ASSOCIATION MAPPING FOR DETECTING QTLS FOR FUSARIUM HEAD BLIGHT
AND YELLOW RUST RESISTANCE IN BREAD WHEAT
By
Carlos Esteban Falconi-Castillo

A DISSERTATION
Submitted to
Michigan State University
in partial fulfillment of the requirements
for the degree of
DOCTOR OF PHILOSOPHY
Plant Breeding, Genetics and Biotechnology – Crop and Soil Sciences – Doctor of
Philosophy
2014

ABSTRACT
ASSOCIATION MAPPING FOR DETECTING QTLS FOR FUSARIUM HEAD BLIGHT
AND YELLOW RUST RESISTANCE IN BREAD WHEAT
By
Carlos Esteban Falconi-Castillo
Yellow rust (YR), caused by Puccinia striiformis, and Fusarium head blight
(FHB), caused by Fusarium graminearum, are two of the most important wheat
diseases in the world. Both pathogens cause severe losses in yield and in the case of
FHB, there is an additional concern related with mycotoxin production, which induces
serious toxicological problems in human and animals. Breeding for resistance for both
diseases has been considered as the most practical strategy of control. To identify
sources of resistance and detect regions responsible of resistance to these diseases in
wheat germplasm, an association mapping panel (AMP) of 297 spring wheat lines
developed by the International Maize and Wheat Improvement Center (CIMMYT) was
assembled. The AMP was evaluated for resistance to P. striiformis and F. graminearum
in Mexico and Ecuador over two years. The AMP was screened with 8,632 SNP
markers included in the wheat 9K chip from Illumina® and 66 SSR markers from the
wheat consensus map. A total of 3,701 SNP and 33 SSR markers were informative and
were used to perform analyses in the wheat AMP. Genotypic data was used to estimate
the population structure and determine the extent of linkage disequilibrium in the panel.
Genotypic and phenotypic data was used to identify marker trait associations.
The structure analysis determined that the panel can be separated in three subpopulations. The extent of LD was different for each genome with major differences
between linkage groups in the D-genome. Association analysis with GLM method

detected significant regions associated with yellow rust resistance on chromosomes 1A,
2A, 5A, 6A, 7A, 2B, 5B, 6B, 7B, and 3D, however, the analysis with the MLM method
detected significant regions on chromosomes 1A and 2A. The association analysis
conducted for Fusarium head blight resistance using the GLM detected regions
significantly associated with resistance on chromosomes 4A, 7A, 2B, 5B, and 7B and
using the MLM method the regions associated with resistance were located on
chromosomes 2B and 7B. In the association analysis for DON concentration with GLM
the regions associated with resistance were detected on chromosomes 4A, 5B, 7B, and
2D. However, no significant regions were detected with the MLM method.
This study allowed the identification of several sources of resistance for yellow rust and
Fusarium head blight as well as the identification of several molecular markers linked to
regions responsible for resistance to these two important diseases. Additionally, the
wheat AMP panel showed to be a source of genetic diversity. The findings reported
here can be applied to wheat breeding by different programs interested in spring wheat.
Finally, the SNP chip utilized to conduct the genotypic analysis was found to be a very
useful tool to conduct association analysis studies. However, more coverage on the Dgenome might be necessary in spring wheat populations.

ACKNOWLEDGEMENTS

I would like to express my appreciations to my advisor, Dr. Karen Cichy. Her
advices, support, and time were extremely important during all these years to continue
and complete my dissertation. I also would like to thank Dr. Cichy for the numberless
lessons of science and humanity.
Special thanks to my co-advisor, Dr. Russell Freed, for his support and
understanding. He was always there to help me and move things forward.
Thanks to my co-advisor Dr.James D. Kelly to keep his doors always open to discuss
and provide constructive comments.
Dr. Dechun Wang and Ray Hammerschmidt, the other members of my
Committee, were also full of support. I can be proud to say that I learn a lot form all of
them.
My thanks to Dr. Zixang Wen for sharing all his knowledge related with
Association mapping with me. Dr. Wen helped me solving most of the problems with
statistical analyses.
All my friends in the Department of Crop and Soil Sciences at MSU for the
everyday help and friendship, especially Halima, Valerio, Dennis, Sue, Kelvin, Beth,
Corlina, Gerardine, and Yuanjie.
Thanks to all the people from CIMMYT and INIAP (Ravi Singh, Pawan Singh,
Jose Crossa, Xavier Garofalo, Jose Ochoa, Mayra Cathme, Segundo Abad, Luis
Ponce, Sibyl Herrera-Fossel, Julio Huerta, Francisco Lopez, Xavier Segura, Xinyao He,
Nerida Lozano, and others) to collaborate with the development of the project,

iv

germplasm, field evaluations, laboratory analysis, and suggestions during all the
research process. It would not be possible without all the help they provided.

v

TABLE OF CONTENTS
LIST OF TABLES ............................................................................................................ix
LIST OF FIGURES ......................................................................................................... xii
CHAPTER 1 .................................................................................................................... 1
YELLOW RUST AND FUSARIUM HEAD BLIGHT IN BREAD WHEAT: IMPORTANCE,
PATHOLOGY AND DISEASE RESISTANCE ................................................................. 1
Bread wheat: Origin and importance ............................................................................ 1
Yellow Rust .................................................................................................................. 3
Biology of Puccinia striiformis ...................................................................................... 4
Yellow rust control........................................................................................................ 4
Resistance to yellow rust ............................................................................................. 6
Fusarium Head Blight ................................................................................................ 10
Control of FHB ........................................................................................................... 12
Resistance to FHB ..................................................................................................... 14
Association mapping .................................................................................................. 17
Association mapping in wheat.................................................................................... 19
Linkage disequilibrium (LD) in plants ......................................................................... 21
REFERENCES .......................................................................................................... 23
CHAPTER 2 .................................................................................................................. 40
STUDY OF THE POPULATION STRUCTURE IN THE WHEAT ASSOCIATION
MAPPING PANEL ......................................................................................................... 40
Abstract ...................................................................................................................... 40
Introduction ................................................................................................................ 41
Materials and Methods ............................................................................................... 43
Plant Material .......................................................................................................... 43
Genotyping ............................................................................................................. 57
Population structure ................................................................................................ 62
Linkage disequilibrium ............................................................................................ 64
Results ....................................................................................................................... 64
Genotyping ............................................................................................................. 64
Linkage disequilibrium (LD) .................................................................................... 67
Population structure analysis .................................................................................. 68
Discussion.................................................................................................................. 69
Genotyping ............................................................................................................. 69
Linkage disequilibrium ............................................................................................ 71
Population structure analysis .................................................................................. 72
Conclusions ............................................................................................................... 74
Acknowledgments ...................................................................................................... 74
APPENDIX .................................................................................................................... 86
Appendix: wheat association mapping panel and membership coefficients. .............. 87
REFERENCES .......................................................................................................... 98
vi

CHAPTER 3 ................................................................................................................ 104
ASSOCIATION MAPPING FOR DETECTING QTLs FOR YELLOW RUST IN BREAD
WHEAT ....................................................................................................................... 104
Abstract .................................................................................................................... 104
Introduction .............................................................................................................. 104
Materials and methods ............................................................................................. 107
Plant material ........................................................................................................ 107
Locations .............................................................................................................. 107
Field management, inoculation, and phenotyping................................................. 108
Genotyping ........................................................................................................... 110
Statistical analysis ................................................................................................ 110
Results ..................................................................................................................... 111
Germplasm evaluation .......................................................................................... 112
Analysis of variance for Yellow Rust Severity ....................................................... 126
Association analysis for yellow rust severity ......................................................... 131
Analysis of variance of flowering time ................................................................... 145
Association Analysis for flowering time ................................................................. 149
Analysis of variance of plant height ...................................................................... 149
Association analysis for plant height ..................................................................... 150
Discussion................................................................................................................ 150
Germplasm evaluation .......................................................................................... 150
Analysis of variance of yellow rust severity ........................................................... 151
Association analysis for yellow rust severity ......................................................... 153
Analysis of variance of flowering time ................................................................... 155
Association Analysis for flowering time ................................................................. 155
Analysis of variance of plant height ...................................................................... 159
Association analysis for plant height ..................................................................... 163
Conclusions ............................................................................................................. 166
Acknowledgements .................................................................................................. 167
APPENDICES ............................................................................................................. 168
Appendix A: Modified Cobb’s scale. ......................................................................... 169
Appendix B: Yellow rust reaction ............................................................................. 170
Appendix C: Temperatures and precipitation in Ecuador and Mexico. 2011-12 ...... 171
REFERENCES ........................................................................................................ 172
CHAPTER 4 ................................................................................................................ 178
ASSOCIATION MAPPING FOR DETECTING QTLs FOR FUSARIUM HEAD BLIGHT
IN BREAD WHEAT ..................................................................................................... 178
Abstract .................................................................................................................... 178
Introduction .............................................................................................................. 179
Materials and Methods ............................................................................................. 181
Plant material ........................................................................................................ 181
Locations .............................................................................................................. 182
Field management, inoculation, and phenotyping................................................. 182
Genotyping ........................................................................................................... 184
Statistical Analyses ............................................................................................... 184
vii

Results ..................................................................................................................... 186
Analysis of variance of Fusarium Head Blight Severity ......................................... 186
Association analysis of Fusarium Head Blight Severity ........................................ 189
Germplasm evaluation .......................................................................................... 198
Analysis of variance of Deoxinivalenol concentration ........................................... 202
Association analysis for DON concentration ......................................................... 203
Discussion................................................................................................................ 209
Statistical analysis FHB severity ........................................................................... 209
Statistical analysis DON concentration ................................................................. 211
Germplasm evaluation .......................................................................................... 211
Association analysis of FHB severity .................................................................... 212
Association analysis for DON concentration ......................................................... 214
Conclusions ............................................................................................................. 215
Acknowledgments .................................................................................................... 216
APPENDIX .................................................................................................................. 217
Appendix: Temperature and precipitation. Mexico and Ecuador. 2011-12 ............... 218
REFERENCES ........................................................................................................ 219

viii

LIST OF TABLES

Table 1-1. QTLs for field or adult plant resistance to yellow rust in wheat. Adapted from
Boyd (2005)..................................................................................................................... 8
Table 1-2. Most common sources of FHB resistance, location of the QTLs and type of
resistance. Adapted from Buerstmayr et al. (2009). ...................................................... 14
Table 2-1. Wheat accessions from the association mapping panel developed by
CIMMYT listed with the germplasm identifier (GID), pedigree and origin from CIMMYT
trials............................................................................................................................... 45
Table 2-2. Microsatellite markers (SSRs) employed to screen the wheat association
mapping panel, sequences of the primers, and comments from the results of the
amplifications. ............................................................................................................... 58
Table 2-3. List of SSR markers that amplified in the wheat AMP genome. ................... 63
Table 2-4. Size of the wheat linkage groups (cM) and number of SNP markers from the
9K SNP chip after filtering for MAF(> 5%) and missing data (< 10%). .......................... 66
Table 2-5. Wheat AMP accessions and membership coefficients for each subpopulation (Q) determined by STRUCTURE software. ................................................. 87
Table 3-1. Locations and years of the wheat association mapping study on Yellow Rust.
.................................................................................................................................... 107
Table 3-2. Codes for recording wheat reaction to Yellow Rust infection as used by
CIMMYT (1986). .......................................................................................................... 109
Table 3-3. Yellow rust severity registered in the wheat AMP in Ecuador and Mexico.
2011-2012. .................................................................................................................. 113
Table 3-4. Analysis of variance of yellow rust severity in the association mapping panel.
Ecuador and Mexico. 2011-12. ................................................................................... 127
Table 3-5. Disease severity in the association mapping panel planted in Ecuador and
Mexico. 2011-12. ......................................................................................................... 128
Table 3-6. Pearson correlation and p-values of correlations for yellow rust severity in the
association mapping panel experiments in two locations and two years. Ecuador and
Mexico. 2011 -12. All values were highly significant (P< 0.001). ................................. 128
Table 3-7. Association analysis for yellow rust severity of the wheat association
mapping panel using GLM model. Mexico and Ecuador. 2011-12. ............................. 134
ix

Table 3-8. Association analysis for yellow rust severity of the wheat association
mapping panel using MLM model. Mexico and Ecuador. 2011-12. ............................. 139
Table 3-9. Analysis of variance of flowering days of the wheat association mapping
panel. Ecuador 2011 – 2012. ...................................................................................... 146
Table 3-10. Flowering days of the wheat association mapping panel grown in Santa
Catalina-Ecuador and El Batan-Mexico. 2011-2012. .................................................. 146
Table 3-11. Analysis of correlation (Pearson) for flowering days between the wheat
association mapping panel planted in two locations and two years. Ecuador and Mexico.
2011-2012. All values were highly significant (P< 0.001). ........................................... 147
Table 3-12. Association analysis for flowering time of the wheat association mapping
panel using GLM model. Mexico and Ecuador. 2011-12. ............................................ 157
Table 3-13. Association analysis for days to flowering of the wheat association mapping
panel using MLM model. Mexico and Ecuador. 2011-12............................................. 157
Table 3-14. Analysis of variance of the wheat association mapping panel for plant
height. Ecuador and Mexico 2011-12. ......................................................................... 160
Table 3-15. Mean and range for plant height of the wheat association mapping panel
planted in Ecuador and Mexico. 2011-12. ................................................................... 160
Table 3-16. Analysis of correlation (Pearson) for plant height in the wheat association
mapping panel between wheat accessions in two locations and two years. Ecuador and
Mexico. 2011-12. All values were highly significant (P< 0.001). .................................. 162
Table 3-17. Association analysis for plant height of the wheat association mapping
panel using GLM model. Mexico and Ecuador. 2011-12. ............................................ 164
Table 3-18. Association analysis for plant height of the wheat association mapping
panel using MLM model. Mexico and Ecuador. 2011-12............................................. 164
Table 3-18. Temperature and precipitation data from Santa Catalina – Ecuador and
Toluca Mexico during 2011-12. ................................................................................... 171
Table 4-1. Locations and years of the wheat association mapping study on Yellow Rust.
.................................................................................................................................... 182
Table 4-2. ANOVA for Fusarium Head Blight severity in the wheat association mapping
panel from two years. Mexico 2011-12........................................................................ 187
Table 4-3. Fusarium head blight severity in the wheat association mapping panel.
Ecuador and Mexico. 2011 – 2012. ............................................................................. 187

x

Table 4-4. Correlations and p-values in the Association Mapping panel between Mexico
2011 and 2012 for Fusarium Head Blight severity. Mexico 2011-12. All values were
highly significant (P< 0.001). ....................................................................................... 188
Table 4-5. Association analysis for Fusarium head blight severity of the wheat
association mapping panel using GLM model. Mexico. 2011-12. ............................... 192
Table 4-6. Association analysis for fusarium head blight severity of the wheat
association mapping panel using MLM model. Mexico. 2011-12. ............................... 195
Table 4-7. Top 25 and bottom 25 accessions based on FHB severity (%) in the wheat
AMP with sub-populations classification. Mexico, 2011-12. ........................................ 199
Table 4-8. ANOVA for DON concentration of 297 wheat accessions in two years.
Mexico 2011-12. .......................................................................................................... 202
Table 4-9. DON concentration in the wheat Association mapping panel. Mexico, 201112. ............................................................................................................................... 202
Table 4-10. Correlations and p-values in the wheat Association Mapping panel between
Mexico 2011 and 2012 for DON concentration. Mexico 2011-12. All values were highly
significant (P< 0.001). ................................................................................................. 203
Table 4-11. Association analysis for DON concentration of the wheat association
mapping panel using GLM model. Mexico. 2011-12. .................................................. 205
Table 4-12. Association analysis for DON concentration of the wheat association
mapping panel using MLM model. Mexico. 2011-12. .................................................. 206
Table 4-13. Temperature and precipitation data from Santa Catalina – Ecuador and
Toluca Mexico during 2011-12. ................................................................................... 218

xi

LIST OF FIGURES

Figure 1-1. Life cycle of Puccinia striiformis Westend. Two types of disease symptoms
may appear on a wheat primary host, the uredinial stage with urediniospores and the
telial stage with teliospores. The two-celled teliospores may germinate with a basidium
developing into four basidiospores. In the alternal host, the pathogen can produce
pycniopores. Finally, aeciospores are produced and wheat can be infected completing
the cycle (Zheng et al., 2013). For interpretation of the references to color in this and all
other figures, the reader is referred to the electronic version of this dissertation. ........... 5
Figure 1-2. Fusarium graminearum life cycle in wheat. The pathogen overwinters on
infested crop residues. Ascospores from perithecium are produced and infect wheat
spikes. Infected seed or crop residues become the source of inoculum for the next
season (Trail, 2009). ..................................................................................................... 12
Figure 2-1. Results from the Illumina® iSelect scan: blue color corresponds to the
percentage of SNP markers from the 9K SNP chip that were detected and red color
corresponds to the percentage of SNP markers placed in the 9K SNP Chip from
Illumina that were not detected. .................................................................................... 75
Figure 2-2. Percentage of SNP markers eliminated after filtering for poor quality or
minimum frequency alleles (<5%) and SNP markers showing good quality and
considered for analysis.................................................................................................. 75
Figure 2-3. Scatter plot of LD values (r2) against genetic distance (cM) of chromosomes
1A – 4A. ........................................................................................................................ 76
Figure 2-4. Scatter plot of LD values (r2) against genetic distance (cM) of chromosomes
5A – 7A. ........................................................................................................................ 77
Figure 2-5. Scatter plot of LD values (r2) against genetic distance (cM) of chromosomes
1B – 4B. ........................................................................................................................ 78
Figure 2-6. Scatter plot of LD values (r2) against genetic distance (cM) of chromosomes
5B – 7B. ........................................................................................................................ 79
Figure 2-7. Scatter plot of LD values (r2) against genetic distance (cM) of chromosomes
1D – 4D. ........................................................................................................................ 80
Figure 2-8. Scatter plot of LD values (r2) against genetic distance (cM) of chromosomes
5D – 7D. ........................................................................................................................ 81
Figure 2-9. Intrachromosomal comparison of LD decay on chromosomes from the A
genome of the wheat AMP. ........................................................................................... 81

xii

Figure 2-10. Intrachromosomal comparison of LD decay on chromosomes from the B
genome of the wheat AMP. ........................................................................................... 82
Figure 2-11. Intrachromosomal comparison of LD decay on chromosomes from the D
genome of the wheat AMP. ........................................................................................... 82
Figure 2-12. Distribution of Delta K values in wheat the association mapping panel
based on STRUCTURE analysis. East Lansing. 2013. ................................................. 83
Figure 2-13. Population structure based on STRUCTURE software of the wheat
association mapping panel. East Lansing. 2013. .......................................................... 83
Figure 2-14. Principal component analysis of the wheat association mapping panel
(red= sub-population one, green= sub-population two, blue= sub-population three)
based on SNP markers. East Lansing. 2013. ................................................................ 84
Figure 2-15. Neighbor joining tree of the wheat Association Mapping Panel. Accessions
have been assigned colores based on STRUCTURE analysis. Red= sub-population 1,
Green= sub-population 2, and Blue= subpopulation 3. ................................................. 85
Figure 3-1. Histograms of yellow rust severity (%) in the wheat AMP evaluated in
Ecuador and Mexico, 2011-12. ................................................................................... 129
Figure 3-2. Histograms of two year averages of yellow rust severity (%) in the wheat
AMP evaluated in Ecuador and Mexico, 2011-12. ...................................................... 130
Figure 3-3. Scatter plots of yellow rust severity data from the wheat AMP evaluated in
Ecuador and Mexico. 2011-12. ................................................................................... 130
Figure 3-4. Manhattan plots of the association analysis for yellow rust severity in the
wheat association mapping panel using GLM and MLM. Mexico 2011 and 2012. ...... 141
Figure 3-5. Q-Q plots of the of the association analysis for yellow rust severity in the
wheat association mapping panel using GLM and MLM. Mexico 2011 and 2012. ...... 142
Figure 3-6. Manhattan plots of the association analysis for yellow rust severity in the
wheat association mapping panel using GLM and MLM. Ecuador 2011 and 2012. .... 143
Figure 3-7. Q-Q plots of the association analysis for yellow rust severity in the wheat
association mapping panel using GLM and MLM. Ecuador 2011 and 2012................ 144
Figure 3-8. Histogram for flowering days in the Association Mapping Panel evaluated in
Ecuador and Mexico. 2011 -2012. .............................................................................. 148
Figure 3-9. Scatter plot of flowering days of the wheat Association Mapping Panel.
Ecuador and Mexico. 2011 – 2012. ............................................................................. 148

xiii

Figure 3-10. Manhattan plots of association analysis for flowering in the wheat
association mapping panel using GLM (left) and MLM (right) method. Mexico 2011 and
2012. ........................................................................................................................... 158
Figure 3-11. Histogram of plant heigh (cm) of the wheat AMP evaluated in Ecuador and
Mexico 2011-12. .......................................................................................................... 161
Figure 3-12. Manhattan plot of the association mapping analysis for plant height with the
GLM method in the wheat association mapping population. Mexico 2011 -2012. ....... 165
Figure 3-13. Q-Q plot for association analysis of the wheat association mapping panel
for plant height. Mexico 2011 – 2012. ......................................................................... 165
Figure 3-14. The modified Cobb’s scale: A: Actual percentage occupied by rust uredinia;
B: Rust severities of the modified Cobb’s scale (Roelfs et al., 1992). ......................... 169
Figure 3-15. Adult plant responses to stripe rust (P. striiformis) (Roelfs et al., 1992). . 170
Figure 4-1. Distribution of percentage of FHB severity in the wheat AMP evaluated in
Mexico 2011-12. .......................................................................................................... 188
Figure 4-2. Scatter plot and regression line of FHB severity from the wheat AMP
evaluated in Mexico, 2011-12. .................................................................................... 189
.................................................................................................................................... 196
Figure 4-3. Manhattan plots of the association analysis for Fusarium head blight severity
in the wheat association mapping panel using GLM and MLM. Mexico 2011 and 2012.
.................................................................................................................................... 196
Figure 4-4. Q-Q plots of the association analysis for fusarium head blight severity in the
wheat association mapping panel using GLM and MLM. Mexico 2011 and 2012. ...... 197
Figure 4-5. Distribution of DON concentration in the wheat AMP evaluated in Mexico
2011-12. ...................................................................................................................... 203
Figure 4-6. Manhattan plots of the association analysis for DON accumulation in the
wheat association mapping panel using GLM and MLM. Mexico 2011-12. ................. 207
Figure 4-7. Q-Q plots of the association analysis for DON accumulation in the wheat
association mapping panel using GLM and MLM. Mexico 2011-12. ........................... 208

xiv

CHAPTER 1
YELLOW RUST AND FUSARIUM HEAD BLIGHT IN BREAD WHEAT: IMPORTANCE,
PATHOLOGY AND DISEASE RESISTANCE
Bread wheat: Origin and importance
The origin of bread wheat (Triticum aestivum L.) can be traced back to southwest Asia
between 8,000 to 12,000 years ago (Giles and Brown, 2006; McFadden and Sears,
1946). Bread wheat is a hexaploid species with three genomes A, B, and D. Hexaploid
wheat arose from the hybridization of cultivated tetraploid emmer wheat (T. turgidum
ssp. dicoccum Schrank) with the wild diploid wheat species Aegilops tauschii
Coss.(Caldwell et al., 2004; Matsuoka, 2011). Each of the three genomes has seven
chromosomes and the total chromosome number is (2n = 6x = 42) (Gill and Friebe,
2009). Triticum aestivum and all polyploidy wheat species are disomic in inheritance
due to genome-specific chromosome-pairing (Gustafson et al., 2009), controlled by
pairing suppressor genes Ph1, Ph2 and other minor genes (Ceoloni and Feldman,
1987; Sears, 1976; Sears, 1977). This characteristic has allowed full fertility in the
species and, moreover, the action of favorable effect of an extra gene dosage or the
build-up of positive inter-genomic interactions (Feldman et al., 2012).
The allelic diversity found in hexaploid wheat is reduced compared with its diploid
ancestors (Haudry et al., 2007). This severe bottleneck originated by limited number of
hybridizations during its formation (Talbert et al., 1998). Fortunately, diploid wheat
species can naturally or artificially be crossed with other polyploid wheat species (Gill
and Raupp, 1987). These interspecific crosses have helped to increase the diversity in
hexaploid wheat (Chen and Li, 2007; Sharma and Gill, 1983). Furthermore, production
1

of interspecific crosses has resulted in the development of wheat lines with resistance to
many biotic and abiotic constrains (Mujeeb-Kazi et al., 1996; van Ginkel and
Ogbonnaya, 2007) and are being used in wheat breeding programs and in some cases
have resulted in improved wheat varieties (Yang et al., 2009).
The wheat genome is one of the largest crop genomes with ~16 000 Mb (Gill et al.,
2004) of which 80% are repetitive sequences (Smith and Flavell, 1975). Wheat has a
complex and extremely large genome compared with other crops, therefore its genome
has not yet been totally sequenced. Efforts to sequence the genome are being led by
the International Wheat Genome Sequencing Consortium (IWGSC) which aims to
establish a high quality reference sequence of the wheat genome using cv. ‘Chinese
Spring’ (www.wheatgenome.org). Currently, only chromosome 3B is completely
sequenced by a French group from INRA.
Wheat is one of the most important crops in the world and is grown on 20% of the
cultivated land area of the world. It is grown on more than 216 million hectares with an
approximate production of 675 million tons of grain annually (FAOSTAT, 2012). It is the
staple food of nearly 35% of the world’s population (Rajaram, 2010). Most of its
production is for human consumption mostly as flour and a small portion as whole grain
is used to feed animals (Harlan, 1981). Wheat provides 20% of the total caloric inputs
and protein to the world population (Reynolds et al., 2008; Shiferaw et al., 2013). It is
also the most widely adapted crop plant and wheat is produced between 30º - 60º north
latitude and between 27º - 40º south latitude (Bockus et al., 2010). Likewise, wheat is
produced at high altitudes in the tropics such as the Andean region or valleys in
equatorial countries in Africa (Dubin and Rajaram, 1996; Lantican et al., 2005). The

2

diversity of environments where wheat is grown also allows the occurrence of vast
number of diseases which affect seed quality and yield. A complete review of diseases
affecting wheat can be found in (Bockus et al., 2010). Among this large group of wheat
diseases, yellow rust (Puccinia striiformis Westend. f. sp. tritici) and fusarium head
blight (Fusarium spp.) are considered two of the most severe.

Yellow Rust
Yellow Rust (YR), also known as stripe rust, is caused by Puccinia striiformis Westend.
f. sp. tritici (McIntosh et al., 1995). Yellow rust is one of the major wheat diseases in
temperate regions around the world (Roelfs et al., 1992). High losses can arise due to
reduced number and size of flowering spikes, shriveled grain, and damaged tillers,
especially when the infection occurs in early growth stages (Wellings, 2010). Losses
from 20 to 75% have been recorded in the western states of the US during severe
epidemics (Roelfs, 1978). Puccinia striiformis has been a constant threat to wheat
production. Significant regional epidemics have been recorded since 1725 (Wellings,
2011). Such recurrent epidemics occur due to a combination of specific virulence in the
pathogens population and wide-scale cultivation of genetically similar varieties (Danial
et al., 1994).
The infection can occur throughout the life of a plant. Symptoms first appear as chlorotic
patches on leaves. Tiny, yellow to orange uredia develop in these chlorotic areas
(Chen, 2010). Narrow stripes are formed on the leaves due to the production of pustules
containing orange-yellow urediospores. Yellow rust usually infects leaves; however, the
disease can also infect the glumes of the spikelets in susceptible cultivars.
3

Biology of Puccinia striiformis
Puccinia striiformis is an obligate parasite that shows optimal development under high
relative humidity conditions and low temperatures (8-15°C), particularly cool nights (<
10°C). The optimum temperature for urediospore germination is between 7 and 12°C,
with limits near 0 and 21°C. Disease development is most rapid between 10 and 18 °C
with intermittent rain or dew (Chen, 2010).
Puccinia striiformis is considered a highly diverse pathogen since large number of
different races have been reported worldwide (Kolmer et al., 2009). This pathogenic
variability has been observed between and within geographical areas (Chen et al.,
2009; Chen et al., 2002; Mboup et al., 2009). The main mechanism generating
variability is thought to be the result of mutations and asexual recombination (Stubbs,
1988). An alternate host of P. striiformis was unknown, so it was though that the
pathogen has a micro-cyclic life cycle (McIntosh et al., 1995). However, Jin et al. (2010)
recently demonstrated that several Berberis spp. in China can be naturally infected by
P. striiformis and act as alternate hosts. In consequence, P. striiformis is a macrocyclic
rust with five different spore stages: uredinial, telial, basidia, pycnial, and aecial stages
(Figure 1-1).

Yellow rust control
The use of resistance genes is considered the most effective strategy to control yellow
rust. The incorporation of resistance genes for yellow rust along with other resistance

4

Figure 1-1. Life cycle of Puccinia striiformis Westend. Two types of disease symptoms
may appear on a wheat primary host, the uredinial stage with urediniospores and the
telial stage with teliospores. The two-celled teliospores may germinate with a basidium
developing into four basidiospores. In the alternal host, the pathogen can produce
pycniopores. Finally, aeciospores are produced and wheat can be infected completing
the cycle (Zheng et al., 2013). For interpretation of the references to color in this and all
other figures, the reader is referred to the electronic version of this dissertation.
genes has been the primary objective of most of the wheat breeding programs
(Johnson, 1992).
Many sources of resistance carrying major or minor genes have been reported (Roelfs
et al., 1992; Wellings, 2011). However, the large genetic variability and high mutation
rate that it exhibits has allowed the yellow rust pathogen to overcome many major
resistance genes. For example, the resistance conferred from Yr27 resistance gene has
broken down in some regions in Asia (Hodson, 2011). For this reason, it is necessary to
5

develop cultivars with high and durable resistance that combine effective genes. A
promising long-term control strategy is to breed and deploy cultivars carrying durable
resistance based on minor, slow rusting genes with additive effects (Singh et al., 2004)
The use of multi-lines has been proposed to control cereal diseases (Wolfe, 1985);
however, the success of this strategy depends on several factors such as the genetic
background of the pathogen race, host, and interaction among pathogen races (Dileone
and Mundt, 1994) resulting in a very complex approach.
Cultural practices, such as the removal of volunteer plants from previous seasons, are
always part of integrated control to avoid early infections. Several fungicides are
effective to control the disease. Seed treatment and timely application of fungicides can
be used (Chen, 2010); however, the use of fungicides significantly increase the
production cost (Wellings, 2007).

Resistance to yellow rust
Genetic resistance to yellow rust is conferred by race-specific and/or non-race-specific
genes. The race-specific resistance is usually conferred by a single dominant gene,
which results in a hypersensitive reaction that can be observed after the pathogen
infection. Whereas non-race-specific resistance or horizontal resistance is controlled by
QTLs that act additively (Lindhout, 2002). Race-specific genes have been extensively
used; however, this type of resistance has been overcome by some rust pathogen
biotypes (Johnson, 2000). The capability of the pathogen to develop new virulent races
via mutations is relatively high (Chen et al., 2009; Sharma-Poudyal et al., 2013;
Wellings et al., 2000). More than 50 yellow resistance genes have been identified and
6

catalogued and several more are under characterization (Boyd, 2005; McIntosh et al.,
2012; Yamazaki et al., 1998). The majority of the genes that have been cataloged are
expressed throughout the life of the plant; however, some genes are expressed at later
growth stages and the resistance type that they confer has been designated as field or
adult plant resistance (APR)(Johnson, 1992), and some particular APR genes are only
expressed at high temperatures (> 10ºC) (Qayoum and Line, 1985; Uauy et al., 2005).
Several QTLs conferring resistance to yellow rust have been reported and mapped
(Table 1-1).

7

Table 1-1. QTLs for field or adult plant resistance to yellow rust in wheat. Adapted from
Boyd (2005).
Chromosomal
location of QTL
Source of QTL
gene name
3BS
‘Opata85’
Singh et al. 2000
3DS
‘Opata85’
5DS
‘Opata85’
‘Opata85’; ‘Yr18/6*AvS’
Yr18
7DS
(Singh et al., 2000b)
2BS
‘Opata85’
Borner et al. 2000
2AL
‘Opata85’
2BS
‘Opata85’
Boukhatem et al. 2002
3DS
‘Opata85’
5AL
‘Opata85’
6DL
‘Opata85’
7DS
‘Opata85’
Yrns-B1
3BS
‘Lgst79-74’
Yr29
1BL
‘Pavon76’
Yr30
3BS
‘Pavon76’; ‘Parula’
4B
‘Pavon76’
6ª
‘AvocetS’
6B
‘Pavon76’
Yr29
1BL
‘Parula’
Yr30
3BS
‘Parula’
Yr18
7DS
‘Parula’
2BS
‘Kariega’
7DS
‘Kariega’
QYr.sun7B
‘Kukri’
7B
Yr54
2D
Quaiu #3
(Basnet et al., 2013)
Yr28
4DS
(Singh et al., 2000b)
The most promising long-term control strategy is to breed and deploy cultivars carrying
durable resistance based on minor, slow rusting genes with additive effects (Singh et
al., 2004). Wheat breeding lines with high yield potential and resistance levels reaching
near-immunity to yellow rust have been successfully developed by CIMMYT through
combination of several QTLs (3 – 5) with small to intermediate effects (Singh et al.,
2000a). In this context, CIMMYT has been successful with the development of hundreds

8

of wheat lines that have been released as new improved cultivars in many countries of
the world, especially in developing countries (Reynolds and Borlaug, 2006).
Resistance genes widely used in developing wheat lines with resistance to yellow
rust are many. Among them, Yr18 is one of the most widely deployed (Reynolds and
Borlaug, 2006). Yr18 confers moderate levels of adult plant resistance (Singh and
Rajaram, 1992). Additionally, this gene is completely linked to other genes that confer
resistance to other diseases such as leaf rust, barley yellow dwarf (BYD) virus, and
powdery mildew (Singh, 1993; Spielmeyer et al., 2005). These combined characteristics
were the reason to develop molecular markers to conduct marker assisted selections for
these specific region (Suenaga et al., 2003).
Yr25 is another gene frequently deployed in wheat cultivars (Boshoff and
Pretorius, 1999) and it is also present in ‘Strubes Dickkopf’ used to differentiate P.
striiformis races. This gene was located on chromosome 1D. Interestingly, it has been
observed that genes located in other chromosomes might suppress or reduce the levels
of resistance of this gene (Calonnec and Johnson, 1998).
Another example of a resistance gene frequently deployed is Yr32 (Hovmøller,
2007). Gene Yr32 is located in chromosome 2AL (Eriksen et al., 2004), and it is present
in the differential cultivar ‘Cartens V’ (McIntosh et al., 1995).
Other genes have been widely deployed in wheat breeding for resistance such
as YrA, Yr1, Yr2, Yr9, and Yr17; however, several reports have been published
indicating that resistance have been overcome by new strains of P. striiformis (Bayles et
al., 2000; Boyd, 2005; Hovmøller, 2001; Lupton and Johnson, 1970; Wellings, 2011).
None of these major genes are recommended to be used alone.

9

Fusarium Head Blight
Fusarium head bight (FHB), also known as Fusarium ear blight or scab, is one of the
most important diseases affecting wheat. The major causal organism of this disease
worldwide is Gibberella zeae (Schwein) Petch (anamorph: Fusarium graminearum
Schwabe) (Schmale III and Bergstrom, 2003). However, FHB several other species of
Fusarium and one species of Microdochium can also cause FHB. Fusarium
graminearum and F. culmorum are the most important species due to their wide
distribution in wheat fields around the world (Bottalico and Perrone, 2002; Parry et al.,
1995). The infection of Fusarium on wheat causes yield reduction and losses as high as
50% (Ireta and Gilchrist, 1994). FHB epidemics are cyclic and severe outbreaks of the
disease have been reported in many regions where the crop is grown resulting in
millions of dollars in crop losses (McMullen et al., 1997). The pathogen also produces
mycotoxins, which are a major concern. These metabolites have toxic effects in humans
and mono-gastric animals (Bottalico and Perrone, 2002). These toxins can induce a
spectrum of effects in farm and laboratory animals including emesis immunotoxic
effects, and suppression of appetite and growth (Voss, 2010). The most common
mycotoxins are Deoxynivalenol (DON), Zearalenone, Moniliformin, 3Acetyldeoxynivalenol (3-ADON), Nivalenol, and T-2 toxin (Bottalico and Perrone, 2002;
Placinta et al., 1999). Mycotoxins are commonly present in wheat fields and the health
risk associated with them has prompted several countries to create a policy regarding
maximum allowable levels in food. For instance, the United States allows a maximum
concentration of DON of 1000 µg/kg in wheat products finished for human consumption
(Richard, 2007); whereas the European Nations do not allow flour with more than 750
10

µg/kg (van Egmond and Jonker, 2004). Unfortunately, several countries lack regulations
for mycotoxins concentrations in food or allow relatively high concentrations in wheat
products (Dohlman, 2004).
FHB was first described in 1884 in England and was considered a major threat to wheat
and barley during the early years of the twentieth century (Stack, 2003). The first
symptoms of FHB appear shortly after flowering. Diseased spikelets exhibit premature
bleaching as the pathogen grows and spreads within the head (Ireta and Gilchrist,
1994). One or more spikelets located on the top, middle, or bottom of the head may be
bleached. Over time, the premature bleaching of the spikelets may progress throughout
the entire head (Schmale III and Bergstrom, 2003). Other symptoms include tan to
brown discoloration at the base of the head, a pink or orange colored mold at the base
of the florets under moist conditions, and kernels that are shriveled, white, and chalky in
appearance (Buhariwalla et al., 2011). The pathogen can infect wheat spikes from
flowering to late stages of kernel development (Del Ponte et al., 2007). Initial source of
Fusarium inoculum comes from the soil, which survives either as saprophytic mycelium
or as chlamydospores (Parry et al., 1995). Later in the season, macroconidia and
ascospores carried by air currents to wheat heads are considered the primary inoculum
(Dill-Macky, 2010). Warm temperatures and high relative humidity favor pathogen
growth, and aggregations of light pink/salmon colored spores (sporodochia) may appear
on the rachis and glumes of individual spikelets (Schmale III and Bergstrom, 2003).
Later in the season, bluish- black perithecia bodies may appear on the surface of
infected spikelets. These bodies are sexual structures of the fungus known as
perithecia. As symptoms progress, the fungus colonizes the developing grain, causing it

11

to shrink and wrinkle inside the head (Dill-Macky, 2010). The cycle is completed when
Fusarium-infected seeds or host residues remaining in the soil provide source of
inoculum for the next cropping cycle (Parry et al., 1995) (Figure 1-2).

Figure 1-2. Fusarium graminearum life cycle in wheat. The pathogen overwinters on
infested crop residues. Ascospores from perithecium are produced and infect wheat
spikes. Infected seed or crop residues become the source of inoculum for the next
season (Trail, 2009).
Control of FHB
There is agreement that no single strategy is 100% effective against FHB (Gilbert and
Haber, 2013). Cultural and management practices, such as crop rotations with at least a
12

1-year break from the cultivation of a host crop (corn, wheat, barley, and other cereals),
thorough tillage (McMullen et al., 2012; Parry et al., 1995; Pereyra and Dill-Macky,
2008) and the use disease-free or treated seeds (Gilbert and Tekauz, 2000), may
reduce the damage caused by FHB in wheat cultivars. However, these practices do not
completely control the disease (Dill-Macky, 2010; Dill-Macky and Jones, 2000).
Fungicides partially control the disease under optimal application conditions (Jones,
2000). However, fungicide application is not always effective because not all fungicides
used can control FHB (Mesterházy et al., 2011). Moreover, it has been reported that
some fungicides such as azoxystrobin partially controlled the disease but resulted in an
increase of DON toxin concentration (Mesterházy et al., 2003). It is also common to get
incomplete crop coverage of spikes because differences in flowering or inadequate
equipment use (Mesterházy, 2003). Incorrect timing of application can also be another
reason for control failure. Some fungicides such as tebuconzole or carbendazim are
reported as useful to control FHB (Dill-Macky, 2010); however these fungicides do not
totally prevent the disease (Jones, 2000; Mesterházy et al., 2011). The increase in cost
is also a constraint for some farmers who want to avoid additional production costs
(Lewis, 2010, pers. com.). Additionally, chemical control may represent health risks to
farmers who are exposed to pesticides and do not take enough care to protect
themselves or simply ignore safety measures (Ecobichon, 2001; Jeyaratnam, 1990).
Therefore, the development of new cultivars, with high levels of FHB resistance, is the
most promising cost-effective strategy for FHB control.

13

Resistance to FHB
The resistance to FHB has been grouped based on mechanisms. The most studied
types of FHB resistance are: type I, (resistance to initial infection) and type II,
(resistance to fungal spread within the inoculated head). Other types are resistance to
deoxynivalenol (DON) accumulation (also known as type III), and resistance to the
development of Fusarium-damaged kernels (FDK) (Schroeder and Christensen, 1963).
Presently, no cultivar has been reported as immune to FHB infection; however, large
genetic variation for FHB resistance has been observed in wheat germplasm
(Mesterhazy et al., 2005; Ruckenbauer et al., 2001). QTL mapping studies have shown
that resistance genes for FHB are present on all wheat chromosomes except
chromosome 7D (Buerstmayr et al., 2009). Several sources of resistance have been
reported and widely used. One of these sources is the Chinese cultivar ‘Sumai 3’, that
possesses two well-known and exploited loci (Fhb1 and Fhb2) (Waldron et al., 1999).
However, none of these genes confer complete resistance to the pathogen (Miller and
Greenhalgh, 1988; Snijders, 1994). Other Chinese wheat cultivars used as sources of
resistance include ‘Ning7840’, ‘Wuhan 1’ and ‘Nyuubai’ (McCartney et al., 2007),
‘Chokwang’ (Yang et al., 2005). Another popular source of resistance widely used for
more than 50 years ago is the Brazilian cultivar ‘Frontana’ (Schroeder and Christensen,
1963). Sources from Europe have been also reported, and the Swiss cultivar ‘Arina’, are
the most studied and used from that region (Snijders, 1990).
Table 1-2. Most common sources of FHB resistance, location of the QTLs and type of
resistance. Adapted from Buerstmayr et al. (2009).
Source of
Country of
Chromosome Type of
resistance
origin
resistance
‘Sumai 3’
China
3BS
FHB spread (II)

14

Table 1-2 (cont’d)
‘Ning 7840’
‘Stoa’
‘ND-2603’
‘CM-82036’
‘Alondra’
‘Ning 894037’
‘Huapei 57-2’
‘Wuhan 1’
‘Patterson’
‘Nyu Bai’

‘Wangshuibai’

6BS
3BS
2BL
2AS
USA
2AL
4BS
USA
3BS
6AS
3AL
Mexico
3BS
5ª
1B
Mexico/Brasil 2DS
1B
China
3BS
6BS
China
3BS
3BL
3AS
China
2DL
USA
5BL
3D
China
3BS
China

China

3BS
5AS
2D
3BS
6B
1B
7A
3BS
2D
4B
5B
2DL
5A
3AS
5DL

‘Frontana’

Brasil

3A
5ª
2B
15

FHB spread (II)
FHB spread (II)
FHB spread (II)
FHB spread (II)
FHB spread (II)
FHB spread (II)
FHB spread (II)
FHB spread (II)
FHB spread (II)
FHB spread (II)
FHB spread (II)
FHB spread (II)
FHB spread (II)
FHB spread (II)
FHB spread (II)
FHB spread (II)
FHB spread (II)
FHB spread (II)
FHB spread (II)
FHB spread (II)
FHB spread (II)
FHB spread (II)
FHB spread (II)
and DON content
FHB Severity
DON content
DON content
FHB spread (II)
FHB spread (II)
FHB spread (II)
FHB spread (II)
FHB spread (II)
and DON content
FHB Severity
FHB Severity
FHB Severity
FHB Severity
FHB Incidence
FHB Incidence
DON content and
FHB Incidence
FHB Severity (II)
and FHB Incidence
FHB Severity
FHB incidence

Table 1-2 (cont’d)
6B
‘Arina’

Switzerland

7AS
4AL
6DL
3BL
5AL
2AL
1BL
6BS
4DS
6BL

FHB Severity and
FHB Incidence
FHB Severity
FHB Severity
FHB Severity
FHB Severity
FHB Severity
FHB Severity
FHB Severity
FHB Severity
FHB Severity
FHB Severity

The selection of wheat germplasm with resistance is conducted mainly in the
field, but greenhouse inoculations can be performed to assess type II resistance
(Buerstmayr et al., 2002). The screening techniques may differ and depend on factors
such as project goals, precision needed, number of lines under evaluation and
resources (Rudd et al., 2001). The environment plays an important role in the
development of the disease, so the infection in the field might be improved with the use
of sprinklers to provide adequate levels of humidity. Since resistance to Fusarium head
blight is horizontal and non-race specific (Mesterhazy et al., 1999), selection of any
aggressive strain of F. graminearum or F. culmorum for screening purposes should be
satisfactory (Eeuwijk et al., 1995). To ensure infection in the trials, some researchers
use a mixture of isolates to do not completely depend in only one isolate (Lu et al.,
2013; Van Ginkel et al., 1996; Yoshida and Nakajima, 2010). The inoculum
concentration is an important factor in screening for resistance. Stein et al. (2009)
reported that disease incidence and severity increased sharply in relation to inoculum

16

concentration. In general, a recommendation will be to use inoculum with concentration
of 50,000 spores/ml (Gilbert and Woods, 2006).
Numerous QTLs in wheat have been mapped onto chromosomes of resistance sources
from many Asian, North American, South American, and European countries using
traditional QTL analysis methods (Ma et al., 2006; Paillard et al., 2004). More than 100
QTLs conditioning FHB resistance in wheat have been reported (Buerstmayr et al.,
2009; Liu et al., 2009; Loeffler et al., 2009). However, the discovery of such QTLs has
been conducted in bi-parental populations (Buerstmayr et al., 2009) and most of the
QTLs have minor effects. New methods to identify QTL for FHB and other wheat
diseases are being employed which are described with more detail in the following two
sections.

Association mapping
Association Mapping (AM), also known as Association Analysis or Linkage
Disequilibrium Mapping, is a method used to detect QTLs controlling traits based on
correlating genotype with phenotype (Neumann et al., 2011). Association mapping can
also be employed as an approach to validate the presence and position of QTLs
previously reported (Aranzana et al., 2005). The principle of AM methodology is based
on linkage disequilibrium (LD) (Breseghello and Sorrells, 2006), which is the nonrandom association of alleles at different loci (Flint-Garcia et al., 2003). LD tends to be
maintained over many generations between loci which are genetically linked to one
another. The approach was developed originally in the field of human genetics (Lander

17

and Schork, 1994) and now, with the development of complex statistical methods,
association mapping is being employed in plants (Thornsberry et al., 2001).
One of the advantages of association mapping is the use of existing populations, which
could be obtained from gene banks or germplasm collections. Therefore, there is no
need to develop specific crosses resulting in saving time (Oraguzie and Wilcox, 2007).
The population can be assembled with breeding lines, cultivars, landaraces or mixtures
of all of them. In order to successfully detect QTLs controlling traits of interest in such
populations using AM approaches, a diverse population with a considerable allelic
variation for the trait/s of interest must be assembled (Yu et al., 2006). If the population
is rich in allele diversity for a specific trait, the likelihood to discover large number of
significantly important and novel alleles will increase. Less frequent alleles significantly
associated with a trait can exist, however, rare alleles are usually not considered for
analysis (Adhikari et al., 2012; Reimer et al., 2008), since association analysis require
rare alleles to be filtered to avoid errors that could lead to false positive associations
(Brachi et al., 2010; Maccaferri et al., 2010).
Two methods are extensively used in association analysis: The general linear model
(GLM) and the mixed linear model (MLM). With the GLM method, associations between
markers and phenotype are detected using the population membership estimates of
each individual as covariates to control for population structure (Pritchard and
Rosenberg, 1999), since population structure can cause spurious associations (Kang et
al., 2008). MLM, additionally to the population structure, incorporates kinship in the
association analysis allowing an improved control of type I and type II error rates over
GLM due to relatedness and population structure (Yu et al., 2006).

18

False discoveries are a common problem in association studies. A false discovery
refers to the situation when one concludes erroneously that a genomic region harbors a
gene contributing to a quantitative trait (Sabatti, 2007). False discoveries are common in
association studies due to the multiple hypotheses testing (Sabatti, 2007; Storey, 2003).
In order to control false discoveries in association studies, several methods have been
proposed. Bonferroni multiple correction test is one of the most well known methods
(Shaffer, 1995), which defines a cut-off value based on the proposed threshold divided
by number of tests (aka markers employed in the analysis) as a new threshold.
However, this method has been considered too conservative (Perneger, 1998). Some
other methods such as Holm-Bonferroni have been cited in the literature of association
mapping studies (Miedaner et al., 2011), which are described as more powerful test
since is more likely to detect an effect it exists (Abdi, 2010). Finally, the Q value method
proposed by Storey (2002) is also used in association studies, where q-values are
calculated based on p-values.

Association mapping in wheat
Association mapping in wheat has become a popular method to detect QTLs, based on
numerous studies published. For example, association mapping have been used to
detect markers associated with agronomic traits (Yao et al., 2009), quality traits such as
kernel size and milling quality (Breseghello and Sorrells, 2006; Reimer et al., 2008), and
resistance to diseases such as yellow rust (Wang and Chen, 2013), leaf rust (Maccaferri
et al., 2010), Fusarium head blight (Hao et al., 2012; Kollers et al., 2013), and Septoria
tritici blotch (Goudemand et al., 2013).
19

The number and distribution of molecular markers in the genome are critical for
association mapping studies. In this sense, microsatellites (SSRs), Diversity Array
Technology (DArT) and single nucleotide polymorphisms (SNP) markers are considered
the best choices (Crossa et al., 2007; Jing et al., 2009; Zhu et al., 2008). These markers
are highly polymorphic in the wheat genome or any plant species and can be automated
or semi-automated (Akbari et al., 2003; Zhu et al., 2008). Association mapping studies
using SSRs have been published where important agronomic traits such as plant
height, spike length, spikelets per spike, grains per spike, thousand kernel weight have
been associated with SSR markers (Maccaferri et al., 2008; Maccaferri et al., 2010;
Reimer et al., 2008; Yao et al., 2009). DArT markers have been successfully employed
in association mapping to find associations between markers and resistance to stem
rust, leaf rust, yellow rust, and powdery mildew, grain yield in wheat from CIMMYT
(Crossa et al., 2007). Currently, there are around 7,000 DArT markers available for
wheat (Goudemand et al., 2013). In the case of SNPs, there is a large list of SNPs
markers available at databases such as Graingenes (http://wheat.pw.usda.gov ), the
Triticease tool box (http://triticeaetoolbox.org/wheat/) or CerealsDB
(http://www.cerealsdb.uk.net/). The wheat community now has a valuable tool which will
facilitate the screening of wheat populations with almost 9,000 SNP markers distributed
in the wheat genome. This is the 9K SNP chip developed by a research consortium
(Cavanagh et al., 2013) and commercialized by Illumina. The chip was developed from
27 wheat cultivars from the US and Australia (Akhunov et al., 2011) funded by USDAAFRI and Grains Research and Development Corporation (GRDC, Australia)
(http://www.triticeaecap.org/). The wheat SNP chip is now available to the wheat

20

community and results from its use are already being published. Wang and Chen (2013)
have used the SNP chip to detect markers linked with regions conferring resistance to
yellow rust, Zhao et al. (2013) detected frost tolerance locus on Central European winter
wheat, and Würschum et al. (2013) used the SNP chip to conduct a study of genetic
diversity in a population of winter wheat.

Linkage disequilibrium (LD) in plants
Linkage disequilibrium is the nonrandom association of alleles at different loci (FlintGarcia et al., 2003). Alleles at two or more loci are said to be in LD if they are nonrandomly co-inherited as determined by their individual and joint allele frequencies
(Slatkin, 2008). Consequently, for two loci, the alleles at one locus are predictive of
those present at the other. Given its dependence on allele frequencies, any measure of
LD is population-specific (Waugh et al., 2009). The extent of LD differs for each crop
species and LD can vary between different populations of he same crop species (Chao
et al., 2010). Factors affecting LD can be domestication, mating system, inbreeding
(Kim et al., 2007; Wright et al., 2005), selection of favorable alleles (Cavanagh et al.,
2013; Kane and Rieseberg, 2007), and admixture (Flint-Garcia et al., 2003).
It has been observed in wheat that LD extends differently depending on population
origin and genome (A, B, or D), however, LD commonly extends more than 10 cM
(Chao et al., 2010). In corn, due the diversity and the mating system, LD decays
relatively fast. The LD decay distance ranged from 1 to 10 kb (Yan et al., 2009). LD
does not decay as fast in self-pollinated crops (Flint-Garcia et al., 2003). LD in soybean
extended from 90 to 574 kb in three cultivated groups which presented highly variable
21

patterns of LD (Hyten et al., 2007). However, this is not always a constant. In wild
barley, a self-pollinated species, LD may decay faster than expected (Morrell et al.,
2005) or in the case of Arabidopsis thaliana, LD decays within 10 kb on average which
is faster than previously estimated (Kim et al., 2007).

22

REFERENCES

23

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39

CHAPTER 2
STUDY OF THE POPULATION STRUCTURE IN THE WHEAT ASSOCIATION
MAPPING PANEL
Abstract
Wheat is the number one cereal grown in the world based on production area and direct
human consumption. Fifty percent of this wheat is produced in developing countries
where spring wheat type is the most abundant. CIMMYT, based in Mexico, and its
branches located in many countries are the main source of spring wheat germplasm in
the world. As a result, thousands of wheat varieties have been released in the world.
CIMMYT continues the effort of producing wheat germplasm with high yield and
enhanced disease resistance to distribute potential new varieties or sources of valuable
alleles with the mission to end hunger in the world. One major concern of breeders at
CIMMYT is the reduction of genetic diversity. Therefore, CIMMYT breeders focus on
maintaining high levels of diversity in international nurseries. In the current study,
population structure and extent of linkage disequilibrium (LD) were examined in a wheat
association mapping panel (AMP) with 297 wheat accessions developed by CIMMYT
with many elite accessions. To conduct this study, a SNP chip with 9K markers and 20
SSR markers were used. Analysis of the population structure determined that the wheat
AMP can be separated in three sub-populations. Linkage disequilibrium extended
between 13 – 15 cM on chromosomes in the A and B-genome. On the D-genome, LD
decayed at different distances from 3 cM on chromosomes 2, 4, and 7D to 40 cM on
chromosome 6D. The results of the population structure analysis showed that the AMP
includes wheat accessions genetically distant which is important to conduct wheat
40

breeding. The LD analysis showed that LD extends considerably as is expected in a
self-crossing species such as wheat. Based on the LD results, it was concluded that
association studies can be accurately conducted with the 9K SNP chip; however, there
is low marker coverage on the D-genome. Therefore, it is necessary to include more
molecular markers on D-genome to increase the likelihood of finding favorable alleles
and increase the confidence of the results in association studies.

Introduction
Wheat (Triticum aestivum L.) is one of the most ancient crops cultivated by humankind
(McFadden and Sears, 1946) and, nowadays, wheat is the most widely cultivated cereal
in the world with approximately 220 million ha planted annually (FAOSTAT, 2012). Fifty
percent of the wheat is produced in developing countries (Shiferaw et al., 2013). Most of
the wheat cultivated in this region of the world is spring wheat type and the spring wheat
germplasm developed by the International Maize and Wheat International Improvement
Center (CIMMYT) is predominant. According to Lantican et al. (2005), 86% of all spring
bread varieties releases in developing countries (excluding Eastern Europe and Former
Soviet Union) were originated by or had some form of CIMMYT ancestry. The genetic
characteristics of CIMMYT’s wheat germplasm are some of the reasons to find this type
of wheat distributed in many regions of the world. CIMMYT germplasm have Rht genes,
which stands for ‘reduced height’ (Ellis et al., 2005), and indirectly increase harvest
index and reduce lodging by inhibition of gibberellin sensitivity in wheat cultivars
(Flintham et al., 1997; Youssefian et al., 1992). Additionally, CIMMYT focuses its efforts

41

on the incorporation of genes to confer resistance to the major and most frequent biotic
and abiotic constraints that occur around the world (Reynolds and Borlaug, 2006).
Concern over the reduction of genetic diversity in crop species by widespread adoption
of modern cultivars by farmers exist which results in replace of local cultivars and land
races (Frankel, 1970). However, CIMMYT gives singular attention to maintain high
levels of genetic diversity to minimize the risk of genetic vulnerability (Dreisigacker et
al., 2012; Reeves, 1999). Evidence of this strategy can be observed in the pedigrees of
wheat lines that are part of the international nurseries distributed by CIMMYT around
the world, where exotic alleles from wild species and landraces are usually incorporated
(Chen and Li, 2007; Mujeeb-Kazi et al., 2000; Mujeeb-Kazi et al., 1996; Reynolds et al.,
2007).
Elite lines from CIMMYT germplasm contain valuable genes for numerous traits of
interest. Assembly of populations from elite germplasm to discover and exploit these
genes can be a useful tool in wheat breeding. However, association studies on existing
populations used to map QTLs require clear estimation of the population structure to
avoid spurious associations between molecular markers and regions in the genome that
have no effect on phenotype (Pritchard and Rosenberg, 1999). Additionally, it is also
important to estimate how linkage disequilibrium extends in this type of population to
determine the proper number and distribution of molecular markers in the genome in
this association studies (Ball, 2005; Ball, 2013).
Population structure occurs when there is a population subdivision caused by nonrandom mating between individuals and an unequal distribution of alleles exists within
these subpopulations (Flint-Garcia et al., 2003). Genetic markers can be used to

42

estimate the genetic structure of germplasm by inferring individual identity or
relatedness between individuals (Dreisigacker et al., 2012). Several methods have been
proposed to estimate population structure. Among the most popular, it is the modelbased clustering method performed by the software STRUCTURE which uses multilocus genotype data to infer population structure and assign individuals to
subpopulations (Porras-Hurtado et al., 2013; Pritchard et al., 2000). Another method
proposed to estimate population structure is principal component analysis (Patterson et
al., 2006), which models ancestry differences between samples of the population giving
accurate estimation of population stratification (Price et al., 2006).
The wheat association mapping panel has been developed by Singh, Huerta-Espino,
and Duveiller at CIMMYT to conduct association mapping studies of yellow rust and
fusarium head blight. Wheat lines come from CIMMYT elite spring wheat yield trials
(IBWSN44, IBWSN45, SAWYT27, HRWSN20), and other lines selected by the wheat
Pathology Program based on response to Fusarium head blight.
This study aims to estimate the population structure and linkage disequilibrium
decay in a 297 line wheat association mapping panel assayed with the 9K SNP chip.

Materials and Methods
Plant Material
A group of 297 spring wheat accessions was assembled to conduct the current study
(Table 2-1). This collection of accessions will be referred to as the association mapping
panel (AMP) from now on. The AMP was obtained from the International Center for
Maize and Wheat Improvement (CIMMYT) and it included breeding lines, cultivars, and
43

landraces from different origins as well as control wheat lines used for Fusarium head
blight (FHB) and yellow rust (YR) studies. The panel was selected because of its
variability for FHB and YR response observed in previous evaluations in experimental
stations at CIMMYT. The AMP represents a considerable number of the resistant alleles
employed by CIMMYT’s to develop improved wheat lines.

44

Table 2-1. Wheat accessions from the association mapping panel developed by CIMMYT listed with the germplasm
identifier (GID), pedigree and origin from CIMMYT trials.
No
GID
Pedigree
Origin*
1
6175653
SAUAL/KRONSTAD F2004
C45IBWSN
2
6178206
TUKURU//BAV92/RAYON*2/3/PVN
C45IBWSN
3
6179223
PBW343*2/KUKUNA*2//FRTL/PIFED
C45IBWSN
4
6176225
FRET2/TUKURU//FRET2/3/MUNIA/CHTO//AMSEL/4/FRET2/TUKURU//
C45IBWSN
FRET2
5
6176235
ROLF07*2/KACHU #1
C45IBWSN
6
6179254
WBLL1*2/4/BABAX/LR42//BABAX/3/BABAX/LR42//BABAX
C45IBWSN
7
6176332
BAV92//IRENA/KAUZ/3/HUITES*2/4/MURGA
C45IBWSN
8
6176335
WBLL1*2/CHAPIO*2//MURGA
C45IBWSN
9
6176395
KACHU/5/REH/HARE//2*BCN/3/CROC_1/AE.SQUARROSA
C45IBWSN
(213)//PGO/4/HUITES/6/KACHU
10
6176409
ATTILA*2/PBW65*2//W485/HD29
C45IBWSN
11
6176428
BAV92//IRENA/KAUZ/3/HUITES*2/4/CROC_1/AE.SQUARROSA
C45IBWSN
(224)//KULIN/3/WESTONIA
12
6176474
KACHU #1/4/CROC_1/AE.SQUARROSA
C45IBWSN
(205)//KAUZ/3/SASIA/5/KACHU
13
6176600
BAV92//IRENA/KAUZ/3/HUITES*2/4/GONDO/TNMU
C45IBWSN
14
6176914
MUNAL #1/FRANCOLIN #1
C45IBWSN
15
6178972
PFAU/SERI.1B//AMAD/3/WAXWING/4/BABAX/LR42//BABAX*2/3/KUR
C45IBWSN
UKU
16
6174887
BECARD/KACHU
C45IBWSN
17
6177148
TRCH/HUIRIVIS #1
C45IBWSN
18
6177159
TRCH/KBIRD
C45IBWSN
19
6177324
ROLF07/MUU
C45IBWSN
20
6177667
PBW343*2/KUKUNA//TECUE #1
C45IBWSN
21
6175076
NAC/TH.AC//3*PVN/3/MIRLO/BUC/4/2*PASTOR/5/KACHU/6/KACHU
C45IBWSN
22

6175172

YAV_3/SCO//JO69/CRA/3/YAV79/4/AE.SQUARROSA (498)/5/LINE
1073/6/KAUZ*2/4/CAR//KAL/BB/3/NAC/5/KAUZ/7/KRONSTAD
F2004/8/KAUZ/PASTOR//PBW343
45

C45IBWSN

Table 2-1 (cont’d)
23

6175216

24
25
26
27
28
29
30
31
32

6175409
6178018
6178123
6179218
6085788
5895861
5686762
5893342
6178964

33
34

6175500
6175667

35
36
37
38

6175679
6175694
6175740
6175757

39
40
41
42
43

6175897
6175902
6175989
6176021
6176024

44

6176045

45
46

6178273
6178335

WAXWING/4/BL 1496/MILAN/3/CROC_1/AE.SQUARROSA
(205)//KAUZ/5/FRNCLN
WAXWING*2/HEILO
KIRITATI/4/2*BAV92//IRENA/KAUZ/3/HUITES
KZA//WH 542/2*PASTOR/3/BACEU #1
KFA/2*KACHU
QUAIU #1
PASTOR//HXL7573/2*BAU/3/WBLL1
KLDR/PEWIT1//MILAN/DUCULA
PUB94.15.1.12/FRTL
FRET2*2/4/SNI/TRAP#1/3/KAUZ*2/TRAP//KAUZ*2/5/BOW/URES//2*W
EAVER/3/CROC_1/AE.SQUARROSA (213)//PGO
ATTILA*2/PBW65//WBLL1*2/VIVITSI
ALTAR 84/AE.SQUARROSA
(221)//3*BORL95/3/URES/JUN//KAUZ/4/WBLL1/5/REH/HARE//2*BCN/
3/CROC_1/AE.SQUARROSA (213)//PGO/4/HUITES
MURGA//WAXWING/KIRITATI
MURGA/KRONSTAD F2004
ATTILA*2/PBW65//MURGA
BAV92//IRENA/KAUZ/3/HUITES/6/ALD/CEP75630//CEP75234/PT7219/
3/BUC/BJY/4/CBRD/5/TNMU/PF85487
WBLL1*2/CHAPIO//HEILO
WBLL1*2/CHAPIO//HEILO
WAXWING*2/4/BOW/NKT//CBRD/3/CBRD
ROLF07*2/3/PRINIA/PASTOR//HUITES
ROLF07*2/4/CROC_1/AE.SQUARROSA (205)//BORL95/3/2*MILAN

C45IBWSN

WBLL1*2/KUKUNA/5/PSN/BOW//SERI/3/MILAN/4/ATTILA/6/WBLL1*2/
KKTS
WAXWING*2/DIAMONDBIRD
BAV92//IRENA/KAUZ/3/HUITES*2/4/MILAN/KAUZ//CHIL/CHUM18

C45IBWSN

46

C45IBWSN
C45IBWSN
C45IBWSN
C45IBWSN
C45IBWSN
C45IBWSN
C45IBWSN
C45IBWSN
C45IBWSN
C45IBWSN
C45IBWSN
C45IBWSN
C45IBWSN
C45IBWSN
C45IBWSN
C45IBWSN
C45IBWSN
C45IBWSN
C45IBWSN
C45IBWSN

C45IBWSN
C45IBWSN

Table 2-1 (cont’d)
47
48
49
50
51
52

6178362
6176134
6178476
6178527
6178539
6178575

BAV92//IRENA/KAUZ/3/HUITES*2/4/PVN
BAV92//IRENA/KAUZ/3/HUITES*2/4/TNMU
WBLL1/DIAMONDBIRD//WBLL1*2/VIVITSI
SAUAL/YANAC//SAUAL
SAUAL/KIRITATI//SAUAL
CS/TH.SC//3*PVN/3/MIRLO/BUC/4/URES/JUN//KAUZ/5/HUITES/6/YA
NAC/7/CS/TH.SC//3*PVN/3/MIRLO/BUC/4/MILAN/5/TILHI

C45IBWSN
C45IBWSN
C45IBWSN
C45IBWSN
C45IBWSN
C45IBWSN

53
54
55

6178591
6179244
6176173

FINSI/METSO//FH6-1-7/3/FINSI/METSO
INQALAB 91*2/KUKUNA*2//PVN
UP2338*2/4/SNI/TRAP#1/3/KAUZ*2/TRAP//KAUZ/5/MILAN/KAUZ//CHI
L/CHUM18/6/UP2338*2/4/SNI/TRAP#1/3/KAUZ*2/TRAP//KAUZ

C45IBWSN
C45IBWSN
C45IBWSN

56
57
58
59
60
61

6178240
6176189
6176903
6176298
6176361
6176368

C45IBWSN
C45IBWSN
C45IBWSN
C45IBWSN
C45IBWSN
C45IBWSN

62

6176403

63

6176431

UP2338*2/KKTS*2//YANAC
WAXWING/2*ROLF07
WBLL1*2/5/CNO79//PF70354/MUS/3/PASTOR/4/BAV92
ATTILA*2/PBW65*2//MURGA
WBLL1/FRET2//PASTOR*2/3/MURGA
KACHU #1/4/CROC_1/AE.SQUARROSA
(205)//BORL95/3/2*MILAN/5/KACHU
SAUAL/4/CROC_1/AE.SQUARROSA
(205)//BORL95/3/2*MILAN/5/SAUAL
ROLF07*2/4/CROC_1/AE.SQUARROSA (224)//KULIN/3/WESTONIA

64
65

6176455
6176480

C45IBWSN
C45IBWSN

66

6176509

67
68

6176556
6176583

KACHU*2/3/CHUM18/BORL95//CBRD
KACHU #1/4/CROC_1/AE.SQUARROSA
(205)//KAUZ/3/SASIA/5/KACHU
SAUAL*2/6/CNDO/R143//ENTE/MEXI_2/3/AEGILOPS SQUARROSA
(TAUS)/4/WEAVER/5/2*PASTOR
ATTILA*2/PBW65*2/4/BOW/NKT//CBRD/3/CBRD
BAV92//IRENA/KAUZ/3/HUITES/4/FN/2*PASTOR/5/BAV92//IRENA/KA
UZ/3/HUITES
47

C45IBWSN
C45IBWSN

C45IBWSN
C45IBWSN
C45IBWSN

Table 2-1 (cont’d)
69

6176584

BAV92//IRENA/KAUZ/3/HUITES/4/FN/2*PASTOR/5/BAV92//IRENA/KA
UZ/3/HUITES
ROLF07*2/4/BOW/NKT//CBRD/3/CBRD
WBLL1/4/BOW/NKT//CBRD/3/CBRD/5/WBLL1*2/TUKURU
WBLL1*2/4/YACO/PBW65/3/KAUZ*2/TRAP//KAUZ*2/5/GONDO

C45IBWSN

70
71
72

6176611
6178881
6176647

73

6176696

C45IBWSN

6178897
6178898
6176829
6176848
6178715
6178734
6178760
6178768
6178790
6177845
6176924
6178999
6179596
6177095
6177127
6177147
6179044
6177439
6177509
6177552
6177562

PFAU/WEAVER*2//BRAMBLING/3/KAUZ//TRAP#1/BOW/4/PFAU/WEA
VER*2//BRAMBLING
KACHU*2//CHIL/CHUM18
KACHU*2//CHIL/CHUM18
SAUAL/3/ACHTAR*3//KANZ/KS85-8-4/4/SAUAL
BAV92//IRENA/KAUZ/3/HUITES*2/4/YUNMAI 47
WAXWING/KIRITATI*2/3/C80.1/3*BATAVIA//2*WBLL1
BAV92//IRENA/KAUZ/3/HUITES*2/4/WHEAR
KACHU #1*2/WHEAR
KACHU #1/3/C80.1/3*BATAVIA//2*WBLL1/4/KACHU
SAUAL/WHEAR//SAUAL
FRNCLN/BECARD
PAURAQ/3/KIRITATI//PRL/2*PASTOR
QUAIU/5/FRET2*2/4/SNI/TRAP#1/3/KAUZ*2/TRAP//KAUZ
BECARD/KACHU
FRANCOLIN #1/HAWFINCH #1
FRNCLN/TECUE #1
TRCH/HUIRIVIS #1
QUAIU/TECUE #1
KBIRD//WBLL1*2/KURUKU
KINGBIRD #1/KACHU
WAXWING/4/SNI/TRAP#1/3/KAUZ*2/TRAP//KAUZ/5/AKURI
WAXWING/4/SNI/TRAP#1/3/KAUZ*2/TRAP//KAUZ/5/TECUE #1

74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96

6177652
6174927

PBW343*2/KUKUNA//TECUE #1
WBLL1*2/BRAMBLING//FN/2*PASTOR

C45IBWSN
C45IBWSN

48

C45IBWSN
C45IBWSN
C45IBWSN

C45IBWSN
C45IBWSN
C45IBWSN
C45IBWSN
C45IBWSN
C45IBWSN
C45IBWSN
C45IBWSN
C45IBWSN
C45IBWSN
C45IBWSN
C45IBWSN
C45IBWSN
C45IBWSN
C45IBWSN
C45IBWSN
C45IBWSN
C45IBWSN
C45IBWSN
C45IBWSN
C45IBWSN

Table 2-1 (cont’d)
97
98
99
100
101

6174952
6177898
6174993
6175057
6175078

102
103

6179159
6175232

104

6175312

105

6175382

106

6175444

107
108
109
110

6177980
6178005
6178080
6178083

111

6179559

112
113
114
115
116
117
118
119

6179479
6179497
6179417
6179293
6176149
6179562
6179345
6177057

QUAIU #3//MILAN/AMSEL
ATTILA*2/PBW65//MUU #1/3/FRANCOLIN #1
ATTILA*2/PBW65*2//TOBA97/PASTOR
WBLL1*2/VIVITSI//PRINIA/PASTOR/3/WBLL1*2/BRAMBLING
SAUAL/5/REH/HARE//2*BCN/3/CROC_1/AE.SQUARROSA
(213)//PGO/4/HUITES/6/KACHU
MUU #1//PBW343*2/KUKUNA/3/MUU
WBLL1*2/KURUKU/6/CNDO/R143//ENTE/MEXI_2/3/AEGILOPS
SQUARROSA (TAUS)/4/WEAVER/5/2*JANZ/7/WBLL1*2/KURUKU

C45IBWSN
C45IBWSN
C45IBWSN
C45IBWSN
C45IBWSN

TUKURU//BAV92/RAYON*2/7/YAV_3/SCO//JO69/CRA/3/YAV79/4/AE.
SQUARROSA (498)/5/LINE
1073/6/KAUZ*2/4/CAR//KAL/BB/3/NAC/5/KAUZ
NG8675/CBRD//FN/2*PASTOR/4/THELIN/3/2*BABAX/LR42//BABAX

C45IBWSN

BAV92//IRENA/KAUZ/3/HUITES/4/GONDO/TNMU/5/BAV92//IRENA/KA
UZ/3/HUITES
CONI#1/2*HUIRIVIS #1
TECUE #1/2*WAXWING
KBIRD//WH 542/2*PASTOR/3/WBLL1*2/BRAMBLING
MUU/5/TRAP#1/BOW/3/VEE/PJN//2*TUI/4/BAV92/RAYON/6/MILAN/S8
7230//BAV92
KFA/3/PFAU/WEAVER//BRAMBLING/4/PFAU/WEAVER*2//BRAMBLIN
G
ATTILA*2/PBW65//KRONSTAD F2004
WBLL1*2/TUKURU//KRONSTAD F2004
CHIL/CHUM18//GONDO
WBLL1*2/KUKUNA//KIRITATI/3/WBLL1*2/KUKUNA
NORM/WBLL1//WBLL1/3/TNMU/4/WBLL1*2/TUKURU
PBW343*2/KHVAKI*2//YANAC
FRANCOLIN #1/4/BABAX/LR42//BABAX*2/3/KURUKU
PANDORA//WBLL1*2/BRAMBLING

C45IBWSN

49

C45IBWSN
C45IBWSN

C45IBWSN

C45IBWSN
C45IBWSN
C45IBWSN
C45IBWSN
C45IBWSN
C45IBWSN
C45IBWSN
C45IBWSN
C45IBWSN
C45IBWSN
C45IBWSN
C45IBWSN
C45IBWSN

Table 2-1 (cont’d)
120
121
122
123

6181746
6177408
6179457
6179471

WBLL1*2/BRAMBLING//JUCHI
WBLL1*2/KKTS//KINGBIRD #1
TACUPETO F2001//WBLL1*2/KKTS/3/WBLL1*2/BRAMBLING
WBLL1/KUKUNA//TACUPETO F2001/3/KRONSTAD F2004/4/ROLF07

C45IBWSN
C45IBWSN
C45IBWSN
C45IBWSN

124

6175213

C45IBWSN

125

6181759

126
127
128

6178136
6179481
6175720

129
130
131
132

6179510
6179534
6179423
6178918

133

6179013

ATTILA*2/PBW65*2/5/REH/HARE//2*BCN/3/CROC_1/AE.SQUARROS
A (213)//PGO/4/HUITES
HEILO/7/IVAN/6/SABUF/5/BCN/4/RABI//GS/CRA/3/AE.SQUARROSA
(190)/8/VORB/FISCAL
KSW/SAUAL//SAUAL
KAUZ/PASTOR//PBW343/3/KRONSTAD F2004
REH/HARE//2*BCN/3/CROC_1/AE.SQUARROSA
(213)//PGO/4/HUITES/5/KRONSTAD F2004
PRL/2*PASTOR//VORB
TRCH*2/3/WUH1/VEE#5//CBRD
KACHU #1/3/SHA3/SERI//SHA4/LIRA/4/KACHU
PBW343/PASTOR*2/6/TURACO/5/CHIR3/4/SIREN//ALTAR
84/AE.SQUARROSA (205)/3/3*BUC
WBLL1*2/BRAMBLING/4/BABAX/LR42//BABAX*2/3/KURUKU

134
135

6177099
6177771

FRANCOLIN #1/KIRITATI
BABAX/LR42//BABAX*2/3/KUKUNA/4/TAM200/PASTOR//TOBA97

C45IBWSN
C45IBWSN

136
137
138
139
140
141
142

6179553
6181750
5793255
5793394
5793395
5793605
5793920

143

5793926

MURGA/KRONSTAD F2004//QUAIU #3
KENYA NYANGUMI/3/2*KAUZ/PASTOR//PBW343
PARUS/PASTOR//INQALAB 91*2/KUKUNA
CROC_1/AE.SQUARROSA (205)//KAUZ/3/SASIA/4/TROST
CROC_1/AE.SQUARROSA (205)//KAUZ/3/SASIA/4/TROST
PFAU/MILAN//SOVA/3/PBW65/2*SERI.1B
PASTOR/KAUZ/6/CNDO/R143//ENTE/MEXI_2/3/AEGILOPS
SQUARROSA (TAUS)/4/WEAVER/5/2*KAUZ
PASTOR/3/VORONA/CNO79//KAUZ/4/MILAN/OTUS//ATTILA/3*BCN
50

C45IBWSN
C45IBWSN
C45IBWSN
C45IBWSN
C45IBWSN
C45IBWSN
C45IBWSN
C45IBWSN
C45IBWSN

C45IBWSN
C45IBWSN
ELITE2NDYEAR
ELITE2NDYEAR
ELITE2NDYEAR
ELITE2NDYEAR
ELITE2NDYEAR
ELITE2NDYEAR

Table 2-1 (cont’d)
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159

5793927
5793974
5793975
5793991
5794010
5794027
5794033
5794348
5794812
5794547
5794843
5794845
5794846
10004
5536
4936163

PASTOR/3/VORONA/CNO79//KAUZ/4/MILAN/OTUS//ATTILA/3*BCN
CHIBIA/WEAVER//KACHU
CHIBIA/WEAVER//KACHU
PRINIA/PASTOR//HUITES/3/MILAN/OTUS//ATTILA/3*BCN
C80.1/3*BATAVIA//2*WBLL1/3/TOBA97/PASTOR
WHEAR/3/PBW343/PASTOR//ATTILA/3*BCN
PBW343/HUITES/3/MILAN/OTUS//ATTILA/3*BCN
WBLL1*2/KURUKU//KRONSTAD F2004
MONARCA F2007/KRONSTAD F2004
PBW343*2/KUKUNA//PBW343*2/KUKUNA/3/PBW343
WHEAR/2*KRONSTAD F2004
C80.1/3*BATAVIA//2*WBLL1/3/2*KRONSTAD F2004
C80.1/3*BATAVIA//2*WBLL1/3/2*KRONSTAD F2004
SUMAI #3
GAMENYA
FALCIN/AE.SQUARROSA (312)/3/THB/CEP7780//SHA4/LIRA

ELITE2NDYEAR
ELITE2NDYEAR
ELITE2NDYEAR
ELITE2NDYEAR
ELITE2NDYEAR
ELITE2NDYEAR
ELITE2NDYEAR
ELITE2NDYEAR
ELITE2NDYEAR
ELITE2NDYEAR
ELITE2NDYEAR
ELITE2NDYEAR
ELITE2NDYEAR
ELITE2NDYEAR
ELITE2NDYEAR
ELITE2NDYEAR

160
161
162
163
164
165
166
167
168
169
170
171
172
173

4877754
2589783
6121919
6121935
6121938
6121967
6121989
6122002
6122022
6122036
6122042
6122072
6122079
6122123

GONDO/CBRD
HEILO
PICUS/3/KAUZ*2/BOW//KAUZ/4/KKTS/5/HEILO
HUIRIVIS #1/GONDO
HUIRIVIS #1/GONDO
KAUZ/PASTOR//PBW343/3/HEILO
FRET2/WBLL1//TACUPETO F2001/3/HEILO
WBLL1*2/4/YACO/PBW65/3/KAUZ*2/TRAP//KAUZ/5/GONDO
WBLL1*2/CHAPIO//HEILO
WBLL1*2/KURUKU//HEILO
WBLL1*2/KURUKU//HEILO
WBLL1*2/VIVITSI//GONDO
ATTILA/2*PASTOR//FN/2*PASTOR
KACHU #1/4/CROC_1/AE.SQUARROSA
(205)//KAUZ/3/SASIA/5/KACHU

ELITE2NDYEAR
ELITE2NDYEAR
PCFUSWRYRG
PCFUSWRYRG
PCFUSWRYRG
PCFUSWRYRG
PCFUSWRYRG
PCFUSWRYRG
PCFUSWRYRG
PCFUSWRYRG
PCFUSWRYRG
PCFUSWRYRG
PCFUSWRYRG
PCFUSWRYRG

51

Table 2-1 (cont’d)
174

6122128

175

6122172

176
177
178
179
180
181
182

6122349
6122353
6122408
6122546
6122554
6122590
6122654

183
184
185
186
187
188
189

6122710
6122741
6122745
6122847
6123007
6123164
6123179

190
191
192
193

6123188
6123193
6123199
6123225

194

6123229

195
196
197
198

6123240
6123281
6123311
6123623

KACHU #1/4/CROC_1/AE.SQUARROSA
(205)//KAUZ/3/SASIA/5/KACHU
SAUAL*2/6/CNDO/R143//ENTE/MEXI_2/3/AEGILOPS SQUARROSA
(TAUS)/4/WEAVER/5/2*PASTOR
BAV92//IRENA/KAUZ/3/HUITES*2/4/GONDO/TNMU
BAV92//IRENA/KAUZ/3/HUITES*2/4/GONDO/TNMU
FRET2*2/KUKUNA*2//SHA4/CHIL
WBLL1*2/KURUKU*2//TNMU
WBLL1*2/TUKURU//WUH1/BOW/3/WBLL1*2/TUKURU
WBLL1/FRET2//PASTOR*2/3/GONDO
PFAU/WEAVER*2//BRAMBLING/7/IVAN/6/SABUF/5/BCN/4/RABI//GS/
CRA/3/AE.SQUARROSA (190)/8/PFAU/WEAVER//BRAMBLING

PCFUSWRYRG

TRCH*2/TNMU
KACHU*2//CHIL/CHUM18
KACHU*2//CHIL/CHUM18
SAUAL #1/TNMU//SAUAL
PRINIA/PASTOR//CHIL/CHUM18/3/PRINIA/PASTOR
PBW343*2/KHVAKI*2//CHIL/CHUM18
PBW343/PASTOR*2/6/TURACO/5/CHIR3/4/SIREN//ALTAR
84/AE.SQUARROSA (205)/3/3*BUC
PBW343/PASTOR*2/3/WUH1/VEE#5//CBRD
PBW343/PASTOR*2/3/WUH1/VEE#5//CBRD
PBW343/PASTOR*2/3/WUH1/VEE#5//CBRD
NG8675/CBRD/7/IVAN/6/SABUF/5/BCN/4/RABI//GS/CRA/3/AE.SQUAR
ROSA (190)/8/WBLL1*2/CHAPIO
NG8675/CBRD/7/IVAN/6/SABUF/5/BCN/4/RABI//GS/CRA/3/AE.SQUAR
ROSA (190)/8/WBLL1*2/CHAPIO
SHA3/CBRD//TNMU/3/KACHU
FN/2*PASTOR//GONDO/TNMU/3/FRANCOLIN #1
HEILO//GONDO/TNMU/3/WBLL1*2/BRAMBLING
CBRD/FILIN

PCFUSWRYRG
PCFUSWRYRG
PCFUSWRYRG
PCFUSWRYRG
PCFUSWRYRG
PCFUSWRYRG
PCFUSWRYRG

52

PCFUSWRYRG
PCFUSWRYRG
PCFUSWRYRG
PCFUSWRYRG
PCFUSWRYRG
PCFUSWRYRG
PCFUSWRYRG
PCFUSWRYRG

PCFUSWRYRG
PCFUSWRYRG
PCFUSWRYRG
PCFUSWRYRG
PCFUSWRYRG
PCFUSWRYRG
PCFUSWRYRG
PCFUSWRYRG
PCFUSWRYRG

Table 2-1 (cont’d)
199
200
201

6123625
6123661
6122202

CBRD/FILIN
CHIL/CHUM18//GONDO
SAUAL/4/CROC_1/AE.SQUARROSA (205)//KAUZ/3/ATTILA/5/SAUAL

PCFUSWRYRG
PCFUSWRYRG
PCFUSWRYRG

202
203

6122272
6122389

PCFUSWRYRG
PCFUSWRYRG

204

6122425

205

6122610

206

6122665

WAXWING/KIRITATI*2/3/SHA3/SERI//SHA4/LIRA
CNO79//PF70354/MUS/3/PASTOR/4/BAV92*2/5/SHA3/SERI//SHA4/LI
RA
FRET2/TUKURU//FRET2/3/WUH1/VEE#5//CBRD/4/FRET2/TUKURU//F
RET2
WBLL1/FRET2//PASTOR/3/SHA3/SERI//SHA4/LIRA/4/WBLL1/TACUP
ETO F2001//PASTOR
PFAU/WEAVER*2//BRAMBLING/7/IVAN/6/SABUF/5/BCN/4/RABI//GS/
CRA/3/AE.SQUARROSA (190)/8/PFAU/WEAVER//BRAMBLING

207
208
209
210

6122704
6122756
6123015
6123133

PCFUSWRYRG
PCFUSWRYRG
PCFUSWRYRG
PCFUSWRYRG

211
212
213
214
215

6123209
6123221
6123242
6123283
6123299

PFAU/WEAVER//BRAMBLING*2/3/SHA3/SERI//SHA4/LIRA
KACHU #1/3/SHA3/SERI//SHA4/LIRA/4/KACHU
PRINIA/PASTOR//CHIL/CHUM18/3/PRINIA/PASTOR
KETUPA*2/PASTOR/6/TURACO/5/CHIR3/4/SIREN//ALTAR
84/AE.SQUARROSA (205)/3/3*BUC/7/KACHU
CHIL/CHUM18//FN/2*PASTOR/3/PRL/2*PASTOR
CHIL/CHUM18//GONDO/3/WBLL1*2/KURUKU
SHA3/CBRD//TNMU/3/KACHU
FN/2*PASTOR//GONDO/TNMU/3/FRANCOLIN #1
NG8675/CBRD//FN/2*PASTOR/4/THELIN/3/2*BABAX/LR42//BABAX

216

6123343

PCFUSWRYRG

217
218
219
220
221
222

5993501
5993950
5994110
5994207
5994481
5994020

HEILO/7/IVAN/6/SABUF/5/BCN/4/RABI//GS/CRA/3/AE.SQUARROSA
(190)/8/VORB/FISCAL
BAV92//IRENA/KAUZ/3/HUITES/4/DOLL
TRCH/SRTU//KACHU
PRL/2*PASTOR//SRTU/3/PRINIA/PASTOR
WAXWING*2/3/PASTOR//HXL7573/2*BAU
ATTILA*2/PBW65*2//TNMU
SERI.1B//KAUZ/HEVO/3/AMAD*2/4/KIRITATI
53

PCFUSWRYRG
PCFUSWRYRG
PCFUSWRYRG

PCFUSWRYRG
PCFUSWRYRG
PCFUSWRYRG
PCFUSWRYRG
PCFUSWRYRG

SELC44IBWSN
SELC44IBWSN
SELC44IBWSN
SELC44IBWSN
SELC44IBWSN
SELC44IBWSN

Table 2-1 (cont’d)
223
224
225
226
227
228
229
230
231
232

5995334
5995338
5995481
5995483
5995487
5995488
5995598
5995609
5995635
5995800

233
234
235

5996086
5996092
5996469

236
237
238

5849348
5996709
5993900

239

5994089

240
241
242
243
244

5996840
3826276
9774
3855011
5685927

245

5685928

246
247

5685929
5685994

WBLL1*2/4/SNI/TRAP#1/3/KAUZ*2/TRAP//KAUZ/5/KACHU
WBLL1*2/4/YACO/PBW65/3/KAUZ*2/TRAP//KAUZ/5/KACHU #1
FRET2*2/4/SNI/TRAP#1/3/KAUZ*2/TRAP//KAUZ/5/KACHU
FRET2*2/KUKUNA//PRINIA/PASTOR
FRET2/KIRITATI/5/NAC/TH.AC//3*PVN/3/MIRLO/BUC/4/2*PASTOR
FRET2/KIRITATI/5/NAC/TH.AC//3*PVN/3/MIRLO/BUC/4/2*PASTOR
KAUZ//ALTAR 84/AOS/3/MILAN/KAUZ/4/SAUAL
KAUZ//ALTAR 84/AOS/3/MILAN/KAUZ/4/OTUS/TOBA97
SAUAL/3/KAUZ/PASTOR//PBW343
NG8675/CBRD//MILAN/3/SAUAL/6/CNDO/R143//ENTE/MEXI_2/3/AEG
ILOPS SQUARROSA (TAUS)/4/WEAVER/5/2*PASTOR
ATTILA/3*BCN//BAV92/3/TILHI/4/SHA7/VEE#5//ARIV92
ATTILA/3*BCN//BAV92/3/TILHI/4/SHA7/VEE#5//ARIV92
BABAX/KS93U76//BABAX/3/ATTILA/3*BCN//TOBA97/4/WBLL1*2/KUR
UKU
ATTILA*2/PBW65//KRONSTAD F2004
KANZ*4/KS85-8-4//2*WBLL1*2/KURUKU
FRET2/KUKUNA//FRET2/3/PASTOR//HXL7573/2*BAU/5/FRET2*2/4/S
NI/TRAP#1/3/KAUZ*2/TRAP//KAUZ
PRL/2*PASTOR//PARUS/5/NAC/TH.AC//3*PVN/3/MIRLO/BUC/4/2*PAS
TOR
WAXWING*2/JUCHI
FUNDACEP 30
SHANGHAI #8
VOROBEY
CPI8/GEDIZ/3/GOO//ALB/CRA/4/AE.SQUARROSA
(208)/5/HAHN/2*WEAVER/6/SKAUZ/BAV92
CPI8/GEDIZ/3/GOO//ALB/CRA/4/AE.SQUARROSA
(208)/5/HAHN/2*WEAVER/6/SKAUZ/BAV92
CPI8/GEDIZ/3/GOO//ALB/CRA/4/AE.SQUARROSA
NING MAI 96035/FINSI//HEILO
54

SELC44IBWSN
SELC44IBWSN
SELC44IBWSN
SELC44IBWSN
SELC44IBWSN
SELC44IBWSN
SELC44IBWSN
SELC44IBWSN
SELC44IBWSN
SELC44IBWSN
SELC44IBWSN
SELC44IBWSN
SELC44IBWSN
SELC44IBWSN
SELC44IBWSN
SELC44IBWSN
SELC44IBWSN
SELC44IBWSN
20HRWSNFHB
20HRWSNFHB
20HRWSNFHB
20HRWSNFHB
20HRWSNFHB
20HRWSNFHB
20HRWSNFHB

Table 2-1 (cont’d)
248
249
250
251
252
253
254
255
256
257

5685998
5686022
5686023
5551988
5398611
5535312
3855011
5423325
5422808
5427957

258
259
260
261
262
263
264
265
266

5428538
5428200
5427842
5427852
5427940
5427955
5423682
5423717
5423751

267
268
269

5436044
5686798
5686808

270
271
272
273
274
275

5687025
5687066
5687067
5687100
5894425
5894548

NING MAI 96035/FINSI//HEILO
ATTILA/HEILO
ATTILA/HEILO
WAXWING//PFAU/WEAVER
BABAX/LR42//BABAX*2/3/KURUKU
ND643//2*PRL/2*PASTOR
VOROBEY
BABAX/LR42//BABAX/3/ER2000
OASIS//TC14/2*SPER/3/ATTILA/4/WBLL4
FILIN/3/CROC_1/AE.SQUARROSA
(205)//KAUZ/4/FILIN/5/VEE/MJI//2*TUI/3/PASTOR
T.DICOCCON PI94625/AE.SQUARROSA (372)//3*PASTOR
PASTOR/4/WEAVER/TSC//WEAVER/3/WEAVER/5/URES/PRL//BAV92
SW94.2690/SUNCO
SW94.2690/SUNCO
VEE/MJI//2*TUI/3/PASTOR/4/BERKUT
BERKUT/3/ATTILA*2//CHIL/BUC
TAN//TEMPORALERA M 87/AGR/3/FRET2/4/URES/PRL//BAV92
A93324S.7197.29/4/KAUZ//ALTAR 84/AOS/3/KAUZ/5/PASTOR
OASIS//TC14/2*SPER/3/ATTILA/10/ATTILA*2/9/KT/BAGE//FN/U/3/BZA
/4/TRM/5/ALDAN/6/SERI/7/VEE#10/8/OPATA
MEX94.27.1.20/3/SOKOLL//ATTILA/3*BCN
KS82W418/SPN//WBLL1/3/BERKUT
CNDO/R143//ENTE/MEXI75/3/AE.SQ/4/2*FCT/5/KAUZ*2/YACO//KAUZ
/6/BERKUT
SOKOLL/EXCALIBUR
PASTOR/SLVS//FRAME
PASTOR/SLVS//FRAME
BAXTER*2/4/CHEN/AEGILOPS SQUARROSA (TAUS)//BCN/3/BAV92
BERKUT/3/ALTAR 84/AE.SQUARROSA (219)//SERI
MILAN/DUCULA//SUNCO/2*PASTOR
55

20HRWSNFHB
20HRWSNFHB
20HRWSNFHB
20HRWSNFHB
20HRWSNFHB
20HRWSNFHB
27SAWSNFHB
27SAWSNFHB
27SAWSNFHB
27SAWSNFHB
27SAWSNFHB
27SAWSNFHB
27SAWSNFHB
27SAWSNFHB
27SAWSNFHB
27SAWSNFHB
27SAWSNFHB
27SAWSNFHB
27SAWSNFHB
27SAWSNFHB
ELITE2NDYEAR
ELITE2NDYEAR
ELITE2NDYEAR
ELITE2NDYEAR
ELITE2NDYEAR
ELITE2NDYEAR
ELITE2NDYEAR
ELITE2NDYEAR

Table 2-1 (cont’d)
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297

5894637
5894655
5894659
5894787
5894800
5894832
5894851
5894933
5895167
5895192
5895200
5895215
5895222
5895241
5895245
5895256
5837084
5895311
5895333
5895337
5895423
5895427

SW89-5124*2/FASAN//PARUS/PASTOR
SOKOLL//SUNCO/2*PASTOR
CROC_1/AE.SQUARROSA (224)//OPATA/3/ALTAR
SUNSTATE/SD 3195//SOKOLL
SOKOLL*2/GLE
TEMPORALERA M 87
FINSI/3/ATTILA/BAV92//PASTOR/4/PBW343*2/KUKUNA
CO99W329/2*BERKUT
PSN/BOW//MILAN/3/2*BERKUT
CROC_1/AE.SQUARROSA (224)//OPATA/3/RAC655/4/SLVS/PASTOR
SLVS/PASTOR/3/PASTOR//MUNIA/ALTAR 84
YAV79//DACK/RABI/3/SNIPE/4/AE.SQUARROSA (460)/5/2*EX
CNDO/R143//ENTE/MEXI_2/3/AEGILOPS SQUARROSA
CROC_1/AE.SQUARROSA (205)//BORL95/3/KENNEDY/6/
D67.2/PARANA 66.270//AE.SQUARROSA
CALINGIRI/SOKOLL
SOKOLL//SLVS/PASTOR/3/ATTILA*2//CHIL/BUC
BERKUT/HTG
SOKOLL/FRAME
SOKOLL/SLVS
ASTREB*2/NING MAI 9558
ASTREB*2/3/WUH1/VEE#5//CBRD

ELITE2NDYEAR
ELITE2NDYEAR
ELITE2NDYEAR
ELITE2NDYEAR
ELITE2NDYEAR
ELITE2NDYEAR
ELITE2NDYEAR
ELITE2NDYEAR
ELITE2NDYEAR
ELITE2NDYEAR
ELITE2NDYEAR
ELITE2NDYEAR
ELITE2NDYEAR
ELITE2NDYEAR
ELITE2NDYEAR
ELITE2NDYEAR
ELITE2NDYEAR
ELITE2NDYEAR
ELITE2NDYEAR
ELITE2NDYEAR
ELITE2NDYEAR
ELITE2NDYEAR

* C45IBWSN = Cycle 45 International Bread Wheat Screening Nursery; ELITE2NDYEAR, PCFUSWRYRG and
PCFUSWRYRG = Selections from the Pathogy Program at CIMMYT; 27SAWSNFHB= Cycle 27 Semi-arid wheat
screening nursery for Fusarium Head Blight; 20HRWSNFHB= Cycle 20 Haigh-reinfall wheat screening nursery for
Fusarium Head Blight.

56

Genotyping
Ten seeds of each accession of the AMP were planted in a greenhouse at Michigan
State University (MSU) in 2011. A leaf sample from one seedling, between 2 and 3 wk
old, was harvested. The tissue was frozen in liquid nitrogen and stored at -80 ºC prior to
DNA extraction. Genomic DNA was extracted with the Wizard® Genomic DNA
purification (Promega®) according to the manufacturer’s protocol to obtain 20 mL
sample of DNA concentration of 50 ng/uL from each sample. The DNA was genotyped
the by Illumina Infinium® genotyping facility at MSU for whole-genome profiling using
8,632 SNP markers integrated in the 9K SNP chip from Illumina (Cavanagh et al.,
2013).
Three day assays using the 9K chip were carried out to genotype the wheat AMP
samples with the 8,632 SNPs at MSU using iScan screener from Illumina®. Quality of
SNP markers was determined by GenomeStudio® data analysis software from
Illumina®. SNP markers with unexpected genotype AB (heterozygous) were recoded as
either AA or BB based on the graphical interface visualization tool of the software. SNP
markers that did not show clear clustering patterns were excluded. In addition, 66
simple sequence repeats (SSR) markers were screened in the AMP in the
Biotechnology laboratory at INIAP using a 4300 DNA analyzer from LI-COR® to obtain
a larger number of polymorphic markers in the D genome. To visualize and score the
SSR markers, the forward primer of each marker was tagged with a M13 tail with the
following primer tag sequence: “5’-CACGACGTTGTAAAACGAC-3’”. The sequences of
the SSR primer markers can be found in Table 2-2.

57

Table 2-2. Microsatellite markers (SSRs) employed to screen the wheat association mapping panel, sequences of the
primers, and comments from the results of the amplifications.
Marker
Chromosome
Forward sequence
Reverse sequence
Comments
name
Location
5' - 3'
5' - 3'
Barc133
AGCGCTCGAAAAGTCAG
GGCAGGTCCAACTCCAG
3BS, 5D
Linked to
Fhb-1
Gwm493
TTCCCATAACTAAAACCGCG
GGAACATCATTTCTGGACTTTG
3BS
Linked to
Fhb-1
Barc19
GCGACCCGAGTAGCCTGAA
GGTGGACCATTAGACGCTTACTT
3AS
Linked to a
G
FHB QTL
Wmc44
GGTCTTCTGGGCTTTGATCCTG
TGTTGCTAGGGACCCGTAGTGG
1BL
Yr29
Gwm261
CTCCCTGTACGCCTAAGGC
CTCGCGCTACTAGCCATTG
2D
Rht8
Xgwm259 AGGGAAAAGACATCTTTTTTTTC CGACCGACTTCGGGTTC
1B
Yr29
Xgwm146 CCAAAAAAACTGCCTGCATG
CTCTGGCATTGCTCCTTGG
7BL
Septoria
Xgwm493 TTCCCATAACTAAAACCGCG
GGAACATCATTTCTGGACTTTG
3BS
Fhb-1
Xgwm533 AAGGCGAATCAAACGGAATA
GTTGCTTTAGGGGAAAAGCC
3BS
Xbarc124 TGC ACC CCT TCC AAA TCT
TGC GAG TCG TGT GGT TGT
2A, 2B, 2D
Yr61
Xgwm359 AGC CGC GAA ATC TAC TTT GA TTA AAC GGA CAG AGC ACA CG
2A
Yr61
Cfa2149
CTT GGA GCT CGG GTA GTA GC AAG GCA GCT CAA TCG GAG TA
4B, 5A
Yr48
Snf-A2
TCC GTC TCC ATC ATT CAA CA
GTG TTG CGC AAG TTT GTG AC
5AL
Yr48
Xgwm130 AGC TCT GCT TCA CGA GGA AG CTC CTC TTT ATA TCG CGT CCC
7D
Yr18
Wmc720
CACCATGGTTGGCAAGAGA
CTGGTGATACTGCCGTGACA
4D
Cfd23
TAGCAGTAGCAGCAGCAGGA
GCAAGGAAGAGTGTTCAGCC
4D
Cfd84
GTTGCCTCGGTGTCGTTTAT
TCCTCGAGGTCCAAAACATC
4D
Barc196
GGTGGGTTTTATCGAATAGATTT GCGTTTCGTCAAGATTAATGCAG
6D
GCT
GTTT
Wmc14
ACCCGTCACCGGTTTATGGATG
TCCACTTCAAGATGGAGGGCAG
7D
Wmc606
CCGATGAACAGACTCGACAAGG GGCTTCGGCCAGTAGTACAGGA
7B, 7D
Easy to score
and saparate
genomes.
Few bands
on 7B
58

Table 2-2 (cont’d)
Gwm297
Wmc581

ATCGTCACGTATTTTGCAATG
CATGTTGCCATCAAACTCGC

Xbarc71

GCGCTTGTTCCTCACCTGCTCAT
A
CCTGTTGCATACTTGACCTTTTT
TTGATATTAAATCTCTCTATGTG
TATAGGCAAATTAATTAAGACG
CGGCTAGTAGTTGGAGTGTTGG
ATTGATGTGTACGATGTGCCTG
GGTTGCAGTTTCCACCTTGT
CCCTCCTCTCTTTAGCCATCC
GAGGAGTAAGACACATGCCC
GGGATGACACATAACGGACA
TTGTGATCCTGGTTGTGTTGTGA
GATCGAGTGATGGCAGATGG
AGGAGCTCCTCTGTGCCAC
AGGATCAGAATAGTGCTACCC
TCTGAACATTACACAACCCTGA

TGCGTAAGTCTAGCATTTTCTG
GCTATTGACATGCAACTATGGAC
CT
GCGTATATTCTCTCGTCTTCTTGT
TGGTT
GGAGTTCAATCTTTCATCACCAT
AATTTTATTTGAGCTATGCG
ATCTTTATGTGAGTACACTGC
ACCGCCTCTAGTTATTGCTCTC
CATGTCAATGTCATGATGAAGC
CATCTATTGCCAAAATCGCA
GCACGTACTATTCGCCTTCACTTA
GTGGCTGGAGATTCAGGTTC
ATCAGCGGCGCTATAGTACG
CACCCAGCCGTTATATATGTTGA
TGTGAATTACTTGGACGTGG
TTCGGGACTCTCTTCCCTG
ATCCCGTGATCAGAATAGTGT
TGCTCTCTCTGAACCTGAAGC

AAGTAGGCGAGCGTTGT
TTGTCACACACGCACTCCC
GCGCTTGTTCCTCACCTGCTCAT
A
CCACTAGGAAGAAGGGGAAACT

TTTCCCTGGCGAGATG
GTCCTTCCCTCGTTCATCCT
GCGTATATTCTCTCGTCTTCTTGT
TGGTT
ATCTGGATTACTGGCCAACTGT

Wmc331
Gwm624
Gdm153
Barc130
Wmc111
Cfd2
Barc228
Gwm301
Cfd35
Wmc11
Gwm161
Gwm314
Wmc492
Gwm456
Wmc656
Wmc549
Barc71
Wmc617

59

7B
7B
3DL
4D
4D
5D
5D
2D
Multilocation
2D
2D
2DL
3A, 3B, 3D
3D
3D
3D, 5A
3D

Linked to
Sr24

linked to Sd-1
character

3D
3D
3D
4A, 4B, 4D

Not possible
to distinguish
between
genomes B
and D

Table 2-2 (cont’d)
Wmc89

ATGTCCACGTGCTAGGGAGGTA

TTGCCTCCCAAGACGAAATAAC

Wmc622

CAGGAAGAAGAGCTCCGAGAAA

CTTGCTAACCCGCGCC

Wmc74

AACGGCATTGAGCTCACCTTGG

TGCGTGAAGGCAGCTCAATCGG

4B, 4D, 5A

Wmc233
Gwm205
Gdm136

GACGTCAAGAATCTTCGTCGGA
CGACCCGGTTCACTTCAG
CTCATCCGGTGAGTGCATC

ATCTGCTGAGCAGATCGTGGTT
AGTCGCCGTTGTATAGTGCC
CCCGCATGTCTACATGAGAA

5DS
5A, 5D
1A, 1B, 3D,
5D

Gwm174
Cfd183

GGGTTCCTATCTGGTAAATCCC
ACTTGCACTTGCTATACTTACGA
A
TGCTGATGTTGTAAGAAGGC
TGAGTTCTTCTGGTGAGGCA
ACCGCTCGGAGAAAATCC
CAACTCAGTGCTCACACAACG
TTTCTTCTGTCGTTCTCTTCCC
GCAATTTCACACGCGACTTA
CGCAGAAGAAAAACCTCGCAGA
AAAACC
GAGGCTTGCATGTGCTTGA

GACACACATGTTCCTGCCAC
GTGTGTCGGTGTGTGGAAAG

Gwm654
Cfd49
Gdm132
Gwm469
Gwm325
Cfd76
Barc204
Wmc773

TGCGTCAGATATGCCTACCT
GAATCGGTTCACAAGGGAAA
AGGGGGGCAGAGGTAGG
CGATAACCACTCATCCACACC
TTTTTACGCGTCAACGACG
CGCTCGACAACATGACACTT
CGCAGTGTATCCAAATGGGCAAG
C
GCCAACTGCAACCGGTACTCT
60

4A, 4B, 4D

4D

5D
5D
5D
6D
6D
5D, 6D
6B, 6D
6D
1A, 6A, 6D
5B, 6D

Easy to
detect bands
from the D
genome
Multilocus all
from the D
genome
Easy to
distinguish.
234 bp on
5A, 256 bp
on 4D, and
310 bp on 4B

Not possible
to distinguish
between
genomes

Table 2-2 (cont’d)
Gwm350

ACCTCATCCACATGTTCTACG

GCATGGATAGGACGCCC

Cfd41
Wmc629
Wmc405

TAAAGTCTCAGGCGACCCAC
TTTGTGTGTTGGATGCGTGC
GTGCGGAAAGAGACGAGGTT

AGTGATAGACGGATGGCACC
AATAAAACGCGACCTCCCCC
TATGTCCACGTTGGCAGAGG

Wmc121
Gwm437
Gwm121

GGCTGTGGTCTCCCGATCATTC
GATCAAGACTTTTGTATCTCTC
TCCTCTACAAACAAACACAC

ACTGGACTTGAGGAGGCTGGCA
GATGTCCAACAGTTAGCTTA
CTCGCAACTAGAGGTGTATG

Gwm37

ACTTCATTGTTGATCTTGCATG

CGACGAATTCCCAGCTAAAC

Multilocation

Cfd175

TGTCGGGGACACTCTCTCTT

ACCAATGGGATGCTTCTTTG

2D, 7D

61

4A, 7A, 7D

7D
7D
Multilocation

7D
7D
5D, 7D

Not possible
to distinguish
between
genomes

Not possible
to distinguish
between
genomes

Possible to
distinguish
Not possible
to distinguish
between
genomes

Population structure
Population structure was estimated with STRUCTURE version 2.3.4 software, which
implements a model-based clustering algorithm (Pritchard et al., 2000). One condition to
perform the analysis is the use of unlinked markers, so this analysis was conducted
with 315 SNP distributed in the 21 wheat chromosomes and 22 SSR markers located
exclusively in the D genome (Table 2-3). The admixture model was selected due the
nature of the wheat AMP. The parameters were set to 10,000 burnings and the number
of MCMC iterations after burning were 100,000 with subpopulation number (k) from k=1
to k=10. The optimum k value was determined with Evanno method, which consists of
identifying the true number of clusters (K) in a sample of individuals using an ad hoc
statistic Delta K based on the rate of change in the log probability of data between
successive K values (Evanno et al., 2005). To apply the Evanno method, the online
software Structure Harvester version 0.6.93 was employed (Earl and Vonholdt, 2012).
The online software can be found at:
(http://taylor0.biology.ucla.edu/structureHarvester/).

EIGENSTRAT software, which infers principal components (Price et al., 2006), was
used to detect and correct for population structure. Principal Component Analysis was
conducted with 3,701 SNP markers distributed in the wheat AMP genome with MAF >
5%.

62

Table 2-3. List of SSR markers that amplified in the wheat AMP genome.
SSR
Chromosome
SSR
Chromosome
SSR
Chromosome
marker
marker
marker
name
name
name
Barc83
Wmc728
Gwm147
1A
1B
1D
Gwm636
Barc133
Wmc216
2A
3B
1D
Wmc658
Gwm493
Gwm261
2A
3B
2D
Barc19
Wmc89
Wmc111
3A
4B
2D
Cfa2149
Gwm297
Barc71
5A
7B
3D
Gwm161
3D
Gwm314
3D
Gwm456
3D
Wmc492
3D
Cfd84
4D
Wmc331
4D
Wmc720
4D
Barc130
5D
Gdm153
5D
Barc204
6D
Cfd49
6D
Gdm132
6D
Gwm325
6D
Gwm469
6D
Wmc773
6D
Gwm121
7D
Gwm437
7D

63

Linkage disequilibrium
For estimating linkage disequilibrium (LD), SNP alleles with minor allele frequency
(MAF) higher than 0.05 were used. Pair-wise linkage disequilibrium (LD) was measured
2

using the squared allele-frequency correlations (r ) (Flint-Garcia et al., 2003). TASSEL
4.0 (Bradbury et al., 2007) was employed to estimate inter and intra-chromosomal LD.
To confirm the results from TASSEL, a set of SNP markers located in different regions
2

of the wheat AMP genome were selected and r was calculated using GGT 2.0:
2

Graphical Genotypes (van Berloo, 2008). LD decay were assessed by calculating r for
pairs of SNP loci and plotting them against genetic distance (cM) and the cutoff was set
2

as r > 0.2 is in LD.

Results
Genotyping
The wheat AMP was screened at MSU with 8,632 SNP markers included in the wheat
SNP chip from llumina®. The total number of markers with missing data (no call) was
2,324 (27%) (Figure 2.1). The remaining SNP markers (6,308) ranged from 100 to 13%
calls. The quality of the 6,308 SNP markers was determined by GenomeStudio® data
analysis software from Illumina®. From the total number of good quality SNP markers,
681 were coded as heterozygous by Genome Studio’s automated SNP calling in some
individuals of the wheat AMP, but they were actually homozygous. The 681 markers
were re-coded from AB to AA or BB allele based on GenomeStudio results. A total of
64

1,629 SNP markers were not considered for the analysis because of poor quality or
because the position in the genome was unknown. Additionally, markers with more than
10% no-calls were also not considered in the analysis. The final number of markers
considered for association analysis were 4,679 SNP (Figure 2.2), which were part of the
7,497 SNP markers with known positions in the wheat genome (Cavanagh et al., 2013)
and additionally 32 SSR markers, out of 66 SSR markers screened, were selected
based on clarity to score and genome specificity. Twenty-two SSR markers out of the
32 were located on the D-genome.
The distribution of the SNP markers in the wheat chromosomes are shown in Table 2-4.
The A and B-genome have the best coverage in every chromosome compared with
marker coverage on D-genome. The number of markers in A and B-genome ranged
from 87 SNP markers on chromosome 4B to 404 SNP markers on chromosome 2B.
The total number of SNP markers distributed on the entire D-genome was only 227.
Chromosomes 3, 5, 6, and 7 from the D-genome were subdivided in three linkage
groups each, according to the original report of SNP positions from the consensus map
(Cavanagh et al., 2013). The number of SNP per linkage group ranged from 6 on
chromosome 4D to 65 on chromosome 1D (Table 2-4).

65

Table 2-4. Size of the wheat linkage groups (cM) and number of SNP markers from the 9K SNP chip after filtering for
MAF(> 5%) and missing data (< 10%).
1
Size of
No of
Chr.
Size of
No of
Chr.
Size of
No of
Chr.
Chr. (cM)
SNPs for
Chr. (cM)
SNPs for
Chr. (cM)
SNPs for
AM
AM
AM
1A
2A
3A
4A
5A
6A
7A

1

183
231
172
211
196
218
194

254
195
241
210
279
255
276

1B
2B
3B
4B
5B
6B
7B

141
272
196
125
227
154
169

Chr. = Chromosome

66

221
404
238
87
369
281
173

1D
2D
3D1
3D2
3D3
4D
5D3
5D2
5D1
6D1
6D2
6D3
7D1
7D2
7D3

145
192
2
2
85
102
48
16
54
8
78
8
7
55
8

65
44
3
0
14
6
15
2
17
17
21
2
3
11
7

Linkage disequilibrium (LD)
Linkage disequilibrium analysis was conducted with 3,701 SNP markers after filtering
the selected 4,679 SNP showing good quality against alleles with minimum frequency >
5%. Linkage disequilibrium decay was different for each genome. In the A-genome, LD
2

decayed to the proposed cutoff of r = 0.2 at about 13 cM (Figure 2.9), while in the Bgenome, LD decayed at 15 cM (Figures 2.10). In the D genome, the lack of good
coverage of markers resulted in unreliable estimate of the LD decay for the entire
genome (Figure 2.11). Therefore, the LD decay calculation was not performed for the
entire genome, but it is reported for each individual chromosome. Thus, LD on
chromosome 1D decayed at 15 cM. LD on chromosomes 2D, 4D and 7D, decayed at 3
cM. On chromosome 3D, LD decayed at 10 cM. On chromosome 5D, LD decayed at 5
cM, and LD on chromosome 6D decayed at 40 cM Figures 2.7 and 2.8).
In the LD analysis, 6,857,956 pair-wise comparisons were performed between SNP
2

markers of the wheat AMP. The percentage of comparisons with an r ≤ 0.2 was 98.8%
and only 1.2% of the pair-wise comparisons between molecular markers were higher
than 0.2.
The intra-chromosomal analysis of the linked markers showed that 16.8 and 16.7% of
the pair-wise comparisons of each chromosome in the A and B-genome respectively
2

had an r > 0.2. However, for the D-genome, 21.3% of the pair-wise comparisons were
2

r > 0.2 (Figures 2.3 – 2.8).
In the A-genome, LD decays at different rates in each chromosome. Analyzing the pair2

wise comparison of r values it was possible to note that Chromosome 3A showed the
67

2

largest percentage of pair-wise comparison of SNP markers with r values > 0.2
(20.9%). On other chromosomes such as 5A showed lower percentage of pair-wise
2

comparisons with r > 0.2 (7.9%) (Figures 2.3 and 2.4).
In the B-genome, Chromosome 6B had the largest percentage of pair-wise comparison
2

of SNP markers with r values > 0.2 (21.3%), while chromosome 7B had the lower
2

percentage of pair-wise comparisons with r > 0.2 (11.7%) (Figures 2.5 and 2.6).

Population structure analysis
The population structure of the AMP was determined with: 1) STRUCTURE software
based on 315 SNP markers separated by at least 4.0 cM in the whole wheat genome,
and 22 SSR markers located on linkage groups of the D-genome exclusively (Table 23), and 2). EIGENSTRAT software, which was employed to perform a principal
component analysis (PCA) with the 3,701 SNP markers from the wheat AMP distributed
on the 21 wheat chromosomes. The output from STRUCTURE was analyzed with
Structure Harvester to obtain Delta K values and determine the number of
subpopulations in the wheat AMP. The results indicated that there were three
subpopulations (k=3) (Figure 2.12). The first subpopulation with 96 accessions, a
second subpopulation with 94 accessions, and the third subpopulation with 107
accessions (Figure 2.13). The principal component analysis also showed three clusters
when PCA1 was plotted against PCA2 as shown in Figure 2.14. In this Figure, colors
have been assigned to each wheat accession based on STRUCTURE results (Red=
sub-population 1, Green= sub-population 2, and Blue= sub-population 3). It can be
68

observed in Figure 2.14 that clusters from the PCA, shows agreement with the
STRUCTURE results. Similar results between the STRUCTURE analysis and the
Neighbor Joining (NJ) tree (Fig 2.15) analysis can also be observed where three
clusters are formed. However, each of the three clusters from the tree includes wheat
accessions that were assigned to a different group according to STRUCTURE results.
Seven accessions that STRUCTURE assigned to subpopulation 3 (blue color) and five
accessions assigned to subpopulation 2 (green color) were clustered in the tree where
most of the wheat accessions that STRUCTURE assigned as subpopulation 1 (red
color). In the same way, twelve accessions from subpopulation 1 (red color) and one
accession from subpopulation two (green color) were clustered in the cluster that
STRUCTURE determined as subpopulation 3 (blue color). Finally, 11 accessions from
supopulation 1 (red color) and 31 accessions from subpopulation 3 (blue color) were
clustered in subpopulation 2 (green color).

Discussion
Genotyping
The final number of SNP markers used for association analysis was 4,679. These
markers were selected for three reasons. First, these markers showed good quality,
which means that presented good allele calls and clustered clearly to differentiate
between one or another allele for each individual. Second, these markers have less
than 10% missing data in the wheat AMP. Third, these markers were part of the 7,497
SNP markers with known positions in the wheat genome (Cavanagh et al., 2013). In
total, 27% of SNP markers did not function (no-call) in the wheat AMP. The percentage
69

of markers with no-calls was similar to the number of markers that did not produce
signals obtained by Würschum et al. (2013), where the number of markers with no-calls
was 26.9%. The number of no-calls differs widely within markers. A SNP marker could
not be detected because poor quality of the DNA sample or the marker did not
hybridize, or simply, the SNP was not present (Illumina, 2008).
Single nucleotide polymorphisms markers are ideal to study genetic structure and
diversity in wheat (Chao et al., 2010) due to abundance and distribution in the whole
genome. Here, using the 9K SNP chip from Illumina (Cavanagh et al., 2013), we have
confirmed that this SNP platform system works for spring wheat.
All the SSR markers used in this study amplified and detected polymorphisms, but, not
all were useful. The main problem observed with the SSR screening was the difficulty to
identify the genome origin of each allele when a marker amplified in more than one
locus. In wheat, it is relatively easy to determine the number of loci expected in the
progeny based on the number of locus observed in the parents and if the marker
amplifies in paralogous loci when biparental populations are screened with molecular
markers (Song et al., 2005). However, in this study, the marker screening of wheat
breeding lines with different ancestry resulted frequently in multi-locus amplification. It
has been observed in complex genomes as wheat (Somers et al., 2004). As a
consequence, only 32 SSR markers were scored and able to be assigned to the proper
genome. Five SSR markers were located on A-genome, five on B-genome, and twenty
two on D-genome (Table 2-3).
The distribution of useful SNP markers for this study was ideal for the A and B-genome
and poor for D-genome. It is common to observe reduced number of polymorphic

70

number of markers at the D-genome in wheat (Pestsova et al., 2000; Somers et al.,
2004). The reason for the reduced polymorphism number is the result of few
hybridizations in the formation of the modern wheat by the fusion of the tetraploid wheat
genome with the T. tauschii genome (Talbert et al., 1998).

Linkage disequilibrium
As expected, the extend of LD for SNP pairs decays as map distance increases (Du et
2

al., 2007; Sorkheh et al., 2008) . In this study, LD declined to r ≤ 0.2 more slowly than in
other studies where LD decayed at about 6.3 cM in the A-genome and 7 cM in the Bgenome (Chao et al., 2007) and at 5 cM in a US winter wheat and a durum wheat
population (Breseghello and Sorrells, 2006; Maccaferri et al., 2005). LD values must be
different for different wheat populations (Chao et al., 2010) since LD is affected by
several factors such as recombination, population size, admixture, or genetic
bottlenecks (Flint-Garcia et al., 2003). In other crops such as soybean, LD decay
pattern differed among four distinct populations of diverse origin (Hyten et al., 2007). LD
can decay faster or slower, depending primary on the mating system. LD usually decays
faster in open pollinated crops. For example, LD in maize, often measured as physical
2

distance, has been shown to decay to an r ≤ 0.2 within 500 – 2,000 bp (Remington et
al., 2001). This is extremely fast compared with hexaploid wheat if we consider that 1
cM from the consensus map (Cavanagh et al., 2013) represents an average of 3.4 Mb
based on the wheat genome size of 16 Gb (Arumuganathan and Earle, 1991).
LD decay distance was different in each chromosome. It has also been observed that
LD decay distance may differ among chromosomes (Yan et al., 2009).
71

The distance over which LD persist defines the number of markers needed to conduct
association mapping analysis (Sorkheh et al., 2008). In this study, LD extended to about
13cM and 15 cM for SNP markers at the A and B- genome, while the distance at the Dgenome varied from 3 to 15 cM with exception ofr chromosome 6D, which decayed at
40cM. The SNP map developed by (Cavanagh et al., 2013) has 3,500 cM, so the
number of SNP markers utilized in this study tell us that there is 1 SNP per cM. This
situation would be ideal, since LD extends > 3cM in every linkage group. However, this
situation is not true for the D-genome due to the reduced presence of markers in this
genome.

Population structure analysis
A population is structured if individuals of the population do not mate at random and
alleles deviate from the Hardy Weinberg equilibrium which results in unequal distribution
of alleles within these subpopulations (Flint-Garcia et al., 2003). In this study, three
subpopulations were identified using three different methods to group individuals based
on genotypic information. The computer software STRUCTURE was able to allocate
each accession of the wheat AMP in one of the three subpopulations based on multiple
locus genotype data using computationally intensive methods (Pritchard et al., 2000).
The results from STRUCTURE differed slightly from the other two methods utilized to
estimate population structure (principal component analysis or NJ three clustering
method). However, it is clear that individuals can be separated in three subpopulations
(Figures 2.13; 2.14; 2.15).

72

The three methods show to be efficient in this study. They separated the wheat
accessions in three subpopulations. Some studies have mentioned that STRUCTURE
software might have some limitations to accurately identify genetic clusters within
species (Kalinowski, 2011; Price et al., 2006); however, for this specific study the results
were similar.
Some wheat accessions assigned to one subpopulation were not genetically distant
from other accessions assigned to other subpopulations as can be observed in the NJ
tree (Fig 2.15). These lines could have same ancestors in common. The analysis with
STRUCTURE using the Admixture model can show how these lines share loci from
different subpopulations. Individual observations of the membership coefficients on
each line from STRUCTURE (Appendix A) show how these lines have close values that
could be used to assign these wheat lines in one or other sub-population. For instance,
accession 135 (BABAX/LR42//BABAX*2/3/KUKUNA/4/TAM200/PASTOR//TOBA97),
line from subpopulation 2 (Green color) according to STRUCTURE analysis, was
clustered with lines from subpopulation three (blue color) in the NJ tree analysis. The
membership coefficients were: Q1=0.38, Q2= 0.44, and Q3= 0.18. So, values for Q1
and Q2 were relatively close. Anthor example is accession 7
(BAV92//IRENA/KAUZ/3/HUITES*2/4/MURGA), assigned to subpopulation 2 (Green
color) by STRUCTURE, was clustered with lines from population 1 (Red color). The
membership coeficients of this accession were Q1= 0.44, Q2= 0.52, and Q3=0.03.
Based on these values, it is not surprising that one analysis produced a different result.

73

Conclusions
Accessions in the wheat association mapping panel can be assigned to three different
sub-populations. Three different methods based on genotypic data coincided to the
allocation of most of the wheat accessions into these three clusters. In the same
manner, these wheat accessions showed rich allele diversity based on SNP and SSR
markers.
Linkage disequilibrium in the wheat AMP extends considerably as expected in selfcrossing species; however, LD decay was different in each chromosome. These results
indicated that 1-3 molecular markers per cM can be enough for association mapping
studies. In other words, 3,000 to 4,000 molecular markers would be needed to
accurately conduct an association study in wheat. The wheat SNP chip with 9K SNP
markers is a great tool to study the genetic diversity of wheat and perform association
mapping studies; however, low coverage and polymorphism was observed in most of
the chromosomes of the D-genome. It will be advisable to include more molecular
markers on the D-genome to provide more complete marker coverage and increase the
chances to discover marker-trait associations.

Acknowledgments
Eduardo Morillo and Miguel Marquez from INIAP-Ecuador helped with SSR marker
screenings.
Daniel Zarka from MSU genotyped the wheat AMP with 9K SNP chip from Illumina.
Zixang Wen from MSU with software analysis.

74

27%

73%

SNP calls
no-calls
Figure 2-1. Results from the Illumina® iSelect scan: blue color corresponds to the
percentage of SNP markers from the 9K SNP chip that were detected and red color
corresponds to the percentage of SNP markers placed in the 9K SNP Chip from
Illumina that were not detected.

26%

74%

Eliminated by: < 5%
MAF or poor quiality
Good quality

Figure 2-2. Percentage of SNP markers eliminated after filtering for poor quality or
minimum frequency alleles (<5%) and SNP markers showing good quality and
considered for analysis.

75

2

Figure 2-3. Scatter plot of LD values (r ) against genetic distance (cM) of chromosomes
1A – 4A.

76

2

Figure 2-4. Scatter plot of LD values (r ) against genetic distance (cM) of chromosomes
5A – 7A.

77

2

Figure 2-5. Scatter plot of LD values (r ) against genetic distance (cM) of chromosomes
1B – 4B.

78

2

Figure 2-6. Scatter plot of LD values (r ) against genetic distance (cM) of chromosomes
5B – 7B.

79

2

Figure 2-7. Scatter plot of LD values (r ) against genetic distance (cM) of chromosomes
1D – 4D.

80

2

Figure 2-8. Scatter plot of LD values (r ) against genetic distance (cM) of chromosomes
5D – 7D.
LD decay in'Genome A' of the wheat AMP
1
0.8

1A
2A

r2

0.6

3A

0.4

4A
5A

0.2

6A

0
0
-0.2

50

100

150

200

250

300

350

400

450

7A

Genetic distance (cM)

Figure 2-9. Intrachromosomal comparison of LD decay on chromosomes from the A
genome of the wheat AMP.
81

r2

LD decay in 'Genome B' of the wheat AMP
0.9
0.8
0.7
0.6
0.5
0.4
0.3
0.2
0.1
0

1B
2B
3B
4B
5B
6B
7B
0

100

200
300
Genetic distance (cM)

400

500

Figure 2-10. Intrachromosomal comparison of LD decay on chromosomes from the B
genome of the wheat AMP.

LD decay in 'Genome D' of the wheat AMP

r2

1.2
1

1D

0.8

2B

0.6

3D

0.4

4D

0.2

5D

0

6D

-0.2 0

50

100
150
Genetic distance (cM)

200

7D

Figure 2-11. Intrachromosomal comparison of LD decay on chromosomes from the D
genome of the wheat AMP.

82

Figure 2-12. Distribution of Delta K values in wheat the association mapping panel
based on STRUCTURE analysis. East Lansing. 2013.

Figure 2-13. Population structure based on STRUCTURE software of the wheat
association mapping panel. East Lansing. 2013.

83

Figure 2-14. Principal component analysis of the wheat association mapping panel
(red= sub-population one, green= sub-population two, blue= sub-population three)
based on SNP markers. East Lansing. 2013.

84

Figure 2-15. Neighbor joining tree of the wheat Association Mapping Panel. Accessions
have been assigned colores based on STRUCTURE analysis. Red= sub-population 1,
Green= sub-population 2, and Blue= subpopulation 3.

85

APPENDIX

86

Appendix: wheat association mapping panel and membership coefficients.
Table 2-5. Wheat AMP accessions and membership coefficients for each sub-population (Q) determined by STRUCTURE
software.
Accession pedigree
Q1
Q2
Q3
1
SAUAL/KRONSTAD F2004
0.97 0.02 0.01
2
TUKURU//BAV92/RAYON*2/3/PVN
0.26 0.16 0.57
3
PBW343*2/KUKUNA*2//FRTL/PIFED
0.70 0.09 0.21
4
FRET2/TUKURU//FRET2/3/MUNIA/CHTO//AMSEL/4/FRET2/TUKURU//FRET2
0.06 0.45 0.50
5
ROLF07*2/KACHU #1
0.03 0.09 0.88
6
WBLL1*2/4/BABAX/LR42//BABAX/3/BABAX/LR42//BABAX
0.04 0.15 0.81
7
BAV92//IRENA/KAUZ/3/HUITES*2/4/MURGA
0.44 0.52 0.03
8
WBLL1*2/CHAPIO*2//MURGA
0.22 0.19 0.59
9
KACHU/5/REH/HARE//2*BCN/3/CROC_1/AE.SQUARROSA
0.97 0.01 0.01
(213)//PGO/4/HUITES/6/KACHU
10
ATTILA*2/PBW65*2//W485/HD29
0.05 0.06 0.90
11
BAV92//IRENA/KAUZ/3/HUITES*2/4/CROC_1/AE.SQUARROSA
0.69 0.25 0.06
12
KACHU #1/4/CROC_1/AE.SQUARROSA (205)//KAUZ/3/SASIA/5/KACHU
0.97 0.01 0.01
13
BAV92//IRENA/KAUZ/3/HUITES*2/4/GONDO/TNMU
0.55 0.39 0.06
14
MUNAL #1/FRANCOLIN #1
0.01 0.01 0.97
15
PFAU/SERI.1B//AMAD/3/WAXWING/4/BABAX/LR42//BABAX*2/3/
0.08 0.02 0.90
KURUKU
16
BECARD/KACHU
0.75 0.02 0.23
17
TRCH/HUIRIVIS #1
0.40 0.10 0.50
18
TRCH/KBIRD
0.20 0.07 0.73
19
ROLF07/MUU
0.13 0.44 0.42
20
PBW343*2/KUKUNA//TECUE #1
0.17 0.33 0.51
21
NAC/TH.AC//3*PVN/3/MIRLO/BUC/4/2*PASTOR
0.83 0.13 0.04
22
YAV_3/SCO//JO69/CRA/3/YAV79/4/AE.SQUARROSA (498)/5/LINE
0.35 0.04 0.61
23
WAXWING/4/BL 1496/MILAN/3/CROC_1/AE.SQUARROSA (205)//KAUZ/5/
0.04 0.04 0.92
FRNCLN
24
WAXWING*2/HEILO
0.02 0.15 0.84
87

Table 2-5 (cont’d)
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51

KIRITATI/4/2*BAV92//IRENA/KAUZ/3/HUITES
KZA//WH 542/2*PASTOR/3/BACEU #1
KFA/2*KACHU
QUAIU #1
PASTOR//HXL7573/2*BAU/3/WBLL1
KLDR/PEWIT1//MILAN/DUCULA
PUB94.15.1.12/FRTL
FRET2*2/4/SNI/TRAP#1/3/KAUZ*2/TRAP//KAUZ*2/5/BOW/URES//2*WEAVER/3/CROC_1/
AE.SQUARROSA (213)//PGO
ATTILA*2/PBW65//WBLL1*2/VIVITSI
ALTAR 84/AE.SQUARROSA
(221)//3*BORL95/3/URES/JUN//KAUZ/4/WBLL1/5/REH/HARE//2*BCN/3/CROC_1/AE.SQU
ARROSA (213)//PGO/4/HUITES
MURGA//WAXWING/KIRITATI
MURGA/KRONSTAD F2004
ATTILA*2/PBW65//MURGA
BAV92//IRENA/KAUZ/3/HUITES/6/ALD/CEP75630//CEP75234/PT7219/3/BUC/BJY/4/CBR
D/5/TNMU/PF85487
WBLL1*2/CHAPIO//HEILO
WBLL1*2/CHAPIO//HEILO
WAXWING*2/4/BOW/NKT//CBRD/3/CBRD
ROLF07*2/3/PRINIA/PASTOR//HUITES
ROLF07*2/4/CROC_1/AE.SQUARROSA (205)//BORL95/3/2*MILAN
WBLL1*2/KUKUNA/5/PSN/BOW//SERI/3/MILAN/4/ATTILA/6/WBLL1*2/KKTS
WAXWING*2/DIAMONDBIRD
BAV92//IRENA/KAUZ/3/HUITES*2/4/MILAN/KAUZ//CHIL/CHUM18
BAV92//IRENA/KAUZ/3/HUITES*2/4/PVN
BAV92//IRENA/KAUZ/3/HUITES*2/4/TNMU
WBLL1/DIAMONDBIRD//WBLL1*2/VIVITSI
SAUAL/YANAC//SAUAL
SAUAL/KIRITATI//SAUAL
88

0.60
0.50
0.80
0.02
0.03
0.05
0.02
0.20

0.29
0.44
0.11
0.11
0.61
0.41
0.94
0.04

0.11
0.05
0.09
0.87
0.36
0.54
0.04
0.76

0.04
0.78

0.06
0.18

0.90
0.04

0.12
0.66
0.01
0.55

0.64
0.30
0.01
0.42

0.25
0.04
0.98
0.03

0.34
0.28
0.06
0.04
0.11
0.09
0.07
0.54
0.71
0.56
0.33
0.89
0.90

0.62
0.53
0.08
0.44
0.23
0.33
0.21
0.04
0.16
0.39
0.14
0.02
0.05

0.04
0.19
0.86
0.51
0.66
0.58
0.72
0.41
0.13
0.05
0.53
0.09
0.05

Table 2-5 (cont’d)
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78

CS/TH.SC//3*PVN/3/MIRLO/BUC/4/URES/JUN//KAUZ/5/HUITES/6/YANAC/7/CS/TH.SC//3
*PVN/3/MIRLO/BUC/4/MILAN/5/TILHI
FINSI/METSO//FH6-1-7/3/FINSI/METSO
INQALAB 91*2/KUKUNA*2//PVN
UP2338*2/4/SNI/TRAP#1/3/KAUZ*2/TRAP//KAUZ/5/MILAN/KAUZ//CHIL/CHUM18/6/UP23
38*2/4/SNI/TRAP#1/3/KAUZ*2/TRAP//KAUZ
UP2338*2/KKTS*2//YANAC
WAXWING/2*ROLF07
WBLL1*2/5/CNO79//PF70354/MUS/3/PASTOR/4/BAV92
ATTILA*2/PBW65*2//MURGA
WBLL1/FRET2//PASTOR*2/3/MURGA
KACHU #1/4/CROC_1/AE.SQUARROSA (205)//BORL95/3/2*MILAN/5/KACHU
SAUAL/4/CROC_1/AE.SQUARROSA (205)//BORL95/3/2*MILAN/5/SAUAL
ROLF07*2/4/CROC_1/AE.SQUARROSA (224)//KULIN/3/WESTONIA
KACHU*2/3/CHUM18/BORL95//CBRD
KACHU #1/4/CROC_1/AE.SQUARROSA (205)//KAUZ/3/SASIA/5/KACHU
SAUAL*2/6/CNDO/R143//ENTE/MEXI_2/3/AEGILOPS SQUARROSA
(TAUS)/4/WEAVER/5/2*PASTOR
ATTILA*2/PBW65*2/4/BOW/NKT//CBRD/3/CBRD
BAV92//IRENA/KAUZ/3/HUITES/4/FN/2*PASTOR/5/BAV92//IRENA/KAUZ/3/HUITES
BAV92//IRENA/KAUZ/3/HUITES/4/FN/2*PASTOR/5/BAV92//IRENA/KAUZ/3/HUITES
ROLF07*2/4/BOW/NKT//CBRD/3/CBRD
WBLL1/4/BOW/NKT//CBRD/3/CBRD/5/WBLL1*2/TUKURU
WBLL1*2/4/YACO/PBW65/3/KAUZ*2/TRAP//KAUZ*2/5/GONDO
PFAU/WEAVER*2//BRAMBLING/3/KAUZ//TRAP#1/BOW/4/PFAU/WEAVER*2//BRAMBLIN
G
KACHU*2//CHIL/CHUM18
KACHU*2//CHIL/CHUM18
SAUAL/3/ACHTAR*3//KANZ/KS85-8-4/4/SAUAL
BAV92//IRENA/KAUZ/3/HUITES*2/4/YUNMAI 47
WAXWING/KIRITATI*2/3/C80.1/3*BATAVIA//2*WBLL1
89

0.31

0.32

0.37

0.08
0.39
0.15

0.66
0.18
0.21

0.26
0.44
0.64

0.34
0.01
0.76
0.12
0.03
0.89
0.92
0.14
0.05
0.97
0.39

0.60
0.02
0.15
0.05
0.94
0.08
0.07
0.41
0.91
0.01
0.60

0.06
0.97
0.09
0.83
0.03
0.02
0.01
0.45
0.04
0.02
0.01

0.01
0.46
0.59
0.21
0.07
0.04
0.42

0.02
0.46
0.29
0.24
0.23
0.47
0.33

0.97
0.08
0.12
0.55
0.70
0.49
0.24

0.94
0.91
0.80
0.55
0.14

0.03
0.07
0.16
0.27
0.19

0.03
0.02
0.04
0.18
0.68

Table 2-5 (cont’d)
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106

BAV92//IRENA/KAUZ/3/HUITES*2/4/WHEAR
KACHU #1*2/WHEAR
KACHU #1/3/C80.1/3*BATAVIA//2*WBLL1/4/KACHU
SAUAL/WHEAR//SAUAL
FRNCLN/BECARD
PAURAQ/3/KIRITATI//PRL/2*PASTOR
QUAIU/5/FRET2*2/4/SNI/TRAP#1/3/KAUZ*2/TRAP//KAUZ
BECARD/KACHU
FRANCOLIN #1/HAWFINCH #1
FRNCLN/TECUE #1
TRCH/HUIRIVIS #1
QUAIU/TECUE #1
KBIRD//WBLL1*2/KURUKU
KINGBIRD #1/KACHU
WAXWING/4/SNI/TRAP#1/3/KAUZ*2/TRAP//KAUZ/5/AKURI
WAXWING/4/SNI/TRAP#1/3/KAUZ*2/TRAP//KAUZ/5/TECUE #1
PBW343*2/KUKUNA//TECUE #1
WBLL1*2/BRAMBLING//FN/2*PASTOR
QUAIU #3//MILAN/AMSEL
ATTILA*2/PBW65//MUU #1/3/FRANCOLIN #1
ATTILA*2/PBW65*2//TOBA97/PASTOR
WBLL1*2/VIVITSI//PRINIA/PASTOR/3/WBLL1*2/BRAMBLING
SAUAL/5/REH/HARE//2*BCN/3/CROC_1/AE.SQUARROSA
(213)//PGO/4/HUITES/6/KACHU
MUU #1//PBW343*2/KUKUNA/3/MUU
WBLL1*2/KURUKU/6/CNDO/R143//ENTE/MEXI_2/3/AEGILOPS SQUARROSA
(TAUS)/4/WEAVER/5/2*JANZ/7/WBLL1*2/KURUKU
TUKURU//BAV92/RAYON*2/7/YAV_3/SCO//JO69/CRA/3/YAV79/4/AE.SQUARROSA
(498)/5/LINE 1073/6/KAUZ*2/4/CAR//KAL/BB/3/NAC/5/KAUZ
NG8675/CBRD//FN/2*PASTOR/4/THELIN/3/2*BABAX/LR42//BABAX
BAV92//IRENA/KAUZ/3/HUITES/4/GONDO/TNMU/5/BAV92//IRENA/KAUZ/3/HUITES
90

0.51
0.86
0.72
0.77
0.05
0.06
0.05
0.81
0.08
0.09
0.25
0.16
0.27
0.74
0.03
0.10
0.09
0.26
0.55
0.02
0.03
0.08
0.93

0.39
0.06
0.24
0.21
0.04
0.08
0.09
0.02
0.01
0.29
0.09
0.23
0.04
0.02
0.02
0.18
0.38
0.12
0.30
0.03
0.12
0.21
0.03

0.10
0.08
0.04
0.03
0.91
0.87
0.86
0.17
0.91
0.61
0.66
0.60
0.69
0.24
0.95
0.72
0.53
0.61
0.15
0.94
0.85
0.71
0.04

0.44
0.21

0.27
0.13

0.29
0.66

0.05

0.25

0.70

0.83
0.48

0.01
0.28

0.16
0.24

Table 2-5 (cont’d)
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133

CONI#1/2*HUIRIVIS #1
TECUE #1/2*WAXWING
KBIRD//WH 542/2*PASTOR/3/WBLL1*2/BRAMBLING
MUU/5/TRAP#1/BOW/3/VEE/PJN//2*TUI/4/BAV92/RAYON/6/MILAN/S87230//BAV92
KFA/3/PFAU/WEAVER//BRAMBLING/4/PFAU/WEAVER*2//BRAMBLING
ATTILA*2/PBW65//KRONSTAD F2004
WBLL1*2/TUKURU//KRONSTAD F2004
CHIL/CHUM18//GONDO
WBLL1*2/KUKUNA//KIRITATI/3/WBLL1*2/KUKUNA
NORM/WBLL1//WBLL1/3/TNMU/4/WBLL1*2/TUKURU
PBW343*2/KHVAKI*2//YANAC
FRANCOLIN #1/4/BABAX/LR42//BABAX*2/3/KURUKU
PANDORA//WBLL1*2/BRAMBLING
WBLL1*2/BRAMBLING//JUCHI
WBLL1*2/KKTS//KINGBIRD #1
TACUPETO F2001//WBLL1*2/KKTS/3/WBLL1*2/BRAMBLING
WBLL1/KUKUNA//TACUPETO F2001/3/KRONSTAD F2004/4/ROLF07
ATTILA*2/PBW65*2/5/REH/HARE//2*BCN/3/CROC_1/AE.SQUARROSA
(213)//PGO/4/HUITES
HEILO/7/IVAN/6/SABUF/5/BCN/4/RABI//GS/CRA/3/AE.SQUARROSA
(190)/8/VORB/FISCAL
KSW/SAUAL//SAUAL
KAUZ/PASTOR//PBW343/3/KRONSTAD F2004
REH/HARE//2*BCN/3/CROC_1/AE.SQUARROSA (213)//PGO/4/HUITES/5/KRONSTAD
F2004
PRL/2*PASTOR//VORB
TRCH*2/3/WUH1/VEE#5//CBRD
KACHU #1/3/SHA3/SERI//SHA4/LIRA/4/KACHU
PBW343/PASTOR*2/6/TURACO/5/CHIR3/4/SIREN//ALTAR 84/AE.SQUARROSA
(205)/3/3*BUC
WBLL1*2/BRAMBLING/4/BABAX/LR42//BABAX*2/3/KURUKU
91

0.36
0.03
0.30
0.30
0.94
0.21
0.56
0.02
0.04
0.15
0.01
0.03
0.36
0.68
0.12
0.02
0.50
0.30

0.11
0.01
0.29
0.44
0.04
0.03
0.01
0.48
0.28
0.03
0.02
0.01
0.12
0.17
0.05
0.36
0.05
0.05

0.53
0.96
0.41
0.25
0.03
0.77
0.43
0.50
0.68
0.83
0.97
0.96
0.52
0.15
0.83
0.62
0.45
0.65

0.06

0.92

0.03

0.83
0.67
0.97

0.10
0.05
0.01

0.06
0.27
0.02

0.03
0.36
0.96
0.49

0.71
0.29
0.02
0.42

0.26
0.35
0.02
0.09

0.17

0.29

0.53

Table 2-5 (cont’d)
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163

FRANCOLIN #1/KIRITATI
BABAX/LR42//BABAX*2/3/KUKUNA/4/TAM200/PASTOR//TOBA97
MURGA/KRONSTAD F2004//QUAIU #3
KENYA NYANGUMI/3/2*KAUZ/PASTOR//PBW343
PARUS/PASTOR//INQALAB 91*2/KUKUNA
CROC_1/AE.SQUARROSA (205)//KAUZ/3/SASIA/4/TROST
CROC_1/AE.SQUARROSA (205)//KAUZ/3/SASIA/4/TROST
PFAU/MILAN//SOVA/3/PBW65/2*SERI.1B
PASTOR/KAUZ/6/CNDO/R143//ENTE/MEXI_2/3/AEGILOPS SQUARROSA
(TAUS)/4/WEAVER/5/2*KAUZ
PASTOR/3/VORONA/CNO79//KAUZ/4/MILAN/OTUS//ATTILA/3*BCN
PASTOR/3/VORONA/CNO79//KAUZ/4/MILAN/OTUS//ATTILA/3*BCN
CHIBIA/WEAVER//KACHU
CHIBIA/WEAVER//KACHU
PRINIA/PASTOR//HUITES/3/MILAN/OTUS//ATTILA/3*BCN
C80.1/3*BATAVIA//2*WBLL1/3/TOBA97/PASTOR
WHEAR/3/PBW343/PASTOR//ATTILA/3*BCN
PBW343/HUITES/3/MILAN/OTUS//ATTILA/3*BCN
WBLL1*2/KURUKU//KRONSTAD F2004
MONARCA F2007/KRONSTAD F2004
PBW343*2/KUKUNA//PBW343*2/KUKUNA/3/PBW343
WHEAR/2*KRONSTAD F2004
C80.1/3*BATAVIA//2*WBLL1/3/2*KRONSTAD F2004
C80.1/3*BATAVIA//2*WBLL1/3/2*KRONSTAD F2004
SUMAI #3
GAMENYA
FALCIN/AE.SQUARROSA (312)/3/THB/CEP7780//SHA4/LIRA
GONDO/CBRD
HEILO
PICUS/3/KAUZ*2/BOW//KAUZ/4/KKTS/5/HEILO
HUIRIVIS #1/GONDO
92

0.36
0.38
0.30
0.39
0.55
0.02
0.55
0.69
0.48

0.11
0.44
0.52
0.22
0.04
0.66
0.04
0.03
0.43

0.54
0.18
0.18
0.39
0.41
0.33
0.41
0.28
0.09

0.47
0.26
0.72
0.62
0.68
0.64
0.18
0.30
0.43
0.23
0.13
0.70
0.71
0.63
0.38
0.50
0.32
0.29
0.54
0.25
0.17

0.35
0.60
0.26
0.36
0.25
0.04
0.44
0.25
0.15
0.05
0.65
0.02
0.14
0.03
0.31
0.41
0.32
0.49
0.21
0.41
0.43

0.18
0.14
0.02
0.02
0.07
0.32
0.38
0.45
0.43
0.73
0.22
0.28
0.15
0.34
0.30
0.09
0.36
0.22
0.25
0.34
0.39

Table 2-5 (cont’d)
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191

HUIRIVIS #1/GONDO
KAUZ/PASTOR//PBW343/3/HEILO
FRET2/WBLL1//TACUPETO F2001/3/HEILO
WBLL1*2/4/YACO/PBW65/3/KAUZ*2/TRAP//KAUZ/5/GONDO
WBLL1*2/CHAPIO//HEILO
WBLL1*2/KURUKU//HEILO
WBLL1*2/KURUKU//HEILO
WBLL1*2/VIVITSI//GONDO
ATTILA/2*PASTOR//FN/2*PASTOR
KACHU #1/4/CROC_1/AE.SQUARROSA (205)//KAUZ/3/SASIA/5/KACHU
KACHU #1/4/CROC_1/AE.SQUARROSA (205)//KAUZ/3/SASIA/5/KACHU
SAUAL*2/6/CNDO/R143//ENTE/MEXI_2/3/AEGILOPS SQUARROSA
(TAUS)/4/WEAVER/5/2*PASTOR
BAV92//IRENA/KAUZ/3/HUITES*2/4/GONDO/TNMU
BAV92//IRENA/KAUZ/3/HUITES*2/4/GONDO/TNMU
FRET2*2/KUKUNA*2//SHA4/CHIL
WBLL1*2/KURUKU*2//TNMU
WBLL1*2/TUKURU//WUH1/BOW/3/WBLL1*2/TUKURU
WBLL1/FRET2//PASTOR*2/3/GONDO
PFAU/WEAVER*2//BRAMBLING/7/IVAN/6/SABUF/5/BCN/4/RABI//GS/CRA/3/AE.SQUARR
OSA (190)/8/PFAU/WEAVER//BRAMBLING
TRCH*2/TNMU
KACHU*2//CHIL/CHUM18
KACHU*2//CHIL/CHUM18
SAUAL #1/TNMU//SAUAL
PRINIA/PASTOR//CHIL/CHUM18/3/PRINIA/PASTOR
PBW343*2/KHVAKI*2//CHIL/CHUM18
PBW343/PASTOR*2/6/TURACO/5/CHIR3/4/SIREN//ALTAR 84/AE.SQUARROSA
(205)/3/3*BUC
PBW343/PASTOR*2/3/WUH1/VEE#5//CBRD
PBW343/PASTOR*2/3/WUH1/VEE#5//CBRD
93

0.12
0.17
0.27
0.03
0.28
0.22
0.23
0.02
0.03
0.95
0.98
0.90

0.67
0.45
0.71
0.64
0.52
0.73
0.47
0.62
0.81
0.01
0.01
0.05

0.21
0.39
0.03
0.33
0.20
0.05
0.30
0.36
0.16
0.03
0.01
0.05

0.50
0.56
0.05
0.16
0.23
0.02
0.45

0.46
0.42
0.16
0.37
0.05
0.96
0.27

0.03
0.02
0.79
0.48
0.72
0.03
0.28

0.30
0.90
0.61
0.68
0.06
0.31
0.19

0.39
0.08
0.14
0.30
0.70
0.18
0.39

0.31
0.02
0.25
0.02
0.24
0.51
0.42

0.09
0.14

0.32
0.32

0.60
0.54

Table 2-5 (cont’d)
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216

PBW343/PASTOR*2/3/WUH1/VEE#5//CBRD
NG8675/CBRD/7/IVAN/6/SABUF/5/BCN/4/RABI//GS/CRA/3/AE.SQUARROSA
(190)/8/WBLL1*2/CHAPIO
NG8675/CBRD/7/IVAN/6/SABUF/5/BCN/4/RABI//GS/CRA/3/AE.SQUARROSA
(190)/8/WBLL1*2/CHAPIO
SHA3/CBRD//TNMU/3/KACHU
FN/2*PASTOR//GONDO/TNMU/3/FRANCOLIN #1
HEILO//GONDO/TNMU/3/WBLL1*2/BRAMBLING
CBRD/FILIN
CBRD/FILIN
CHIL/CHUM18//GONDO
SAUAL/4/CROC_1/AE.SQUARROSA (205)//KAUZ/3/ATTILA/5/SAUAL
WAXWING/KIRITATI*2/3/SHA3/SERI//SHA4/LIRA
CNO79//PF70354/MUS/3/PASTOR/4/BAV92*2/5/SHA3/SERI//SHA4/LIRA
FRET2/TUKURU//FRET2/3/WUH1/VEE#5//CBRD/4/FRET2/TUKURU//FRET2
WBLL1/FRET2//PASTOR/3/SHA3/SERI//SHA4/LIRA/4/WBLL1/TACUPETO
F2001//PASTOR
PFAU/WEAVER*2//BRAMBLING/7/IVAN/6/SABUF/5/BCN/4/RABI//GS/CRA/3/AE.SQUARR
OSA (190)/8/PFAU/WEAVER//BRAMBLING
PFAU/WEAVER//BRAMBLING*2/3/SHA3/SERI//SHA4/LIRA
KACHU #1/3/SHA3/SERI//SHA4/LIRA/4/KACHU
PRINIA/PASTOR//CHIL/CHUM18/3/PRINIA/PASTOR
KETUPA*2/PASTOR/6/TURACO/5/CHIR3/4/SIREN//ALTAR 84/AE.SQUARROSA
(205)/3/3*BUC/7/KACHU
CHIL/CHUM18//FN/2*PASTOR/3/PRL/2*PASTOR
CHIL/CHUM18//GONDO/3/WBLL1*2/KURUKU
SHA3/CBRD//TNMU/3/KACHU
FN/2*PASTOR//GONDO/TNMU/3/FRANCOLIN #1
NG8675/CBRD//FN/2*PASTOR/4/THELIN/3/2*BABAX/LR42//BABAX
HEILO/7/IVAN/6/SABUF/5/BCN/4/RABI//GS/CRA/3/AE.SQUARROSA
(190)/8/VORB/FISCAL
94

0.13
0.40

0.36
0.38

0.51
0.22

0.65

0.23

0.12

0.61
0.04
0.10
0.10
0.28
0.02
0.94
0.02
0.04
0.19
0.03

0.32
0.37
0.60
0.62
0.67
0.87
0.03
0.08
0.33
0.08
0.85

0.08
0.60
0.31
0.28
0.05
0.11
0.02
0.90
0.63
0.73
0.12

0.58

0.38

0.03

0.27
0.96
0.09
0.68

0.41
0.02
0.75
0.16

0.33
0.02
0.16
0.16

0.04
0.26
0.63
0.40
0.02
0.06

0.77
0.11
0.33
0.37
0.59
0.91

0.19
0.64
0.04
0.23
0.39
0.03

Table 2-5 (cont’d)
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244

BAV92//IRENA/KAUZ/3/HUITES/4/DOLL
TRCH/SRTU//KACHU
PRL/2*PASTOR//SRTU/3/PRINIA/PASTOR
WAXWING*2/3/PASTOR//HXL7573/2*BAU
ATTILA*2/PBW65*2//TNMU
SERI.1B//KAUZ/HEVO/3/AMAD*2/4/KIRITATI
WBLL1*2/4/SNI/TRAP#1/3/KAUZ*2/TRAP//KAUZ/5/KACHU
WBLL1*2/4/YACO/PBW65/3/KAUZ*2/TRAP//KAUZ/5/KACHU #1
FRET2*2/4/SNI/TRAP#1/3/KAUZ*2/TRAP//KAUZ/5/KACHU
FRET2*2/KUKUNA//PRINIA/PASTOR
FRET2/KIRITATI/5/NAC/TH.AC//3*PVN/3/MIRLO/BUC/4/2*PASTOR
FRET2/KIRITATI/5/NAC/TH.AC//3*PVN/3/MIRLO/BUC/4/2*PASTOR
KAUZ//ALTAR 84/AOS/3/MILAN/KAUZ/4/SAUAL
KAUZ//ALTAR 84/AOS/3/MILAN/KAUZ/4/OTUS/TOBA97
SAUAL/3/KAUZ/PASTOR//PBW343
NG8675/CBRD//MILAN/3/SAUAL/6/CNDO/R143//ENTE/MEXI_2/3/AEGILOPS
SQUARROSA (TAUS)/4/WEAVER/5/2*PASTOR
ATTILA/3*BCN//BAV92/3/TILHI/4/SHA7/VEE#5//ARIV92
ATTILA/3*BCN//BAV92/3/TILHI/4/SHA7/VEE#5//ARIV92
BABAX/KS93U76//BABAX/3/ATTILA/3*BCN//TOBA97/4/WBLL1*2/KURUKU
ATTILA*2/PBW65//KRONSTAD F2004
KANZ*4/KS85-8-4//2*WBLL1*2/KURUKU
FRET2/KUKUNA//FRET2/3/PASTOR//HXL7573/2*BAU/5/FRET2*2/4/SNI/TRAP#1/3/KAUZ
*2/TRAP//KAUZ
PRL/2*PASTOR//PARUS/5/NAC/TH.AC//3*PVN/3/MIRLO/BUC/4/2*PASTOR
WAXWING*2/JUCHI
FUNDACEP 30
SHANGHAI #8
VOROBEY
CPI8/GEDIZ/3/GOO//ALB/CRA/4/AE.SQUARROSA
(208)/5/HAHN/2*WEAVER/6/SKAUZ/BAV92
95

0.85
0.62
0.02
0.02
0.09
0.83
0.37
0.91
0.91
0.03
0.15
0.16
0.92
0.90
0.61
0.03

0.11
0.02
0.69
0.07
0.07
0.08
0.03
0.04
0.02
0.84
0.76
0.73
0.02
0.07
0.22
0.94

0.04
0.36
0.29
0.92
0.84
0.09
0.60
0.05
0.07
0.13
0.09
0.11
0.05
0.03
0.17
0.03

0.47
0.47
0.57
0.46
0.13
0.16

0.04
0.03
0.05
0.02
0.37
0.40

0.49
0.50
0.38
0.53
0.51
0.44

0.01
0.01
0.45
0.35
0.04
0.05

0.95
0.01
0.41
0.32
0.88
0.90

0.04
0.98
0.14
0.33
0.08
0.05

Table 2-5 (cont’d)
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272

CPI8/GEDIZ/3/GOO//ALB/CRA/4/AE.SQUARROSA
(208)/5/HAHN/2*WEAVER/6/SKAUZ/BAV92
CPI8/GEDIZ/3/GOO//ALB/CRA/4/AE.SQUARROSA
(208)/5/HAHN/2*WEAVER/6/SKAUZ/BAV92
NING MAI 96035/FINSI//HEILO
NING MAI 96035/FINSI//HEILO
ATTILA/HEILO
ATTILA/HEILO
WAXWING//PFAU/WEAVER
BABAX/LR42//BABAX*2/3/KURUKU
ND643//2*PRL/2*PASTOR
VOROBEY
BABAX/LR42//BABAX/3/ER2000
OASIS//TC14/2*SPER/3/ATTILA/4/WBLL4
FILIN/3/CROC_1/AE.SQUARROSA (205)//KAUZ/4/FILIN/5/VEE/MJI//2*TUI/3/PASTOR
T.DICOCCON PI94625/AE.SQUARROSA (372)//3*PASTOR
PASTOR/4/WEAVER/TSC//WEAVER/3/WEAVER/5/URES/PRL//BAV92
SW94.2690/SUNCO
SW94.2690/SUNCO
VEE/MJI//2*TUI/3/PASTOR/4/BERKUT
BERKUT/3/ATTILA*2//CHIL/BUC
TAN//TEMPORALERA M 87/AGR/3/FRET2/4/URES/PRL//BAV92
A93324S.7197.29/4/KAUZ//ALTAR 84/AOS/3/KAUZ/5/PASTOR
OASIS//TC14/2*SPER/3/ATTILA/10/ATTILA*2/9/KT/BAGE//FN/U/3/BZA/4/TRM/5/ALDAN/6
/SERI/7/VEE#10/8/OPATA
MEX94.27.1.20/3/SOKOLL//ATTILA/3*BCN
KS82W418/SPN//WBLL1/3/BERKUT
CNDO/R143//ENTE/MEXI75/3/AE.SQ/4/2*FCT/5/KAUZ*2/YACO//KAUZ/6/BERKUT
SOKOLL/EXCALIBUR
PASTOR/SLVS//FRAME
PASTOR/SLVS//FRAME
96

0.02

0.95

0.03

0.02

0.96

0.02

0.27
0.31
0.04
0.05
0.27
0.05
0.08
0.06
0.11
0.04
0.06
0.01
0.02
0.04
0.03
0.07
0.10
0.23
0.02
0.03

0.47
0.39
0.01
0.02
0.02
0.14
0.83
0.85
0.03
0.05
0.71
0.98
0.97
0.94
0.95
0.14
0.74
0.69
0.90
0.45

0.26
0.29
0.94
0.93
0.71
0.81
0.10
0.09
0.87
0.91
0.23
0.01
0.02
0.03
0.02
0.79
0.17
0.08
0.09
0.52

0.05
0.07
0.36
0.03
0.04
0.06

0.84
0.88
0.59
0.95
0.94
0.92

0.10
0.04
0.04
0.02
0.02
0.02

Table 2-5 (cont’d)
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297

BAXTER*2/4/CHEN/AEGILOPS SQUARROSA (TAUS)//BCN/3/BAV92
BERKUT/3/ALTAR 84/AE.SQUARROSA (219)//SERI
MILAN/DUCULA//SUNCO/2*PASTOR
SW89-5124*2/FASAN//PARUS/PASTOR
SOKOLL//SUNCO/2*PASTOR
CROC_1/AE.SQUARROSA (224)//OPATA/3/ALTAR 84/AE.SQ//2*OPATA
SUNSTATE/SD 3195//SOKOLL
SOKOLL*2/GLE
TEMPORALERA M 87/ROMO96/3/ATTILA/BAV92//PASTOR/4/PRL/2*PASTOR
FINSI/3/ATTILA/BAV92//PASTOR/4/PBW343*2/KUKUNA
CO99W329/2*BERKUT
PSN/BOW//MILAN/3/2*BERKUT
CROC_1/AE.SQUARROSA (224)//OPATA/3/RAC655/4/SLVS/PASTOR
SLVS/PASTOR/3/PASTOR//MUNIA/ALTAR 84
YAV79//DACK/RABI/3/SNIPE/4/AE.SQUARROSA
(460)/5/2*EXCALIBUR/6/VEE/LIRA//BOW/3/BCN/4/KAUZ
CNDO/R143//ENTE/MEXI_2/3/AEGILOPS SQUARROSA
(TAUS)/4/WEAVER/5/2*JANZ/6/D67.2/PARANA 66.270//AE.SQUARROSA
(320)/3/CUNNINGHAM
CROC_1/AE.SQUARROSA
(205)//BORL95/3/KENNEDY/6/CNDO/R143//ENTE/MEXI_2/3/AEGILOPS SQUARROSA
(TAUS)/4/WEAVER/5/2*JANZ
D67.2/PARANA 66.270//AE.SQUARROSA (320)/3/CUNNINGHAM/4/PASTOR/SLVS
CALINGIRI/SOKOLL
SOKOLL//SLVS/PASTOR/3/ATTILA*2//CHIL/BUC
BERKUT/HTG
SOKOLL/FRAME
SOKOLL/SLVS
ASTREB*2/NING MAI 9558
ASTREB*2/3/WUH1/VEE#5//CBRD

97

0.18
0.03
0.31
0.04
0.05
0.09
0.13
0.03
0.02
0.11
0.30
0.02
0.32
0.02
0.17

0.80
0.94
0.59
0.93
0.92
0.75
0.83
0.94
0.96
0.43
0.44
0.96
0.60
0.97
0.78

0.02
0.04
0.10
0.03
0.03
0.17
0.05
0.03
0.02
0.46
0.26
0.02
0.08
0.02
0.05

0.26

0.29

0.46

0.34

0.27

0.39

0.14
0.03
0.03
0.39
0.50
0.26
0.16
0.03

0.52
0.53
0.25
0.51
0.21
0.68
0.76
0.89

0.34
0.44
0.72
0.11
0.29
0.06
0.09
0.08

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98

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103

CHAPTER 3
ASSOCIATION MAPPING FOR DETECTING QTLs FOR YELLOW RUST IN BREAD
WHEAT
Abstract
Yellow rust (Puccinia striiformis) is one of the most aggressive diseases of bread wheat
(Triticum aestivum L.), which drastically can reduce the yield. Currently, yellow rust is a
global concern, since the pathogen is now present in areas where it was not previously
reported before. Adult plant resistance genes (APR) are considered the better approach
to generate new wheat varieties with high levels of non-race specific resistance. In the
current study, a wheat association mapping panel (AMP) with 297 spring wheat
accessions developed by CIMMYT was evaluated in Mexico and Ecuador during two
years to identify markers linked to regions in the wheat genome responsible for yellow
rust resistance. SNP markers significantly associated with the resistance to P. striiformis
were detected on chromosomes 1A, 2A, 5A, 6A, 7A, 2B, 5B, 6B, 7B, and 3D using the
GLM method; whereas, the association analysis detected SNP markers significantly
associated with the trait on chromosomes 1A and 2A using the MLM method.

Introduction

Yellow rust or stripe rust, caused by Puccinia striiformis, is considered one the most
severe diseases of wheat (Roelfs et al., 1992) and also one of most frequent diseases
to occur along with stem and leaf rust (McIntosh et al., 1995). Yield losses arise due to
leaf tissue damaged by the infection, reduced number and size of flowering spikes,
104

shriveled grain, and damaged tillers, especially when the infection occurs in early
growth stages (Wellings, 2010). It is possible to have yield losses over 70% when
susceptible cultivars are planted and the weather favors pathogen development
(Sharma-Poudyal and Chen, 2010). In the past, yellow rust was considered a disease
common only in areas where cool and moist weather conditions prevail (Stubbs, 1988).
Severe epidemics are now often reported in warmer areas, where yellow rust was
absent before or not considered important (Hovmøller et al., 2010).
Complete resistance to the pathogen conferred by major resistance genes, which are
race specific, have has been extensively used by wheat breeders in the past (Zadoks,
1961); however, it has been demonstrated that this mechanism of resistance is
commonly overcome by the pathogen (Johnson, 1992). Some cases of these failures
have been reported in the literature. For example, Yr6 was released in the UK cultivar
Rothwell Perdix in 1964, but isolates virulent to this cultivar were detected only two
years later (Boyd, 2005). Yr17 was introduced into northern European wheat cultivars in
the mid-70s and after 20 years of extensive use of this gene in new wheat cultivars, the
gene was no longer effective in some countries of this region (Bayles et al., 2000).
Partial resistance conferred by genes with minor effects in the control of yellow rust is
currently the most popular mechanism of resistance employed in wheat breeding since
it has been more durable over time (Morgounov et al., 2012; Qayoum and Line, 1985).
Partial resistance is also non-race specific (Singh et al., 2004), and genes involved in
the disease resistance possess additive effects, therefore these genes can be
pyramided to provide high levels of resistance near immunity. Singh et al. (2011)
reported that CIMMYT lines with combinations of 4 – 5 minor, slow rusting genes were

105

able to acquire high levels of resistance near-immunity to yellow rust (1 – 5% of disease
severity) in environments which favors the development of the pathogen located in
hotspots in Ecuador, Mexico and Kenya.
Many major and minor resistance genes for yellow rust resistance have been identified.
From those, more than 50 genes have been catalogued and some more potential novel
genes remains temporally catalogued (Boyd, 2005; McIntosh et al., 2012). Breeders
have been taking advantage of QTL analysis studies to discover, map, and quantify the
effects of these genes in plant germplasm with the purpose of using this knowledge to
develop new improved varieties in more efficient ways. In wheat, many yellow rust
resistance genes have been identified, but many still remain undiscovered. Additionally,
it is important to validate already reported new QTL effects in different genetic
backgrounds. Association mapping is a technique to map QTLs using existing
populations. Using this approach in wheat elite, exotic, or landraces germplasm will
allow the discovery of novel alleles and quantify these effects in different genetic
backgrounds at the same time. Moreover, association mapping uses large numbers of
molecular markers distributed in plants genome, therefore, new SNP markers closely
linked to resistance genes are likely to be discovered and contribute to wheat breeding
efforts.
The current research aims to evaluate the resistance against Puccinia striiformis in the
wheat AMP and detect QTLs for yellow rust resistance using association mapping
approach in this collection of germplasm.

106

Materials and methods
Plant material
A group of 297 spring wheat accessions was assembled to conduct the current study
(Table 2-1, Chapter II). This collection of accessions will be referred to as the
association mapping panel (AMP). The AMP was obtained from the International Center
for Maize and Wheat Improvement (CIMMYT) located in Mexico. The wheat AMP
contains breeding lines, cultivars, and landraces from different origins as well as control
wheat lines used for yellow rust (YR) studies. The panel was selected because most of
the wheat lines in the panel have shown variability for YR response observed in
previous evaluations at CIMMYT. The AMP represents a considerable number of the
resistant alleles employed by CIMMYT’s to develop improved wheat lines.

Locations
The field research was conducted in Toluca - Mexico and Santa Catalina – Ecuador
during 2011 and 2012. Phenotypic and genotypic data analyses were conducted at
MSU as well as CIMMYT (Table 3-1).
Table 3-1. Locations and years of the wheat association mapping study on Yellow Rust.
Location
Years
Altitude (masl) Type of study
East Lansing-MSU-USA
2011
262
Genotyping
Santa Catalina-INIAP2011 - 2012
3,050
Field evaluation
Ecuador
Toluca-CIMMYT-Mexico
2011 - 2012
2,640
Field evaluation

107

Field management, inoculation, and phenotyping
The wheat AMP nurseries for YR studies were arranged in an alpha lattice design. Each
plot was 1.0 m long with two rows separated by 0.25 m. Two replications of the wheat
AMP were planted in Ecuador in 2011 and 2012 while one replication was sown in
Mexico during 2011 and 2012.
For YR evaluations in Ecuador, the AMP was surrounded by a mixture of the
susceptible cultivars ‘Morocco’, ‘Tungurahua’ and ‘Cotopaxi’. The susceptible cultivars
planted around the experiments were selected because these lines reach the highest
level of susceptibility at different periods of time so a continuous supply of inoculum is
produced. In 2011, the wheat AMP and the susceptible cultivars were inoculated three
times every five days staring 45 DAP. In 2012, only the susceptible cultivars were
inoculated three times every five days starting 45 DAP. The inoculum concentration was
80,000 spores per mL. The wheat plants were inoculated using a Micron Ulva-8 sprayer
(Distributed by Micron Sprayers, Bromyard, UK.).
In the YR field experiments in Mexico 2011 and 2012, the AMP was surrounded by a
mixture of six susceptible wheat cultivars derived from the cross ‘Avocet /Attila’ known
to carry the Yr27 stripe rust resistance gene. The cultivars were inoculated with the P.
striiformis isolates Mex96.11 and Mex08, which are virulent to genes Yr27 and Yr31.
The variables recorded from the field studies were:
Yellow Rust severity (%).- Percentage of surface area of the plant showing yellow rust
infection according to the modified Cobb’s scale recorded as: 0, 1, 5, 10, 20, 30, 40, 50,
60, 70, 80, 90 or 100% (Roelfs et al., 1992) (See Appendix B for visual representation).

108

Yellow Rust reaction.- Also known as the field response, recorded by using the codes
listed in Table 3-2 (CIMMYT, 1986) (See also Appendix C for visual representation).
Yield data was collected to keep record of the seed produced in the experiments,
however, yield was not part of any statistical analysis since plots size were too small for
yield evaluation.
Table 3-2. Codes for recording wheat reaction to Yellow Rust infection as used by
CIMMYT (1986).
Reaction
Code
Description
No symptoms
0
No visible infection on plants.
Resistant
R
Visible chlorosis or necrosis without presence of
uredia.
Moderately Resistant MR
Small uredia are present and surrounded by
chlorotic or necrotic areas.
Intermediate
M
Variable sized uredia are present, some with
chlorosis, necrosis, or both
Moderately
MS
Medium sized uredia are present and possibly
Susceptible
surrounded by chlorotic areas
Large uredia are present, generally with little or no
Susceptible
S
chlorosis and no necrosis
A Seedling test to confirm adult plant resistance (APR) was conducted in the
greenhouse at INIAP-Ecuador with two isolates of P. striiformis collected from Santa
Catalina Research Station on 2013. Plastic trays (60 x 40 x 10 cm) were used to plant
the wheat AMP. Each tray was divided in 15 cells of 20 x 8 cm where 15 seeds were
planted from each accession. Twenty days after planting, the wheat AMP was
inoculated with each isolate separatedly. The test was replicated two times with each
isolate. After 10 – 15 days, seedlings were evaluated and the infection type was
recorded following the protocol described by Roelfs et al. (1992). Seedling with scores 0
– 3 were considered resistant reactions and scores from 4 – 9 were considered
susceptible reaction according to Uauy et al. (2005).

109

Genotyping
A total of 1,666 SNP markers (selected by MAF > 5%) generated from the screening of
297 accessions from the wheat AMP with the 9K SNP chip and 32 microsatellites
markers (SSR) were employed to conduct the association analysis (See chapter II).

Statistical analysis
The data were tested for normality using the Shapiro-Wilk normality test (Shapiro and
Wilk, 1965) with R (Ihaka and Gentleman, 1996) version 2.15.3. Data sets that were not
normal were transformed with square root function. Analysis of variance (ANOVA) for
every trait was conducted in R with packages Agricolae version 1.1-4 and PBIB.test
using REML (de Mendiburu, 2013).
The number of subpopulations in the wheat AMP was estimated with the software
STRUCTURE v.2.3.4 (http://pritchardlab.stanford.edu/structure.html). Default setting of
admixture model for the ancestry of individuals and correlated allele frequencies were
used. Population structure was modelled with a burning of 10,000 cycles followed by
100,000 Markov Chain Monte Carlo (MCMC) repeats for assumed subpopulation
number, k= 1,…10 according to Pritchard et al. (2010). The optimum k value was
determined with Evanno method described in Chapter II (Evanno et al., 2005).
Principal component analysis (PCA) was used to validate the number of subpopulations
estimated by STRUCTURE. The software used to perform the PCA was EIGENSTRAT.
The Principal Component Analysis was conducted with 3,701 SNP markers distributed
in the wheat AMP genome with MAF > 5%.

110

Association analyses between markers and traits were conducted with TASSEL v
4.0 (http://www.maizegenetics.net/) using the general linear model (GLM) and the mixed
linear model (MLM). The GLM includes population structure as covariable, whereas the
MLM method includes, in addition to the population structure, a matrix of relatedness
(Kinship matrix), which was estimated with TASSEL. The Kinship Matrix and the
association analysis were performed with a set of 3,701 SNP markers, which were
selected from the original set of 9 K SNP markers included in the SNP chip. The
selection criteria to select these SNP markers were less than 10% of calls and MAF >
5%. Once the association analyses were done and p-values were obtained with both
methods (GLM and MLM), significant markers linked to the traits were selected using
false discovery rate (FDR) method, which controls the rate of false positives when
testing several hypotheses simultaneously (Storey, 2002). FDR analysis was conducted
with R using Q-value package version 1.0 (Dabney et al., 2004).

Results
The field evaluations of the wheat association mapping panel composed of 297 wheat
accessions were conducted in Mexico and Ecuador for two years (2011 and 2012).
Agronomic data and disease response to Yellow Rust were collected form the two
locations as shown in Table 3-1. Yellow rust response (percentage of severity) from
Mexico and Ecuador was used to conduct statistical analysis. The two agronomic
variables analyzed from the experiments were flowering (Days after planting- DAP) and
plant height (cm). The analyses of variance of these traits detected significant
differences among locations, so the traits were analyzed independently as follows:
111

Germplasm evaluation
In the two years of evaluations in Ecuador and Mexico, more than 50% of the
accessions from the wheat AMP showed high levels of resistance (0 – 5% of disease
severity) (Figures 3-6 and 3-7). The resistance of most of the cultivars was conferred by
APR genes since less than 25% of the accessions showed hypersensitivity to P.
striiformis infection in the field evaluations in Mexico and Ecuador. Additionally, the
seedling test conducted in Ecuador with two P. striiformis isolates showed that only 19%
of the 297 wheat accessions showed resistance reaction according to Uauy et al. (2005)
protocol.
The wheat accession ‘KAUZ’ was one of the common parents observed in the wheat
AMP pedigrees. It was present in 58 accessions. Most of these wheat accessions were
susceptible at seedling stage and resistant as adult plants in Mexico and Ecuador.
Another wheat accession which is common in the pedigrees of the wheat accessions in
the AMP with adult plant resistance in the two locations was ‘ATTILA’. This wheat
accession was present in 32 wheat accessions in the wheat AMP according their
pedigrees. Most of the lines with ‘ATTILA’ in its pedigree showed high levels of adult
plant resistance. Finally, another wheat line present 17 times in the pedigrees of the
accessions was ‘HEILO’. The lines with ‘HEILO’ in the pedigrees also showed
susceptibility as seedlings and resistance in the field.
Another wheat line present in the pedigree of the accessions of the wheat AMP was
‘QUAIU’. This line was present in the pedigree of five lines and all of them but one
(MURGA/KRONSTADF2004//QUAIU) showed high levels of yellow rust resistance in
the field.
112

Table 3-3. Yellow rust severity registered in the wheat AMP in Ecuador and Mexico. 2011-2012.
Acc.
Pedigree
Ecu.
Ecu.
Mex. Mex. 2012
No.
2011
2012 2011
% of disease severity
7
BAV92//IRENA/KAUZ/3/HUITES*2/4/MURGA
0
0
0
0
65
KACHU #1/4/CROC_1/AE.SQUARROSA
0
0
0
0
(205)//KAUZ/3/SASIA/5/KACHU
86
BECARD/KACHU
0
0
0
0
88
FRNCLN/TECUE #1
0
0
0
0
90
QUAIU/TECUE #1
0
0
0
0
92
KINGBIRD #1/KACHU
0
0
0
0
116 NORM/WBLL1//WBLL1/3/TNMU/4/WBLL1*2/TUKURU
0
0
0
0
162 PICUS/3/KAUZ*2/BOW//KAUZ/4/KKTS/5/HEILO
0
0
0
0
190 PBW343/PASTOR*2/3/WUH1/VEE#5//CBRD
0
0
0
0
223 WBLL1*2/4/SNI/TRAP#1/3/KAUZ*2/TRAP//KAUZ/5/KACHU
0
0
0
0
226 FRET2*2/KUKUNA//PRINIA/PASTOR
0
0
0
0
9
KACHU/5/REH/HARE//2*BCN/3/CROC_1/AE.SQUARROSA
0
0
0
1
(213)//PGO/4/HUITES/6/KACHU
17
TRCH/HUIRIVIS #1
0
0
0
1
27
KFA/2*KACHU
0
0
0
1
35
MURGA//WAXWING/KIRITATI
0
0
0
1
36
MURGA/KRONSTAD F2004
0
0
0
1
43
ROLF07*2/4/CROC_1/AE.SQUARROSA (205)//BORL95/3/2*MILAN
0
0
0
1
44
WBLL1*2/KUKUNA/5/PSN/BOW//SERI/3/MILAN/4/ATTILA/6/WBLL1*2/
0
0
1
0
KKTS
59
ATTILA*2/PBW65*2//MURGA
0
0
0
1
61
KACHU #1/4/CROC_1/AE.SQUARROSA
0
0
0
1
(205)//BORL95/3/2*MILAN/5/KACHU
74
KACHU*2//CHIL/CHUM18
0
0
0
1
76
SAUAL/3/ACHTAR*3//KANZ/KS85-8-4/4/SAUAL
0
0
1
0
89
TRCH/HUIRIVIS #1
0
0
0
1
105 NG8675/CBRD//FN/2*PASTOR/4/THELIN/3/2*BABAX/LR42//BABAX
0
0
0
1
113

Table 3-3 (cont’d)
125
130
132
173
185
191
209
229
282
15
32
54
147
208
5
62
67
131
169
192
249
3
16
21

HEILO/7/IVAN/6/SABUF/5/BCN/4/RABI//GS/CRA/3/AE.SQUARROSA
(190)/8/VORB/FISCAL
TRCH*2/3/WUH1/VEE#5//CBRD
PBW343/PASTOR*2/6/TURACO/5/CHIR3/4/SIREN//ALTAR
84/AE.SQUARROSA (205)/3/3*BUC
KACHU #1/4/CROC_1/AE.SQUARROSA
(205)//KAUZ/3/SASIA/5/KACHU
KACHU*2//CHIL/CHUM18
PBW343/PASTOR*2/3/WUH1/VEE#5//CBRD
PRINIA/PASTOR//CHIL/CHUM18/3/PRINIA/PASTOR
KAUZ//ALTAR 84/AOS/3/MILAN/KAUZ/4/SAUAL
FINSI/3/ATTILA/BAV92//PASTOR/4/PBW343*2/KUKUNA
PFAU/SERI.1B//AMAD/3/WAXWING/4/BABAX/LR42//BABAX*2/3/KU
FRET2*2/4/SNI/TRAP#1/3/KAUZ*2/TRAP//KAUZ*2/5/BOW/URES//2*W
EAVER/3/CROC_1/AE.SQUARROSA (213)//PGO
INQALAB 91*2/KUKUNA*2//PVN
PRINIA/PASTOR//HUITES/3/MILAN/OTUS//ATTILA/3*BCN
KACHU #1/3/SHA3/SERI//SHA4/LIRA/4/KACHU
ROLF07*2/KACHU #1
SAUAL/4/CROC_1/AE.SQUARROSA
(205)//BORL95/3/2*MILAN/5/SAUAL
ATTILA*2/PBW65*2/4/BOW/NKT//CBRD/3/CBRD
KACHU #1/3/SHA3/SERI//SHA4/LIRA/4/KACHU
WBLL1*2/KURUKU//HEILO
PBW343/PASTOR*2/3/WUH1/VEE#5//CBRD
ATTILA/HEILO
PBW343*2/KUKUNA*2//FRTL/PIFED
BECARD/KACHU
NAC/TH.AC//3*PVN/3/MIRLO/BUC/4/2*PASTOR/5/KACHU/6/KACHU
114

0

0

0

1

0
0

0
0

0
0

1
1

0

0

0

1

0
0
0
0
0
0
0

0
0
0
0
0
0
0

0
0
0
0
0
1
1

1
1
1
1
1
1
1

0
0
0
2.5
2.5

0
0
0
0
0

1
1
1
0
0

1
1
1
0
0

2.5
2.5
2.5
2.5
2.5
2.5
2.5
0

0
0
0
0
0
0
0
2.5

0
0
0
0
0
0
0
0

0
0
0
0
0
1
1
1

Table 3-3 (cont’d)
34
38
60
97
107
164
170
184
194
216
222
233
235
247
255
291
58
174
182
199
225
286

ALTAR 84/AE.SQUARROSA
(221)//3*BORL95/3/URES/JUN//KAUZ/4/WBLL1/5/REH/HARE//2*BCN/3
/CROC_1/AE.SQUARROSA (213)//PGO/4/HUITES
BAV92//IRENA/KAUZ/3/HUITES/6/ALD/CEP75630//CEP75234/PT7219/
3/BUC/BJY/4/CBRD/5/TNMU/PF85487
WBLL1/FRET2//PASTOR*2/3/MURGA
QUAIU #3//MILAN/AMSEL
CONI#1/2*HUIRIVIS #1
HUIRIVIS #1/GONDO
WBLL1*2/KURUKU//HEILO
KACHU*2//CHIL/CHUM18
NG8675/CBRD/7/IVAN/6/SABUF/5/BCN/4/RABI//GS/CRA/3/AE.SQUAR
ROSA (190)/8/WBLL1*2/CHAPIO
HEILO/7/IVAN/6/SABUF/5/BCN/4/RABI//GS/CRA/3/AE.SQUARROSA
(190)/8/VORB/FISCAL
SERI.1B//KAUZ/HEVO/3/AMAD*2/4/KIRITATI
ATTILA/3*BCN//BAV92/3/TILHI/4/SHA7/VEE#5//ARIV92
BABAX/KS93U76//BABAX/3/ATTILA/3*BCN//TOBA97/4/WBLL1*2/KUR
UKU
NING MAI 96035/FINSI//HEILO
BABAX/LR42//BABAX/3/ER2000
CALINGIRI/SOKOLL
WBLL1*2/5/CNO79//PF70354/MUS/3/PASTOR/4/BAV92
KACHU #1/4/CROC_1/AE.SQUARROSA
(205)//KAUZ/3/SASIA/5/KACHU
PFAU/WEAVER*2//BRAMBLING/7/IVAN/6/SABUF/5/BCN/4/RABI//GS/
CRA/3/AE.SQUARROSA (190)/8/PFAU/WEAVER//BRAMBLING
CBRD/FILIN
FRET2*2/4/SNI/TRAP#1/3/KAUZ*2/TRAP//KAUZ/5/KACHU
SLVS/PASTOR/3/PASTOR//MUNIA/ALTAR 84
115

2.5

0

0

1

2.5

0

0

1

0
0
2.5
2.5
2.5
2.5
2.5

2.5
2.5
0
0
0
0
0

1
0
0
0
1
0
0

0
1
1
1
0
1
1

0

2.5

0

1

2.5
2.5
2.5

0
0
0

0
0
1

1
1
0

2.5
2.5
2.5
0
0

0
0
0
2.5
2.5

0
1
0
1
1

1
0
1
1
1

2.5

0

1

1

2.5
2.5
2.5

0
0
0

1
1
1

1
1
1

Table 3-3 (cont’d)
95
108
133
165
45
80
109
114
160
163
176
186
187
248
287
6
20
40
142
210
284
24
29
46
55
94

PBW343*2/KUKUNA//TECUE #1
TECUE #1/2*WAXWING
WBLL1*2/BRAMBLING/4/BABAX/LR42//BABAX*2/3/KURUKU
KAUZ/PASTOR//PBW343/3/HEILO
WAXWING*2/DIAMONDBIRD
KACHU #1*2/WHEAR
KBIRD//WH 542/2*PASTOR/3/WBLL1*2/BRAMBLING
CHIL/CHUM18//GONDO
GONDO/CBRD
HUIRIVIS #1/GONDO
BAV92//IRENA/KAUZ/3/HUITES*2/4/GONDO/TNMU
SAUAL #1/TNMU//SAUAL
PRINIA/PASTOR//CHIL/CHUM18/3/PRINIA/PASTOR
NING MAI 96035/FINSI//HEILO
YAV79//DACK/RABI/3/SNIPE/4/AE.SQUARROSA
(460)/5/2*EXCALIBUR/6/VEE/LIRA//BOW/3/BCN/4/KAUZ
WBLL1*2/4/BABAX/LR42//BABAX/3/BABAX/LR42//BABAX
PBW343*2/KUKUNA//TECUE #1
WBLL1*2/CHAPIO//HEILO
PASTOR/KAUZ/6/CNDO/R143//ENTE/MEXI_2/3/AEGILOPS
SQUARROSA (TAUS)/4/WEAVER/5/2*KAUZ
KETUPA*2/PASTOR/6/TURACO/5/CHIR3/4/SIREN//ALTAR
84/AE.SQUARROSA (205)/3/3*BUC/7/KACHU
PSN/BOW//MILAN/3/2*BERKUT
WAXWING*2/HEILO
PASTOR//HXL7573/2*BAU/3/WBLL1
BAV92//IRENA/KAUZ/3/HUITES*2/4/MILAN/KAUZ//CHIL/CHUM18
UP2338*2/4/SNI/TRAP#1/3/KAUZ*2/TRAP//KAUZ/5/MILAN/KAUZ//CHI
L/CHUM18/6/UP2338*2/4/SNI/TRAP#1/3/KAUZ*2/TRAP//KAUZ
WAXWING/4/SNI/TRAP#1/3/KAUZ*2/TRAP//KAUZ/5/TECUE #1
116

5
5
5
0
5
2.5
5
0
2.5
5
5
5
5
5
5

0
0
0
0
0
2.5
0
5
2.5
0
0
0
0
0
0

0
0
0
5
0
0
1
0
0
0
1
0
1
0
1

0
0
0
0
1
1
0
1
1
1
0
1
0
1
0

5
0
5
2.5

0
5
0
2.5

1
1
1
1

1
1
1
1

2.5

2.5

1

1

5
7.5
2.5
2.5
7.5

0
0
0
0
0

1
0
5
5
0

1
1
1
1
1

7.5

0

0

1

Table 3-3 (cont’d)
166
195
212
215
250
268
64
135
143
31
66
81
145
153
218
22
85
234
264
270
39
52
87
150
252
262

FRET2/WBLL1//TACUPETO F2001/3/HEILO
SHA3/CBRD//TNMU/3/KACHU
CHIL/CHUM18//GONDO/3/WBLL1*2/KURUKU
NG8675/CBRD//FN/2*PASTOR/4/THELIN/3/2*BABAX/LR42//BABAX
ATTILA/HEILO
KS82W418/SPN//WBLL1/3/BERKUT
KACHU*2/3/CHUM18/BORL95//CBRD
BABAX/LR42//BABAX*2/3/KUKUNA/4/TAM200/PASTOR//TOBA97
PASTOR/3/VORONA/CNO79//KAUZ/4/MILAN/OTUS//ATTILA/3*BCN
PUB94.15.1.12/FRTL
SAUAL*2/6/CNDO/R143//ENTE/MEXI_2/3/AEGILOPS SQUARROSA
(TAUS)/4/WEAVER/5/2*PASTOR
KACHU #1/3/C80.1/3*BATAVIA//2*WBLL1/4/KACHU
CHIBIA/WEAVER//KACHU
PBW343*2/KUKUNA//PBW343*2/KUKUNA/3/PBW343
TRCH/SRTU//KACHU
YAV_3/SCO//JO69/CRA/3/YAV79/4/AE.SQUARROSA (498)/5/LINE
1073/6/KAUZ*2/4/CAR//KAL/BB/3/NAC/5/KAUZ/7/KRONSTAD
F2004/8/KAUZ/PASTOR//PBW343
QUAIU/5/FRET2*2/4/SNI/TRAP#1/3/KAUZ*2/TRAP//KAUZ
ATTILA/3*BCN//BAV92/3/TILHI/4/SHA7/VEE#5//ARIV92
TAN//TEMPORALERA M 87/AGR/3/FRET2/4/URES/PRL//BAV92
SOKOLL/EXCALIBUR
WBLL1*2/CHAPIO//HEILO
CS/TH.SC//3*PVN/3/MIRLO/BUC/4/URES/JUN//KAUZ/5/HUITES/6/YA
NAC/7/CS/TH.SC//3*PVN/3/MIRLO/BUC/4/MILAN/5/TILHI
FRANCOLIN #1/HAWFINCH #1
PBW343/HUITES/3/MILAN/OTUS//ATTILA/3*BCN
BABAX/LR42//BABAX*2/3/KURUKU
VEE/MJI//2*TUI/3/PASTOR/4/BERKUT
117

7.5
2.5
7.5
2.5
5
7.5
7.5
5
7.5
7.5
5

0
0
0
0
2.5
0
0
2.5
0
2.5
0

0
1
0
5
0
1
1
1
1
0
5

1
5
1
1
1
0
1
1
1
0
0

0
5
0
5
5

10
0
10
0
0

0
0
0
0
5

0
5
0
5
1

5
2.5
5
7.5
7.5
5

0
2.5
0
2.5
0
7.5

1
1
5
1
1
0

5
5
1
1
5
1

5
2.5
5
7.5

2.5
10
2.5
0

5
0
5
5

1
1
1
1

Table 3-3 (cont’d)
51
297
137
28
75
77
141
177

SAUAL/KIRITATI//SAUAL
ASTREB*2/3/WUH1/VEE#5//CBRD
KENYA NYANGUMI/3/2*KAUZ/PASTOR//PBW343
QUAIU #1
KACHU*2//CHIL/CHUM18
BAV92//IRENA/KAUZ/3/HUITES*2/4/YUNMAI
PFAU/MILAN//SOVA/3/PBW65/2*SERI.1B
BAV92//IRENA/KAUZ/3/HUITES*2/4/GONDO/TNMU

2.5
12.5
0
10
0
10
5
10

10
0
0.5
0
10
5
10
0

1
1
10
5
1
1
0
1

1
1
5
1
5
0
1
5

214
259
280
148
50
168
196
261
119
219
70
118
138
140
204

FN/2*PASTOR//GONDO/TNMU/3/FRANCOLIN #1
PASTOR/4/WEAVER/TSC//WEAVER/3/WEAVER/5/URES/PRL//BAV92
SOKOLL*2/GLE
C80.1/3*BATAVIA//2*WBLL1/3/TOBA97/PASTOR
SAUAL/YANAC//SAUAL
WBLL1*2/CHAPIO//HEILO
FN/2*PASTOR//GONDO/TNMU/3/FRANCOLIN #1
SW94.2690/SUNCO
PANDORA//WBLL1*2/BRAMBLING
PRL/2*PASTOR//SRTU/3/PRINIA/PASTOR
ROLF07*2/4/BOW/NKT//CBRD/3/CBRD
FRANCOLIN #1/4/BABAX/LR42//BABAX*2/3/KURUKU
PARUS/PASTOR//INQALAB 91*2/KUKUNA
CROC_1/AE.SQUARROSA (205)//KAUZ/3/SASIA/4/TROST
FRET2/TUKURU//FRET2/3/WUH1/VEE#5//CBRD/4/FRET2/TUKURU//F
RET2
WBLL1*2/CHAPIO*2//MURGA
FINSI/METSO//FH6-1-7/3/FINSI/METSO
OASIS//TC14/2*SPER/3/ATTILA/10/ATTILA*2/9/KT/BAGE//FN/U/3/BZA
/4/TRM/5/ALDAN/6/SERI/7/VEE#10/8/OPATA

5
5
5
5
12
2.5
2.5
7.5
12.5
2.5
5
20
5
0
5

0
0
5
10
5
15
10
0
5
15
0
0
0
0
15

10
10
5
1
1
1
5
10
1
1
10
0
10
15
0

1
1
1
1
0
0
1
1
1
1
5
0
5
5
0

0
2.5
10

20
17.5
0

0
0
10

1
1
1

8
53
266

118

Table 3-3 (cont’d)
213
244
257
267
285
12
258
260
296
271
100
279
33
82
121
112
211
265
272
295
4
220
13
106
63

SHA3/CBRD//TNMU/3/KACHU
CPI8/GEDIZ/3/GOO//ALB/CRA/4/AE.SQUARROSA
(208)/5/HAHN/2*WEAVER/6/SKAUZ/BAV92
FILIN/3/CROC_1/AE.SQUARROSA
(205)//KAUZ/4/FILIN/5/VEE/MJI//2*TUI/3/PASTOR
MEX94.27.1.20/3/SOKOLL//ATTILA/3*BCN
CROC_1/AE.SQUARROSA
(224)//OPATA/3/RAC655/4/SLVS/PASTOR
KACHU #1/4/CROC_1/AE.SQUARROSA
(205)//KAUZ/3/SASIA/5/KACHU
T.DICOCCON PI94625/AE.SQUARROSA (372)//3*PASTOR
SW94.2690/SUNCO
ASTREB*2/NING MAI 9558
PASTOR/SLVS//FRAME
WBLL1*2/VIVITSI//PRINIA/PASTOR/3/WBLL1*2/BRAMBLING
SUNSTATE/SD 3195//SOKOLL
ATTILA*2/PBW65//WBLL1*2/VIVITSI
SAUAL/WHEAR//SAUAL
WBLL1*2/KKTS//KINGBIRD #1
ATTILA*2/PBW65//KRONSTAD F2004
CHIL/CHUM18//FN/2*PASTOR/3/PRL/2*PASTOR
A93324S.7197.29/4/KAUZ//ALTAR 84/AOS/3/KAUZ/5/PASTOR
PASTOR/SLVS//FRAME
SOKOLL/SLVS
FRET2/TUKURU//FRET2/3/MUNIA/CHTO//AMSEL/4/FRET2/TUKURU
WAXWING*2/3/PASTOR//HXL7573/2*BAU
BAV92//IRENA/KAUZ/3/HUITES*2/4/GONDO/TNMU
BAV92//IRENA/KAUZ/3/HUITES/4/GONDO/TNMU/5/BAV92//IRENA/K
AUZ/3/HUITES
ROLF07*2/4/CROC_1/AE.SQUARROSA (224)//KULIN/3/WESTONIA
119

15
7.5

2.5
0

0
10

5
5

2.5

10

10

1

5
7.5

2.5
0

15
15

1
1

0

25

0

0

5
5
15
5
7.5
5
7.5
2.5
7.5
10
5
7.5
15
5
20
5
30
20

0
0
0
0.5
2.5
15.5
0
25
5
17.5
0
7.5
0
0
5
2.5
2.5
7.5

10
15
5
15
15
5
15
0
15
1
10
10
10
20
5
20
0
1

10
5
5
5
1
1
5
1
1
1
15
5
5
5
1
5
1
5

20

5

5

5

Table 3-3 (cont’d)
181
203
274
263
104
228
30
117
139
149
197
224
47
122
277
101
11
124
273
72
198
245

WBLL1/FRET2//PASTOR*2/3/GONDO
CNO79//PF70354/MUS/3/PASTOR/4/BAV92*2/5/SHA3/SERI//SHA4/LI
RA
BERKUT/3/ALTAR 84/AE.SQUARROSA (219)//SERI
BERKUT/3/ATTILA*2//CHIL/BUC
TUKURU//BAV92/RAYON*2/7/YAV_3/SCO//JO69/CRA/3/YAV79/4/AE.
SQUARROSA (498)/5/LINE
1073/6/KAUZ*2/4/CAR//KAL/BB/3/NAC/5/KAUZ
FRET2/KIRITATI/5/NAC/TH.AC//3*PVN/3/MIRLO/BUC/4/2*PASTOR
KLDR/PEWIT1//MILAN/DUCULA
PBW343*2/KHVAKI*2//YANAC
CROC_1/AE.SQUARROSA (205)//KAUZ/3/SASIA/4/TROST
WHEAR/3/PBW343/PASTOR//ATTILA/3*BCN
HEILO//GONDO/TNMU/3/WBLL1*2/BRAMBLING
WBLL1*2/4/YACO/PBW65/3/KAUZ*2/TRAP//KAUZ/5/KACHU #1
BAV92//IRENA/KAUZ/3/HUITES*2/4/PVN
TACUPETO F2001//WBLL1*2/KKTS/3/WBLL1*2/BRAMBLING
SOKOLL//SUNCO/2*PASTOR
SAUAL/5/REH/HARE//2*BCN/3/CROC_1/AE.SQUARROSA
(213)//PGO/4/HUITES/6/KACHU
BAV92//IRENA/KAUZ/3/HUITES*2/4/CROC_1/AE.SQUARROSA
(224)//KULIN/3/WESTONIA
ATTILA*2/PBW65*2/5/REH/HARE//2*BCN/3/CROC_1/AE.SQUARROS
A (213)//PGO/4/HUITES
BAXTER*2/4/CHEN/AEGILOPS SQUARROSA (TAUS)//BCN/3/BAV92
WBLL1*2/4/YACO/PBW65/3/KAUZ*2/TRAP//KAUZ*2/5/GONDO
CBRD/FILIN
CPI8/GEDIZ/3/GOO//ALB/CRA/4/AE.SQUARROSA
(208)/5/HAHN/2*WEAVER/6/SKAUZ/BAV92
120

5
5

5
0

20
15

5
15

5
25
12.5

0
2.5
2.5

20
5
15

10
5
10

10
30
15
5

0
5
15
20

20
1
10
15

10
5
1
1

10
30
5
20
22.5
12.5
40

30
5
30
20
0
0
2.5

0
5
1
1
15
20
1

1
1
5
1
5
10
0

40

2.5

1

1

22.5

20

1

1

25
10
0
5

17.5
0
0
0

1
30
30
20

1
5
15
20

Table 3-3 (cont’d)
256
269
278
293
71
146
179
48
69
2
98
231
73
152
232
239
243
83
127
276
205
251

OASIS//TC14/2*SPER/3/ATTILA/4/WBLL4
CNDO/R143//ENTE/MEXI75/3/AE.SQ/4/2*FCT/5/KAUZ*2/YACO//KAUZ
/6/BERKUT
CROC_1/AE.SQUARROSA (224)//OPATA/3/ALTAR
84/AE.SQ//2*OPATA
BERKUT/HTG
WBLL1/4/BOW/NKT//CBRD/3/CBRD/5/WBLL1*2/TUKURU
CHIBIA/WEAVER//KACHU
WBLL1*2/KURUKU*2//TNMU
BAV92//IRENA/KAUZ/3/HUITES*2/4/TNMU
BAV92//IRENA/KAUZ/3/HUITES/4/FN/2*PASTOR/5/BAV92//IRENA/KA
UZ/3/HUITES
TUKURU//BAV92/RAYON*2/3/PVN
ATTILA*2/PBW65//MUU #1/3/FRANCOLIN #1
SAUAL/3/KAUZ/PASTOR//PBW343
PFAU/WEAVER*2//BRAMBLING/3/KAUZ//TRAP#1/BOW/4/PFAU/WEA
VER*2//BRAMBLING
MONARCA F2007/KRONSTAD F2004
NG8675/CBRD//MILAN/3/SAUAL/6/CNDO/R143//ENTE/MEXI_2/3/AEGI
LOPS SQUARROSA (TAUS)/4/WEAVER/5/2*PASTOR
PRL/2*PASTOR//PARUS/5/NAC/TH.AC//3*PVN/3/MIRLO/BUC/4/2*PAS
TOR
VOROBEY
FRNCLN/BECARD
KAUZ/PASTOR//PBW343/3/KRONSTAD F2004
SW89-5124*2/FASAN//PARUS/PASTOR
WBLL1/FRET2//PASTOR/3/SHA3/SERI//SHA4/LIRA/4/WBLL1/TACUPE
TO F2001//PASTOR
WAXWING//PFAU/WEAVER

121

22.5
5

2.5
0

15
30

5
10

5

0

20

20

5
30
20
15
30
30

0
15
5
0
15
15

30
0
20
30
1
1

10
1
1
1
1
1

15
30
40
45

7.5
2.5
2.5
5

20
10
5
1

5
5
1
1

12.5
12.5

0
0

20
30

20
10

42.5

0

5

5

7.5
40
40
40
5

0
2.5
2.5
2.5
0

30
10
10
10
30

15
1
1
1
20

30

5

15

5

Table 3-3 (cont’d)
23
115
238
126
56
236
57
110
41
129
144
200
237
283
202
221
254
188
183
151
253
281
113
49

WAXWING/4/BL 1496/MILAN/3/CROC_1/AE.SQUARROSA
(205)//KAUZ/5/FRNCLN
WBLL1*2/KUKUNA//KIRITATI/3/WBLL1*2/KUKUNA
FRET2/KUKUNA//FRET2/3/PASTOR//HXL7573/2*BAU/5/FRET2*2/4/S
NI/TRAP#1/3/KAUZ*2/TRAP//KAUZ
KSW/SAUAL//SAUAL
UP2338*2/KKTS*2//YANAC
ATTILA*2/PBW65//KRONSTAD F2004
WAXWING/2*ROLF07
MUU/5/TRAP#1/BOW/3/VEE/PJN//2*TUI/4/BAV92/RAYON/6/MILAN/S8
7230//BAV92
WAXWING*2/4/BOW/NKT//CBRD/3/CBRD
PRL/2*PASTOR//VORB
PASTOR/3/VORONA/CNO79//KAUZ/4/MILAN/OTUS//ATTILA/3*BCN
CHIL/CHUM18//GONDO
KANZ*4/KS85-8-4//2*WBLL1*2/KURUKU
CO99W329/2*BERKUT
WAXWING/KIRITATI*2/3/SHA3/SERI//SHA4/LIRA
ATTILA*2/PBW65*2//TNMU
VOROBEY
PBW343*2/KHVAKI*2//CHIL/CHUM18
TRCH*2/TNMU
WBLL1*2/KURUKU//KRONSTAD F2004
ND643//2*PRL/2*PASTOR
TEMPORALERA M
87/ROMO96/3/ATTILA/BAV92//PASTOR/4/PRL/2*PASTOR
WBLL1*2/TUKURU//KRONSTAD F2004
WBLL1/DIAMONDBIRD//WBLL1*2/VIVITSI
122

50

5

1

1

7.5
15

15
2.5

20
30

15
10

40
30
35
22.5
40

20
30
10
20
7.5

0
0
15
15
10

0
1
1
5
5

20
20
10
5
40
50
50

0
20
0
0
5
10
7.5

30
20
40
40
15
5
10

15
5
15
20
5
0
1

30
5
50
5
40
50
20

15
5
15
2.5
10
0
5

15
40
5
50
15
15
40

10
20
1
15
10
10
10

50
15

10
12.5

15
30

1
20

Table 3-3 (cont’d)
189
25
275
175
18
42
227
68
294
161
103
120
159
1
246
167
292
111
123
96
26
240
201
154

PBW343/PASTOR*2/6/TURACO/5/CHIR3/4/SIREN//ALTAR
84/AE.SQUARROSA (205)/3/3*BUC
KIRITATI/4/2*BAV92//IRENA/KAUZ/3/HUITES
MILAN/DUCULA//SUNCO/2*PASTOR
SAUAL*2/6/CNDO/R143//ENTE/MEXI_2/3/AEGILOPS SQUARROSA
(TAUS)/4/WEAVER/5/2*PASTOR
TRCH/KBIRD
ROLF07*2/3/PRINIA/PASTOR//HUITES
FRET2/KIRITATI/5/NAC/TH.AC//3*PVN/3/MIRLO/BUC/4/2*PASTOR
BAV92//IRENA/KAUZ/3/HUITES/4/FN/2*PASTOR/5/BAV92//IRENA/KA
UZ/3/HUITES
SOKOLL/FRAME
HEILO
WBLL1*2/KURUKU/6/CNDO/R143//ENTE/MEXI_2/3/AEGILOPS
SQUARROSA (TAUS)/4/WEAVER/5/2*JANZ/7/WBLL1*2/KURUKU
WBLL1*2/BRAMBLING//JUCHI
FALCIN/AE.SQUARROSA (312)/3/THB/CEP7780//SHA4/LIRA
SAUAL/KRONSTAD F2004
CPI8/GEDIZ/3/GOO//ALB/CRA/4/AE.SQUARROSA
(208)/5/HAHN/2*WEAVER/6/SKAUZ/BAV92
WBLL1*2/4/YACO/PBW65/3/KAUZ*2/TRAP//KAUZ/5/GONDO
SOKOLL//SLVS/PASTOR/3/ATTILA*2//CHIL/BUC
KFA/3/PFAU/WEAVER//BRAMBLING/4/PFAU/WEAVER*2//BRAMBLIN
G
WBLL1/KUKUNA//TACUPETO F2001/3/KRONSTAD F2004/4/ROLF07
WBLL1*2/BRAMBLING//FN/2*PASTOR
KZA//WH 542/2*PASTOR/3/BACEU #1
WAXWING*2/JUCHI
SAUAL/4/CROC_1/AE.SQUARROSA (205)//KAUZ/3/ATTILA/5/SAUAL
WHEAR/2*KRONSTAD F2004
123

10

7.5

30

30

60
50
52.5

20
30
15

1
1
10

0
1
5

50
40
22.5
50

15
30
2.5
35

15
10
40
1

5
5
20
0

50
32.5
60

25
30
15

10
10
10

1
15
5

40
50
70
12.5

25
25
20
30

20
10
1
30

5
5
1
20

7.5
60
55

0
22.5
30

50
10
10

40
5
5

35
70
60
70
60
80

35
25
22.5
10
40
25

20
5
10
20
5
1

10
1
10
5
1
1

Table 3-3 (cont’d)
10
102
79
99
217
78
19
37
178
14
134
91
84
93
242
157
128
171
172
155
206
193
230
288

ATTILA*2/PBW65*2//W485/HD29
MUU #1//PBW343*2/KUKUNA/3/MUU
BAV92//IRENA/KAUZ/3/HUITES*2/4/WHEAR
ATTILA*2/PBW65*2//TOBA97/PASTOR
BAV92//IRENA/KAUZ/3/HUITES/4/DOLL
WAXWING/KIRITATI*2/3/C80.1/3*BATAVIA//2*WBLL1
ROLF07/MUU
ATTILA*2/PBW65//MURGA
FRET2*2/KUKUNA*2//SHA4/CHIL
MUNAL #1/FRANCOLIN #1
FRANCOLIN #1/KIRITATI
KBIRD//WBLL1*2/KURUKU
PAURAQ/3/KIRITATI//PRL/2*PASTOR
WAXWING/4/SNI/TRAP#1/3/KAUZ*2/TRAP//KAUZ/5/AKURI
SHANGHAI #8
SUMAI #3
REH/HARE//2*BCN/3/CROC_1/AE.SQUARROSA
(213)//PGO/4/HUITES/5/KRONSTAD F2004
WBLL1*2/VIVITSI//GONDO
ATTILA/2*PASTOR//FN/2*PASTOR
C80.1/3*BATAVIA//2*WBLL1/3/2*KRONSTAD F2004
PFAU/WEAVER*2//BRAMBLING/7/IVAN/6/SABUF/5/BCN/4/RABI//GS/
CRA/3/AE.SQUARROSA (190)/8/PFAU/WEAVER//BRAMBLING
NG8675/CBRD/7/IVAN/6/SABUF/5/BCN/4/RABI//GS/CRA/3/AE.SQUAR
ROSA (190)/8/WBLL1*2/CHAPIO
KAUZ//ALTAR 84/AOS/3/MILAN/KAUZ/4/OTUS/TOBA97
CNDO/R143//ENTE/MEXI_2/3/AEGILOPS SQUARROSA
(TAUS)/4/WEAVER/5/2*JANZ/6/D67.2/PARANA
66.270//AE.SQUARROSA (320)/3/CUNNINGHAM

124

70
60
70
60
70
60
50
80
80
80
70
80
50
80
60
80
70

25
40
35
45
27.5
30
50
20
42.5
35
20
12.5
45
40
50
50
65

10
5
5
5
10
20
10
15
1
5
30
20
20
10
15
5
1

5
5
1
1
5
5
10
5
1
5
5
15
15
1
10
1
1

15
80
60
80

12.5
17.5
30
55

70
30
30
5

40
10
20
1

50

30

50

15

80
70

50
55

10
20

5
1

Table 3-3 (cont’d)
290
156
136
207
241
289
180
158

D67.2/PARANA 66.270//AE.SQUARROSA
(320)/3/CUNNINGHAM/4/PASTOR/SLVS
C80.1/3*BATAVIA//2*WBLL1/3/2*KRONSTAD F2004
MURGA/KRONSTAD F2004//QUAIU #3
PFAU/WEAVER//BRAMBLING*2/3/SHA3/SERI//SHA4/LIRA
FUNDACEP 30
CROC_1/AE.SQUARROSA
(205)//BORL95/3/KENNEDY/6/CNDO/R143//ENTE/MEXI_2/3/AEGILOP
S SQUARROSA (TAUS)/4/WEAVER/5/2*JANZ
WBLL1*2/TUKURU//WUH1/BOW/3/WBLL1*2/TUKURU
GAMENYA

125

80

50

15

5

70
70
70
80
80

15
30
80
50
85

40
40
10
30
10

30
30
10
20
5

80
90

65
65

60
60

30
40

Analysis of variance for Yellow Rust Severity
The ANOVA of the Ecuador trials indicated significant differences for yellow rust
severity (%) among accessions in each location and year (Table 3-3). The average
severity scores were 19.01 and 9.17% in 2011 and 2012, respectively. In 2011, the
severity ranged from 0 - 90%, whereas, in 2012 the severity ranged from 0 – 65%
(Table 3-4; Figure 3-1; Figure 3-2). The combined analysis among the two years
detected significant differences between treatments, but the coefficient of variance was
high cv = 82.9% (Table 3-3).
The ANOVA of the experiments evaluated in Mexico 2011 and 2012 for YR severity
detected significant differences between accessions and years (Table 3-3). The means
were 8.37 and 4.25% in 2011 and 2012, respectively. The percentage of severity
ranged from 0 – 70% in 2011 and from 0 – 40 % in 2012 (Figure 3-6; Figure 3-7).
According to Shapiro-Wilk normality test, the data distribution for yellow rust severity
were not normal and showed skewedness in each experiment for each year and
location (Figure 3-6)
A high positive correlation was observed between locations in the two years (Table 3-5).
2

In Ecuador, the correlation r = 0.77 (P< 0.001) between years. In Mexico the
2

correlation was r = 0.85 (P< 0.001) between years. However, the correlation between
2

the two locations was relatively low (r = 0.30; P< 0.001) (Figure 3-8; Table 3-5). Some
wheat accessions were susceptible in Ecuador but resistant in Mexico. These
differences of disease response in each location lowered the correlation between

126

locations and may be caused by a race specific effect of some major genes against
different local races.
Table 3-4. Analysis of variance of yellow rust severity in the association mapping panel.
Ecuador and Mexico. 2011-12.
Sources of variation
Df
Mean
F-value
P-value
squares
Yellow rust (Ecuador 2011-12)
14384
105.47
< 0.001
Year
1
693.2
5.0827
< 0.001
Accession
296
125.5
0.9199
0.69
Block/Group
196
136.4
Error
100
CV(%)=
82.9
Mean (%)=
14.1
Yellow rust (Ecuador 2011)
296
808.79
28.943
<0.001
Accession
100
27.94
Error
CV(%) =
27.8
Mean(%) =
19.0
Yellow rust (Ecuador 2012)
Accession
Error
CV(%) =
Mean(%) =
Yellow rust (Mexico 2011-12)
Accession
Error
CV(%) =
Mean(%) =

296
100
84.2
9.17

455.83
59.65

7.6414

<0.0001

296
280
28.9
6.31

14.9
0.9

17.2

<0.001

127

Table 3-5. Disease severity in the association mapping panel planted in Ecuador and
Mexico. 2011-12.
Location

Year

Range (%)

Average (%)

Santa Catalina –
Ecuador

2011

0 - 90

19.01

El Batan – Mexico

2012
2011
2012

0 - 65
0 - 70
0 - 40

9.17
8.37
4.25

Table 3-6. Pearson correlation and p-values of correlations for yellow rust severity in the
association mapping panel experiments in two locations and two years. Ecuador and
Mexico. 2011 -12. All values were highly significant (P< 0.001).
Ecuador
Mexico
Ecuador Ecuador Mexico
Mexico
2011-12 2011-12
2011
2012
2011
2012
Ecuador 2011-12
1
Mexico 2011-12
Ecuador 2011
Ecuador 2012
Mexico 2011
Mexico 2012

0.30
0.97
0.91
0.30
0.27

1
0.30
0.25
0.98
0.94

1
0.77
0.31
0.26

128

1
0.24
0.24

1
0.85

1

Figure 3-1. Histograms of yellow rust severity (%) in the wheat AMP evaluated in
Ecuador and Mexico, 2011-12.

129

Figure 3-2. Histograms of two year averages of yellow rust severity (%) in the wheat
AMP evaluated in Ecuador and Mexico, 2011-12.

Figure 3-3. Scatter plots of yellow rust severity data from the wheat AMP evaluated in
Ecuador and Mexico. 2011-12.

130

Association analysis for yellow rust severity
A total of 4,679 SNPs and 33 SSR markers showing good quality were considered for
the association analysis with the traits collected in Mexico and Ecuador during 2011 and
2012. The markers were filtered to retain polymorphic markers with minor allele
frequencies over 5% and one marker per locus avoiding markers in clusters with the
same polymorphic pattern. The final number of molecular markers employed to perform
the association analysis was 1,666.
The association analysis conducted in Mexico using the GLM method detected 17 and 9
significant SNP markers during 2011 and 2012, respectively. (Table 3-6; Figure 3-4;
Figure 3-5). These SNP markers were located on chromosomes 2A, 5A, 6A, 7A, 2B,
5B, 6B, 3D, and 5D. On chromosome 2A, the markers were distributed between 5 and
53 cM. Two SNP markers (wsnp_Ku_c33374_42877546 and
wsnp_RFL_Contig1951_1127302) showed the most significant p-valuel in both years
(p-values < 3.7 x 10-8). On chromosome 5A, two significant markers were detected only
in the association analysis conducted in 2011. These two SNP markers were located at
121 and 172 cM. On chromosome 7A the region associated with the YR resistance was
located at 41 and 51 cM. On chromosome 2B the region associated with the YR
resistance was located at 5, 15, 112, and 220 cM. On chromosome 5B, only one
significant SNP marker was detected at 100 cM. In the same way, only one marker was
detected on chromosome 6B at 22 cM. On chromosome 7B there were two SNP
markers located at 45 and 160 cM. On the D-genome, chromosomes 3D and 5D
showed markers associated with yellow rust resistance at 15 and 13 cM, respectively.

131

In Ecuador, the association analysis conducted in the AMP with the combined data set
collected in Ecuador 2011-12 detected regions associated with YR resistance on
chromosome 1A, 2A, 5A, 6A, 7A, 1B, 2B, 3B, 4B, 5B, 6B, 7B, 1D, 2D, 3D, 5D, 6D, and
7D (Table 3-6; Figure 3-6; Figure 3-7).
The association analysis from the combined experiments in Ecuador 2011-12 resulted
in more significant markers linked to YR resistance compared with those found in
Mexico. In total, 72 and 56 SNP markers were associated with YR resistance in 2011
and 2012, respectively. However, some markers located on chromosome 2A
(wsnp_Ku_c33374_42877546 and wsnp_RFL_Contig1951_1127302) were common for
both locations (Table 3-6). Additionally, the analysis detected markers located on
chromosomes 1A, 6A, 6B, and 7B. SNP marker on chromosome 1A was detected at
171 cm. SNP markers on chromosome 2A were distributed from 5 to 88 cM. SNP
marker on chromosome 6A was located at 106 cM. SNP markers on chromosome 6B
were found at 191 – 192 cM. Finally, SNP markers on chromosome 7B were found at
66 -67 cM.
The individual analysis in Ecuador 2011 using the GLM method detected significant
markers on chromosomes 1A, 2A, 5A, 6A, 7A, 1B, 2B, 3B, 4B, 5B, 6B, 7B, 1D, 2D, 3D,
5D, 6D, and 7D (Table 3-6; Figure 3-6). On chromosome 2A, the significant SNP
markers were distributed from 5 to 76 cM. On chromosome 5B the significant markers
were located at 8, 23, 69, 71, 100, and 181 cM. On chromosome 7B, two significant
SNP markers were detected at 31and 100 cM. Finally, chromosome 7D contained
significant markers at 0-8 cM.

132

The association analysis conducted with the data collected in Ecuador 2012 for yellow
rust severity with the GLM method detected SNP markers significantly associated with
resistance to YR located on chromosomes 1A, 2A, 3A, 4A, 5A, 6A, 7A, 1B, 2B, 3B, 5B,
7B, 2D, 4D, and 7D (Figure 3-6; Figure 3-7). On chromosome 2A, eight SNP markers
significantly associated with YR located at 5, 7, 10, 72, and 76 cM were detected. On
chromosome 6A, eight SNP markers were detected at 21, 90. 99. 106, 117, 139, 189,
and 206 cM. On chromosome 7A, three SNP markers were detected located at 104,
105, and 107 cM. On chromosome 1B, the significant markers were located at 46, 86,
91 cM. On chromosome 5B, three SNP markers were detected at 23, 39, and 134 cM.
On chromosome 6B, there was one SNP marker detected at 192 cM. Finally, on
chromosome 7B, three SNP markers were detected at 41, 47, 51, and 99 cM.
The association analysis conducted with the combined data set and individually data set
per location in Ecuador and Mexico in the wheat AMP using the MLM method detected
SNP markers significantly linked to YR resistance on chromosomes 1A, 2A, 3A, 6A, 1B,
2B, 7B, 6D, 7D . On chromosome 2A, the SNP markers were located at 7 and 10 cM
(Table 3-7). The same results were found in the association analysis conducted with
data collected from Ecuador using MLM method. In addition to the SNPs located on
chromosome 2A, there two SNP markers located on chromosome 1A position 104 and
111cM.

133

Table 3-7. Association analysis for yellow rust severity of the wheat association mapping panel using GLM model. Mexico
and Ecuador. 2011-12.
Marker
Chr
Position
P-value
r2
Alleles
Allele 1
Allele 2
Effect
(cM)
Sev(%)
Sev(%) Sev(%)
Ecuador 2011
wsnp_Ex_c48087_53105842
1A
36
0.00119
0.04
A/G
17.2
30.3
13.1
wsnp_BE403956A_Ta_2_3
1A
71
4.07E-04 0.05
T/C
17
21.7
4.7
wsnp_Ex_c6817_11761300
1A
71
6.58E-04 0.05
T/C
30
17
13
wsnp_BE517729A_Ta_2_1
1A
100
0.00445
0.04
A/G
13.5
19.4
5.9
wsnp_Ex_rep_c68085_66839109
1A
104
9.24E-05 0.07
A/G
20.3
15.1
5.2
wsnp_Ex_c43228_49605281
1A
104
0.00182
0.04
A/G
20.1
14.5
5.6
wsnp_Ex_c5550_9779698
1A
176
0.00262
0.04
T/C
17.1
27.3
10.2
wsnp_Ku_c23598_33524490
2A
5
7.61E-05 0.06
A/C
23.1
16.1
7
wsnp_Ku_c33374_42877546
2A
7
1.11E-24 0.31
A/G
26.8
3.4
23.4
wsnp_RFL_Contig1951_1127302
2A
10
1.41E-29 0.37
A/G
3.4
27.6
24.2
wsnp_Ex_rep_c68113_66877517
2A
72
6.25E-04 0.05
T/C
22.6
10
12.6
wsnp_CAP11_rep_c8768_3788007
2A
76
0.00229
0.04
T/G
22.5
10.2
12.3
wsnp_JG_c2509_1153697
3A
57
0.00315
0.04
A/G
18.5
16.8
1.7
wsnp_Ex_c5047_8963671
3A
100
4.18E-04 0.05
T/C
18.6
15.5
3.1
wsnp_Ex_c5623_9891584
3A
123
0.00149
0.04
A/G
21.2
13.3
7.9
wsnp_Ex_c361_708712
3A
162
6.47E-04 0.05
A/G
27.3
16.1
11.2
wsnp_Ex_c5072_9006666
4A
4
4.30E-05 0.07
A/G
16.8
31.5
14.7
wsnp_Ku_c9746_16265584
4A
5
1.02E-05 0.08
A/G
32.7
16.6
16.1
wsnp_Ex_c1246_2393978
4A
47
2.17E-05 0.07
T/G
16.1
26.1
10
wsnp_JD_c27162_22206547
4A
63
5.76E-04 0.05
T/C
21.3
14.6
6.7
wsnp_RFL_Contig4086_4599222
5A
114
9.91E-05 0.06
A/G
17.4
28.1
10.7
wsnp_Ex_c10231_16783750
5A
153
0.00424
0.04
T/C
20.4
17.3
3.1
wsnp_Ku_c9559_16000086
5A
185
9.30E-04 0.05
T/C
22.1
15
7.1
wsnp_Ex_c13230_20872924
6A
21
9.72E-04 0.05
T/C
17.5
26.7
9.2
wsnp_Ku_c17618_26749729
6A
43
0.00161
0.04
A/G
14.4
22.4
8
wsnp_Ex_c18965_27868480
6A
90
5.17E-04 0.05
A/G
17.3
21.1
3.8
wsnp_Ex_c34641_42914170
6A
139
0.00113
0.05
T/C
19.3
18.3
1
134

Table 3-7 (cont’d)
wsnp_JD_c5872_7032077
wsnp_Ex_rep_c66939_65371026
wsnp_BG313770A_Ta_2_1
wsnp_Ex_c20062_29096408
wsnp_Ra_c26491_36054023
wsnp_Ku_rep_c103889_90513365
wsnp_Ex_c6142_10746442
wsnp_Ex_c52474_56060204
wsnp_Ku_rep_c69901_69397257
wsnp_BG606986B_Ta_2_1
wsnp_Ex_c194_381656
wsnp_Ex_c1597_3045682
wsnp_Ex_c7776_13247654
wsnp_JD_c12687_12877994
wsnp_Ex_c30447_39360584
wsnp_Ex_c17845_26604587
wsnp_Ku_c48694_54811376
wsnp_CAP11_c3742_1796552
wsnp_Ku_c33335_42844594
wsnp_Ex_c48922_53681502
wsnp_Ex_c26285_35531493
wsnp_JD_c12221_12509932
wsnp_JD_c8978_9893945
wsnp_Ex_rep_c68600_67448893
wsnp_Ra_c20970_30293078
wsnp_Ex_c2264_4243233
wsnp_Ex_rep_c103024_88075347
wsnp_JD_c15167_14703349
wsnp_Ku_c4910_8793327
wsnp_Ex_c6731_11634168

6A
7A
7A
7A
7A
7A
7A
1B
1B
1B
1B
1B
2B
2B
2B
2B
2B
3B
3B
4B
4B
5B
5B
5B
5B
5B
5B
6B
6B
6B

187
6
20
51
105
134
173
46
48
86
91
141
5
76
91
170
220
12
61
80
86
8
23
69
71
100
181
12
140
153

0.00633
0.00624
0.00474
4.55E-04
0.00119
0.00319
9.03E-04
0.00753
0.003
0.00186
0.00172
9.95E-04
2.58E-05
0.00269
0.00721
0.00521
0.00304
1.33E-04
2.34E-04
0.00333
0.00556
0.00157
2.38E-05
0.00337
0.00411
2.39E-05
0.00722
0.00295
0.00305
0.00851
135

0.03
0.04
0.04
0.05
0.04
0.04
0.05
0.03
0.04
0.04
0.04
0.05
0.07
0.04
0.03
0.04
0.03
0.06
0.06
0.03
0.04
0.04
0.07
0.04
0.04
0.07
0.03
0.04
0.04
0.04

A/G
A/G
T/C
T/C
A/G
A/G
A/G
A/G
T/C
T/C
T/C
A/G
T/C
T/C
A/G
T/C
T/C
T/C
A/G
T/C
T/C
A/C
T/C
T/G
A/C
A/C
T/C
T/C
A/G
T/G

20.1
15.3
21.4
30.9
37.2
22.5
16.9
19.8
18.6
16.6
20.1
22.2
28.9
20.5
13.4
25.2
17.4
5.3
17.4
20.1
20.5
21.4
15.8
19.8
22.4
5.4
15
8.3
15.3
19.8

17.1
27.7
16
16.6
17.9
14.9
25.2
14.8
17.3
20.3
16.3
15.4
17.2
11.9
19.7
14.4
32
20.5
32.6
4
17.2
9.8
31.2
16.7
14.3
21
23.8
21.5
23.2
14.6

3
12.4
5.4
14.3
19.3
7.6
8.3
5
1.3
3.7
3.8
6.8
11.7
8.6
6.3
10.8
14.6
15.2
15.2
16.1
3.3
11.6
15.4
3.1
8.1
15.6
8.8
13.2
7.9
5.2

Table 3-7 (cont’d)
wsnp_Ku_c665_1371448
wsnp_Ex_c8963_14948293
wsnp_Ex_c278_538285
wsnp_Ex_c15396_23659859
wsnp_Ex_c14779_22892053
wsnp_Ex_c6400_11123059
wsnp_BE444144D_Ta_1_1
wsnp_Ex_rep_c70527_69450183
wsnp_BE497160D_Ta_2_1
wsnp_JD_c825_1223454
wsnp_Ex_c6942_11966469
wsnp_Ex_c1690_3206784
wsnp_Ex_c43083_49499652
wsnp_CAP11_c2839_1425826
wsnp_CAP11_c176_177381

7B
7B
1D
1D
2D
2D
2D
3D
3D
5D
6D
6D
7D
7D
7D

Ecuador 2012
wsnp_BE403956A_Ta_2_3
wsnp_BE495786A_Ta_2_1
wsnp_Ex_c1255_2411550
wsnp_Ku_c23598_33524490
wsnp_Ku_c33374_42877546
wsnp_RFL_Contig1951_1127302
wsnp_Ex_c5412_9565733
wsnp_BQ168780B_Ta_2_1
wsnp_Ex_rep_c68113_66877517
wsnp_JD_c13086_13174510
wsnp_CAP11_rep_c8768_3788007
wsnp_JG_c2509_1153697
wsnp_Ku_rep_c68484_67499824

1A
1A
1A
2A
2A
2A
2A
2A
2A
2A
2A
3A
3A

31
100
0
91
0
89
101
2
53
13
0
44
34
0
8
71
81
178
5
7
10
53
67
72
72
76
57
100

0.00759
0.00323
7.78E-04
0.00772
1.39E-05
0.00395
0.00382
0.00626
0.00379
1.30E-04
6.98E-04
0.00445
0.00389
1.37E-06
0.00203

0.03
0.04
0.05
0.02
0.07
0.04
0.04
0.03
0.04
0.06
0.05
0.04
0.04
0.09
0.04

2.36E-04
5.18E-04
1.83E-04
0.00375
1.99E-11
7.19E-12
9.89E-07
2.79E-05
0.00137
0.00343
0.00337
0.00157
0.00352

0.06
0.05
0.06
0.04
0.15
0.16
0.09
0.07
0.04
0.04
0.04
0.04
0.04

136

A/G
T/C
T/C
A/G
T/C
A/G
A/G
T/G
T/C
T/C
T/C
A/G
A/G
A/G
T/C
T/C
T/G
A/C
A/C
A/G
A/G
T/C
A/G
T/C
A/G
T/G
A/G
T/C

15.2
16.7
26.3
21.6
17.6
19.7
11.9
20.5
21.7
20.7
17.4
18
16.5
15.8
19.2

23.3
30.5
16.9
18.2
31.5
13.1
20.2
16.1
12.7
14.6
19.4
31.9
20.8
31.8
12.4

8.1
13.8
9.4
3.4
13.9
6.6
8.3
4.4
9
6.1
2
13.9
4.3
16
6.8

7.6
10.9
10.1
10.9
12.1
3.7
4.9
6.6
11.2
4.1
11.2
8.6
2.1

20.6
4.6
8.1
7.7
3.4
12
13.3
14.3
3.4
11.2
3.9
9.4
8.3

13
6.3
2
3.2
8.7
8.3
8.4
7.7
7.8
7.1
7.3
0.8
6.2

Table 3-7 (cont’d)
wsnp_Ra_c4858_8709000
wsnp_Ex_c5623_9891584
wsnp_Ex_c361_708712
wsnp_Ex_c5072_9006666
wsnp_Ex_c1246_2393978
wsnp_Ku_c46057_52907637
wsnp_Ra_c25624_35192195
wsnp_JD_c21776_19013462
wsnp_RFL_Contig4086_4599222
wsnp_Ra_c11420_18529863
wsnp_Ex_c13230_20872924
wsnp_BF200644A_Ta_1_1
wsnp_Ex_c35545_43677576
wsnp_Ex_c2350_4403690
wsnp_Ku_c22358_32187765
wsnp_Ex_c34641_42914170
wsnp_Ex_c10718_17457870
wsnp_Ex_c1153_2213588
wsnp_Ku_c34643_43968242
wsnp_Ra_c26491_36054023
wsnp_BM134363A_Ta_2_4
wsnp_Ex_c52474_56060204
wsnp_BG606986B_Ta_2_1
wsnp_Ex_c194_381656
wsnp_Ex_c7776_13247654
wsnp_BE499478B_Ta_2_1
wsnp_Ex_rep_c101906_87187119
wsnp_Ku_c3000_5638635
wsnp_Ex_c10796_17575074
wsnp_Ku_c48694_54811376

3A
3A
3A
4A
4A
4A
5A
5A
5A
5A
6A
6A
6A
6A
6A
6A
6A
6A
7A
7A
7A
1B
1B
1B
2B
2B
2B
2B
2B
2B

105
123
162
4
47
152
12
25
114
153
21
90
99
106
117
139
189
206
104
105
107
46
86
91
5
94
112
160
185
220

2.40E-04
0.00302
0.00446
0.00308
3.42E-04
0.00114
0.00476
0.00219
0.0048
0.00107
0.00198
1.14E-04
0.00141
0.00167
0.00394
0.0023
1.23E-04
0.00459
0.00171
0.0011
0.00499
1.78E-04
0.00171
0.00237
0.00248
0.0032
4.70E-04
0.00168
0.00189
2.54E-04

137

0.06
0.04
0.04
0.04
0.05
0.05
0.04
0.04
0.04
0.05
0.04
0.05
0.04
0.04
0.04
0.04
0.06
0.04
0.04
0.05
0.03
0.06
0.04
0.04
0.04
0.04
0.05
0.04
0.04
0.04

A/C
A/G
A/G
A/G
T/G
A/C
T/C
A/G
A/G
T/C
T/C
T/G
A/G
A/G
A/G
T/C
A/G
A/G
T/C
A/G
A/G
A/G
T/C
T/C
T/C
T/C
A/C
A/G
T/C
T/C

7.2
10.7
13.8
7.9
7.4
8.7
17.6
10.8
8.2
7.2
8.3
0
13
13.7
13.6
12
8.3
9.1
8.4
18.5
20.5
10.1
6.6
10
12.3
14.3
5.2
11.1
12.1
19.9

9.2
5.2
7.7
15.7
13.3
8.4
8.2
8
16.2
10.8
14.2
8.2
7.7
7.7
7.5
8.4
19.8
7.5
14.2
8.4
9.6
5.9
11.4
7.3
8.8
7
12.3
3.3
7.1
8

2
5.5
6.1
7.8
5.9
0.3
9.4
2.8
8
3.6
5.9
8.2
5.3
6
6.1
3.6
11.5
1.6
5.8
10.1
10.9
4.2
4.8
2.7
3.5
7.3
7.1
7.8
5
11.9

Table 3-7 (cont’d)
wsnp_CAP11_c3742_1796552
wsnp_Ex_c29623_38630871
wsnp_JD_c8978_9893945
wsnp_Ku_c8953_15094606
wsnp_Ex_rep_c66375_64566565
wsnp_CAP7_c90_52035
wsnp_CAP11_rep_c6622_3044459
wsnp_Ex_c2539_4733110
wsnp_Ex_c10550_17231294
wsnp_Ex_c14779_22892053
wsnp_Ku_c13442_21433358
wsnp_Ex_c43083_49499652
wsnp_CAP11_c176_177381

3B
3B
5B
5B
5B
7B
7B
7B
7B
2D
4D
7D
7D

12
102
23
39
134
41
47
51
99
0
46
34
8

0.0013
0.00221
0.00181
0.00418
0.00348
9.38E-05
0.00131
0.00345
0.00207
0.00156
0.00475
0.00224
0.00274

0.04
0.04
0.04
0.03
0.04
0.06
0.05
0.04
0.03
0.04
0.04
0.04
0.04

T/C
A/G
T/C
A/G
T/C
T/C
A/G
A/G
T/C
T/C
A/G
A/G
T/C

3.5
15.4
7.7
17.7
13.1
7.9
7.2
7.4
8.3
8.4
5.3
8.1
9.4

9.8
8.4
16
8.2
7.3
23.8
14.1
12.5
23
14.9
9.2
9.4
4

6.3
7
8.3
9.5
5.8
15.9
6.9
5.1
14.7
6.5
3.9
1.3
5.4

Mexico 2011
wsnp_Ku_c33374_42877546
wsnp_RFL_Contig1951_1127302
wsnp_Ex_c5412_9565733
wsnp_Ku_rep_c68259_67171095
wsnp_Ku_c29319_39227528
wsnp_Ku_c139_279238
wsnp_Ex_c20062_29096408
wsnp_Ex_c7776_13247654
wsnp_Ex_c25688_34949297
wsnp_Ex_rep_c101906_87187119
wsnp_Ku_c48694_54811376
wsnp_Ex_c2264_4243233
wsnp_Ex_c4815_8597139
wsnp_Ex_c27914_37074773
wsnp_BE445506B_Ta_2_2

2A
2A
2A
5A
5A
7A
7A
2B
2B
2B
2B
5B
6B
7B
7B

7
10
53
121
172
41
51
5
15
112
220
100
22
45
160

3.95E-18
1.14E-18
1.40E-05
4.13E-04
4.56E-04
3.75E-05
2.64E-04
2.27E-07
5.85E-04
2.75E-04
1.87E-04
2.49E-07
1.88E-04
8.97E-05
3.19E-04

0.21
0.22
0.07
0.05
0.05
0.06
0.05
0.09
0.04
0.05
0.04
0.09
0.05
0.05
0.05

A/G
A/G
T/C
T/C
A/G
T/C
T/C
T/C
T/C
A/C
T/C
A/C
T/C
T/C
T/C

12.1
1.7
6
11.7
5.5
13.2
11.5
15
7.6
11.9
15
2
5.1
9.7
7.1

1
12.4
11.5
4.5
11.1
5.9
7.7
7.4
14.2
4.6
7.7
9.3
9
0.5
9.6

11.1
10.7
5.5
7.2
5.6
7.3
3.8
7.6
6.6
7.3
7.3
7.3
3.9
9.2
2.5

138

Table 3-7 (cont’d)
wsnp_Ku_c7264_12545135
wsnp_JD_c825_1223454

3D
5D

15
13

2.31E-05
3.87E-04

0.06
0.05

T/C
T/C

7
10.4

14.2
3.6

7.2
6.8

Mexico 2012
wsnp_Ku_c33374_42877546
wsnp_RFL_Contig1951_1127302
wsnp_Ex_c5412_9565733
wsnp_Ex_c7546_12900094
wsnp_Ex_c7776_13247654
wsnp_Ku_c48694_54811376
wsnp_Ra_c13424_21239985
wsnp_Ex_c1498_2868339
wsnp_Ex_c4815_8597139

2A
2A
2A
6A
2B
2B
5B
5B
6B

7
10
53
217
5
220
182
182
22

3.71E-08
8.64E-08
9.52E-05
1.11E-04
1.09E-04
7.99E-05
5.34E-05
8.87E-05
1.90E-04

0.10
0.10
0.06
0.06
0.06
0.05
0.06
0.06
0.06

A/G
A/G
T/C
T/C
T/C
T/C
A/C
T/C
T/C

5.8
1.6
2.8
4
7.2
8.7
3.1
3.1
2.1

1.3
5.9
6
4.3
3.8
3.8
7.3
7.3
4.6

4.5
4.3
3.2
0.3
3.4
4.9
4.2
4.2
2.5

Table 3-8. Association analysis for yellow rust severity of the wheat association mapping panel using MLM model. Mexico
and Ecuador. 2011-12.
Marker
Chr.
Pos.
P-value
r2
Alleles
Allele 1
Allele 2
Effect
(cM)
(% Sev.) (% Sev.) (% Sev.)
Ecuador 2011
wsnp_RFL_Contig1951_1127302
2A
10
2.56E-12
0.212
A/G
24.2
3.4
27.6
wsnp_Ku_c33374_42877546
2A
7
8.12E-12
0.192
A/G
23.4
26.8
3.4
wsnp_Ex_c34641_42914170
6A
139
1.01E-04
0.066
T/C
1
19.3
18.3
wsnp_Ex_rep_c68085_66839109
1A
104
1.02E-04
0.071
A/G
5.2
20.3
15.1
wsnp_Ex_c6942_11966469
6D
0
1.14E-04
0.064
T/C
2
17.4
19.4
wsnp_Ex_c43083_49499652
7D
34
1.24E-04
0.065
A/G
4.3
16.5
20.8
wsnp_Ex_c1597_3045682
2B
141
1.24E-04
0.068
A/G
6.8
22.2
15.4
wsnp_BG606986A_Ta_2_1
1A
111
1.31E-04
0.065
A/C
15.1
20.5
5.4
wsnp_Ex_c43228_49605281
1A
104
2.06E-04
0.063
A/G
5.2
20.3
15.1
wsnp_Ex_c2350_4403690
6A
106
2.75E-04
0.060
A/G
24.3
16.9
7.4
139

Table 3-8 (cont’d)
wsnp_Ex_c5623_9891584
wsnp_Ex_c17692_26437459
wsnp_Ex_c24777_34031473
wsnp_Ex_c908_1754208

3A
6A
1B
7B

123
90
141
41

3.33E-04
4.02E-04
4.76E-04
5.21E-04

0.05805
0.05482
0.05651
0.05521

A/G
A/G
A/G
T/C

7.9
3.8
6.8
15.1

21.2
17.3
22.2
31.7

13.3
21.1
15.4
15.6

Ecuador 2012
wsnp_Ku_c33374_42877546
wsnp_RFL_Contig1951_1127302

2A
2A

7
10

5.08E-08
2.87E-07

0.12013
0.11185

T/C
T/G

7.6
10.9

20.6
4.6

13
6.3

Mexico 2011
wsnp_RFL_Contig1951_1127302
wsnp_Ku_c33374_42877546

2A
2A

10
7

2.30E-10
1.03E-09

0.16857
0.15126

A/G
A/G

12.1
1.7

1
12.4

11.1
10.7

Mexico 2012
wsnp_Ku_c33374_42877546
wsnp_RFL_Contig1951_1127302

2A
2A

7
10

1.31E-05
1.50E-05

0.07789
0.07825

A/G
A/G

5.8
1.6

1.3
5.9

4.5
4.3

140

Manhattan plot of yellow rust severity –
Mexico 2011 using MLM

Manhattan plot of yellow rust severity –
Mexico 2011 using GLM

Manhattan plot of yellow rust severity –
Mexico 2012 using GLM

Manhattan plot of yellow rust severity –
Mexico 2012 using MLM

Figure 3-4. Manhattan plots of the association analysis for yellow rust severity in the wheat association mapping panel
using GLM and MLM. Mexico 2011 and 2012.

141

Q-Q plot of yellow rust severity –
Mexico 2011 using GLM

Q-Q plot of yellow rust severity –
Mexico 2011 using MLM

Q-Q plot of yellow rust severity –
Mexico 2012 using GLM

Q-Q plot of yellow rust severity –
Mexico 2012 using MLM

Figure 3-5. Q-Q plots of the of the association analysis for yellow rust severity in the wheat association mapping panel
using GLM and MLM. Mexico 2011 and 2012.

142

Manhattan plot of yellow rust severity –
Ecuador 2011 using GLM

Manhattan plot of yellow rust severity –
Ecuador 2011 using MLM

Manhattan plot of yellow rust severity –
Ecuador 2012 using GLM

Manhattan plot of yellow rust severity –
Ecuador 2012 using MLM

Figure 3-6. Manhattan plots of the association analysis for yellow rust severity in the wheat association mapping panel
using GLM and MLM. Ecuador 2011 and 2012.

143

Q-Q plot of yellow rust severity –
Ecuador 2011 using GLM

Q-Q plot of yellow rust severity –
Ecuador 2011 using MLM

Q-Q plot of yellow rust severity –
Ecuador 2012 using GLM

Q-Q plot of yellow rust severity –
Ecuador 2012 using GLM

Figure 3-7. Q-Q plots of the association analysis for yellow rust severity in the wheat association mapping panel using
GLM and MLM. Ecuador 2011 and 2012.

144

Analysis of variance of flowering time
The analysis of variance of the wheat AMP detected significant differences between
accessions in Ecuador in 2011 and 2012 (P < 0.05) (Table 3-8). In 2011, the wheat
accessions started flowering at 80 DAP and finished at 101 DAP with an average of
91.8 DAP and 21 days range. In 2012, the flowering started at 85 DAP and finished 103
DAP with an average of 94.7 DAP (Table 3-9; Figure 3-8).
The Shapiro-Wilk normality test determined that data distribution for flowering days in
Ecuador was not normally distributed (P = 0.04).
In Mexico, the analysis of variance detected significant differences between treatments
(Table 3-8). In 2011, the wheat accessions started flowering at 66 DAP and finished 85
DAP with an average of 76.7 DAP and a range of 19 days. In 2012, the wheat
accessions started flowering 68 DAP and finished 93 DAP with an average of 77.5 DAP
and 25 a range of 25 days (Table 3-9; Figure 3-8).
The Shapiro-Wilk normality test from the experiments carried out in combined data from
Mexico 2011 and 2012 determined that data distribution was not normal (P= 0.02).
The analysis of correlation in the wheat AMP showed significant correlations between
the two years of study in Ecuador and Mexico (Table 3-10).

145

Table 3-9. Analysis of variance of flowering days of the wheat association mapping panel. Ecuador 2011 – 2012.
Sources of variation
Df
Mean squares
F-value
P-value
Flowering days (Ecuador 2011-12)
Year
1
1235
265.4
< 0.001***
Accession
296
18.95
4.1
< 0.001***
Block/Group
8
35.66
7.7
<0.001***
Error
288
4.65
CV(%)=
2.3
Mean (%)=
93.3
Flowering days (Mexico 2011-12)
Year
1
86.75
30.4
< 0.001***
Accession
296
22.5
7.9
< 0.001***
Block/Group
8
5.8
2
0.0422*
Error
288
2.9
CV(%)=
2.2
Mean (%)=
77.1
Table 3-10. Flowering days of the wheat association mapping panel grown in Santa Catalina-Ecuador and El BatanMexico. 2011-2012.
Location
Year
Start (DAP)
End (DAP)
Average (days)
Range
(days)
Santa Catalina – Ecuador
2011
80
101
91.8
21
2012
87
103
94.7
16
El Batan – Mexico
2011
66
85
76.7
19
2012
68
93
77.5
25

146

Table 3-11. Analysis of correlation (Pearson) for flowering days between the wheat association mapping panel planted in
two locations and two years. Ecuador and Mexico. 2011-2012. All values were highly significant (P< 0.001).
Mexico 2011
Mexico 2012
Ecuador 2011
Ecuador 2012
Mexico 2011
1
Mexico 2012
0.77
1
Ecuador 2011
0.46
0.47
1
Ecuador 2012
0.3
0.35
0.57
1

147

Figure 3-8. Histogram for flowering days in the Association Mapping Panel evaluated in
Ecuador and Mexico. 2011 -2012.

Figure 3-9. Scatter plot of flowering days of the wheat Association Mapping Panel.
Ecuador and Mexico. 2011 – 2012.

148

Association Analysis for flowering time
The association analysis using the GLM method performed with data collected in
Mexico during 2011 and 2012 in the wheat AMP detected SNP markers significantly
associated with flowering time on chromosomes 3A, 5A, and 6D. On chromosome 3A,
two markers located at 35 cM explained between 5.8 to 6.2% of the phenotypic variance
of this trait. On chromosome 5A, there was only one SNP marker associated with
flowering time. This marker was located at 146 cM and explained 6.1% of the
phenotypic variance. Finally, on chromosome 6D, there were two SNP markers
associated with flowering time. These markers were located at 58 cM and expalned
between 6.2 and 6.5% of the phenotypic variance.
There were no SNP markers significantly associated with flowering time in Ecuador
neither SNP markers significantly associated with the trait using the mixed model.

Analysis of variance of plant height
According to the analysis of variance, there were significant differences between
accessions for plant height in Ecuador and Mexico (Table 3-13 and 3-8). The average
plant height in Ecuador was 96.5 cm in 2011 and the average was 99.2 cm in 2012, with
a overall average of 97.9 cm. The range for plant height was 75 – 125 cm in 2011 and
from 80 – 125 cm in 2012 (Table 3-14; Figure 3-3; Figure 3-4).
The average plant height in Mexico 2011 was 94.3 cm with a range from 75 – 117 cm,
whereas the AMP in 2012, the average in plant height was 102.7 with a range from 84 –
132 cm. The general average for plant height was 98.5 cm (Table 3-14; Figure 3-11).

149

Plant height in Mexico (p-value = 0.40) and Ecuador (p-value = 0.32) were normally
distributed according to Shapiro-Wilk normality test.
The analysis of correlation for plant height in the wheat AMP detected significant
correlation among data collected in 2011 and 2012. There were a high correlation
between location and year for plant height (Table 3-15).

Association analysis for plant height
The association analysis using the general linear model (GLM) in the combined data set
of Mexico 2011 and 2012 detected significant SNP markers related with plant height on
chromosomes 2A, A, 7A, 2B, 6D (Table 3-16; Figure 3-12). SNP markers located on
chromosome 2A were at 119 cM. This region explained from 5.5 to 5.9% of the
phenotypic variation of the trait with an effect of 3.4 – 3.5 cm. The lagest effects were
observed on SNP markers located on chromosome 7A at 8cM with 5 cm. No significant
markers were detected in evaluations conducted in Ecuador using the GLM.
Furthermore, no significant markers were located in any of the two locations using the
MLM.

Discussion
Germplasm evaluation
In general, the wheat AMP has a large number of wheat accessions with high levels of
disease resistance against yellow rust, especially adult plant resistance. Resistance
was demonstrated with the yellow rust response in the greenhouse and the field. Adult
150

plant resistance genes can confer high levels of resistance near immunity (Singh et al.,
2000). Analysis of the pedigrees showed that most of the wheat breeding lines in the
wheat AMP have ‘ATTILA’, ‘KAUZ’, ‘PASTOR’ as one of its progenitor in complex
crosses. These lines are not necessary the source of resistance for yellow rust in this
study, but it is important to mention that these lines have been very popular in CIMMYT
germplasm because they have wide range of adaptation and good agronomical and
physiological traits (Rajaram et al., 2002). Some lines that may have more than one Yr
gene are those that possess ‘Quaiu’ in their pedigrees, since it has been reported that
this accession has Yr54 gene (Basnet et al., 2013) and in this study almost all of these
lines have high levels of disease resistance. The large number of wheat accessions in
the panel with high levels of resistance to yellow rust demonstrates the value of the
AMP as sources of resistance to any breeding program. This is expecially relevant
because the two locations of the field evaluations are hot spots for P. striiformis where
very aggressive races of this pathogen exist. CIMMYT has been evaluating germplasm
in these two location for several years to enhance resistance (Singh et al., 2011).

Analysis of variance of yellow rust severity
Generally, the wheat breeding program relies on natural infection for wheat germplasm
evaluations since environmental conditions of Santa Catalina favor YR infection and
development annually (Bonjean and Angus, 2001; Dubin and Rajaram, 1996); however,
in this study inoculations were carried out to ensure the infection.
The yellow rust severity data collected in the two locations where the evaluations were
conducted did not follow a normal distribution. The reason for non normal distribution is
151

caused by the large number of wheat lines showing high resistance in the AMP.
CIMMYT has been selecting for this characteristic in previous germplasm evaluation
over years. The wheat accessions included in the AMP were chosen based on the
diverse pedigree and segregation for disease response with the purpose to find a large
number of novel alleles for disease resistance.
The high coefficient of variance (82.9%) observed in the analysis of variance of the
experiments evaluated in Ecuador during 2011 and 2012 might be the result of the
reduced disease pressure observed in the experiment in 2012. The overall disease
severity mean in 2011 was 19.0%, whereas the overall mean in 2012 was 9.2%. The
severity was higher in 2011 due to climatic conditions, since cooler temperatures and
higher humidity allowed more rapid development of the disease in the susceptible wheat
cultivars (McIntosh et al., 1995).
In Mexico, the overall mean in 2012 was also significantly lower than the mean in 2011.
Similar to Ecuador, 2012 was a less humid year.
In general, around the 50% of the population showed resistance with a disease severity
between 0 – 5%. In other to conduct further analysis, the data were transformed using
root square transformation method to adjust to normality.
The correlation analysis between the two years in each location was high, however, low
correlation was observed between the two locations. It was observed that some wheat
accessions were susceptible in Ecuador but resistant in Mexico. These differences of
disease response in each location reduced the correlation between locations and can
be caused by a race specific effect of some major genes with local races. Broad sense

152

heritability (H2) estimates were high in both locations. In Mexico 2011-12 the heritability
for yellow rust severity (%) was 0.97 and heritability in Ecuador 2011-12 was 0.80.

Association analysis for yellow rust severity
The association analysis in the wheat AMP detected markers significantly associated
with yellow rust resistance in each location and each year using GLM and MLM
methods.
Genes for yellow rust resistance have been found in almost every chromosome of the
wheat genome (Boyd, 2005). In this study, analyses conducted with data collected in
Ecuador and Mexico detected significant SNP markers on chromosome 2A. The
association analysis using the MLM method, which is a very conservative method of
analysis, detected SNP markers located between 5 and 40 cM on chromosome 2A. One
gene for yellow rust resistance on chromosome 2A is Yr17 (Bariana and McIntosh,
1993). Yr17 has been located in the short arm of chromosome 2A (Bariana and
McIntosh, 1993; Jia et al., 2011), which is the region where the association analyses
have detected significant markers. Interestingly, the same chromosome segment that
contains Yr17 also contains genes Lr37 and Sr38 which confers resistance to leaf rust
and stem rust, respectively (Helguera et al., 2003). Yr17 has been extensively used in
CIMMYT’s germplasm (Singh and Huerta-Espino, 2000) and it would not be surprising
that these markers are linked to Yr17. Another well-known gene located on the long
arm of chromosome 2A is Yr1 (McIntosh and Arts, 1996). The probability that the gene
associated with the SNP markers significantly associated with resistance to yellow rust
detected in this study is lower since SNP markers identified in this study were located in
153

short arm of chromosome 2A. Additionally, races of P. striiformis occurring in Ecuador
overcome Yr1 (Ochoa et al., 2007) therefore phenotypic variation of resistance at this
gene was unlikely in Ecuador.
Another gene that might be linked to the SNP markers detected on chromosome 2B
(Mexico 2011) might be Yr27. The reason to make this assumption is that CIMMYT
uses this gene frequently in the development of improved wheat lines. A known source
of this gene is the accession ‘Kauz’. This accession carries Yr9 and Yr27 and this
accession was part of the pedigree of 58 lines in the wheat AMP. The isolates that
were employed in Mexico to inoculate the susceptible cultivars overcome the resistance
conferred by Yr27 and the cultivars planted around the experiments carried Yr27. For
this reason, the population of YR isolates was expected to be infective against Yr27 so
the QTL identified on chromosome 2B might be a different QTL or the population of YR
contained isolates compatible and incompatible for Yr27 (McDonald et al. 2004).
On chromosome 5A, the association analysis detected a significant region at 141 cM.
Bariana et al. (2006) reported a gene on chromosome 5AL which confers APR. The
origin of the source is the breeding line WAWHT-2046 from Australia
(http://www.wheatpedigree.net/sort/show/82706). Yr54 is another gene that has been
reported on Chromosome 5AL. This gene comes from a synthetic derivative from
CIMMYT’s Wide Cross Program (Lowe et al., 2011). The wheat AMP includes 49
genotypes that have synthetic lines in the pedigrees, so it is not surprising that the
significant region detected in this study contains Yr54.
Two genome regions associated with yellow rust resistance located on chromosome
7A were detected by the association analysis using both Mexio 2011 and Ecuador 2012
154

data using the GLM method. The region was located at 62 cM with Mexico 2011 data
and at 159 – 161 cM with Ecuador 2012 data. This region is interesting since no QTLs
have previously been reported on chromosome 7A (McIntosh et al., 2012).

Analysis of variance of flowering time
Accessions in the wheat AMP started flowering earlier in Mexico than Ecuador. The
difference in days was expected since the wheat association mapping panel was
planted at 2,640 masl at Toluca – Mexico whereas in Ecuador, the wheat panel was
planted at 3,050 masl (Table 3-1). The temperatures were higher and days were
warmer in Mexico as compared to Ecuador (Appendix D) which hastened the growth
rate (Altenbach et al., 2003; Wiegand and Cuellar, 1980). There were statistical
differences betwewen accessions for flowering days. The range observed in the two
locations during the two years for flowering time (around 20 days) demonstrated that the
wheat AMP includes accessions with a considerable diversity for this trait.

Association Analysis for flowering time
Three major groups of genes control flowering time in wheat. Those are photoperiod
response genes (Ppd genes), vernalization response genes (Vrn genes), and
developmental rate genes (‘earliness per se’, Eps genes) (Snape et al., 2001). From
those, Vrn-A1a is located on chromosome 5A (Iwaki et al., 2002). Vrn-A1a gene is one
of the major genes responsible for change in growth habit (spring vs. winter wheat). Itis
highly conserved among spring wheat cultivars (Fu et al., 2005). It is known that Vrn
genes contribute indirectly to yield by influencing flowering time, which makes this gene
155

important for plant breeders. A previous study conducted with spring wheat accessions
from CIMMYT determined that Vrn-D1 is the most frequent gene found in this specific
germplasm (van Beem et al., 2005); however, no significant markers were associated
with flowering time in any region on chromosome 5D.

156

Table 3-12. Association analysis for flowering time of the wheat association mapping panel using GLM model. Mexico and
Ecuador. 2011-12.
Marker
Chr.
Pos. (cM) p-value
r2
Allele
Allele 1 Allele 2 Effect
Mexico 2011-12
wsnp_Ex_c33765_42199371
3A
35
7.34E-05 0.06276
A/G
77.6
75.5
2.1
wsnp_Ex_rep_c69816_68774932
3A
35
1.16E-04 0.05802
A/G
75.6
77.6
2
wsnp_BE444644A_Ta_2_2
5A
146
8.93E-05 0.06075
A/C
78.4
76.7
1.7
wsnp_Ex_c23383_32628864
6D
58
3.83E-05 0.06505
A/G
77.8
76.6
1.2
wsnp_Ex_c37749_45436366
6D
58
7.98E-05 0.06196
A/C
77.8
76.7
1.1
Ecuador 2011-12

NS

Table 3-13. Association analysis for days to flowering of the wheat association mapping panel using MLM model. Mexico
and Ecuador. 2011-12.
Marker
Chr.
Pos. (cM) p-value
r2
Allele
Allele 1 Allele 2 Effect
Mexico 2011-12
NS
Ecuador 2011-12
NS

157

Manhattan plot of flowering days –
Mexico 2011-12 using GLM

Manhattan plot of flowering days –
Mexico 2011-12 using GLM

Figure 3-10. Manhattan plots of association analysis for flowering in the wheat association mapping panel using GLM (left)
and MLM (right) method. Mexico 2011 and 2012.

158

Analysis of variance of plant height
The ANOVA detected statistical differences for plant height among accessions and also
between years in the experiments evaluated in Mexico and Ecuador. Average plant
height registered in Ecuador 2011 (96.5 cm) was slightly lower than the registered in
2012 (99.2 cm) and the difference (2.7 cm) was minor. However, the differences for
plant height observed in Mexico 2011 (94.3 cm) versus Mexico 2012 (102.7 cm) were
larger (8.4 cm). According to the literature, Rht genes can respond differently to different
environments and plant height differences of more than 20 cm in the same genotype at
different environment have been observed (Flintham et al., 1997). Irrigation and
nitrogen fertilization can also have such effect on this trait (Cooper, 1980). However, the
wheat plants carrying Rht genes tend to be always smaller than wheat genotypes
without those genes, since Rht genes encode growth repressors that are normally
suppressed by GA (Hedden, 2003). So, the differences observed for plant height in
Mexico are considered normal.

159

Table 3-14. Analysis of variance of the wheat association mapping panel for plant
height. Ecuador and Mexico 2011-12.
Mean
Sources of variation
Df
F-value
P-value
squares
Plant height (Ecuador 2011-12)
Year
Accession
Block/Group
Error
CV(%)=
Mean (cm) =
Plant height (Mexico 2011-12)
Year
Accession

1
296
16
280
2.6
97.9

269.4
91.5
17
6.4

42.3
14.4
2.7

<0.0001***
<0.0001***
0.0006**

1
296

9616.3
76

322.6
2.6

<0.0001***
<0.0001***

Block/Group

16

38.2

1.3

Error
CV(%) =
Mean (cm) =

280
5.6
98.5

29.8

Table 3-15. Mean and range for plant height of the wheat association mapping panel
planted in Ecuador and Mexico. 2011-12.
Range
Location
Year
Average (cm)
(cm)
Santa Catalina –
2011
75 - 125
96.5
Ecuador
2012
80 – 125
99.2
El Batan – Mexico
2011
75 – 117
94.3
2012

84 - 132

160

102.7

ns

0.21

Figure 3-11. Histogram of plant heigh (cm) of the wheat AMP evaluated in Ecuador and
Mexico 2011-12.

161

Table 3-16. Analysis of correlation (Pearson) for plant height in the wheat association mapping panel between wheat
accessions in two locations and two years. Ecuador and Mexico. 2011-12. All values were highly significant (P< 0.001).
Mexico
Ecuador
Ecuador
Ecuador
Mexico
Average
Average
Mex.2011
2012
2011
2012
2011-12
2011-12 2011
2012
1
Mexico 2011
0.67
1
Mexico 2012
0.54
0.49
1
Ecuador 2011
0.47
0.47
0.86
1
Ecuador 2012
Ecuador
0.52
0.5
0.96
0.97
1
2011-12
Mexico
0.92
0.91
0.57
0.52
0.56
1
2011-12
Average
0.87
0.66
0.88
0.76
0.85
0.84
1
2011
Average
0.65
0.83
0.81
0.89
0.88
0.81
0.83
1
2012

162

Association analysis for plant height
In Mexico, the association analysis conducted in 2011 and 2012 using the GLM method
detected one significant SNP markers related with plant height on chromosomes 2A,
4A, 7A, 2B, and 6D. A QTL has been reported on chromosome 2B (Talaat et al., 2000)
with minor effects on plant height. Another QTL previously reported is located on
chromosome 7A (Cadalen et al., 1998). Other plant height related genes expected to be
present in the AMP population were Rht-B1b or Rht-D1b, which are known to be GA
insensitive dwarfing genes and are present in the majority of the world semi-dwarf
wheat lines (Flintham et al., 1997); however, the association analysis did not detect
these since there was no segregation for these genes in the population.

163

Table 3-17. Association analysis for plant height of the wheat association mapping panel using GLM model. Mexico and
Ecuador. 2011-12.
Marker
Chr
Pos.
p-value
r2
Alleles
Allele 1
Allele 2
Effect
.
(cM)
(cm)
(cm)
(cm)
Mexico 2011-12
wsnpCAP11_rep_c8469_3658252
2A
119
7.84E-05
0.059
A/G
95.6
99
3.4
wsnp_BF145580A_Ta_2_1
2A
119
1.48E-04
0.05518
A/G
95.3
99
3.7
wsnp_BF474615A_Ta_1_1
4A
133
2.13E-04
0.05419
A/G
98.9
96.4
2.5
wsnp_Ra_c5008_8947135
7A
8
9.15E-05
0.04774
A/G
93.8
98.8
5
wsnp_Ku_c5874_10384659
7A
8
1.37E-04
0.04558
A/C
93.8
98.8
5
wsnp_Ex_c22018_31193171
2B
112
5.96E-05
0.06218
T/C
96.5
99.7
3.2
wsnp_Ku_c7096_12264232
2B
112
2.22E-04
0.05412
T/C
100.3
97.2
3.1
wsnp_Ex_c37749_45436366
6D
58
1.46E-06
0.08451
A/G
100.7
96.8
3.9
wsnp_Ex_c23383_32628864
6D
58
9.77E-06
0.07182
A/C
100.7
96.8
3.9
Ecuador 2011-12

NS

Table 3-18. Association analysis for plant height of the wheat association mapping panel using MLM model. Mexico and
Ecuador. 2011-12.
Marker
Chr
Pos.
p-value
r2
Alleles
Allele 1
Allele 2
Effect
.
(cM)
(cm)
(cm)
(cm)
Mexico 2011-12
NS
Ecuador 2011-12
NS

164

Manhattan plot of plant height –
Mexico 2011-12 using GLM

Manhattan plot of plant height –
Mexico 2011-12 using MLM

Figure 3-12. Manhattan plot of the association mapping analysis for plant height with the GLM method in the wheat
association mapping population. Mexico 2011 -2012.
Q-Q plot of plant height –
Mexico 2011-12 using GLM

Q-Q plot of plant height –
Mexico 2011-12 using MLM

Figure 3-13. Q-Q plot for association analysis of the wheat association mapping panel for plant height. Mexico 2011 –
2012.
165

Conclusions
A large majority of the accessions in the wheat AMP have yellow rust resistance. The
resistance was demonstrated during two years of evaluations in two locations with high
disease pressure and favorable environmental conditions for disease progress. The two
locations are considered as yellow rust hot spots where aggressive races of the
pathogen occur. Based on the high level of resistance showed by most of the wheat
accessions and the field and the greenhouse responses, the resistance of these wheat
accessions appears to be conferred by several adult plant resistance genes combined
in single accessions. For these reasons, we conclude that the germplasm evaluated in
this study have great potential as sources of favorable alleles to develop future spring
wheat populations with yellow rust resistance. Additionally, all the accessions in the
wheat AMP were adapted to the two environments where the evaluations were
conducted.
The association analyses detected markers significantly linked to regions responsible
for yellow rust resistance. These regions could contain genes for yellow rust resistance
that have been previously identified such as Yr17; however, these genes have been
identified mostly using SSR markers. One interesting finding in this study is the
discovery of new SNP markers linked to these genes. Other regions not reported
previously are also valuable findins from this study. These regions have shown low
effects, but it can be always useful to wheat breedeers to conduct indirect selection with
molecular markers.

166

Acknowledgements
Ravi Singh, Sybil Herrera, Julio Huerta, Lan Caixa from CIMMYT
Javier Garofalo, Jose Ochoa, Mayra Cathme, Segundo Abad, Luis Ponce from INIAP
Zixang Wen from MSU with software analysis.

167

APPENDICES

168

Appendix A: Modified Cobb’s scale.

Figure 3-14. The modified Cobb’s scale: A: Actual percentage occupied by rust uredinia;
B: Rust severities of the modified Cobb’s scale (Roelfs et al., 1992).

169

Appendix B: Yellow rust reaction

Figure 3-15. Adult plant responses to stripe rust (P. striiformis) (Roelfs et al., 1992).

170

Appendix C: Temperatures and precipitation in Ecuador and Mexico. 2011-12
Table 3-18. Temperature and precipitation data from Santa Catalina – Ecuador and
Toluca Mexico during 2011-12.
Location
Year
Months Average Temp. Temp. Precipitation (mm)
temp.
max.
min.
(°C)
(°C)
(°C)
Santa Catalina*
2011 February
11.3
19.6
3.8
206
March
11.2
20.5
2.6
143.7
April
11.1
19.9
2.5
262.2
May
12.1
21.6
2
91.7
June
12
20.6
2.2
61.5
2012 February
11.1
18.6
4.5
227.3
March
12.2
20.6
5
197.4
April
11.1
23.7
3.2
219.3
May
11.8
19.8
4.2
62.9
June
11.8
21.2
2.6
10.2
Toluca**
2011 Aug
15.2
21.1
9.9
113.3
Sep
14.5
20.8
8.6
74.1
Oct
12.2
20.5
4.6
51.6
2012 Aug
14.8
19.9
9.9
177
Sep
14.5
20.6
9.2
110.7
Oct
13.2
21.6
5.4
118.3
* Data collected from the weather station of Santa Catalina Researc Station
** Data collected from the weather station located at the Lic. Adolfo López Mateos
International Airport (Toluca, Mexico)
(http://weatherspark.com/history/32602/2012/Toluca-Mexico)

171

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172

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177

CHAPTER 4
ASSOCIATION MAPPING FOR DETECTING QTLs FOR FUSARIUM HEAD
BLIGHT IN BREAD WHEAT
Abstract
Fusarium head bight (FHB) caused by Fusarium graminearum Schwabe is one of
the most important diseases in wheat due to the yield reduction, seed damage, and
mycotoxins that results from the pathogen infection. Yield reduction and seed damage
cause severe economic impacts, however, societal impacts caused by toxins produced
by the pathogen, such as Deoxynivalenol (DON), deserve special attention. Cultivars
with high levels of resistance are the most practical way to control the disease and
CIMMYT has consider this disease as one of its priorities in the development of wheat
germplasm with enhanced disease resistance. In the present study, a wheat association
mapping panel from CIMMYT with 297 wheat accessions has been evaluated for
Fusarium head blight resistance. The objectives of this study were to identify sources of
resistance in the wheat AMP and conduct an association mapping study with 3,701
SNP markers incorporated in the 9K SNP wheat chip from Illumina and 32 SSR
markers. The evaluations conducted in Mexico during 2011 and 2012 revealed that the
wheat AMP has several wheat accessions with high FHB resistance that can be used in
breeding programs focused on spring wheat. The wheat AMP showed allelic diversity
for FHB resistance that come from different origins according to their pedigrees. Some
of these accessions have synthetic wheat parents in its pedigrees. The association
mapping studies for FHB resistance conducted with the GLM method detected SNP
markers on chromosomes 4A, 7A, 2B, 5B, and 7B. When the MLM method was used,
178

significant markers were detected only on chromosome 2B and 7B. The association
analysis also detected SNP markers associated with DON concentration on different
chromosomes using the GLM method (4A, 5B, 7B, and 2D); however, no SNP markers
were detected when the MLM method was used.

Introduction
Wheat (Triticum aestivum L.) is the most important cereal crop for human
consumption as the global production of wheat almost reaches 700 million tons per year
(FAOSTAT, 2012) and provides 20 % of the total dietary calories and proteins
worldwide (Shiferaw et al., 2013). It has been estimated that global wheat production
must increase 1.6% annually to meet the wheat demands from the growing population
by 2020 (Dixon et al., 2009). However, the world production of wheat in the last two
decades only increased 1.1% annually (Ortiz, 2011). It is evident that the increase in
wheat production is not keeping pace with the future demand of the crop, so rapid
action in the next years to increase yield potential is needed to avoid social and
economic problems caused by food scarcity. One of the main causes for poor yields
and increasing of the gap between potential and actual yield are wheat diseases
(Bockus et al., 2010). One of the most important diseases affecting wheat production is
Fusarium head bight (FHB), also known as Fusarium ear blight or scab (Dill-Macky,
2010). The major causal organism of this disease worldwide is Gibberella zeae
(Schwein) Petch (anamorph: Fusarium graminearum Schwabe) (Schmale III and
Bergstrom, 2003). However, there are 17 species in total associated with this disease
(Parry et al., 1995). The infection of Fusarium on wheat causes yield reduction and
179

losses as high as 50% have been reported (Ireta and Gilchrist, 1994). The infection also
affects wheat quality by reducing test weight, milling quality, and baking performance
(Dexter et al., 1996; Dexter et al., 1997; Gilbert and Tekauz, 2000). However, the major
concern with FHB is the fact that the pathogen produces secondary metabolites
(mycotoxins), such as DON (Bottalico and Perrone, 2002; Placinta et al., 1999). This
metabolite produces toxic effects in animals and humans (Pestka, 2010; Pestka, 2007),
since it induces a spectrum of effects in farm and laboratory animals including emesis
immunotoxic effects, and suppression of appetite and growth (Voss, 2010). As a
consequence, strong regulations have been created in some countries, where limits for
DON concentration have been established. This is the case of the United States where
a maximum concentration 1000 µg/kg of DON is allowed (Richard, 2007) or no more
than 750 µg/kg in the European Nations for wheat flour (van Egmond and Jonker,
2004).
Genetic resistance is considered the most practical way to control FHB disease
(Bai and Shaner, 2004). The resistance to FHB has been grouped in four types based
on the mechanisms used by the plant. Type I refers to resistance to initial infection, type
II is used to describe resistance to fungal spread within the inoculated spike, type III
refers to resistance to DON accumulation in the kernels, and type IV denotes resistance
to the development of Fusarium-damaged kernels (FDK) (Schroeder and Christensen,
1963).
Quantitative trait loci (QTLs) studies to identify resistance for all four types of
FHB resistance have been conducted, however, QTLs studies related to type II
resistance are the most abundant in the literature (Buerstmayr et al., 2009). Discovery

180

of QTLs with medium to large effects or validate already reported QTLs in different
genetic backgrounds can contribute to develop improved varieties with high levels of
resistance to FHB. Association mapping is a novel approach which allows QTL mapping
or validation in existing populations. Additionally, the QTLs detected through association
mapping are associated with tightly linked SNP markers due to the dense coverage of
SNP markers employed and the historical recombination exploited in breeding lines
usually used to conduct such studies (Zhu et al., 2008).
The current research aims to detect QTLs for fusarium head blight in the wheat
AMP using association mapping approach and evaluate the resistance against
Fusarium graminearum in this collection of germplasm.

Materials and Methods
Plant material
A group of 297 spring wheat accessions was assembled to conduct the current
study (Table 2-1). This collection of accessions will be referred to as the association
mapping panel (AMP). The AMP was obtained from CIMMYT and it included breeding
lines, cultivars, and landraces from different origins as well as control wheat lines used
for Fusarium head blight (FHB) studies. Wheat accessions in the AMP panel were
selected based on the variability for FHB response observed in previous evaluations at
CIMMYT. Additionally, the AMP panel includes wheat accessions that are part of
CIMMYT’s elite germplasm and have showed wide adaptation, high yield, and
resistance for several diseases.

181

Locations
The field research was conducted in El Batan – Mexico and Santa Catalina Ecuador during 2011 and 2012. Genotyping was performed at Michigan State University
(MSU), East Lansing, Michigan, USA in 2011 (Table 4-1). Phenotypic data for FHB
were collected from El Batan and Santa Catalina during 2011 and 2012. At Santa
Catalina Experimental Station of the National Institute for Agricultural Research (INIAP),
the disease was evaluated at 3,050 masl. In Mexico, the AMP was evaluated for FHB in
El Batan at 2,249 masl. Phenotypic and genotypic data analyses were conducted at
MSU and CIMMYT.
Table 4-1. Locations and years of the wheat association mapping study on Yellow Rust.
Location
Years
Altitude (masl)
Type of study
East Lansing-MSU-USA
2011
262
Genotyping
Santa Catalina-INIAP-Ecuador 2011 - 2012
3,050
Field evaluation
El Batan-CIMMYT-Mexico
2011 - 2012
2,249
Field evaluation
Field management, inoculation, and phenotyping
The AMP nurseries for FHB studies were arranged in an alpha lattice design.
Each plot was 1.0 m long with two rows separate with 0.25 m. Two replications of the
wheat AMP for FHB were planted in Ecuador in 2011 and 2012 while one replication for
FHB was sown in Mexico during 2011. In 2012, two replications for FHB evaluation
were sown. The FHB nursery in Ecuador was inoculated with one F. graminearum
isolate (SC01) collected from Santa Catalina Experimental Station. In 2011, the field
was inoculated with corn seeds infected with the pathogen. The inoculum was
2

broadcasted at rate of 50 g of infected seed/m . The inoculations with F. graminearum
were performed twice, 3 and 2 weeks before the anticipated start of flowering. In 2012,
182

inoculum was broadcasted directly to the soil similar to 2011 and, additionally, the
wheat spikes were sprayed with macroconidial suspension (50,000 spores/mL) at the
rate of 50 mL per plot using 1-L hand sprayer. YR pressure in 2011 was high; therefore
the FHB nursery was sprayed with Propiconazole (48.1%), which controls YR but does
not control FHB (Paul et al., 2008), before flag leaf emergence to avoid or reduce rust
infection.
In Mexico, plots were inoculated with five isolates of F. graminearum (CIMFU235,
702, 715, 720, and 770) at flowering (50% anthesis) by spraying a 30 mL macroconidial
suspension of F. graminearum (50,000 spores/mL) using a CO2-powered backpack
sprayer (model T R&D Sprayers - Opelousas, LA) calibrated to 40 psi. A second
inoculation was repeated after two days. Ten spikes from each inoculated plot were
tagged to collect data. High relative humidity in the field site was maintained by a mist
irrigation system which was activated for 10 min. every hour.
The FHB severity data were collected 20, 25, and 30 days after inoculation by
counting spikelets showing FHB symptoms on tagged spikes. Data were transformed to
percentage (FHB severity). Incidence (percentage of tagged spikes with symptoms) was
also recorded at 30 days after inoculation.
At maturity, the plots were hand harvested. Spikes from each plot were air-dried
in the greenhouse inside meshpolypropylene bags for 4 – 7 days. Each sample was
threshed by a belt thresher Wintersteiger LD180 (Ecuador) and with a Large Vogel Plot
Thresher (Mexico). In the two locations, Fusarium damaged kernels (FDK) from each
plot was registered. The FDK refers to the percentage of visibly scabby kernels in a
sample of seed.

183

From each plot, 50 – 100g sub-samples were collected. Sub-samples were
ground to produce particles similar to whole wheat flour, with at least 60 % of the flour
able to pass through a No. 20 sieve. A laboratory mill (Retsch ZM 200) was employed to
grind the samples in Ecuador, and a coffee grinder was used at CIMMYT. Ground
samples were analyzed for DON concentration at CIMMYT in the laboratory of wheat
pathology with the Ridascreen® Fast DON

TM

(R-Biopharm) enzyme linked immuno-

assay (ELISA) according to the manufacturer’s instructions and at INIAP by the
Laboratory of Nutrition and Quality with an Agilent 1100 series HPLC value system
(Agilent Technologies) using the water extraction method in conjunction with DONPREP
(R-Biopharm).

Genotyping
The genotypic data to conduct the association analysis included 3,701 SNP
markers from the 9K SNP chip from Illumina®, which were selected based on good
quality and MAF > 5%, and 32 microsatellites markers (SSR) distributed mostly in the D
genome (20 SSRs) (See chapter II).

Statistical Analyses
Phenotypic data from 297 wheat accessions from the AMP were tested for
normality using the Shapiro-Wilk normality test (Shapiro and Wilk, 1965) with the
statistical package R ver.2.15.3 (Ihaka and Gentleman, 1996). Phenotypic data sets,
which did not show normal distribution, were transformed using the square root method

184

of transformation (McDonald, 2009). Analysis of variance (ANOVA) for every trait was
conducted in R with packages Agricolae version 1.1-4 and PBIB.test using REML (de
Mendiburu, 2013).
A total of 3,701 SNP markers were utilized from the whole set of 8,632 SNP
markers included in the 9K SNP wheat chip from Illumina®. The markers were selected
based on minimum frequency of alleles ≥ 0.05 and missing data ≤10%.
Marker-trait association analyses were conducted with software TASSEL v.4.0
(http://www.maizegenetics.net/) using the general linear model (GLM), which includes
population structure as co-variable, and the mixed linear model (MLM), which
incorporates population structure (Q) and relative kinship (K) (Yu et al., 2006).
To estimate the population structure, a subset of 315 SNP and 22 SSR markers
loosely linked and evenly distributed in the 21 wheat chromosomes were selected to be
analyzed under the software STRUCTURE v. 2.3.4
(http://pritchardlab.stanford.edu/structure.html). STRUCTURE uses a Bayesian modelbased clustering method which allows obtaining the optimum number of hypothetical
sub-populationss and membership coefficients for each individual to create the Q matrix
(Pritchard et al., 2000) that was included in the Association analysis.
The Kinship matrix, which estimates the relationships between individuals, was
obtained with TASSEL using the genotypic data (Bradbury et al., 2007).
Significant markers linked to the traits were selected using false discovery rate
(FDR) method described by Storey (2002). FDR analysis was conducted with R using
Qvalue package version 1.0 (Dabney et al., 2004).

185

Marker effects were also calculated with TASSEL. It is important to note that the
resulting marker effects calculated by TASSEL is not decomposed into additive and
dominance effects but simply tested for overall significance (Bradbury et al., 2007).
Graphics of Q-Q plots were generated by TASSEL and Manhattan plots were
generated by R using with all the p-values from each marker-trait association analysis
and an R code developed by Turner (2011).

Results
Analysis of variance of Fusarium Head Blight Severity
The ANOVA for FHB severity in Mexico 2011 and 2012 detected significant
differences between treatments and years (Table 4-2). In 2011, FHB severity ranged
from 2.3 – 64.0% with a mean of 22.4%. In 2012 in this location, the FHB severity
ranged from 1.0 – 80.0% with a mean of 9.6% (Table 4-3). Statistical analysis was not
conducted for the wheat AMP in Ecuador 2011 and 2012. The reason to exclude this
location from the analysis is due to the low disease pressure observed in the two years.
The severity for FHB in Ecuador ranged from 0 -10% with a mean of 3.8%. Broad sense
2

heritability of Fusarium head blight severity was H = 0.44.
2

The correlations were very low across years in Mexico (r = 0.3, p-value=<0.001)
(Table 4-4 and Figure 4-2).

186

Table 4-2. ANOVA for Fusarium Head Blight severity in the wheat association mapping
panel from two years. Mexico 2011-12.
Sources of
Df
Mean
F value
Pr(>F)
variation
Squares
Year
1
24385.3
302.6
< 0.001***
Accession
296
143
1.8
< 0.001***
Block/Group
8
248.9
3.1
0.002**
Error
288
80.6
CV(%)=
56.0
Mean (%)=
16.0
2
H = 0.44
Table 4-3. Fusarium head blight severity in the wheat association mapping panel.
Ecuador and Mexico. 2011 – 2012.
Range
Average
Location
Year
(%)
(%)
Santa Catalina – Ecuador
2011
0.0 – 10.0
3.8
2012
NA
NA
El Batan – Mexico
2011
2.30 – 64.00
22.4
2012
0.0– 80.0
9.6

187

Figure 4-1. Distribution of percentage of FHB severity in the wheat AMP evaluated in
Mexico 2011-12.
Table 4-4. Correlations and p-values in the Association Mapping panel between Mexico
2011 and 2012 for Fusarium Head Blight severity. Mexico 2011-12. All values were
highly significant (P< 0.001).
Mexico 2011
Mexico 2012
Mexico 2011-12
1
Mexico 2011
Mexico 2012
0.3
1
0.9
0.7
1
Mexico 2011-12

188

80
Fusarium head blight severity. Mexico 2011-12

70
60
50
FHB severity (%)
40
30
20
10
0
0

10

20

30
40
50
60
70
FHB severity (%)
Figure 4-2. Scatter plot and regression line of FHB severity from the wheat AMP
evaluated in Mexico, 2011-12.
Association analysis of Fusarium Head Blight Severity
The association analysis for FHB conducted in Mexico using the GLM method
detected 59 SNP markers significantly associated with FHB resistance on
chromosomes 7A, 2B, 5B, and 7B during 2011 and 31 SNP markers located on
chromosomes 1A, 2A, 3A, 5A, 7A, 2B, 3B, 5B, 7B, and 2D during 2012 (Table 4-5;
Figure 4-3). In 2011, the region showing the largest effect related with FHB resistance
(9.5 and 12.3%) were located on chromosome 7A at 5-6 cM. At this region, SNP
markers wsnp_ku_c14220_22456923 and wsnp_Ex_rep_c66939_65371026 were
located. These markers explained 8.0 and 11.0% of the phenotypic variance observed
189

in the trait. Another region with a significant effect was located on chromosome 7B at
41-45 cM. Three markers located in this region also presented a relatively large effect
for FHB resistance. The effects observed ranged from 11.1 – 12.9 % for FHB severity.
These significant SNP markers were wsnp_CAP7_c90_52035,
wsnp_be352570B_Ta_2_2, and wsnp_CAP8_c3593_1773371. The phenotypic
variance (r2) observed for FHB severity explained in this region ranged from 5-7%.
Another region with moderate effect over FHB severity was located on
chromosome 2B. Several SNP markers with significant effects were observed along this
chromosome. Most SNP markers were located from 122 to 160 cM. SNP marker
wsnp_BE445278B_Ta_2_1 showed the largest effect (5.9%). The phenotypic variance
explained by this marker was 4%.
The largest number of SNP markers associated singnificantly with FHB severity
were located on chromosome 5B with 46 SNP markers significantly associated with
FHB resistance located in a region from 225 – 247 cM. The phenotypic variance
explained by the regions where these SNP markers were located ranged from 6.3 –
9.8%.
The association analysis conducted with the data collected from Mexico 2012 from
the wheat AMP for FHB resistance using the GLM method detected 31 SNP markers
significantly associated with FHB resistance on chromosomes 1A, 2A, 3A, 5A, 7A, 2B,
3B, 5B, 7B, and 2D. All of them explained low percentages of the phenotypic variance
for the trait with low effects. On chromosome 2B, four SNP markers were located at 122
– 126 cM. The phenotypic variance explained by the QTL ranged from 3.0 to 4 with
effects between 5.4 to 11.1% for FHB severity. On chromosome 7B, SNP markers

190

associated with FHB resistance were detected at 32 - 45 cM with effects between 3.9 to
5.0% of disease severity.
The association analysis with the combined data from Mexico 2011-12 in the
wheat AMP for FHB severity using the MLM method did not detected any SNP markers
significantly associated with FHB resistance on any chromosome; however, the
association analysis conducted with data collected from Mexico 2011 detected three
SNP markers located at chromosome 7B at 41 – 51 cM. The QTL detected in this
region explained from 5.0 – 8.4% of the phenotypic variance observed for FHB severity
during this specific year. The association analysis conducted with data collected from
Mexico 2012 detected one SNP marker associated with FHB resistance on
chromosome 2B located at 126 cM. The QTL detected in this region explained 8.3% of
the phenotypic variance observed for FHB severity.

191

Table 4-5. Association analysis for Fusarium head blight severity of the wheat association mapping panel using GLM
model. Mexico. 2011-12.
Marker
Chr.
Pos.
P-value
r2
Alleles Allele 1 Allele 2
Effect
(%Sev.) (%Sev.) (%Sev.)
Mexico 2011
wsnp_Ku_c14220_22456923
7A
5
2.04E-06
0.08
T/C
28,4
18,9
9.5
wsnp_Ex_rep_c66939_65371026
7A
6
7.15E-08
0.11
A/G
18,4
30,7
12.3
wsnp_Ku_c1809_3536072
7A
9
0.00266
0.04
A/G
20,9
24,9
4
wsnp_Ex_c14219_22169892
7A
11
5.57E-04
0.05
A/G
25,9
18,6
7.3
wsnp_Ex_rep_c66476_64726880
7A
13
2.58E-05
0.07
T/C
19,3
26,9
7.6
wsnp_JD_c6179_7344980
7A
16
8.12E-04
0.05
T/C
18,2
24,6
6.4
wsnp_BG313770A_Ta_2_1
7A
20
5.57E-06
0.07
T/C
26,4
19,1
7.3
wsnp_BG313770A_Ta_2_3
7A
20
9.19E-06
0.07
A/G
19,2
26,5
7.3
wsnp_Ku_rep_c105954_91953127
7A
57
9.48E-04
0.04
T/G
21
25,8
4.8
wsnp_Ex_c7776_13247654
2B
5
0.00424
0.03
T/C
19,1
23,5
4.4
wsnp_Ra_c16822_25566950
2B
73
1.21E-04
0.06
A/G
21
25,5
4.5
wsnp_Ku_c13905_22034406
2B
73
3.09E-04
0.05
A/C
20,1
25,7
5.6
wsnp_CAP8_c303_286918
2B
122
5.04E-04
0.05
T/G
22,1
24,5
2.4
wsnp_Ra_c2842_5399988
2B
126
6.79E-05
0.06
T/C
27,7
21,9
5.8
wsnp_BF291736B_Ta_1_1
2B
126
2.29E-04
0.05
T/C
21,9
26,9
5
wsnp_CAP11_c5474_2542512
2B
126
2.32E-04
0.04
A/G
22
25,3
3.3
wsnp_BE445278B_Ta_2_1
2B
126
3.29E-04
0.04
A/G
21,8
27,7
5.9
wsnp_BE445278B_Ta_2_3
2B
126
6.45E-04
0.04
A/G
22
24,9
2.9
wsnp_Ex_c38739_46195930
2B
126
0.00133
0.03
T/C
21,9
32,9
11
wsnp_Ku_c3000_5638635
2B
160
2.73E-04
0.05
A/G
23,9
18,1
5.8
wsnp_Ku_c3102_5811860
5B
97
3.49E-04
0.05
A/G
24
16,6
7.4
wsnp_Ku_c3102_5810751
5B
97
6.05E-04
0.05
T/C
16,3
24,1
7.8
wsnp_Ex_rep_c67690_66354931
5B
163
3.37E-06
0.08
A/G
21,1
24,7
3.6
wsnp_Ex_c48257_53217539
5B
163
1.92E-05
0.07
T/C
26,4
20,9
5.5
wsnp_Ex_c38105_45710671
5B
163
9.27E-04
0.04
A/G
26,3
20,7
5.6
wsnp_Ex_c4826_8610827
5B
164
7.80E-07
0.09
A/G
20,7
25,3
4.6
wsnp_Ku_c8270_14083963
5B
164
3.35E-06
0.08
A/G
20,7
26,3
5.6
192

Table 4-5 (cont’d)
wsnp_CAP8_c1594_914839
wsnp_BE606403B_Ta_2_1
wsnp_Ex_rep_c108314_91592072
wsnp_Ku_c28491_38419391
wsnp_Ex_c39535_46808105
wsnp_Ku_c15630_24304954
wsnp_Ex_c7469_12780118
wsnp_Ex_c1938_3656802
wsnp_Ku_c1661_3262505
wsnp_Ex_c49809_54305634
wsnp_Ex_c658_1293780
wsnp_Ku_c1661_3262637
wsnp_Ex_c658_1294440
wsnp_BF473658B_Ta_2_1
wsnp_Ex_c658_1295291
wsnp_Ku_c23836_33776356
wsnp_Ex_c658_1294003
wsnp_Ku_c57172_60417550
wsnp_Ex_c7173_12319519
wsnp_Ex_rep_c67549_66173636
wsnp_Ex_c5217_9237399
wsnp_Ex_rep_c66921_65344887
wsnp_BQ166999B_Ta_2_1
wsnp_Ex_c20988_30107609
wsnp_Ku_c11721_19085513
wsnp_Ra_c13646_21523723
wsnp_Ex_c13496_21243167
wsnp_Ku_rep_c103274_90057407
wsnp_CAP7_c90_52035
wsnp_be352570B_Ta_2_2

5B
5B
5B
5B
5B
5B
5B
5B
5B
5B
5B
5B
5B
5B
5B
5B
5B
5B
5B
5B
5B
5B
5B
5B
5B
5B
5B
5B
7B
7B

164
164
164
166
166
167
168
168
168
168
168
168
168
168
168
168
168
168
168
168
169
170
174
174
175
176
178
213
41
45

1.20E-05
4.72E-05
4.47E-04
3.54E-05
4.07E-05
4.52E-04
1.11E-04
1.21E-04
1.40E-04
1.45E-04
1.50E-04
1.64E-04
1.67E-04
1.69E-04
1.85E-04
3.03E-04
3.61E-04
4.02E-04
4.29E-04
8.11E-04
1.78E-04
2.84E-04
9.56E-05
0.00133
7.67E-05
3.10E-05
4.12E-04
0.00141
1.54E-04
2.09E-06

193

0.07
0.06
0.05
0.06
0.06
0.05
0.06
0.06
0.05
0.05
0.05
0.05
0.05
0.05
0.05
0.05
0.05
0.05
0.05
0.04
0.05
0.06
0.06
0.04
0.06
0.06
0.05
0.04
0.05
0.08

A/G
T/C
A/G
T/C
A/C
A/G
T/C
T/C
T/C
A/C
A/G
A/C
T/G
T/C
T/C
A/G
T/C
A/G
A/C
A/G
T/C
A/G
T/G
A/G
A/G
A/G
A/G
A/G
T/C
T/C

28,4
21
20,7
20,9
21
19,7
29,4
21
21,2
28,4
28,4
28,4
28,4
21,2
20,1
28
28,4
28,1
21,2
21,1
21,2
26,1
19,4
27,2
26,1
20,1
19,7
21,4
21,2
21,1

20,7
23,7
28,2
26,8
25,5
26
20,1
28,4
28,4
21,2
21,2
21,1
21,1
28,4
29,4
21,2
21,1
21,1
28,4
27,6
28,4
19,4
25,5
19,9
19,4
25,5
27,3
25,2
32,3
33,4

7.7
2.7
7.5
5.9
4.5
6.3
9.3
7.4
7.2
7.2
7.2
7.3
7.3
7.2
9.3
6.8
7.3
7
7.2
6.5
7.2
6.7
6.1
7.3
6.7
5.4
7.6
3.8
11.1
12.3

Table 4-5 (cont’d)
wsnp_CAP8_c3593_1773371
wsnp_Ex_c2539_4733110
Mexico 2012
wsnp_Ex_rep_c66562_64849366
wsnp_Ex_c1767_3341220
wsnp_be498599A_Ta_2_2
wsnp_BE406351A_Ta_2_2
wsnp_BE403597A_Ta_2_1
wsnp_Ex_c28204_37349164
wsnp_Ex_rep_c69816_68774932
wsnp_JD_c940_1381378
wsnp_BG313770A_Ta_2_3
wsnp_Ra_c250_526345
wsnp_Ra_c26491_36054023
wsnp_Ku_c13905_22034406
wsnp_BF202681B_Ta_2_2
wsnp_CAP8_c303_286918
wsnp_Ra_c2842_5399988
wsnp_Ex_c46576_52042185
wsnp_Ku_c48694_54811376
wsnp_Ex_c11246_18191079
wsnp_Ex_c4888_8713275
wsnp_JD_c5067_6187376
wsnp_Ex_rep_c108114_91468537
wsnp_Ex_c3130_5789888
wsnp_Ra_c69_149394
wsnp_BE443187B_Ta_2_1
wsnp_Ex_c48257_53217539
wsnp_Ex_rep_c67549_66173636

7B
7B

45
51

4.84E-06
0.0017

0.07
0.04

T/C
A/G

21
20,2

33,9
26,7

12.9
6.5

1A
2A
2A
2A
2A
2A
3A
5A
7A
7A
7A
2B
2B
2B
2B
2B
2B
3B
3B
3B
3B
3B
3B
5B
5B
5B

71
79
93
113
116
119
35
184
20
82
105
73
94
122
126
167
220
63
70
83
100
132
132
146
163
168

0.00271
4.43E-04
0.00447
8.65E-04
9.14E-04
0.00147
0.0034
0.00209
0.00414
0.0015
0.00422
5.09E-05
0.00392
1.44E-04
3.27E-05
9.41E-05
1.65E-04
4.14E-04
0.00339
3.07E-04
3.17E-04
9.71E-06
4.69E-05
0.0018
2.86E-04
2.63E-05

0.03
0.03
0.00
0.03
0.02
0.02
0.02
0.04
0.01
0.02
0.04
0.02
0.03
0.04
0.03
0.01
0.02
0.02
0.03
0.02
0.02
0.01
0.02
0.01
0.02
0.04

T/C
A/G
A/G
T/C
A/G
T/C
A/G
T/G
A/G
A/G
A/G
A/G
A/C
T/G
T/C
T/C
T/C
A/C
A/G
T/C
T/C
T/C
T/C
A/C
T/C
A/G

10.4
13.3
8.9
14.2
14.2
13.7
7.5
9.9
8.9
14.8
17.2
8.4
13.8
8.9
9
10
14.6
5
10.2
9.1
9.1
8.8
11.1
11.3
11.5
13

8.9
8.8
13.4
9.1
9.1
8.9
10.3
11.6
10.6
9
9.2
12.5
8.9
14.3
20.1
8.3
9
10
7.4
14.9
11.8
11.4
8.9
9.2
9.1
9

1.5
4.5
4.5
5.1
5.1
4.8
2.8
1.7
1.7
5.8
8
4.1
4.9
5.4
11.1
1.7
5.6
5
2.8
5.8
2.7
2.6
2.2
2.1
2.4
4

194

Table 4-5 (cont’d)
wsnp_Ex_c658_1293780
wsnp_Ex_c17882_26646153
wsnp_BF474552B_Ta_1_1
wsnp_CAP8_c3593_1773371
wsnp_Ku_c8712_14751858

5B
7B
7B
7B
2D

168
32
32
45
139

3.50E-05
8.32E-05
8.56E-05
0.00386
6.88E-05

0.02
0.01
0.01
0.04
0.04

A/C
T/C
A/G
T/C
T/C

13.2
9.2
14.2
9.2
10.1

9
14.2
9.2
13.1
6.1

Table 4-6. Association analysis for fusarium head blight severity of the wheat association mapping panel using MLM
model. Mexico. 2011-12.
Position
Marker
Chr.
P-value
(cM)
Mexico 2011-12
NS
Mexico 2011
wsnp_Ex_c11860_19030807
wsnp_RFL_Contig3854_4205716
wsnp_RFL_Contig2167_1484520
Mexico 2012
wsnp_Ex_c55735_58127324

7B
7B
7B

74
78
85

8.74E-06
7.81E-05
1.81E-05

2B

242

5.00E-06

195

4.2
5
5
3.9
4

Manhattan plot of FHB severity –
Mexico 2011 using GLM

Manhattan plot of FHB severity –
Mexico 2011 using MLM

Manhattan plot of FHB severity –
Mexico 2012 using GLM

Manhattan plot of FHB severity –
Mexico 2012 using MLM

Figure 4-3. Manhattan plots of the association analysis for Fusarium head blight severity in the wheat association
mapping panel using GLM and MLM. Mexico 2011 and 2012.

196

Q-Q plot of FHB severity –
Mexico 2011 using GLM

Q-Q plot of FHB severity –
Mexico 2011 using MLM

Q-Q plot of FHB severity –
Mexico 2012 using GLM

Q-Q plot of FHB severity –
Mexico 2012 using MLM

Figure 4-4. Q-Q plots of the association analysis for fusarium head blight severity in the wheat association mapping panel
using GLM and MLM. Mexico 2011 and 2012.

197

Germplasm evaluation
The wheat AMP includes wheat accessions with high levels of resistance to FHB.
In table 4-7, the top 25 accessions showed reduced percentage of severity (<7.0%),
evaluated in two years under high disease pressure and adequate environmental
conditions provided at El Batan. The Structure analysis (Chapter II) separated the wheat
accessions in three sub-populationss. From the top 25 FHB resistant genotypes, there
were 10 genotypes from sub-population 1, 10 genotypes from sub-populations 2, and
five genotypes from sub-populations 3. In the other hand, from the bottom 25, most of
the susceptible wheat accessions were assigned to sub-population 1 and 3, with eight
and 11 accessions respectively. In the case of sub-population 2, six wheat accessions
were located in the bottom 25.

198

Table 4-7. Top 25 and bottom 25 accessions based on FHB severity (%) in the wheat AMP with sub-populations
classification. Mexico, 2011-12.
FHB 2011
FHB 2012
SubAcc.
Severity
Severity
population
Pedigree
Number
(%)
(%)
Top 25
250
157
181
131

213
219
249
118
152
167
199
210
223
232

244

ATTILA/HEILO (b)
SUMAI #3
WBLL1/FRET2//mazar 99*2/3/GONDO
KAUZ//ALTAR
84/AOS/3/MILAN/KAUZ/4/HUITES/5/SHA3/SERI//SHA4/LIRA/6/KA
UZ//ALTAR 84/AOS/3/MILAN/KAUZ/4/HUITES
SHA3/CBRD//TNMU/5/KAUZ//ALTAR
84/AOS/3/MILAN/KAUZ/4/HUITES
PRL/2*mazar 99//SRTU/3/PRINIA/PASTOR
ATTILA/HEILO (a)
FRANCOLIN #1/4/BABAX/LR42//BABAX*2/3/KURUKU
WBLL1*2/TUKURU//KRONSTAD F2004
WBLL1*2/4/YACO/PBW65/3/KAUZ*2/TRAP//KAUZ/5/GONDO
CBRD/FILIN
KETUPA*2/mazar 99/6/TURACO/5/CHIR3/4/SIREN//ALTAR
84/AE.SQUARROSA (205)/3/3*BUC/7/KAUZ//ALTAR
84/AOS/3/MILAN/KAUZ/4/HUITES
WBLL1*2/4/SNI/TRAP#1/3/KAUZ*2/TRAP//KAUZ/5/KAUZ//ALTAR
84/AOS/3/MILAN/KAUZ/4/HUITES
NG8675/CBRD//MILAN/7/CAL/NH//H567.71/3/SERI/4/CAL/NH//H56
7.71/5/2*KAUZ/6/mazar
99/8/CNDO/R143//ENTE/MEXI_2/3/AEGILOPS SQUARROSA
(TAUS)/4/WEAVER/5/2*mazar 99
CPI8/GEDIZ/3/GOO//ALB/CRA/4/AE.SQUARROSA
(208)/5/HAHN/2*WEAVER/6/SKAUZ/BAV92
199

2
3
3
4

1
1
1
3

3
1
2
131

5

3

213

5
5
6
6
6
6
6

3
1
2
1
2
2
2

2
3
3
3
2
2
1

6

2

3

6

4

2

6

1

2

Table 4-7 (cont’d)
132
PBW343/PASTOR*2/6/TURACO/5/CHIR3/4/SIREN//ALTAR
84/AE.SQUARROSA (205)/3/3*BUC
146
CHIBIA/WEAVER/5/KAUZ//ALTAR
84/AOS/3/MILAN/KAUZ/4/HUITES
148
C80.1/3*BATAVIA//2*WBLL1/3/TOBA97/PASTOR
154
WHEAR/2*KRONSTAD F2004
160
GONDO/CBRD
162
PICUS/3/KAUZ*2/BOW//KAUZ/4/KKTS/5/HEILO
173
KAUZ//ALTAR
84/AOS/3/MILAN/KAUZ/4/HUITES/5/CROC_1/AE.SQUARROSA
(205)//KAUZ/3/SASIA/6/KAUZ//ALTAR
84/AOS/3/MILAN/KAUZ/4/HUITES
182
PFAU/WEAVER*2//BRAMBLING/7/IVAN/6/SABUF/5/BCN/4/RABI//
GS/CRA/3/AE.SQUARROSA
(190)/8/PFAU/WEAVER//BRAMBLING
216
HEILO/7/IVAN/6/SABUF/5/BCN/4/RABI//GS/CRA/3/AE.SQUARRO
SA (190)/8/VORB/FISCAL
248
NING MAI 96035/FINSI//HEILO

76

91
122
134
28
54

Bottom 25
CAL/NH//H567.71/3/SERI/4/CAL/NH//H567.71/5/2*KAUZ/6/PASTO
R/7/ACHTAR*3//KANZ/KS85-84/8/CAL/NH//H567.71/3/SERI/4/CAL/NH//H567.71/5/2*KAUZ/6/PAS
TOR
KBIRD//WBLL1*2/KURUKU
TACUPETO F2001//WBLL1*2/KKTS/3/WBLL1*2/BRAMBLING
FRANCOLIN #1/KIRITATI
QUAIU #1
INQALAB 91*2/KUKUNA*2//PVN

200

7

4

1

7

2

1

7
7
7
7
7

3
1
1
4
1

1
1
2
2
1

7

1

1

7

2

2

7

1

2

35

10

1

35
36
36
38
40

13
10
14
10
11

3
3
3
1
3

Table 4-7 (cont’d)
57
WAXWING/2*ROLF07
67
ATTILA*2/PBW65*2/4/BOW/NKT//CBRD/3/CBRD
98
ATTILA*2/PBW65//MUU #1/3/FRANCOLIN #1
277
SOKOLL//SUNCO/2*PASTOR
39
WBLL1*2/CHAPIO//HEILO
102
MUU #1//PBW343*2/KUKUNA/3/MUU
20
PBW343*2/KUKUNA/3/PGO/SERI//BAV92
46
BAV92//IRENA/KAUZ/3/HUITES*2/4/MILAN/KAUZ//CHIL/CHUM18
87
FRANCOLIN #1/5/HE1/3*CNO79//2*SERI/3/ATTILA/4/WH 542
159
FALCIN/AE.SQUARROSA (312)/3/THB/CEP7780//SHA4/LIRA
275
MILAN/DUCULA//SUNCO/2*PASTOR
53
FINSI/METSO//FH6-1-7/3/FINSI/METSO
158
GAMENYA
289
CROC_1/AE.SQUARROSA
(205)//BORL95/3/KENNEDY/6/CNDO/R143//ENTE/MEXI_2/3/AEGI
LOPS SQUARROSA (TAUS)/4/WEAVER/5/2*JANZ
107
CONI#1/6/2*HPO/TAN//VEE/3/2*PGO/4/MILAN/5/SSERI1
58
WBLL1*2/5/CNO79//PF70354/MUS/3/PASTOR/4/BAV92
294
SOKOLL/FRAME
77
BAV92//IRENA/KAUZ/3/HUITES*2/4/YUNMAI 47
110
MUU/5/TRAP#1/BOW/3/VEE/PJN//2*TUI/4/BAV92/RAYON/6/MILA
N/S87230//BAV92

201

42
42
42
42
45
48
49
49
50
51
51
52
52
53

9
8
11
8
7
21
10
14
7
22
13
10
80
10

1
3
3
2
2
1
3
1
3
3
2
2
1
3

54
57
57
61
64

13
11
10
16
16

3
1
1
2
2

Analysis of variance of Deoxinivalenol concentration
The ANOVA for DON concentration in Mexico detected significant differences
among accessions in each year (Table 4-8). The mean was 3.8 ppm. The average for
DON concentration in 2011 was 5.0 ppm and 2.6 ppm in 2012. In 2011, the DON
concentration ranged from 0.2 – 16.3 ppm, meanwhile, in 2012 the DON concentration
ranged from 0.1 – 12.7 ppm. The coefficient of variance was CV= 53.8%. Broad sense
2

heritability of DON concentration was H = 0.51.
The data distribution showed skewedness to the left to low levels (Figure 4-5).
The correlation on the two years of experiments on Mexico was 0.3 (Table 4-10).
Table 4-8. ANOVA for DON concentration of 297 wheat accessions in two years.
Mexico 2011-12.
Df
Mean
F value
P-value
Sources of variation
Squares
Year
1
810.1
193.0
<0.0001***
Accession
296
8.6
2.1
<0.0001***
Block/Group
8
29.8
7.1
<0.0001***
Error
288
4.2
CV(%)=
53.8
Mean (%)
3.8
2
H = 0.51
Table 4-9. DON concentration in the wheat Association mapping panel. Mexico, 201112.
Location

Year

Range (%)

Average (%)

El Batan – Mexico

2011
2012

0.2 - 16.3
0.1 - 12.7

5
2.6

202

Table 4-10. Correlations and p-values in the wheat Association Mapping panel between
Mexico 2011 and 2012 for DON concentration. Mexico 2011-12. All values were highly
significant (P< 0.001).
Mexico
Mexico
Mexico
2011
2012
2011-12
Mexico 2011
1
Mexico 2012
0.29
1
Mexico 2011-12
0.87
0.73
1

Figure 4-5. Distribution of DON concentration in the wheat AMP evaluated in Mexico
2011-12.
Association analysis for DON concentration
The association analysis conducted with the data collected from Mexico during
2011 and 2012 using the GLM method detected SNP markers significantly associated
with DON concentration on chromosomes 1A, 3A, 5A, 7A, 2B, 5B, and 2D (Table 4-11).
The analysis conducted with data collected on 2011 identied SNP markers located on
203

chromosomes 2B, 5B, and 2 D with the lagest effects. On chromosome 2B, one SNP
marker located at 126 cM explained 5.2% of the phenotypic variance of the trait. The
estimated effect of this SNP was 2.7 ppm. Other interesting region was located on
chromosome 5B at positions 162 - 167. The effect of this region on the trait was 2.6
ppm. On chromosome 2B, one marker located at 139 cM explained 7.9% of the
phenotypic variance and showed an effect of 2.5 ppm.
The association analysis conducted with data collected on Mexico 2012 detected 5
SNP markers associated with DON concentration. These markers were located on
chromosomes 4A, 7A, 2B, and 2D. The regions found in this analysis were different
from those found in 2011 exept for the SNP marker located on chromosome 2D at 139
cM. In this analysis, the QTL associated with this marker explained 6.3% of the
phenotypic variance. The effect over the trait was 1.2 ppm.
The MLM model did not detected any significant SNP marker associated with DON
concentration in the AMP.

204

Table 4-11. Association analysis for DON concentration of the wheat association mapping panel using GLM model.
Mexico. 2011-12.
Marker
Ch Pos.
p-value
r2
Allele Allele 1
Allele 2
Effect
r.
(cM)
s
(ppm)
(ppm)
(ppm)
Mexico 2011
wsnp_Ex_c12123_19388313
1
148
1.19E-04
0.06064
A/G
5.4
4.4
1
wsnp_Ku_rep_c109724_94227136 1
149
2.38E-04
0.05649
T/C
5.4
4.5
0.9
wsnp_BQ167580A_Ta_1_1
3
123
6.96E-05
0.06341
T/C
3.7
5.2
1.5
wsnp_BE399966A_Ta_2_3
5
193
2.01E-05
0.07136
A/G
5.2
3.7
1.5
wsnp_Ex_c14219_22169892
7
11
6.30E-04
0.05044
A/G
5.6
4.3
1.3
wsnp_Ex_rep_c66476_64726880
7
13
3.13E-05
0.07097
T/C
4.4
6
1.6
wsnp_BG313770A_Ta_2_1
7
20
1.90E-04
0.05734
T/C
5.7
4.4
1.3
wsnp_Ex_c7776_13247654
9
5
4.63E-04
0.05161
T/C
4
5.3
1.3
wsnp_Ra_c2842_5399988
9
126
3.81E-04
0.05206
T/C
4.9
7.6
2.7
wsnp_Ra_c39562_47242455
12
70
5.41E-05
0.06538
A/G
4.4
5.5
1.1
wsnp_Ex_c5155_9140608
12
77
6.39E-04
0.04872
A/C
5.3
4.1
1.2
wsnp_Ku_c3102_5810751
12
97
2.54E-06
0.08499
A/G
5.5
3.5
2
wsnp_JD_c11594_12033647
12
162
2.84E-04
0.0431
A/G
4.8
7.4
2.6
wsnp_Ku_c15630_24304954
12
167
4.66E-04
0.0518
A/G
4.4
5.9
1.5
wsnp_Ex_rep_c66921_65344887
12
170
4.38E-04
0.05922
A/G
5.9
4.3
1.6
wsnp_BQ166999B_Ta_2_1
12
174
9.17E-04
0.04841
T/G
4.4
5.7
1.3
wsnp_Ku_c11721_19085513
12
175
2.20E-04
0.05771
A/G
5.9
4.4
1.5
wsnp_Ku_c8712_14751858
16
139
5.39E-06
0.07887
T/C
5.3
2.8
2.5
Mexico 2012
wsnp_Ex_c1373_2628597
wsnp_Ex_c9971_16412345
wsnp_Ku_c13905_22034406
wsnp_Ra_c16822_25566950
wsnp_Ku_c8712_14751858

4
7
9
9
16

138
154
73
73
139

1.92E-04
1.40E-04
7.41E-05
8.58E-05
2.34E-05

205

0.05226
0.05728
0.05894
0.05691
0.06346

A/G
C/T
A/G
C/T
A/C

3
2.1
2.3
2.9
2.3

1.7
2.8
3.4
1.3
3.5

1.3
0.7
1.1
1.6
1.2

Table 4-12. Association analysis for DON concentration of the wheat association mapping panel using MLM model.
Mexico. 2011-12.
Marker
Chromosome Position (cM)
P-value
Mexico 2011-12
NS

206

Manhattan plot of DON concentration –
Mexico 2011 using GLM

Manhattan plot of DON concentration –
Mexico 2011 using MLM

Manhattan plot of DON concentration –
Mexico 2012 using MLM

Manhattan plot of DON concentration –
Mexico 2012 using GLM

Figure 4-6. Manhattan plots of the association analysis for DON accumulation in the wheat association mapping panel
using GLM and MLM. Mexico 2011-12.
207

Q-Q plot of DON concentration –
Mexico 2011 using GLM

Q-Q plot of DON concentration –
Mexico 2011 using MLM

Q-Q plot of DON concentration – Mexico 2012
using GLM

Q-Q plot of DON concentration – Mexico 2012
using MLM

Figure 4-7. Q-Q plots of the association analysis for DON accumulation in the wheat association mapping panel using
GLM and MLM. Mexico 2011-12.

208

Discussion
The Fusarium Head Blight data (percentage of severity and concentration of
Deoxinivalenol in parts per million-ppm) were analyzed only from the experiments
planted in Mexico. The weather conditions in Mexico ranged from 15 – 25ºC (Appendix
E), which are ideal to the development of the disease. According to Schmale III and
Bergstrom (2003) the optimum range of temperatures which favors the disease
development are 15 - 30ºC. Additionally, the wheat AMP planted in El Batan - Mexico
received additional irrigation from sprinklers which increased the relative humidity in the
experiment.
The analyses of variance of these traits detected significant differences among
locations, so the traits were analyzed independently as follows:

Statistical analysis FHB severity
In Ecuador, statistical analyses were not conducted with the data collected on 2011
and 2012 from the wheat AMP. The disease severity in 2011 was very low with an
average of 3.8% in the whole population. More than 50% of the accessions did not
show any symptoms in the spikes or seeds. It could be attributed to unfavorable
environmental conditions for the development of the disease since wheat accessions
possessing immunity to the disease are not expected (Miller and Greenhalgh, 1988;
Snijders, 1994). In 2012, the disease was not present in the experiment, despite ground
inoculations and the two inoculations at flowering time. Macroconidia require relative

209

humidity higher than 80% to germinate (Beyer et al., 2005) and Santa Catalina did not
meet that condition in 2011 nor 2012.
In El Batan-Mexico the situation was different. The first year of evaluations (2011)
was better in terms of disease severity. The average infection for the whole experiment
was 22.4%. The disease severity for second year of study in Mexico (2012) was
significantly lower compared with the first year. Even though the FHB severity ranged
from 1.0 -80.0%, the average for the population was lower (9.6% of disease severity).
The reason is that only one genotype (Gamenya) showed a high percentage of disease
severity. The second accession more susceptible in 2012 was FALCIN/
AE.SQUARROSA(312) /3/THB/CEP7780//SHA4/LIRA with 22% of disease severity.
Low correlations were observed between FHB severity in Mexico 2011 versus
2012. Low correlations of FHB severity experiments conducted in the same location but
different seasons are not uncommon (Somers et al., 2003). Accessions that were
susceptible in Mexico 2011 (over 35% of FHB severity) were moderately susceptible in
Mexico 2012 (5-22%) except from Gamenya (the susceptible control) with 80% of FHB
severity in 2012.
Heritability estimates were low in the experiments conducted in Mexico. In the
literature, the heritability for FHB traits is variable. Some studies such as Buerstmayr et
2

al. (2000) or Miedaner et al. (2011) reported H > 0.7; however, Verges et al. (2006)
reported heritability values for FHB traits lower than 0.3. In this study, the low heritability
resulted by the complexity of the trait and the GxE effect could result in slow progress in
breeding for resistance.

210

Statistical analysis DON concentration
The data distribution showed skewedness to the left to low levels (Figure 4-3),
giving the impression that most of the wheat accessions had low levels of DON.
However, levels of 2 ppm of DON or higher are not accepted by the industry (van
Egmond and Jonker, 2004). Considering that limit or threshold, in 2011, 85.5% of the
wheat accessions exceeded 2.0 ppm, meanwhile, in 2012, 49.9% exceeded 2.0 ppm.
The DON concentration was higher in 2011 due to climatic conditions which favored
disease development. The same results were observed for FHB severity. The
coefficient of variance was high CV= 53.8%. The reason for such high coefficients of
variation might be the result of the reduced disease pressure observed in the
experiment evaluated in 2012.The correlation between the two years of experiments in
Mexico was low 0.3 (p value <0.001). It is not uncommon to find low correlation between
DON concentration between two or more different seasons or between DON
concentration and FHB severity (Bruins et al., 1993; McCormick et al., 2003). The
reason for these observations could be caused by the high influence of the environment
on the development of the disease and the various mechanisms of resistance that can
be combined in the plant (Mesterházy et al., 2003; Somers et al., 2003). For example,
Type I resistance can be more efficient with less relative humidity as occurred in 2012.

Germplasm evaluation
The evaluation of FHB severity and DON concentration in the wheat AMP
allowed the identification of accessions with high levels of disease resistance to FHB
(Table 4-7). The maximum percentage of FHB severity observed in these lines was 7%
211

in the evaluation conducted in 2011 in Mexico under high disease pressure. Based on
the pedigree, it was possible to identify some wheat lines that are frequently present.
For instance, there were 11 accessions developed from the synthetic wheat line
(‘ALTAR84/Ae. squarrosa) which has been previously used at CIMMYT to provide
resistance to several biotic and abiotic constraints (Warburton et al., 2006) and was
used to introgress Fusaium head blight resistance (Mujeeb-Kazi et al., 2001). Other
ancestors frequently found in the list of the top 25 accessions was ‘Heilo’ (five times).
Heilo, which showed resistance to yellow rust as well (Chapter III), was one of the
parents in the last cross of most of the resistant accessions. ‘Heilo’ is also a wheat
accession of special interest since it has high end-use quality and has two QTLs related
with low-molecular weight glutenin subunits (Liu et al., 2010; McIntosh et al., 2012).
Another accession found nine times in the pedigrees of the top 25 lines with resistance
to FHB was ‘Kauz’ (nine times). This accession in commonly found in CIMMYT wheat
lines, since it provides resistance to abiotic stresses and has improved nutrient use
efficiency (N and P) and shows high yield in low and high input conditions in a wide
range of different environments (Rajaram et al., 2002).

Association analysis of FHB severity
The association analysis conducted with data from Mexico 2011 and 2012 using
the GLM method detected SNP markers significantly associated with FHB severity on
chromosomes 1A, 2A, 3A, 5A, 7A, 2B, 3B, 5B, and 7B. Markers located on
chromosome 2B and 5B were the same in the analysis conducted separately for each
year.
212

When the MLM method was used, the analysis did not detect markers
significantly linked to FHB severity in the data set from Mexico 2011-12. However, the
individual analysis for each season using the MLM method detected markers on
chromosome 7B (Mexico 2011) and 2B (Mexico 2012). MLM method is highly
conservative compared with the general linear model (Yu et al., 2006) and this is the
reason why in this study few SNP markers were detected using MLM.
Quantitative trait loci for FHB resistance have been mapped on every chromosome
of the hexaploid wheat genome except on chromosome 7D (Buerstmayr et al., 2009). In
this study, the association analyses with data collected from Mexico from 2011 and
2012 using the GLM method and the individual analysis from Mexico 2011 using GLM
and MLM detected SNP markers significantly associated with FHB resistance on
chromosome 7A position 8-9 cM. Several QTLs have been reported to be located on
chromosome 7A. One of them was found in the Chinese source of resistance
‘Wangshuibai’ (Zhou et al., 2004). Following the report of the QTL discovered in
‘Wangshuibai’, two other QTL found in ‘Frontana’ (Mardi et al., 2006) and NK93604
(Semagn et al., 2007) were reported in the same chromosome 7A. The last report of a
QTL located on chromosome 7A was a QTL discovered in ‘Sumai 3’ named as Fhb7AC
was found near the centromere of chromosome 7A (Jayatilake et al., 2011). From all the
QTLs reported previously, the only QTL located in the short arm of chromosome 7A was
the QTL from ‘Frontana’, which is the region were the SNP markers were significant.
This finding added to the fact that ‘Frontana’ was extensively used in CIMMYT
germplasm to develop spring wheat lines with resistance to FHB suggested that the
QTL found in this study could be the same QTL present in ‘Frontana’.

213

Chromosome 5B was the other chromosome where several SNP markers were
detected in the combined data set of Mexico 2011-12 and Mexico 2011alone using the
GLM method. SNP markers were located in the distal region at 225 – 247 cM. QTLs in
different regions of the long arm of chromosome 5 have been reported previously in
winter wheat (Bourdoncle and Ohm, 2003; Klahr et al., 2007; Paillard et al., 2004) and
spring wheat (Jia et al., 2005). One of these QTLs was detected in the cultivar ‘Forno’
which has been of interest, not only for FHB resistance and significant percentage of
variation of the FHB severity explained (14.3%), but plant height or flowering time
variation indicating linkage or pleiotropic effects (Buerstmayr et al., 2009; Paillard et al.,
2004). Another source of resistance with a QTL detected on chromosome 5B is the
Chinese landrace ‘Wangshuibai’ (Jia et al., 2005).

Association analysis for DON concentration
Even though, the number of studies conducted to detect QTLs controlling DON
concentration are abundant, not many regions in the wheat genome have been
identified compared with other traits such as FHB severity, incidence or spread
(Buerstmayr et al., 2009). In this study, SNP markers significantly associated with DON
concentration were found on chromosomes 1A, 3A, 4A, 5A, 7A, 2B, 5B, 7B, and 2D. In
this study, chromosome 5A position 193 cM presented one marker significantly
associated with DON concentration. In chromosome 5A one QTL has been reported in
a population obtained by the cross Wuhan 1 x Nyu Bai (Somers et al., 2003). The QTL
found in this study was discovered in the short arm of chromosome 5A and the source
belongs to Chinesse germplasm. In other study (Jiang et al., 2007), a QTL located on
214

chromosome 5A was reported in spring wheat. One of the partent in this population was
Veery, which is one of the most populat accessions from CIMMYT utilized as a source
of multiple traits. Several QTLs associated with FHB severity have been reported in
these chromosomes, but only one study has previously reported a QTL on chromosome
2D, present in ‘Maringa’ (Somers et al., 2003). The mechanism to control DON
concentration has been elucidated by Lemmens et al. (2005), which found that DON
was converted to DON-3-O-glycoside which is a less phytotoxic compound. The two
possible ways proposed from the authors after this observation are that a gene encodes
the enzyme DON-glucosyltransferase or regulates the expression of such an enzyme.
Several regions wich had effect over FHB severity and DON concentrations were
detected. These regions were located on chromosomes 2B, 5B, and 2D. On
chromosome 2B, SNP marker wsnp_Ra_c2842_5399988 located at 126 cM showed
effect related with reduction on FHB severity and specially DON concentration with
reduction of 2.7 ppm when the favorable allele was present. Similarly, SNP marker
wsnp_ku_c15630_24304954 showed effect in the reduction of FHB severity and DON
concentration. On chromosome 2D, SNP marker wsnp_ku_c8712_14751858 had effect
on both traits with notable reduction on DON concentration (2.5 ppm) when the
favorable allele was present.

Conclusions
The wheat AMP includes several wheat accessions with high levels of resistance
to FHB. These accessions have shown allelic diversity for FHB resistance and are
valuable sources of many genes to control FHB. Based on the pedigrees and the
215

classification of the wheat accessions in different sub-populations, it can be inferred that
the allelic richness and potential contribution for breeding are not limited to FHB
resistance but are valuable for many other traits.
Association mapping approach detected several regions associated with
resistance to FHB severity and DON concentration. The number of regions and markers
were drastically reduced when the MLM method was used instead of the GLM method.
Special attention must be considered to this situation, which is commonly reported in
the literature, and it will be important to validate these SNP markers and QTLs in
mapping or breeding populations.
The wheat SNP chip is a valuable tool to conduct association mapping studies,
but the reduced number of polymorphic markers detected in the D-genome in spring
wheat populations needs to be addressed with the incorporation of additional markers in
the D-Genome. For example, SSR markers that have been reported to be specific for Dgenome.

Acknowledgments
Wheat Pathology Program at CIMMYT: Pawan Singh, Nerida Lozano, Monica,
Mary, Francisco, and INIAP: Jose Ochoa, Mayra Cathme, Javier Garofalo, Luis Ponce,
Segundo Abad y Segundo Guaynalla.
Zixang Wen from MSU with software analysis (R, STRUCTURE and TASSEL)

216

APPENDIX

217

Appendix: Temperature and precipitation. Mexico and Ecuador. 2011-12
Table 4-13. Temperature and precipitation data from Santa Catalina – Ecuador and El
Batan - Mexico during 2011-12.
Location
Year
Months Average Temp. Temp.
Precipitation
temp.
max.
min.
(mm)
(°C)
(°C)
(°C)
Santa Catalina*
2011 February
11.3
19.6
3.8
206
March
11.2
20.5
2.6
143.7
April
11.1
19.9
2.5
262.2
May
12.1
21.6
2
91.7
June
12
20.6
2.2
61.5
2012 February
11.1
18.6
4.5
227.3
March
12.2
20.6
5
197.4
April
11.1
23.7
3.2
219.3
May
11.8
19.8
4.2
62.9
June
11.8
21.2
2.6
10.2
El Batan
2011 Aug
17.6
25.7
9.6
66.1
Sep
15.7
24.7
6.6
68.5
Oct
15.0
25.1
4.9
94.6
2012 Aug
16.2
22.6
10.9
75.6
Sep
16.1
23.8
10.1
51.1
Oct
15.1
25.6
5.3
9.3
* Data collected from the weather station of Santa Catalina Research Station
** Data collected from Wunderground.com ®
(http://www.wunderground.com/weatherstation/WXDailyHistory.asp?ID=IESTADOD2)

218

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219

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