IDENTIFICATION AND EVALUATION OF QUANTITATIVE TRAIT LOCI
INFLUENCING GROWTH, CARCASS COMPOSITION, AND MEAT QUALITY
TRAITS IN PIGS
By
Igseo Choi

A DISSERTATION
Submitted to
Michigan State University
in partial fulfillment of the requirements
for the degree of
DOCTOR OF PHILOSOPHY
Animal Science
2011

 
 

ABSTRACT
IDENTIFICATION AND EVALUATION OF QUANTITATIVE TRAIT LOCI
INFLUENCING GROWTH, CARCASS COMPOSITION, AND MEAT QUALITY
TRAITS IN PIGS
By
Igseo Choi
A three-generation resource population for identifying quantitative trait loci (QTL) for
growth, carcass and meat quality traits was previously constructed at Michigan State University
by crossing pigs from the Duroc and Pietrain breeds. The initial genome scan of this population
included 510 F2 animals genotyped with 124 microsatellite markers and analyzed using a linecross model. For the second scan, 20 additional markers on 9 chromosomes (SSC3 – 7, 12, 15,
16 and 18) were genotyped for 954 F2 animals, and 20 markers used in the first scan were
genotyped for 444 additional F2 animals. Three least-squares Mendelian models for QTL
analysis were fit to each trait: a line-cross (LC) model, a half-sib (HS) model, and a combined
line-cross and half-sib (CB) model.
A total of 41 QTL for growth traits were identified on SSC4, 6 – 9, 11, 15, 16, and 18. The
LC analysis revealed 26 QTL, including 7 new QTL (SSC7, 15 and 18) which were not detected
in the first scan. The HS analysis revealed 12 QTL, and 3 additional QTL were detected using
the CB model. A total of 91 QTL for carcass and meat quality traits were identified. The LC
analysis revealed 50 QTL including 14 new QTL (SSC3, 6, 7, 12, 16, and 18). The HS analysis
revealed 38 QTL including 13 QTL detected on SSC15. In addition, 3 QTL were detected using
the CB model. Increasing the number of markers and animals facilitated detection of new QTL,

 
 

as well as confirmation of previously identified QTL. Also, three different least-squares models
made it possible to detect new QTL segregating either between or within breeds.
Based on results of the initial genome scan, five putative QTL regions (SSC3, 6, 12, 15, and
18) for carcass and meat quality traits were selected for further evaluation in an US purebred
Duroc population. A total of 81 gene-specific single-nucleotide polymorphisms (SNP) were
genotyped of which 33 were segregating and were analyzed for associations with pH, color,
marbling, days to 113 kg, backfat thickness, and LMA. The MDH1 SNP on SSC3 was associated
with pH traits. A combined genotype of PRKAG3 T30N and I199V on SSC15 was associated
with ultimate pH. The HSPG2 SNP on SSC6 was associated with marbling score and days to 113
kg. Markers for NUP88 and FKBP10 on SSC12 were associated with 45-min pH and L*,
respectively. Significant associations were observed for the SSC15 marker SF3B1 with L* and
LMA, and for the SSC18 marker ARF5 with ultimate pH and color score. Thus, results observed
for SNP markers in the US Duroc population were consistent with our previous genome scan.
Based on QTL detected on SSC18 and its biological function, corticotropin-releasing
hormone receptor 2 (CRHR2) was evaluated as a potential candidate gene affecting stress
response and influencing carcass and meat quality traits. Association analyses were performed
for carcass and meat quality traits recorded for the MSU resource population and for a
population exposed to a halothane challenge test, as well as for stress response phenotypes
recorded for the halothane challenge population. CRHR2 genotype was significantly associated
with pH and temperature, drip loss, cook yield, moisture and protein percent, b*, ham weight,
and dressing percent in the resource population, and CRHR2 genotype exhibited a suggestive
association with blotching, L* and color score in the halothane challenge population.

 
 

Identification and subsequent validation of QTL influencing growth, carcass and meat
quality traits in resource and commercial populations will facilitate successful implementation of
marker assisted selection programs in order to achieve genetic improvement.

 
 

This dissertation is dedicated to my father and mother.

v
 

Acknowledgments
A lot of support from many people made it possible for me to reach this moment. First of all,
I would like to thank my major professor, Dr. Catherine Ernst, who gave me an opportunity to
attend Michigan State University and has always encouraged and inspired me at every single step
of the process during my whole Ph.D program. Without the tremendous efforts of Dr. Ernst, it is
obviously impossible for me to finish my project. Dr. Ernst has provided me the opportunity with
her efforts to perform outstanding research in the animal genetics area, present the outcomes at
scientific meetings, and publish the results in peer-reviewed journals. Beyond the scientific
aspects, Dr. Ernst was deeply involved in my life in many ways and her truly indescribable
efforts are holding a special place in my heart and will stay forever.
I would also like to thank the members of my guidance committee for their valuable insight
in my project and Ph.D program. Especially, Drs. Ronald Bates and Juan Pedro Steibel who gave
me countless advice for every chapter presented in this dissertation. Dr. Bates and his family (Ms.
Teresa Bates and Ms. A’Lana Bates) provided tremendous support to continue my program as
well as Dr. Bates’ scientific advice for my research project. Dr. Steibel helped with statistical
analysis in detail, and his scientific view and rational thought deeply influenced me. Dr.
Tempelman gave me a clear solution to construct the genetic map, even impressively asking
other scientists to help me when faced with a difficult problem, as well as providing statistical
ideas and help. Dr. Doumit provided me the opportunity to attend his classes as a student and
teaching assistant, and his enthusiastic teaching inspired me to ruminate about research and
teaching in academia. Dr. Gale Strasburg has always been available for listening and offering
suggestions about my research and personal life.

vi
 

I especially thank Ms. Nancy Raney for teaching and supporting me in everything starting
from pipetting conducted in the laboratory. I thank Dr. David Edwards for providing answers to
connect between previous and present research, and I also thank Ms. Emily Helman for working
together. And I certainly appreciate Mr. Al Snedegar and Mr. Kevin Turner at the MSU Swine
Teaching & Research Center as well as Routh Packing Company, National Swine Registry,
Whiteshire Farm, Comparts Farm, and Waldo Farm who contributed to my research.
I would like to thank all faculty, staff and graduate students in the Department of Animal
Science. Especially, Drs. Karen Plaut and Janice Swanson, former and present department
chairpersons, and Dr. Janice Rumph who supported me to continue accomplishing my program.
Also, Drs. Steve Bursian and Paul Coussens for their concern and encouragement during my
program. I also would like to thank Mr. Thomas Forton and Dr. John Lee for concerns and
prayers for me and my family.
The time shared with other students and researchers was invaluable. I would like to thank
Bruna Sollero, Enrico D’Alessandro, Sombat Prasongsook, and Michelle Dawes, former students
in our group, for working together. Especially, Enrico D’Alessandro and Sombat Prasongsook
who helped to gas the pigs and collect data presented in this dissertation. Bruna Sollero helped
me learn quantitative real-time PCR and Michelle Dawes worked together with me to extract
DNA from muscle samples. Also, I thank Yvonne Badke for sharing the office and ideas.
Again, I deeply appreciate Drs. Ernst, Bates, and Steibel for their contributions to my
research project and inspiring me to be a better scientist and man.
Finally, I would like to thank my children and my parents for all their support.

vii
 

TABLE OF CONTENTS
 

LIST OF TABLES ...........................................................................................................................x
LIST OF FIGURES ....................................................................................................................... xi
LIST OF ABBREVIATIONS ....................................................................................................... xii
INTRODUCTION ...........................................................................................................................1
CHAPTER I
APPLICATION OF ALTERNATIVE MODELS TO IDENTIFY QTL FOR GROWTH TRAITS
IN AN F2 DUROC x PIETRAIN PIG RESOURCE POPULATION ...........................................12
Abstract ...................................................................................................................................13
Background .............................................................................................................................14
Results.....................................................................................................................................15
Discussion ...............................................................................................................................18
Conclusions.............................................................................................................................22
Methods ..................................................................................................................................23
Animals and phenotypes ....................................................................................................23
Markers and genotyping ....................................................................................................23
Statistical analysis ..............................................................................................................24
Tables......................................................................................................................................28
Figures ....................................................................................................................................33
Supplementary Table ..............................................................................................................37
References...............................................................................................................................42
 

CHAPTER II
IDENTIFICATION OF CARCASS AND MEAT QUALITY QTL IN AN F2 DUROC x
PIETRAIN PIG RESOURCE POPULATION USING DIFFERENT LEAST-SQUARES
ANALYSIS MODELS ..................................................................................................................47
Abstract ...................................................................................................................................48
Introduction.............................................................................................................................49
Materials and methods ............................................................................................................50
Animals and phenotypic data ..........................................................................................50
Genotypic data .................................................................................................................51
Statistical analysis ...........................................................................................................52
Results.....................................................................................................................................54
Line-cross analysis ..........................................................................................................55
Half-sib analysis ..............................................................................................................56
Combined analysis...........................................................................................................57
Effect of additional markers and animals on QTL detection ..........................................58
Discussion ...............................................................................................................................59
viii
 

Conclusion ..............................................................................................................................63
Tables......................................................................................................................................65
Figures ....................................................................................................................................70
References...............................................................................................................................75 
 

CHAPTER III
EVALUATION OF QTL FOR CARCASS MERIT AND MEAT QUALITY TRAITS IN AN
US COMMERCIAL DUROC POPULATION .............................................................................81
Abstract ...................................................................................................................................82
Introduction.............................................................................................................................83
Materials and methods ............................................................................................................85
Animals and phenotypic data ..........................................................................................85
Genotypic data .................................................................................................................86
Statistical analysis ...........................................................................................................86
Results.....................................................................................................................................87
Allele and genotype frequency ........................................................................................87
Association analysis ........................................................................................................88
Discussion ...............................................................................................................................90
Tables......................................................................................................................................97
Supplementary Table ............................................................................................................104
References.............................................................................................................................107
 

CHAPTER IV
ASSOCIATION OF A CORTICOTROPIN-RELEASING HORMONE RECEPTOR 2 (CRHR2)
POLYMORPHISM WITH CARCASS MERIT, MEAT QUALITY AND STRESS RESPONSE
IN PIGS........................................................................................................................................112
Abstract .................................................................................................................................113
Introduction...........................................................................................................................114
Materials and methods ..........................................................................................................115
Animals and traits ..........................................................................................................115
CRHR2 genotyping ........................................................................................................117
Statistical analysis .........................................................................................................118
Results...................................................................................................................................120
Allele and genotype frequency ......................................................................................120
Resource population ......................................................................................................121
Halothane challenge population ....................................................................................122
Discussion .............................................................................................................................123
Tables....................................................................................................................................126
References.............................................................................................................................131
 

DISCUSSION ..............................................................................................................................135
References.............................................................................................................................140

ix
 

LIST OF TABLES
 

Table I.1. Position and significance level of growth trait QTL ...................................................28
Table I.2. Number of records, means, and SD for growth traits measured ..................................31
Supplementary Table I.1. Genetic maps constructed for the first and second genome scans of
the Michigan State University Duroc x Pietrain resource population ...........................................37
 
 

Table II.1. Number of records, means, and standard deviations for carcass and meat quality traits
........................................................................................................................................................65
Table II.2. Position and significance level of carcass and meat quality trait QTL .......................66
Table III.1. Number of records, means, and standard deviation for 9 traits evaluated in this study
........................................................................................................................................................97
Table III.2. Information for SNP markers evaluated in this study ..............................................98
Table III.3. Genotypic and allelic frequencies for SNP markers ...............................................100
Table III.4. LS means of significant SNP markers for carcass and meat quality traits .............101
Table III.5. LS means of combined PRKAG3 SNP genotypes for ultimate pH ........................103
Supplementary Table III.1. Probe sequences spanning SNP positions used for Illumina
Goldengate assays .......................................................................................................................104
 
 

Table IV.1. Number of records, means, and SD for MSU Duroc x Pietrain F2 resource
population ...................................................................................................................................126
Table IV.2. Number of records, means, and SD for halothane-challenge population ...............127
Table IV.3. Allele and genotype frequencies for CRHR2 in two pig populations .....................128
Table IV.4. Least squares means by CRHR2 genotype for carcass and meat quality traits
significant in resource population ...............................................................................................129
Table IV.5. Least squares means by CRHR2 genotype for traits significant in halothane
challenge population ...................................................................................................................130
 

 

x
 

LIST OF FIGURES
 
Figure I.1. Effect of additional markers and animals on information content .............................33
Figure I.2. Effect of additional markers and animals on statistical power for QTL detection ....34
Figure I.3. QTL results determined by different models for longissimus muscle area (LMA) at
19 weeks of age on SSC7...............................................................................................................35
th

Figure I.4. QTL results determined by different models for 10 rib backfat (BF10) at 19 and 22
weeks of age on SSC15..................................................................................................................36
 

Figure II.1. Genome scan results for ham weight determined using different analysis models ..70
Figure II.2. Line-cross analysis indicated strong evidence for QTL influencing fat deposition
traits on SSC6 ................................................................................................................................71
Figure II.3. Half-sib analysis indicated strong evidence for QTL influencing meat quality traits
on SSC15 .......................................................................................................................................72
Figure II.4. QTL results determined by different models for pH related traits on SSC3 .............73
Figure II.5. Effect of additional markers and animals for detecting meat color QTL on SSC12 ....
........................................................................................................................................................74
 

xi
 

LIST OF ABBREVIATIONS

BF10 = tenth rib backfat
BW = body weight
CB = combined line-cross and half-sib
CRHR2 = corticotropin-releasing hormone receptor 2
EBPRO = empty body protein
EBLIPID = empty body lipid
FFTOLN = fat-free total lean tissue
FDR = false discovery rate
GC = glucocorticoid
HCW = hot carcass weight
HS = half-sib
HPA = hypothalamic-pituitary-adrenal
HWE = Hardy-Weinberg equilibrium
LC = line-cross
LD = linkage disequilibrium
LMA = longissimus muscle area
LRF = last-rib backfat
NSR = National Swine Registry
pHu = ultimate pH
PRKAG3 = protein kinase AMP-activated gamma 3
PSE = pale, soft, and exudative
xii
 

PSS = porcine stress syndrome
QTL = quantitative trait loci
RFLP = restriction fragment length polymorphism
RYR1 = ryanodine receptor 1
SNP = single-nucleotide polymorphisms
SSC = sus scrofa chromosome
TOTFAT = total body fat tissue

xiii
 

INTRODUCTION

The advent of genomics has brought opportunities for quantitative trait loci (QTL) mapping
and gene discovery. A comprehensive pig genome sequence, tens of thousands of molecular
markers, and microarrays using several high-throughput technologies are being applied to
understand the pig genome [1, 2]. As a result, QTL mapping in the pig has been remarkably
successful and numerous publications have demonstrated the existence of QTL for major
production traits at various positions in the pig genome [3]. The pork industry, however, has
difficulties in applying QTL to commercial populations. The power of QTL mapping is reduced
within commercial pig populations because only a limited proportion of parents will be
heterozygous for any QTL [4]. Also, it is not practical to involve exotic breeds in commercial
populations, although such breeds maximize the likelihood of detecting QTL due to large
phenotypic variation within experimental resource populations. In addition, most economically
important traits are multi-factorial polygenic traits influenced by interaction between
environmental effects and many genes [5]. The confirmation and validation of QTL in
commercial populations, and fine mapping and candidate gene analyses are promising strategies
to overcome the limitations of QTL mapping [6].

1. Identification of quantitative trait loci for growth, carcass and meat quality traits
Genetic improvement of production efficiency such as growth traits has been shown to be
remarkably successful by conventional breeding schemes. To obtain additional rapid and
accurate genetic gain, marker-assisted selection (MAS) schemes are promising using QTL and
candidate genes. Numerous studies have demonstrated the existence of QTL for growth traits at
various positions in the pig genome. Putative QTL have been reported for birth weight on
1 
 

chromosomes 1 [7-9], 3 [1, 5], 4 [10-13], 6 [14], 7 [10, 12, 15], 12 [9] and 13 [9, 16, 17]. QTL
for backfat thickness at different developmental stages have been reported on chromosomes 1, 2
and 4 – 7 [10]. Also average daily gain has been documented on chromosomes 1 [5, 10, 12, 18],
2 [1, 19, 20], 3 [21, 22], 4 [1, 9, 12, 13, 23-25], 6 – 10 [5, 15, 26], 13 [9, 23], and X [27].
Genetic improvement of carcass composition and meat quality traits by conventional
breeding methods is often challenging because these traits are difficult and expensive to measure,
and they are expressed only late in life. Marker-assisted selection has been suggested as a
strategy for genetic improvement of such recording intensive traits [28]. Swine industries are
now paying more attention to meat quality and are including quality traits as an integral part of
selection programs to make simultaneous improvements in both quality and production traits
[29]. However, a limited number of studies have attempted to map QTL for meat quality traits in
commercial populations [30], and the identification of the underlying genes causing the QTL
effect still remains a challenging task. Putative QTL have been reported for backfat thickness on
all chromosomes except 12, 16 and 17 [1, 14, 18, 20, 21, 31-36]. In addition, QTL for several
meat quality traits have been reported for chromosomes 1 – 8, 12, 13, 15, 17 and X [32, 37-41].
A F2 Duroc x Pietrain resource population has previously been developed at Michigan State
University. An initial genome scan of this population using 124 microsatellite markers revealed
many QTL regions for growth, carcass and meat quality traits [42, 43]. A total of 55 QTL for 22
of the 29 measured growth traits were found to be significant at the 5% chromosome-wise level.
Of the 55 QTL, 16 were significant at the 1% chromosome-wise, 11 at the 5% genome-wise, and
10 at the 1% genome-wise significance thresholds. Putative QTL were discovered for 10th rib
and last rib backfat on SSC6, body composition traits on SSC9, backfat and lipid composition
traits on SSC11, and 10th rib and last rib backfat on SSC18. A total of 94 QTL for 35 of the 38
2 
 

carcass and meat quality traits were found to be significant at the 5% chromosome-wise level. Of
these, 43 were significant at the 1% chromosome-wise, 27 at the 5% genome-wise, and 14 at the
1% genome-wise thresholds. Putative QTL were discovered for 45 min pH and pH decline on
SSC3, marbling score and carcass backfat on SSC6, carcass length and number of ribs on SSC7,
marbling score on SSC12, and color measurements and tenderness score on SSC15.

2. Evaluation of SNPs located in putative QTL regions in commercial populations
Validation studies for identified QTL are necessary because QTL effects can be biased
upward and the identification of the causative mutations underlying QTL by association analyses
either in experimental or commercial populations is required to implement MAS. In pig breeding
programs, commercially available SNPs are limited and include insulin-like growth factor 2
(IGF2) and melanocortin 4 receptor (MC4R) for growth traits [44, 45], and ryanodine receptor 1
(RYR1) and protein kinase adenosine monophosphate activated γ3-subunit (PRKAG3) for carcass
and meat quality traits [46, 47]. Numerous QTL have been identified over the past two decades,
but there are several reasons for the limited number of QTL incorporated into MAS. The use of
commercially relevant populations is important for transferring QTL results into breeding
programs. Also, QTL identified in a specific population may not be detected across other
populations because the segregation of a QTL gene or marker is not guaranteed, and the allele
frequency at the causative locus can differ between populations [48]. In addition, low map
resolution and small sample sizes have been recognized as limiting factors in QTL mapping.
Evaluation of putative QTL regions in commercial populations using markers segregating in
these populations is important for developing MAS programs for the industry.

3 
 

3. Association of corticotropin-releasing hormone receptor 2 with carcass merit and meat
quality traits
Pale, soft and exudative (PSE) pork was recognized and described a half century ago [49].
Numerous reports on the development of PSE pork have focused on major gene effects,
including the halothane gene (RYR1) [46] and Napole gene (PRKAG3) [50, 51]. Concurrently,
stress susceptibility of pigs was also described as a syndrome rather than a single disease entity
and was identified as porcine stress syndrome (PSS) [52]. PSS is one of the problems to cause
inferior meat quality. PSS has been directly associated with the HAL-1843 polymorphism caused
by a substitution of T for C at nucleotide 1843 in the RYR1 gene [46]. RYR1 has been examined
extensively and a genetic marker for HAL-1843 permits genetic testing, thus allowing producers
to remove the allele deleterious to meat quality. Despite an attempt to eliminate HAL-1843 and
considerable achievements in improving slaughter conditions, the frequency of PSE pork in the
U.S. increased from 10.2% in 1996 to 15.5% in 2002 [53]. This implies that many genes
contribute to pork quality. Corticotropin-releasing hormone receptor 2 (CRHR2) is a G-protein
coupled receptor in the secretin family that functions in stress response and energy homeostasis.
It is one of two receptors for CRH which plays a key role in mediating endocrine, autonomic,
behavioral, and immune responses to stress trough the hypothalamus-pituitary-adrenal (HPA)
axis [54]. CRHR2 is a potential candidate gene affecting stress response and influencing carcass
and meat quality traits in pigs.

4 
 

4. Specific Aims
The research conducted for this dissertation project addresses the following specific aims:
1. Perform a second genome scan of the MSU Duroc x Pietrain resource population using
additional microsatellite markers and F2 animals, and applying different least-squares
analysis models to identify quantitative trait loci (QTL) for growth, carcass merit and
meat quality traits;
2. Conduct an evaluation study using gene-specific SNP markers located in five QTL
regions identified from the genome scan of the resource population in an US purebred
Duroc population; and,
3. Examine the association of corticotropin releasing hormone receptor 2 (CRHR2) with
carcass merit and meat quality traits in the Duroc x Pietrain resource population and a
population exposed to a halothane challenge test.

5 
 

References

6 
 

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11 
 

 

Chapter I
 

Application of alternative models to identify QTL for growth traits
in an F2 Duroc x Pietrain pig resource population

Igseo Choi, Juan P Steibel, Ronald O Bates, Nancy E Raney, Janice M Rumph, Catherine W
Ernst*

Department of Animal Science, Michigan State University, East Lansing, Michigan, U.S.A.

*Corresponding author. 3385 Anthony Hall, Department of Animal Science,
Michigan State University, East Lansing, MI 48824-1225, USA
Tel:+1 517 432 1941; Fax:+1 517 432 0753
E-mail: ernstc@msu.edu (C.W. Ernst)

 
 
 

This chapter was published in the journal BMC Genetics in November, 2010.
Choi, I., Steibel, J. P., Bates, R. O., Raney, N. E., Rumph, J. M., & Ernst, C. W. (2010).
Application of alternative models to identify QTL for growth traits in an F2 Duroc x Pietrain pig
resource population. BMC Genetics, 11(1), 97.

12 
 

Abstract  
Background
A variety of analysis approaches have been applied to detect quantitative trait loci (QTL) in
experimental populations. The initial genome scan of our Duroc x Pietrain F2 resource
population included 510 F2 animals genotyped with 124 microsatellite markers and analyzed
using a line-cross model. For the second scan, 20 additional markers on 9 chromosomes were
genotyped for 954 F2 animals and 20 markers used in the first scan were genotyped for 444
additional F2 animals. Three least-squares Mendelian models for QTL analysis were applied for
the second scan: a line-cross model, a half-sib model, and a combined line-cross and half-sib
model.
Results
In total, 26 QTL using the line-cross model, 12 QTL using the half-sib model and 3
additional QTL using the combined line-cross and half-sib model were detected for growth traits
with a 5% false discovery rate (FDR) significance level. In the line-cross analysis, highly
significant QTL for fat deposition at 10-, 13-, 16-, 19-, and 22-wk of age were detected on SSC6.
In the half-sib analysis, a QTL for loin muscle area at 19-wk of age was detected on SSC7 and
QTL for 10th-rib backfat at 19- and 22-wk of age were detected on SSC15.
Conclusions
Additional markers and animals contributed to reduce the confidence intervals and increase the
test statistics for QTL detection. Different models allowed detection of new QTL which
indicated differing frequencies for alternative alleles in parental breeds.

13 
 

Background
A variety of analysis approaches have been applied to detect quantitative trait loci (QTL) in
experimental populations. For an F2 population design, a line-cross model is most commonly
used to detect QTL segregating between divergent lines. This model assumes the founder lines
are fixed for alternative QTL alleles [1] and under such assumption is the most powerful [2].
However, the QTL effects under the line-cross model can be biased downwards since not all
QTL alleles are completely fixed, especially in domestic animals [3]. In addition, introgression
of QTL detected using the line-cross model is difficult since genetic improvement in the pig
breeding industry has been achieved largely by within breed selection [4]. To identify QTL
segregating within parental breeds, a half-sib model that does not assume fixation of QTL alleles
in the founder lines was introduced by Knott et al. [5]. A general model that accounts for
between and within line segregation has been proposed [3], but it is computationally prohibitive
to implement in many populations. Kim et al. [6] subsequently developed a combined model
which accounts for both line effects and half-sib family effects.
Along with appropriate statistical methods for QTL mapping, marker density and sample
size are also determining factors for estimating QTL locations and effects with accuracy and
precision. Although increasing marker density is becoming routine for high resolution mapping
[7], a two-step strategy of adding markers and animal genotypes into previously identified QTL
regions is efficient and cost effective.
We have previously reported results for a whole genome scan of our Duroc x Pietrain F2
population using a line-cross analysis [8, 9]. Both the Duroc and Pietrain breeds are used in
commercial pig production and they exhibit variation in growth phenotypes [8]. The objective of

14 
 

this study was to detect new QTL for growth traits using three different models, and to refine
previously identified QTL regions with addition of new markers and additional F2 animals.

Results
A linkage map was constructed with 136 microsatellite markers including 116 markers used
in the first genome scan of the MSU Duroc x Pietrain population [8] distributed across the 18
autosomes and 20 additional markers located on 9 chromosomes (SSC3 – 7, 12, 15, 16 and 18; 1
to 4 markers per chromosome; Supplementary table I.1). All animals were genotyped for new
markers, and 444 additional F2 animals not included in the first scan were also genotyped for 20
of the markers used in the first scan located on the 9 targeted chromosomes. The total genome
length excluding the sex chromosomes was 3,089.6 Haldane cM with an average marker interval
of 19.5 cM for the 9 chromosomes having additional markers and 28.2 cM for other
chromosomes. The information content was increased by adding markers and animals (Figure
I.1).
Three least-squares Mendelian models for QTL analysis were fit to each trait for this study:
a line-cross model, a half-sib model, and a combined line-cross and half-sib model, whereas only
a line-cross model was applied to the first scan of this population which used 510 F2 pigs [8, 9].
A total of 41 QTL were identified (Table I.1). The line-cross analysis revealed 26 QTL,
including 7 new QTL not detected in the first scan. The half-sib analysis revealed 12 QTL, and
three additional QTL were detected using the combined line-cross and half-sib model. A total of
23 QTL were identified with the combined model, but 20 of these were already detected with
either the line-cross or half-sib model. Thus, the combined model used in tandem with the line-

15 
 

cross and half-sib models facilitated identification of additional QTL not detected by either
independent analysis. The significance threshold was determined by False Discovery Rate (FDR)
and FDR was compared to conventional permutation tests for selected traits. A 5% FDR was
more stringent than a 5% chromosome-wise level threshold and a 1% FDR was more stringent
th

than a 5% genome-wise level threshold. For example, for 10 rib backfat (BF10) at 19-wk of
age the 5% FDR F-ratio of 6.79 was higher than the 5% chromosome-wise level threshold Fratios of minimum 4.46 and maximum 5.69, and the 1% FDR F-ratio of 8.68 was higher than the
5% genome-wise level threshold F-ratio of 8.38.
Twelve highly significant QTL affecting fat deposition at different developmental stages
were detected using the line-cross model on SSC6 between 164 and 174 cM (FDR ≤ 0.002),
which is consistent with results of the first scan [8] (Table I.1). The estimates of the additive
effects of these QTL indicated that the Duroc alleles contributed to higher fat deposition (Table
I.1). For example, the QTL affecting BF10 at 22-wk of age had an estimated additive effect of
2.17 mm indicating that Duroc alleles contribute to larger measures of BF10. The addition of
two markers into the SW122 – SW18 interval on SSC6, as well as the addition of more F2 pigs,
narrowed the estimated QTL region and increased the statistical power. For last-rib backfat (LRF)
at 19-wk of age, the 95% confidence interval decreased from 16 cM (160 cM – 176 cM) to 12
cM (163 cM – 175 cM) and the test statistic ( log 10 P increased from 15.43 to 27.25 under the
same model (Figure I.2). Similarly for BF10 at 19-wk, the 95% confidence interval narrowed
from 11 cM (163.5 cM – 174.5 cM) to 9.5 cM (164.5 – 174 cM) and the test statistic ( log 10 P
increased from 14.89 to 25.29 under the same model (Figure I.2).
Results for SSC6 using the line-cross model also revealed significant QTL for 22-wk fatfree total lean tissue (FFTOLN) and 22-wk empty body protein (EBPRO) at 129 cM, consistent
16 
 

with results of the first scan [8]. In addition, three new QTL were discovered under the half-sib
model. QTL for 22-wk empty body lipid (EBLIPID) and 22-wk total body fat tissue (TOTFAT)
were detected at 25 and 26 cM, respectively, and a QTL for FFTOLN was detected at 229 cM.
Additional markers and F2 animals contributed to detection of new QTL using the line-cross
model that were not detected in the first scan on SSC7, 15 and 18. In addition, QTL for fat traits
detected in the first scan on SSC11 and SSC16 were confirmed in the second scan. For SSC7,
QTL were detected for longissimus muscle area (LMA) at 19-wk of age (FDR < 0.002) and for
LMA at 10- and 13-wk (FDR <0.04). Half-sib analysis of SSC7 revealed QTL for LMA at 10and 19-wk of age in the S0064 – SW1369 interval that differed in location by 125 cM and 90 cM,
respectively (Table I.1). QTL for LMA at 19-wk were detected by both line-cross and half-sib
analyses and were significant at the 1% FDR level, but their locations were in completely
different positions (Figure I.3).
No QTL for growth traits were observed on SSC15 in the first QTL scan of this population
[8]. However, the second scan of this chromosome using the line-cross model revealed QTL at
39 and 69 cM for LMA at 16- and 19-wk, respectively (FDR ≤ 0.04, Table I.1). Using the halfsib model for SSC15, significant QTL were detected for BF10 at 19- and 22-wk at 96 and 74 cM,
respectively (FDR ≤ 0.005). However, the line-cross analysis did not detect significant BF10
QTL in this SSC15 region (Figure I.4).
A QTL for body weight at 22-wk was detected using the line-cross model in the SW21 –
SW983 marker interval at the proximal end of SSC9 (FDR ≤ 0.05). QTL for body weight at 19wk and EBLIPID were also detected in this same chromosomal region using the combined model
(FDR ≤ 0.05). A new QTL for BF10 at 16-wk was mapped by line-cross analysis at 4 cM
between markers SW2540 and SW1023 on SSC18. The SW1808 and SW2540 markers were
17 
 

added to SSC18 proximal to SW1023, thus extending the SSC18 map and facilitating further
QTL detection on this chromosome.
Analysis of SSC4 using the line-cross model confirmed a QTL at 57 cM for 22-wk LMA
that was observed in the first scan [8]. This QTL had an estimated additive effect of -1.1 mm2 of
LMA indicating that Pietrain alleles contribute to a larger LMA. Significant SSC4 QTL were
identified using the half-sib model for BF10 at 22-wk and LRF at 16-wk in the S0301 – SW871
region and also for TOTFAT at 25 cM.
New QTL were also detected using the half-sib model on SSC8 for EBLIPID at 137 cM
(FDR ≤ 0.04) and on SSC16 for 19-wk BF10 at 93 cM (FDR ≤ 0.04). This position on SSC16
also included a QTL for TOTFAT detected using the line-cross analysis and a QTL for body
weight at 22-wk detected using the combined analysis.

Discussion
Increasing the number of markers and animals for the genome scan of our Duroc x Pietrain
resource population facilitated detection of new QTL (SSC7, 15 and 18), as well as confirmation
of previously identified QTL affecting growth traits (SSC4, 6, and 16) using the line-cross
analysis [8]. A QTL for LMA at 22-wk was confirmed on SSC4. On SSC6, 14 QTL affecting fat
deposition traits were confirmed, although three QTL for LMA identified in the first scan were
not significant in the second scan. On SSC7, three new QTL for LMA were identified, whereas a
QTL for ADG located in the S0115 – SWR773 interval in the first scan was not detected. Two
new QTL for LMA were detected on SSC15. Six QTL on SSC16 had been detected in the first
scan but only one QTL for TOTFAT was confirmed in the second scan. Many of these QTL
which were significant at the 5% chromosome-wise level in the first scan were not detected in

18 
 

the second scan, either because the 5% FDR threshold used for the second scan was slightly
more stringent than the 5% chromosome-wise level threshold or because these were false
positives in the first scan.
A QTL for LMA at 22-wk detected with the line-cross analysis, and QTL for LRF at 16-wk
and BF10 at 22-wk detected with the half-sib analysis were localized in the S0301 – SW871
interval on SSC4. A possible explanation for these results could be QTL alleles of Pietrain origin
affecting LMA, and QTL alleles influencing backfat thickness segregating in each founder breed.
A QTL affecting fatness in this interval has been confirmed by many previous studies [10-12],
including a report by Andersson et al. [13] of the first pig QTL for growth and fatness on SSC4
in a Wild boar x Large White cross. Also, Cepica et al. [10] reported a QTL for LMA in this
same region and demonstrated that Pietrain alleles were associated with increased meat and
decreased fat content in a Wild boar x Pietrain cross.
Strong evidence for QTL affecting fatness was revealed at marker interval SW1647 –
SW1881 on SSC6. QTL for BF10 and LRF at different stages of growth (measured by ultrasound)
were highly significant (FDR < 0.001). The estimates of the additive effects suggest that Duroc
alleles contributed to larger measures of BF10 and LRF. However the estimates of the
dominance effects for these QTL were negative. In the first scan, the predominant location for
most of the BF10 and LRF QTL was estimated to be distal to marker SW1881. However, with
the addition of marker SW1647, these QTL were determined to be located in the interval SW1647
– SW1881 in the second scan. This region includes the leptin receptor (LEPR) gene located on
SSC6q3.3-3.5 [14] and Óvilo et al. [15] reported a QTL for fatness in this same region. Muñoz et
al. [16] reported the effect of QTL and LEPR alleles in this region to be significant for backfat
thickness and also for body weight. Our results are in agreement with those of Muñoz et al. [16]

19 
 

regarding backfat QTL. However, we did not find evidence for a QTL affecting body weight on
SSC6, although body weight was positively correlated with BF10 and LRF in our population.
Three other regions on SSC6 included QTL for body composition traits determined by either
the line-cross analysis or the half-sib analysis. In the line-cross analysis, QTL for FFTOLN and
EBPRO at 22-wk mapped to 129 cM and this result is in agreement with a study reported by
Mohrmann et al. [17]. Two additional QTL regions affecting EBLIPID and TOTFAT, and
FFTOLN were detected by the half-sib analysis and were located at the proximal and distal ends
of SSC6, respectively.
QTL for LMA at 10- and 19-wk of age were detected using both the line-cross and the halfsib models on different regions of SSC7. A newly detected QTL for LMA at 19-wk under the
line-cross model was mapped to 138 cM with the contribution of the Pietrain allele increasing
LMA, whereas the half-sib model revealed a QTL for LMA at 19-wk at 48 cM. Nagamine et al.
[18] reported an LMA QTL in a Large White population that spanned most of SSC7 and Uemoto
et al. [18] reported an LMA QTL segregating in a Duroc population with a relative peak location
in between the QTL detected using the line-cross and half-sib models in the current study.
QTL for LMA located on SSC15 were also identified in the second scan. The QTL for LMA
at 19-wk was detected in the S0088 – SW1683 interval in a region that includes MSTN (myostatin)
which is considered to be a candidate gene for muscle hypertrophy [19]. Stinckens et al. [20]
reported a polymorphism in the porcine MSTN promoter region MEF3 binding site, which could
potentially abolish enhancer activity, and that had a very high allele frequency in the Pietrain
breed. Thus, its effect could be associated with the higher muscularity of the Pietrain breed. Two
QTL influencing backfat thickness were identified on SSC15 using the half-sib analysis. A QTL
for BF10 at 22-wk was located in the SW1683 – SW906 interval and a QTL for BF10 at 19-wk

20 
 

was located in the SW1983 – SW1119 interval. This latter region is consistent with a QTL for 1st
rib fatness detected in a four-way cross by Harmegnies et al. [21].
The marker SW2517 located on SSC16q2.2 has been reported to be linked to a QTL
affecting fatness at later stages of growth [22]. In the first scan, five QTL (body weight and
BF10 at 19-wk, body weight and LRF at 22-wk, and TOTFAT) were detected near SW2517. A
QTL for ADG was also identified on SSC16 distal to this region. In the second scan, QTL for
TOTFAT detected with the line-cross analysis, BF10 at 19-wk detected with the half-sib analysis
and body weight at 22-wk detected with the combined analysis were mapped to 93 cM at marker
SW2517. In addition, suggestive QTL for EBLIPID (FDR < 0.06), TOTFAT (FDR < 0.066) and
Age at 105 kg (FDR < 0.073) under the combined model were located at the same position. No
additional SW2517 animal genotypes were included in the second scan, although two new
markers flanking SW2517 were genotyped across the full population. For marker SW2517, three
SW2517 alleles were segregating and the number of phase known informative meioses was 385
of 510 animals. The allele associated with fatness and heavier body weight originated only from
the Pietrain, whereas the other two alleles were segregating in both the Pietrain and Duroc
founder breeds. Thus segregation patterns of alleles at this marker allowed detection of QTL by
all three models. Liu et al. [23, 24] reported that a QTL influencing backfat thickness using both
line-cross and combined analyses was located in the same SSC16 region for their Duroc x
Pietrain population. The prolactin receptor (PRLR) gene located in this region [25] is wellknown as a candidate gene affecting reproductive traits in pigs [26-29]. Prolactin also stimulates
fat deposition and weight gain, and stimulates increases in white adipose tissue leptin mRNA and
plasma leptin levels [30, 31]. Freemark et al. [32] provided evidence that the absence of PRLR in
knockout mice was accompanied by reduced body weight gain after 16 weeks of age and

21 
 

decreased abdominal fat mass. Recently, Lu et al. [33] demonstrated that polymorphisms in the
PRLR gene were associated with growth traits in cattle. Based on results for other species and
our localization of QTL on SSC16, PRLR may be a candidate gene for growth and fat deposition
in pigs and further research is warranted.
A QTL for BF10 at 16-wk was identified at the proximal end of SSC18. Malek et al. [34]
detected QTL for backfat thickness in the same SSC18 region in a Berkshire x Yorkshire
population. A novel QTL for body weight at 22-wk was also identified on SSC9 in the second
scan. This QTL had a dominance effect of 4.12 kg, which indicated that the heterozygous
genotype contributed to heavier body weight at 22-wk. A QTL for body weight at 19-wk
detected by the combined analysis was located in the same interval. A QTL for body weight at 3wk was reported in the same SSC9 region [23], but no other body weight QTL have been
reported in the SW21 – SW983 interval.

Conclusions
Additional markers and animal genotypes contributed to refine QTL positions and increase
the statistical power. The application of different QTL analysis models made it possible to detect
new QTL segregating either between or within breeds. In total, 26 QTL with the line-cross
model, 12 QTL with the half-sib model and 3 additional QTL with the combined line-cross and
half-sib model were detected for pig growth traits. Analysis using the line-cross model was most
powerful for detecting QTL, whereas the combined model which assumed QTL to be segregating
at different allelic frequencies in the founder populations was less powerful than the line-cross or
half-sib models. This result was not unexpected because the population was designed to exploit
between breed differences and markers were selected in such a way that they were more

22 
 

informative to declare breed of origin QTL than for detecting QTL using the half-sib analysis.
However, our analysis shows that there is substantial segregation within breed that can be
tracked (although to a lesser extent) by using the sire haplotype probabilities either alone (halfsib analysis) or jointly with the breed origin probabilities (combined analysis).

Methods
Animals and phenotypes
Animals from a three-generation Duroc x Pietrain resource population developed at
Michigan State University and described by Edwards et al. [8] were used for this study. Animal
protocols were approved by the Michigan State University All University Committee on Animal
Use and Care (AUF# 09/03-114-00). The population was established from 4 F0 Duroc sires and
15 F0 Pietrain dams. The F2 pigs were produced from 50 F1 females and 6 F1 males, and were
born in 141 litters across 11 farrowing groups. The second genome scan for this study included
the 510 F2 animals used in the first genome scan along with an additional 444 F2 animals. The
954 total pigs included all F2 animals from this population for which complete growth
phenotypes are available. Descriptive statistics for phenotypes used in this study are presented in
Table I.2.

Markers and genotyping
Based on the first genome scan results using 124 markers [8, 9], 9 chromosomes (SSC3, 4, 5,
6, 7, 12, 15, 16 and 18) with significant QTL were selected for additional marker genotyping.
Twenty new microsatellite markers were selected from the publicly available pig genome linkage
23 
 

map [http://www.marc.usda.gov/genome/swine/swine.html] that map within the QTL regions on
these chromosomes (Supplementary table I.1). New markers were confirmed to be informative in
the MSU population by genotyping of F0 pigs. All F0, F1, and the 954 F2 pigs were genotyped
for the 20 new markers, and the 444 additional F2 pigs were also genotyped for 20 markers
flanking the QTL regions on the 9 selected chromosomes. Genotyping was performed at a
commercial laboratory (GeneSeek Inc., Lincoln, NE). Sex-averaged genetic linkage maps were
constructed using CRI-MAP version 2.4 [35] and converted to the Haldane map function [36].

Statistical analysis
QTL mapping was performed using least-squares regression with line-cross, half-sib and
combined line-cross and half-sib models. Genome-wise significance thresholds were determined
by false discovery rate (FDR) [37]. QTL detected using the line-cross or half-sib model were
declared using a FDR threshold of 5%, and then additional QTL detected with the combined
line-cross and half-sib model were declared when such QTL had not previously been detected by
either the line-cross or half-sib model.
Under the line-cross model it is assumed that the two founder lines are fixed for alternative
alleles at the QTL affecting the traits of interest [1]. The QTL Express software [38] was used to
estimate the probability of each F2 individual being homozygous for two Duroc alleles (P11),
homozygous for two Pietrain alleles (P22), or heterozygous (P12 or P21) at fixed 1-cM intervals
across the genome. By denoting the effect of P11 as positive additive (a), the effect of P12 + P21

24 
 

as dominance (d) and the effect of P22 as negative additive (-a), the following linear model was
fitted at every cM across the genome.

j

j

j

j

j

Where j is the phenotype of F2 progeny , j and b are the design matrix and solution
vector for the fixed effects, respectively, a and d are the estimated additive and dominance
effects of a putative QTL at the given location, respectively,
animal to carry two Duroc alleles,

j is the conditional probability of

j is the conditional probability of animal to be

heterozygous, and j is the residual error. The model for all traits included sex of animal and
litter as fixed effects and the model for 10- to 22-wk ADG included 10-wk body weight as a
covariate.
For the half-sib analysis, the F2 individuals were treated as 6 paternal half-sib families
which assumes no fixation of the QTL alleles in the founder lines. QTL Express [38] was used to
calculate the probabilities of individuals of allele (P1) or allele (P2) from the common Duroc
parent (P12 or P21) [5]. In these analyses contrasts were made between the two haplotypes of
every F1 sire.

ij

ij

HSi

i

ij

ij

Where ij is the phenotype of F2 progeny of F1 sire , ij and b are the design matrix and
the solution vector for fixed effects, respectively, i is the effect of the th F1 sire, αHSi is the
substitution effect for the two putative QTL alleles (P1 or P2) carried by the th F1 sire,

25 
 

ij is

the probability that the F2 individuals inherited the arbitrary allele (P1) from F1 sire , and ij is
the residual error.
The combined line-cross and half-sib model included,

ij

ij

i

ij

ij

Where ij is the phenotype of F2 progeny of F1 sire ,
the solution vector for fixed effects, respectively,

CBi

ij

ij

and b are the design matrix and

is the effect of the th F1 sire, a and d are the

additive and dominance effects of breed-origin alleles, respectively,

and

are the

corresponding breed-origin coefficients, CB is the substitution effect for the two putative QTL
alleles carried by the th F1 sire,

is the probability that the F2 individuals inherited the

arbitrary allele (P1) from F1 sire , and

is the residual error. In this model, a and d account for

the average effects of breed origin alleles through both the F1 sire and the F1 dam and CB
represents the difference between the two QTL alleles that a given F1 sire received from the two
parental breeds as a deviation from their average additive effect [6].

Authors' contributions
IC carried out the data analyses and drafted the manuscript. JPS participated in design of the
study and supervised the data analyses. ROB coordinated development of the resource
population and participated in design of the study. NER prepared the DNA samples and
identified segregating markers. JMR contributed to design of the study. CWE coordinated the
project, participated in design of the study and helped draft the manuscript. All authors read and
approved the final manuscript.
26 
 

 

Authors' information
JMR is currently Senior Scientist, Pfizer Animal Genetics, Kalamazoo, MI.

Acknowledgements
This project was supported by National Research Initiative Competitive Grant no. 200435604-14580 from the USDA National Institute of Food and Agriculture. We thank D. Edwards
for technical contributions in development of the pig resource population.
 

27 
 

Table I.1. Position and significance level of growth trait QTL.
Chr1 Position2 Trait
4

Type3 -log10P4 FDR5

Flanking Markers

Additive6

Dominance7

-1.10 (0.29) -0.66 (0.51)

HS
HS
LC

3.17 0.0484
3.65 0.0209
3.29 0.0351

SW2509 – S0301
S0301 – SW871
S0301 – SW871

16-wk last-rib backfat, mm

HS

3.97 0.0120

S0301 – SW871

25

22-wk empty body lipid, kg

HS

3.76 0.0179

S0099 – SW2406

26
129

22-wk total body fat tissue, kg
22-wk fat-free total lean, kg

HS
LC

3.29 0.0398
5.30 0.0006

S0099 – SW2406
S0220 – SW122

-0.35 (0.09)

0.44 (0.14)

129

22-wk empty body protein, kg

LC

4.85 0.0016

S0220 – SW122

-0.13 (0.03)

0.14 (0.05)

164

13-wk 10th-rib backfat, mm

LC

21.81 0.0000

SW1647 – SW1881

0.98 (0.10) -0.74 (0.15)

164
164

22-wk 10th-rib backfat, mm
22-wk last-rib backfat, mm

LC
LC

18.49 0.0000
22.25 0.0000

SW1647 – SW1881
SW1647 – SW1881

2.17 (0.25) -1.37 (0.36)
1.55 (0.17) -1.24 (0.24)

165

10-wk 10th-rib backfat, mm

LC

22.90 0.0000

SW1647 – SW1881

0.70 (0.07) -0.57 (0.11)

165

13-wk last-rib backfat, mm

LC

21.85 0.0000

SW1647 – SW1881

0.58 (0.06) -0.39 (0.09)

165
166

16-wk last-rib backfat, mm
10-wk last-rib backfat, mm

LC
LC

22.26 0.0000
20.30 0.0000

SW1647 – SW1881
SW1647 – SW1881

0.86 (0.10) -0.81 (0.14)
0.43 (0.05) -0.30 (0.07)

166

19-wk last-rib backfat, mm

LC

27.25 0.0000

SW1647 – SW1881

1.32 (0.13) -1.03 (0.18)

168

22-wk total body fat tissue, kg

LC

4.69 0.0023

SW1881 – SW322

0.57 (0.13) -0.38 (0.19)

169
172

19-wk 10th-rib backfat, mm
16-wk 10th-rib backfat, mm

LC
LC

25.29 0.0000
23.45 0.0000

SW1881 – SW322
SW1881 – SW322

1.78 (0.18) -1.61 (0.27)
1.38 (0.14) -0.97 (0.22)

174

22-wk empty body lipid, kg

LC

8.61 0.0000

SW1881 – SW322

0.46 (0.09) -0.50 (0.14)

229
7

22-wk total body fat tissue, kg
22-wk 10th-rib backfat, mm
22-wk LM area, cm2

65
6

25
42
57

22-wk fat-free total lean, kg
10-wk LM area, cm2
19-wk LM area, cm2

HS

3.38 0.0343

SW607 – SW2419

HS
HS

3.41 0.0325
4.78 0.0024

S0064 – SW1369
S0064 – SW1369

44
48

28
 

Table I.1. (Cont’d).
Chr1 Position2 Trait
7

138
169
185

19-wk LM area, cm2†
10-wk LM area, cm2†
13-wk LM area, cm2†

8

137

9

7

22-wk empty body lipid, kg
22-wk body weight, kg†

9
12

Type3 -log10P4 FDR5

Flanking Markers

Additive6

Dominance7

-1.38 (0.29) -0.55 (0.62)
-0.43 (0.11) 0.05 (0.16)
-0.49 (0.13) 0.26 (0.20)

3.30 0.0397

S0017 – SW2160

LC

3.10 0.0485

SW21 – SW983

22-wk empty body lipid, kg
19-wk body weight, kg

CB
CB

3.55 0.0433
3.78 0.0297

SW21 – SW983
SW21 – SW983

91

19-wk last-rib backfat, mm

LC

3.98 0.0093

S0230 – SW66

0.20 (0.19) -1.11 (0.27)

19-wk 10th-rib backfat, mm

LC

3.37 0.0307

S0230 – SW66

-0.63 (0.28) -1.28 (0.41)

106
39

22-wk empty body lipid, kg
16-wk LM area, cm2†

LC
LC

3.28 0.0356
3.19 0.0413

S0230 – SW66
S0148 – SW1989

-0.22 (0.15) -1.03 (0.29)
-0.75 (0.19) 0.05 (0.30)

19-wk LM area, cm2†

LC

3.28 0.0356

S0088 – SW1683

-0.86 (0.22) -0.01 (0.35)

22-wk 10th-rib backfat, mm

HS

4.43 0.0048

SW1683 – SW906

96
93

19-wk 10th-rib backfat, mm
22-wk total body fat tissue, kg

HS
LC

4.38 0.0053
3.64 0.0186

SW1983 – SW1119
SW2517

93

19-wk 10th-rib backfat, mm

HS

3.36 0.0355

SW2517

93
18

HS

74
16

SW859 – SW2040
SW2040 – S0115
S0115 – SW632

69

15

5.02 0.0011
3.51 0.0236
3.22 0.0391

93

11

LC
LC
LC

22-wk body weight, kg

CB

3.57 0.0424

SW2517

16-wk 10th-rib backfat, mm†

LC

3.54 0.0222

SW2540 – SW1023

4

-0.52 (0.71)

4.12 (1.10)

-0.47 (0.18) -0.89 (0.31)

0.55 (0.14)

0.23 (0.20)

1

Chr = chromosome

2

Position in Haldane cM

3

LC = QTL declared as line-cross type; HS = half-sib type; CB = combined type.

4

Negative logarithm of the comparison-wise p value of the test statistic against the null hypothesis of no QTL at the most likely
position for the inferred QTL model.
5
FDR = false discovery rate
30
 

Table I.1. (Cont’d).
6
Estimates of additive effects with standard errors for LC QTL. The effects are expressed as (DD-PP)/2, where D = Duroc allele and
P = Pietrain allele.
7
Estimates of dominance effects with standard errors for LC QTL. The effects are expressed as DP-PD, where D = Duroc allele and
P = Pietrain allele.
†
New QTL detected with the line-cross model in the second scan.

30
 

Table I.2. Number of records, means, and SD for growth traits measured.
Trait
N
Mean
SD
Birth weight (kg)
954
1.53
0.32
3-wk weight (kg)
954
5.69
1.48
6-wk weight (kg)

953

12.04

2.84

10-wk weight (kg)
10-wk 10th-rib backfat (mm)1
10-wk longissimus muscle area (cm2)1

954

26.43

4.84

954
954

7.96
11.55

1.77
2.54

10-wk last-rib backfat (mm)1

954

6.11

1.06

13-wk weight (kg)
13-wk 10th-rib backfat (mm)1
13-wk longissimus muscle area (cm2)1

954

41.66

6.60

954
954

9.74
16.98

2.68
3.35

13-wk last-rib backfat (mm)1

954

7.13

1.38

16-wk weight (kg)
16-wk 10th-rib backfat (mm)1
16-wk longissimus muscle area (cm2)1

954

62.28

8.27

954
954

12.35
24.85

3.44
3.82

16-wk last-rib backfat (mm)1

954

9.57

2.28

19-wk weight (kg)
19-wk 10th-rib backfat (mm)1
19-wk longissimus muscle area (cm2)1

954

80.79

9.84

954
954

15.90
31.39

5.02
4.19

19-wk last-rib backfat (mm)1

954

11.79

3.29

22-wk weight (kg)
22-wk 10th-rib backfat (mm)1
22-wk longissimus muscle area (cm2)1

954

100.05

10.87

954
954

19.89
37.09

6.40
4.83

22-wk last-rib backfat (mm)1

954

14.35

4.16

10 - 22 wk ADG (g/d)
Age at 105 (kg)2
22-wk total body fat tissue (kg)3

954

878.04

105.42

954
954

157.42
24.94

13.64
6.96

22-wk fat-free total lean tissue (kg)3

954

38.35

4.45

954

15.01

1.67

954

21.96

4.23

22-wk empty body protein (kg)
22-wk empty body lipid (kg)

3

3

1

10th rib backfat, last rib backfat, and longissimus muscle area at 10, 13, 16, 19, and 22 wk of
age estimated using B-mode ultrasound (Pie Medical 200SLC, Classic Medical Supply Inc.,
Tequesta, FL).
2
Age at 105 kg calculated following National Swine Improvement Federation guidelines [39].

31 
 

Table I.2. (Cont’d).
3
Total body fat tissue, fat-free total lean tissue, empty body protein, and empty body lipid at 22
wk of age calculated by using equations similar to those used by Wagner et al. [40].

32 
 

Figure I.1. Effect of additional markers and animals on information content.
Information content was determined for both the first QTL scan (dotted lines) and the second
scan (solid lines) which include additional animals and markers. Blue lines indicate additive
effects and red lines indicate dominance effects. Marker positions are shown as triangles on the
X-axis (gray, markers used for both QTL scans and genotyped only in 510 animals; green,
markers used for both QTL scans and genotyped in all animals; red, markers used for second
scan only and genotyped in all animals).
For interpretation of the references to color in this and all other figures, the reader is referred to
the electronic version of this dissertation.
33 
 

Figure I.2. Effect of additional markers and animals on statistical power for QTL detection.
Blue lines indicate last rib fat (LRF) QTL at 19-wk and red lines indicate 10th rib backfat (BF10)
QTL at 19-wk on SSC6. Solid lines are second scan results and dotted lines are first scan results.
Marker positions are shown as triangles on the X-axis (gray, markers used for both QTL scans
and genotyped only in 510 animals; green, markers used for both QTL scans and genotyped in
all animals; red, markers used for second scan only and genotyped in all animals). Horizontal
lines indicate significance thresholds (lower line, 5% FDR; upper line, 1% FDR).

34 
 

Figure I.3. QTL results determined by different models for longissimus muscle area (LMA)
at 19 weeks of age on SSC7.
Blue and red lines indicate LMA QTL at 19-wk detected by line-cross and half-sib models,
respectively (FDR ≤ 0.002). Marker positions are shown as triangles on the X-axis (gray,
markers used for both QTL scans and genotyped only in 510 animals; green, markers used for
both QTL scans and genotyped in all animals; red, markers used for second scan only and
genotyped in all animals). Horizontal lines indicate significance thresholds (lower line, 5% FDR;
upper line, 1% FDR).

35 
 

Figure I.4. QTL results determined by different models for 10th rib backfat (BF10) at 19
and 22 weeks of age on SSC15.
Blue and red solid lines indicate BF10 at 19- and 22-wk, respectively, detected with the half-sib
model (FDR ≤ 0.005). Blue and red dotted lines indicate BF10 at 19- and 22-wk, respectively,
detected with the line-cross model. Marker positions are shown as triangles on the X-axis (gray,
markers used for both QTL scans and genotyped only in 510 animals; green, markers used for
both QTL scans and genotyped in all animals; red, markers used for second scan only and
genotyped in all animals). Horizontal lines indicate significance thresholds (lower line, 5% FDR;
upper line, 1% FDR)

36 
 

Supplementary Table I.1. Genetic maps constructed for the first and second genome
scans of the Michigan State University Duroc x Pietrain resource population.
2nd scan
1st scan2
Chr1
Marker
Kosambi cM
Haldane cM
1
SW1514
0.0
0.0
SW1515

49.7

61.8

S0331
SW974

77.5
108.9

97.0
137.6

S0056

179.7

247.4

SW1301

235.1

328.7

SWR2516
SW240

0.0
41.0

0.0
56.2

S0170

53.7

70.5

SW1026

64.8

82.8

S0370
SW1844

93.4
103.2

119.2
129.9

S0378
3

25.4

S0008

2

21.1

110.1

137.3

S0036
SW274
SW2021
S0206‡

142.0
0.0
22.7

178.8
0.0
27.7

68.7

94.8

SWR978

†

112.9

ACTG2
SW2141†

86.4

116.5
127.1

SW2047‡

101.3

134.8

SW2408

124.3

163.4

S0002
SW1327

132.4
141.2

174.1
182.8

SW2532

159.6

204.5

37 
 

Supplementary Table I.1. (Cont’d).
Chr1
4

Marker
SW2404‡

1st scan2
Kosambi cM
0.0

2nd scan
Haldane cM
0.0

SW2509†
S0301

‡

10.0
54.1
61.8

66.3
74.6

S0107

73.8

88.2

S0214

88.0

104.2

S0097

131.3

164.5

SW413
ACR

0.0
11.5

0
12.8

SWR453

53.4

70.5

SW2
S0005

81.5
108.9

106
140.4

S0018
IGF1†

127.4

162.8

SW995
SW378
6

35.3

SW871
SW2454

5

29.2

147.6
159.7

185.5
199.6

S0099
SW2406

0.0
22.4

0.0
27.3

SW2525
S0087‡

50.7
81.4

64.2
103.4

S0220
SW122‡

98.2

123.6

103.9

129.5

179.1

†

SW2173
SW1647†
SW1881‡
SW322

135.8

167.0

164.8

‡

SW1328
SW607†

140.0
153.7
204.1

†

SW2419

208.9
216.1
181.1

229.6

38 
 

Supplementary Table I.1. (Cont’d).
Chr1
7

Marker
S0025
S0064
SW1369‡
SW2019

2nd scan
Haldane cM
0.0
35.6

48.0

59.8

†

‡

SW859
SW2040†

70.7
92.5

109.2
164.5

135.8

S0115‡
SW632

1st scan2
Kosambi cM
0.0
28.1

177.7

†

SWR773
S0101‡

185.5
151.6
164.0

197.7
209.7

S0212†

219.4

SW764
SW2410
SW905

0.0
22.9

0.0
28.0

55.0

69.7

S0017

95.1

124.4

SW2160
SW1085

110.7
124.4

142.4
158.0

S0178
9

240.7

SWR1101

8

186.7

165.5

214.4

SW21
SW983
SW911

0.0
13.2
43.1

0.0
14.9
53.3

SW2401

63.7

78.0

SW539

72.4

87.5

SW989
SW2116

97.0
127.4

117.9
157.0

39 
 

Supplementary Table I.1. (Cont’d).
Chr1
10

Marker
SWR136
SW249

1st scan2
Kosambi cM
0.0
20.2

2nd scan
Haldane cM
0.0
24.2

SWC19

56.8

67.9

SW920

79.4

95.5

S0391
S0071

0.0
53.0

0.0
77.0

S0230

65.2

90.7

SW66

119.0

169.1

SW2490
SW957‡

0.0
31.2

0
40.4

SW874
SW37†

47.2

59

S0090
SWC23†

12

53.9

SW1041
11

44.3

61.0

64.8
76.2
97.5

‡

94.1

117.4

S0219
SWR1941

0.0
13.7

0.0
15.6

SW344

40.9

49.8

SWR1008

54.2

64.9

S0068
SW398

65.1
85.8

76.9
101.8

SW2440

103.2

122.2

S0215

122.6

145.3

SW857
SW510

0.0
26.6

0.0
33.4

SW210

45.9

56.3

SW886

64.7

78.6

SW55
SW1557

85.2
95.6

103.1
114.6

SWC27

117.6

141.3

SW2180
13

14

40 
 

Supplementary Table I.1. (Cont’d).
Chr1

Marker

15

SW1204
S0148‡

1st scan2
Kosambi cM
0.0
24.7

2nd scan
Haldane cM
0.0
31.2

SW1989†

56.3

S0088

64.5

SW1683
SW906†

64.5

73.6
82.8

SW1983‡

82.2

95.9

SW1119
16

54.8

96.0

111.4

S0111
SW419‡

0.0
30.9

0.0
39.4

SW1454†

66.0

SW2517
SW1897†
S0061‡

18

93.0

99.5

127.7
143.0

SWR1004
SW2441

0.0
22.7

0.0
27.7

SW1031
SW2427

17

64.8

42.9
94.9

51.9
127.1

SW1808†
SW2540†

0.0
3.2

SW1023‡
SW1984‡

0.0
43.1

37.9
96.6

S0062

54.9

109.6

1

Chr = chromosome

2

First scan reported in Edwards et al.

†

Additional markers used for the second scan

‡

Flanking markers used for the second scan

41 
 

References

42 
 

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meat quality, and carcass traits within a Duroc purebred population. Journal of
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Liu G, Jennen DGJ, Tholen E, Juengst H, Kleinwachter T, Holker M, Tesfaye D, Un G,
Schreinemachers HJ, Murani E et al: A genome scan reveals QTL for growth, fatness,
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Liu G, Kim JJ, Jonas E, Wimmers K, Ponsuksili S, Murani E, Phatsara C, Tholen E,
Juengst H, Tesfaye D et al: Combined line-cross and half-sib QTL analysis in DurocPietrain population. Mammalian Genome 2008, 19(6):429-438.

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Prolactin Stimulates Leptin Secretion by Rat White Adipose Tissue. Endocrinology
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analysis to identify chromosomal regions influencing economic traits in the pig. I.
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46 
 

 

Chapter II

Identification of carcass and meat quality QTL in an F2 Duroc x Pietrain pig resource
population using different least-squares analysis models
Igseo Choi, Juan P. Steibel, Ronald O. Bates, Nancy E. Raney, Janice M. Rumph and Catherine
W. Ernst*

Department of Animal Science, Michigan State University, East Lansing, MI, U.S.A.

*Corresponding author. 3385 Anthony Hall, Department of Animal Science,
Michigan State University, East Lansing, MI 48824-1225, USA
Tel:+1 517 432 1941; Fax:+1 517 432 0753
E-mail: ernstc@msu.edu (C.W. Ernst)

This chapter was published in the journal Frontiers in Genetics (Livestock Genomics) in May,
2011.
Choi, I., Steibel, J. P., Bates, R. O., Raney, N. E., Rumph, J. M., & Ernst, C. W. (2011).
Identification of carcass and meat quality QTL in an F2 Duroc x Pietrain pig resource population
using different least-squares analysis models. Frontiers in Genetics, 2, 18.

47 
 

Abstract
A three-generation resource population was constructed by crossing pigs from the Duroc
and Pietrain breeds. In this study, 954 F2 animals were used to identify quantitative trait loci
(QTL) affecting carcass and meat quality traits. Based on results of the first scan analyzed with a
line-cross model using 124 microsatellite markers and 510 F2 animals, 9 chromosomes were
selected for genotyping of additional markers. Twenty additional markers were genotyped for
954 F2 animals and 20 markers used in the first scan were genotyped for 444 additional F2
animals. Three different Mendelian models using least-squares for QTL analysis were applied
for the second scan: a line-cross model, a half-sib model, and a combined line-cross and half-sib
model. Significance thresholds were determined by false discovery rate (FDR). In total, 50 QTL
using the line-cross model, 38 QTL using the half-sib model and 3 additional QTL using the
combined line-cross and half-sib model were identified (q < 0.05). The line-cross and half-sib
th

models revealed strong evidence for QTL regions on SSC6 for carcass traits (e.g., 10 -rib
backfat; q < 0.0001) and on SSC15 for meat quality traits (e.g., tenderness, color, pH; q < 0.01),
respectively. QTL for pH (SSC3), dressing percent (SSC7), marbling score and moisture percent
(SSC12), CIE a* (SSC16) and carcass length and spareribs weight (SSC18) were also significant
(q < 0.01). Additional marker and animal genotypes increased the statistical power for QTL
detection, and applying different analysis models allowed confirmation of QTL and detection of
new QTL.

Keywords: Pig, QTL, Carcass merit, Meat quality

48 
 

1. Introduction
Quantitative trait loci (QTL) mapping has been conducted using numerous pig populations
to identify genomic regions controlling phenotypic variation for hundreds of traits
(http://www.animalgenome.org/cgi-bin/QTLdb/SS/index). Nevertheless, the implementation of
QTL into breeding programs which is a major goal of QTL mapping has been limited not only
due to insufficient numbers of identified causative mutations, but because of unknown linkage
disequilibrium (LD) phase between markers and QTL resulting from cross breeding systems
(Hayes et al. 2009; Spelman and Bovenhuis 1998). We have developed a F2 Duroc x Pietrain
resource population at Michigan State University (Edwards et al. 2008b) and reported QTL for
carcass merit and meat quality traits (Edwards et al. 2008a). The Duroc and Pietrain breeds are
used in breeding programs as sire breeds worldwide, and these breeds exhibit variation in carcass
merit and meat quality phenotypes. Pietrain pigs have been shown to have less backfat
(Affentranger et al. 1996; Edwards et al. 2003) and larger longissimus muscle area (LMA)
(Edwards et al. 2003). Duroc and Duroc-sired pigs generally have more favorable meat quality
(Affentranger et al. 1996; Edwards et al. 2003; Jeremiah et al. 1999; Langlois and Minvielle
1989), whereas Pietrain and Pietrain-sired pigs are leaner with average meat quality (Edwards et
al. 2003).
A line-cross (LC) model, which assumes the founder lines to be fixed for alternative QTL
alleles, has been most commonly used to identify QTL for F2 population designs (Haley et al.
1994). The first genome scan for our Duroc x Pietrain population was performed using a LC
analysis (Edwards et al. 2008a; Edwards et al. 2008b). However, for crosses between outbred
lines such as domestic animals, not all QTL alleles are completely fixed so effects under the LC
model can be biased downwards (Pérez-Enciso and Varona 2000). To identify QTL segregating
49 
 

within parental breeds, a half-sib (HS) model that does not assume fixation of QTL alleles in the
founder lines was introduced by Knott et al. (1996), and Kim et al. (2005) developed a combined
line-cross and half-sib (CB) model that accounts for both line and half-sib effects. We have
recently utilized LC, HS, and CB models to identify QTL for growth traits in our Duroc x
Pietrain population (Choi et al. 2010). The objective of this study was to confirm previously
identified carcass merit and meat quality QTL regions with addition of new marker genotypes
and additional F2 animals, and to detect new QTL for carcass merit and meat quality traits using
three different least-squares models under different assumptions; 1) founders fixed for alternative
QTL alleles (LC model), 2) segregation of QTL alleles at similar frequencies in founders (HS
model), and 3) segregation of QTL alleles at different frequencies in founders (CB model).

2. Materials and methods
2.1 Animals and phenotypic data
A three-generation resource population developed at Michigan State University was used for
this study. A detailed description of the animals and phenotypic data was previously reported
(Edwards et al. 2008a; Edwards et al. 2008b). All grandparents were confirmed to be
homozygous normal for the polymorphism at position 1843 in the RYR1 gene (Edwards et al.
2008b). Animal protocols were approved by the Michigan State University All University
Committee on Animal Use and Care (AUF# 09/03-114-00). A total of 954 F2 pigs were used
which included the 510 animals evaluated in the first genome scan. These pigs were produced
from 6 F1 boars and 50 F1 sows which were retained from 4 F0 Duroc sires and 15 F0 Pietrain
dams. The F2 pigs were analyzed for 38 carcass and meat quality traits.

50 
 

Details of carcass and meat quality phenotype collection were reported in Edwards et
al.(2008a). Briefly, animals were slaughtered at the Michigan State University Meat Laboratory
(East Lansing, MI) or a federally inspected commercial plant (DeVries Meats, Coopersville, MI).
Slaughter age was 165.8 ± 9.2 days and the minimum off-farm body weight (BW) for slaughter
was 82.54 kg. Hot carcass weight (HCW), and pH and temperature of the longissimus muscle
(LM) at 45-min and 24-h postmortem were obtained. After overnight chilling, backfat thickness,
number of ribs and carcass length were measured, and the weights of primal cuts were recorded.
A single trained evaluator scored color, marbling and firmness using two 2.54-cm thick chops
cut from the LM, and objective color scores of CIE L*, a*, and b* were obtained using a Minolta
colorimeter. The remaining section of the LM was used to determine drip loss, cook yield,
Warner-Bratzler shear force, proximate analysis measures, and sensory attributes. A trained
sensory panel evaluated juiciness, tenderness, overall tenderness, connective tissue, and offflavor using an 8-point hedonic scale. Descriptive statistics for phenotypes used in this study are
presented in Table II.1.

2.2 Genotypic data
Nine chromosomes (SSC3 – 7, 12, 15, 16 and 18) were selected based on results of the first
genome scan (Edwards et al. 2008a; Edwards et al. 2008b) which had been completed using 510
F2 animals and 124 microsatellite markers. For the second scan 20 additional microsatellite
markers were selected on these chromosomes (1 – 4 markers per chromosome; Choi et al. 2010)
in order to increase the power of QTL detection and to narrow the QTL locations. All F0, F1, and
the 954 F2 pigs were genotyped for the 20 new markers, and the 444 additional F2 pigs were also

51 
 

genotyped for 20 markers flanking the QTL regions on the 9 selected chromosomes. Sexaveraged genetic linkage maps were estimated for all autosomes using CRI-MAP version 2.4
(Green et al. 1990) and converted to the Haldane map function (Choi et al. 2010).

2.3 Statistical analysis
Three different models using least-squares (LC, HS, and CB models) were adopted for QTL
analysis (Kim et al. 2005) and analyses were performed using the methods described in Choi et
al. (2010). Significance thresholds were determined by False Discovery Rate (FDR; Weller et al.
1998).
The LC analysis assumes the QTL to be fixed for alternative alleles in the founder lines.
Probabilities of each F2 individual being homozygous for two Duroc alleles (P11), homozygous
for two Pietrain alleles (P22), or heterozygous (P12 or P21) were estimated at fixed 1-cM
intervals across the genome using the QTL Express software (Seaton et al. 2002). By denoting
the mean of homozygous animals for the Duroc allele as positive additive (a), the mean of
heterozygous animals as dominance (d) and the mean of homozygous animals for the Pietrain
allele as negative additive (-a), the following linear model was fitted at every cM across the
genome.

j

j

j

j

j

Where j is the phenotype of F2 progeny , j and b are the design matrix and solution
vector for the fixed effects, respectively, a and d are the estimated additive and dominance
effects of a putative QTL at the given location, respectively,

52 
 

j = P11 – P22 is the conditional

expectation of the number of Duroc alleles carried by animal ,

j = P12 + P21 is the

conditional probability of animal to be heterozygous, and j is the residual error.
The HS analysis assumes the QTL to be segregating in the parental breeds, and the 6 F1
sires were regarded as common parents. QTL Express (Seaton et al. 2002) was used to calculate
the probabilities of individuals inheriting allele (A1) or allele (A2) from the common F1 sire (A1
or A2) at fixed 1-cM intervals (Knott et al. 1996). In these analyses contrasts were made between
the two haplotypes of every F1 sire.

ij

ij

HSi

i

ij

Where ij is the phenotype of F2 progeny of F1 sire ,

ij
and b are the design matrix and

the solution vector for fixed effects, respectively, i is the effect of the th F1 sire, αHSi is the
substitution effect for the two putative QTL alleles (A1 or A2) carried by the th F1 sire,

ij is

the probability that the F2 individuals inherited the arbitrary allele (Ai1) from F1 sire , and ij is
the residual error.
The CB model assumes the QTL to be segregating in the parental breeds.

ij

ij

i

ij

ij

CBi

ij

ij

Where ij is the phenotype of F2 progeny of F1 sire , ij and b are the design matrix and
the solution vector for fixed effects, respectively, i is the effect of the th F1 sire, a and d are
the additive and dominance effects of breed-origin alleles, respectively,

ij and

ij are the

corresponding breed-origin coefficients as described above, CBi is the substitution effect for
53 
 

the two putative QTL alleles carried by the th F1 sire,

ij is the probability that the F2

individuals inherited the arbitrary allele (Ai1) from F1 sire , and ij is the residual error. In this
model, a and d account for the average effects of breed origin alleles through both the F1 sire and
the F1 dam and CBi represents the difference between the two QTL alleles that a given F1 sire
received from the two parental breeds as a deviation from their average additive effect (Kim et al.
2005). To avoid increasing Type I error rate due to multiple testing, a significance threshold of q
< 0.05 was used, where q is the FDR corrected p-value. QTL detected using the LC, HS or CB
models were declared using the following criteria:
1) LC QTL declared if qLC = min(qLC, qHS) < 0.05
2) HS QTL declared if qHS = min(qLC, qHS) < 0.05
3) CB QTL declared if qCB < 0.05 and qLC > 0.05 and qHS > 0.05
A QTL was declared under the CB model only if it had not been previously detected using
the LC or HS models.

3. Results
Three different models for QTL analysis revealed a total of 91 QTL for carcass and meat
quality traits on all autosomes except SSC11 and 17. The LC analysis revealed 50 QTL (Table
II.2) including 14 new QTL on 6 chromosomes (SSC3, 6, 7, 12, 16, and 18) which had not been
identified in the first genome scan of this population (Edwards et al. 2008a). The HS analysis
revealed 38 QTL, and 3 additional QTL were detected using the CB model (Table II.2). The
thresholds used in this study were –log10(P) = 3.78 and –log10(P) = 2.88 at the 1% and 5% FDR
54 
 

levels, respectively. As an example, the genome scan for ham weight is shown in Figure II.1. At
the 1% FDR level, two QTL were identified using the LC model on SSC6 and 7, and one QTL
was identified using the HS model on SSC7. At the 5% FDR level, additional QTL were
revealed on SSC3 with the LC model and on SSC5, 8 and 9 with the HS model.

3.1 Line-cross analysis
A total of 50 significant QTL were identified on SSC1, 3-10, 12, 14, 16 and 18 using the LC
model (Table II.2). Of these, 29 QTL were below the 1% FDR threshold on SSC1, 3, 6, 7, 12, 14
and 18. On SSC1, QTL affecting LMA and spareribs weight detected at 12 cM and 236 cM
supported our previous results, but a QTL for dressing percent which was significant at the 1%
chromosome-wise level in the first scan of this population (Edwards et al. 2008a) did not reach
significance in the second scan. On SSC3, a QTL for 45-min pH was significant at the 1% FDR
level, also confirming results from our first scan (Edwards et al. 2008a), and a QTL for ham
weight was newly identified at the 5% FDR level.
On SSC6, QTL for moisture and firmness were located in the S0087 – S0220 interval, QTL
influencing meat quality traits were mapped to the SW2173 – SW1647 interval, and QTL
affecting fat deposition and carcass traits were identified in the SW1647 – SW1881 – SW322
interval (Figure II.2). The QTL detected in these marker intervals showed additive pleiotropic
effects indicating that the Duroc allele contributed to increased fat deposition and reduced
muscularity. In contrast to SSC6, QTL affecting muscle mass located in the SW2019 – SW859
interval on SSC7 showed negative additive effects, and the Pietrain allele was associated with
higher muscularity. The incorporation of the new SSC7 marker SW2019 in the SW1369 – SW850
marker interval allowed refining the QTL position detected in the first scan, as well as increasing

55 
 

the statistical power and narrowing the QTL interval. A QTL for LMA detected in the SW859 –
S0115 interval in the first scan was repositioned at 86 cM in the SW2019 – SW859 interval in the
second scan.
On SSC12, QTL for fat related traits including marbling score, belly weight and
intramuscular fat percent detected in the SW874 – S0090 interval in the first scan were identified
in the second scan in the SW957 – SW874 interval at the 1% FDR level. In addition, at the 5%
FDR level, QTL for a* and b* not identified in the first scan were mapped to 93 cM and 110 cM
of SSC12, respectively. A QTL for LMA mapped to 42 cM and QTL for intramuscular fat and
moisture percent located at 143 cM were newly discovered on SSC16 in the second scan. In the
SW2540 – SW1023 interval of SSC18, not only was a QTL for spareribs weight confirmed from
the first scan, but QTL for carcass length and last-lumber backfat were also newly identified in
the second scan.

3.2 Half-sib analysis
HS analysis revealed a total of 38 QTL on SSC2, 3, 5, 7-10, 13-16 and 18 (Table II.2). Of
these, 20 QTL indentified on SSC3, 5, 7, 8, 15, 16 and 18 were significant at the 1% FDR level
including 13 QTL detected on SSC15.
A QTL affecting 45-min carcass temperature (q < 0.01) was detected at 47 cM on SSC3. On
SSC5, a QTL for first-rib backfat was declared as a HS QTL (q < 0.01) in the second scan,
whereas a first-rib backfat QTL had previously been identified in this location with the LC
analysis in the first scan (Edwards et al. 2008a). On SSC8, a QTL affecting ham weight was
identified at 39 cM, and QTL for off-flavor and cook yield were mapped to the distal region of
SSC8 near S0178. In addition, a QTL for ham weight on SSC9 and a QTL for protein percent on

56 
 

SSC10 were identified (q < 0.01). On SSC16, HS QTL were identified for L* (q < 0.02), a* (q <
0.01) and moisture percent (q < 0.02). A highly significant HS QTL influencing spareribs weight
(q < 0.0007) was detected on SSC18 with an estimated location at 70 cM. The location of the LC
QTL for spareribs weight on SSC18 was estimated at 24 cM so these QTL were considered to be
unique QTL.
On SSC7, QTL affecting ham weight and number of ribs were identified in the SW859 –
SW2040 – S0115 interval (q < 0.01), and QTL for marbling score and loin weight significant at
the 5% FDR level were located in the same interval. For ham weight, the QTL identified with the
HS analysis was mapped to 139 cM (q < 0.0002), whereas the ham weight QTL revealed with
the LC analysis was mapped to 104 cM (q < 0.006). Since these QTL detected by the different
models mapped to distinct locations, they were considered to be separate unique QTL.
The HS analysis revealed evidence for QTL influencing meat quality traits in the SW1683 –
SW906 – SW1983 interval on SSC15 (Figure II.3). In the SW1683 – SW906 interval, a QTL for
protein percent had the highest test statistic (–log10(P) = 27.55; q < 0.0001) among the QTL
detected on SSC15. In addition, a QTL for 24-h pH, a trait that is associated with many other
meat quality traits, was highly significant (q < 0.0001; Figure II.3). The LC analysis also
revealed significant QTL for these traits in the same interval, but the HS model showed much
higher statistical evidence.

3.3 Combined analysis
In addition to QTL identified with the LC and HS analyses, three additional QTL exceeded
the 5% FDR significance threshold using the CB analysis. A QTL for pH decline from 45-min to
24-h was mapped to 117 cM on SSC3, a QTL for spareribs weight was detected in the SW2019 –
57 
 

SW859 interval on SSC7 and a QTL for 24-h carcass temperature was found in the SW2540 –
SW1023 interval on SSC18. Although the statistical power was sufficient to detect QTL, the CB
model revealed a small number of additional QTL because most QTL had been declared using
either the LC or HS models due to higher test statistics with these analyses.

3.4 Effect of additional markers and animals on QTL detection
The QTL analyses under three different models revealed QTL for pH associated traits at
different positions on SSC3 (Figure II.4). The first scan of this chromosome using the LC model
had revealed QTL for 45-min pH and pH decline from 45-min to 24-h postmortem (Edwards et
al. 2008a). The second scan included two additional markers and genotypes for additional F2
pigs. A QTL for 45-min pH using the LC model (q ≤ 0.0076) was mapped at 135 cM near
marker SW2047 (134.8 cM) and a QTL for pH decline from 45-min to 24-h was detected using
the CB model (q ≤ 0.0469) at 117 cM near marker ACTG2 (116.5 cM) which did not reach the
significance threshold in the LC analysis (q ≤ 0.065). In addition, a QTL for 45-min carcass
temperature was detected using the HS model (q ≤ 0.002) located at 47 cM in the SW2021 –
S0206 marker interval. These results confirm results of the first scan for 45-min pH and pH
decline, and add new results for 45-min carcass temperature.
We have recently used LC, HS and CB models to identify QTL for growth traits in our
Duroc x Pietrain resource population, and we reported that additional markers and animals
contributed to reduce the confidence intervals and increase the test statistics for QTL detection
(Choi et al. 2010). For the present study, QTL affecting the a* and b* objective color measures
were newly detected on SSC12 (q < 0.04). In order to determine how the QTL peaks for these
traits were changed, analyses were performed under 4 different scenarios; 5 and 7 markers with
58 
 

510 and 948 animals (Figure II.5). The results indicated that increasing the number of animals or
increasing the number of markers was effective in increasing the power to detect QTL on this
chromosome, and that increasing the numbers of both animals and markers allowed detection of
the a* and b* QTL.

4. Discussion
This study identified 91 QTL for pig carcass and meat quality traits located on all autosomes
except SSC11 and 17 using three least-squares Mendelian analysis models. The LC analysis,
which detected QTL segregating between breeds, revealed 50 QTL including 13 new QTL on 6
chromosomes (SSC3, 6, 7, 12, 16, and 18) that had not been identified in the first genome scan
of this population (Edwards et al. 2008a). The HS analysis, which detected QTL segregating
within breeds, revealed 38 QTL including 18 on SSC15. Three additional QTL were detected
using the CB model (Kim et al. 2005).
Application of the three different models for SSC3 identified not only QTL influencing
muscularity under the LC model, but also QTL affecting pH and carcass temperature using all
three models. The LC QTL for 45-min pH detected at 135 cM near SW2047 confirmed the 45min pH QTL observed in the first scan (Edwards et al. 2008a). Beeckmann et al. (2003) reported
a QTL for 45-min pH at the same interval in a Wild boar x Meishan F2 population. Several
studies (de Koning et al. 2003; Evans et al. 2003; Óvilo et al. 2002a; Wimmers et al. 2006)
reported QTL affecting muscle pH in the SW2021 – S0206 marker interval, a region where we
identified a QTL for 45-min carcass temperature under the HS model. In addition, Duan et al.
(2009) reported a QTL for pH decline from 45-min to 3-h in the SW2021 – S0206 interval in a

59 
 

White Duroc x Chinese Erhualian population. We also detected a QTL for pH decline from 45min to 24-h under the CB model, however, our QTL was located at 117 cM near ACTG2.
Significant QTL affecting backfat thickness were located on SSC6 within the SW1647 –
SW1881 – SW322 marker interval at 160 – 174 cM. A 10,000 bootstrap permutation analysis
showed the average QTL positions for each backfat trait to be located in the 160.12 – 167.96 cM
th

region. The 95% confidence interval for 10 -rib backfat was estimated to be 159 – 165 cM (6
Haldane cM), which was considerably narrowed from the 38.5 Haldane cM interval observed for
the first scan (Edwards et al. 2008a). Not only were QTL affecting fat deposition traits observed
in this region, but QTL influencing muscularity were also identified at the same marker interval
since Duroc alleles contributed to both fat accumulation and reduced muscle content.
Our results for backfat thickness traits were in agreement with other studies (Malek et al.
2001b; Óvilo et al. 2002b; Varona et al. 2002) that identified QTL for fatness traits in this region
of SSC6. This region includes the leptin receptor (LEPR) gene which is considered as a potential
candidate gene for fatness (Ernst et al. 1997; Mohrmann et al. 2006; Óvilo et al. 2005), and
studies to identify a causal mutation in LEPR have been conducted (Mackowski et al. 2005;
Muñoz et al. 2009). We also observed a QTL for intramuscular fat percent using the LC model in
a position more proximal to this region of SSC6 at 141 cM, which coincided with a backfat
thickness QTL detected with the HS model in a Duroc x Pietrain population by Liu et al. (2008).
The SSC6 region affecting intramuscular fat percent also included QTL for marbling score and
a*, which were all mapped to 141 – 152 cM in the SW2173 – SW1647 interval. The confidence
th

interval for these QTL did not overlap with the confidence interval for 10 -rib backfat. This
result was consistent with previous studies (Óvilo et al. 2005; Szyda et al. 2003) which reported
that QTL effects for backfat and intramuscular fat content resulted from different closely linked
60 
 

loci on SSC6. QTL affecting intramuscular fat content have been reported (de Koning et al. 2000;
Grindflek et al. 2001) in the same region where we detected a QTL for marbling score, although
no other reports of subjective marbling score QTL in this SSC6 region have been reported. Also,
Harmegines et al. (2006) identified QTL for a* as well as fat thickness in this same region.
The different models revealed distinct QTL regions on SSC7 with LC and HS QTL
identified at 84 – 104 cM and at 130 – 178 cM, respectively. A highly significant QTL
influencing muscle mass identified in the SW2019 – SW859 interval had an additive effect for
which Duroc alleles increased carcass length and decreased dressing percent, LMA and ham
weight. In this region, Yue et al. (2003) found a 1% genome-wide level significant QTL
influencing carcass composition traits such as carcass length in a Wild boar x Meishan
population. Liu et al. (2008) reported QTL for carcass length and dressing percent with similar
allelic substitution effect in their Duroc x Pietrain population as we observed in our study.
However, Nezer et al. (2002) identified a QTL for carcass length at the more distal position from
our QTL in a Pietrain x Large White population. In addition, Sato et al. (2003) detected a QTL
for dressing percent in a Duroc x Meishan population in the same region as our study. A QTL for
number of ribs was detected using the HS analysis. A QTL for number of ribs had been detected
in this position at the 1% genome-wise significance level using the LC analysis in the first scan
(Edwards et al. 2008a), however, evidence from the second scan suggests the HS model better
describes the QTL allele frequency in the parental breeds. Also on SSC7, analyses using both the
LC and HS models identified QTL for ham weight at different locations, which were in the
SW2019 – SW859 interval with the LC model and in the SW859 – SW2040 interval with the HS
model. Similarly, Milan et al (2002) also reported suggestive QTL for ham weight at different

61 
 

positions using LC and HS models, and their LC QTL detected in the SLA – S0102 marker
interval was in a similar region to our LC QTL.
We have recently used LC, HS and CB models to identify QTL for growth traits in our
Duroc x Pietrain resource population, and we reported that additional markers and animals
contributed to reduce the confidence intervals and increase the test statistics for QTL detection
(Choi et al. 2010). In the present study, genotyping of additional markers and animals increased
the statistical power and facilitated discovery of new QTL which had not been observed in the
first scan (Edwards et al. 2008a). For example, QTL for the objective color measures of a* and
b* were identified on SSC12 with the addition of more F2 pigs and more marker genotypes using
the LC analysis. The LC analysis also identified QTL on SSC12 related to intramuscular fat
percent and moisture at 47 – 50 cM and at 69 cM, respectively. A QTL for marbling was located
in the SW957 – SW874 marker interval, whereas the position of this QTL had been more distal
for the first scan (Edwards et al. 2008a). The additive effects of these QTL indicated that Duroc
alleles increased marbling and intramuscular fat percent, and decreased moisture percent.
Harmegnies et al. (2006) also reported a QTL for a* although at a more distal position than our
current result, and Malek et al. (2001a) detected a QTL for subjective color score in the same
region as our result.
The half-sib analysis revealed strong evidence for QTL affecting meat quality traits on
SSC15 at 74 – 90 cM in the SW1683 – SW1983 marker interval where 13 and 2 QTL were
significant at the 1% and 5% FDR levels, respectively, including a highly significant QTL for
protein percent. Significant QTL had been identified in this region using the LC analysis in the
first scan (Edwards et al. 2008a), and a negative additive effect had been seen for protein percent,
color and tenderness traits suggesting contributions from segregation of Pietrain alleles. The
62 
 

pleiotropic effects of Pietrain alleles contributing to leanness resulted in effects on other meat
quality traits resulting in more muscularity, paler muscle color and less tenderness. QTL for 24-h
pH, L* and tenderness significant at the 1% genome-wise level were identified in this region of
SSC15 in a Berkshire x Yorkshire population (Kim et al. 2005; Malek et al. 2001a; Thomsen et
al. 2004). Very few studies have measured the trait of protein percent and no QTL for protein
percent have been reported on SSC15.
Several candidate genes such as myostatin (MSTN), Titin (TTN) and protein kinase AMPactivated gamma 3 (PRKAG3) are located in the SW1683 – SW1983 interval (Davoli et al. 2003;
Milan et al. 2000; Sonstegard et al. 1998). Stinckens et al. (2008) reported that pigs of the
Pietrain breed had higher muscularity as a result of association between a polymorphism in the
MSTN MEF3 binding site and muscle mass. Wimmers et al. (2007) reported that a polymorphism
in TTN was associated with leanness in a Pietrain population. Also, Milan et al. (2000) mapped
the PRKAG3 gene in the SW1683 – SW1983 marker interval and Ciobanu et al. (2001) identified
three polymorphic sites in the PRKAG3 gene that affect meat quality traits including 24-h pH.

5. Conclusion
This study used a Duroc x Pietrain F2 resource population and identified a total of 91 QTL
for carcass merit and meat quality phenotypes. Three different least-squares models were applied
under different assumptions; 1) founders fixed for alternative QTL alleles (line-cross model), 2)
segregation of QTL alleles at similar frequencies in founders (half-sib model), and 3) segregation
of QTL alleles at different frequencies in founders (combined model). The addition of new
marker and animal genotypes contributed to increasing the statistical power for QTL detection,

63 
 

and the application of alternative models allowed confirmation of QTL and detection of new
QTL segregating either between or within breeds.

Acknowledgements
This project was supported by National Research Initiative Competitive Grant no. 200435604-14580 from the USDA National Institute of Food and Agriculture. We thank D. Edwards
for technical contributions in development of the pig resource population.
 

 

64 
 

Table II.1. Number of records, means, and standard deviations for carcass and meat
quality traits.
Traits
N
Mean
SD
Carcass measure
Off-farm BW, kg
948
112.08
8.56
Hot carcass weight, kg
948
81.84
6.81
Dressing percent, %
948
73.01
2.11
45 min carcass temperature, °C
947
39.47
2.23
24 h carcass temperature, °C
945
2.91
1.19
45 min pH
934
6.37
0.22
24 h pH
927
5.51
0.14
45 min - 24h pH decline
914
0.86
0.22
carcass length, cm
947
78.72
2.51
Number of ribs
669
14.83
0.85
First rib backfat, mm
859
40.67
7.09
Last rib backfat, mm
947
28.69
6.38
Last lumbar vertebra backfat, mm
946
22.25
6.23
Tenth rib backfat, mm
941
24.16
7.35
2
942
40.61
4.74
Longissimus muscle area, cm
Primal cut weight
Ham weight, kg
947
9.63
0.77
Loin weight, kg
947
8.28
0.83
Boston shoulder weight, kg
947
3.90
0.56
Picnic shoulder weight, kg
947
3.72
0.57
Belly weight, kg
947
5.03
0.67
Spareribs weight, kg
943
1.53
0.20
Meat quality evaluation
Color, 1-6
945
3.16
0.82
Marbling, 1-10
946
2.82
0.84
Firmness, 1-5
932
2.86
0.79
L*
900
53.77
2.24
a*
900
17.25
1.83
b*
900
9.13
1.61
Proximate analysis
Moisture, %
936
73.94
1.53
Fat, %
936
3.18
1.40
Protein, %
935
23.44
1.13
Laboratory analyses
Drip loss, %
946
1.85
1.18
Cook yield, %
936
77.26
2.83
Warner-Bratzler shear force, kg
935
3.21
0.69
Sensory panel analyses
Juiciness, 1-8
942
5.23
0.59
Tenderness, 1-8
942
5.55
0.62
Overall tenderness, 1-8
942
5.63
0.55
Connective tissue, 1-8
942
6.39
0.39
Off-flavor, 1-8
942
1.14
0.21
65 
 

Table II.2. Position and significance level of carcass and meat quality trait QTL.
Chr1 Position2
1
12
236
2
81
100
3
47
97
117
135
151
4
19
21
5
94
151
173
6
103
114
124
141
146
152
160
162
162
163
164
168
174
174
175
179
182

Trait
LM area, cm2
Spareribs wt, kg
Juiciness, 1 to 8
45-m carcass temperature, °C
45-m carcass temperature, °C
Ham wt, kg
45-min to 24-h pH decline
45-min pH
Loin wt, kg
Off-farm BW, kg
HCW, kg
First-rib backfat, mm
24-h carcass temperature, °C
Ham wt, kg
Picnic shoulder wt, kg
Moisture, %
Firmness, 1 to 5
Fat, %
Marbling, 1 to 10
a*
First-rib backfat, mm
Tenth-rib backfat, mm
Carcass length, cm
Loin wt, kg
Last lumbar vertebra backfat, mm
Belly wt, kg
Ham wt, kg
Last-rib backfat, mm
LM area, cm2
Protein, %
HCW, kg

Type3 -log10P4
LC
3.48
LC
5.36
HS
2.98
HS
3.00
HS
4.73
LC
3.68
CB
3.21
LC
3.92
LC
3.36
LC
2.91
LC
3.11
HS
3.95
LC
3.14
HS
3.13
LC
3.39
LC
5.12
LC
4.31
LC
19.03
LC
16.18
LC
4.40
LC
6.71
LC
35.82
LC
10.42
LC
19.86
LC
14.87
LC
3.78
LC
10.38
LC
7.47
LC
8.30
LC
5.41
LC
3.10
66 

 

FDR5
0.0177
0.0005
0.0471
0.0460
0.0020
0.0121
0.0469
0.0076
0.0215
0.0473
0.0334
0.0083
0.0317
0.0364
0.0204
0.0008
0.0037
0.0000
0.0000
0.0031
0.0000
0.0000
0.0000
0.0000
0.0000
0.0101
0.0000
0.0000
0.0000
0.0005
0.0341

Flanking Markers
SW1514 – SW1515
SW974 – S0056
S0170 – SW1026
SW1026 – S0370
SW2021 – S0206
S0206 – SWR978
ACTG2 – SW2141
SW2047 – SW2408
SW2047 – SW2408
SW2509 – S0301
SW2509 – S0301
SWR453 – SW2
S0005 – S0018
S0018 – IGF1
SW2525 – S0087
S0087 – S0220
S0220 – SW122
SW2173 – SW1647
SW2173 – SW1647
SW2173 – SW1647
SW1647 – SW1881
SW1647 – SW1881
SW1647 – SW1881
SW1647 – SW1881
SW1647 – SW1881
SW1881 – SW322
SW1881 – SW322
SW1881 – SW322
SW1881 – SW322
SW1881 – SW322
SW1881 – SW322

Additive6 Dominance7
-1.27 (0.32) 0.45 (0.59)
0.02 (0.02) -0.12 (0.02)

0.11 (0.03)

0.03 (0.05)

-0.04 (0.01)
0.02 (0.03)
-1.58 (0.45)
-1.30 (0.35)

0.00 (0.02)
-0.20 (0.05)
0.78 (0.77)
0.56 (0.61)

0.04 (0.03)

0.22 (0.06)

-0.05 (0.02)
-0.25 (0.08)
0.17 (0.04)
0.56 (0.06)
0.34 (0.04)
0.12 (0.05)
1.54 (0.30)
3.20 (0.25)
-0.46 (0.11)
-0.21 (0.02)
1.95 (0.25)
0.06 (0.02)
-0.16 (0.02)
1.28 (0.27)
-1.30 (0.21)
-0.30 (0.06)
0.44 (0.35)

0.05 (0.02)
0.48 (0.13)
-0.03 (0.06)
-0.23 (0.09)
-0.14 (0.06)
0.24 (0.07)
-1.19 (0.47)
-1.65 (0.38)
0.55 (0.16)
0.18 (0.04)
-1.51 (0.39)
-0.06 (0.02)
0.08 (0.04)
-1.62 (0.43)
0.61 (0.35)
0.09 (0.10)
-2.23 (0.61)

Table II.2. (Cont’d).
Chr1 Position2
6
183
7
15
75
84
86
97
104
130
139
141
178
8
39
126
205
214
9
0
25
10
0
21
72
12
47
50
50
69
93
110
13
122
14
62
73
136
15
70

Trait
24-h carcass temperature, °C
Protein, %
Spareribs wt, kg
Dressing percent, %
Carcass length, cm
LM area, cm2
Ham wt, kg
Marbling, 1 to 10
Ham wt, kg
Loin wt, kg
Number of ribs
Ham wt, kg
LM area, cm2
Off-flavor, 1 to 8
Cook yield, %
Drip loss, %
Ham wt, kg
Overall tenderness, 1 to 8
Protein, %
Connective tissue, 1 to 8
Marbling, 1 to 10
Belly wt, kg
Fat, %
Moisture, %
b*
a*
Last-rib backfat, mm
a*
Boston shoulder wt, kg
Belly wt, kg
Loin wt, kg

Type3 -log10P4
LC
4.98
LC
3.44
CB
3.40
LC
12.04
LC
11.50
LC
8.25
LC
4.05
HS
2.97
HS
6.13
HS
3.83
HS
9.23
HS
2.96
LC
2.97
HS
4.48
HS
3.73
LC
2.94
HS
3.11
LC
2.90
HS
3.06
LC
2.99
LC
5.50
LC
4.14
LC
3.97
LC
5.80
LC
3.72
LC
3.07
HS
2.96
LC
3.30
HS
3.18
LC
4.26
HS
3.57
67 

 

FDR5
0.0010
0.0186
0.0339
0.0000
0.0000
0.0000
0.0060
0.0480
0.0002
0.0102
0.0000
0.0490
0.0429
0.0031
0.0122
0.0449
0.0380
0.0484
0.0414
0.0410
0.0004
0.0051
0.0070
0.0002
0.0112
0.0360
0.0488
0.0239
0.0336
0.0041
0.0160

Flanking Markers
SW1881 – SW322
S0025 – S0064
SW2019 – SW859
SW2019 – SW859
SW2019 – SW859
SW2019 – SW859
SW2019 – SW859
SW859 – SW2040
SW859 – SW2040
SW859 – SW2040
S0115 – SW632
SW905 – SWR1101
S0017 – SW2160
SW1085 – S0178
SW1085 – S0178
SW21
SW983 – SW911
SWR136
SWR136 – SW249
SW1041 – SW920
SW957 – SW874
SW957 – SW874
SW957 – SW874
SW37 – S0090
S0090 – SWC23
SWC23 – SW2180
SW398 – SW2440
SW210 – SW886
SW210 – SW886
SW1557 – SWC27
S0088 – SW1683

Additive6 Dominance7
0.13 (0.03) -0.11 (0.05)
-0.27 (0.07) -0.23 (0.15)
-0.81 (0.11)
0.88 (0.13)
-1.60 (0.26)
-0.12 (0.03)

-0.05 (0.19)
0.29 (0.23)
0.21 (0.46)
-0.02 (0.04)

-0.84 (0.24)

-0.40 (0.37)

-0.04 (0.07)

0.35 (0.10)

0.04 (0.04)

0.19 (0.05)

0.08 (0.04)
0.23 (0.04)
0.09 (0.02)
0.30 (0.07)
-0.36 (0.07)
0.17 (0.04)
0.19 (0.05)

0.17 (0.07)
0.03 (0.07)
-0.01 (0.03)
-0.11 (0.12)
0.08 (0.11)
0.08 (0.07)
0.08 (0.08)

-0.29 (0.07)

0.06 (0.13)

0.06 (0.03)

-0.26 (0.06)

Table II.2. (Cont’d).
Chr1 Position2 Trait
15
71
First-rib backfat, mm
72
Tenth-rib backfat, mm
74
Color, 1 to 6
76
L*
78
Juiciness, 1 to 8
78
Moisture, %
80
Warner-Bratzler shear force, kg
80
Overall tenderness, 1 to 8
80
Protein, %
80
Tenderness, 1 to 8
81
a*
81
24-h pH
82
Firmness, 1 to 5
83
Drip loss, %
85
Cook yield, %
87
Connective tissue, 1 to 8
90
Belly wt, kg
16
42
LM area, cm2
66
L*
70
a*
97
Moisture, %
143
Fat, %
143
Moisture, %
18
0
Tenth-rib backfat, mm
4
Carcass length, cm
24
Spareribs wt, kg
30
Last lumbar vertebra backfat, mm
33
24-h carcass temperature, °C
70
Spareribs wt, kg
1
Chr = chromosome

Type3 -log10P4
HS
3.11
HS
3.77
HS
4.43
HS
4.74
HS
4.56
HS
5.59
HS
5.51
HS
10.22
HS
27.55
HS
9.72
HS
4.51
HS
10.48
HS
3.35
HS
10.36
HS
16.06
HS
4.89
HS
3.38
LC
3.58
HS
3.48
HS
4.91
HS
3.45
LC
3.24
LC
3.35
LC
3.00
LC
5.67
LC
5.13
LC
4.40
CB
3.40
HS
5.34

68 
 

FDR5
0.0383
0.0115
0.0033
0.0020
0.0027
0.0004
0.0005
0.0000
0.0000
0.0000
0.0029
0.0000
0.0240
0.0000
0.0000
0.0015
0.0226
0.0145
0.0191
0.0015
0.0199
0.0265
0.0219
0.0408
0.0003
0.0008
0.0031
0.0339
0.0007

Flanking Markers
S0088 – SW1683
S0088 – SW1683
SW1683 – SW906
SW1683 – SW906
SW1683 – SW906
SW1683 – SW906
SW1683 – SW906
SW1683 – SW906
SW1683 – SW906
SW1683 – SW906
SW1683 – SW906
SW1683 – SW906
SW1683 – SW906
SW906 – SW1983
SW906 – SW1983
SW906 – SW1983
SW906 – SW1983
SW419 – SW1454
SW1454
SW1454 – SW2517
SW2517 – SW1897
S0061
S0061
SW1808
SW2540 – SW1023
SW2540 – SW1023
SW2540 – SW1023
SW2540 – SW1023
SW1023 – SW1984

Additive6

Dominance7

0.80 (0.20)

-0.23 (0.32)

0.18 (0.06)
-0.14 (0.07)
0.96 (0.26)
-0.49 (0.10)
-0.04 (0.01)
1.60 (0.35)

0.22 (0.09)
-0.31 (0.09)
0.04 (0.35)
-0.23 (0.14)
0.00 (0.02)
0.01 (0.64)

Table II.2. (Cont’d).
2
Position in Haldane cM
3

LC = QTL declared as line-cross type; HS = half-sib type; CB = combined type.

4

Negative logarithm of the comparison-wise p value of the test statistic against the null hypothesis of no QTL at the most likely
position for the inferred QTL model.
5
FDR=false discovery rate
6

Estimates of additive effects with standard errors for LC QTL. The effects are expressed as (DD-PP)/2, where D = Duroc allele and
P = Pietrain allele.
7
Estimates of dominance effects with standard errors for LC QTL. The effects are expressed as DP-PD, where D = Duroc allele and
P = Pietrain allele.

69 
 

Figure II.1. Genome scan results for ham weight determined using different analysis models.
A whole genome scan to identify QTL for the trait ham weight was performed using three different analysis models (line-cross, red
line; half sib, blue line; combined line-cross and half-sib, green line). The X-axis indicates positions of chromosomes 1 to 18.
Horizontal lines indicate significance thresholds (lower line, 5% FDR; upper line, 1% FDR).

70 
 

Figure II.2. Line-cross analysis indicated strong evidence for QTL influencing fat deposition traits on SSC6.
Highly significant QTL for traits related to fat deposition were identified on SSC6. Confidence intervals for fat percentage and 10th rib
backfat (BF10) were estimated using 10,000 bootstrap permutations as 136 – 146 cM (blue bar) and 159 – 165 cM (red bar),
respectively. Marker positions are shown as triangles on the X-axis (black, markers used for both QTL scans and genotyped only in
510 animals; green, markers used for both QTL scans and genotyped in all animals; red, markers used for second scan only and
genotyped in all animals). Horizontal lines indicate significance thresholds (lower line, 5% FDR; upper line, 1% FDR).
71 
 

Figure II.3. Half-sib analysis indicated strong evidence for QTL influencing meat quality traits on SSC15.
Highly significant QTL for meat quality traits were identified on SSC15. Confidence interval for protein percentage was estimated by
10,000 bootstrap permutations as 77 – 85 cM (red bar). Marker positions are shown as triangles on the X-axis (black, markers used for
both QTL scans and genotyped only in 510 animals; green, markers used for both QTL scans and genotyped in all animals; red,
markers used for second scan only and genotyped in all animals). Horizontal lines indicate significance thresholds (lower line, 5%
FDR; upper line, 1% FDR).
72 
 

Figure II.4. QTL results determined by different models for pH related traits on SSC3.
Line-cross model detected a QTL (q < 0.01) for 45 min pH (red line) and half-sib model detected a QTL (q < 0.01) for 45 min carcass
temperature (blue line). Combined model identified a QTL (q < 0.01) affecting pH decline from 45 min to 24 h (green line). Marker
positions are shown as triangles on the X-axis (black, markers used for both QTL scans and genotyped only in 510 animals; green,
markers used for both QTL scans and genotyped in all animals; red, markers used for second scan only and genotyped in all animals).
Horizontal lines indicate significance thresholds (lower line, 5% FDR; upper line, 1% FDR).
73 
 

Figure II.5. Effect of additional markers and animals for detecting meat color QTL on SSC12.
Effects of additional marker genotypes and animals for detecting QTL for a* and b* objective meat color measures on SSC12 were
compared under four different scenarios (5 and 7 markers with 510 F2 animals, 5 and 7 markers with 948 F2 animals). Blue lines
indicate 5 markers (black and green triangles on the X-axis) and red lines indicate 7 markers (black, green and red triangles on the Xaxis). Solid lines indicate 948 animals and dotted lines indicate 510 animals. Marker positions are shown as triangles on the X-axis
(black, markers used for both QTL scans and genotyped only in 510 animals; green, markers used for both QTL scans and genotyped
in all animals; red, markers used for second scan only and genotyped in all animals). Horizontal lines indicate significance thresholds
(lower line, 5% FDR; upper line, 1% FDR).
74 
 

References

75 
 

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(2003). Mapping, identification of polymorphisms and analysis of allele frequencies in the
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Chapter III

Evaluation of QTL for carcass merit and meat quality traits
in an US commercial Duroc population
Igseo Choi, Ronald O. Bates, Nancy E. Raney, Juan P. Steibel and Catherine W. Ernst*

Department of Animal Science, Michigan State University, East Lansing, MI 48823, U.S.A.

*Corresponding author. 3385 Anthony Hall, Department of Animal Science, Michigan State
University, East Lansing, MI 48824-1225, USA
Tel:+1 517 432 1941; Fax:+1 517 432 0753
E-mail: ernstc@msu.edu (C.W. Ernst)

This chapter formatted for submission to the journal Meat Science

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Abstract
Efforts for deciphering the genetic basis of polygenic traits such as carcass merit and
meat quality traits aim to identify genetic markers that can be used for marker assisted
selection in the pig industry. Our previous genome scans revealed quantitative trait loci (QTL)
influencing carcass merit and meat quality traits in a Duroc x Pietrain F2 resource population.
Putative QTL regions on 5 chromosomes (SSC3, 6, 12, 15, and 18) were selected for further
evaluation in an US commercial Duroc population. A total of 81 gene-specific single
nucleotide polymorphism (SNP) markers spanning these QTL regions were genotyped and
33 markers were segregating. These markers were evaluated for associations with pH, color,
marbling, days to 113 kg, backfat thickness, and longissimus muscle area (LMA) in the
Duroc population (n = 331). Association analyses were performed using a linear mixed
model, and additive and dominance effects were estimated for each trait. The MDH1 SNP on
SSC3 was associated with 45-min and ultimate pH, and pH decline from 45-min to 24-h. A
combined genotype of PRKAG3 T30N and I199V on SSC15 was associated with ultimate
pH. The HSPG2 SNP on SSC6 was associated with marbling score and days to 113 kg.
Markers for NUP88 and FKBP10 on SSC12 were associated with 45-min pH and L*,
respectively. A significant association was observed for the SSC15 marker SF3B1 with L*
and LMA and for the SSC18 marker ARF5 with ultimate pH and color score. The results of
the present study in an US commercial Duroc population showed a general consistency with
our previous genome scan.

Keywords: pig, carcass merit, meat quality, single nucleotide polymorphisms

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1. Introduction
Meat quality has always been an important consideration for consumers along with cost,
and the recent development of niche markets for pork represents consumers’ various needs
and concerns for meat quality (Brewer, Zhu, & McKeith, 2001; Honeyman, Pirog, Huber,
Lammers, & Hermann, 2006). Conventional swine selection programs have been
implemented mainly for production efficiency such as growth rate, feed conversion, and sow
productivity since meat quality phenotypic data must usually be collected postmortem, and
carcass value predictions varied with different carcass evaluation technology (Boland, et al.,
1995). Selection for improvements in lean growth efficiency resulted in increased lean
content and lean gain per day, but showed less backfat thickness, higher shear force and
greater drip loss (Lonergan, Huff-Lonergan, Rowe, Kuhlers, & Jungst, 2001). Also, strong
selection for leanness may give rise to the segregation of linked unfavorable alleles causing
inferior pork quality. Suzuki et al. (2005) reported that selection for decreased backfat
thickness had a negative influence on meat tenderness and color.
Rapid advancements in molecular genetics are contributing to localize quantitative trait
loci (QTL) and to identify genes causing variation in economically important traits such as
meat quality. Two well known achievements are the identification of substitutions within the
ryanodine receptor 1 (RYR1) and protein kinase adenosine monophosphate activated γ3subunit (PRKAG3) genes. A recessive C1843T polymorphism (HAL-1843) in the RYR1 gene
was associated with the incidence of porcine stress syndrome (PSS) and an increase in the
frequency of pale, soft, and exudative (PSE) pork (Apple, et al., 2005; Fujii, et al., 1991), and
the identification of a dominant R200Q polymorphism in the PRKAG3 gene was associated
with reduced meat quality by high glycogen content in skeletal muscle (Milan, et al., 2000).
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However, many genes are involved with additive effects even though assuming a single gene
can have a larger effect on meat quality traits (Dekkers, 2004; van der Steen, Prall, &
Plastow, 2005). In addition to RYR1 and PRKAG3, Kim et al. (2000) reported a missense
mutation D298N in the melanocortin-4 receptor gene (MC4R), a candidate gene for growth
rate and fatness, and this gene was subsequently associated with darker meat and less drip
loss (Otto, et al., 2007). In addition, Ciobanu et al. (2004) reported a haplotype of calpastatin
(CAST; R249K and S638R) to be associated with decreased shear force and increased
juiciness.
In order to identify a causative mutation, the positional candidate gene approach using
QTL mapping has been conducted by many research groups and results have been confirmed
in various populations (de Vries, Sosnicki, Garnier, & Plastow, 1998). After completion of
the pig genome sequencing, it will be more feasible to identify genes underlying complex
traits such as meat quality (Archibald, et al., 2010; Gao, Zhang, Hu, & Li, 2007; Mullen,
Stapleton, Corcoran, Hamill, & White, 2006).
In our previous studies, putative QTL regions for meat quality traits were identified
through two genome scans of our Duroc x Pietrain F2 resource population (Choi, et al., 2011;
Edwards, Ernst, Raney, et al., 2008). The objective of this study was to determine if five
QTL regions identified from our previous studies were segregating in an US commercial
purebred Duroc population. For this study 81 gene-specific single-nucleotide polymorphisms
(SNP) on 5 chromosomes (SSC3, 6, 12, 15, and 18) were selected that map to QTL regions
identified from our first genome scan result (Edwards, Ernst, Raney, et al., 2008) and
association analyses were performed to evaluate the genetic relationship with meat quality
traits in an US purebred Duroc population.
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2. Materials and methods
2.1. Animals
A commercial purebred Duroc population was used. Pigs were from three herds as
suggested by the National Swine Registry (NSR; http://www.nationalswine.com) in the
United States. Animal protocols were approved by the Michigan State University All
University Committee on Animal Use and Care (AUF#04/06-048-00). A total of 331 pigs
were produced from three sire families per herd which were chosen to represent a crosssection of the sire families within the US Duroc breed. Pigs across 103 litters were born from
December 2006 to July 2008 and harvested from June 2007 to December 2008. The NSR
provided muscle tissue samples taken at harvest for DNA extraction as well as records of live
animal traits. The B-mode ultrasound estimates of 10th rib backfat thickness and loin muscle
area (LMA) on test day were determined. The number of days to 113 kg was calculated, and
backfat thickness and LMA were also adjusted to a common weight of 113 kg based on
National Swine Improvement Federation guidelines (2006). After harvesting, 45-min pH and
24-h pH (Ultimate pH; pHu) were measured on longissimus muscle adjacent to the 10th rib,
and pH decline from 45-min to 24-h was calculated. The fresh loin was cut at the 10th rib
and allowed to bloom for a minimum of 10 min and evaluated for subjective color and
marbling scores (NPPC, 2000). The color score scale ranged from 1 (pale pinkish gray) to 6
(dark purplish red) and the marbling score scale was 1 (rarely marbled) to 10 (best marbled)
corresponding to approximately 1 – 10% intramuscular lipid (NPPC, 2000). Objective color
score of CIE L* was also measured using a Minolta colorimeter. Descriptive statistics for
phenotypes used in this study are presented in Table III.1.
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2.2. Genotyping
Genomic DNA was extracted from muscle tissue using a PureLink Genomic DNA kit
TM

(Invitrogen, Carlsbad, CA, USA) and quality of DNA was verified using a Quant-iT

Assay Kit (Invitrogen, Carlsbad, CA, USA) with an Invitrogen Qubit fluorometer. Genespecific SNP markers were chosen from our previous work or from publicly available
sources. A total of 81 SNPs from 80 candidate genes on 5 chromosomes (SSC3, 6, 12, 15
and 18) were selected that mapped to putative QTL regions identified in our first genome
scan using 510 F2 animals (Edwards, Ernst, Raney, et al., 2008; Edwards, Ernst, Tempelman,
et al., 2008). Markers were included in an Illumina Goldengate 96 plex SNP multiplex
custom assay panel (Illumina, Inc. San Diego, CA), which was read using the Illumina
BeadXpress Reader. The Illumina Goldengate assay was performed at the Michigan State
University Research Technology Support Facility. Genotype segregation was determined
using the Illumina Genome Studio software. A total of 6 SSC3 (out of 18), 5 SSC6 (out of
19), 10 SSC12 (out of 18), 8 SSC15 (out of 20) and 4 SSC18 (out of 6) markers were found
to be segregating in the commercial Duroc population (minor allele frequency > 0.05).
Information about these 33 SNPs is presented in Table III.2 and further details regarding the
SNP assay probes are presented in Supplementary Table III.1.

2.3. Statistical analyses
Association analyses between SNP genotypes and meat quality traits were performed
using linear mixed models. The model specifications are as follows;
1) For pH traits, L*, color and marbling:
86 
 

Yijklmno    S j   Gkl  H m  Dmn  Lo  eijklmnop
k
where Yijklmno is the trait measured on animal i,  is the overall mean for the trait, S j is the
sex (j = 1 or 2),

 Gkl is the lth genotypic effects of all kth SNPs (k = 1 – 33, l = 1 – 3),
k

H m is herd (m = 1 – 3), Dmn is harvest date within herd (n = 1 – 14), Lo is the random
effect of litter (o = 1 – 103), and eijklmnop is the residual term,
2) For days to 113 kg, backfat thickness and LMA:

Yijklmo    S j   Gkl  H m  Lo  eijklmop.
k
For all analyses, only trait-gene associations for which a SNP was significant in the
model were considered further for comparison among genotype classifications. Additive
genetic effects were estimated as half of the difference between homozygous genotypes and
dominance genetic effects were estimated as the deviation of the heterozygous genotype
from the mean of the homozygous genotypes. For PRKAG3, a combined genotype was
considered in the analysis because the T30N and I199V markers were in high linkage
disequilibrium, and these loci are physically separated by only 98 kb. All statistical analyses
were completed using the SAS software (SAS Inst., Inc., Cary, NC).

3. Results
3.1. Allele and genotype frequencies

The SNP allele and genotype frequencies are shown in Table III.3. Eighteen of 33 SNPs
in this analysis were in Hardy-Weinberg equilibrium and overall allele frequencies showed a
range of segregation patterns. Five markers, RNF40, PCGF1, CEBPZ, COL16A1, and SF3B1
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exhibited only two of three possible genotypes. As these SNPs were not prescreened in the
Duroc population and this population is under selection, it was not unexpected for some
markers to exhibit unequal genotype frequencies.

3.2. Association analyses

The malate dehydrogenase 1 (MDH1) marker located on SSC3 was significantly
associated with 45-min pH and pHu, and pH decline from 45-min to 24-h (P < 0.01; Table
III.4). Pigs with the GG genotype exhibited a 45-min pH of 6.87 compared with 6.46 and
6.50 for pigs with the AA and AG genotypes, respectively. For pHu, pigs with the AG
genotype were significantly higher than pigs with the AA genotype (P < 0.05) (Table III.4).
Consequently, GG genotype pigs also had a significantly greater pH decline from 45-min to
24-h than AA and GG genotype pigs. Pigs with the GG genotype exhibited higher 45-min pH
and greater pH decline from 45-min to 24-h compared to pigs with the AA genotype, and
pigs with the AG genotype exhibited lower 45-min pH and smaller pH decline compared to
the average value of pigs with the AA and GG genotypes. The polycomb group ring finger 1
(PCGF1) marker also located on SSC3 showed a significant association with 45-min pH (P
< 0.01; Table III.4) with pigs of the AG genotype exhibiting higher pH than pigs of the GG
genotype. The nucleoporin 88 kDa (NUP88) marker on SSC12 was also associated with 45min pH (P < 0.05; Table III.4) with AC heterozygotes exhibiting higher 45-min pH than AA
homozygotes, and the dominance effect at this locus was significant (P < 0.05; Table III.4).
Similarly pigs with the heterozygous AG genotype for the ADP-ribosylation factor 5 (ARF5)
marker on SSC18 showed higher pHu values than pigs with the AA genotype (P < 0.05) and

88 
 

again the dominance effect at this locus was significant (P < 0.05; Table III.4). For PRKAG3
the markers for T30N and I199V were combined (6 genotypes observed; CC/AA, CC/AG,
CC/GG, AC/AG, AC/GG, AA/GG) and evaluated. The combined PRKAG3 genotypes were
associated with pHu (P < 0.05; Table III.5). Pigs with the CC/AG genotype had significantly
higher pHu (5.95) than pigs with the AC/AG (5.84), AC/GG (5.84) or CC/GG (5.81)
genotypes (P < 0.05).
The FK506 binding protein 10, 65 kDa (FKBP10) marker on SSC12 and the splicing
factor 3b, subunit 1 (SF3B1) marker on SSC15 were significantly associated with L* (P <
0.01). Pigs with the heterozygous AG genotype at the FKBP10 locus were lighter in color (P
< 0.05; Table III.4) than pigs with the AA genotype, and the observed dominance effect of
1.34 was significant (P < 0.01). Pigs with the TT genotype at SF3B1 on SSC15 had higher
L* values than pigs with the AT genotype (P < 0.01). The PCGF1 SNP on SSC3, which was
associated with 45-min pH, also exhibited a significant effect on L* (P < 0.05) and on color
score (P < 0.01). Pigs with the PCGF1 GG genotype exhibited lighter colored meat than pigs
with the AG genotype as measured by both L* and subjective color score (Table III.4). The
ARF5 marker on SSC18, which was associated with pHu, was also associated with color

score (P < 0.05; Table III.4). For this marker a significant dominance effect (P < 0.01) was
observed and pigs with the heterozygous genotype exhibited significantly higher color scores
than pigs with either homozygous genotype (P < 0.05).
For heparan sulfate proteoglycan 2 (HSPG2) on SSC6, pigs with the GG genotype
exhibited higher marbling scores and took more days to reach 113 kg (P < 0.05) than pigs of
the CC genotype (Table III.4). Pigs with the AG genotype of the 95 kDa retinoblastoma

89 
 

protein binding protein (RNF40) SNP on SSC3 exhibited more backfat thickness than pigs
with the GG genotype (P < 0.05; Table III.4). Two SSC15 and one SSC12 markers were
significantly associated with LMA (Table III.4). Pigs with the GG genotype for the mitogenactivated protein kinase kinase kinase 2 (MAP3K2) marker had smaller LMA (P < 0.01) than
pigs of the AG genotype. For the SF3B SNP, pigs of the TT genotype had larger LMA than
pigs of the AT genotype (P < 0.05), and for the phospholipase D2 (PLD2) marker pigs of the
AA genotype had less LMA than pigs of either the AG or GG genotypes (P < 0.05).

4. Discussion
We have previously reported results for whole genome scans of our Duroc x Pietrain F2
population (Choi, et al., 2010; Choi, et al., 2011; Edwards, Ernst, Raney, et al., 2008;
Edwards, Ernst, Tempelman, et al., 2008). For the present study, five QTL regions were
selected for further evaluation in an unrelated US Duroc population through analysis of genespecific SNP markers that are located within the QTL regions. Significant QTL for 45-min
pH and pH decline from 45-min to 24-h were identified in the middle of SSC3 in the marker
interval S0206 to SW2408 (Edwards, Ernst, Raney, et al., 2008) and this result was
confirmed in a subsequent QTL analysis (Choi et al., 2011). Eighteen SNP markers spanning
this region of SSC3 were selected for evaluation, and 6 of the 18 markers were found to be
segregating in the Duroc population. Our QTL analyses revealed numerous QTL on SSC6
influencing carcass length, LMA, backfat thickness, marbling score, fat and protein percent,
and ham and loin weight (Choi, et al., 2010; Choi, et al., 2011; Edwards, Ernst, Raney, et al.,
2008; Edwards, Ernst, Tempelman, et al., 2008). Nineteen SNPs were selected in the SSC6
S0087 – SW322 marker interval although only 5 SNPs were used in the analysis because 13

90 
 

SNPs were not segregating and 1 SNP was excluded because it was in complete LD with a
neighboring SNP marker and thus would not have contributed any additional information.
QTL on SSC12 were identified for pHu, fat and moisture percent, marbling score and belly
weight in the SW957 – SW2180 marker interval (Edwards, Ernst, Raney, et al., 2008). Ten of
the 19 selected SSC12 SNP markers were segregating in the Duroc population and were used
in the analysis. A region on SSC15 in the marker interval S0148 – SW1983 was considered
as a strong QTL region affecting many meat quality traits (i.e., protein percent, meat color,
sensory panel tenderness, shear force, and cook yield; Edwards, Ernst, Raney, et al., 2008).
Twenty SNPs were selected in this region and 8 SNPs segregating in the Duroc population
were analyzed. On SSC18, we detected QTL for backfat thickness and pHu. Six SNP
markers were selected spanning a large portion of SSC18, and 4 of these markers segregating
in the Duroc population were used in the analysis.
Of the 33 segregating SNPs in the five putative QTL regions, 11 SNPs exhibited
significant associations with one or more carcass merit or meat quality traits measured in the
purebred Duroc population. The association of MDH1 genotypes with 45-min pH and pH
decline from 45-min to 24-h was in agreement with our previous results (Choi, et al., 2011;
Edwards, Ernst, Raney, et al., 2008). The MDH1 gene encodes a 334 amino acid cytoplasmic
malate dehydrogenase which catalyzes the oxidation of malate to oxaloacetate utilizing the
NAD/NADH cofactor system in the citric acid cycle and catalyzes the oxidation of NADH,
+

+

H into NAD in glycolysis. Campbell et al. (2001) found the MDH1 gene in mice to be
expressed 2.2 fold higher in red skeletal muscle than in white skeletal muscle consistent with
its role in glycolysis. In Duroc sires, the expression of MDH1 protein in longissimus

91 
 

lumborum muscle was highly correlated with pHu (r = 0.629) which resulted from increased
glycolysis by MDH1 protein (Kwasiborski, et al., 2008). Our observation of MDH1 genotype
associations with 45-min pH, pHu and pH decline from 45-min to 24-h in Duroc pigs
continue to support the potential of MDH1 as a candidate gene for longissimus muscle pH
traits.
The PRKAG3 gene, which encodes a subunit of AMP-activated protein kinase (AMPK),
is well known as a candidate gene affecting meat quality caused from increased glycogen
level in skeletal muscle. Milan et al. (2000) identified 7 SNPs (at amino acid positions 30, 53,
193, 194, 199, 200, and 372) in the PRKAG3 gene and found the R200Q substitution to be
associated with a higher glycogen content and a consequent lower pHu which corresponded
to the dominant RN– allele of the Napole gene (Fernandez, Lafaucheur, Gueblez, & Monin,
1991). An additional missense mutation (G52S) in PRKAG3 was revealed from putative
QTL regions affecting muscle glycogen content and pHu in a Berkshire x Yorkshire F2
population in which the R200Q substitution was not segregating, and the effects of G52S
along with T30N and I199V were investigated in commercial populations (Ciobanu, et al.,
2001). The large effects of the I199V substitution showed a significant association with pH
and meat color across populations (Landrace, Large White, Berkshire, Duroc, and Duroc
Synthetic breeds), and the T30N substitution showed a significant association with pHu
exclusively in a Duroc population where the genotype frequencies of TT, TN, and NN were
0.34, 0.46, and 0.2, respectively (Ciobanu, et al., 2001). Huang et al. (2004) reported that the
frequency of favorable alleles 30T and 199I were 0.35 and 0.24 respectively in Duroc, and
the 200Q allele was found to segregate only in Hampshire. In the present study, the allele
92 
 

frequencies of 30T (C allele at this locus) and 199I (A allele at this locus) were 0.66 and 0.37,
respectively. Evaluation of combined genotypes for the T30N and I199V markers revealed
an association with pHu. Animals with the CC genotype at T30N in combination with at least
one A allele at the I199V locus (AA or AG) tended to have higher pHu compared to other
allelic combinations. This provides evidence of the favorable role of these two PRKAG3
SNPs on pHu in the US Duroc breed.
The PCGF1 gene consists of 9 exons and encodes a 259 amino acid polypeptide that is a
component of the polycomb group multiprotein complex (Nunes, et al., 2001). The PCGF1
mRNA which was ubiquitous in adult tissue and abundant in nervous tissue, heart and testis
in mouse was highly expressed in the developing nervous system and acts as a transcriptional
repressor via protein kinase C phosphorylation (Gong, et al., 2005; Nunes, et al., 2001).
Genotypes of the PCGF1 SNP marker located on SSC3 were significantly associated with
45-min pH, L*, and color score. Pigs with the AG genotype exhibited a higher 45-min pH
and consequently darker meat (i.e., lower L* and higher subjective color score) than pigs
with the GG genotype. The associations of MDH1 and PCGF1 with pH on SSC3 were
consistent with our previous studies which identified QTL for 45-min pH and pH decline
from 45-min to 24-h (Choi, et al., 2011; Edwards, Ernst, Raney, et al., 2008). Also,
Beeckmann et al. (2003) reported a QTL for 45-min pH at the same interval in a Wild boar x
Meishan F2 population. In addition, Harmegnies et al. (2006) and Ma et al. (2009) reported
QTL affecting L* in a similar region of SSC3. Other color traits QTL have also been
identified on SSC3. Harmegnies et al. (2006) reported a QTL for a* in the same region in a
commercial four-way cross population and Soma et al. (2011) reported QTL for L* and b* at
93 
 

the proximal end of SSC3 as well as a QTL for color score located in a position proximal to
that of PCGF1.
FKBP10 encodes a 65kD rough endoplasmic reticulum FK506 binding protein that is

part of a large family of FK binding proteins which possesses both peptidyl-prolyl cis-trans
isomerization and chaperone functions (Davis, Broekelmann, Ozawa, & Mecham, 1998).
The FKBP10 SNP on SSC12 was associated with L* in this study. Li et al. (2010) evaluated
an FKBP10 SNP in a Korean Native Pig x Landrace F2 population and found associations
with growth traits at 21 days, although they did not identify an association with meat color or
other meat quality traits. We had identified QTL influencing the objective color parameters
of a* and b* at a more distal position on SSC12 in our second genome scan (Choi, et al.,
2011), but significant QTL influencing meat color were not observed in our first genome
scan in this region (Edwards, Ernst, Raney, et al., 2008). Ma et al. (2009) reported a QTL for
color score in this region of SSC12 in a Duroc x Erhualian population, and Harmegnies et al.
(2006) reported a QTL for a* at a position distal to FKBP10 in a commercial four-way cross
population.
The HSPG2 gene encodes a large proteoglycan that is a component of the extracellular
matrix. It is located on SSC6 (Aldenhoven, Spötter, & Distl, 2005) and was associated with
marbling score and days to 113 kg. Our results indicated that animals with the GG genotype
grew more slowly and had higher marbling scores. Schwab et al. (2010) reported a positive
moderate genetic correlation between intramuscular fat and growth rate in a Duroc
population, which agreed with the genetic control of this gene for these two traits. The
location of the HSPG2 gene is within the confidence interval of a highly significant QTL
region for fat percent and marbling score that was identified by both our first and second
94 
 

genome scans (Choi, et al., 2011; Edwards, Ernst, Raney, et al., 2008). Also, detection of
significant QTL for fat-free total lean and empty body protein at 22 wk of age was reported
in this same region in our previous genome scans (Choi, et al., 2010; Edwards, Ernst,
Tempelman, et al., 2008) and supports HSPG2 as a possible candidate gene for fat deposition
traits.
On SSC15, the MAP3K2 and SF3B1 markers were associated with LMA and the SF3B1
marker was also associated with L*. The MAP3K2 gene maps to human chromosome 2 and
encodes a 619 amino acid protein that activates MAP2K which regulate MAPK in a protein
kinase signal transduction cascade (Blank, Gerwins, Elliott, Sather, & Johnson, 1996; W.
Chen, White, & Cobb, 2002). The MAPK signaling cascade in the c-Jun N-terminal kinase
(JNK) and extracellular signal regulated kinase (ERK) pathways regulates cell differentiation,
proliferation and apoptosis, and is involved in cardiac muscle defects during development
(Regan, et al., 2002; Widmann, Gerwins, Johnson, Jarpe, & Johnson, 1998). The SF3B1 gene
encodes subunit 1 of the splicing factor 3B protein which plays an essential role for premRNA processing (Golas, Sander, Will, Luhrmann, & Stark, 2003). An association of
SF3B1 with L* in the present study was consistent with our genome scan results identifying

a QTL for L* on SSC15, and also associations of MAP3K2 and SF3B1 with LMA were
consistent with our previous studies identifying QTL affecting LMA on SSC15 (Choi, et al.,
2011; Edwards, Ernst, Raney, et al., 2008).
Based on genome scans using our Duroc x Pietrain F2 resource population, gene-specific
SNP markers located in the regions of five putative QTL (SSC3, 6, 12, 15, and 18) were
selected for evaluation in an US purebred Duroc population. Of the 81 gene-specific SNPs
genotyped, 33 were found to be segregating and were analyzed for associations with carcass
95 
 

merit and meat quality traits in the Duroc population. While the Duroc population had fewer
phenotypic traits recorded than had been measured for the resource population, 11 of the
SNP markers showed a significant association with at least one trait. Significant associations
were observed for the SSC3 marker MDH1 with all pH traits and for the SSC3 marker
PCGF1 with 45-min pH, L* and color score. The SSC6 marker HSPG2 was associated with

marbling score and days to 113 kg. On SSC12, significant associations were found for
NUP88 with 45-min pH, and FKBP10 with L*. The SSC15 marker SF3B1 was associated

with L* and LMA, and markers for the SSC15 gene PRKAG3 showed a significant
association with pHu. On SSC18, ARF5 was associated with pHu and color score. The results
obtained in the present study were consistent with our previous genome scans for pH on
SSC3, marbling score on SSC6, pH on SSC12, L* on SSC15 and pH on SSC18. Further
study is warranted to confirm segregation of QTL for these traits using larger commercial
Duroc populations with these SNPs as well as additional SNPs in order to facilitate
incorporation into pig breeding programs.

Acknowledgments
This project was supported in part by National Research Initiative Competitive Grant no.
2004-35604-14580 from the USDA National Institute of Food and Agriculture. We thank the
National Swine Registry for phenotypic data and samples.

96 
 

Table III.1. Number of records, means, and standard deviation for 9 traits evaluated
in this study.
Trait
N
Mean
SD
45 min pH

245
326

6.33
5.86

0.43
0.23

224
313

0.58
51.19

0.36
2.69

Color score, 1-6

326

2.99

0.63

Marbling score, 1-10

327

2.37

1.16

Backfat thickness, mm
Longissimus muscle area, cm2

277
277

13.12
47.15

2.74
4.76

Days to 113 kg

277

166.56

15.27

24 h pH (Ultimate pH; pHu)
45 min - 24 h pH decline
L*

97 
 

Table III.2. Information for SNP markers evaluated in this study.
Chr1
3

6

12

Full name

RNF40
IFT172
PCGF1
TIA1
MDH1
CEBPZ
MLL4
HSPG2
COL16A1
LEPR
STMN1
FKBP10
CACNB1
CACNA1G

95 kDa retinoblastoma protein binding protein
Intraflagellar transport 172 homolog
Polycomb group ring finger 1
TIA1 cytotoxic granule-associated RNA binding protein
Malate dehydrogenase 1, NAD
CCAAT/enhancer binding protein (C/EBP), zeta
Myeloid/lymphoid or mixed-lineage leukemia protein 4
Heparan sulfate proteoglycan 2
Collagen, type XVI, alpha 1
Leptin receptor
Stathmin 1
FK506 binding protein 10, 65 kDa
Calcium channel, voltage-dependent, beta 1 subunit
Calcium channel, voltage-dependent, T type, alpha 1G
subunit
Distal-less homeobox 4
Protein phosphatase, Mg2+/Mn2+ dependent, 1E
Solute carrier family 6 (neurotransmitter transporter,
serotonin), member 4
Small G protein signaling modulator 2
Phospholipase D2
Notchless homolog 1 (Drosophila)
Nucleoporin 88kDa
Phosphatidic acid phosphatase type 2 domain containing 1B
Oculocutaneous albinism II

DLX4
PPM1E
SLC6A4

15

SGSM2
PLD2
NLE1
NUP88
PPAPDC1B
OCA2
ABCB11

ATP-binding cassette, sub-family B (MDR/TAP), member
11

98 
 

16,106,853
43,823,993
63,084,541
66,216,145
72,272,863
96,014,633
30,161,015
54,946,906
60,507,310
103,462,536
–
18,725,878
21,096,363
24,128,357

SNP
type
A/G
T/C
T/C
A/G
A/G
T/C
A/C
C/G
A/G
T/C
A/G
T/C
A/G
T/G

ss23130356
ss23131537
ss23133110
ss16337136
ss23132846
ss23129373
ss16337525
ss23130687
ss23129728
Chen et al. 2004
ss99306908
ss23129646
ss86353491
ss86353487

24,905,314
33,289,660
44,338,635

T/C
T/C
T/G

ss23129839
ss23130731
ss99306907

45,906,070
49,274,975
–
–
45,833,191
53,553,470

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

70,887,762

G/C

ss23130795
ss23131423
ss23132413
ss16336982
ss23131638
Fernandez et al.
2006
ss23131708

Position2

Gene symbol

SNP source3

Table III.2. (Cont’d).
Chr1
15

Full name

PRKAG3 T30N

Protein kinase, AMP-activated, gamma 3 non-catalytic
subunit
Protein kinase, AMP-activated, gamma 3 non-catalytic
subunit
Mitogen-activated protein kinase kinase kinase 2
Splicing factor 3b, subunit 1
Titin
ADP-ribosylation factor 5
Inhibitor of growth family, member 3
Releasing hormone receptor 2
H2A histone family, member V

PRKAG3 I199V
MAP3K2
SF3B1
TTN
18 ARF5
ING3
CRHR2
H2AFV
1
Chr = chromosome

113,850,723

SNP
type
A/C

Milan et al. 2000

113,949,491

T/C

Milan et al. 2000

–
–
–
18,821,285
24,115,025
40,666,448
49,063,850

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

ss23130119
MSU
ss16337371
MSU
MSU
ss86353512
ss23132945

Position2

Gene symbol

2

SNP source3

Position is estimated based on pig genome sequence (Build 9; http://www.ensemble.org) and – indicates unidentified position in the
current pig genome sequence.
3
SNP were obtained from public database sources or publications, or were developed in our laboratory, and are identified by dbSNP
accession number, reference citation or as MSU for new SNP
.

99 
 

Table III.3. Genotypic and allelic frequencies for SNP markers.
Chr
3

6

12

15

18

1

1

Gene symbol
RNF40
PCGF1
TIA1
MDH1
CEBPZ
IFT172
MLL4
HSPG2
COL16A1
LEPR
STMN1
FKBP10
CACNB1
CACNA1G
DLX4
PPM1E
SLC6A4
SGSM2
PLD2
NLE1
NUP88
PPAPDC1B
OCA2
ABCB11
PRKAG3 T30N
PRKAG3 I199V
MAP3K2
SF3B1
TTN
ARF5
ING3
CRHR2
H2AFV

Genotype frequency
0.85(GG)
0.32(GG)
0.05(AA)
0.85(AA)
0.79(AA)
0.32(AA)
0.22(AA)
0.28(CC)
0.88(AA)
0.52(AA)
0.61(AA)
0.08(AA)
0.13(AA)
0.24(AA)
0.08(AA)
0.77(AA)
0.05(AA)
0.02(AA)
0.33(AA)
0.51(AA)
0.18(AA)
0.4(AA)
0.57(AA)
0.01(CC)
0.09(AA)
0.11(AA)
0.15(AA)
0.23(TT)
0.36(AA)
0.2(AA)
0.2(AA)
0.26(AA)
0.26(AA)

0.15(AG)
0.68(AG)
0.35(AG)
0.13(AG)
0.21(AG)
0.52(AG)
0.47(AC)
0.44(CG)
0.12(AG)
0.37(AG)
0.38(AG)
0.34(AG)
0.31(AG)
0.38(AC)
0.31(AG)
0.21(AG)
0.48(AC)
0.34(AC)
0.53(AG)
0.26(AG)
0.49(AC)
0.48(AG)
0.35(AG)
0.1(CG)
0.5(AC)
0.52(AG)
0.42(AG)
0.77(AT)
0.5(AC)
0.47(AG)
0.48(AG)
0.51(AG)
0.44(AG)

Chr = chromosome

100 
 

–
–
0.6(GG)
0.02(GG)
–
0.16(GG)
0.32(CC)
0.28(GG)
–
0.11(GG)
0.02(GG)
0.58(GG)
0.56(GG)
0.38(CC)
0.61(GG)
0.02(GG)
0.47(CC)
0.64(CC)
0.14(GG)
0.22(GG)
0.34(CC)
0.12(GG)
0.08(GG)
0.9(GG)
0.41(CC)
0.37(GG)
0.43(GG)
–
0.14(CC)
0.33(GG)
0.32(GG)
0.24(GG)
0.29(GG)

Allele frequency
0.93 (G)
0.66 (G)
0.77 (G)
0.92 (A)
0.9 (A)
0.58 (A)
0.55 (C)
0.5 (G)
0.94 (A)
0.7 (A)
0.8 (A)
0.75 (G)
0.71 (G)
0.57 (C)
0.76 (G)
0.88 (A)
0.71 (C)
0.81 (C)
0.59 (A)
0.64 (A)
0.58 (C)
0.64 (A)
0.74 (A)
0.94 (G)
0.66 (C)
0.63 (G)
0.64 (G)
0.62 (T)
0.61 (A)
0.56 (G)
0.56 (G)
0.51 (A)
0.51 (G)

0.07 (A)
0.34 (A)
0.23 (A)
0.08 (G)
0.1 (G)
0.42 (G)
0.45 (A)
0.5 (C)
0.06 (G)
0.3 (G)
0.2 (G)
0.25 (A)
0.29 (A)
0.43 (A)
0.24 (A)
0.12 (G)
0.29 (A)
0.19 (A)
0.41 (G)
0.36 (G)
0.42 (A)
0.36 (G)
0.26 (G)
0.06 (C)
0.34 (A)
0.37 (A)
0.36 (A)
0.38 (A)
0.39 (C)
0.44 (A)
0.44 (A)
0.49 (G)
0.49 (A)

P value
(χ2 test)
HWE
<0.05
HWE
<0.05
<0.05
HWE
HWE
<0.05
HWE
<0.05
<0.05
HWE
<0.05
<0.05
<0.05
HWE
<0.05
<0.05
HWE
<0.05
HWE
HWE
HWE
HWE
<0.05
HWE
HWE
<0.05
HWE
HWE
HWE
HWE
<0.05

Table III.4. LS means of significant SNP markers for carcass and meat quality traits.
MDH1

Chr1
3

PCGF1

3

NUP88

12

ARF5

18

MDH1

3

pH decline

MDH1

CIE L*

Trait

Gene

Homozygote

Heterozygote

Homozygote

a

a

b

Dominance

-0.21±0.06**

-0.17±0.07**

Marbling

6.49± 0.13de
(CC)

-0.03±0.03

0.08±0.03*

5.83± 0.10d
(AA)
5.86± 0.09d
(AA)

5.91± 0.1e
(AG)
5.95± 0.1e
(AG)

5.89± 0.1de
(GG)
5.81± 0.13de
(GG)

-0.03±0.02

0.05±0.02*

0.02±0.05

0.12±0.06*

3

0.61± 0.18a
(AA)

0.55± 0.18a
(AG)

1.08± 0.23b
(GG)

-0.24±0.08**

-0.30±0.09**

FKBP10

12

-0.38±0.42

1.34±0.47**

3

54.88±1.43e
(AG)
53.30±1.36b
(AT)
53.53±1.37e
(AG)

53.90±1.39de
(GG)

15

53.17±1.52d
(AA)
54.67±1.46a
(TT)
54.44±1.44d
(GG)

PCGF1

3

ARF5

Color

6.50± 0.13
(AG)
6.53± 0.13b
(AG)
6.53± 0.13e
(AC)

PCGF1

pHu

6.46± 0.12
(AA)
6.42± 0.13a
(GG)
6.42± 0.13d
(AA)

SF3B1

pH45

18

2.15± 0.37a
(GG)
2.13± 0.36d
(AA)

2.45± 0.35b
(AG)
2.46± 0.35e
(AG)

2.31± 0.37d
(GG)

-0.09±0.07

0.24±0.09**

HSPG2

6

1.59± 0.57d
(CC)

1.87± 0.55de
(CG)

2.07± 0.56e
(GG)

-0.23±0.10*

0.04±0.13

101 
 

6.87± 0.17
(GG)

Additive

Table III.4. (Cont’d).
Days

HSPG2

Chr1
6

Backfat

RNF40

3

13.36± 1.74d
(GG)

15.01± 1.84e
(AG)

LMA

MAP3K2

15

51.40± 2.78ab
(AA)

51.71± 2.66a
(AG)

SF3B1

15

51.95± 2.76d
(TT)

49.84± 2.65e
(AT)

PLD2

12

49.67± 2.67d
(AA)

51.1± 2.66e
(AG)

Trait

Gene

Homozygote
156.71± 8.58
(CC)

Heterozygote
d

158.15± 8.22
(CG)

1

Chr = chromosome

a,b

LS mean P < 0.01

d,e

LS mean P < 0.05
*P < 0.05 and ** P < 0.01

102 
 

d

Homozygote
162.5± 8.26
(GG)

-2.77±1.34*

Dominance
-1.46±1.70

49.57± 2.72b
(GG)

0.79±0.53

1.29±0.61*

51.3± 0.82e
(GG)

-1.16±0.50*

0.20±0.62

e

Additive

Table III.5. LS means of combined PRKAG3 SNP genotypes for ultimate pH.
CC/AA
CC/AG
CC/GG
AC/AG
ab
a
c
5.95± 0.10
5.81± 0.11
5.84± 0.10c
5.93± 0.11
pHu
No. of
37
73
21
95
animals
a,b,c
LS mean P < 0.05

103 
 

AC/GG
5.84± 0.10bc

AA/GG
5.88± 0.09abc

68

31

Supplementary Table III.1. Probe sequences spanning SNP positions used for Illumina Goldengate assays.
chr1 SNP
3

RNF40
PCGF1
TIA1
MDH1
CEBPZ
IFT172

6

MLL4
HSPG2
COL6A1
LEPR
STMN1

12 FKBP10
CACNB1
CACNA1G

Probe sequence
CCCCCTTCTG
TAGTAGTACA
GAGAAACGGA
CAGCCCAGTG
CCTTCGATCG
TGGGTGGAAA
TTAAAGCCTC
TTTCTCTGGC
TTGATGATAA
AGTGGATGGC
GGTCACGGGT
ATGATTTCAT
GGCACCACAC
CCATGGGGAT
TCTGTGTGAC
CCTAGACAGG
GAGGCGCCTA
GCCGATTTTG
CTCAAAGAAC
GGAACTTAAT
GAAGGTTGGT
ATGCACCGCT
GGTGGCTCCC
GGGGCTGCTG
AGACCCGGGG
GCCTGCATGA
AGCCGGGACC
GGGGTGGGTC

GTCCCCTTGC
CAGCCTGCAT
TCCGAGAATT
GGGAAGAGCC
TAGTACCATT
ATCTAAGAGA
TATTGGGCCA
CTGCTTCACA
ATACGGGAGG
AGATGACCTT
GTCTCTGAAA
CATCTTATTA
CCCAGACCTC
TCAGGGGTGG
TGGAAGCAAG
AAGGAAGTTC
GCTCTTCTCT
ATATCCCAGG
ACTTCAACTT
TCAAGTGGTA
GTCTCTTCGT
GAAGTGGCTT
GGGAAAAACT
ATGAGCAGGA
GAAGGGAAGG
GAGAGTGACC
CCTCAGGGAG
TCCTCTTGGT

CTCTGAATGT
TTGCTCAGTG
CTACCAGTCC
AGCCCTGAGC
CACTGAGACA
AAGAGAATGA
TAGCTCCTCT
CATAGATCAG
AAGAAAGGAA
AGGCTGTGGC
AACGTCCACA
CCATCTTTCC
TCACCGGAGA
TTCCTCTTGG
TCCCTGGATC
CCCATCTGCT
GAGGCAGGGA
CTGGGGTGGG
TGAATGGACA
CCTACTTATC
GGCTTTGAGG
GAACTCTTGA
CAGCTTAGGT
AAGCGCTCTG
GGGGACGTTT
AGGAACACCT
CGCTGGGCAC
TTTACAGGTG
104 

 

TGGAATTCTC
GTTCCTCGGT
CGAGACTTGG
AACCTCGGCC
ATGGCATGGG
GGTTTTTTTG
GTTTATAAGA
AAGAGTAGAA
AGCAACAAAG
AGACCAGTTC
CCTACACACG
TGCTTTTTAG
TAGACCTCAG
CCCTCTCCCT
TCTCAGGTTC
CAAGTTCCAG
GGAGAGCTGG
GACAGTGGAG
TGATGAGGCA
AAACTTATCT
GGGTTAAGCG
CCTTGATCAC
CTCGCAAGGG
GAAACCAGGG
GGGTTCTCCT
GGCCCGGGTC
CCTACAGGGC
AGAACACTGA

CAAACTTCTT
CAGCCGTGTG
ACAGGATCAC
TCCCCTTCAG
CTGCACTTTC
TTTTTGATTT
TGAGGGTTAT
TGAGATAATA
GGGGTTTGCA
CTGTTACTTA
AAATTTCACA
GGCCGAACCA
CGCGGGCTGC
GCTGGTGGTA
CTCTGGGACA
GAGGGGGTGG
GAGTGTCAGG
AGACAGAGCT
GTTGTTGAAA
TCTAAAACAA
CTTGGAGTTC
ACGCTTGGTG
AAGAACCAAC
TTGGCGCCTT
GGTTTCCTCT
CTTAGGTGCA
GTCCACCTCT
GGCTCAGAGA

[A/G]
[T/C]
[A/G]
[A/G]
[T/C]
[T/C]
[A/C]
[C/G]
[A/G]
[T/C]
[A/G]
[T/C]
[A/G]
[T/G]

Supplementary Table III.1. (Cont’d).
chr1 SNP
12 DLX4
PPM1E
SLC6A4
SGSM2
PLD2
NLE1
NUP88

15 PPAPDC1B
OCA2
ABCB11
PRKAG3 T30N
PRKAG3 I199V
MAP3K2
SF3B1-1

Probe sequence
GAGCCACGCC
CA TTTTTC C T
AGCCACACAA
ATGCCACAAA
TCGGGTGGAT
CGGGGAGCTA
AGCCTGTTCC
GGAGGGGCCC
GACTTCAGCC
GCAGAGCCCT
CAAGTAAGGG
TGCCCTGGAA
TTC TTATG TT
GTTTCCGACG
TCCACTCTGG
GTAACTGGTC
GCTTGGAAGT
TCCAGAGTTC
GTCTGCAGAG
ACGCATGCTT
CATCCCGAGC
CAAGGCCTC
ACACCATGCT
TCCGAGCGGC
TTGAGAGAAC
GTCTCCCAGC
CTAATGGTTT
AACCAGCTTA

CGTCCACACC
CCTTTCCCCC
ACCTGACAGA
GGCAGTTTTC
GGAGGGGTCT
GGGACCTCCT
TTTGGGGCAG
TTGGAAACCT
CGCTGCCCCA
CCTCTCCCGG
ACTTCCCCAG
GAGCCCTTCC
GAGATAGCTT
GGTAGGGACG
CTCAAGATAC
ATTTAGTATA
TTCTCCCAAG
ATCACTGCTG
AAAAGATGGA
GCTCCCTGGC
TGTAACCACC
TAGATGGACA
GGAGATCAAG
ACCTTTGTGG
AAATACCGCC
ACGGGAGTGC
GGTGTTTTTA
TTAGATGTAT

TACACTCCTC
CATAGGTAAA
GAGGTAAGTA
TAATTATTAA
TCCAAGAAAG
GGAATCAGTC
GGAGATGGGG
CAGTCCCTTC
GCCCCCTCTC
GGTCACCCAC
TGGAGTTTGG
TGGACTTGAC
AGAAGTGCTC
TAATTCACGG
GGGTTCTGAG
CGGGTGATGG
AGGAAGGGTC
ATGAACCTTG
AAGGTTGAGG
CCACCATGGT
AGCTCAGAAA
AGGCAGGAGG
AAGGCCTTCT
GACAGCAAGA
CACAAACCCA
CACCAACCCA
ACATAAAAGG
TTATGAAAAT
105 

 

CTGACCTTTC
GATCTGGTTT
TGGTCTGTTC
T TTTC ATTTA
TGGTGATTTC
AGGCCTTAGG
AGCCAGGCCA
CAGCTAGAAA
CCTGACCCGA
CACGTCGTAT
TCCCCAAAGG
CTTGGGGTTG
TGCTAAAATG
AAGGCCAGCA
GACGCTTTAG
GTGCTCCTGG
ACGGTGCATA
GGAGAACAGC
CAAT [C/G]
GAGAGTTCTG
GAAGCCATGG
ATGTAGAGGA
TTGCCCTGGT
AGCAGAGCTT
TCCCCTCTCC
AGGCTTAGCC
AATTTGTGGA
TTAAAACTCT

TTTCAATCCT
CAGAACAAAC
TGTTAGGACA
TCTCAGCAAC
CATCAGTGTT
TATCCCCAGG
CAGGAACCCA
CAGGGCAGAG
GAGCCCCCC
TCCCCGTCCC
CCAAGGATAC
GGGTTCCTAG
ACTGGCTTAA
GCAGCAAGTA
GGCGCCCGG
TGGTACTGTG
CCTGTTTACC
TCAGCTGAGT
CCTCATACC
GGACCAGGGG
AGGGGGGCCT
GGCCAACGGC
CGTGGGGATG
CACAGTCTGC
AGCATTGTGG
CTTTTTTTTT
AAAACAATAC

[T/C]
[T/C]
[T/G]
[T/G]
[T/C]
[A/G]
[T/G]
[A/G]
[A/G]

A [A/C]
CC
[A/G]
CTGAC
[T/C]
[A/T]

Supplementary Table III.1. (Cont’d).
chr1 SNP
15 TTN
18 ARF5
ING3-1
CRHR2
H2AFV
1

Probe sequence
TCGTAAGTAC
TTTTTATTTT
AGCTGGGAAA
CGCCTCTGCC
CACGACTGAT
TTTTTTC ATG
CTCCACCTGC
GCCTTGAACCC
TGAAAATTGA
TGGCCTCACA

ACCTTTACCA
TGGCTTTTGT
AGGGAGCCTG
TTCTCTCAGT
TGAGAAACAA
CAATTACTGG
GGCTGGCACA
GGGCCCCAGC
TTCCTGTATA
TTTAAAGACT

TTTTCA TTAC
CTTCTGAGCA
GGGGCCCCCA
TGCAGGAAGA
ATATCCCTCC
TGGATTTATC
CTCAAAGCCG
CCCGTCACGC
TAACTTAGTG
TCTTCGAAGT

Chr = chromosome

106 
 

ATGCTTAAGG
CGGCAAATCC
GAGACCCCCC
CGACGTGCGC
CCTTTTATTT
TTCTTCATTT
TGCA [T/C]
CTACCGCCC
AATTTTTACT
TGAACGTTAA

CCTCTATGTG [T/G]
TCAGAAACCT
TCAGATGCCA [T/C]
GATGCGGTGC
TGTGAGATAT [A/G]
GGGAA

TCTGCATACT [T/C]
ACTGTCATGG

References

107
 

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Chapter IV

Association of a corticotropin-releasing hormone receptor 2 (CRHR2) polymorphism
with carcass merit, meat quality and stress response traits in pigs
Igseo Choi, Ronald O. Bates, Nancy E. Raney and Catherine W. Ernst*

Department of Animal Science, Michigan State University, East Lansing, MI, U.S.A.

*Correspondence:
Catherine W. Ernst
3385 Anthony Hall
Department of Animal Science
Michigan State University
East Lansing, MI 48824-1225, USA
ernstc@msu.edu

This chapter formatted for submission to the journal Molecular Biology Reports

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Abstract
Corticotropin-releasing hormone receptor 2 (CRHR2) was evaluated as a potential candidate
gene affecting stress response, carcass merit and meat quality traits in pigs. CRHR2 is one of two
receptors for corticotropin-releasing hormone and plays a key role in mediating stress response
through the hypothalamic-pituitary-adrenal (HPA) axis. A single nucleotide polymorphism (SNP)
was identified within the pig CRHR2 gene and its position was localized to pig chromosome 18.
The CRHR2 SNP was used to screen a Duroc x Pietrain F2 resource population and a LandraceYorkshire population that was exposed to a halothane challenge test. Association analyses were
performed for carcass merit and meat quality traits recorded for each population, and for stress
response phenotypes recorded for the halothane challenge population. For the resource
population, CRHR2 genotype was significantly associated with 45-min pH, ultimate pH (pHu),
45-min temperature, drip loss, cook yield, moisture percent, protein percent, CIE b*, ham weight,
and dressing percent. Pigs with the GG genotype had a lower 45-min carcass temperature which
led to maintenance of a higher 45-min pH and pHu. Consequently, early postmortem temperature
and pH may have influenced cook yield and protein percent. For the halothane challenge
population, CRHR2 genotype exhibited a suggestive association with CIE L* and color score, as
well as with blotching although no other stress response phenotypes showed an association with
CRHR2 genotype. This study supports an association of CRHR2 genotype with meat quality and

suggests additional research with additional pig populations may be warranted.
Keywords: Corticotropin-releasing hormone receptor 2, stress response, carcass merit, meat
quality

113
 

Introduction
Tremendous efforts to obviate inferior meat quality resulting from animal stress have been
continuing in the pig breeding industry since pale, soft, exudative (PSE) pork was recognized in
the 1950s. Thereafter the prevalence of the porcine stress syndrome (PSS) condition and its
effect on the occurrence of PSE pork became a concern, which the National Pork Producers
Council affirmed as the most important quality problem in the 1990s [1-4]. PSE pork induced
from the rate and extent of muscle pH decline postmortem and high muscle temperature [5] is
influenced by genetic, biochemical and environmental factors. PSS is a non-pathological
disorder affected by diverse stressors such as transportation, temperature, restraint, weaning and
handling [6], and its symptoms are characterized by rapid respiration, muscle tremors and
rigidity, reddish blotches on the skin, increases in body temperature, and even death. In order to
identify stress susceptible pigs, a halothane challenge test has been used [7]. This test was unable
to distinguish heterozygous (N/n) stress carriers from homozygous normal (N/N) individuals.
Identification of a polymorphism at position 1843 in the skeletal muscle ryanodine receptor 1
(RYR1) gene that was highly associated with PSS facilitated development of a DNA-based assay
for detecting the C1843T mutation which allowed identification of heterozygous carriers (N/n)
[8,9]. However, despite efforts to reduce the incidence of PSE pork, PSE pork was 15.5 percent
of US slaughter pigs in 2002 which corresponds to $90 million per year in lost revenue [1].
Another potential candidate gene for animal stress may be corticotropin-releasing hormone
receptor 2 (CRHR2). Corticotropin-releasing hormone (CRH) plays a key role in mediating the
endocrine, autonomic, behavioral, and immune responses to stress through the hypothalamicpituitary-adrenal (HPA) axis stimulating cortisol release [10,11]. CRHR1 and CRHR2, two
receptors of CRH, are expressed in distinct locations implying different physiological functions

114
 

[12]. CRHR2 is found in skeletal, smooth, and cardiac muscle as well as brain, whereas CRHR1
is found in ovary, testis, skin, and adrenal gland. At the DNA level, a C/T polymorphism at
position 233 within the CRH gene was associated with meat color in Pietrain pigs [13]. In
addition, mRNA abundance of CRH in brain of stressed pigs was higher than in unstressed pigs
[14]. A relationship of CRHR2 and meat quality is plausible, but no studies have been done to
consider potential associations between CRHR2 and meat quality.
In the present study, CRHR2 was evaluated as a potential candidate gene affecting stress
response and influencing polygenic carcass and meat quality traits. The effect of CRHR2
genotype was investigated using an experimental F2 resource population and a commercial pig
population that was exposed to a halothane challenge test.

Materials and methods
Animals and traits
Two independent pig populations, a Duroc x Pietrain F2 resource population and a halothane
challenge population, were used for this study. Animal protocols were approved by the Michigan
State University (MSU) Institutional Animal Care and Use Committee (AUF# 09/03-114-00 and
AUF# 02/07-011-00, respectively). The MSU resource population was derived from 4 F0 Duroc
sires and 15 F0 Pietrain dams, and 438 F2 pigs were genotyped for this study. All grandparents
were confirmed to be homozygous normal for the polymorphism at position 1843 in the RYR1
gene [14]. A detailed description of the animals and phenotypic data was previously reported
[15,16]. Briefly, animals were slaughtered at the Michigan State University Meat Laboratory
(East Lansing, MI) or a federally inspected commercial plant (DeVries Meats, Coopersville, MI).
115
 

Slaughter age was 165.8 ± 9.2 days and the average off-farm body weight (BW) at slaughter was
111.15 ± 8.98 kg. Hot carcass weight (HCW), and pH and temperature of the longissimus muscle
(LM) at 45-min and 24-h (pHu) postmortem were obtained. After overnight chilling, backfat
thickness, number of ribs and carcass length were measured, and the weights of primal cuts were
recorded. A single trained evaluator scored color, marbling and firmness using two 2.54-cm thick
chops cut from the LM, and objective color scores of CIE L*, a*, and b* were obtained using a
Minolta colorimeter. The remaining section of the LM was used to determine drip loss, cook
yield, Warner-Bratzler shear force, proximate analysis measures, and sensory attributes. A
trained sensory panel evaluated juiciness, tenderness, overall tenderness, connective tissue, and
off-flavor using an 8-point hedonic scale. Descriptive statistics for phenotypes used in this study
are presented in Table IV.1.
The second population was comprised of 363 pigs produced in four farrowing groups from
Yorkshire-Landrace F1 sows bred to Landrace boars at the MSU Swine Teaching and Research
Center. Pigs were subjected to a halothane gas challenge test at 56 ± 3 d of age with the test
repeated 2 d after the first test. Pigs were chosen at random for halothane gas challenge which
consisted of pig exposure to 5% halothane gas in a closed system at a rate of 2 L/min for three
minutes per pig for the first farrowing group and two minutes per pig for subsequent farrowing
groups. After 1 min inhalation of halothane, limb rigidity, blotching of the skin, and muscle
tremors were recorded on a binary scale (0 = no response; 1 = response) and body temperature
was measured. Animals were slaughtered at a federally inspected commercial facility (J.H.
Routh Packing Co, Sandusky, OH). The pH of the LM at 45-min and 18-h (pHu) postmortem
were measured and pH decline from 45-min to 18-h was calculated. After overnight chilling,

116
 

subjective color and marbling scores were evaluated, objective color scores of CIE L*, a*, and
b* were measured using a Hunter colorimeter, and drip loss was estimated from LM using filter
paper [17]. Descriptive statistics for phenotypes used in this study are presented in Table IV.2.

CRHR2 genotyping

DNA was isolated from white blood cells for pigs from the resource population using
standard procedures as previously reported [14]. Primers for use in the polymerase chain reaction
®

(PCR) were designed from heterologous CRHR2 sequences using the OLIGO 5.0 Primer
Analysis Software (NBI, Plymouth, MN) to amplify a portion of the pig CRHR2 gene (forward
primer 5′-GCA TCA CCT ACA TGC TCT TCT T-3′; reverse primer 5′-CTC TCC ATT GAA
GAA GCA GTA GA-3′), and identity of the pig CRHR2 sequence-tagged site (STS) was
confirmed by sequencing. A single nucleotide polymorphism (SNP) in CRHR2 was identified by
sequencing [18] and information on the SNP and the pig STS has been deposited into appropriate
databases (dbSNP ID: ss86353512, GenBank accession no. BV727800). A PCR restriction
fragment length polymorphism (RFLP) approach was used to genotype CRHR2 in the MSU
resource population. Approximately 25 ng of genomic DNA was amplified in a final volume of
10 μl containing 0.5 μM each primer, 200 μM dNTPs, 1.5 mM MgCl2 , 1x PCR buffer, and 0.05
units Taq DNA polymerase. Amplifications were performed in a MJ Research thermal cycler
with a profile that included an initial denaturation at 94 °C for 10 min, 30 cycles of denaturation
at 94°C for 1 min, annealing at 58 °C for 1 min, and extension at 72 °C for 1 min followed by a
final 10 min extension at 72 °C. Subsequently, the 422 bp amplicons were digested with the
endonuclease NlaIII resulting in two alleles (cut fragments 258 bp and 164 bp designated allele
A; uncut fragment 422 bp designated allele G).
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For the halothane challenge population, DNA was purified from white blood cells using an
Invitrogen PureLink Genomic DNA minikit (Invitrogen Corp., Carlsbad, CA) following the
manufacturer’s instructions. Quantity and quality of purified DNA was measured on a Nanodrop
spectrophotometer (Thermo Fisher Scientific Inc., Waltham, MA). An Invitrogen Quant-iT
dsDNA assay was also used to quantify extracted DNA, according the manufacturer’s
instructions, and samples were read using an Invitrogen Qubit fluorometer. In order to more
rapidly genotype this population, the CRHR2 SNP was included in an Illumina Goldengate 96
plex SNP multiplex custom assay panel (Illumina, Inc. San Diego, CA). Pigs were genotyped
with the custom assay, and the assay was read using an Illumina BeadXpress Reader at the MSU
Research Technology Support Facility. Genotype segregation was then determined using the
Illumina Genome Studio software.

Statistical analysis
Allele and genotype frequencies for the CRHR2 polymorphism were estimated by the
ALLELE procedure of SAS (version 9.1.3). Hardy-Weinberg equilibrium (HWE) was tested by
using 10,000 permutations to approximate p-values for HWE, and 1,000 bootstrap samples were
used to calculate 95% confidence intervals for allele frequency and Hardy-Weinberg
disequilibrium coefficients. The association analyses between CRHR2 genotypes and traits were
performed using the MIXED or GLIMMIX procedure software of SAS (version 9.1.3),
depending on the response variable distribution. The phenotypic traits for which CRHR2
genotype was significant in the model were considered further for comparison of least-squares
means. Additive effect of CRHR2 was estimated as half of the difference between two
homozygous genotypes (a = ½ (AA – GG)) and dominance effect was estimated as the

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difference between the heterozygous genotype and the average of the two homozygous
genotypes (d = AG – ½(AA + GG)).
For the resource population, the statistical model was as follows;
Yijklmn    Sex j  CRHR 2 k  Farrowing l  Harvest m  Littern  eijklmno

where Yijklmn is the trait measured on animal i, μ is the overall mean for the trait, Sexj is the fixed
effect of the animal’s gender (j = barrow or gilt) and CRHR2k is the fixed effect of CRHR2
genotype (k = AA, AG or GG). Farrowingl, Harvestm, and Littern are random effects of
farrowing group (l = 11 levels), harvest date (m = 33 levels) and litter (n = 55 levels),
respectively, and eijklmno is the residual term.
For the halothane challenge population, a generalized linear mixed model with a logit link
function was used to analyze the binomially distributed response traits (blotching, rigidity and
tremor) and then estimates were transformed back to the original scale via the inverse link
function in order to compare least-squares means. The statistical model for halothane response
traits was as follows [1];
Yijklm ~ Bernoulli ( Pijklm )
logit ( Pijklm )    Sex j  CRHR 2 k  Farrowing l  Litterm  eijklmn   

[1]

where Pijklm is the probability of the i th animal exhibiting a positive response for the measured
trait, μ is the overall mean for the trait, Sexj and CRHR2k are the fixed effects. Farrowingl, and
litterm, are random effects of farrowing group (l = 4 levels) and litter (m = 38 levels),

respectively. Body temperature was analyzed with the same fixed and random effects as [1]
using a linear mixed model.
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For carcass and meat quality traits in the halothane challenge population, the summation of
binary recorded blotching, rigidity and tremor per animal was treated as an overall halothane
effect (denoted as Gas in the model) and used as a fixed effect along with sex and CRHR2
genotype. The statistical model used was as follows [2];
Yijklmno    Sex j  CRHR 2 k  Gas l  Farrowing m  Harvest n  Litter o  e ijklmnop [2]

where Yijklmno is the trait measured on animal i, μ is the overall mean for the trait, Sexj, CRHR2k
and Gas (l = 0 – 3) are the fixed effects. Farrowingm, Harvestn and littero, are the random
effects of farrowing group (m = 4 levels), harvest date (n = 8 levels) and litter (o = 38 levels),
respectively.
For growth traits in the halothane challenge population, the model was as follows [3];
Yijklmn    Sex j  CRHR 2 k  Gas l  Farrowing m  Litter n  e ijklmno   

[3]

where Yijklmn is the trait measured on animal i, μ is the overall mean for the trait, Sexj, CRHR2k
and Gasl are the fixed effects. Farrowingm, and littern, are the random effects of farrowing
group (m = 4 levels) and litter (n = 38 levels), respectively. For average daily gain from weaning
to off-farm date, body weight of age at weaning was included as a covariate.

Results
Allele and genotype frequencies
Allele and genotype frequencies for CRHR2 in both the resource population and the
halothane challenge population are shown in Table IV.3. The frequency of the CRHR2 allele A
was 0.38 and that of allele G was 0.62 in the resource population, whereas the frequency of allele

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A was 0.60 and that of allele G was 0.40 in the halothane challenge population. Frequencies in
both populations were in agreement with Hardy-Weinberg equilibrium.

Resource population
Based on the location of CRHR2 on human chromosome 7 (GenBank accession no.
NM_001883), pig CRHR2 was expected to be located on SSC18. This was previously confirmed
using the CRHR2 STS by both linkage mapping with the PigMap reference families and
radiation hybrid (RH) mapping using the INRA University of Minnesota porcine RH panel [18].
The position of CRHR2 was determined to be near marker S0062 (lod score = 16.83) on SSC18
by RH mapping and CRHR2 also showed evidence of linkage with marker S0062 (lod score =
4.86) in the PigMap families. In our Duroc x Pietrain resource population, CRHR2 was localized
to 95.4 Kosambi cM which was at the distal end of SSC18 on the map for this population
(SW1808 – SW2540 – SW1023 – SW1984 – S0062 – CRHR2), and was consistent with the results
obtained by RH mapping and linkage mapping in the PigMap population. Localization of the pig
CRHR2 STS to the pig genome sequence (Bulid 9; http://www.ensemble.org) indicates a start

position for the STS of 40,666,139 bp on SSC18.
Results of the association analysis of CRHR2 genotypes with carcass merit and meat quality
traits for the resource population are presented in Table IV.4. Pigs exhibiting the GG genotype
had significantly higher LM 45-min pH and pHu values than pigs with the AA genotype (P <
0.05), and consequently GG pigs also had a lower moisture percent and a greater cook yield (P <
0.05). The GG pigs also exhibited a higher LM protein percent than the AA and AG pigs (P <
0.05) consistent with the decreased moisture percent. A significant additive effect was observed
for these traits such that the addition of a G allele increased pH values, cook yield and protein
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percent, and decreased moisture percent. Pigs with the GG genotype also exhibited a lower LM
drip loss than pigs with the AG genotype (P < 0.05), although pigs with the AA genotype were
intermediate and not significantly different from either AG or GG pigs. In addition pigs with the
GG genotype exhibited a lower 45-min carcass temperature than pigs with the AG genotype (P <
0.05), with no significant difference from AA pigs observed.
Higher values for CIE b* indicating more yellowness for LM color were observed for pigs
with the GG and AG genotypes compared to pigs with the AA genotype (P < 0.05), and this trait
exhibited a significant additive effect. Pigs with the GG genotype also exhibited a lower ham
weight compared to pigs with the AA or AG genotypes (P < 0.05). Interestingly, a significant
dominance effect was observed for dressing percent (P < 0.05) such that pigs with the AG
genotype exhibited a higher dressing percent than pigs with either homozygous genotype.

Halothane challenge population
Results of the association analysis of CRHR2 genotypes with stress response, carcass merit
and meat quality traits for the halothane challenge population are presented in Table IV.5.
Association analyses were performed to investigate the relationship between CRHR2 genotypes
and four halothane challenge response traits: blotching, rigidity, tremor and body temperature.
Pigs with the GG genotype exhibited less blotching (P = 0.07) than pigs with the AG genotype.
For carcass and meat quality traits, meat from pigs with the GG genotype exhibited a lighter
muscle color as measured by CIE L* compared to AG genotype pigs (P = 0.07). Subjective color
score also tended toward significance for this population such that AG genotype pigs had higher
scores (darker color) than AA pigs (P = 0.08). A significant dominance effect (P < 0.05) was

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observed for both CIE L* and color score indicating that heterozygous animals exhibited darker
meat than homozygous animals.

Discussion
CRHR2 is one of two receptors for CRH ,and it is a G-protein-coupled receptor that

functions in stress response and energy homeostasis [12]. The major ligands for CRHR2 are
urocortin II and urocortin III which are two CRH-related neuropeptides and selective agonists

[19,20]. CRHR2 contributes to an animal’s ability to cope with stressful situations such as
feeding suppression, hypotension, and anxiolysis, and it is also involved in weight regulation.
The CRHR2 gene contains 17 exons, and three expression variants have been reported in human
and rodents: CRHR2α, CRHR2β and CRHR2γ [21-23] with CRHR2β being the major CRHR2
isoform expressed in skeletal muscle tissue in rodents [24].
Glucocorticoid (GC) that is synthesized and secreted from the adrenal cortex as a negative
feedback response to stress has been shown to induce skeletal muscle proteolysis [25], and to
differentially regulate the expression of the CRHRs [26]. Elevated GC under stress response
stimulates muscle proteolysis and affects meat quality [27-29]. Shaw et al. [25] reported that
elevated cortisol levels in skeletal muscle were highly correlated with cortisol levels in plasma,
and were also associated with meat quality traits exhibiting a positive correlation with ultimate
pH and color score and a negative correlation with drip loss [27]. A positive relationship has also
been shown between plasma cortisol level and muscle rigidity in humans [30].
For the present study CRHR2 was evaluated as a candidate gene for potential effects on
carcass merit and meat quality traits, and also effects on response to stress induced by a
halothane gas challenge test. In the F2 resource population, CRHR2 genotypes were significantly
123
 

associated with 45-min pH, pHu, 45-min carcass temperature, drip loss, cook yield, moisture
percent, protein percent, CIE b*, ham weight, and dressing percent. In the halothane challenge
population, CRHR2 genotype influenced blotching, CIE L* and color at a suggestive significance
level.
The CRHR2 gene was highly associated with pHu in the resource population (P < 0.002). A
previous genome scan of this population had revealed a QTL for pHu on SSC18, although the
position of this QTL did not overlap the map position of CRHR2 [16]. Other QTL studies have
identified QTL for pHu in the region where CRHR2 is located [31,32]. A significant effect of
CRHR2 genotype on carcass temperature and pH supports a potential role for this gene in meat

quality since early postmortem temperature and pH influence variation in expression of other
meat quality traits [33]. For pigs with the GG genotype, a lower 45-min carcass temperature led
to maintaining a higher 45-min pH and pHu, thus resulting in higher cook yield. An interesting
allelic interaction known as overdominance was observed for dressing percent which surpasses
both homozygous genotypes. No QTL have been reported for dressing percent in this region of
SSC18.
In the halothane challenge population, CRHR2 genotype exhibited only a suggestive
association with L* and color score, and with blotching. Muráni et al. [34] also evaluated
CRHR2 as a potential candidate gene for stress response phenotypes. In contrast to the present

study that used halothane gas as a measure of stress susceptibility, Muráni et al. [34] induced
stress by mixing of individuals with different aggressive temperaments. They measured plasma
concentrations of cortisol, creatine kinase, glucose and lactose, adrenal weight, and also lesion

124
 

scores as an assessment of aggressive behavior. No significant associations were identified
between CRHR2 genotypes and stress response traits [34].
The present study used two populations to evaluate CRHR2 as a potential candidate gene
affecting carcass merit, meat quality and stress response phenotypes. Results for the Duroc x
Pietrain F2 resource population support an association of CRHR2 with meat quality, and indicate
the potential for animals with the GG genotype to have more favorable meat quality. The
halothane challenge population had fewer carcass and meat quality trait phenotypes recorded
than were available for the resource population. However, there was little evidence for
association of CRHR2 genotypes with meat quality in this population, with only L* and color
score exhibiting a suggestive association. Differences in response between the two populations
could be due to breed (genetic background) and other differences between the populations. In
addition, stress response phenotypes measured in the halothane challenge population did not
exhibit significant associations with CRHR2 genotype with only blotching showing a suggestive
relationship. This result taken together with a previous study of CHRH2 associations with stress
response [32] suggest that CRHR2 may not be a strong candidate for stress response traits.
Further research considering the relationship of CRHR2 with pig carcass merit and meat quality
phenotypes in additional populations is warranted.

Acknowledgements
This project was supported in part by MSU AgBioResearch, and the halothane challenge
population was developed through a MSU Animal Industry Initiative Coalition Grant. The
authors thank M.E. Doumit, E.E. Helman, and D.B. Edwards for assistance with data collection
for the pig populations, and J.P. Steibel for advice on statistical analysis.
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Table IV.1. Number of records, means, and SD for MSU Duroc x Pietrain F2 resource
population.
Trait
N
Mean
SD
Off-farm BW, kg
438
111.15
8.98
Hot carcass weight, kg
438
80.91
7.18
Dressing percent, %
438
72.77
2.01
45-min carcass temperature, °C
437
39.15
2.36
24-h carcass temperature, °C
436
2.74
1.18
45-min pH
431
6.37
0.23
24-h pH
430
5.53
0.15
45-min to 24-h pH decline
424
0.84
0.23
Carcass length, cm
437
78.82
2.57
Number of ribs
294
14.83
0.89
First-rib backfat, mm
373
40.49
7.21
Last-rib backfat, mm
437
28.47
6.27
Last-lumbar vertebra backfat, mm
436
22.15
6.67
Tenth-rib backfat, mm
433
23.66
7.10
2
LM area, cm
434
40.98
4.66
Ham weight, kg
437
9.52
0.81
loin weight, kg
437
8.18
0.87
Boston shoulder weight, kg
437
3.72
0.63
Picnic shoulder weight, kg
437
3.84
0.60
Belly weight, kg
437
4.92
0.70
Spareribs weight, kg
436
1.48
0.21
Color, 1 to 6
436
3.26
0.80
Marbling, 1 to 10
437
2.88
0.80
Firmness, 1 to 5
423
2.86
0.80
CIE L*
421
53.60
2.17
CIE a*
421
17.42
1.96
CIE b*
421
8.98
1.64
Moisture, %
429
73.94
1.51
Fat, %
429
3.27
1.32
Protein, %
428
23.33
1.11
Drip loss, %
436
1.77
1.19
Cook yield, %
434
77.40
2.91
Warner-Bratzler shear force, kg
434
3.18
0.70
Juiciness, 1 to 8
435
5.28
0.59
Tenderness, 1 to 8
435
5.58
0.61
Overall tenderness, 1 to 8
435
5.65
0.56
Connective tissue, 1 to 8
435
6.39
0.39
Off-flavor, 1 to 8
435
1.14
0.21

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Table IV.2. Number of records, means, and SD for halothane challenge population.
Trait
N
Mean
SD
Weight at birth, kg
363
1.49
0.33
Weight at weaning, kg
363
6.48
1.43
Weight at slaughter, kg
348
116.30
13.13
45-min pH
323
5.93
0.24
18-h pH
323
5.49
0.17
45-min to 18-h pH decline
326
0.43
0.24
CIE L*
327
55.15
4.15
CIE a*
327
7.74
1.36
CIE b*
327
15.39
1.01
Subjective color score, 1-6
327
2.12
0.88
Subjective marbling score, 1-10
325
1.95
0.71
Drip loss, %
324
5.08
1.87
ADG, g/day
348
692.45
99.44
Body temperature on day 1 of halothane challenge, °C
363
39.65
0.37
Limb rigidity score on day 1 of halothane challenge, 0 or 1
363
0.74
0.44
Front leg tremors on day 1 of halothane challenge, 0 or 1
363
0.17
0.37
Blotch score on day 1 of halothane challenge, 0 or 1
363
0.21
0.41

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Table IV.3. Allele and genotype frequencies for CRHR2 in two pig populations.
Population
Allele frequency P-value
Genotype frequency
2
A
G
AA
AG
GG
(χ test)
Resource population
0.38
0.62
0.64
0.14
0.48
0.38
Halothane challenge
0.60
0.40
0.62
0.37
0.47
0.17

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Table IV.4. Least squares means by CRHR2 genotype for carcass and meat quality traits significant in resource population.
Trait
obs
AA (n=61)
AG (n=211)
GG (n=171)
Additive1
Dominance1
24-h pH (ultimate pH)
45-min pH
45-min carcass temp
CIE b*

430

5.50 ± 0.02 a

5.51 ± 0.02 a

5.57 ± 0.02 b

-0.03 ± 0.01 **

-0.03 ± 0.02

431

6.30 ± 0.04

a

6.37 ± 0.03

b

6.42 ± 0.03

b

-0.06 ± 0.02 **

0.01 ± 0.03

39.28 ± 0.43

ab

39.36 ± 0.37

a

38.92 ± 0.38

b

0.18 ± 0.16

0.26 ± 0.18

8.90 ± 0.38

a

9.23 ± 0.37

b

-0.16 ± 0.07 *

0.16 ± 0.09

a

78.01 ± 0.34

b

-0.67 ± 0.28 *

-0.05 ± 0.34

437
421

9.23 ± 0.37
a

Cook yield, %

434

76.67 ± 0.49

Dressing percent

438

72.56 ± 0.35 ab

73.03 ± 0.26 a

72.51 ± 0.27 b

0.02 ± 0.17

0.49 ± 0.20 *

436

1.79 ± 0.20

ab

a

1.53 ± 0.15

b

0.13 ± 0.10

0.24 ± 0.13

9.62 ± 0.08

a

9.44 ± 0.06

b

0.09 ± 0.04 *

0.03 ± 0.05

74.26 ± 0.28

a

73.58 ± 0.23

b

0.34 ± 0.13 **

0.06 ± 0.16

23.15 ± 0.18

a

23.55 ± 0.13

b

Drip loss, %
Ham weight, kg
Moisture percent
Protein percent
1

437
429
428

77.29 ± 0.31
1.90 ± 0.14
9.56 ± 0.05
73.98 ± 0.22

a

23.32 ± 0.12

a

Significant additive and dominance effect at the * P < 0.05 and ** P < 0.01

a,b

LS mean different at P < 0.05

129
 

-0.20 ± 0.10 *

-0.03 ± 0.13

Table IV.5. Least squares means by CRHR2 genotype for traits significant in halothane challenge population.
Trait

obs

AA (n=129)

AG (n=171)

GG (n=60)

Additive1

Dominance1

Blotch

345

0.07 ± 0.08 ab

0.08 ± 0.09 a

0.03 ± 0.04 b

Color score
CIE L*
1

324
324

2.01 ± 0.16

a

55.55 ± 1.13

ab

2.20 ± 0.15

b

55.01 ± 1.10

a

Significant additive and dominance effect at the * P < 0.05

a,b

LS mean different at P < 0.10

130
 

0.36 ± 0.26

0.47 ± 0.34

2.00 ± 0.19

ab

0.01 ± 0.08

0.19 ± 0.10 *

56.11 ± 1.20

b

-0.28 ± 0.33

-0.82 ± 0.42 *

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Discussion

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Discussion

Quantitative trait loci (QTL) are genomic regions that affect phenotypic variation and that
are generally influenced by several polymorphic genes and environmental factors. QTL mapping
performed with phenotypic and genotypic data has often been underestimated for its important
role in contributing to the understanding of the genetic basis of phenotypic variation due to an
emphasis on the limitations of QTL mapping. This is because not only have a limited number of
causative genes been identified as a result of coarse map resolution and different gene
segregation patterns across or within breeds, but also in pigs more than 6,000 QTL have been
reported for growth, carcass, meat quality, reproduction and animal health traits
(http://www.animalgenome.org/cgi-bin/QTLdb/SS/index). However, according to the Pig QTL
database, a limited number of pig resource populations have been constructed for QTL mapping
in the United States [1-8]. Furthermore, few studies have used the Duroc breed in their resource
population, and Duroc is one of the most popular sire breeds in the United States. In addition, the
size of many Duroc-containing populations was relatively small with relatively few phenotypic
traits measured. Among US populations that include Duroc parentage and have data reported,
our Duroc x Pietrain resource population developed at MSU is larger and more comprehensive
[6-9].
A Duroc x Pietrain F2 resource population was previously constructed at MSU to discover
quantitative trait loci (QTL) influencing growth, carcass merit and meat quality traits. The Duroc
and Pietrain breeds were selected because they are utilized in commercial pig breeding
worldwide and they exhibit widely different phenotypic variation [10, 11]. In our previous study,
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Edwards et al. [7, 9] revealed 55 QTL for growth traits and 94 QTL for carcass and meat quality
traits through a genome scan which used a line-cross analysis model with 124 microsatellite
markers in 510 F2 animals.
In order to identify additional QTL, three least-squares Mendelian models for QTL analysis
were applied for a second genome scan (SSC1 – 18). This scan included all of the marker data
from the first scan along with 20 new markers on 9 selected chromosomes (SSC3 – 7, 12, 15, 16
and 18) and 20 markers from the first scan for 444 additional F2 animals. Increasing the number
of markers and animals facilitated detection of new QTL (SSC7, 15 and 18), as well as
confirmation of previously identified QTL affecting growth traits (SSC4, 6, and 16) using the
line-cross analysis [7]. Also, the second scan narrowed the confidence intervals of QTL and
increased the statistical power compared to results of the first scan. The half-sib analysis for
which F2 individuals were treated as 6 paternal half-sib families revealed 12 QTL for growth
traits on SSC4 – 8, 15 and 18. For carcass and meat quality traits, the line-cross analysis revealed
50 QTL including 14 new QTL on 6 chromosomes (SSC3, 6, 7, 12, 16, and 18) which had not
been identified in the first genome scan [9]. The half-sib analysis revealed 38 QTL, and 3
additional QTL were detected using the combined line-cross and half-sib model.
In order for QTL results to lead to applications for the industry, putative QTL identified in
resource populations require validating the segregation of QTL in commercial populations.
Based on the first genome scan, QTL regions on 5 chromosomes (SSC3, 6, 12, 15, and 18) were
selected for further evaluation. Gene-specific SNP markers located in the QTL regions were
genotyped in an U.S. commercial purebred Duroc population for a SNP association study.
Thirty-three of 81 gene-specific SNPs were segregating and were evaluated for associations with

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pH, color, marbling, days to 113 kg, backfat thickness, and LMA. Eleven SNPs exhibited
significant effects on one or more carcass merit or meat quality traits measured in this population.
The SNP evaluation study showed a consistency with our previous genome scans for pH on
SSC3, marbling score on SSC6, pH on SSC12, L* on SSC15 and pH on SSC18. As expected,
the effect of the SNP markers for the analyzed traits was not as significant as the genome scan.
There are several reasons to explain the reduced statistical power. First, a limited number of SNP
markers were genotyped across the putative QTL regions. For example, the confidence interval
(CI) for the 10th-rib backfat QTL on SSC6 which was highly significant in the genome scan was
estimated to span a 30 cM interval (118 – 148 cM) [9], but only 5 SNP markers were segregating
and used to analyze traits in the Duroc population. Considering a genetic distance of 1 cM
roughly corresponds to about 1 million base pairs (bp) or 10 genes in mammals [12], the use of 5
SNP markers was not enough to expect high linkage disequilibrium with the QTL. Second, there
were genetic differences between the MSU resource population and the purebred Duroc
population. Finally, the size of the Duroc population was relatively small and many more
animals would be needed to do a thorough QTL validation study.
Based on QTL detected on SSC18 and the biological function of corticotropin-releasing
hormone receptor 2 (CRHR2), CRHR2 was evaluated as a potential candidate gene affecting
stress response and influencing polygenic carcass and meat quality traits in the MSU resource
population and also in a halothane challenge population. To prevent inferior meat quality caused
by animal stressors, mutations within the RYR1 and PRKAG3 genes have been used for selection
in commercial breeding programs [13, 14]. However, these two mutations cannot explain all of
the phenotypic variation in pork quality because pale, soft, exudative (PSE) pork resulting from
animal stress is still a major concern in the pig industry. CRHR2 contributes to an animal’s
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ability to cope with stressful situations such as feeding suppression, hypotension, and anxiolysis,
and it is also involved in weight regulation. CRHR2 genotype was associated with carcass and
meat quality in the MSU resource population supporting it as a potential candidate gene for the
relationship between stress and meat quality traits. However, CRHR2 genotype showed only a
suggestive association with blotching, L* and color score in the halothane challenge population.
Reasons for this difference include breed differences between the two populations as well as the
relatively small size of the halothane challenge population.
Genome scans in the Duroc x Pietrain F2 population revealed many QTL influencing growth,
carcass merit and meat quality traits and also, evaluation of identified QTL using SNP markers
and candidate gene analysis were performed in commercial populations. With the pig genome
sequence, the availability of high-throughput SNP genotyping will help to overcome the
limitations of traditional QTL mapping. Putative QTL regions identified in the genome scan can
be narrowed with the high density SNP chip. Also, confirmation of QTL effects using SNP
genotyping in large commercial populations will be promising. In addition, comparison of QTL
results with the results of expression QTL analyses in the resource population and conducting
genome-wide association studies will facilitate understanding of the genetic basis of complex
traits and incorporation into pig breeding programs.

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