REDESIGNING DIPLOID POTATO BREEDING WITH SELF-COMPATIBILITY By Maher Alsahlany A DISSERTATION Submitted to Michigan State University in partial fulfillment of the requirements for the degree of Plant Breeding, Genetics and Biotechnology—Crop and Soil Sciences—Doctor of Philosophy 2019 ABSTRACT REDESIGNING DIPLOID POTATO BREEDING WITH SELF-COMPATIBILITY By Maher Alsahlany The majority of cultivated potato varieties (Solanum tuberosum Grp. Tuberosum 2n=4x=48) are tetraploid. For over a 100 years yield increases have been obtained from improvements in production management rather than through genetic improvement. The goal of this study is to develop diploid germplasm that is self-compatible (SC) that can be used as parental material F1 hybrid variety breeding scheme. Wild and cultivated species and dihaploid produced from cultivated tetraploid potato are self-incompatible (SI). We designed our study to develop breeding strategies to generate two germplasm pools using the benefit of having the S- locus inhibitor (Sli) gene from Solanum chacoense (M6). The first goal was to create a SC diploid potato multi-species germplasm pool. The objectives of recurrent selection (RS) are to introgress and improve SC in a multi-species potato germplasm pool that has essential cultivated tuber and canopy traits. SC was increased from 16% to 85% in the progeny over five cycles of RS. Genetic variability analysis based on 4885 single nucleotide polymorphisms (SNPs) was maintained in the germplasm based on heterozygosity, and Neighbor-joining tree (NJ), Principal component analysis (PCA), and Structure analyze. Agronomic trait measurements showed that the RS population has genetic variability for many agronomic traits. SC combined with essential tuber traits is a valuable germplasm resource for inbred/F1 hybrid variety development. The second goal was to introgress self-compatibility into S. tuberosum dihaploids by crossing SC donor lines to cultivated dihaploids (2n=2x=24) produced from cultivated lines with traits such as chip processing, disease resistance, and virus resistance. A set of three crosses with dihaploid selections was used to select against undesirable traits and reduce the genetic contribution of the SC donor parents. The SC progeny were used in each round of crosses to a set of cultivated dihaploids. This set of three crosses is referred to as S. tuberosum backcross. Genetic diversity analysis based on over 6000 SNPs and agronomic traits measurements showed that the backcross (BC) population has genetic variability for many agronomic traits. The SC germplasm is a valuable resource for future development of dihaploid F1 hybrid varieties. Third, a study was conducted to examine the results of chloroplast counting in stomatal guard cells, SNP genotyping calling, and flow cytometry to determine ploidy level. All three methods of ploidy determination agreed for evaluating ploidy. Twenty-eight clones with known ploidy level were used as reference samples (14 diploid lines (2n=2x=24), 14 tetraploid varieties and advanced breeding lines (2n=4x=48)) and 102 samples of unknown ploidy level from the RS and the BC populations. These results demonstrate the usefulness of chloroplast counting as an efficient and inexpensive method for breeders to differentiate ploidy between diploid and tetraploid potato. SNP-based heterozygosity, an unweighted pair-group method with arithmetic averages (UGPMA), PCA, and Structure analysis were done using the RS and BC germplasm pools. The NJ tree and PC analysis show the BC2 selections are distinct from the cycle 4 RS selections. The BC2 selections were clustered in one distinct group, and genetic variability was maintained within the group. Developing SC multi-species germplasm pool by using recurrent selection and SC S. tuberosum pool using backcross has led to two germplasm pools that could be tested to identify heterotic combinations. These two germplasm pools may help us develop F1 hybrid diploid potato varieties in the future. Copyright by MAHER ALSAHLANY 2019 I dedicate this dissertation to my beloved parents Abood and Anisa Alsahlany and my family for their love, endless support, encouragement and sacrifices. v ACKNOWLEDGMENTS I would like to thank my super advisor Dr. David Douches, for his mentoring my graduate research, especially during planning and developing my research. Also, for all his life advice for me during my study at Michigan State University. I want to thank the members of my graduate committee, Drs. David Douches, James Kelly, Dechun Wang, and Cholani Weebadde for dedicating their time and advice that helped to develop my research skills. I want to thank my potato family the Douches lab who provided support in the lab, field, greenhouse, and life. I would like to thank Dr. Norma Manrique-Carpintero and Mr. Joseph Coombs for helping to develop my knowledge of using marker assistant breeding, data analysis, and experimental designing of my research. Especial thanks to Greg Steere, Matt Zuehlke, Donna Kells, Nick Garrity, Damen Kurzer, and Azamat Sardarbekov for helping with pollination and field planting. I would like to thank the graduate students Felix Enciso- Rodriguez, Natalie Kaiser, Susan Otieno, Shafiqual Islam, and Emily Pawa in Douches lab for their support and friendship. I would also like to thank my Iraqi friends Mr. Ali Alwatifi and Drs. Ameer Aljanaby, Mohammed Al-Rubaiai, and Montassar Sharif for their friendship and support. My special thanks to my brother Mustafa Alsahlany, for taking over the responsibility to do all the paperwork I need back home in Iraq. I want to thank my Brothers and sisters for all the support during my study. I would like to thank my partner Katy Seidel for standing and supporting me during my last two years of study. vi I want to thank the Higher Committee for Education Development in Iraq for sponsoring my Ph.D. program at Michigan State University. vii TABLE OF CONTENTS ...................................................................................................... viii TABLE OF CONTENTS LIST OF TABLES .................................................................................................................. xi LIST OF FIGURES .............................................................................................................. xiii KEY TO ABBREVIATIONS ................................................................................................ xvi CHAPTER 1 ............................................................................................................................ 1 INTRODUCTION ................................................................................................................... 1 Challenges to Producing Tetraploid Inbred Lines ....................................................................... 1 The Limitations of Conventional Cultivated Tetraploid Breeding .............................................. 2 Potato Genomics .......................................................................................................................... 3 Genomic Selection ....................................................................................................................... 5 Challenges and Benefits of Switching from Tetraploid to Diploid Potato Breeding .................. 6 Diploid Breeding Using the Self-compatible Advantage ............................................................ 8 Diploid Breeding Strategies ........................................................................................................ 9 Dissertation Organization and Objectives ................................................................................. 10 LITERATURE CITED .............................................................................................................. 12 CHAPTER 2 .......................................................................................................................... 18 DEVELOPING SELF-COMPATIBLE DIPLOID POTATO GERMPLASM THROUGH RECURRENT SELECTION .................................................................................................. 18 Abstract ...................................................................................................................................... 19 Introduction ............................................................................................................................... 20 Materials and Methods .............................................................................................................. 22 Plant Materials ....................................................................................................................... 22 One Year Cycle of Recurrent Selection ................................................................................ 23 Single Nucleotide Polymorphisms (SNP) Genotyping ......................................................... 24 Heterozygosity and Population Structure Analysis ............................................................... 24 Phenotyping of Clonal Selections ......................................................................................... 25 Common Scab Resistance ..................................................................................................... 26 Tuber Traits Evaluation ......................................................................................................... 26 Statistical Analyses ................................................................................................................ 27 Results and Discussion .............................................................................................................. 27 Introgress and Improve Self-compatibility ............................................................................ 27 Heterozygosity and Population Structure Analysis ............................................................... 30 Phenotyping of Clonal Selections ......................................................................................... 32 Common Scab Resistance ..................................................................................................... 33 Tuber Traits Evaluation ......................................................................................................... 33 APPENDIX ............................................................................................................................... 36 LITERATURE CITED .............................................................................................................. 90 CHAPTER 3 .......................................................................................................................... 95 viii INTROGRESSING SELF-COMPATIBILITY TO (Solanum tuberosum) DIHAPLOIDS FOR DIPLOID POTATO VARIETY DEVELOPMENT ................................................................. 95 Abstract ...................................................................................................................................... 96 Introduction ............................................................................................................................... 97 Materials and Methods .............................................................................................................. 99 Plant Materials ....................................................................................................................... 99 S. tuberosum Backcross Generation Cycle .......................................................................... 100 Single Nucleotide Polymorphisms (SNPs) Genotyping ...................................................... 101 Heterozygosity and Population Structure Analysis ............................................................. 101 Clonal Selection Phenotyping ............................................................................................. 102 Common Scab Resistance ................................................................................................... 102 Tuber Traits Evaluation ....................................................................................................... 103 Statistical Analyses .............................................................................................................. 103 Results and Discussion ............................................................................................................ 104 Introgress and Improve Self-compatibility .......................................................................... 104 Heterozygosity and Population Structure Analysis ............................................................. 107 Clonal Selection Phenotyping ............................................................................................. 109 Common Scab Resistance ................................................................................................... 110 Tuber Traits Evaluation ....................................................................................................... 110 APPENDIX ............................................................................................................................. 114 LITERATURE CITED ............................................................................................................ 151 CHAPTER 4 ........................................................................................................................ 155 COMPARISON OF METHOD TO DISTINGUISH DIPLOID AND TETRAPLOID POTATO FOR APPLIED DIPLOID BREEDING ................................................................................ 155 Abstract .................................................................................................................................... 156 Introduction ............................................................................................................................. 156 Materials and Methods ............................................................................................................ 160 Plant Material ...................................................................................................................... 160 Chloroplast Counts in Guard Cells ...................................................................................... 161 Genome-wide Single Nucleotide Polymorphisms (SNPs) Genotyping .............................. 161 Flow Cytometry ................................................................................................................... 162 Statistical Analyses .............................................................................................................. 162 Results and Discussion ............................................................................................................ 163 APPENDIX ............................................................................................................................. 167 LITERATURE CITED ............................................................................................................ 180 CHAPTER 5 ........................................................................................................................ 184 CONCLUSION ................................................................................................................... 184 Recurrent Selection Germplasm Pool ..................................................................................... 184 Backcross Germplasm Pool ..................................................................................................... 187 Examine Recurrent Selection and Backcross Germplasm Pools for Future Direction ........... 188 Impact of Diploid Inbred SC Potato ........................................................................................ 190 Genome Editing ....................................................................................................................... 190 Ploidy Determination ............................................................................................................... 191 Dissertation Funds ................................................................................................................... 192 ix APPENDIX ............................................................................................................................. 193 LITERATURE CITED ............................................................................................................ 199 x LIST OF TABLES Table 2. 1. Parental lines genetic background. .............................................................................. 37 Table 2. 2. Selections summary of each cycle of the recurrent selection. ..................................... 38 Table 2. 3. Solanum tuberosum dihaploid references. ................................................................... 39 Table 2. 4. Self-compatibility in selections from five cycles of recurrent selection. .................... 40 Table 2. 5. Heterozygosity based on Individuals basis using a total of 6429 SNPs for five cycles of recurrent selection compared with the parental lines. ............................................................... 41 Table S2. 1. Selections pedigree from all cycles for screening self-compatibility. The first 12 rows are the parental lines; the lines started with (MSBB) they are cycle 0 selections. The lines started with (MSCC); they are selections from cycle 1. The lines started with (MSDD); they are cycle 2 selections. The lines started with (MSEE); they are cycle 3 selections. The lines started with (MSFF); they are cycle 4 selections. ..................................................................................... 51 Table S2. 2. Frequency distribution of cycle 0, cycle 1, cycle 2, cycle 3, and cycle 4 clone means for eight agronomic traits. Maturity (1 = dead, 5 = late, full green vines and flowering); scab resistance (0 = resistance, and 5 = susceptible); tuber appearance (1= very poor, and 9= excellent); tuber shape (1= compressed, 2= round, 3= oval, 4= oblong, and 5= long); average tuber number (No. tuber/ plant); average tuber weight (g); average tuber yield (g); specific gravity. ........................................................................................................................................... 72 Table 3. 1. Solanum tuberosum dihaploid parental lines used to introgress self-compatibility (SC) and SC donors. ............................................................................................................................. 115 Table 3. 2. Summary of total selections and self-compatible (SC) selections from each generation of backcross. ................................................................................................................................ 118 Table 3. 3. Introgression and Improvement of self-compatibility after three generations of crossing dihaploids. ..................................................................................................................... 119 Table 3. 4. Heterozygosity based on Individuals basis using a total of 6612 SNPs for two backcrosses and F1 generations compared with parental lines. ................................................... 120 Table S3. 1. Dihaploid parental lines with F1, BC1, and BC2 selections pedigree……...……...129 Table S3. 2. S. tuberosum dihaploid lines and Atlantic tetraploid cultivar phenotype and tuber traits. ............................................................................................................................................ 142 Table S3. 3. Frequency distribution of F1, BC1, and BC2 clone mean for eight agronomic traits. Maturity (1 = dead, 5 = late, full green vines and flowering); scab resistance (0 = resistance, and xi 5 = susceptible); tuber appearance (1 = very poor, and 9 = excellent); tuber shape (1 = compressed, 2 = round, 3 = oval, 4 = oblong, and 5 = long); average tuber number (No. tuber/ plant); average tuber weight (g); average tuber yield (g); specific gravity. ................................ 143 Table 4. 1. Comparison of chloroplast count, SNP genotyping and flow cytometry to determine ploidy level in potato hybrids. ..................................................................................................... 168 Table 4. 2. Advantages and disadvantages of the chloroplast count, SNP genotyping, and flow cytometry ploidy determination methods. ................................................................................... 174 xii LIST OF FIGURES Figures 2. 1.Recurrent selection breeding scheme. Developing self-compatible diploid potato germplasm adapted to the long-photoperiod growing season of northern latitudes. ..................... 42 Figures 2. 2. Improvement of self-compatibility through five cycles of recurrent selection. ....... 43 Figures 2. 3. SNP Heterozygosity for five cycles of recurrent selection using 6429 SNPs. ......... 44 Figures 2. 4. Neighbor-joining tree using 4885 SNPs; for 39 selections of cycle 4 (magenta), eight parental lines/ species introgression (black), four parental lines/self-compatibility donors (green) and 27 dihaploid S. tuberosum (orange). .......................................................................... 45 Figures 2. 5. Principal component analysis using 4885 SNPs; for 39 self-compatible selections of cycle 4 (magenta), eight parental lines/ species introgression (black), four parental lines/ self- compatibility donors (green) and 27 dihaploid S. tuberosum (orange). ........................................ 46 Figures 2. 6. Structure analysis using 4885 SNPs for 39 self-compatible selections of cycle 4 of RS and 12 parental lines (eight species introgression and four SC donors). The arrows refer to the 12 parental lines were used to generate the RS population. .................................................... 47 Figures 2. 7. Frequency distribution for seven agronomic traits of cycle 0 to cycle 4. (a) maturity (1 = dead, 5 = late, full green vines and flowering); (b) tuber appearance (1= very poor, and 9= excellent); (c) tuber shape (1= compressed, 2= round, 3= oval, 4= oblong, and 5= long); (d) average tuber yield (g); (e ) average tuber number (No. tuber/ plant); (f) average tuber weight (g); (g) specific gravity. ................................................................................................................. 48 Figure S2. 1. Comparison of cycle 0 to cycle 4 neighbor-joining trees with parental lines using 4885 SNPs. (a) cycle 0, (b) cycle 1, (c) cycle 2, (d) cycle 3, and (e) cycle 4 of RS. The black color is representing the species introgression parental lines, Green self-compatible parental lines; each cycle selections was representing in a different color. ................................................ 82 Figure S2. 2. Comparison of five cycles of recurrent selection with neighbor-joining trees with parental lines using 4885 SNPs. .................................................................................................... 85 Figure S2. 3. Comparison of cycle 0 to cycle 4 principal component analysis with parental lines using 4885 SNPs. (a) cycle 0, (b) cycle 1, (c) cycle 2, (d) cycle 3. The black color is representing the species introgression parental lines, Green self-compatible parental lines; each cycle selections was representing in a different color. ............................................................................ 86 Figure S2. 4. Comparison of cycle 0 to cycle 4 principal component analysis with parental lines using 4885 SNPs. (a) cycle 0, (b) cycle 1, (c) cycle 2, (d) cycle 3. The black color is representing the species introgression parental lines, Green self-compatible parental lines; each cycle selections was representing in a different color. ............................................................................ 88 xiii Figure S2. 5. Determining appropriate final K value for Structure analysis. ................................ 89 Figure 3. 1. Neighbor-joining tree generated by using 6023 SNPs, , 34 self-compatible selections (14 F1, 15 BC1, and 5 BC2) and 20 parental lines (15 dihaploids and 5 SC donors). Atlantic and Superior varieties were used as references. Each color represents a different generation for dihaploid parental lines (black), self-compatibility donors (green), BC1 (orange), and BC2 (magenta). .................................................................................................................................... 121 Figure 3. 2. Principal Component Analysis (6023 SNPs), 34 self-compatible (SC) selections (14 F1, 15 BC1, and 5 BC2) and 20 parental lines (15 dihaploids and 5 SC donors). Cvs. Atlantic and Superior were used as references. ............................................................................................... 122 Figure 3. 3. Backcross breeding scheme to introgress self-compatibility (SC) to S. tuberosum dihaploids. ................................................................................................................................... 123 Figure 3. 4. Introgression and improvement of self-compatibility through S. tuberosum backcross strategy. ....................................................................................................................................... 124 Figure 3. 5. SNP Heterozygosity among F1 progeny, and two backcross generation using 6612 SNPs. ........................................................................................................................................... 125 Figure 3. 6. Frequency distribution for eight agronomic traits of F1, BC1 and BC2 generation. (a) maturity (1 = dead, 5 = late, full green vines and flowering); (b) scab resistance (0 = resistance, and 5 = susceptible); (c) tuber appearance (1 = very poor, and 9= excellent); (d) tuber shape ((1= compressed, 2= round, 3= oval, 4= oblong, and 5= long); (e ) average tuber number (No. tuber/ plant); (f) average tuber weight (g); (g) average tuber yield (g); (h) specific gravity. ................ 126 Figure S3. 1. Neighbor-Joining tree using 6023 SNPs. All selections, 33 parental lines (27 dihaploids and 5 SC donors), 30 F1, 30 BC1, 5 BC2, and two references varieties (Atlantic and Superior). Each colored represents different generation, dihaploids (black), SC donors (green), F1 (blue), BC1 (orange), BC2 (magenta). ................................................................................... 149 Figure S3. 2. Principal Component Analysis. All selections, 33 parental lines (28 dihaploids and 5 SC donors), 30 F1, 30 BC1, 5 BC2, and two references cultivars. .......................................... 150 Figure 4. 1. Guard cells of (a) diploid (2n-2x=24) and (b) tetraploid (2n=4x=48) potato leaf sections with chloroplasts stained with PIPI buffer (600x magnification). ................................. 175 Figure 4. 2. Comparison of chloroplast count, SNP genotyping and flow cytometry data to determine ploidy level in potato hybrids (reference samples (left), breeding lines (right) in each panel). (a) Average chloroplast counts from ten guard cells per plant. (b) Frequency of simplex (AAAB) and triplex (ABBB) SNP genotype cluster summaries for 4571 SNPs. (c) Comparison of DNA content using flow cytometry. ....................................................................................... 176 xiv Figure 4. 3. SNP genotype summary examples of tetraploid and diploid reference samples using a tetraploid genotyping model (five cluster calling). The 28 reference samples shown include 14 tetraploid samples (top) and 14 diploid samples (bottom). ......................................................... 177 Figure 4. 4. SNP genotype summary examples of breeding lines using a tetraploid genotyping model (five-cluster calling). The 102 breeding lines shown include 22 tetraploid samples (top) and 84 diploid samples (bottom). ................................................................................................ 178 Figure 4. 5. Flow cytometry method analysis of DNA content for select tetraploid and diploid reference samples and breeding lines. The DNA content (pg/2C) is shown for 14 tetraploid reference samples (red), two tetraploid breeding lines (grey), 14 diploid reference samples (blue), and 12 diploid breeding lines (green). ......................................................................................... 179 Figure 5. 1. Best selections from five cycles of recurrent selection. ........................................... 194 Figure 5. 2. Best selections from F1, BC1, and BC1 generations of backcross. ........................... 195 Figure 5. 3. DNA amplification of 18 BC1 progeny that have NY148 HP #1 dihaploid genetic background. R and S indicate resistance susceptible progeny to PVY, respectively. Sample 12 (MSZ219DH-15) and 13 (NY152) are positive controls. While sample 14 is negative control (water). ......................................................................................................................................... 196 Figure 5. 4. Neighbor-joining tree using 4787 SNPs. Clustering cycle 4 of recurrent selection germplasm (magenta) and BC2 of backcross germplasm (green) (39 selections of RS and five selections of BC2). ....................................................................................................................... 197 Figure 5. 5. Principal component analysis. Comparison of cycle 4 of recurrent selection germplasm and BC2 of backcross germplasm (39 selections of RS and five selections of BC2) using 4787 SNPs. ......................................................................................................................... 198 xv KEY TO ABBREVIATIONS ANOVA One-way analysis of variance BC GS ha MAB MT MS NF NJ PCA REML RILs RS SC Sli SI SNP TPS Backcross population Genomic selection hectares Marker-assisted breeding Metric ton Male sterile Non-flowering plant Neighbor-joining tree Principal Component Analysis Residual Maximum Likelihood Recombinant inbred lines Recurrent selection Self-compatibility S-locus inhibitor gene Self-incompatibility Single Nucleotide Polymorphism True potato seeds xvi CHAPTER 1 INTRODUCTION Potato (Solanum tuberosum L.) is the most significant non-grain food crop in the world and is central to global food security (Potato Genome Sequencing Consortium 2011). Potato tubers are a source of starch, protein, antioxidants, and vitamins that are important for human dietary needs (Brown 2005; Zehra 2012). Potato production is increasing in developing countries, and worldwide production has reached 376 million metric tons in 2016 http://www.fao.org/faostat/en/#data/QC/visualize. Between 2007 and 2016, the world potato production has increased from 314 to 376 million metric tons. Moreover, the yield has increased from 17.3 to 19.5 MT ha-1, and the harvested area increased from 18 to 19.2 million hectares (http://www.fao.org/faostat/en/#data/QC/visualize). Challenges to Producing Tetraploid Inbred Lines There are numerous challenges in producing inbred potato lines at the tetraploid level. For example, low recombination rates, long generation breeding cycle (8-12 years), autopolyploidy, and inbreeding depression are some of the factors that contribute to these challenges (Lightbourn and Veilleux 2007; Visser et al. 2009; Lindhout et al. 2011; Leisner et al. 2018). Crossing between two tetraploid potato varieties or advanced breeding lines will produce F1 progeny that are segregating for all the traits due to a combination of tetraploidy and parental heterozygosity. Potato breeders in North America discard more than 90% of the progeny in the first year of their breeding scheme due to a high level of trait segregation (Haynes et al. 2012). The remaining 10% of the progeny will be grown in the field for further selection based upon agronomic biotic and abiotic resistance. Some of these advanced breeding lines will be released 1 as new varieties after growing and testing in the commercial farms along with current varieties for multiple years (Douches and Kazimierz 1993). Currently, breeding methods for tetraploid potatoes have made limited improvements, and there is no genetic gain in the past century for total yield and small but a significant genetic gain for specific gravity and processing quality (Douches et al. 1996). Converting to a diploid breeding strategy to create inbred lines in potato could be a rational and directional approach for future potato breeding to improve genetic gain in yield and other traits (Jansky et al. 2016). The Limitations of Conventional Cultivated Tetraploid Breeding Cultivated potato varieties are tetraploid (2n=4x=48), clonally propagated, and highly heterozygous (Lindhout et al. 2011; Manrique-Carpintero et al. 2018). Increasing potato yield through conventional breeding is difficult compared to other major food crops. The difficulties relate to genetic gain that cannot be easily fixed in potato due to obligatory outbreeding (Lindhout et al. 2011). Most potato cultivars are tetraploid and self-compatible (SC). However, self-pollinating cultivated tetraploid potato leads to severe inbreeding depression (Lindhout et al. 2011). Furthermore, to reach 99% homozygosity, a tetraploid clone must be self-pollinated for 20 generations (Hawkes 1958). As a consequence, it has been difficult to breed new tetraploid varieties that are higher yielding and have better tuber characteristics that also addresses the expanding disease resistance needs. Breeding potato at the tetraploid level is more challenging compared with grain and forage crops due to more market-specific traits to be considered in new potato variety (Slater et al. 2014; Slater et al. 2016). Potato yield increases mostly correlated with the improvement in production techniques, rather than the improvement of germplasm in the past 100 years (Vos 1992). 2 Potato, like other crops, is susceptible to several pathogens such as late blight (Phytophthora infestans) that led to the Irish potato famine in the mid-19th century as well as other destructive diseases, pests, nematodes, and viruses (Vos et al. 1998). Breeding new tetraploid varieties with resistance to disease or pest resistance to meeting the specific requirements of commercial cultivars is challenging due to a high level of segregation in a F1 generation (Slater et al. 2014). A recurrent selection strategy based on phenotypic has been applied in most conventional breeding programs to develop new potato varieties that could take over 10 years (Hirsch et al. 2014; Slater et al. 2014). To accelerate gain for some traits in conventional potato breeding programs, the genetics and molecular biology tools have been developed, and are available to breeders (Slater et al. 2014). Potato Genomics The plant breeding revolution depends on the availability of genetic and genomic resources to understand the relationship between the genotype and phenotype (Perez-de-Castro et al. 2012). Potato has a large complex genome such that the development of genetic and genomic resources lags behind other crop species. Due to a high level of heterozygosity and autotetraploidy (2n=4x=48) genomic analysis has been challenging (Leisner et al. 2018). Improving potato varieties requires a better understanding of the species through genetic and genomic resources (Hirsch et al. 2016). The diploid genome sequence of potato was published in 2011, and has an estimated size of 844 Mb (Potato Genome Sequencing Consortium 2011). The genomic tools and resources are considered to be more efficient when the crop is diploid, homozygous and has a small genome size (Krens and Kamo 2013). Diploid potato 3 species are mostly self-incompatible (SI) which prevents the production of inbred lines for potato genome sequencing. Lightbourn and Veilleux (2007) were able to produce a doubled monoploid S. tuberosum Group Phureja (2n=2x=24) referred to as DM1-3 516 R44 that was used for the reference genome. Over 39,000 protein-coding genes were predicted (Potato Genome Sequencing Consortium 2011). The whole genome sequence has led to the development of genetic tools and resources for potato. The potato SNP array was developed, (SolCAP Infinium 8303 Potato SNP V1) and subsequently two additional public arrays (Infinium V2 12K and V3 22K Potato SNP Array) were released in 2012, 2015, and 2017, respectively (Hamilton et al. 2011; Felcher et al. 2012; Vos et al. 2015; Schmitz et al. 2017). The sequence of an inbred diploid S. chacoense, M6 line was released in 2018 (Leisner et al. 2018). These genomic tools and resources helped potato breeders to combine conventional techniques and genomic tools to improve efficiency in potato improvement (Potato Genome Sequencing Consortium 2011). A large genomic, genetic, and phenotypic dataset has been developed as a result of the development of potato genome sequencing and genotyping method. A diversity panel of 250 potato clones that includes both phenotypic and genotypic data, along with genome sequence and associated annotation datasets has been developed as a website called Spud DB that provides access to potato breeders (Hirsch et al. 2014). The genomic tools and resources have been used to measure of potato genetic diversity. Potato has the highest genomic diversity compared with other crops species (Hardigan et al. 2017). High genetic diversity can be attributed to introgression of wild species and ploidy level. Genetic variation has been maintained within the cultivated tetraploid potato due to autopolyploidy and introgression of wild species. The genomic tools and resources have been used to compare varieties that were developed from different backgrounds. Hardigan et al., 4 (2017) found that 80% of the selected genes in S. tuberosum grp. Tuberosum or Andean landraces (Andigena) have potentially untapped heterotic alleles from South American landraces. Tuber bearing genome diversity was analyzed to evaluate the history and domestication of cultivated potato. A total of 2,622 genes control carbohydrate metabolism, glycoalkaloid biosynthesis, the shikimate pathway, the cell cycle, and circadian rhythm were identified in a diversity panel including wild potato species, cultivated landraces, and cultivars. The study indicates approximately 14-16% of the genes are shared between North American and Andean cultivars that explain the diversity of potato cultivars (Hardigan et al. 2017). Genomic Selection Conventional tetraploid potato breeding programs discard a high percentage of the progeny, due to segregation of a large number of traits, making the development of new cultivars a slow process (Slater et al. 2016). There are approximately 40 traits such as yield, tuber quality characteristics, and biotic, and abiotic stress resistance that should be considered during the development of new potato variety (Gebhardt 2013; Slater et al. 2014). To improve selection, breeders use the benefit of potato genome sequence and marker-assisted breeding (MAB) that are linked to the important genes such as potato virus Y(PVY), potato virus X (PVX), golden nematode (Globodera rostochiensis (Woll.), verticillium wilt (VW), potato leafroll virus (PLRV), and late blight (LB) (Phytophthora infestans) (Hämäläinen et al. 1997; van Der Voort et al. 1997; Colton et al. 2006; Mihovilovich et al. 2014; Felcher and Douches 2012). MAB can significantly reduce the duration and cost of breeding cycles (Slater et al. 2016). Genomic selection (GS) has been successfully used to improve quantitative traits (Enciso-Rodriguez et al. 2018). Breeding value for lines in a population could be predicted by analyzing their phenotypes 5 and genotypes (high-density marker) using GS. GS can accurately correlate 0.85 of the predicted breeding value with true breeding value even for polygenic, low heritability traits such as yield in crops such as rice (Oryza sativa), wheat (Triticum aestivum), and corn (Zea mays ssp. Mays) (Heff et al. 2009). In tetraploid potato, based on traits heritability and the reference population size, the predicted accuracy ranged between 0.19 to 0.77 (Slater et al. 2016). Using GS at the tetraploid level could accelerate potato breeding schemes and increase genetic gain over the conventional breeding schemes, but it is challenging to use due to a high level of heterozygosity and outbreeding that increase the segregation in the target population. Challenges and Benefits of Switching from Tetraploid to Diploid Potato Breeding Solanum species are found across a wide range of environments, spread from the southwestern United States (38-N) to central Argentina and Chile (41-S) (Spooner and Hijmans 2001). Cultivated potato varieties range in ploidy from diploid (2n=2x=24) to pentaploid (2n= 5x=60) (Jackson et al., 1980) with tetraploid potato varieties being the most commonly grown. The diploid wild species have genetic diversity for economic-related traits that the potato breeders can use to produce improved varieties (Bradshaw et al. 2006). The idea of switching the current potato breeding system from a heterozygous tetraploid crop to a diploid inbred crop has been proposed (Haynes and Guedes 2018). Several studies have been conducted to develop the germplasm and resources to change from a tetraploid asexually produced crop into a diploid, inbred, true seeded crop. The uses of the diploid inbred line‑based F2 population for genetic mapping in potato (Endelman and Jansky 2016) has been developed. Meijer et al., (2018) have identified quantitative trait loci (QTL) for tuber shape, flesh color, tuber skin color, and maturity 6 in F2 and F3 populations. True potato seeds were grown in East Africa and reported 29 ton ha-1 yield for the best hybrid (De Vries et al. 2016). Potato breeding depends on interspecific hybridization with wild diploid potato species that have resistance to diseases or pests. Most wild diploid potato species do not tuberize under long-photoperiod conditions of temperate region (Hermundstad and Peloquin 1985). However, potato breeders can obtain adapted hybrids by crossing wild diploid species to S. tuberosum dihaploids then selecting against undesirable traits (Hermundstad and Peloquin 1985). These hybrids can be easily crossed to tetraploid cultivars by using 2n gametes (Hermundstad and Peloquin 1985; Carputo and Barone 2005). Jansky et al., (2018) have introgressed resistance to common scab caused by Streptomyces sp. from M6 (S. chacoense), an inbred clone, into the US- W4 S. tuberosum dihaploid to produce resistance diploid hybrids. Diploid potatoes have several positive features: simpler genetic system, large genetic diversity, and a source of economic traits (Bonierbale et al. 1993; Jansky et al. 2016). Most wild and cultivated diploid species have gametophytic self-incompatibility (GSI) (Hawkes 1958; Phumichai et al. 2005; Jansky et al. 2014). Self-incompatibility prevents inbreeding and promotes mating with individuals that differ by at least one-allele at the S-locus (Byers and Meagher 1992). The S-locus recognition system is controlled by the S-locus gene(s) and can be inhibited by the observation of S-locus inhibitor (Sli) that fixes self-incompatibility in pollen of diploid potatoes (Hosaka and Hanneman, Jr. 1998). Sli from the self-compatible (SC) diploid clone M6 (S. chacoense) has been used to overcome SI in diploid potato (Jansky et al. 2014; Sanetomo et al. 2014). 7 S. chacoense selection, M6, (also known as chc523-3) is a diploid SC inbred line in contrast to most wild diploid potato species. M6 has the Sli gene in a homozygous state. After seven generations of selfing, M6 has become fixed for SC. M6 is a vigorous plant producing highly fertile male and female individuals (Jansky et al. 2014). M6 can be crossed to other diploid accessions to introgress Sli which could then lead to inbred line development across a broad germplasm base (Jansky et al. 2014). Diploid Breeding Using the Self-compatible Advantage Jansky et al. (2016) reviewed the obstacles faced in the last 100 years in confronting in potato production through improvement of germplasm. Also, they discussed the benefits of conducting breeding potato at the diploid level utilizing inbred lines. Some potato breeders have switched to breeding potato at the diploid level (Haynes and Guedes 2018). Inbreeding in diploids is much more efficient than in tetraploid potatoes (Hosaka and Hanneman 1998). Self- compatibility provides a new opportunity to produce a F1 hybrid diploid potato from TPS (Phumichai et al. 2005). A cross between a SC wild diploid potato species and SI cultivated diploid potato species is expected to give a F1 hybrid with a dominant S-locus inhibitor gene (Sli) in a heterozygous condition (Hosaka and Hanneman 1998). The SI lines can be used to select the best clones by phenotypic evaluations then use selfing to fix or remove alleles from the resulting progeny (Lindhout et al. 2011). High phytosanitary seed quality, as well as less demanding seed storage conditions, are some of the advantages of using true seed as planting material for potato growers. In developing countries, the farmers maintain their tuber seeds that frequently get infected with virus. Moreover, tubers attract and transport pests and diseases such as late blight, Andean potato weevil (Premnotrypes suturicallus), nematodes, and tuber moth 8 (Phthorimaea operculella). True potato seed could help to solve some of these issues (Jansky et al. 2016). Diploid Breeding Strategies Developing inbred diploid potato lines could be completed faster through selfing (Haynes and Guedes 2018). However, developing recombinant inbred lines (RILs) at the diploid level is challenging due to the loss of SC and severe inbreeding depression during the advancement of the population from F2 to F4 generations. Recurrent selection breeding strategy has previously been used in potato to improve both qualitative and quantitative traits such as diseases and pest resistances and remove undesirable characteristics such as adaptation for short-day photoperiod sensitivity (Plaisted et al. 1981; Sanford and Ladd 1987; Hermsen 1989; Haynes 2001; Benites and Pinto 2011). Producing SC germplasm with traits of interest such as abiotic and biotic resistances will help to develop a F1 diploid hybrid breeding approach. Backcrossing breeding strategy has also been used to improve qualitative and quantitative traits in potato (Tarn and Tai 1983). Indeed, Frisch et al., (1999) utilized a backcross breeding strategy to improve the recurrent parent and remove undesirable traits inherited from the donor parent. Developing a scheme of inbred/F1 diploid hybrid breeding approach requires S. tuberosum diploid SC germplasm. The MSU Potato Breeding and Genetics Program is focusing on developing germplasm that progresses chip-processing and table market varieties that have desirable traits for tuber appearance, tuber shape, combined with resistance to common scab (Streptomyces scabies), PVY, late blight (P. infestans), and Colorado potato beetle (Leptinotarsa decemlineata). Wild diploid potato species do not tuberize under long-photoperiod conditions, that can be adapted by crossing wild diploid species to S. tuberosum dihaploids then select against undesirable traits 9 (Hermundstad and Peloquin 1985). This is a promising approach for diploid inbred/F1 development after fixing SC in inbred parental materials. However, most wild and cultivated diploid species are SI (Hawkes 1958; Phumichai et al. 2005; Jansky et al. 2014), and this has led to the idea of introgressing SC from S. chacoense to multi-species germplasm and S. tuberosum dihaploids to overcome SI. The main objective of this study is to produce self-compatible germplasm adapted to a long-photoperiod with commercial tuber traits. Dissertation Organization and Objectives The dissertation is organized into three research chapters and a concluding chapter. The objectives of chapter 2 entitled, “Developing Self-Compatible Diploid Potato Germplasm through Recurrent Selection” are as follows: 1. Introgress self-compatibility into multi-species germplasm via recurrent selection 2. Select progeny for photoperiod adaptation and commercial tuber traits 3. Maintain genetic diversity in the RS population 4. Examine genetic structure The objectives of chapter 3 entitled, “Introgressing Self-compatibility to Solanum tuberosum Dihaploids for Diploid potato Variety Development” are as follows: 1. Introgress self-compatibility via S. chacoense to a set of S. tuberosum dihaploid germplasm pool over a set of three crosses. 2. Examine genetic diversity and genetic structure in the germplasm developed. 10 The objectives of chapter 4 entitled, “Comparison of Methods to Distinguish Diploid and Tetraploid Potato for Applied Diploid Breeding” are as follows: 1. Determine an efficient method to identify ploidy level in potato. 2. Examine results of chloroplast count in stomatal guard cells, SNP genotyping calls, and flow cytometry methods for ploidy level determination. The conclusion Chapter 5 summaries the experiments, findings of the dissertation and provides prospects for future diploid breeding research in potato. 11 LITERATURE CITED 12 LITERATURE CITED Benites, F. R. G., and C. A. B. P. 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Some analytical quality characteristics for evaluating the utilization and consumption of potato (Solanum tuberosum L.) tubers. African Journal of Biotechnology 10: 6001–6010. doi:10.5897/AJB11.042. 17 CHAPTER 2 DEVELOPING SELF-COMPATIBLE DIPLOID POTATO GERMPLASM THROUGH RECURRENT SELECTION 18 Abstract Most wild and cultivated diploid potato species are self-incompatible which prevents inbreeding by promoting mating with individuals. In our study, we used a set of diploid clones that are composed of five diploid potato species (S. berthaultii, S. chacoense, S. microdontum, S. tuberosum Grp. Phureja, and S. tuberosum Grp. Tuberosum) to generate a self-compatible (SC) population selected for commercial tuber traits. For this purpose, SC was introgressed from a set of four SC donors to eight introgression species to develop a multi-species potato germplasm pool using recurrent selection (RS) adapted to Michigan's long day growing environment while selecting for cultivated tuber traits. Five cycles of RS demonstrated a significant increase in SC and improvement of tuber traits in the population. Male fertility was also increased by reducing male sterility (MS) from 32% to 8% between cycle 0 and cycle 4, respectively. The self- compatibility was significantly increased from 16% to 83% over five cycles of RS. Progeny with no-flowers decreased from 19% to 2% between cycles 1 and cycle 4. Over 6,000 genome-wide single-nucleotide polymorphic (SNP) markers were used to characterize germplasm diversity, heterozygosity, and population structure over five RS cycles. SNP heterozygosity was significantly decreased after five cycles of RS. A neighbor-joining cluster (NJ) tree and Principal Component Analysis (PCA) were generated using cycle 4 selections of RS cultivated dihaploid lines, and both analyses showed cycle 4 selections were distinct from the cultivated dihaploids. Diversity analysis and trait measurements showed that the RS population has genetic variation (alleles) for many agronomically adapted traits such as vine maturity, tuber appearance, tuber shape, and average tuber weight, average tuber yield, and specific gravity for breeding. Cycle 4 progeny were significantly improved for maturity, average tuber number, and average tuber yield. This SC germplasm can be used to select for commercially important tuber trait adaptation 19 in a genetically diverse diploid species germplasm population and parent development of inbreds for a F1 hybrid program. Introduction Potato (Solanum tuberosum L.) is the third most important food crop in the world after wheat and rice (Visser et al. 2009; Ríos 2015). The genus Solanum contains nearly 200 tuber- bearing species out of 2,000 species (Spooner and Salas 2006; Ruiz de Galarreta et al. 2015). Native Solanum species are found across a wide range of environments, from the southwestern United States (latitude 38º N) to central Argentina and Chile (latitude 41º S) (Spooner and Hijmans 2001). Cultivated potato ranges in ploidy from diploid (2n=2x=24) to pentaploid (2n= 5x=60) (Hawkes 1958; Jackson and Hawkes 1980). In general, cultivated potatoes are autotetraploid (2n=4x=48) ( Jansky et al. 2016) and are facultatively outcrossing tetraploids with a highly heterozygous genome that is asexually propagated. This combination of traits makes breeding improved varieties challenging (Jansky et al. 2016). Other factors such as low recombination rates, long generation breeding cycle (5-12 years), autopolyploids (Visser et al. 2009; Lindhout et al. 2011) and inbreeding depression also contribute to this challenge limiting the fixation of genetic gains (Lindhout et al. 2011; Jansky et al. 2014). Diploid potato species offer a simpler genetic system, genetic diversity and many economically important traits needed in cultivated varieties (Bonierbale et al. 1993). Specially, wild diploid species have diversity for economic traits that can be used to breed improved varieties for many traits including heat and frost tolerance, resistance to fungal, bacterial, virus, pathogens, nematode, and pest (Bradshaw et al. 2006; Spooner and Salas 2006; Acquaah 2009). However, most wild and cultivated diploid species are controlled by the gametophytic self- 20 incompatibility (GSI) system (Hawkes 1958; Phumichai et al. 2005; Jansky et al. 2014) preventing inbreeding by promoting mating with individuals that differ by at least one-allele at the S-locus (Byers and Meagher 1992). The S-locus is defined as a multigene complex that encodes at least two tightly linked polymorphic genes; a female determinant (S-RNase) which prevents self-pollen tube growth and a male determinant (SFL) factor which recognizes and degrades S-RNases (Entani et al. 2003, McClure et al. 2011). To overcome SI, an inhibitor of the S-locus (Sli) has been used (Sanetomo et al. 2014) to generate the diploid self-compatible (SC) clone M6 derived from the wild species S. chacoense (Jansky et al. 2014). In M6, Sli has been fixed after seven generations of selfing. Moreover, M6 is also highly male and female fertile and does not show severe inbreeding depression. M6 can be used for developing diploid SC potatoes by selecting clones phenotypically, reducing possible linkage drag effects by using backcrosses and then selfing to fix or remove undesirable alleles from the gene pool to obtain cultivated desirable traits (Lindhout et al. 2011). Ultimately, inbreeding can lead to the production of true potato seed varieties or F1 hybrids. Multiple studies have reported using RS to improve potato for qualitative and quantitative traits (Sanford and Ladd 1987; Haynes 2001; Benites and Pinto 2011). RS breeding strategy applies breeding and reselection technique for improvement of agronomic traits generation after generation (Jenkins 1940; Hull 1945). RS has been successfully used in potato for maximizing heterozygosity and removing undesirable characters, such as short day-length, leafhopper resistance, and heat tolerance (Plaisted et al. 1981; Gautney and Haynes 1983; Sanford and Ladd 1987; Hermsen 1989; Benites and Pinto 2011). Haynes (2001) used an RS strategy to estimate narrow sense heritability and phenotypic variance of yield and specific gravity. RS has been reported as a useful strategy for improving forage crops such as birdsfoot trefoil (Lotus 21 corniculatus) for high seed set (Peacock and Wilsie 1960; Villegas et al. 1971). Increasing seed set through RS will benefit the F1 hybrid diploid breeding programs. The RS breeding strategy is also useful to improve complex traits, compared with using parental-line breeding (Haynes and Xinshun 2018). Breeding programs have traditionally used a parental line breeding strategy to combine desirable traits. The purpose of this study was using an RS strategy to introgress the Sli-based SC into multi-species diploid breeding germplasm and create a SC diploid germplasm that is adapted to the long-photoperiod growing season of northern latitudes. Moreover, the multi-species germplasm pool. Several traits of interest to Michigan State University (MSU) potato breeding program such as potato virus Y(PVY), potato virus X (PVX), golden nematode (Globodera rostochiensis (Woll.), verticillium wilt (VW), potato leafroll virus (PLRV), and late blight (LB) (Phytophthora infestans) were included. Five cycles of recurrent selection have been completed over a five-year period. This study is the first report to use RS to select for SC as well as for improved economically important tuber traits in a genetically diverse diploid potato breeding population. Materials and Methods Plant Materials Twelve diploid parental breeding lines composed of germplasm including S. berthaultii, S. chacoense, S. microdontum, S. tuberosum Grp. Phureja, and S. tuberosum Grp. Tuberosum were planted in a greenhouse crossing block in the winter of 2013 at Michigan State University (MSU) in East Lansing, Michigan. Four lines were SC donors, and eight lines were SI diploid advanced breeding clones (Table 2. 1, Figure 2. 1). Twenty-four combinations of SC×SI crosses 22 were generated. These 24 families were used to manage the selection and future cycles of crossing (Table 2. 2). One Year Cycle of Recurrent Selection A one-year RS cycle was used to introgress SC, photoperiod adaptation and commercial tuber traits. The goal was to maintain genetic diversity throughout the cycles of selection in a multi-species germplasm pool. Each cycle was started in fall (October) by making selection of tubers produced from field-grown transplants based on plant maturity and tuber traits such as tuber shape, tuber appearance, tuber number, average tuber weight, and yield. In winter, selections were planted in 5-gallon pots in the greenhouse 16-h-light/8-h-night photoperiod at 20-25 °C. Chloroplast counts in stomatal guard cells were used to determine ploidy level prior to flowering, and then discarded tetraploid lines. Leaf tissue was collected for DNA isolation for SNP genotyping for confirming ploidy level, and examine population genetics (Felcher et al. 2012). SC was tested by collecting pollen from an open flower on the plant and self-pollinating the remaining inflorescences with fresh pollen in the greenhouse. After determining SC, an equal amount of pollen (2 mg each) from selected SC progeny was collected to obtain bulk pollen for crossing to selected (SC and SI) progeny (Table 2. 2). The one-month-old fruit was harvested, and seeds were extracted and treated with 1500 ppm gibberellic acid (GA3) overnight at room temperature to break seed dormancy. Seeds from each family were sown in 10 cm pots in the greenhouse. After germination, 10-15 days old seedlings were transplanted to 50-cell trays. One- month-old seedlings were transplanted at the MSU’s Montcalm Research Center (MRC, Lakeview, MI) (June 15 – July 15) and harvested in October (10-15) after vine desiccation. Five cycles of recurrent selection were completed between 2013 - 2018. 23 Single Nucleotide Polymorphisms (SNP) Genotyping DNA was extracted using young leaf tissue from a total of 200 selections across the five cycles of RS (21, 25, 45, 70, and 39 from cycle 0, cycle 1, cycle 2, cycle 3, and cycle 4, respectively) and 12 parental lines (Table S2. 1) using the QIAGEN DNeasy Plant Mini Kit (QIAGEN, Germantown, MD). DNA was quantified using Quant-iT PicoGreen dsDNA Assay Kit (Invitrogen, San Diego, CA) and normalized to a concentration of 50 ng µl-1. The Infinium 8303 potato array (V1), Infinium 12K potato (V2) array and Infinium 22K potato array (V3) were used to genotype samples using an Illumina iScan Reader (Illumina, San Diego, CA) according to the manufacturer’s suggested protocol. GenomeStudio software was used for calling SNP genotypes (Illumina, San Diego, CA). The SNP marker data were filtered to exclude low- quality SNPs, excluding SNPs with a minimal allele frequency < 0.03 and missing data > 10%. For ploidy confirmation, a tetraploid genotyping ploidy prediction method used previously by Ellis et al. (2018) and Alsahlany et al. (2018) was employed to determine potato ploidy level using a tetraploid genotyping cluster call based upon a custom Perl script for five clusters genotype calling based on theta value thresholds (Hirsch et al. 2013). Heterozygosity and Population Structure Analysis Heterozygosity was calculated on an individual basis by summing heterozygous SNPs (AB) over the total number of SNPs. Population structure was assessed using high-quality polymorphic SNP markers by three different methods. First, cluster analysis performed a using a neighbor-joining method with the R package ‘ape’ (http://ape-package.ird.fr) in R version 3.5.0. Second, principal component analysis (PCA) was conducted using the ‘ggbioplot’ (https://cloud.r-project.org/web/packages/ggplot2/index.html) R package. Finally, the individual 24 ancestry of each progeny and the parental line was assessed using Structure v2.3.4 (Pritchard et al. 2000), with K populations ranging from 2 to 10, 10,000 burn-in iterations and 50,000 Markov Chain Monte Carlo replicates. The final K was selected using the DK method with Structure harvester (Earl and vonHoldt 2012). The parental lines were used in the structure analysis to assign species genetic background contributing to the RS population. Atlantic dihaploid selections were used as S. tuberosum references to estimate the genetic distance to the RS germplasm pool combination on the genetic basis. Twenty-seven dihaploids previously produced from cultivar Atlantic variety to examine the genetics of the germplasm pool (Table 2. 3). These dihaploids were used to generate SC S. tuberosum diploid germplasm. Phenotyping of Clonal Selections Selections were made in the fall for each cycle. Plants were grown in the field in the second-year evaluate traits. Vine maturity was recorded based on the 5-hill plot. Maturity after 110 days from planting was evaluated in the 2016 - 2018 field seasons using a scale ranging from 1 to 5 (1 = dead, 5 = late, full green vines and flowering) (Manrique-Carpintero et al. 2015; Braun et al. 2017). A total of 839 selections were screened for vine maturity over five seasons. In 2016 field season, 149 from cycle 0, 118 from cycle 1, and 132 from cycle 2 selections were screened due to seed availability while 248 selections of cycle 3 were screened in 2017 field season. One hundred-ninety-seven selections of cycle 4 were screened in 2018 field season. The selections were planted May 11 at MRC 2017 and 2018 and harvested September 27 and October 4 for 2017 and 2018, respectively. 25 Common Scab Resistance A total of 97 selections (27 of cycle 0, 15 of cycle 1, and 15 of cycle 2) were screened for reaction to common scab (Streptomyces scabies) in the 2017 growing season. In 2018 growing season a total of 40 selections (26 cycle 3 and 14 cycle 4) were screened for common scab. Selections from each cycle were screened for scab resistance due to the availability of seed tuber. Selections were planted in a common scab disease nursery at MRC which is managed to promote high levels of S. scabies in the field. The scab trial was planted as single observation 5-hill plots on May 8,2017 and May 1, 2018. Tubers were harvested on August 30, 2017 and 27, 2018 and visually scored for common scab lesion coverage and severity using 0 – 5 scale (0 = highly resistant -no lesion and 5 = highly susceptible) (Driscoll et al. 2009). A score of 1.5 or less was classified as resistant. Tuber Traits Evaluation From 2017 and 2018, clonal selections from previous years were grown to evaluate tuber appearance, tuber shape, average tuber weight, average tuber yield, and specific gravity from seed tubers rather than as seedlings transplants. Tuber appearance was evaluated using a 1 – 9 scale (1= very poor, and 9= excellent) (De Haan et al. 2014). Tuber shape was scored on a 1 – 5 scale (1= Compressed, 2= Round, 3= Oval, 4= Oblong, and 5= Long) (Domański 2001). Average tuber number per plant, tuber weight (g/tuber), and tuber yield per plant (kg/plant) were measured. Specific gravity was calculated as weight in air (dry weight – wet weight) −1 on samples over 1 Kg (total of 10 plants yield) (Manrique-Carpintero et al. 2015). 26 Statistical Analyses A chi-square test was used to compare the improvement of SC after five cycles of RS with cycle 0 and the SI, MS, and non-flowering plants reduction in the germplasm. Individual heterozygosity, maturity, scab resistance, and tuber-related traits data were analyzed each using a general linear model (GLM) that was fitted using Residual Maximum Likelihood (REML), with RS-cycles as random effects. Differences between RS-cycles were conducted using a Student’s t- test (a = 0.05) in JMP version 14.0.0 (SAS Institute, Cary, NC). Student’s t-test was used due to an uneven number of genotypes that observed within each cycle of RS based on tuber seed availability. A total of 31 selections from cycle 0 were used as references to determine the improvement in SC as the RS-cycle advanced. Twelve parental lines were used to compare differences in the cycle means for SNP heterozygosity. For tuber traits, the cycle 0 selections were used as a reference to compare the changes between RS cycles (Table S2. 2). The commercial variety Atlantic and dihaploid lines were used as reference cultivars in the trials. Results and Discussion Introgress and Improve Self-compatibility A recurrent selection breeding strategy was used to increase SC in a multi-species diploid potato population also selected for marketable tuber traits and for long-photoperiod adaptation. In the summer of 2013, 50 seeds per family were grown in seedling trays. One to two individuals were selected from each of the 24 families based on tuber shape, yield and tuber number as a foundation for the RS population. In the winter of 2014, 31 individuals were planted from 24 F1 hybrid families to establish an RS cycle crossing block (cycle 0) in the greenhouse. Equal amounts of pollen (2 mg each) were collected from 12 male fertile F1 hybrid clones (5 SC and 7 27 SI) and bulked. Bulk pollen was used to cross to 31 F1 hybrids from cycle 0 to create half-sib families (cycle 1). In the summer of 2014, 50 seedlings from each half-sib family (cycle 1) were transplanted between July 15 to August 5 to the field at the MRC field and harvested on October 13. In the fall, 92 selections from 18 families were selected to create cycle 2 of the RS population (Table 2. 2, Figure 2. 1). In the winter of 2015, the selections chosen based on yield, tuber shape, and earlier maturity were planted in the greenhouse. Self-compatibility was determined by self-pollinating the plants in the greenhouse, and then an equal amount of pollen was taken from the 12 SC selections to create 31 half-sib families. In the summer of 2015, 50 seedlings per half-sib family were transplanted (July 3 to 25) to the field at MRC for the third cycle of recurrent selection and harvested on October 12. In the fall, 123 selections were made based on desirable tuber traits from 28 families to generate cycle 3 (Table 2. 2, Figure 2. 1). In the winter of 2016, these selections were planted in the greenhouse and screened for SC. Thirty-seven SC individuals out of 50 SC selections were used that represent the families in cycle 2 to maintain diversity in the germplasm for bulked pollen to generate 47 full-sib families (cycle 3). In the summer of 2016, 50 seedlings from each full-sib family were transplanted (June 22 to July 19) to MRC and harvested on October 10. In fall, 187 selections were made from 47 families to create the RS cycle 4 population. In winter of 2017, the 187 selections of cycle 4 were planted in the greenhouse and screened for SC via self-pollination. Ninety-nine SC individuals were identified. An equal amount of pollen (2 mg each) was taken from 55 SC selections that represent the families in cycle 3 to maintain diversity in the germplasm to generate seventy-one full-sib crosses for cycle 4. In the summer of 2017, 50 seedlings per full-sib family were transplanted between June 21 to 28 July 14to MRC for the fifth cycle of recurrent selection and harvested in October 3. In fall 2017, 197 selections were made to generate cycle 5 (Table 2. 2, Figure 2. 1). In the winter of 2018, 53 of 197 selections were planted to screen for SC. Forty-four SC lines were identified. Then an equal amount of pollen was taken from 19 SC selections due to pollen amount availability and time consuming to generate 46 half-sib families. Thirty-five crosses from the best selections were used to generate the cycle 5 of half-sib families. In the summer of 2018, 50 seedlings per half-sib family were transplanted (June 21 and 29) to MRC and harvested on October 10 for the sixth cycle of recurrent selection in the field to advance the germplasm for SC and photoperiod adaptation (Table 2. 2, Figure 2. 1). Five cycles of RS have shown a significant increase in SC values from 16% (cycle 0) to 83% (cycle 4) over five cycles (Chi-square p=0.0025, Table 2. 4, Figure 2. 2). A significant decrease in SI, male sterile, and non-flowering plants through five cycles of RS was also observed (Table 2. 4, Figure 2. 2). Using bulk pollen from only SC selections from cycle 1 and later to advance each cycle of RS led to a decrease SI from 32% (cycle 0) and 26% (cycle 1) to 8% (cycle 4) (Chi-square P < 0.0001). The percentage of non-flowering plants decreased from 19% to 2% between cycle 0 and cycle 4 (Chi-square P < 0.0001). The number of male sterile plants decreased from 32% in cycle 0 to 8% in cycle 4 (Chi-square P < 0.0001, Table 2. 4, Figure 2. 2). In cycle 0 and cycle 1, the bulk pollen was mixed from male fertile selections (whether the male selections were SC or SI) to maintain a broad genetic base in the population. As a consequence, we did not see an improvement in SC in cycle 0 and cycle 1. In all subsequent cycles, bulk pollen was collected only from SC selections in the population. This may account for the change in the SC ratio after cycle 1 of recurrent selection. Similar results were observed 29 by Villegas et al. (1971) using a recurrent selection strategy to select for self-fertility in alfalfa (Medicago sativa L.). The bulk SC pollen strategy improved SC in the germplasm pool and reduced SI, male sterility, and non-flowering plants. Heterozygosity and Population Structure Analysis A total of 200 selections from the five cycles of RS and 12 parental lines were SNP genotyped to examine germplasm structure. Heterozygosity was measured on an individual basis using 6429 SNPs after applying quality filters. The parental lines that were used to generate base germplasm pool had a wide range of heterozygosity from 6% for M6 to 35% for XD3. The average of SNP heterozygosity for the parental lines was 25% ± 19% (Table 2. 5). The average heterozygosity in cycle 0, cycle 1, cycle 2, cycle 3, and cycle 4 was 26% ± 11%, 24% ± 8%, 23% ± 8%, 23% ± 7%, and 21% ± 5%, respectively (Figure 2. 3). These results showed a significant heterozygosity decrease in cycle 4 compared to the cycle 0 of RS (p < 0.0001) (Table 2. 5, Figure 2. 3). Crossing with bulk pollen may increase the possibility of having self-progeny or share a genetic background in our population and could partially explain the decrease in heterozygosity through RS cycles for some of the selections. Similar results were observed by Labate et al. (1997), De Koeyer et al. (1999), and Yuan et al. (2004) in rapeseed (Brassica napus), maize (Zea mays L.) and oat (Avena sativa L.). These studies demonstrated that using recurrent selection led to reduced genetic variability after 12, seven, and four cycles of RS, respectively. In contrast, a study reported by Liu et al. (2007) maintaining heterozygosity using recurrent selection for improving five morphological traits in wheat (Triticum aestivum L.). Population structure was evaluated using nearest neighbor-joining (NJ) clustering and Principal Component Analysis (PCA). After applying quality filters, 2254 monomorphic SNPs 30 were removed resulting in 4885 polymorphic SNPs for NJ tree and PCA analysis. The NJ tree and PC were generated by using 4885 SNPs with 65 selections (39 selections cycle 4, 12 parental lines, and 14 dihaploid references). The NJ tree shows that the RS cycle 4 selections are distinguished from dihaploid S. tuberosum references (Figure 2. 4). The cycle 4 selections are clustered in one distinct group with the parental lines, maintaining the genetic variation within the group. PCA was also used to examine population structure. Figure S2.1 shows the selections group close to each other as the RS cycles advanced due to population selected through RS for SC and photoperiod adaptation and refers to reduce the genetic distance between individuals. Figure S2. 2 shows genetic diversity has been maintained in the germplasm pool even the selection was conducted based on SC and tuber traits. PC1 and PC2 explained 23.3% and 5.5% of the total genetic variance present in the population (Figure 2. 5). The cycle 4 population grouped together in comparison with the dihaploid reference samples (Figure 2. 5). Figure S2. 3 shows the selections group closer to each other as the RS cycles advanced due to population selected through RS for SC and photoperiod adaptation and refers to maintain the genetic distance between individuals. Similar to the NJ tree, the cycle 4 selections, parental lines, and SC donors formed a distinct group from the S. tuberosum dihaploids (Figure 2. 5). The PC analysis supported the NJ tree that the genetic diversity maintained in the germplasm pool and Figure S2. 4 shows the selections cross all cycle of RS were group in one group and there is no draft in the analysis and selection based on SC and tuber traits did not affect genetic diversity in the germplasm pool. The NJ trees from cycle 0 through cycle 4 of the recurrent selection show the process of gradually more uniform clustering as the cycles advanced (Figure S2. 1). Figure S2. 1 shows the parental lines (Black) are grouped together after five cycles of RS as the cycle 4 selections 31 become more uniform and distinct from the parental lines. The cycles phenotypic data supported the NJ tree and agreed with Liu et al. (2007) study that used RS to maintain wheat genetic variability while the population is improved for a specific traits. Structure software was also used to determine the contribution of the parental lines in their progeny after five cycles of RS. Thirty-nine selections from cycle 4 and 12 parental lines were used in this analysis. Five subpopulations were observed in the germplasm pool (Figure 2. 6 and S2. 5). Each subpopulation has a major contribution of each independent parental line as observed in Figure 2. 6. The fifth subpopulation is an undefined mix of species. The analysis indicated the RS breeding strategy helped to maintain diversity from different genetic sources in cycle 4 even though the population was selected for SC, maturity and commercial tuber traits for five cycles. Phenotyping of Clonal Selections The self-compatibility source in this population is from S. chacoense germplasm, which also has late maturity in northern latitudes. Early vine maturation in cycle 4 selections was significantly reduced compared with cycle 0 (p= 0.001). The maturity mean was reduced from 2.5 in cycle 0 to 1.4 in cycle 4 of RS. Approximately 92% of cycle 4 selections were early maturing (maturity rating 1 or 2). The selections within previous cycles of RS showed a wide range of vine maturity under long day conditions (Table S2. 2, Figure 2. 7a). Cycle 3 selections showed a wider range and a greater mean maturity (Figure 2. 7a). The RS cycles helped to reduce undesirable traits such as adaptation to short day-length. Gebhardt et al. (2004) used recurrent selection to adapt a population of 600 potato cultivars (IPK Genbank Außenstelle Nord, 18190 Groß Lüsewitz, Germany) from short day-length to long day-length and observed a wide 32 variation in maturity. In our study, commercial tuber traits were prioritized over early vine maturity since vine maturity could not be assessed at tuber harvest at the single hill stage (Table S2. 2). Variation for maturity decreased in the population over the cycles of selection (Figure 2. 7a). However, there were still late maturing selections in the population as early maturity was not a criterion for inclusion in the crossing block. Common Scab Resistance Potato scab is a common disease that causes lesions damage to tubers in many regions of production worldwide. Common scab evaluations were conducted for a total of 97 selections in 2017 and 2018 due to seed availability. Clones with common scab resistance were identified in the population (scab rating ≤ 1.5). Sixty-two percent of the screened germplasm showed resistance to common scab. These results did not differ among RS cycles (p= 0.0029) (Table S2. 2). However, there was a wide range of common scab resistance within each cycle of RS. The MSU RS population was transplanted in a field that had a high level of disease pressure. Therefore, highly susceptible progeny were excluded during the selection that may have increased common scab (Streptomyces scabies) resistance in the germplasm. Tuber Traits Evaluation Single plants were selected based on tubers produced from field-grown transplants, based on tuber appearance, tuber shape, tuber number, and total plant yield. Tubers were also evaluated for these traits the following year from seed-tuber field-grown plants. A wide variation for tuber appearance was noted within each cycle of RS. In general, the tuber appearance was significantly improved in cycle 4 compared with cycle 0 (p < 0.0001). The Student’s t-test for mean 33 compression was not able to detect the difference between cycles’ means. However, the tuber appearance increased from 84% of cycle 4 selections compared with 73% of cycle 0 selections having acceptable tuber appearance (> 5 on scale 1 - 9) (Table S2. 2, Figure 2. 7c). These results agree with Bradshaw et al. (2009) and Benites and Pinto (2011) that tuber appearance can be improved through RS breeding system. The variation for tuber appearance was decreased as the RS cycles advanced. The germplasm was selected for round tuber shapes during the recurrent selection cycles. The tuber shape population mean significantly shifted to oval in cycle 4 compared with previous cycles of RS (p < 0.0001) (Table S2. 2, Figure 2. 7d). The reduction of the percentage of selections with round tuber shape may be due to the selection in the crossing block for SC over tuber shape in the greenhouse. Selecting for tuber appearance and shape may have slowed progress towards increasing SC since SC was not selected in the field but post- harvest in the greenhouse. The yield was measured for a total of 208 selections across all RS cycles (46 of cycle 0, 33 of cycle 1, 46 of cycle 2, 24 of cycle 3, and 59 of cycle 4) (Table S2. 2). Tuber yield per plant significantly increased from 430 g in cycle 0 to 522 g in cycle 4 (p= 0.0006) (Table S2. 2, Figure 2. 7d). These results agree with Bradshaw et al. (2009) that indicated increasing yield after three cycles of recurrent selection mix of wild diploid species (S. vernei, S. tuberosum Grp. Andigena, S. acaule, S. chacoense, S. demissum, S. microdontum and S. stoloniferum). Also, tuber yield was improved after five cycles of the RS for Solanum tuberosum ssp. Andigena for (Cubillos and Plaisted 1976). Average tuber number per plant was screened, and tuber number was significantly increased from 8.9 in cycle 0 to 10.9 in cycle 4 tubers (p < 0.0001). Tuber number per plant has a wide range of variation (Table S2. 2, Figure 2. 7e). There was an improvement in the tuber number per plant compared with previous cycles of the RS. Screening 34 selections for average tuber weight are showing there is a wide range of variation within and between the cycle(s) (Table S2. 2 and Figure 2. 7f). Average tuber weight significantly decreased in 38.7 g in cycle 3 compared with 50.4 g in cycle 0 and 50.6 g in cycle 4 of (p= 0.0001). The difference between cycle 0 and cycle 4 was not significant in average tuber weight (Table S2. 2). In general, the yield component is improving slowly due to the selection have been made based on SC in the greenhouse. Tuber specific gravity was measured, and there was significant difference between the mean of RS cycles (p < 0.0001) (Table S2. 2, Figure 2. 7g). The average tuber specific gravity increased from 1.066 in cycle 0 to 1.076 in cycle 4 of RS. The specific gravity improved approximately 0.010 in cycle 4 compared with cycle 0, and these results agreed with Plaisted and Peterson (1963) and Benites and Pinto (2011) that specific gravity can be improved using recurrent selection. These results also agreed with Bradshaw et al. (2009) study which used recurrent selection to improve potato processing quality. RS breeding strategy improved SPGR after five cycles. 35 APPENDIX 36 Table 2. 1. Parental lines genetic background. MSM269-HORG S. tuberosum Grp. Tuberosum/S. tuberosum Grp. Parental lines MS2XLB-75 Scab4-48 M6 BER83 chc524_8 HS66 MCR205 MSM267-B MSS703-5 XD3 MS DMMS Background S. tuberosum Grp. Tuberosum/S. tuberosum Grp. Phureja/S. chacoense/S. microdontum/S. berthaultii S. chacoense S. chacoense S. tuberosum Grp. Tuberosum/S. tuberosum Grp. Phureja/S. chacoense/S. berthaultii S. chacoense S. tuberosum Grp. Tuberosum/S. tuberosum Grp. Phureja S. tuberosum Grp. Tuberosum/S. tuberosum Grp. Phureja/S. chacoense/S. microdontum S. tuberosum Grp. Tuberosum/S. tuberosum Grp. Phureja Phureja S. tuberosum Grp. Phureja /S. tuberosum Grp. Tuberosum/S. chacoense S. tuberosum Grp. Tuberosum/S. chacoense S. tuberosum Grp. Tuberosum/S. tuberosum Grp. Phureja S. tuberosum Grp. Tuberosum/S. tuberosum Grp. Phureja Source MSU UW, Madison UW, Madison MSU UW, Madison MSU MSU MSU MSU MSU UW, Madison MSU MSU 37 No. of female crossed to create next cycle - 24 17 47 71 35 Year of field selection - - 2014 2015 2016 2017 Table 2. 2. Selections summary of each cycle of the recurrent selection. Recurrent selection cycle Total Selections No. families Parental lines cycle 0 cycle 1 cycle 2 cycle 3 cycle 4 12 31 92 123 187 197 - 24 18 28 47 40 No. Self-compatible selections for Bulk pollen - 12 12 37 55 19 38 Table 2. 3. Solanum tuberosum dihaploid references. Sources ID Variety ATL_M_113 MSU Atlantic ATL_M_114 Atlantic MSU ATL_M_120 MSU Atlantic ATL_M_159 MSU Atlantic ATL_M_170 MSU Atlantic ATL_M_179 MSU Atlantic ATL_M_182 MSU Atlantic ATL_M_188 MSU Atlantic ATL_M_192 MSU Atlantic ATL_M_198 MSU Atlantic ATL_M_403 MSU Atlantic ATL_M_404 MSU Atlantic ATL_M_405 Atlantic MSU ATL_M_422 MSU Atlantic ATL_M_423 MSU Atlantic ATL_M_424 MSU Atlantic ATL_M_429 MSU Atlantic ATL_V_023 MSU Atlantic ATL_V_024 MSU Atlantic ATL_V_030 MSU Atlantic ATL_V_033 MSU Atlantic NY148 HP# 1 Breeding line MSU R127-2 HP #1 Breeding line MSU VT_SUP_08 VT_SUP_19 VT_SUP_70 VT_SUP_96 Superior Superior Superior Superior Virginia Tech Virginia Tech Virginia Tech Virginia Tech 39 Table 2. 4. Self-compatibility in selections from five cycles of recurrent selection. Cycle Intercept cycle 0 cycle 1 cycle 2 cycle 3 Cycle Intercept cycle 0 cycle 1 cycle 2 cycle 3 Term Intercept cycle 0 cycle 1 cycle 2 cycle 3 Cycle Intercept cycle 0 cycle 1 cycle 2 cycle 3 9.12 11.76 29.00 0.04 9.01 Self-compatibility% Chi-Square 83.01 16.12 14.13 40.65 52.94 Self-incompatibility% Chi-Square 7.54 32.25 26.08 47.15 40.10 Male sterile% 52.46 0.94 0.06 16.97 8.32 Chi-Square 254.79 20.31 8.42 10.77 9.48 7.54 32.25 16.30 1.62 2.19 Non-flowering plant% Chi-Square 1.88 19.35 43.47 10.56 4.81 210.83 1.31 55.76 0.00 3.83 Prob>ChiSq 0.0025* 0.0006* <0.0001* 0.848 0.0027* Prob>ChiSq <0.0001* 0.3311 0.8135 <.0001* 0.0039* Prob>ChiSq <0.0001* <0.0001* 0.0037* 0.0010* 0.0021* Prob>ChiSq <0.0001* 0.2522 <0.0001* 1 0.0503 * Means significance level for improve germplasm fertility based on the Chi-square distribution test. 40 Table 2. 5. Heterozygosity based on Individuals basis using a total of 6429 SNPs for five cycles of recurrent selection compared with the parental lines. RS No. selections Heterozygosity % Parental lines cycle0 cycle1 cycle2 cycle3 cycle4 12 21 25 45 70 39 25 ± 19 AB* 26 ± 11 A 24 ± 8 AB 23 ± 8 BC 23 ± 7 BC 21 ± 5 C Standard error mean 0.03 0.05 0.03 0.03 0.03 0.03 * Means with the same letter designation are not significantly different as determined by Student’s t-test a = 0.05. 41 Figures 2. 1.Recurrent selection breeding scheme. Developing self-compatible diploid potato germplasm adapted to the long-photoperiod growing season of northern latitudes. 42 Figures 2. 2. Improvement of self-compatibility through five cycles of recurrent selection. 43 Figures 2. 3. SNP Heterozygosity for five cycles of recurrent selection using 6429 SNPs. 44 Figures 2. 4. Neighbor-joining tree using 4885 SNPs; for 39 selections of cycle 4 (magenta), eight parental lines/ species introgression (black), four parental lines/self-compatibility donors (green) and 27 dihaploid S. tuberosum (orange). 45 Figures 2. 5. Principal component analysis using 4885 SNPs; for 39 self-compatible selections of cycle 4 (magenta), eight parental lines/ species introgression (black), four parental lines/ self- compatibility donors (green) and 27 dihaploid S. tuberosum (orange). 46 Figures 2. 6. Structure analysis using 4885 SNPs for 39 self-compatible selections of cycle 4 of RS and 12 parental lines (eight species introgression and four SC donors). The arrows refer to the 12 parental lines were used to generate the RS population. 47 l e a c S y t i r u t a M 3 2 1 5 Mean Mean cycle 4 Mean 5 4 2 1 cycle 0 cycle 1 cycle 3 cycle 4 cycle 0 cycle 1 cycle 0 cycle 1 cycle 3 cycle 4 cycle 0 cycle 1 2 1 4 3 5 4 3 2 1 9 8 7 6 Mean cycle 2 RS cycle 2 RS cycle 2 RS l e a c S y t i r u t a M l e a c S y t i r u a M t cycle 3 3 l e a c S y t i r u t a M (c) Tuber shape (a) Vine maturity (b) Tuber appearance Figures 2. 7. Frequency distribution for seven agronomic traits of cycle 0 to cycle 4. (a) maturity (1 = dead, 5 = late, full green vines and flowering); (b) tuber appearance (1= very poor, and 9= excellent); (c) tuber shape (1= compressed, 2= round, 3= oval, 4= oblong, and 5= long); (d) average tuber yield (g); (e ) average tuber number (No. tuber/ plant); (f) average tuber weight (g); (g) specific gravity. e c n a r a e p p a r e b u T e p a h S r e b u T cycle 2 RS Mean cycle 1 cycle 2 5 4 3 2 1 cycle 2 RS cycle 4 cycle 3 cycle 0 cycle 1 cycle 0 5 4 3 2 cycle 4 cycle 3 cycle 4 cycle 3 1 RS 48 4 3 l e a c S y t i r u t a M RS cycle 3 2 1 400 500 600 cycle 2 cycle 1 cycle 0 Figure 2.7 (cont’d). (d) Average plant weight (g) (f) Average tuber weight (g) cycle 2 cycle 0 cycle 1 300 200 100 0 ) g ( d e Y i l l t n a P e g a r e v A RS cycle 3 12 10 8 6 4 2 r e b m u N r e b u T e g a r e v A l e a c S y t i r u t a M 0 cycle 4 l e a c S R G P S l e a c S y t i r u t a M 4 3 2 (e) Average tuber number cycle 4 Mean 1 cycle 0 cycle 1 cycle 2 RS cycle 3 cycle 4 Mean Mean 5 4 3 2 cycle 0 cycle 1 cycle 2 RS cycle 3 cycle 4 (g) Specific gravity 1 1.1 cycle 0 cycle 1 cycle 2 RS cycle 3 cycle 4 Mean 1.08 1.06 1.04 1.02 1 cycle 0 cycle 1 cycle 2 RS cycle 3 cycle 4 49 SUPPLEMENTAL TABLES 50 Table S2. 1. Selections pedigree from all cycles for screening self-compatibility. The first 12 rows are the parental lines; the lines started with (MSBB) they are cycle 0 selections. The lines started with (MSCC); they are selections from cycle 1. The lines started with (MSDD); they are cycle 2 selections. The lines started with (MSEE); they are cycle 3 selections. The lines started with (MSFF); they are cycle 4 selections. Family MSX2XLB_75 Scab4_48R M6 BER_83 chc_524_8 HS66 MCD205 MSM267_B MSM269_HORG MSS703_5 XD3 MS DMMS MSBB901_A MSBB902_A MSBB902_B MSBB902_C MSBB902_D MSBB907_A MSBB909_A Female Male Female Male Female Male Groups Fertility - - - - - - - - - - - - - Scab4_48 S703_5 S703_5 S703_5 S703_5 A151_16 HS66 - - - - - - - - - - - - - M6 M6 M6 M6 M6 M6 M6 - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - 51 - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - 10 9 9 9 9 18 7 SI SI SC SI SC SI SI SI SI SI SC SI SC MS SC MS MS MS NF SI SNP genotype MSX2XLB_75 Scab4_48R M6 BER_83 chc_524_8 HS66 MCD205 MSM267_B MSM269_HORG MSS703_5 XD3 MS DMMS MSBB901_A MSBB902_A MSBB902_B MSBB902_C - - MSBB909_A Table S2. 1 (cont’d) MSBB912_A MSBB912_B MSBB917_A MSBB918_A MSBB919_A MSBB920_A MSBB921_A MSBB921_B MSBB925_A MSBB927_A MSBB930_A MSBB930_B MSBB932_A MSBB934_A MSBB935_A MSBB936_A MSBB938_A MSBB939_A MSBB940_A MSBB942_A MSBB943_A MSBB946_A MSBB953_A MSBB953_B MSCC804_01 MSCC804_02 MSCC804_03 M6 M6 MCD205 MCD205 M267_B chc524_8 M269_1Y chc524_8 MCD205 chc524_8 chc524_8 chc524_8 S703_5 S703_5 chc524_8 chc524_8 BER83 chc524_8 2XLB_075 MS MS HS66 Scab4_48 M269_1Y XD3 XD3 XD3 BER83 BER83 S703_5 XD3 XD3 XD3 XD3 XD3 A133_134 XD3 M267_B 2XLB_60 DMMS XD3 DMMS DMMS MCD205 MCD205 MCD205 BB901_A BB901_A BB901_A XD3 XD3 Bulk1 Bulk1 Bulk1 - - - - - - - - - - - - - - - - - - - - - - - - Scab4_48 Scab4_48 Scab4_48 - - - - - - - - - - - - - - - - - - - - - - - - M6 M6 M6 - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - 8 8 3 4 19 5 6 6 14 1 2 2 15 13 20 21 11 22 17 23 16 24 12 12 10 10 10 MS SI MS SI MS SC MS MS SI SI SC SI SC SI SI MS NF NF SI NF SI NF SC SI SC NF NF 52 MSBB912_A MSBB912_B MSBB917_A MSBB918_A MSBB920_A MSBB925_A MSBB927_A MSBB930_A MSBB930_B MSBB932_A MSBB934_A - - - - - - - - - - MSBB938_A MSBB940_A MSBB943_A MSBB953_A MSBB953_B MSCC804_01 Table S2. 1 (cont’d) MSCC804_04 MSCC804_05 MSCC804_06 MSCC805_01 MSCC805_02 MSCC805_03 MSCC805_04 MSCC806_01 MSCC806_02 MSCC806_03 MSCC806_04 MSCC806_05 MSCC806_06 MSCC807_01 MSCC807_02 MSCC807_03 MSCC807_04 MSCC807_05 MSCC807_06 MSCC809_01 MSCC809_02 MSCC809_03 MSCC809_04 MSCC809_05 MSCC809_06 MSCC810_01 MSCC810_02 BB901_A BB901_A BB901_A BB902_A BB902_A BB902_A BB902_A BB902_B BB902_B BB902_B BB902_B BB902_B BB902_B BB902_C BB902_C BB902_C BB902_C BB902_C BB902_C BB909_A BB909_A BB909_A BB909_A BB909_A BB909_A BB912_A BB912_A Scab4_48 Scab4_48 Scab4_48 S703_5 S703_5 S703_5 S703_5 S703_5 S703_5 S703_5 S703_5 S703_5 S703_5 S703_5 S703_5 S703_5 S703_5 S703_5 S703_5 HS66 HS66 HS66 HS66 HS66 HS66 Bulk1 Bulk1 Bulk1 Bulk1 Bulk1 Bulk1 Bulk1 Bulk1 Bulk1 Bulk1 Bulk1 Bulk1 Bulk1 Bulk1 Bulk1 Bulk1 Bulk1 Bulk1 Bulk1 Bulk1 Bulk1 Bulk1 Bulk1 Bulk1 Bulk1 Bulk1 MCD205 Bulk1 MCD205 M6 M6 M6 M6 M6 M6 M6 M6 M6 M6 M6 M6 M6 M6 M6 M6 M6 M6 M6 M6 M6 M6 M6 M6 M6 M6 M6 - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - 10 10 10 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 7 7 7 7 7 7 8 8 SI SC NF NF NF SI SI NF SC MS SI SC MS NF SI SI NF MS MS SI SC SI SC SI NF NF NF MSCC804_05 MSCC805_03 MSCC806_02 MSCC806_05 MSCC807_01 MSCC807_04 MSCC809_02 MSCC809_04 MSCC810_01 - - - - - - - - - - - - - - - - - - 53 Table S2. 1 (cont’d) MSCC811_01 MSCC811_02 MSCC811_03 MSCC811_04 MSCC811_05 MSCC812_01 MSCC812_02 MSCC812_03 MSCC813_01 MSCC813_02 MSCC813_03 MSCC813_04 MSCC815_01 MSCC815_02 MSCC817_01 MSCC817_02 MSCC817_03 MSCC817_04 MSCC819_01 MSCC819_02 MSCC819_03 MSCC820_01 MSCC820_02 MSCC820_03 MSCC820_04 MSCC820_05 MSCC820_06 BB912_B BB912_B BB912_B BB912_B BB912_B BB917_A BB917_A BB917_A BB918_A BB918_A BB918_A BB918_A BB920_A BB920_A BB921_B BB921_B BB921_B BB921_B BB925_A BB925_A BB925_A BB927_A BB927_A BB927_A BB927_A BB927_A BB927_A M6 M6 M6 M6 M6 Bulk1 MCD205 Bulk1 MCD205 Bulk1 MCD205 Bulk1 MCD205 Bulk1 MCD205 M267_B chc524_8 Bulk1 M267_B chc524_8 Bulk1 Bulk1 M267_B chc524_8 Bulk1 M269_1Y chc524_8 Bulk1 M269_1Y chc524_8 Bulk1 M269_1Y chc524_8 Bulk1 M269_1Y chc524_8 Bulk1 chc524_8 chc524_8 Bulk1 chc524_8 Bulk1 S703_5 Bulk1 chc524_8 S703_5 chc524_8 Bulk1 S703_5 S703_5 Bulk1 chc524_8 chc524_8 BER83 Bulk1 Bulk1 chc524_8 BER83 chc524_8 BER83 Bulk1 chc524_8 2XLB_075 Bulk1 Bulk1 chc524_8 2XLB_075 chc524_8 2XLB_075 Bulk1 chc524_8 2XLB_075 Bulk1 Bulk1 chc524_8 2XLB_075 chc524_8 2XLB_075 Bulk1 MS MS - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - 8 8 8 8 8 3 3 3 4 4 4 4 5 5 6 6 6 6 14 14 14 1 1 1 1 1 1 NF NF SI SC SC NF NF NF MS MS MS NF MS NF SI NF NF NF SI NF NF NF SI SI SI SI NF 54 MSCC811_04 MSCC811_05 MSCC812_02 MSCC819_03 MSCC813_04 MSCC815_01 MSCC815_02 MSCC817_02 - - - - - - - - - - - - - - - - - - - Table S2. 1 (cont’d) MSCC822_01 MSCC822_02 MSCC822_03 MSCC822_04 MSCC822_05 MSCC822_06 MSCC823_01 MSCC823_02 MSCC823_03 MSCC823_04 MSCC823_05 MSCC825_01 MSCC825_02 MSCC825_03 MSCC825_04 MSCC825_05 MSCC825_06 MSCC827_01 MSCC827_02 MSCC827_03 MSCC827_04 MSCC827_05 MSCC827_06 MSCC831_01 MSCC831_02 MSCC831_03 MSCC831_04 BB930_A BB930_A BB930_A BB930_A BB930_A BB930_A BB930_B BB930_B BB930_B BB930_B BB930_B BB934_A BB934_A BB934_A BB934_A BB934_A BB934_A BB938_A BB938_A BB938_A BB938_A BB938_A BB938_A BB953_A BB953_A BB953_A BB953_A XD3 XD3 XD3 XD3 XD3 XD3 XD3 XD3 XD3 XD3 XD3 Bulk1 Bulk1 Bulk1 Bulk1 Bulk1 Bulk1 Bulk1 Bulk1 Bulk1 Bulk1 Bulk1 Bulk1 Bulk1 Bulk1 Bulk1 Bulk1 Bulk1 Bulk1 Bulk1 Bulk1 Bulk1 Bulk1 Bulk1 Bulk1 MCD205 Bulk1 MCD205 Bulk1 MCD205 Bulk1 MCD205 Scab4_48 Scab4_48 Scab4_48 Scab4_48 Scab4_48 Scab4_48 HS66 HS66 HS66 HS66 HS66 HS66 BER83 BER83 BER83 BER83 BER83 BER83 BER83 BER83 BER83 BER83 BER83 XD3 XD3 XD3 XD3 XD3 XD3 XD3 XD3 XD3 XD3 XD3 XD3 XD3 XD3 XD3 XD3 - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - 2 2 2 2 2 2 2 2 2 2 2 13 13 13 13 13 13 11 11 11 11 11 11 12 12 12 12 SC NF NF NF SC SI NF NF SC NF SI MS SI NF SC NF NF SI SI SI SI SI SC MS NF SI NF 55 MSCC822_01 MSCC823_03 MSCC823_05 MSCC825_02 MSCC827_06 MSCC831_03 - - - - - - - - - - - - - - - - - - - - - Table S2. 1 (cont’d) MSCC831_05 MSCC831_06 MSCC832_01 MSCC832_02 MSCC832_03 MSCC832_04 MSCC832_05 MSCC832_06 MSDD802_01 MSDD802_02 MSDD802_04 MSDD802_06 MSDD803_01 MSDD803_05 MSDD804_06 MSDD804_07 MSDD804_09 MSDD805_01 MSDD805_02 MSDD805_03 MSDD805_05 MSDD805_06 MSDD805_07 MSDD805_08 MSDD805_09 MSDD805_12 MSDD807_01 BB953_A BB953_A BB953_B BB953_B BB953_B BB953_B BB953_B BB953_B CC804_1 CC804_1 CC804_1 CC804_1 CC804_5 CC804_5 CC806_2 CC806_2 CC806_2 CC806_5 CC806_5 CC806_5 CC806_5 CC806_5 CC806_5 CC806_5 CC806_5 CC806_5 CC807_4 XD3 XD3 XD3 XD3 XD3 XD3 XD3 XD3 - - - - - - - - Bulk1 MCD205 Bulk1 MCD205 Bulk1 MCD205 Bulk1 MCD205 Bulk1 MCD205 Bulk1 MCD205 Bulk1 MCD205 Bulk1 MCD205 Bulk1 Bulk1 Bulk1 Bulk1 Bulk1 Bulk1 Bulk1 Bulk1 Bulk1 Bulk1 Bulk1 Bulk1 Bulk1 Bulk1 Bulk1 Bulk1 Bulk1 Bulk1 Bulk1 BB901_A Bulk1 Scab4_48 BB901_A Bulk1 Scab4_48 BB901_A Bulk1 Scab4_48 BB901_A Bulk1 Scab4_48 BB901_A Bulk1 Scab4_48 BB901_A Bulk1 Scab4_48 S703_5 BB902_B Bulk1 BB902_B Bulk1 S703_5 S703_5 BB902_B Bulk1 S703_5 BB902_B Bulk1 BB902_B Bulk1 S703_5 S703_5 BB902_B Bulk1 S703_5 BB902_B Bulk1 S703_5 BB902_B Bulk1 BB902_B Bulk1 S703_5 S703_5 BB902_B Bulk1 S703_5 BB902_B Bulk1 BB902_B Bulk1 S703_5 S703_5 BB902_C Bulk1 56 - - - - - - - - M6 M6 M6 M6 M6 M6 M6 M6 M6 M6 M6 M6 M6 M6 M6 M6 M6 M6 M6 12 12 12 12 12 12 12 12 10 10 10 10 10 10 9 9 9 9 9 9 9 9 9 9 9 9 9 NF NF MS NF MS MS MS MS SC SC SC SC SI SC SI SI SC SC SI SI SC SI SI SC SI SI SI - - - - - - - - - - - - - - - - - - - - MSDD803_05 MSDD804_09 MSCC832_06 MSDD802_01 MSDD802_04 MSDD805_05 MSDD805_08 CC807_4 CC807_4 CC807_4 CC807_4 CC809_2 CC809_2 CC809_4 Table S2. 1 (cont’d) MSDD807_02 MSDD807_03 MSDD807_05 MSDD807_06 MSDD808_10 MSDD808_12 MSDD809_02 MSDD809_04 MSDD809_09 MSDD809_11 MSDD809_13 MSDD812_01 MSDD812_02 MSDD812_03 MSDD812_05 MSDD812_06 MSDD814_01 MSDD814_04 MSDD821_02 MSDD821_04 MSDD821_05 MSDD821_06 MSDD821_08 MSDD821_09 MSDD821_10 MSDD824_01 MSDD825_01 CC809_4 CC809_4 CC809_4 CC811_5 CC811_5 CC811_5 CC811_5 CC811_5 CC813_4 CC813_4 CC817_2 CC817_2 CC817_2 CC817_2 CC817_2 CC817_2 CC817_2 CC819_3 CC820_3 Bulk1 Bulk1 Bulk1 Bulk1 Bulk1 Bulk1 Bulk1 Bulk1 Bulk1 Bulk1 Bulk1 Bulk1 Bulk1 Bulk1 Bulk1 Bulk1 Bulk1 Bulk1 Bulk1 Bulk1 Bulk1 Bulk1 Bulk1 Bulk1 Bulk1 Bulk1 Bulk1 M6 M6 M6 M6 M6 M6 M6 M6 M6 M6 M6 M6 M6 M6 M6 M6 S703_5 S703_5 S703_5 S703_5 HS66 HS66 HS66 HS66 HS66 HS66 HS66 BB902_C Bulk1 BB902_C Bulk1 BB902_C Bulk1 BB902_C Bulk1 BB909_A Bulk1 BB909_A Bulk1 BB909_A Bulk1 BB909_A Bulk1 BB909_A Bulk1 BB909_A Bulk1 BB909_A Bulk1 BB912_B Bulk1 MCD205 BB912_B Bulk1 MCD205 BB912_B Bulk1 MCD205 BB912_B Bulk1 MCD205 BB912_B Bulk1 MCD205 BB918_A Bulk1 M269_1Y chc524_8 BB918_A Bulk1 M269_1Y chc524_8 BB921_B Bulk1 MCD205 BB921_B Bulk1 MCD205 BB921_B Bulk1 MCD205 BB921_B Bulk1 MCD205 BB921_B Bulk1 MCD205 BB921_B Bulk1 MCD205 BB921_B Bulk1 MCD205 BB925_A Bulk1 BB927_A Bulk1 M6 M6 M6 M6 M6 M6 M6 chc524_8 BER83 chc524_8 2XLB_075 9 9 9 9 7 7 7 7 7 7 7 8 8 8 8 8 4 4 6 6 6 6 6 6 6 14 1 NF SC SC SC SC SI SC SI SC NF SI NF SC SC SI SI NF SC SI NF SI SC SC SC SC SC SC 57 MSDD807_03 MSDD807_05 MSDD808_10 MSDD809_09 - - - - - - - - - - - - - - - - MSDD812_02 MSDD812_03 MSDD814_04 MSDD821_09 MSDD821_10 MSDD824_01 MSDD825_01 Table S2. 1 (cont’d) chc524_8 2XLB_075 MSDD829_01 MSDD825_02 MSDD829_01 MSDD829_02 MSDD829_03 MSDD829_04 MSDD829_06 MSDD829_07 MSDD829_08 MSDD829_09 MSDD829_10 MSDD829_11 MSDD831_01 MSDD837_02 MSDD837_03 MSDD837_04 MSDD837_05 MSDD837_06 MSDD837_07 MSDD837_08 MSDD837_09 MSDD837_11 MSDD838_01 MSDD838_02 MSDD844_03 MSDD845_02 MSDD845_03 MSDD847_01 CC820_3 CC825_2 CC825_2 CC825_2 CC825_2 CC825_2 CC825_2 CC825_2 CC825_2 CC825_2 CC825_2 CC827_6 CC831_3 CC831_3 CC831_3 CC831_3 CC831_3 CC831_3 CC831_3 CC831_3 CC831_3 CC832_6 CC832_6 CC823_3 CC823_5 CC823_5 CC805_3 Bulk1 Bulk1 Bulk1 Bulk1 Bulk1 Bulk1 Bulk1 Bulk1 Bulk1 Bulk1 Bulk1 Bulk1 Bulk1 Bulk1 Bulk1 Bulk1 Bulk1 Bulk1 Bulk1 Bulk1 Bulk1 Bulk1 Bulk1 Bulk1 Bulk1 Bulk1 Bulk2 HS66 BB927_A Bulk1 BB934_A Bulk1 Scab4_48 BB934_A Bulk1 Scab4_48 BB934_A Bulk1 Scab4_48 BB934_A Bulk1 Scab4_48 BB934_A Bulk1 Scab4_48 BB934_A Bulk1 Scab4_48 BB934_A Bulk1 Scab4_48 BB934_A Bulk1 Scab4_48 BB934_A Bulk1 Scab4_48 BB934_A Bulk1 Scab4_48 BB938_A Bulk1 BB953_A Bulk1 MCD205 BB953_A Bulk1 MCD205 BB953_A Bulk1 MCD205 BB953_A Bulk1 MCD205 BB953_A Bulk1 MCD205 BB953_A Bulk1 MCD205 BB953_A Bulk1 MCD205 BB953_A Bulk1 MCD205 BB953_A Bulk1 MCD205 BB953_B Bulk1 MCD205 BB953_B Bulk1 MCD205 BB930_B Bulk1 BB930_B Bulk1 BB930_B Bulk1 BB902_A Bulk1 XD3 XD3 XD3 S703_5 XD3 XD3 XD3 XD3 XD3 XD3 XD3 XD3 XD3 XD3 XD3 XD3 XD3 XD3 XD3 XD3 XD3 XD3 XD3 XD3 XD3 XD3 BER83 BER83 BER83 M6 1 13 13 13 13 13 13 13 13 13 13 11 12 12 12 12 12 12 12 12 12 12 12 2 2 2 9 SI SC SI SI SI SI NF SI SI SC SI SI SC SI SI NF SI SC SC SI SI SI SI SI SC SI MS - - - - - - - - - - - - - - - - - MSDD829_10 MSDD831_01 MSDD837_02 MSDD837_08 MSDD838_01 MSDD844_03 MSDD845_02 MSDD845_03 - 58 Table S2. 1 (cont’d) MSDD847_02 MSDD847_03 MSDD847_04 MSDD847_05 MSDD847_06 MSDD848_01 MSDD848_02 MSDD848_03 MSDD848_04 MSDD849_01 MSDD849_02 MSDD849_03 MSDD849_04 MSDD849_05 MSDD849_06 MSDD849_07 MSDD849_08 MSDD850_03 MSDD850_06 MSDD850_07 MSDD851_02 MSDD851_03 MSDD851_05 MSDD851_06 MSDD851_07 MSDD851_08 MSDD852_02 CC805_3 CC805_3 CC805_3 CC805_3 CC805_3 CC806_2 CC806_2 CC806_2 CC806_2 CC806_5 CC806_5 CC806_5 CC806_5 CC806_5 CC806_5 CC806_5 CC806_5 CC809_4 CC809_4 CC809_4 CC810 CC810 CC810 CC810 CC810 CC810 CC812_2 Bulk2 Bulk2 Bulk2 Bulk2 Bulk2 Bulk2 Bulk2 Bulk2 Bulk2 Bulk2 Bulk2 Bulk2 Bulk2 Bulk2 Bulk2 Bulk2 Bulk2 Bulk2 Bulk2 Bulk2 Bulk2 Bulk2 Bulk2 Bulk2 Bulk2 Bulk2 Bulk2 S703_5 S703_5 S703_5 S703_5 S703_5 S703_5 S703_5 S703_5 S703_5 S703_5 S703_5 S703_5 S703_5 S703_5 S703_5 S703_5 S703_5 HS66 HS66 HS66 BB902_A Bulk1 BB902_A Bulk1 BB902_A Bulk1 BB902_A Bulk1 BB902_A Bulk1 BB902_B Bulk1 BB902_B Bulk1 BB902_B Bulk1 BB902_B Bulk1 BB902_B Bulk1 BB902_B Bulk1 BB902_B Bulk1 BB902_B Bulk1 BB902_B Bulk1 BB902_B Bulk1 BB902_B Bulk1 BB902_B Bulk1 BB909_A Bulk1 BB909_A Bulk1 BB909_A Bulk1 BB912_A Bulk1 MCD205 BB912_A Bulk1 MCD205 BB912_A Bulk1 MCD205 BB912_A Bulk1 MCD205 BB912_A Bulk1 MCD205 BB912_A Bulk1 MCD205 BB917_A Bulk1 M267_B 59 M6 M6 M6 M6 M6 M6 M6 M6 M6 M6 M6 M6 M6 M6 M6 M6 M6 M6 M6 M6 M6 M6 M6 M6 M6 M6 chc524_8 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 7 7 7 8 8 8 8 8 8 3 NF SI SI SC SI SC SC SI SI SI SC SI SI SI SC SC SC SC SC NF SI SI SC SC SI SC SC - - - - - - - - - - - - - - - - - MSDD847_05 MSDD847_06 MSDD848_01 MSDD848_02 MSDD849_06 MSDD849_07 MSDD850_03 MSDD850_06 MSDD851_06 MSDD851_08 CC812_2 CC812_2 CC812_2 CC812_2 CC812_2 CC812_2 CC813_4 CC813_4 CC813_4 CC813_4 CC813_4 CC817_2 CC817_2 CC817_2 CC817_2 CC820_3 CC820_3 CC820_3 CC815_2 CC815_2 CC815_2 CC815_2 CC815_2 Table S2. 1 (cont’d) MSDD852_03 Bulk2 MSDD852_04 Bulk2 MSDD852_05 Bulk2 MSDD852_06 Bulk2 MSDD852_08 Bulk2 MSDD852_09 Bulk2 MSDD853_01 Bulk2 MSDD853_02 Bulk2 MSDD853_04 Bulk2 MSDD853_05 Bulk2 MSDD853_08 Bulk2 MSDD855_01 Bulk2 MSDD855_02 Bulk2 MSDD855_03 Bulk2 MSDD855_05 Bulk2 MSDD857_01 Bulk2 MSDD857_02 Bulk2 MSDD857_03 Bulk2 MSDD865_02 Bulk2 MSDD865_03 Bulk2 MSDD865_04 Bulk2 MSDD865_05 Bulk2 MSDD865_06 Bulk2 MSEE700_01 DD802_01 Bulk1 MSEE700_03 DD802_01 Bulk1 MSEE700_05 DD802_01 Bulk1 MSEE700_06 DD802_01 Bulk1 chc524_8 BB917_A Bulk1 M267_B chc524_8 BB917_A Bulk1 M267_B chc524_8 BB917_A Bulk1 M267_B chc524_8 BB917_A Bulk1 M267_B chc524_8 BB917_A Bulk1 M267_B BB917_A Bulk1 M267_B chc524_8 BB918_A Bulk1 M269_1Y chc524_8 BB918_A Bulk1 M269_1Y chc524_8 BB918_A Bulk1 M269_1Y chc524_8 BB918_A Bulk1 M269_1Y chc524_8 BB918_A Bulk1 M269_1Y chc524_8 chc524_8 BB921_B Bulk1 S703_5 BB921_B Bulk1 chc524_8 S703_5 chc524_8 BB921_B Bulk1 S703_5 BB921_B Bulk1 chc524_8 S703_5 chc524_8 2XLB_075 BB927_A Bulk1 chc524_8 2XLB_075 BB927_A Bulk1 chc524_8 2XLB_075 BB927_A Bulk1 chc524_8 BB920_A Bulk1 BB920_A Bulk1 chc524_8 chc524_8 BB920_A Bulk1 chc524_8 BB920_A Bulk1 BB920_A Bulk1 chc524_8 CC804_1 CC804_1 CC804_1 CC804_1 Bulk1 BB901_A Bulk1 BB901_A Bulk1 BB901_A Bulk1 BB901_A Bulk1 Bulk1 Bulk1 Bulk1 MS MS MS MS MS 3 3 3 3 3 3 4 4 4 4 4 6 6 6 6 1 1 1 5 5 5 5 5 10 10 10 10 SI SC SI SC SI SI MS SI SC SI NF SI SI SC SI SI NF SC SC SC NF NF SI SC SC SI SC 60 MSDD852_04 MSDD852_08 - - - - - - - - - - - - - - MSDD853_04 MSDD853_05 MSDD855_01 MSDD855_03 MSDD857_03 MSDD865_02 MSDD865_03 MSEE700_01 - - - Table S2. 1 (cont’d) MSEE701_02 DD802_04 Bulk1 MSEE701_03 DD802_04 Bulk1 MSEE701_06 DD802_04 Bulk1 MSEE702_04 DD803_05 Bulk1 MSEE702_05 DD803_05 Bulk1 MSEE702_06 DD803_05 Bulk1 MSEE702_07 DD803_05 Bulk1 MSEE703_02 DD804_09 Bulk1 MSEE703_04 DD804_09 Bulk1 MSEE703_05 DD804_09 Bulk1 MSEE703_06 DD804_09 Bulk1 MSEE703_07 DD804_09 Bulk1 MSEE703_08 DD804_09 Bulk1 MSEE704_01 DD805_05 Bulk1 MSEE704_03 DD805_05 Bulk1 MSEE704_06 DD805_05 Bulk1 MSEE704_08 DD805_05 Bulk1 MSEE705_04 DD805_08 Bulk1 MSEE705_06 DD805_08 Bulk1 MSEE705_07 DD805_08 Bulk1 MSEE705_08 DD805_08 Bulk1 MSEE706_03 DD807_03 Bulk1 MSEE706_04 DD807_03 Bulk1 MSEE706_06 DD807_03 Bulk1 MSEE707_01 DD807_05 Bulk1 MSEE707_02 DD807_05 Bulk1 MSEE707_03 DD807_05 Bulk1 CC804_1 CC804_1 CC804_1 CC804_5 CC804_5 CC804_5 CC804_5 CC806_2 CC806_2 CC806_2 CC806_2 CC806_2 CC806_2 CC806_5 CC806_5 CC806_5 CC806_5 CC806_5 CC806_5 CC806_5 CC806_5 CC807_4 CC807_4 CC807_4 CC807_4 CC807_4 CC807_4 Bulk1 Bulk1 Bulk1 Bulk1 Bulk1 Bulk1 Bulk1 Bulk1 Bulk1 Bulk1 Bulk1 Bulk1 Bulk1 Bulk1 Bulk1 Bulk1 Bulk1 Bulk1 Bulk1 Bulk1 Bulk1 Bulk1 Bulk1 Bulk1 Bulk1 Bulk1 Bulk1 Bulk1 BB901_A Bulk1 BB901_A Bulk1 BB901_A Bulk1 BB901_A Bulk1 BB901_A Bulk1 BB901_A Bulk1 BB901_A Bulk1 BB902_B Bulk1 BB902_B Bulk1 BB902_B Bulk1 BB902_B Bulk1 BB902_B Bulk1 BB902_B Bulk1 BB902_B Bulk1 BB902_B Bulk1 BB902_B Bulk1 BB902_B Bulk1 BB902_B Bulk1 BB902_B Bulk1 BB902_B Bulk1 BB902_B Bulk1 BB902_C Bulk1 BB902_C Bulk1 BB902_C Bulk1 BB902_C Bulk1 BB902_C Bulk1 BB902_C 61 10 10 10 10 10 10 10 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 SC SI SC SI SC SC SI SI SI SC SI SC SI SI SC SC SC SC SC NF SC SC SI SI SC SI SI MSEE701_02 MSEE701_06 MSEE702_05 MSEE702_06 MSEE703_07 - - - - - - - - - - - - - - - - - MSEE704_03 MSEE704_08 MSEE705_04 MSEE705_06 MSEE706_03 Table S2. 1 (cont’d) MSEE707_05 DD807_05 Bulk1 MSEE707_06 DD807_05 Bulk1 MSEE708_01 DD808_10 Bulk1 MSEE708_02 DD808_10 Bulk1 MSEE708_04 DD808_10 Bulk1 MSEE708_06 DD808_10 Bulk1 MSEE708_07 DD808_10 Bulk1 MSEE709_01 DD809_09 Bulk1 MSEE709_03 DD809_09 Bulk1 MSEE709_04 DD809_09 Bulk1 MSEE710_03 DD812_02 Bulk1 MSEE710_06 DD812_02 Bulk1 MSEE710_09 DD812_02 Bulk1 MSEE710_10 DD812_02 Bulk1 MSEE711_01 DD812_03 Bulk1 MSEE711_03 DD812_03 Bulk1 MSEE711_04 DD812_03 Bulk1 MSEE711_06 DD812_03 Bulk1 MSEE712_02 DD814_04 Bulk1 MSEE712_04 DD814_04 Bulk1 MSEE712_06 DD814_04 Bulk1 MSEE712_07 DD814_04 Bulk1 MSEE713_07 DD821_10 Bulk7 MSEE713_08 DD821_10 Bulk8 MSEE713_09 DD821_10 Bulk9 MSEE714_04 DD821_10 Bulk1 MSEE714_05 DD821_10 Bulk1 CC807_4 CC807_4 CC809_2 CC809_2 CC809_2 CC809_2 CC809_2 CC809_4 CC809_4 CC809_4 CC811_5 CC811_5 CC811_5 CC811_5 CC811_5 CC811_5 CC811_5 CC811_5 CC813_4 CC813_4 CC813_4 CC813_4 CC817_2 CC817_2 CC817_2 CC817_2 CC817_2 Bulk1 Bulk1 Bulk1 Bulk1 Bulk1 Bulk1 Bulk1 Bulk1 Bulk1 Bulk1 Bulk1 Bulk1 Bulk1 Bulk1 Bulk1 Bulk1 Bulk1 Bulk1 Bulk1 Bulk1 Bulk1 Bulk1 Bulk1 Bulk1 Bulk1 Bulk1 Bulk1 Bulk1 BB902_C Bulk1 BB902_C Bulk1 BB909_A Bulk1 BB909_A Bulk1 BB909_A Bulk1 BB909_A Bulk1 BB909_A Bulk1 BB909_A Bulk1 BB909_A Bulk1 BB909_A Bulk1 BB912_B Bulk1 BB912_B Bulk1 BB912_B Bulk1 BB912_B Bulk1 BB912_B Bulk1 BB912_B Bulk1 BB912_B Bulk1 BB912_B Bulk1 BB918_A Bulk1 BB918_A Bulk1 BB918_A Bulk1 BB918_A Bulk1 BB921_B Bulk1 BB921_B Bulk1 BB921_B Bulk1 BB921_B Bulk1 BB921_B 62 9 9 7 7 7 7 7 7 7 7 8 8 8 8 8 8 8 8 4 4 4 4 6 6 6 6 6 SC SI SI SC SC SC SI SI MS SI SC SC SC SC SC SI SI SI SC SC SC SC SC SC SI SC SC MSEE707_05 MSEE708_02 MSEE708_04 - - - - - - - - - - - - - MSEE709_03 MSEE709_04 MSEE710_06 MSEE710_09 MSEE712_02 MSEE712_07 MSEE713_07 MSEE713_08 - MSEE714_04 MSEE714_05 Table S2. 1 (cont’d) MSEE714_06 DD821_10 Bulk1 MSEE716_05 DD825_01 Bulk1 MSEE716_06 DD825_01 Bulk1 MSEE716_07 DD825_01 Bulk1 MSEE717_01 DD829_01 Bulk1 MSEE717_03 DD829_01 Bulk1 MSEE717_04 DD829_01 Bulk1 MSEE717_05 DD829_01 Bulk1 MSEE718_01 DD829_10 Bulk1 MSEE718_02 DD829_10 Bulk1 MSEE718_04 DD829_10 Bulk1 MSEE719_01 DD831_01 Bulk1 CC817_2 CC820_3 CC820_3 CC820_3 CC825_2 CC825_2 CC825_2 CC825_2 CC825_2 CC825_2 CC825_2 CC827_6 Bulk1 BB921_B Bulk1 BB927_A Bulk1 BB927_A Bulk1 BB927_A Bulk1 BB934_A Bulk1 BB934_A Bulk1 BB934_A Bulk1 BB934_A Bulk1 BB934_A Bulk1 BB934_A Bulk1 BB934_A Bulk1 BB938_A Bulk1 Bulk1 Bulk1 Bulk1 Bulk1 Bulk1 Bulk1 Bulk1 Bulk1 Bulk1 Bulk1 Bulk1 MSEE719_02 DD831_01 MSEE719_03 DD831_01 MSEE719_06 DD831_01 Bulk1 CC827_6 Bulk1 BB938_A Bulk1 Bulk1 CC827_6 Bulk1 BB938_A Bulk1 Bulk1 CC827_6 Bulk1 BB938_A Bulk1 CC831_3 CC831_3 CC831_3 CC831_3 CC831_3 CC831_3 CC831_3 CC831_3 CC832_6 Bulk1 BB953_A Bulk1 BB953_A Bulk1 BB953_A Bulk1 BB953_A Bulk1 BB953_A Bulk1 BB953_A Bulk1 BB953_A Bulk1 BB953_A Bulk1 BB953_B Bulk1 Bulk1 Bulk1 Bulk1 Bulk1 Bulk1 Bulk1 Bulk1 Bulk1 63 SI SI SI SI SI MSEE720_03 DD837_02 Bulk1 MSEE720_04 DD837_02 Bulk1 MSEE720_05 DD837_02 Bulk1 MSEE720_08 DD837_02 Bulk1 MSEE721_01 DD837_08 Bulk1 MSEE721_03 DD837_08 Bulk1 MSEE721_05 DD837_08 Bulk1 MSEE721_06 DD837_08 Bulk1 MSEE722_05 DD838_01 Bulk1 6 1 1 1 13 13 13 13 13 13 13 11 11 11 11 12 12 12 12 12 12 12 12 12 SI SI SC SC SC SC SI SC SC SC SI SC SC SI MS SI SC MS SC SI SC SI SC SC - - - - - - - - - - - - - MSEE716_06 MSEE716_07 MSEE717_01 MSEE717_05 MSEE718_01 MSEE718_02 MSEE719_02 - - MSEE720_04 MSEE721_06 Table S2. 1 (cont’d) MSEE722_06 DD838_01 MSEE722_07 DD838_01 MSEE723_01 DD838_02 MSEE723_03 DD838_02 MSEE723_04 DD838_02 MSEE723_06 DD838_02 SI SI SI SI SI SI Bulk1 CC832_6 Bulk1 BB953_B Bulk1 Bulk1 CC832_6 Bulk1 BB953_B Bulk1 Bulk1 CC832_6 Bulk1 BB953_B Bulk1 Bulk1 CC832_6 Bulk1 BB953_B Bulk1 Bulk1 CC832_6 Bulk1 BB953_B Bulk1 Bulk1 CC832_6 Bulk1 BB953_B Bulk1 MSEE724_04 DD845_02 Bulk1 MSEE724_05 DD845_02 Bulk1 MSEE724_06 DD845_02 Bulk1 MSEE724_07 DD845_02 Bulk1 MSEE725_02 DD847_05 Bulk1 MSEE725_04 DD847_05 Bulk1 MSEE725_05 DD847_05 Bulk1 MSEE725_06 DD847_05 Bulk1 MSEE726_02 DD847_06 Bulk1 CC823_5 CC823_5 CC823_5 CC823_5 CC805_3 CC805_3 CC805_3 CC805_3 CC805_3 Bulk1 BB930_B Bulk1 BB930_B Bulk1 BB930_B Bulk1 BB930_B Bulk2 BB902_A Bulk2 BB902_A Bulk2 BB902_A Bulk2 BB902_A Bulk2 BB902_A Bulk1 Bulk1 Bulk1 Bulk1 Bulk1 Bulk1 Bulk1 Bulk1 Bulk1 MSEE726_04 DD847_06 MSEE726_09 DD847_06 MSEE726_12 DD847_06 MSEE727_02 DD847_06 Bulk1 CC805_3 Bulk2 BB902_A Bulk1 Bulk1 CC805_3 Bulk2 BB902_A Bulk1 Bulk1 CC805_3 Bulk2 BB902_A Bulk1 Bulk1 CC805_3 Bulk2 BB902_A Bulk1 SI SI SI SI SI 64 12 12 12 12 12 12 2 2 2 2 9 9 9 9 9 9 9 9 9 SC SC SI SI SI SC SI SC SI SI SC SI SC SI SI SI SI SC NF - MSEE722_07 - - - MSEE723_06 MSEE724_06 MSEE725_02 - - - - - - - - MSEE726_09 - - Table S2. 1 (cont’d) MSEE727_04 DD847_06 MSEE727_05 DD847_06 MSEE727_06 DD847_06 MSEE728_02 DD845_03 SI SI SI SI MSEE729_03 DD849_06 Bulk1 MSEE729_04 DD849_06 Bulk1 MSEE729_05 DD849_06 Bulk1 MSEE730_01 DD849_07 Bulk1 MSEE730_03 DD849_07 Bulk1 MSEE730_07 DD849_07 Bulk1 MSEE730_08 DD849_07 Bulk1 MSEE730_09 DD849_07 Bulk1 MSEE732_03 DD850_06 Bulk1 MSEE732_04 DD850_06 Bulk1 MSEE732_06 DD850_06 Bulk1 MSEE732_07 DD850_06 Bulk1 MSEE733_02 DD851_06 Bulk1 MSEE733_04 DD851_06 Bulk1 MSEE733_05 DD851_06 Bulk1 MSEE733_07 DD851_06 Bulk1 MSEE734_02 DD851_08 Bulk1 MSEE734_03 DD851_08 Bulk1 MSEE734_06 DD851_08 Bulk1 MSEE735_01 DD852_04 Bulk1 MSEE735_02 DD852_04 Bulk1 Bulk1 CC805_3 Bulk2 BB902_A Bulk1 Bulk1 CC805_3 Bulk2 BB902_A Bulk1 Bulk1 CC805_3 Bulk2 BB902_A Bulk1 Bulk1 CC823_5 Bulk1 BB930_B Bulk1 CC806_5 CC806_5 CC806_5 CC806_5 CC806_5 CC806_5 CC806_5 CC806_5 CC809_4 CC809_4 CC809_4 CC809_4 CC810 CC810 CC810 CC810 CC810 CC810 CC810 CC812_2 CC812_2 Bulk2 BB902_B Bulk2 BB902_B Bulk2 BB902_B Bulk2 BB902_B Bulk2 BB902_B Bulk2 BB902_B Bulk2 BB902_B Bulk2 BB902_B Bulk2 BB909_A Bulk2 BB909_A Bulk2 BB909_A Bulk2 BB909_A Bulk2 BB912_A Bulk2 BB912_A Bulk2 BB912_A Bulk2 BB912_A Bulk2 BB912_A Bulk2 BB912_A Bulk2 BB912_A Bulk2 BB917_A Bulk2 BB917_A 65 Bulk1 Bulk1 Bulk1 Bulk1 Bulk1 Bulk1 Bulk1 Bulk1 Bulk1 Bulk1 Bulk1 Bulk1 Bulk1 Bulk1 Bulk1 Bulk1 Bulk1 Bulk1 Bulk1 Bulk1 Bulk1 9 9 9 2 9 9 9 9 9 9 9 9 7 7 7 7 8 8 8 8 8 8 8 3 3 SI SC SI SC SC SC SI SI SC SC SC SC SC SI SC SI SC SC SC SC MS NF NF NF SC - - - - - - - - - - - - - - - - MSEE729_03 MSEE729_04 MSEE730_03 MSEE730_09 MSEE732_03 MSEE732_06 MSEE733_05 MSEE733_07 MSEE735_01 Table S2. 1 (cont’d) MSEE735_04 DD852_04 Bulk1 MSEE736_01 DD852_08 Bulk1 CC812_2 CC812_2 Bulk2 BB917_A Bulk2 BB917_A Bulk1 Bulk1 MSEE736_02 DD852_08 MSEE736_03 DD852_08 MSEE736_04 DD852_08 MSEE736_05 DD852_08 MSEE736_06 DD852_08 Bulk1 CC812_2 Bulk2 BB917_A Bulk1 Bulk1 CC812_2 Bulk2 BB917_A Bulk1 Bulk1 CC812_2 Bulk2 BB917_A Bulk1 Bulk1 CC812_2 Bulk2 BB917_A Bulk1 Bulk1 CC812_2 Bulk2 BB917_A Bulk1 MSEE737_03 DD853_04 Bulk1 MSEE737_05 DD853_04 Bulk1 MSEE737_09 DD853_04 Bulk1 MSEE737_10 DD853_04 Bulk1 MSEE738_01 DD853_05 Bulk1 CC813_4 CC813_4 CC813_4 CC813_4 CC813_4 Bulk2 BB918_A Bulk2 BB918_A Bulk2 BB918_A Bulk2 BB918_A Bulk2 BB918_A Bulk1 Bulk1 Bulk1 Bulk1 Bulk1 MSEE738_04 DD853_05 MSEE738_05 DD853_05 MSEE738_06 DD853_05 MSEE739_01 DD855_01 MSEE739_02 DD855_01 MSEE739_03 DD855_01 Bulk1 CC813_4 Bulk2 BB918_A Bulk1 Bulk1 CC813_4 Bulk2 BB918_A Bulk1 Bulk1 CC813_4 Bulk2 BB918_A Bulk1 Bulk1 CC817_2 Bulk2 BB921_B Bulk1 Bulk1 CC817_2 Bulk2 BB921_B Bulk1 Bulk1 CC817_2 Bulk2 BB921_B Bulk1 SI SI SI SI SI SI SI SI SI SI SI SI SI 66 3 3 3 3 3 3 3 4 4 4 4 4 4 4 4 6 6 6 SI SC SI SI SI SI SI NF SC SC SI SC SC NF NF SC SC SC - MSEE736_01 - - - - - - - MSEE737_05 MSEE737_09 MSEE738_01 MSEE738_04 - - - - MSEE739_03 Bulk1 CC817_2 Bulk2 BB921_B Bulk1 Bulk1 CC817_2 Bulk2 BB921_B Bulk1 CC817_2 CC817_2 CC817_2 CC817_2 CC817_2 CC820_3 CC815_2 CC815_2 CC815_2 CC815_2 CC815_2 CC815_2 CC815_2 CC815_2 CC815_2 CC804_1 CC804_1 CC804_1 CC804_1 CC806_2 Bulk2 BB921_B Bulk2 BB921_B Bulk2 BB921_B Bulk2 BB921_B Bulk2 BB921_B Bulk2 BB927_A Bulk2 BB920_A Bulk2 BB920_A Bulk2 BB920_A Bulk2 BB920_A Bulk2 BB920_A Bulk2 BB920_A Bulk2 BB920_A Bulk2 BB920_A Bulk2 BB920_A Bulk1 BB901_A Bulk1 BB901_A Bulk1 BB901_A Bulk1 BB901_A Bulk1 BB902_B Bulk1 Bulk1 Bulk1 Bulk1 Bulk1 Bulk1 Bulk1 Bulk1 Bulk1 Bulk1 Bulk1 Bulk1 Bulk1 Bulk1 Bulk1 Bulk1 Bulk1 Bulk1 Bulk1 Bulk1 Table S2. 1 (cont’d) MSEE739_04 DD855_01 MSEE739_05 DD855_01 SI SI MSEE740_01 DD855_03 Bulk1 MSEE740_02 DD855_03 Bulk1 MSEE740_03 DD855_03 Bulk1 MSEE740_04 DD855_03 Bulk1 MSEE740_05 DD855_03 Bulk1 MSEE741_08 DD857_03 Bulk1 MSEE742_03 DD865_02 Bulk1 MSEE742_05 DD865_02 Bulk1 MSEE742_06 DD865_02 Bulk1 MSEE742_07 DD865_02 Bulk1 MSEE743_01 DD865_03 Bulk1 MSEE743_03 DD865_03 Bulk1 MSEE743_04 DD865_03 Bulk1 MSEE743_05 DD865_03 Bulk1 MSEE743_06 DD865_03 Bulk1 MSEE744_03 DD802_02 Bulk1 MSEE744_04 DD802_02 Bulk1 MSEE744_05 DD802_02 Bulk1 MSEE744_06 DD802_02 Bulk1 MSEE745_03 DD804_06 Bulk1 MSEE745_04 DD804_06 MSEE745_06 DD804_06 Bulk1 CC806_2 Bulk1 BB902_B Bulk1 Bulk1 CC806_2 Bulk1 BB902_B Bulk1 SI SI SI 67 6 6 6 6 6 6 6 1 5 5 5 5 5 5 5 5 5 10 10 10 10 9 9 9 SC SC SC SI SI SC SC SI SC SC SC SC SI SI SC SI SI SI SI SI SC SI SC SI - MSEE739_05 MSEE740_01 MSEE740_04 MSEE741_08 MSEE742_06 MSEE743_04 MSEE744_06 - - - - - - - - - - - - - - - - Table S2. 1 (cont’d) MSEE745_10 DD804_06 MSEE747_04 DD812_05 MSEE747_05 DD812_05 MSEE747_09 DD812_05 MSEE747_10 DD812_05 MSEE747_13 DD812_05 SI SI SI SI SI SI Bulk1 CC806_2 Bulk1 BB902_B Bulk1 Bulk1 CC811_5 Bulk1 BB912_B Bulk1 Bulk1 CC811_5 Bulk1 BB912_B Bulk1 Bulk1 CC811_5 Bulk1 BB912_B Bulk1 Bulk1 CC811_5 Bulk1 BB912_B Bulk1 Bulk1 CC811_5 Bulk1 BB912_B Bulk1 MSEE748_02 DD807_06 Bulk1 MSEE748_05 DD807_06 Bulk1 MSEE748_06 DD807_06 Bulk1 MSEE748_07 DD807_06 Bulk1 MSEE749_02 DD812_01 Bulk1 MSEE749_03 DD812_01 Bulk1 MSEE749_04 DD812_01 Bulk1 MSEE749_05 DD812_01 Bulk1 MSEE749_06 DD812_01 Bulk1 MSEE827_04 DD847_03 Bulk1 MSEE827_05 DD847_03 Bulk1 MSEE827_08 DD847_03 Bulk1 MSEE827_11 DD847_04 Bulk1 MSFF600_01 MSFF603 MSFF604_01 MSFF608_08 Bulk1 BB902_C Bulk1 BB902_C Bulk1 BB902_C Bulk1 BB902_C Bulk1 BB912_B Bulk1 BB912_B Bulk1 BB912_B Bulk1 BB912_B Bulk1 BB912_B Bulk2 BB902_A Bulk2 BB902_A Bulk2 BB902_A Bulk2 BB902_A EE700_01 Bulk1 DD802_01 Bulk1 CC804_1 EE702_06 Bulk1 DD803_05 Bulk1 CC804_5 EE703_07 Bulk1 DD804_09 Bulk1 CC806_2 EE705_04 Bulk1 DD805_08 Bulk1 CC806_5 CC807_4 CC807_4 CC807_4 CC807_4 CC811_5 CC811_5 CC811_5 CC811_5 CC811_5 CC805_3 CC805_3 CC805_3 CC805_3 Bulk1 Bulk1 Bulk1 Bulk1 Bulk1 Bulk1 Bulk1 Bulk1 Bulk1 Bulk1 Bulk1 Bulk1 Bulk1 Bulk1 Bulk1 Bulk1 Bulk1 68 9 8 8 8 8 8 9 9 9 9 8 8 8 8 8 9 9 9 9 10 10 9 9 SC SC SI SC SI SC SC SI SC SI SI SI NF SC SI SI SC SC SI SC SC SC SC MSEE745_10 - - MSEE747_09 - MSEE747_13 MSEE748_02 MSEE748_06 MSEE749_04 MSEE749_05 MSEE827_05 MSFF600_01 MSFF603 - - - - - - - - - - Table S2. 1 (cont’d) MSFF609_02 MSFF611_02 MSFF611_03 MSFF612_03 MSFF613_03 MSFF620_03 MSFF621_01 MSFF625_01 MSFF627_01 MSFF627_02 MSFF631_01 EE705_06 Bulk1 DD805_08 Bulk1 CC806_5 EE707_05 Bulk1 DD807_05 Bulk1 CC807_4 EE707_05 Bulk1 DD807_05 Bulk1 CC807_4 EE708_02 Bulk1 DD808_10 Bulk1 CC809_2 EE708_04 Bulk1 DD808_10 Bulk1 CC809_2 EE712_07 Bulk1 DD814_04 Bulk1 CC813_4 EE713_07 Bulk1 DD821_10 Bulk1 CC817_2 EE716_06 Bulk1 DD825_01 Bulk1 CC820_3 EE717_05 Bulk1 DD829_01 Bulk1 CC825_2 EE717_05 Bulk1 DD829_01 Bulk1 CC825_2 Bulk1 CC827_6 EE719_02 Bulk1 DD831_01 Bulk1 Bulk1 Bulk1 Bulk1 Bulk1 Bulk1 Bulk1 Bulk1 Bulk1 Bulk1 Bulk1 EE719_02 Bulk1 DD831_01 EE719_02 Bulk1 DD831_01 Bulk1 CC827_6 Bulk1 Bulk1 CC827_6 Bulk1 EE720_04 Bulk1 DD837_02 Bulk1 CC831_3 EE720_04 Bulk1 DD837_02 Bulk1 CC831_3 EE722_06 Bulk1 DD838_01 Bulk1 CC832_6 Bulk1 Bulk1 Bulk1 EE722_06 Bulk1 DD838_01 Bulk1 CC832_6 Bulk1 EE724_06 Bulk1 DD845_02 Bulk1 CC823_5 EE724_06 Bulk1 DD845_02 Bulk1 CC823_5 EE725_02 Bulk1 DD847_05 Bulk1 CC805_3 EE727_05 Bulk1 DD847_06 Bulk1 CC805_3 EE729_04 Bulk1 DD849_06 Bulk1 CC806_5 EE730_03 Bulk1 DD849_07 Bulk1 CC806_5 Bulk1 Bulk1 Bulk2 Bulk2 Bulk2 Bulk2 MSFF631_02 MSFF631_08 MSFF632_01 MSFF632_02 MSFF634_01 MSFF634_02 MSFF636_01 MSFF636_02 MSFF637_01 MSFF640_01 MSFF641_01 MSFF642_01 9 9 9 7 7 4 6 1 13 13 11 11 11 12 12 12 12 2 2 9 9 9 9 SC SC SC SC SC SI SI NF SC SC SC SC SC SC SC SC SC SC MS SC SC SC SC - - - - - MSFF611_02 MSFF611_03 MSFF612_03 MSFF613_03 MSFF620_03 MSFF627_01 MSFF627_02 MSFF631_01 MSFF631_02 MSFF631_08 MSFF632_01 MSFF634_01 MSFF634_02 MSFF636_01 MSFF637_01 MSFF640_01 MSFF641_01 MSFF642_01 SI SI SI SI SI SI 69 Table S2. 1 (cont’d) MSFF644_01 MSFF645_01 MSFF645_02 MSFF648_02 MSFF648_03 MSFF649_01 MSFF650_01 MSFF651_01 EE730_09 Bulk1 DD849_07 Bulk1 CC806_5 EE732_03 Bulk1 DD850_06 Bulk1 CC809_4 EE732_03 Bulk1 DD850_06 Bulk1 CC809_4 EE733_05 Bulk1 DD851_06 Bulk1 CC810 CC810 EE733_05 Bulk1 DD851_06 Bulk1 CC810 EE733_07 Bulk1 DD851_06 Bulk1 EE734_01 Bulk1 DD851_08 Bulk1 CC810 Bulk1 CC812_2 EE736_01 Bulk1 DD852_08 MSFF653_01 MSFF654_01 MSFF655_03 MSFF656_01 MSFF658_02 MSFF658_04 MSFF664_01 MSFF666_03 MSFF670_02 MSFF671_01 MSFF671_03 MSFF678_01 MSFF683_01 MSFF683_02 SI SI SI SI SI SI SI Bulk2 Bulk2 Bulk2 Bulk2 Bulk2 Bulk2 Bulk2 Bulk2 Bulk2 Bulk2 Bulk2 Bulk2 Bulk2 Bulk2 Bulk1 9 7 7 8 8 8 8 3 4 4 6 6 1 1 9 8 8 10 10 6 4 4 SC SC SC SC SC SC SC SC SC SI SC SC SC SC SC SC SC SC MS SI MS SC MSFF644_01 MSFF645_01 MSFF645_02 MSFF648_02 - MSFF649_01 MSFF650_01 MSFF651_01 MSFF653_01 - MSFF655_03 MSFF656_01 MSFF658_02 MSFF658_04 MSFF664_01 MSFF666_03 MSFF670_02 MSFF671_01 - - - MSFF683_02 EE737_09 Bulk1 DD853_04 Bulk1 CC813_4 Bulk1 CC813_4 EE738_01 Bulk1 DD853_05 EE740_01 Bulk1 DD855_03 Bulk1 CC817_2 EE740_04 Bulk1 DD855_03 Bulk1 CC817_2 EE741_08 Bulk1 DD857_03 Bulk1 CC820_3 EE741_08 Bulk1 DD857_03 Bulk1 CC820_3 EE745_10 Bulk1 DD804_06 Bulk1 CC806_2 EE747_13 Bulk1 DD812_05 Bulk1 CC811_5 Bulk1 EE749_05 Bulk1 DD812_01 Bulk1 CC811_5 EE701_02 Bulk1 DD802_04 Bulk1 CC804_1 EE701_02 Bulk1 DD802_04 Bulk1 CC804_1 EE739_02 Bulk1 DD855_01 Bulk1 CC817_2 Bulk1 Bulk1 Bulk1 Bulk2 EE738_02 Bulk1 DD853_05 EE738_02 Bulk1 DD853_05 Bulk1 CC813_4 Bulk2 Bulk1 CC813_4 Bulk2 70 Table S2. 1 (cont’d) MSFF684_01 MSFF684_03 MSFF688_01 MSFF689_02 EE735_01 Bulk1 DD852_04 Bulk1 CC812_2 EE735_01 Bulk1 DD852_04 Bulk1 CC812_2 EE722_05 Bulk1 DD838_01 Bulk1 CC832_6 EE827_05 Bulk1 DD847_03 Bulk1 CC805_3 SI Bulk2 Bulk2 Bulk1 Bulk2 3 3 12 9 SC SC MS SC MSFF684_03 - - MSFF689_02 71 Table S2. 2. Frequency distribution of cycle 0, cycle 1, cycle 2, cycle 3, and cycle 4 clone means for eight agronomic traits. Maturity (1 = dead, 5 = late, full green vines and flowering); scab resistance (0 = resistance, and 5 = susceptible); tuber appearance (1= very poor, and 9= excellent); tuber shape (1= compressed, 2= round, 3= oval, 4= oblong, and 5= long); average tuber number (No. tuber/ plant); average tuber weight (g); average tuber yield (g); specific gravity. ID RS Maturity SCAB Tuber appearance Tuber shape cycle 0 cycle 0 cycle 0 cycle 0 cycle 0 cycle 0 cycle 0 cycle 0 cycle 0 cycle 0 cycle 0 cycle 0 cycle 0 cycle 0 cycle 0 cycle 0 cycle 0 cycle 0 cycle 0 MSBB902_10 MSBB902_18 MSBB903_03 MSBB905_03 MSBB906_06 MSBB907_01 MSBB909_03 MSBB909_04 MSBB910_07 MSBB911_05 MSBB913_09 MSBB914_06 MSBB915_01 MSBB917_01 MSBB918_02 MSBB919_10 MSBB920_03 MSBB920_04 MSBB921_08 1 1 3 4 3 3 1 3 1 3 5 2 2 4 5 2 2 1 3 1.0 1.5 1.0 - - 1.5 1.5 1.0 - 1.5 1.5 - - 0.5 1.0 0.5 2.0 2.5 - 4 5 3 5 5 5 4 7 5 5 5 4 5 5 5 5 4 5 6 1 2 3 2 1 4 3 3 1 3 2 1 3 1 2 1 3 4 2 72 Average tuber number 3.2 4.9 5.4 3.6 13.5 6.8 4.3 12.6 5.9 4.6 17.7 4.6 7.2 6.6 15.6 7.1 4.7 5.1 9.4 Average tuber weight (g) 34.9 45.5 43.5 76.5 63.5 68.3 53.6 62.4 78.0 46.2 53.9 24.6 73.8 32.1 32.2 58.4 60.2 46.2 32.0 Tuber Yield (g) 111.1 223.9 233.6 271.9 857.2 460.9 227.7 785.5 460.9 214.1 953.5 112.6 528.6 211.4 502.6 414.3 280.8 235.0 300.7 SG 1.092 1.085 1.031 1.089 1.079 1.092 1.076 1.090 1.063 1.077 1.089 1.084 1.064 1.046 1.073 1.035 1.076 1.074 1.094 Table S2. 2. (cont’d) MSBB922_01 MSBB923_01 MSBB925_04 MSBB927_06 MSBB930_01 MSBB930_06 MSBB932_05 MSBB933_06 MSBB934_04 MSBB935_06 MSBB936_02 MSBB938_01 MSBB942_10 MSBB943_01 MSBB943_05 MSBB943_13 MSBB943_14 MSBB945_06 MSBB946_02 MSBB947_01 MSBB947_02 MSBB947-04 MSBB948_01 MSBB949_03 MSBB952_06 MSBB953_10 MSBB963_03 MSBB963_08 cycle 0 cycle 0 cycle 0 cycle 0 cycle 0 cycle 0 cycle 0 cycle 0 cycle 0 cycle 0 cycle 0 cycle 0 cycle 0 cycle 0 cycle 0 cycle 0 cycle 0 cycle 0 cycle 0 cycle 0 cycle 0 cycle 0 cycle 0 cycle 0 cycle 0 cycle 0 cycle 0 cycle 0 2 2 2 2 5 3 3 4 1 1 4 2 4 3 1 2 1 1 1 1 1 1 2 2 2 3 1 1 1.5 - - - 2.0 2.0 - 2.0 - 2.0 1.0 - 0.5 0.5 - 1.0 - 1.5 - - - 1.0 2.5 3.0 - 2.0 - - 7 5 4 4 4 7 5 5 8 7 5 8 6 3 6 5 6 4 5 4 5 - 5 6 7 6 5 4 2 3 1 2 1 2 2 1 3 2 2 1 2 3 2 2 1 3 3 1 2 - 1 3 2 3 3 4 73 10.4 18.3 3.3 6.3 8.7 6.8 3.9 9.4 10.1 0.7 8.8 2.0 12.7 6.7 15.7 14.6 12.4 6.1 7.7 11.3 4.1 - 13.8 15.2 9.3 9.8 11.9 9.5 79.3 20.2 50.4 25.3 57.9 78.7 51.4 44.7 69.5 39.8 50.2 56.5 79.3 46.8 43.2 38.2 41.2 59.3 52.1 27.9 50.4 - 29.0 68.2 54.0 69.2 57.8 32.7 826.1 371.0 168.1 160.4 501.9 536.8 201.5 421.8 700.8 28.4 443.1 113.1 1013.7 311.8 676.2 556.3 512.4 361.2 399.1 316.2 205.9 - 400.0 1034.0 504.0 680.2 688.3 312.0 1.063 1.090 1.081 1.088 1.052 1.065 1.084 1.063 1.078 1.038 1.052 1.069 1.047 1.051 1.045 1.025 1.027 1.043 1.025 1.082 1.077 - 1.071 1.058 1.057 1.078 1.054 1.043 Table S2. 2. (cont’d) Mean/ cycle 0 MSCC804-01 MSCC805_01 MSCC806_02 MSCC806_05 MSCC807_01 MSCC807_02 MSCC807_07 MSCC808_01 MSCC809_02 MSCC809_04 MSCC811_03 MSCC811_04 MSCC811_05 MSCC812_01 MSCC813_01 MSCC813_02 MSCC815_01 MSCC816_03 MSCC817_02 MSCC819_02 MSCC820_03 MSCC822_01 MSCC822_03 MSCC822_05 MSCC823_03 MSCC824_01 MSCC825_06 cycle 1 cycle 1 cycle 1 cycle 1 cycle 1 cycle 1 cycle 1 cycle 1 cycle 1 cycle 1 cycle 1 cycle 1 cycle 1 cycle 1 cycle 1 cycle 1 cycle 1 cycle 1 cycle 1 cycle 1 cycle 1 cycle 1 cycle 1 cycle 1 cycle 1 cycle 1 cycle 1 2.5 B* 1.50 A 5.0 A 2.2 A 8.9 B 50.4 A 429.8 BC 1.066 C 1 2 4 2 1 4 1 2 1 3 3 2 2 2 1 1 1 3 1 5 2 1 1 2 3 1 5 1.5 2.0 2.0 1.5 2.0 - - 1.0 - - 1.0 - - 0.5 - - 1.0 1.5 - - - - 1.5 - - - - - 5 5 6 5 4 5 5 5 6 6 4 5 5 5 5 7 5 7 3 5 5 6 5 5 4 5 - 2 2 2 1 3 2 1 2 3 2 3 4 1 2 2 2 2 2 3 3 3 1 3 1 1 1 - 6.8 5.8 5.2 15.3 7.7 13.5 7.4 10.7 7.1 11.6 14.5 8.1 21.0 5.0 11.7 4.8 21.5 5.9 13.6 4.7 4.9 11.3 4.3 3.8 7.3 18.5 - 49.1 40.9 37.8 29.6 55.1 43.6 51.2 49.4 66.2 40.7 60.2 28.6 33.2 28.6 46.3 33.0 25.1 26.9 31.5 32.8 55.5 61.7 43.9 36.5 22.8 45.9 - 331.7 238.5 195.4 451.2 422.2 590.3 378.9 526.5 469.2 470.6 872.2 232.0 697.8 143.1 541.7 158.3 540.0 159.4 429.1 154.3 273.1 699.3 190.1 140.1 165.6 848.9 - 1.062 1.072 1.084 1.070 1.068 1.079 1.066 1.038 1.068 1.062 1.069 1.075 1.076 1.071 1.081 1.076 1.069 1.077 1.077 1.070 1.088 1.061 1.085 1.061 1.064 1.077 74 cycle 1 cycle 1 cycle 1 cycle 1 cycle 1 cycle 1 cycle 1 Table S2. 2. (cont’d) MSCC826_01 MSCC827_06 MSCC828_01 MSCC831_03 MSCC832_02 MSCC832_09 MSCC832_14 Mean/ cycle 1 MSDD802_01 MSDD802_02 MSDD803_01 MSDD803_05 MSDD804_09 MSDD805_01 MSDD805_05 MSDD805_08 MSDD807_03 MSDD807_05 MSDD808_10 MSDD809_02 MSDD809_09 MSDD812_02 MSDD814_04 MSDD821_06 MSDD821_08 MSDD821_10 MSDD825_01 MSDD829_01 cycle 2 cycle 2 cycle 2 cycle 2 cycle 2 cycle 2 cycle 2 cycle 2 cycle 2 cycle 2 cycle 2 cycle 2 cycle 2 cycle 2 cycle 2 cycle 2 cycle 2 cycle 2 cycle 2 cycle 2 1 2 2 2 3 2 3 2.0 1.5 - 2.0 - - 1.0 5 8 5 5 6 5 6 2 3 2 3 2 2 2 14.8 3.9 11.8 7.0 7.9 3.8 7.0 29.0 74.1 26.0 36.0 48.5 36.3 69.1 429.0 288.0 305.6 251.7 384.1 139.1 483.4 2.1 C 1.50 A 5.0 A 2.2 A 9.4 AB 43.0 BC 390.4 BC 1 1 1 2 1 3 4 4 1 2 1 2 1 3 1 3 1 2 3 1 0.5 - - - 1.0 - 1.0 - - 1.5 - - - - 2.0 0.5 - - - - 5 5 6 4 6 5 4 5 5 5 5 4 4 4 6 6 3 5 1 6 3 2 2 3 1 2 2 2 3 1 2 3 4 3 3 1 3 2 2 2 75 8.3 3.3 10.5 10.3 9.8 8.6 16.7 9.4 5.6 5.8 8.4 9.6 7.2 10.2 11.2 11.6 1.4 11.3 5.8 5.5 28.5 24.1 52.3 60.1 46.9 44.2 69.4 29.5 69.6 56.9 36.7 26.4 64.1 47.3 47.7 26.5 17.9 18.8 47.2 40.9 235.0 78.5 549.5 621.4 459.4 379.7 1158.2 277.1 389.9 330.2 308.4 253.3 461.8 482.8 534.7 307.2 25.0 211.1 271.2 225.0 1.072 1.077 1.050 1.071 1.060 1.070 1.063 1.070 C 1.059 1.065 1.055 1.060 1.062 1.069 1.066 1.078 1.078 1.078 1.060 1.047 1.073 1.079 1.059 1.052 1.053 1.065 1.053 1.061 Table S2. 2. (cont’d) MSDD829_09 MSDD831_01 MSDD837_02 MSDD837_07 MSDD837_08 MSDD838_02 MSDD844_06 MSDD845_02 MSDD847_06 MSDD848_01 MSDD848_02 MSDD849_02 MSDD849_06 MSDD849_08 MSDD850_03 MSDD850_06 MSDD851_05 MSDD851_07 MSDD852_02 MSDD852_04 MSDD852_05 MSDD853_02 MSDD855_03 MSDD855_05 MSDD857_03 MSDD865_02 MSDD865_03 Mean/ cycle 2 cycle 2 cycle 2 cycle 2 cycle 2 cycle 2 cycle 2 cycle 2 cycle 2 cycle 2 cycle 2 cycle 2 cycle 2 cycle 2 cycle 2 cycle 2 cycle 2 cycle 2 cycle 2 cycle 2 cycle 2 cycle 2 cycle 2 cycle 2 cycle 2 cycle 2 cycle 2 cycle 2 4 3 2 2 3 3 2 1 1 2 2 2 2 1 1 1 1 1 1 2 1 1 1 2 2 1 2 2.0 - - - 2.0 - 1.5 - - 0.5 - 1.0 - 1.5 - - - - 1.0 - - 2.0 0.5 - - - - 7 5 5 6 7 4 5 5 5 3 3 5 3 6 7 5 5 6 7 6 4 5 - 7 6 6 5 2 2 2 3 2 2 2 3 3 3 2 2 1 2 2 1 2 2 2 2 2 2 - 2 2 2 2 1.9 C 1.5 A 5.1 A 2.2 A 76 18.3 5.7 13.5 5.2 11.0 10.5 20.6 2.0 10.0 5.6 8.8 7.4 7.0 9.4 2.3 3.0 13.7 17.7 11.7 11.5 17.0 15.6 - 14.7 6.7 7.0 10.2 9.5 AB 70.1 50.3 60.6 66.9 47.7 55.2 49.9 39.8 34.3 39.9 67.4 42.9 50.6 67.0 26.9 34.6 26.2 37.4 62.1 51.6 70.9 42.8 - 42.4 67.0 33.5 34.5 1282.0 285.2 817.8 347.6 524.7 579.7 1028.4 79.5 342.7 223.3 589.7 317.2 354.5 630.1 60.6 103.9 357.7 660.7 724.2 593.5 1204.7 668.4 - 622.0 446.6 234.6 352.0 46.2 AB 451.1 AB 1.062 1.060 1.069 1.061 1.079 1.067 1.069 1.048 1.078 1.079 1.078 1.076 1.067 1.072 1.072 1.050 1.069 1.067 1.062 1.062 1.077 1.078 - 1.068 1.060 1.072 1.074 1.066 C Table S2. 2. (cont’d) MSEE700_06 MSEE701_02 MSEE702_02 MSEE702_03 MSEE702_05 MSEE703_05 MSEE704_08 MSEE705_04 MSEE710_03 MSEE710_10 MSEE711_01 MSEE714_05 MSEE720_04 MSEE721_03 MSEE722_07 MSEE725_05 MSEE730_07 MSEE730_09 MSEE733_04 MSEE735_02 MSEE736_01 MSEE737_05 MSEE738_04 MSEE739_04 MSEE740_01 MSEE747_09 MSEE747_13 MSEE827_08 cycle 3 cycle 3 cycle 3 cycle 3 cycle 3 cycle 3 cycle 3 cycle 3 cycle 3 cycle 3 cycle 3 cycle 3 cycle 3 cycle 3 cycle 3 cycle 3 cycle 3 cycle 3 cycle 3 cycle 3 cycle 3 cycle 3 cycle 3 cycle 3 cycle 3 cycle 3 cycle 3 cycle 3 1 3 1 4 2 3 3 2 2 4 3 2 1 3 5 2 3 1 5 2 2 2 2 1 3 4 5 1.0 0.5 2.0 - 2.0 - 2.0 1.5 2.0 1.5 1.0 0.5 1.5 2.0 1.5 2.5 0.5 2.0 1.5 2.0 0.5 1.5 1.0 0.5 1.0 2.0 2.5 3.0 6 5 5 4 5 4 - 5 - 4 6 5 5 5 5 - 5 5 6 - 5 5 5 5 5 4 4 6 2 2 2 2 3 3 - 2 - 3 2 2 3 1 2 - 2 2 3 - 2 2 2 2 2 2 2 3 77 12.1 8.7 9.0 4.4 7.4 2.2 - 13.0 - 6.7 3.4 17.1 9.5 4.4 7.3 - 12.4 11.9 5.7 - 5.1 9.8 13.2 11.4 8.9 7.9 9.4 7.3 15.8 29.7 49.9 63.1 48.2 45.0 - - 23.0 62.7 25.6 17.8 35.4 54.6 39.0 - 27.3 33.8 17.9 - 47.6 45.1 13.9 26.0 34.0 41.3 40.7 33.3 191.0 258.0 449.2 277.5 357.0 99.0 - - 298.8 420.0 86.3 305.6 336.3 242.8 284.5 340.0 402.5 102.0 - - 243.0 443.3 183.5 297.8 302.5 326.1 383.0 241.3 1.081 1.091 1.086 1.068 1.092 1.088 - - 1.077 1.090 1.083 1.078 1.087 1.086 1.079 1.091 1.080 1.078 - - 1.082 1.073 1.076 1.085 1.083 1.092 1.079 1.110 Table S2. 2. (cont’d) Mean/ cycle 3 MSFF600_01 MSFF600-02 MSFF603_01 MSFF604_01 MSFF608_08 MSFF609_01 MSFF609_02 MSFF611_03 MSFF612_01 MSFF612_03 MSFF612_04 MSFF613_03 MSFF617-01 MSFF617_04 MSFF620_03 MSFF621-01Y MSFF621_02 MSFF624_02 MSFF625_01 MSFF627_01 MSFF627_02 MSFF631_01 MSFF631_02 MSFF631_04 MSFF632_01 MSFF632_02 MSFF632_04 cycle 4 cycle 4 cycle 4 cycle 4 cycle 4 cycle 4 cycle 4 cycle 4 cycle 4 cycle 4 cycle 4 cycle 4 cycle 4 cycle 4 cycle 4 cycle 4 cycle 4 cycle 4 cycle 4 cycle 4 cycle 4 cycle 4 cycle 4 cycle 4 cycle 4 cycle 4 cycle 4 2.9 A 2 2 1 1 1 1 2 2 1 1 1 1 1 2 1 1 1 1 1 2 1 1 1 1 1 1 5 1.5 A 1.0 1.5 - - - - - - - - - - 2.5 - - 2.0 - - - 1.0 - - - - - - - 5.1 A 2.2 A 9.1 AB 4 - 6 4 6 5 5 5 4 5 5 5 - 4 5 - 5 7 4 5 5 5 5 5 4 4 5 4 - 2 4 3 3 2 2 4 2 2 2 - 4 3 - 3 2 4 4 1 4 2 3 1 4 1 78 9.6 - 5.8 9.2 7.4 15.1 13.6 8.0 9.2 12.4 14.2 6.3 - 9.4 6.0 - 11.4 4.6 10.7 15.1 6.8 7.1 7.0 4.6 8.8 7.8 11.2 38.7 C 43.8 - 37.1 79.7 89.5 41.6 55.8 39.1 91.5 37.0 44.9 62.5 68.0 39.1 - - 40.1 81.8 56.4 31.6 44.3 61.1 33.6 59.5 67.1 33.4 28.8 322.7 C 418.7 - 215.2 733.6 662.6 628.4 759.0 312.4 841.8 458.6 637.6 393.8 639.2 234.6 - - 457.4 376.4 603.6 478.2 301.4 435.3 235.2 273.6 590.4 260.8 322.6 1.082 A 1.085 - 1.071 1.069 1.080 1.082 1.071 1.069 1.085 1.078 1.075 1.082 1.079 1.069 - - 1.061 1.083 1.071 1.084 1.073 1.074 1.060 1.091 1.078 1.070 1.076 Table S2. 2. (cont’d) MSFF634_02 MSFF636_02 MSFF637_03 MSFF638-01 MSFF639-02 MSFF641-01 MSFF641-02 MSFF641_05 MSFF642_01 MSFF642-03 MSFF644_01 MSFF645_01 MSFF645_02 MSFF645_03 MSFF648_02 MSFF648_03 MSFF648_04 MSFF648_05 MSFF650_01 MSFF650_02 MSFF651_01 MSFF651_03 MSFF651_05 MSFF654_03 MSFF655_03 MSFF655_05 MSFF656_01 MSFF656_03 cycle 4 cycle 4 cycle 4 cycle 4 cycle 4 cycle 4 cycle 4 cycle 4 cycle 4 cycle 4 cycle 4 cycle 4 cycle 4 cycle 4 cycle 4 cycle 4 cycle 4 cycle 4 cycle 4 cycle 4 cycle 4 cycle 4 cycle 4 cycle 4 cycle 4 cycle 4 cycle 4 cycle 4 1 3 1 1 1 1 1 3 2 2 1 1 2 4 1 1 2 1 1 1 1 1 1 1 3 1 2 3 - - - 1.0 1.0 3.0 2.5 - - 2.0 - - - - - - - - - - - - - - - - - 1.5 5 5 5 - - - - 5 5 - 5 5 5 5 5 4 5 4 5 6 5 5 5 5 5 6 5 - 1 1 4 - - - - 2 3 - 3 3 2 2 4 4 2 3 4 3 2 3 4 2 2 2 3 - 79 8.9 10.4 11.5 - - - - - 11.4 11.6 17.0 19.0 23.4 15.1 18.9 5.2 6.6 5.2 6.2 7.2 23.6 7.7 7.0 4.2 21.0 12.0 17.7 - 57.7 48.3 32.6 - - - - - 50.1 71.0 44.3 23.4 31.7 48.6 44.6 58.0 61.2 60.3 48.1 69.9 11.6 44.6 72.3 54.4 61.4 40.0 33.5 - 513.2 504.2 375.1 - - - - - 573.2 823.8 753.3 444.4 741.6 733.7 841.3 301.4 404.0 313.8 298.1 503.0 273.6 342.1 505.8 228.6 1289.5 479.6 592.4 - 1.066 1.077 1.085 - - - - - 1.071 1.071 1.062 1.079 1.085 1.078 1.069 1.092 1.065 1.067 1.070 1.074 1.080 1.085 1.082 1.082 1.070 1.075 1.073 - Table S2. 2. (cont’d) MSFF658_02 MSFF658_04 MSFF662_02 MSFF665_03 MSFF666_02 MSFF666_03 MSFF670_01 MSFF671_01 MSFF678_02 MSFF678_04 MSFF684_01 MSFF684_02 MSFF686_01 MSFF688_03 MSFF689_03 MSFF689_04 Mean/ cycle 4 cycle 4 cycle 4 cycle 4 cycle 4 cycle 4 cycle 4 cycle 4 cycle 4 cycle 4 cycle 4 cycle 4 cycle 4 cycle 4 cycle 4 cycle 4 cycle 4 1 1 1 1 2 1 2 1 1 1 1 1 2 2 2 2 - - - - - - 2.0 - 2.5 - - - - - 2.0 - 5 5 5 6 5 5 - 5 - 5 5 5 5 5 - 6 2 2 4 2 2 1 - 2 - 1 3 1 3 2 - 1 14.5 18.2 7.0 18.4 12.7 21.0 - 19.8 - 5.8 7.9 24.2 17.4 8.7 - 4.8 1.4 D 1.6 A 5.2 A 2.5 A 10.9 A 32.0 34.4 59.3 31.4 45.0 22.0 - - 63.9 62.0 71.3 43.9 67.5 69.2 - 85.0 50.6 A 464.1 625.6 414.9 578.6 571.8 461.3 - - 1262.5 359.4 563.6 1063.1 1174.1 600.0 - 407.9 522.0 A 1.076 1.085 1.092 1.092 1.075 1.081 - - 1.074 1.068 1.079 1.091 1.068 1.066 - 1.063 1.076 B * Means with the same letter designation are not significantly different as determined by Student’s t-test a = 0.05. 80 SUPPLEMENTAL FIGURES 81 (a) (b) Figure S2. 1. Comparison of cycle 0 to cycle 4 neighbor-joining trees with parental lines using 4885 SNPs. (a) cycle 0, (b) cycle 1, (c) cycle 2, (d) cycle 3, and (e) cycle 4 of RS. The black color is representing the species introgression parental lines, Green self-compatible parental lines; each cycle selections was representing in a different color. 82 Figure S2. 1 (cont’d). (c) (d) 83 Figure S2. 1 (cont’d). (e) 84 Parental lines Cycle 0 Cycle 1 Cycle 2 Cycle 3 Cycle 4 Figure S2. 2. Comparison of five cycles of recurrent selection with neighbor-joining trees with parental lines using 4885 SNPs. 85 (b) (d) (a) (c) Figure S2. 3. Comparison of cycle 0 to cycle 4 principal component analysis with parental lines using 4885 SNPs. (a) cycle 0, (b) cycle 1, (c) cycle 2, (d) cycle 3. The black color is representing the species introgression parental lines, Green self-compatible parental lines; each cycle selections was representing in a different color. 86 Figure S2. 3 (cont’d). 87 Figure S2. 4. 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Recurrent Selection for High Self-Fertility in Vernal Alfalfa ( Medicago Sativa L .). Crop Science 11: 881–883. Visser, R. G. F., C. W. B. Bachem, Jan M. de Boer, G. J. Bryan, S. K. Chakrabati, S. Feingold, R. Gromadka, et al. 2009. Sequencing the Potato genome: Outline and first results to come from the Elucidation of the sequence of the world’s third most important food crop. American Journal of Potato Research 86: 417–429. doi:10.1007/s12230-009-9097-8. Yuan, M., Y. Zhou, and D. Liu. 2004. Genetic diversity among populations and breeding lines from recurrent selection in Brassica napus as revealed by RAPD markers. Plant Breeding 123: 9–12. 94 CHAPTER 3 INTROGRESSING SELF-COMPATIBILITY TO (Solanum tuberosum) DIHAPLOIDS FOR DIPLOID POTATO VARIETY DEVELOPMENT 95 Abstract Dihaploids of cultivated potato (Solanum tuberosum L.) have been produced for over 50 years in order to reduce the breeding and genetic challenges of autopolyploidy. Most dihaploids are male sterile (MS) which reduces the benefit of lower ploidy level of cultivated tetraploid potato. In this study, we used three self-compatibility (SC) donors to introgress SC into a wide range of dihaploid germplasm through a series of crosses to dihaploids which we refer to as S. tuberosum backcrossing. The SC progeny were selected from each generation of crosses to dihaploids. Eighteen SC F1 were used to generate BC1. Seventeen SC BC1 selections were used to generate BC2. The SC increased from 11% in the F1 generation to 33% in the BC2 generations. Over 6,000 genome-wide SNPs were used to characterize the germplasm diversity, heterozygosity, and structure in two backcrossing generations. SNP heterozygosity was maintained at 35% over two backcross generations. Using Principal Component Analysis, the BC2 progeny grouped with S. tuberosum dihaploids while the BC1 germplasm pool is distinct from the SC donor lines. Diversity analysis and field-based trait measurements showed that the BC germplasm pool has genetic variability for many agronomically adapted traits including tuber appearance, tuber shape, average tuber weight, average tuber yield, and specific gravity. The BC2 generation was significantly improved for maturity, scab resistance, average tuber number, however, the yield in BC2 was not greater than the F1 and BC1 generations. A backcross breeding strategy was successfully used to reduce the contribution of the S. chacoense SC donor genetic background and select for commercially important tuber trait adaptations in a genetically diverse dihaploid germplasm pool that could be used to develop inbreds for a F1/hybrid breeding strategy. 96 Introduction Improving potato varieties is challenging as cultivated potato (S. tuberosum L.) varieties are tetraploid, outcrossing and asexually propagated (Visser et al. 2009; Lindhout et al. 2011; Jansky et al. 2016). Outbreeding at the tetraploid level makes fixing genetic gains difficult in the cultivated varieties (Lindhout et al. 2011). Furthermore, inbreeding depression and slow progression to inbreeding at the tetraploid level limit the production of inbred tetraploid potatoes (Jansky et al. 2014). Dihaploids of cultivated potatoes (2n=2x=24) are the most useful ploidy level to understand cultivated tetraploid potato breeding and genetics (Hougas et al. 1958). Numerous efforts have been made to produce dihaploids for diploid breeding approaches (Hougas et al. 1958; Phumichai et al. 2005; Jansky et al. 2016; Manrique-Carpintero et al. 2018). Producing dihaploids from S. tuberosum tetraploid (2n=4x=48) is accomplished by crossing tetraploids with a haploid inducer (e.g., IVP101: GM12) (Hougas et al. 1958) or using anther culture of gametophytic cells (n=2X=24) from tetraploid varieties (Veilleux et al. 1985). Santini et al. (2000) reported an evaluation of 245 plants from five families (three Argentinian tetraploid cultivars and three diploid wild species, S. gourlayi, S. chacoense, and S. spegazzinii) were androsterile: the plants produced normal anthers shape and color, but the anthers shed and shrivelled pollen grains that did not stain. Manrique-Carpintero et al. (2018) reported that dihaploids induced from cv. Superior uncovered genetic load of the material parent. The dihaploid breeding programs have had a low impact due to loss of plant vigor (Hougas et al. 1958), and most of the dihaploids are male sterile and/or self-incompatible (SI). However, a large number of dihaploids from S. tuberosum have been reported to readily tuberize in the field 97 which have benefit of being able to cross to diploid wild species and obtain tubering hybrid species (Hermundstad and Peloquin 1985). Previous studies have reported using backcross to improve potato for qualitative and quantitative traits such as maturity, persistence of stolons, and average tuber weight (Tarn and Tai 1983). A backcross strategy was used to accelerate the recovery of the recurrent genome in maize (Zea mays L.) by using marker-assisted selections (Frisch et al. 1999). Luthra et al. (2016) reported somatic hybrids (S. tuberosum dihaploids x wild diploid species S. pinnatisectum Dun.) require several backcrosses to the S. tuberosum dihaploid to improve acceptable phenotypes and acceptable yield components. Potato breeding programs in Canada and the US have previously used backcrossing to enhance resistance and quality gene(s) within S. tuberosum species by crossing with wild species desired traits donor (Hermsen 1989). Amoah and Grun (1988) used backcross to introgress genes that control low berry set and high seed number from ssp. tuberosum to ssp. andigena (2n=2x=24). Therefore, backcross breeding strategy can be used to incorporate major gene(s) for disease and pest resistance in potato cultivars. The backcrossing strategy was used by Condon et al., (2002) to improve wheat (Triticum aestivum L.), durum wheat (T. turgidum L.), and barley (Hordeum vulgare L.) for water-use efficiency. Introgressing SC to self-incompatible (SI) dihaploids increases the opportunity to produce inbred lines at the diploid level (Hosaka and Hanneman, 1998). SI in diploid potato is controlled by the gametophytic system (Jansky et al. 2016) which is due to the presence of S- locus encoding two polymorphic genes, called the S-determinant genes (SLF (S-locus F-box) and S-RNase (style-specific ribonuclease)) (Entani et al. 2003; Takayama and Isogai 2005). The SI at the diploid and dihaploid potatoes could be overcome by introgression of SC from wild diploid S. chacoense selection (M6) (Jansky et al. 2014). Diploid potato SI pollen is controlled by S- 98 locus and to overcome SI, the inhibitor of the S-locus (Sli) has been used (Sanetomo et al. 2014). M6 can transmit Sli to develop SC line, as it is highly male and female fertile and does not show severe inbreeding depression. Self-compatible lines can be inbred to fix or remove alleles related with traits of agronomic interest (Lindhout et al. 2011). Introgressing SC to cultivated dihaploids provides the opportunity to use selfing to remove undesirable traits as the first step in producing inbreds as parents in F1 diploid hybrid potato (Jansky et al., 2014; Jansky et al., 2016). Ultimately, inbreeding can lead to the production of true potato seed F1 hybrids. Using M6 as wild species SC donor transmits late maturity and small tubers (Jansky et al. 2014). A backcross breeding strategy to remove undesirable traits associated with M6 must be employed. In this study, we plan to introgress SC to cultivated dihaploids to create a SC S. tuberosum diploid germplasm pool. We report progress made to introgress and improve SC in the population as well as a genetic variation for other economic traits observed in the population such as early maturity, common scab resistance, larger tuber weight and yield. Three generations of crosses with S. tuberosum dihaploids have been completed. Materials and Methods Plant Materials Dihaploids were previously generated from cultivated tetraploid varieties S. tuberosum (2n=4x=48) by crossing with haploid inducer IVP101 (S. tuberosum Grp. Phureja) (Manrique- Carpintero et al. 2018). For this study, a total 45 dihaploids that have been developed from tetraploid varieties (Atlantic, Superior, Kalkaska) and advanced breeding lines (MSR127-2 and NY148) (Table 3. 1) were used to develop backcross germplasm. Three sources of SC 99 donors were used to generate SC F1 hybrids; MSBB912 (MCD205 (A133-57 x TF75.5) x M6), Bulk 3 pollen created by equally mixing pollen of four SC F1 diploid hybrids (MSBB912_B, MSBB920_A, MSBB930_A, and MSBB932_A) from a SC recurrent selection population (Table 3. 1) called here after as Bulk3, and the S. chacoense line M6. The germplasm was defined as three groups based on SC donors: MSBB912, Bulk 3, and M6. All clonal selections from crosses between dihaploids and SC donors (F1), first backcross to dihaploids (BC1), and second backcross (BC2) were grown in the field at Michigan State University’s Montcalm Research Center (MRC, Lakeview, MI) from 2014-2017 to evaluate tuber appearance, tuber shape, average tuber weight, tuber yield, and specific gravity. S. tuberosum Backcross Generation Cycle The yearly crossing cycle has four key steps. The cycle started each year in the fall by making selections based on tuber traits such as tuber shape, tuber appearance, tuber number, average tuber weight, yield, and maturity. In the winter, one tuber from each selection was planted in 5-gallon pots in the greenhouse 16-h-light/8-h-night photoperiod at 20-25 °C. When the plants were one-month-old, ploidy was checked by chloroplast counting in stomatal guard cells and confirmed using SNP genotyping (Alsahlany et al. 2018). Fresh pollen was collected from open flowers and at least five flowers were self-pollinated to check for self-compatibility. Similarly, pollen from a SC selection was used to cross with selected dihaploid lines from different chip processing, table markets varieties and advanced breeding lines (Table S3. 1). After crossing, seeds were extracted from mature fruits and treated with 1500 ppm gibberellic acid (GA3) overnight. In early summer (June 15 – July 15), 25 – 50 one-month-old seedlings 100 from each family were transplanted to the MRC field. Three S. tuberosum backcross generations were completed between 2014 -2018. Single Nucleotide Polymorphisms (SNPs) Genotyping DNA from 61 selections (30 F1, 26 BC1, and 5 BC2) and 32 dihaploid parental lines (Table S3. 1) was extracted from young leaf tissue using the QIAGEN DNeasy Plant Mini Kit (QIAGEN, Germantown, MD). The Quant-iT PicoGreen dsDNA Assay Kit (Invitrogen, SanDiego, CA) was used to quantify DNA and normalized to a concentration of 50 ng µl-1. The Infinium 8303 potato array (V1), Infinium 12K potatoV2 array or Infinium 22K potato array (V3) were used to SNP genotype the selections (Illumina, San Diego, CA) according to the manufacturer’s protocol. SNP genotype calls were obtained using the GenomeStudio software (Illumina, San Diego, CA). Low-quality SNPs were excluded using a cutoff of ≥10% and < 0.03 minimal allele frequency. Tetraploid genotyping cluster calls were based on a custom Perl script to obtain five clusters using theta value thresholds (Hirsch et al. 2013). SNP-based ploidy determination was performs according to Alsahlany et al. (2018) and Ellis et al. (2018). Heterozygosity and Population Structure Analysis Individual SNP heterozygosity was calculated for each marker dividing the sum of heterozygous SNPs (AB) by the total number of SNPs. Population structure was assessed using high-quality polymorphic SNP markers by three different methods. The neighbor-joining (NJ) tree clustering method (NJ tree) and Principal Component (PC) analysis were generated using R- software version 3.5.0. The NJ tree clustering was generated by using the ‘ape’ (http://ape- package.ird.fr/) R package. PC analysis using the ‘ggbioplot’ (https://github.com/vqv/ggbiplot) R 101 package was used. FigTree v1.4.3 software was used to view the NJ tree and color-coded. The NJ tree and PCA were generated using 34 SC selections (14 F1, 15 BC1, and 5 BC2), 20 parental lines (15 dihaploids and 5 SC donors), and cvs. Atlantic and Superior (Figure 3. 1 and 3. 2). Clonal Selection Phenotyping Tuber traits and vine maturity were evaluated in the field using tubers from the previous season. Four dihaploids (ATL_M_120, ATL_M_429, NY148 HP# 1 and VT_SUP_19) and cv. Atlantic were grown in the field at MRC as checks (Table S3. 2). Five plants per plot with 30 cm in row spacing were planted. Maturity for the F1, BC1, and BC2 was evaluated after 110 days from planting using a maturity scale (1 = early, dead, 5 = late, full green vines and flowering) (Manrique-Carpintero et al. 2015; Braun et al. 2017) in 2017 and 2018 field seasons, respectively. Common Scab Resistance A total of 66 selections from the F1, BC1, and BC2 generations were screened for common scab resistance during the 2018 growing season in a common scab disease nursery at MRC. Tubers were planted in five-plant plots with 30 cm in row spacing. The scale (0 = resistance and 5 = susceptible for the common scab) was used to evaluate the tubers in the plots for scab resistance at the harvest time (Driscoll et al. 2009). A plot score of 1.5 or less is classified as resistant. 102 Tuber Traits Evaluation Selections were evaluated for tuber appearance, shape while average tuber number per plant, tuber weight, tuber yield, and specific gravity were measured for the second-year growing. Selections from F1 and BC1 generations were screened for tuber appearance in the field season 2017 and BC2 generation screened in the 2018 field season. A 1 – 9 scale (1= very poor, 9= excellent) was used to evaluate tuber appearance (De Haan et al. 2014). Tuber shape was evaluated using a 1 – 5 scale ( 1= compressed, 2= round, 3= oval, 4= oblong, and 5= long) (Domański 2001). Average tuber number per plant, average tuber weight (g tuber-1), and average tuber yield per plant (g plant-1) were conducted post-harvest. Specific gravity was calculated as weight in air (dry weight – wet weight) −1 for tuber samples over 1 Kg (total of 10 plants yield) (Manrique-Carpintero et al. 2015). Statistical Analyses A Chi-square test was used to compare the improvement of SC after three crosses generations to S. tuberosum with F1 generation and the SI, MS, and non-flowering plants reduction in the germplasm. A general linear model (GLM) was fitted using Residual Maximum Likelihood (REML) to test generation effects on individual heterozygosity, maturity, common scab resistance, and six tuber traits, with backcross generations (F1, BC1, BC2) as random effects. Differences between backcross generations were conducted using a Student’s t-test (a = 0.05) in JMP version 14.0.0 (SAS Institute, Cary, NC). Student’s t-test means compression was used due to an uneven number of genotypes that observed within each backcross generation based on tuber seed availability. A total of 150 F1 selections were used as references to determine the improvement in SC in the BC1 and BC2 S. tuberosum backcross generations. Fourteen F1 103 progeny were used as references to determine differences between the backcross generation means of an individual’s heterozygosity. For tuber-related traits, the F1 selections were used as a reference to compare the change among the backcross generations (Table S3. 3). Dihaploid lines and cv. Atlantic were used as references. Results and Discussion Introgress and Improve Self-compatibility A series of crosses were made to introgress SC from SC donors into a round, white market class, self-incompatible S. tuberosum dihaploid germplasm while reducing the contribution of the S. chacoense SC donor (Table 3. 1, Figure 3. 3). The series of crosses were referred to as S. tuberosum backcross strategy to reduce the wild species contribution in the S. tuberosum dihaploid germplasm. In winter 2014, 21 Atlantic dihaploids and one Superior dihaploid (Table 3. 1 and 7) were selected based on plant vigor, female fertility, and desirable tuber characteristics to generate F1 progeny. These dihaploid selections were crossed with SC donors to develop a total of 24 families (17 families with MSBB912 genetic background, two families with Bulk 3 genetic background, and five families with M6 genetic background) (Figure 3. 3). In summer 2014, 50 seedlings from each family with MSBB912 and Bulk 3 genetic background were transplanted between July 15 to August 5 in the field at MRC and harvested on October 13. Eighty-eight selections from 19 families of the F1 population were selected based on tuber trait and maturity. Fifty selections were of MSBB912 genetic background, and 38 selections of Bulk 3 genetic background selected to create BC1 (Table 3. 2 and S3. 1). 104 In winter 2016, these selections from MSBB912 and Bulk 3 genetic background with earlier maturity, greater yield, tuber appearance, and shape were screened for SC in the greenhouse. Sixty-two progeny that represent five families of dihaploid x M6 genetic background were planted in the greenhouse to select for SC (Table 3. 1 and S3. 1). A total of 150 of F1 selections from three genetic background groups were checked for ploidy level by the chloroplast count method (Alsahlany et al. 2018) and self-pollinated to determine self- compatibility in the greenhouse. Five SC selections of MSBB912 genetic background were obtained two SC selections of Bulk 3 genetic background, and ten SC selections of M6 genetic background. The SC selections were used as males, and crossed to 17 dihaploid selections (11, 4, 1, and 1 from Atlantic, Superior, Kalkaska, and NY148, respectively) used as females to generate the S. tuberosum BC1 populations (Figure 3. 3). It is expected that the BC1 progeny were 75% S. tuberosum dihaploid and 25% wild species. A total of 79 families were developed including 31 families of MSBB912 genetic background, 22 families of Bulk 3 genetic background, and 26 families of M6 genetic background. In summer 2016, 25 seedlings per family were transplanted (June 22 to July 19) in the field at MRC and harvested on October 10 for the BC1 population selection. In the fall, 123 selections were made for earlier maturity, greater yield, tuber appearance, and shape from 78 families of the three genetic background groups (Table S3. 1 and 3. 2). These selections were checked for ploidy level and self-pollinated to select for SC. A total of 17 SC selections were identified: four from MSBB912 group, three from the Bulk 3 group, and ten from M6 group. Crosses between the SC selections as males with eight dihaploid selections (Table 3. 1) were used to generate S. tuberosum BC2 populations (Table S3. 1, Figure 3. 3). It is expected that the BC2 progeny have an average 87.5% S. tuberosum dihaploid and 12.5% wild species in their genetic background. Forty families were 105 developed that included 15 families of the MSBB912 group, six families of the Bulk 3 group, and 19 families of the M6 group. In summer 2017, 25 seedlings per family were transplanted between June 21 to July 14 to MRC and harvested in October 3. In the fall 127 selections were made from 40 families (Table S3. 1 and 3. 2). In winter 2018, 21 selections out of 127 selections representing 15 families were selected in the fall 2017 (seven families from the MSBB912, two families from the Bulk 3, and six families from the M6 groups) were planted in the greenhouse. Selections were checked for ploidy level and SC. Seven SC selections were obtained of which four from the MSBB912 group and three from the M6 group. The SC selections were used as males and crossed to six dihaploid selections (Table 3. 1) as females to generate S. tuberosum BC3 population (Table S3. 1, Figure 3. 3). It is expected that the BC3 progeny have an average 93.75% S. tuberosum dihaploid and 6.25% wild species. A total of 42 families were developed that included 20 families from the MSBB912 group and 22 families from the M6 group. In summer 2018, 50 seedlings per family were transplanted (June 21 and 29) in MRC and harvested on October 10 for the S. tuberosum BC3 population evaluation. In fall 2018, 205 selections were made from 42 families of the two groups (Table S3. 1 and 3. 2). In the three cycles of crosses referred to as F1, BC1, and BC2, SC was significantly increased in BC2 (33%) compared with F1 (11%) generation (Chi-square p< 0.0001) (Table 3. 3). The percentage of SI in BC2 (61%) was not significantly different from BC1 (59%) generation (Chi-square p< 0.0001). Non-flowering plants were significantly reduced from 18% in BC1 to 0% in a BC2 generation (Chi-square p< 0.0001). Male sterility was significantly reduced from 13% in the F1 to 2% and 5% in the BC1 and BC2 generations, respectively (Chi-square p< 0.0001). Selecting for SC in the crossing block to create each new generation of S. tuberosum 106 backcross progeny has improved the germplasm fertility regarding SC, MS, and non-flowering plants. These results agree with Amoah and Grun (1988) which showed that backcrossing F1 tetraploid (S. phureja X S. chacoense) X S. tuberosum ssp. andigena for four backcross generations to S. tuberosum ssp. tuberosum have improved seed set and fertility in the germplasm. The complexity of these traits indicated that they are negatively correlated showing increasing/decreasing one of them will lead to opposite results in another (Table 3. 3, Figure 3. 4). Heterozygosity and Population Structure Analysis Heterozygosity and population structure were examined using genome-wide SNPs in 61 selections from the F1, BC1, BC2 generations (SC and SI selections) and 32 dihaploid lines. A total of 6612 SNPs was used to measure the individual heterozygosity. The SNP heterozygosity ranged from 6% for M6 to 50% for ATL_M_404, and for SC donors and dihaploid lines the heterozygosity mean was 39% ± 32% (Table 3. 4). The mean SNP heterozygosity was 37% ± 4%, 37% ± 22%, and 35% ± 5% in F1, BC1, and BC2 generations, respectively. The SNP heterozygosity in each generation was maintained during the three crossing cycles (Table 3. 4, Figure 3. 5) with no significant decrease among the backcross generations (p =0.3302). Unlike a classic backcrossing scheme, using these dihaploids from different varieties and advanced breeding lines for backcross led to the maintenance of heterozygosity in the population over two generations of backcrossing. Principal component analysis and neighbor-joining (NJ) tree were generated to evaluate germplasm structure using 6023 SNPs after removing 589 monomorphic SNPs (Figure 3. 1). The NJ tree has four clusters based on the dihaploid parental lines that were used to cross SC 107 selections and cycles of the crossing. The four clusters are two groups of cv. Atlantic and one group of cv. Superior, and a group of SC donors. The NJ clusters show that the F1, BC1, and BC2 generations are distinct from the SC donors and they are clustering with dihaploids and cvs. Atlantic and Superior. This clustering agrees with Phillips and Blok (2008) study that reported the subpopulations clustered based on their genetic background Solanum vernei or from S. tuberosum spp. andigena CPC2802. To examine the germplasm pool structure and diversity, the NJ tree were generated for all SC and SI selections and parental lines (33 parental lines, 30 F1, 30 BC1, 5 BC2, cvs. Atlantic and Superior (Table S3. 1, Figure S3. 1). Figures 3. 1 and S3. 1 showed the two generations of backcrosses and F1 selections were distinct from the SC donors. Furthermore, the NJ tree clusters showed that BC1 and BC2 selections were distinct from SC donors. The crossing the SC lines to different S. tuberosum dihaploids limited inbreeding in the S. tuberosum BC process, while reducing the contribution of the S. chacoense SC donor in the germplasm. A principal component analysis (PCA) was used to examine germplasm relationships for 34 SC selections (14 F1, 15 BC1, and 5 BC2), 15 dihaploids, 5 SC donors (Figure 3. 2). Principal component 1 and 2 explained 14% and 10.7% of the total genetic variance of the population (Figure 3. 2). The clustering of the PCA shows that the SC F1, BC1, and BC2 generations maintained their genetic variation across cycles, and are distinct from the SC donors. PCA showed F1 selections grouped closer to each other due to use of only Atlantic dihaploids to generate F1 selections. Meanwhile, the BC1 and BC2 generations were generated using a diverse group of dihaploids to which led to germplasm with a wider genetic variation. A second PCA was generated for all SC and SI selections to examine the germplasm pool structure and diversity (Figure S3. 2). Twenty-eight dihaploids and 5 SC donors, 30 F1, 30 BC1, 5 BC2, and cvs. Atlantic 108 and Superior. Principal Component 1 and 2 explained 10.9% and 8.7% of the genetic variance of the population (Figure S3. 2). Based on the dihaploid backcross, the PCA showed that the two generations of backcross selections were distinct from the SC donors and the BC selections are grouping close to the S. tuberosum dihaploids. The BC2 progeny have an average 87.8% S. tuberosum in their genetic background, so the PCA shows that the BC2 generation has shifted toward the dihaploids. Even though, the introgression of S. tuberosum to the SC selections seemed to be slow due to selection in greenhouse based on SC and was not on other traits. Clonal Selection Phenotyping Introgressing self-compatibility from wild diploid potato species can lead to late maturity in the progeny (Jansky et al. 2016). To overcome late maturity when using S. chacoense as SC donor, a series of crosses with S. tuberosum dihaploid lines have been implemented. Twenty-six F1 and 191 BC1 selections were grown at MRC for the 2017 field season, and 124 BC2 selections were grown for the 2018 field season to measure maturity and tuber traits. The vine maturity means in BC2 generation (1.8) was significantly reduced compared with F1 (3.9) and BC1 (3.1) generations (p= 0.0001). Seventy-eight percent of the BC2 selections had early maturity (similar to dihaploids and Atlantic) (Table S3. 2) compared with 36% and 11% selections in BC1 and F1, respectively (Table S3. 3, Figure 3. 6a). Some selections in the F1 generation phenotypically resembled the wild SC donor demonstrating late maturity and long stolons. These results agreed with Tarn and Tai, (1983) that indicated backcross strategy has the potential to improve maturity by backcross to S. tuberosum. However, the variation for maturity within each generation reduced and the desirable tuber traits increased as the germplasm was crossed to S. tuberosum dihaploids (Bradeen and Chittaranjan 2011). 109 Common Scab Resistance Some of the dihaploids were developed from round, white, chip-processing, and common scab resistant cultivated tetraploid potato varieties. Resistance to common scab significantly increased in BC2 generation compared with F1 and BC1 generations (p= 0.0001, Table S3. 3, Figure 3. 6b). The percentage of selections that were characterized as common scab resistant increased from 35% - 38% for F1 - BC1 to 63% in BC2 (Table S3. 3). Crossing SC selections to common scab resistant dihaploids may have improved scab resistance in a BC2 generation. Tuber Traits Evaluation Tuber appearance was not significantly different among the backcross generations (p= 0.2112). The F1, BC1, and BC2 results showed a wide variation in tuber appearance within each generation (Table S3. 3, Figure 3. 6c). A total of 79%, 90%, and 96% of the selections had an acceptable tuber appearance greater (> 5 on scale 1 - 9) for F1, BC1, and BC2 generation, respectively compared with dihaploids and the cv. Atlantic which have good tuber appearance and scored between 7 – 8 on the scale (1 – 9) (Table S3. 2). Crossing the SC lines to S. tuberosum dihaploid varieties improved tuber appearance 17% in BC2 generation compared with F1 generation. These results agreed with Carputo et al. (2000) who reported that tuber characteristics were improved by producing backcrossed potato hybrids compared with their F1 parents. Selections were screened for tuber shape as one of the important traits for chip processing, and from the first step in the breeding scheme selecting for round tuber, selections have been applied. Tuber shape was not significantly different among the backcross generations (p= 0.4778). In general, a total of 79%, 74%, and 55% of the selections have round tuber shape for F1, BC1, and BC2 generation, respectively (Table S3. 3, Figure 3. 6d). Dihaploid check lines 110 ATL_M_120 and ATL_M_429 have oval tuber shape, and NY148 HP# 1, VT_SUP_19, and Atlantic variety displayed round tubers shape (Table S3. 3). Round tuber shape selections were not significantly reduced which may be due to two stages of selection (p= 0.4778): first, in the field from seedlings as a single plant transplant in the field at harvest; then second, selection applied based on SC over tuber shape in the crossing block in the greenhouse. Variation for tuber traits such as tuber appearance and shape were maintained in the germplasm pool by BC to the heterozygous dihaploids that are segregating for tuber. These results agreed with Luthra et al. (2016) who reported that phenotypic variation was observed in BC1 progeny were generated from somatic hybrids (S. tuberosum dihaploids x wild diploid species S. pinnatisectum Dun.) crossed to S. tuberosum dihaploid lines. Tuber number per plant varied widely within each generation of backcross (Table S3. 3). However, the average tuber number significantly increased from an average of 5.3 for F1 and 5.9 for BC1 generations to 7.1 for BC2 generation (p= 0.0154) (Table S3. 3, Figure 3. 6e). Crossing SC progeny to S. tuberosum dihaploids improved the average tuber number per plant each cycle of crossing advanced. The average tuber weight was screened for F1, BC1, and BC2 generations and there a slight decrease in the average tuber weight in BC1 and BC2 which was not significantly different compared with F1 (p= 0.5428) (Table S3. 3, Figure 3. 6f). The average tuber weight in a BC2 generation has improved compared with BC1 generation. However, the dihaploids and cv. Atlantic references had an average tuber weight greater than 89 g except for NY148 HP# 1 which was 19 g which could be attributed to inbreeding (Table S3. 2). Improvement in yield can be attributed to two components: tuber number and tuber weight. An increase in any of these two will lead to increase tuber yield. The average tuber yield increased slightly in 540 g plant-1 BC2 generation but was not significantly different compared with 462 g 111 plant-1 and 457 g plant-1 in F1 and BC1 generations, respectively (p= 0.0693) (Table S3. 3, and Figure 3. 6g). These results agreed with Condon et al., (2002) who showed that wheat backcross progeny were higher yielding compared to their recurrent parents. BC1 generation has lower yield compared with F1 generation, while the BC2 generation has higher yield compared with F1 and BC1 generations. Using Atlantic dihaploids for generating BC1 generation after using them as parental lines to generate F1 led to inbreeding depression. Therefore, different dihaploids have been considered for generating BC2 generation. However, three dihaploid lines (ATL_M_120, ATL_M_429, and NY148 HP# 1) yielded lower than 457 g plant-1. While VT_SUP_19 dihaploid and cv. Atlantic have yielded greater than 540 g plant-1 (Table S3. 2). There was broad variation within each generation for average tuber number, tuber weight, and tuber yield. In general, crossing to S. tuberosum dihaploids improved both, average tuber number and tuber yield in BC2 generation compared with F1 and BC1 generations. Selections showed variability within each generation that may have been due to the genetic variation in the germplasm. These results agreed with Carputo et al. (2000) who reported that a BC1 generation showed variability in potato tuber yield. The F1, BC1, and BC2 selections were screened for tuber specific gravity (SPGR). The SPGR was significantly reduced to 1.075 for BC2 generation compared with 1.086 for F1 and 1.086 for BC1 (p < 0.0001) (Table S3. 3, Figure 3. 6h). A total of 33%, 18% and 42% of the selections scored between 1.070 – 1.080 on specific gravity, respectively. While, 13%, 46%, and 21% of the selections scored between 1.081 – 1.090 for specific gravity in F1, BC1, and BC2 generations, respectively (Table S3. 3, Figure 3. 6h). Thirty-one percent F1, 33% BC1, and 2% BC2 have a specific gravity greater than 1.091. The specific gravity data showed low variation within selections in BC2, compared with F1 and BC1. Moreover, BC2 selections scored lower 112 specific gravity compared with F1, and BC1 selections. Crossing SC progeny to S. tuberosum dihaploid varieties has reduced the variation within each generation as the backcross cycle advanced (Table S3. 3). The dihaploid lines and cv. Atlantic references presented a wide range of specific gravity between 1.066 – 1.085 (Table S3. 2). These results were agreed with Carputo et al. (2000) study who reported a wide variability and improvement for specific gravity in potato in BC1 generation. 113 APPENDIX 114 Table 3. 1. Solanum tuberosum dihaploid parental lines used to introgress self-compatibility (SC) and SC donors. ID Female Male - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - ATL_M_113 ATL_M_114 ATL_M_120 ATL_M_128 ATL_M_129 ATL_M_159 ATL_M_170 ATL_M_179 ATL_M_182 ATL_M_186 ATL_M_188 ATL_M_192 ATL_M_198 ATL_M_402 ATL_M_403 ATL_M_404 ATL_M_405 ATL_M_422 ATL_M_423 ATL_M_424 ATL_M_429 ATL_V_023 ATL_V_024 Fertility Used to generate F1 F1 BC1 F1 F1 F1 BC2 F1 F1 BC1 F1, BC1, SI SI SI SI SI SI SI SI SI SI SI SI SI SI SI SI SI SI SI SI SI SI SI F1, BC1 F1 F1 F1, BC1 F1, BC1 BC1, BC2 BC1 F1 F1 F1, BC1 F1, BC1 F1, BC1 F1 Samples Background Source Parental/dihaploid Parental/dihaploid Parental/dihaploid Parental/dihaploid Parental/dihaploid Parental/dihaploid Parental/dihaploid Parental/dihaploid Parental/dihaploid Parental/dihaploid Parental/dihaploid Parental/dihaploid Parental/dihaploid Parental/dihaploid Parental/dihaploid Parental/dihaploid Parental/dihaploid Parental/dihaploid Parental/dihaploid Parental/dihaploid Parental/dihaploid Parental/dihaploid Parental/dihaploid S. tuberosum S. tuberosum S. tuberosum S. tuberosum S. tuberosum S. tuberosum S. tuberosum S. tuberosum S. tuberosum S. tuberosum S. tuberosum S. tuberosum S. tuberosum S. tuberosum S. tuberosum S. tuberosum S. tuberosum S. tuberosum S. tuberosum S. tuberosum S. tuberosum S. tuberosum S. tuberosum MSU MSU MSU MSU MSU MSU MSU MSU MSU MSU MSU MSU MSU MSU MSU MSU MSU MSU MSU MSU MSU MSU MSU 115 Table 3. 1. (cont’d) ATL_V_030 - ATL_V_033 - Kal Hp #5 - Kal Hp #16 - Kal Hp #31 - Ny148 HP# 1 - R127_2 HP #1 RH_89_039_16 UW_W4 MSS576DH_3 MSS576DH_5 MSS576DH_15 MSS576DH_17 VT_SUP_08 VT_SUP_19 VT_SUP_ 47 VT_SUP_70 VT_SUP_79 VT_SUP_96 MSZ219DH_3 - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - SI SI SI SI SI SI SI SI SI SI SI SI SI SI SI SI SI SI SI SI F1 F1 BC1 BC2 BC2 BC1, BC2, BC3 BC2, BC3 BC3 BC3 BC3 BC3 BC3 BC3 BC1, BC2 BC1, BC2 F1 BC2 BC1 BC1 BC3 Parental/dihaploid Parental/dihaploid Parental/dihaploid Parental/dihaploid Parental/dihaploid Parental/dihaploid S. tuberosum S. tuberosum S. tuberosum S. tuberosum S. tuberosum S. tuberosum MSU MSU MSU MSU MSU MSU Parental/dihaploid S. tuberosum MSU Parental/dihaploid S. tuberosum Parental/dihaploid Parental/dihaploid Parental/dihaploid Parental/dihaploid Parental/dihaploid Parental/dihaploid S. tuberosum S. tuberosum S. tuberosum S. tuberosum S. tuberosum S. tuberosum Parental/dihaploid S. tuberosum Parental/dihaploid S. tuberosum Parental/dihaploid S. tuberosum Parental/dihaploid S. tuberosum Parental/dihaploid S. tuberosum Parental/dihaploid S. tuberosum Virginia Tech University UW, Madison MSU MSU MSU MSU Virginia Tech University Virginia Tech University Virginia Tech University Virginia Tech University Virginia Tech University Virginia Tech University MSU 116 Table 3. 1. (cont’d) MSZ219DH_27 MSZ219DH_28 MSBB912_B - - MCD205 M6 - - SI SI SC BC3 BC3 F1 Parental/dihaploid Parental/dihaploid Parental/SC donor S. tuberosum S. tuberosum S. microdontum/ S. chacoense MSBB912_B MCD205 M6 SC F1 Parental/SC donor/ Bulk 3 S. microdontum/ S. chacoense MSBB920_A MS Chc524-8 SC F1 Parental/SC donor/ Bulk 3 S. tuberosum Grp. Phureja/S. chacoense MSBB930_A XD3 BER83 SC F1 MSBB932_A XD3 S703-5 SC F1 Parental/SC donor/ Bulk 3 Parental/SC donor/ Bulk 3 M6 - - S F1 Parental/SC donor S. tuberosum Grp. Tuberosum/S. chacoense/S. berthaultii S. tuberosum Grp. Phureja /S. tuberosum Grp. Tuberosum/S. chacoense S. chacoense MSU MSU MSU/SC recurrent selection germplasm pool MSU/SC recurrent selection germplasm pool MSU/SC recurrent selection germplasm pool MSU/SC recurrent selection germplasm pool MSU/SC recurrent selection germplasm pool UW, Madison 117 Table 3. 2. Summary of total selections and self-compatible (SC) selections from each generation of backcross. Generation SC donor F1 BC1 BC2* BC3** Total Selections No. families SC selection 3 150 123 21 99 NA 24 78 15 42 3 17 17 7 - Atlantic dihaploids for backcross - 21 17 8 - Year of field selection - 2014 2016 2017 2018 * BC2: Selected best 21 selections based on tuber traits out of 127 selections made in the fall season 2017 to screen for SC. ** BC3: Selected best 99 selections based on tuber traits out of 205 selections made in the fall season 2018 to screen for SC. 118 Table 3. 3. Introgression and Improvement of self-compatibility after three generations of crossing dihaploids. Generation Intercept F1 BC1 Generation Intercept F1 BC1 Generation Intercept F1 BC1 Generation Intercept F1 BC1 Self-compatible (%) 33.33 11.33 14.29 Self-incompatible (%) 61.19 59.33 65.55 Male sterile (%) 4.76 13.33 2.52 Non-flowering plant (%) 0.00 16.00 17.65 Chi-Square Prob>ChiSq 62.43 4.81 1.24 <.0001* 0.0282* 0.2649 Chi-Square Prob>ChiSq 8.78 0.40 0.47 0.0030* 0.5279 0.4945 Chi-Square Prob>ChiSq 140.03 7.03 1.96 <.0001* 0.0080* 0.1614 Chi-Square Prob>ChiSq 101.91 4.09 5.45 <.0001* 0.0432* 0.0196* * Means significance level for improving germplasm fertility based on the Chi-square distribution test. 119 Table 3. 4. Heterozygosity based on Individuals basis using a total of 6612 SNPs for two backcrosses and F1 generations compared with parental lines. Generation Parental lines F1 BC1 BC2 No. selections Heterozygosity % Standard Error mean 32 30 31 5 39 ± 32 37 ± 4 a* 37 ± 22 a 35 ± 5 a 0.02 0.02 0.06 0.02 * Means with the same letter designation are not significantly different as determined by Student’s t-test a = 0.05. 120 Figure 3. 1. Neighbor-joining tree generated by using 6023 SNPs, 34 self-compatible selections (14 F1, 15 BC1, and 5 BC2) and 20 parental lines (15 dihaploids and 5 SC donors). Atlantic and Superior varieties were used as references. Each color represents a different generation for dihaploid parental lines (black), self-compatibility donors (green), BC1 (orange), and BC2 (magenta). 121 Figure 3. 2. Principal Component Analysis (6023 SNPs), 34 self-compatible (SC) selections (14 F1, 15 BC1, and 5 BC2) and 20 parental lines (15 dihaploids and 5 SC donors). Cvs. Atlantic and Superior were used as references. 122 Figure 3. 3. Backcross breeding scheme to introgress self-compatibility (SC) to S. tuberosum dihaploids. 123 Figure 3. 4. Introgression and improvement of self-compatibility through S. tuberosum backcross strategy. 124 Figure 3. 5. SNP Heterozygosity among F1 progeny, and two backcross generation using 6612 SNPs. 125 Mean 5 4 3 l e a c S y t i r u t (a) Vine maturity a M 2 (b) Scab resistance cycle 0 cycle 1 cycle 2 RS cycle 3 cycle 4 1 cycle 0 cycle 1 cycle 2 RS cycle 3 cycle 4 Mean Mean l e a c S y t i r u t a M (c) Tuber appearance cycle 0 cycle 1 cycle 2 RS cycle 3 cycle 4 5 4 3 2 1 (d) Tuber shape cycle 0 cycle 1 cycle 2 RS cycle 3 cycle 4 4 3 2 1 5 4 3 2 1 l e a c S y t i r u t a M l e a c S y t i r u t a M Figure 3. 6. Frequency distribution for eight agronomic traits of F1, BC1 and BC2 generation. (a) maturity (1 = dead, 5 = late, full green vines and flowering); (b) scab resistance (0 = resistance, and 5 = susceptible); (c) tuber appearance (1 = very poor, and 9= excellent); (d) tuber shape ((1= compressed, 2= round, 3= oval, 4= oblong, and 5= long); (e ) average tuber number (No. tuber/ plant); (f) average tuber weight (g); (g) average tuber yield (g); (h) specific gravity. 126 Mean Mean l e a c S y t i r u a M t 5 4 3 2 1 (f) Average tuber weight (g) cycle 0 cycle 1 cycle 2 RS cycle 3 cycle 4 5 4 l e a c S y t i r u t a M 3 Figure 3.6 (cont’d). 2 1 (e) Average tuber number cycle 0 cycle 1 cycle 2 RS cycle 3 cycle 4 600 500 ) m g ( l d e Y i r e b u T e g a r e v A 400 300 200 100 0 ) m g ( l d e Y i r e b u T e g a r e v A l e a c S y t i r u t a M Mean 5 4 3 2 1 (g) Specific gravity cycle 0 cycle 1 cycle 2 RS cycle 3 cycle 4 Mean Mean (g) Average plant yield (g) F1 BC1 Generation BC2 F1 BC1 Generation BC2 600 500 400 300 200 100 0 127 SUPPLEMENTAL TABLES 128 Table S3. 1. Dihaploid parental lines with F1, BC1, and BC2 selections pedigree. ID MSCC841_04 ATL_M_113 Female MSCC842_01 ATL_M_114 MSCC842_02 ATL_M_114 MSCC843_01 ATL_M_128 MSCC843_02 ATL_M_129 MSCC844_01 ATL_M_159 MSCC844_02 ATL_M_159 MSCC844_03 ATL_M_159 MSCC845_05 ATL_M_170 MSCC845_06 ATL_M_170 MSCC845_07 ATL_M_170 MSCC846_06 ATL_M_179 MSCC846_07 ATL_M_179 MSCC846_08 ATL_M_179 MSCC847_02 ATL_M_182 MSCC847_06 ATL_M_182 MSCC848_02 ATL_M_192 MSCC848_04 ATL_M_192 Male MSBB912 MSBB912 MSBB912 MSBB912 MSBB912 MSBB912 MSBB912 MSBB912 MSBB912 MSBB912 MSBB912 MSBB912 MSBB912 MSBB912 MSBB912 MSBB912 MSBB912 MSBB912 MSCC848_05 ATL_M_192 MSBB912 MSCC848_06 ATL_M_192 MSCC848_08 ATL_M_192 MSBB912 MSBB912 Female - Male - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - SI SI SI SI SI No SI SC SI SI SI SI No SI SC SI No flower flower No Pollen SI SI Fertility Samples No Pollen flower F1 F1 F1 F1 F1 F1 F1 F1 F1 F1 F1 F1 F1 F1 F1 F1 F1 F1 F1 F1 F1 129 Table S3. 1. (cont’d) MSCC849_01 ATL_M_198 MSBB912 MSCC849_02 ATL_M_198 MSBB912 MSCC849_04 ATL_M_198 MSCC851_02 ATL_M_403 MSCC851_07 ATL_M_403 MSBB912 MSBB912 MSBB912 MSCC851_09 ATL_M_403 MSBB912 MSCC851_11 ATL_M_403 MSCC852_02 ATL_M_422 MSCC852_05 ATL_M_422 MSBB912 MSBB912 MSBB912 MSCC852_07 ATL_M_422 MSBB912 MSCC852_08 ATL_M_422 MSCC853_02 ATL_M_423 MSCC854_01 ATL_M_429 MSCC854_03 ATL_M_429 MSCC854_11 ATL_M_429 MSCC854_12 ATL_M_429 MSCC856_03 ATL_V_023 MSBB912 MSBB912 MSBB912 MSBB912 MSBB912 MSBB912 MSBB912 MSCC857_03 ATL_V_024 MSBB912 MSCC857_05 ATL_V_024 MSCC857_06 ATL_V_024 MSCC857_08 ATL_V_024 MSBB912 MSBB912 MSBB912 - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - No Pollen No flower flower No Pollen SI SI No SI SI No flower No flower flower No flower flower SI No SC SI SI SI No SC SI SI F1 F1 F1 F1 F1 F1 F1 F1 F1 F1 F1 F1 F1 F1 F1 F1 F1 F1 F1 F1 F1 130 Table S3. 1. (cont’d) MSCC858_03 ATL_V_030 MSCC858_04 ATL_V_030 MSBB912 MSBB912 MSCC858_06 ATL_V_030 MSBB912 MSCC859_01 ATL_V_033 MSBB912 MSCC859_04 ATL_V_033 MSCC859_06 ATL_V_033 MSCC859_07 ATL_V_033 MSCC859_08 ATL_V_033 MSCC863_02 ATL_M_170 MSCC863_05 ATL_M_170 MSCC863_06 ATL_M_170 MSCC863_07 ATL_M_170 MSCC863_11 ATL_M_170 MSCC863_12 ATL_M_170 MSCC863_14 ATL_M_170 MSCC863_17 ATL_M_170 MSCC863_20 ATL_M_170 MSCC863_22 ATL_M_170 MSCC863_24 ATL_M_170 MSCC863_25 ATL_M_170 MSBB912 MSBB912 MSBB912 MSBB912 Bulk3 Bulk3 Bulk3 Bulk3 Bulk3 Bulk3 Bulk3 Bulk3 Bulk3 Bulk3 Bulk3 Bulk3 - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - SC No flower No flower No flower flower SI No SI No flower No flower No Pollen SI No Pollen SI SI SI SI No flower No flower No flower SC F1 F1 F1 F1 F1 F1 F1 F1 F1 F1 F1 F1 F1 F1 F1 F1 F1 F1 F1 F1 131 Table S3. 1. (cont’d) MSCC863_27 ATL_M_170 MSCC863_33 ATL_M_170 MSCC863_36 ATL_M_170 MSCC863_40 ATL_M_170 MSCC863_45 ATL_M_170 MSCC863_46 ATL_M_170 MSCC863_49 ATL_M_170 MSCC863_53 ATL_M_170 MSCC863_58 ATL_M_170 MSCC863_60 ATL_M_170 MSCC863_63 ATL_M_170 MSCC863_69 ATL_M_170 MSCC863_70 ATL_M_170 MSCC863_73 ATL_M_170 MSCC863_74 ATL_M_170 MSCC863_77 ATL_M_170 MSCC863_78 ATL_M_170 MSCC863_79 ATL_M_170 MSCC864_01 ATL_M_403 MSCC864_06 ATL_M_403 MSCC864_10 ATL_M_403 MSCC864_17 ATL_M_403 MSCC864_20 ATL_M_403 Bulk3 Bulk3 Bulk3 Bulk3 Bulk3 Bulk3 Bulk3 Bulk3 Bulk3 Bulk3 Bulk3 Bulk3 Bulk3 Bulk3 Bulk3 Bulk3 Bulk3 Bulk3 Bulk3 Bulk3 Bulk3 Bulk3 Bulk3 - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - flower No Pollen flower SI SI SI No SI No SI SI SI SI No flower SI No Pollen SI SI No flower SI SI SI No Pollen SI SC F1 F1 F1 F1 F1 F1 F1 F1 F1 F1 F1 F1 F1 F1 F1 F1 F1 F1 F1 F1 F1 F1 F1 132 Table S3. 1. (cont’d) MSCC864_24 ATL_M_403 MSCC864_28 ATL_M_403 MSCC864_29 ATL_M_403 MSDD881_01 ATL_M_170 MSDD881_03 ATL_M_170 MSDD881_05 ATL_M_170 MSDD881_06 ATL_M_170 MSDD881_07 ATL_M_170 MSDD881_09 ATL_M_170 MSDD881_10 ATL_M_170 MSDD881_11 ATL_M_170 MSDD881_12 ATL_M_170 MSDD881_14 ATL_M_170 MSDD881_15 ATL_M_170 MSDD881_16 ATL_M_170 MSDD881_17 ATL_M_170 MSDD881_18 ATL_M_170 MSDD881_19 ATL_M_170 MSDD881_20 ATL_M_170 MSDD882_01 ATL_M_188 MSDD882_02 ATL_M_188 MSDD882_03 ATL_M_188 MSDD882_04 ATL_M_188 MSDD882_06 ATL_M_188 MSDD882_07 ATL_M_188 Bulk3 Bulk3 Bulk3 M6 M6 M6 M6 M6 M6 M6 M6 M6 M6 M6 M6 M6 M6 M6 M6 M6 M6 M6 M6 M6 M6 - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - No flower No Pollen SI SC SI SC SI SC SC SI SI SI SC SI SI SC SI SI SI No pollen SI SI SI No pollen SI F1 F1 F1 F1 F1 F1 F1 F1 F1 F1 F1 F1 F1 F1 F1 F1 F1 F1 F1 F1 F1 F1 F1 F1 F1 133 Table S3. 1. (cont’d) MSDD882_09 ATL_M_188 MSDD882_10 ATL_M_188 MSDD882_11 ATL_M_188 MSDD882_12 ATL_M_188 MSDD882_14 ATL_M_188 MSDD882_15 ATL_M_188 MSDD882_16 ATL_M_188 MSDD882_17 ATL_M_188 MSDD882_18 ATL_M_188 MSDD882_19 ATL_M_188 MSDD882_20 ATL_M_188 MSDD883_01 ATL_M_403 MSDD883_02 ATL_M_403 MSDD883_03 ATL_M_403 MSDD883_05 ATL_M_403 MSDD883_06 ATL_M_403 MSDD883_07 ATL_M_403 MSDD883_08 ATL_M_403 MSDD883_09 ATL_M_403 MSDD883_10 ATL_M_403 MSDD883_11 ATL_M_403 MSDD883_12 ATL_M_403 MSDD883_13 ATL_M_403 MSDD883_14 ATL_M_403 M6 M6 M6 M6 M6 M6 M6 M6 M6 M6 M6 M6 M6 M6 M6 M6 M6 M6 M6 M6 M6 M6 M6 M6 - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - SI SC No pollen SI No pollen SI SI SI SI SI SI SI SI No pollen No pollen SI SC SI SI SI SI SI SI No pollen F1 F1 F1 F1 F1 F1 F1 F1 F1 F1 F1 F1 F1 F1 F1 F1 F1 F1 F1 F1 F1 F1 F1 F1 134 Table S3. 1. (cont’d) MSDD883_15 ATL_M_403 MSDD883_16 ATL_M_403 MSDD883_17 ATL_M_403 MSDD883_18 ATL_M_403 MSDD883_19 ATL_M_403 MSDD883_20 ATL_M_403 MSDD883_21 ATL_M_403 MSDD883_22 ATL_M_403 MSDD884_05 ATL_M_402 MSDD884_12 ATL_M_402 MSDD884_13 ATL_M_402 MSDD884_15 ATL_M_402 MSDD884_17 ATL_M_402 MSDD885_02 ATL_M_424 MSDD886_03 VT_SUP_ 47 MSDD886_06 VT_SUP_ 47 MSEE758_03 ATL_M_404 MSEE760_02 ATL_M_188 MSEE763_07 Kal Hp # 5 MSEE764_01 ATL_M_186 M6 M6 M6 M6 M6 M6 M6 M6 M6 M6 M6 M6 M6 M6 - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - M6 M6 MSCC858_03 ATL_V_030 MSBB912 MSCC845_05 ATL_M_170 MSBB912 MSCC845_06 ATL_M_170 MSBB912 MSCC845_06 ATL_M_170 MSBB912 MSEE766_01 ATL_M_403 MSEE766_04 ATL_M_403 MSCC845_06 ATL_M_170 MSBB912 MSCC845_06 ATL_M_170 MSBB912 MSEE766_05 ATL_M.403 MSCC845_06 ATL_M_170 MSBB912 SI SI SI SI SC SI SI SI SI No pollen SI SI No pollen No pollen SC SI SC SI SI No flower SI No flower No flower F1 F1 F1 F1 F1 F1 F1 F1 F1 F1 F1 F1 F1 F1 F1 F1 BC1 BC1 BC1 BC1 BC1 BC1 BC1 135 Table S3. 1. (cont’d) MSEE767_02 ATL_M_404 MSEE767_03 ATL_M_404 MSEE768_04 ATL_M_405 MSEE768_05 ATL_M_405 MSCC845_06 ATL_M_170 MSBB912 MSCC845_06 ATL_M_170 MSBB912 MSCC845_06 ATL_M_170 MSBB912 MSCC845_06 ATL_M_170 MSBB912 MSEE769_03 ATL_M_120 MSCC845_06 ATL_M_170 MSBB912 MSEE770_02 ATL_V_023 MSCC845_06 ATL_M_170 MSBB912 MSEE772_01 ATL_M_198 MSCC845_06 ATL_M_170 MSBB912 MSEE773_01 ATL_M_188 MSCC845_06 ATL_M_170 MSBB912 MSEE773_02 ATL_M_188 MSCC845_06 ATL_M_170 MSBB912 MSEE774_02 NY148 HP # 1 MSCC854_01 ATL_M_429 MSBB912 MSEE774_03 NY148 HP # 1 MSCC854_01 ATL_M_429 MSBB912 MSEE775_03 ATL_M_404 MSCC854_01 ATL_M_429 MSBB912 MSEE776_07 ATL_M_405 MSCC854_01 ATL_M_429 MSBB912 MSEE778_04 ATL_M_188 MSEE779_01 Kal Hp # 5 MSCC854_01 ATL_M_429 MSBB912 MSCC857_05 ATL.V.024 MSBB912 MSEE779_05 Kal Hp # 5 MSCC857_05 ATL.V.024 MSBB912 MSEE780_02 NY148 HP # 1 MSCC857_05 ATL.V.024 MSBB912 MSEE781_01 ATL_M_170 MSCC857_05 ATL.V.024 MSBB912 MSEE781_02 ATL_M_170 MSEE781_03 ATL_M_170 MSEE782_06 ATL_M_404 MSEE783_03 ATL_M_405 MSCC857_05 ATL.V.024 MSBB912 MSCC857_05 ATL.V.024 MSBB912 MSCC857_05 ATL.V.024 MSBB912 MSCC857_05 ATL.V.024 MSBB912 SI SI SI No flower No pollen No flower No flower flower SI SI SI No pollen SI SI SI SI SI No SI No SI SI No flower flower BC1 BC1 BC1 BC1 BC1 BC1 BC1 BC1 BC1 BC1 BC1 BC1 BC1 BC1 BC1 BC1 BC1 BC1 BC1 BC1 BC1 BC1 136 Table S3. 1. (cont’d) MSEE783_05 ATL_M_405 MSEE786_10 ATL_M_198 MSEE787_01 ATL_M_188 MSEE789_03 ATL_M_120 MSEE790_04 ATL_V_023 MSEE790_05 ATL_V_023 MSEE810_04 VT_SUP_08 MSEE813_01 VT_SUP_19 MSEE814_01 VT_SUP_19 MSEE815_06 VT_SUP_19 MSCC857_05 ATL.V.024 MSBB912 MSCC857_05 ATL.V.024 MSBB912 MSCC858_03 ATL_V_030 MSBB912 MSCC858_03 ATL_V_030 MSBB912 MSCC858_03 ATL_V_030 MSBB912 MSCC858_03 ATL_V_030 MSBB912 MSCC845_06 ATL_M_170 MSBB912 MSCC845_05 ATL_M_170 MSBB912 MSCC845_06 ATL_M_170 MSBB912 MSCC858_03 ATL_V_030 MSBB912 MSEE815_07 VT_SUP_19 MSEE819_02 VT_SUP_79 MSCC858_03 ATL_V_030 MSBB912 MSCC857_05 ATL.V.024 MSBB912 MSEE822_02 VT_SUP_96 MSCC846_06 ATL_M_170 MSBB912 MSEE823_03 VT_SUP_96 MSEE823_04 VT_SUP_96 MSCC857_05 ATL.V.024 MSBB912 MSCC857_05 ATL.V.024 MSBB912 MSEE791_02 NY148 HP # 1 MSCC863_25 ATL_M_170 Bulk3 MSEE791_03 NY148 HP # 1 MSCC863_25 ATL_M_170 Bulk3 MSEE793_04 ATL_M_120 MSCC863_25 ATL_M_170 Bulk3 MSEE793_05 ATL_M_120 MSCC863_25 ATL_M_170 Bulk3 MSEE795_02 ATL_M_404 MSCC863_25 ATL_M_170 Bulk3 MSEE795_07 ATL_M_404 MSCC863_25 ATL_M_170 Bulk3 MSEE796_06 ATL_M_405 MSCC863_25 ATL_M_170 Bulk3 MSEE798_02 NY148 HP # 1 MSCC864_20 ATL_M_403 Bulk3 MSEE798_08 NY148 HP # 1 MSCC864_20 ATL_M_403 Bulk3 flower SI SI SI No SC SC SI SI SI No SC No SI SI SI SI SI SI SI SI flower flower No flower SI No flower No flower BC1 BC1 BC1 BC1 BC1 BC1 BC1 BC1 BC1 BC1 BC1 BC1 BC1 BC1 BC1 BC1 BC1 BC1 BC1 BC1 BC1 BC1 BC1 BC1 137 Table S3. 1. (cont’d) MSEE799_01 ATL_M_186 MSEE800_01 ATL_M_188 MSEE800_04 ATL_M_188 MSEE801_01 ATL_M_424 MSEE802_01 ATL_M_404 MSEE802_02 ATL_M_404 MSEE803_02 ATL_M_405 MSEE803_03 ATL_M_405 MSEE804_01 ATL_M_120 MSCC864_20 ATL_M_403 Bulk3 MSCC864_20 ATL_M_403 Bulk3 MSCC864_20 ATL_M_403 Bulk3 MSCC864_20 ATL_M_403 Bulk3 MSCC864_20 ATL_M_403 Bulk3 MSCC864_20 ATL_M_403 Bulk3 MSCC864_20 ATL_M_403 Bulk3 MSCC864_20 ATL_M_403 Bulk3 MSCC864_20 ATL_M_403 Bulk3 MSEE804_02 ATL_M_120 MSCC864_20 ATL_M_403 Bulk3 MSEE805_01 ATL_V_023 MSEE805_02 ATL_V_023 MSEE806_03 ATL_M_198 MSEE806_05 ATL_M_198 MSEE808_02 Kal Hp # 5 MSEE808_03 Kal Hp # 5 MSEE809_01 ATL_M_403 MSEE812_05 VT_SUP_08 MSEE817_02 VT_SUP_19 MSEE820_03 VT_SUP_79 MSEE820_04 VT_SUP_79 MSEE821_06 VT_SUP_79 MSEE821_08 VT_SUP_79 MSEE824_04 MSEE824_05 VT_SUP_96 MSEE825_07 VT_SUP_96 MSCC864_20 ATL_M_403 Bulk3 MSCC864_20 ATL_M_403 Bulk3 MSCC864_20 ATL_M_403 Bulk3 MSCC864_20 ATL_M_403 Bulk3 MSCC864_20 ATL_M_403 Bulk3 MSCC864_20 ATL_M_403 Bulk3 MSCC864_20 ATL_M_403 Bulk3 MSCC864_20 ATL_M_403 Bulk3 MSCC864_20 ATL_M_403 Bulk3 MSCC863_25 ATL_M_170 Bulk3 MSCC863_25 ATL_M_170 Bulk3 MSCC864_20 ATL_M_403 Bulk3 MSCC864_20 ATL_M_403 Bulk3 MSCC863_25 ATL_M_170 Bulk3 MSCC863_25 ATL_M_170 Bulk3 MSCC864_20 ATL_M_403 Bulk3 138 SI SI SI SI SI SI SI SI No SC SI SI SI SI SI SC SI SI SI SI No SI SC SI SI flower No flower flower BC1 BC1 BC1 BC1 BC1 BC1 BC1 BC1 BC1 BC1 BC1 BC1 BC1 BC1 BC1 BC1 BC1 BC1 BC1 BC1 BC1 BC1 BC1 BC1 BC1 BC1 Table S3. 1. (cont’d) MSEE825_08 VT_SUP_96 MSCC864_20 ATL_M_403 Bulk3 MSEE826_03 ATL_M_403 MSCC863_25 ATL_M_170 Bulk3 MSEE826_04 ATL_M_403 MSCC863_25 ATL_M_170 Bulk3 MSEE757_01 ATL_M_404 MSDD883_05 ATL_M_403 M6 MSEE828_01 ATL_M_405 MSDD881_05 ATL_M_170 M6 MSEE830_04 NY148 HP # 1 MSDD881_01 ATL_M_170 M6 MSEE831_01 ATL_M_170 MSDD881_01 ATL_M_170 M6 MSEE834_01 ATL_M_405 MSDD881_01 ATL_M_170 M6 MSEE834_02 ATL_M_405 MSDD881_01 ATL_M_170 M6 MSEE835_01 ATL_M_120 MSDD881_01 ATL_M_170 M6 MSEE835_02 ATL_M_120 MSDD881_01 ATL_M_170 M6 MSEE836_01 ATL_M_198 MSDD881_01 ATL_M_170 M6 MSEE840_03 ATL_M_198 MSDD881_05 ATL_M_170 M6 MSEE844_06 NY148 HP # 1 MSDD881_14 ATL_M_170 M6 MSEE844_09 NY148 HP # 1 MSDD881_14 ATL_M_170 M6 MSEE847_02 NY148 HP # 1 MSDD881_17 ATL_M_170 M6 MSEE847_03 NY148 HP # 1 MSDD881_17 ATL_M_170 M6 MSEE848_01 MSDD881_17 ATL_M_170 M6 ATL_M_170 MSEE849_01 ATL_M_120 MSDD881_17 ATL_M_170 M6 MSEE851_06 ATL_M_403 MSDD881_17 ATL_M_170 M6 MSEE853_05 NY148 HP # 1 MSDD883_05 ATL_M_403 M6 MSEE853_08 NY148 HP # 1 MSDD883_05 ATL_M_403 M6 MSEE853_09 NY148 HP # 1 MSDD883_05 ATL_M_403 M6 MSEE853_11 NY148 HP # 1 MSDD883_05 ATL_M_403 M6 MSEE853_27 NY148 HP # 1 MSDD883_05 ATL_M_403 M6 MSEE854_02 ATL_M_170 MSDD883_05 ATL_M_403 M6 SI SI SI SC SC SI SI SI SC SC SC SI SI SI SI SI No SI SC SC SI SI SI SI No flower flower SI BC1 BC1 BC1 BC1 BC1 BC1 BC1 BC1 BC1 BC1 BC1 BC1 BC1 BC1 BC1 BC1 BC1 BC1 BC1 BC1 BC1 BC1 BC1 BC1 BC1 BC1 139 Table S3. 1. (cont’d) MSEE854_03 MSEE854_07 MSDD883_05 ATL_M_403 M6 ATL_M_170 MSDD883_05 ATL_M_403 M6 MSEE855_02 ATL_M_403 MSEE856_02 ATL_M_188 MSEE856_06 ATL_M_188 MSEE857_01 ATL_M_404 MSEE858_01 ATL_M_405 MSEE858_02 ATL_M_405 MSEE860_10 ATL_V_023 MSEE861_03 ATL_M_429 MSEE861_04 ATL_M_429 MSEE862_03 ATL_M_198 MSEE869_01 VT_SUP_19 MSEE869_03 VT_SUP_19 MSEE870_03 VT_SUP_08 MSEE870_06 VT_SUP_08 MSEE871_09 VT_SUP_19 MSEE872_03 VT_SUP_96 MSFF690_01 ATL_M_170 MSFF691_01 ATL_M_404 MSFF694_01 Kal Hp #16 MSFF696_01 VT_SUP_19 MSFF706_01 ATL_M_170 MSFF713_01 Kal Hp #31 MSFF713_04 Kal Hp #31 MSFF716_01 R127-2 HP #1 MSDD883_05 ATL_M_403 M6 MSDD883_05 ATL_M_403 M6 MSDD883_05 ATL_M_403 M6 MSDD883_05 ATL_M_403 M6 MSDD883_05 ATL_M_403 M6 MSDD883_05 ATL_M_403 M6 MSDD883_05 ATL_M_403 M6 MSDD883_05 ATL_M_403 M6 MSDD883_05 ATL_M_403 M6 MSDD883_05 ATL_M_403 M6 MSDD881_14 ATL_M_170 M6 MSDD881_14 ATL_M_170 M6 MSDD883_05 ATL_M_403 M6 MSDD883_05 ATL_M_403 M6 MSDD883_05 ATL_M_403 M6 MSDD883_05 ATL_M_403 M6 MSEE758_03 ATL_M_404 MSCC858_03 MSEE758_03 ATL_M_404 MSCC858_03 MSEE758_03 ATL_M_404 MSCC858_03 MSEE758_03 ATL_M_404 MSCC858_03 MSEE815_07 VT_SUP_19 MSCC858_03 MSEE815_07 VT_SUP_19 MSCC858_03 MSEE815_07 VT_SUP_19 MSCC858_03 MSEE815_07 VT_SUP_19 MSCC858_03 SI No flower SI SC SI SI SI SC SI SI SI SC SI No pollen SI SI SI SI SC SI SI SC SC SI SI No pollen BC1 BC1 BC1 BC1 BC1 BC1 BC1 BC1 BC1 BC1 BC1 BC1 BC1 BC1 BC1 BC1 BC1 BC1 BC2 BC2 BC2 BC2 BC2 BC2 BC2 BC2 140 Table S3. 1. (cont’d) MSFF716_02 R127-2 HP #1 MSEE815_07 VT_SUP_19 MSCC858_03 MSFF716_03 R127-2 HP #1 MSEE815_07 VT_SUP_19 MSCC858_03 MSFF729_01 ATL_M_170 MSEE809_01 ATL_M_403 MSCC864_20 MSFF733_01 Kal Hp #31 MSEE809_01 ATL_M_403 MSCC864_20 MSFF744_01 ATL_M_404 MSEE757_01 ATL_M_404 MSDD883_05 MSFF749_02 NY148 HP # 1 MSEE757_01 ATL_M_404 MSDD883_05 MSFF749_03 NY148 HP # 1 MSEE757_01 ATL_M_404 MSDD883_05 MSFF749_04 NY148 HP # 1 MSEE757_01 ATL_M_404 MSDD883_05 MSFF751_01 VT_SUP_19 MSEE757_01 ATL_M_404 MSDD883_05 MSFF752_01 VT_SUP_70 MSEE757_01 ATL_M_404 MSDD883_05 MSFF752_04 VT_SUP_70 MSEE757_01 ATL_M_404 MSDD883_05 MSFF756_01 Kal Hp #31 MSEE828_01 ATL_M_405 MSDD881_05 MSFF774_03 ATL_M_170 MSEE862_03 ATL_M_198 MSDD883_05 SI SC SI SI SC SI SI SI SC SI SC SI SI BC2 BC2 BC2 BC2 BC2 BC2 BC2 BC2 BC2 BC2 BC2 BC2 BC2 141 Table S3. 2. S. tuberosum dihaploid lines and Atlantic tetraploid cultivar phenotype and tuber traits. ID Maturit y Scab Tuber appearance Tuber shape ATL_M_120 ATM_M_42 9 NY 148 HP #1 VT_SUP_19 Atlantic 2 1 1 1 1 2.5 2.5 1.5 2.0 3.0 7 7 8 7 8 3 3 2 2 2 Average Tuber Number 2.8 3.4 1.9 11.6 6.6 Average Tuber Weight (g) 88 102 Average Tuber Yield (g) 242 346 19 77 112 37 890 740 Specific gravity 1.07 1.07 1.08 1.07 1.08 142 Table S3. 3. Frequency distribution of F1, BC1, and BC2 clone mean for eight agronomic traits. Maturity (1 = dead, 5 = late, full green vines and flowering); scab resistance (0 = resistance, and 5 = susceptible); tuber appearance (1 = very poor, and 9 = excellent); tuber shape (1 = compressed, 2 = round, 3 = oval, 4 = oblong, and 5 = long); average tuber number (No. tuber/ plant); average tuber weight (g); average tuber yield (g); specific gravity. Average Average Tuber Weight Tuber Yield 76.1 A 25 279 462 A 158 1508 ID Maturity Scab Tuber Appearance Tuber Shape MSCC845_06 MSCC846_05 MSCC851_01 MSCC853_01 MSCC857_05 MSCC858_03 MSCC863_36 MSCC864-17 MSCC864_19A MSCC864_20 MSCC864-28 MSDD881_14 MSDD883_05 MSDD883_19 MSDD886_03 Mean/ F1 MSEE757_01 MSEE774_01 3 4 4 1 4 4 3 1 1 4 4 4 5 5 5 3.9 A 2 4 3.0 2.0 2.5 2.5 2.5 1.5 1.5 2.0 2.0 2.0 2.5 1.0 1.0 1.5 - 1.9 A 1.0 2.0 5 4 7 7 5 6 5 8 8 7 7 4 - 5 4 3 2 2 2 2 2 2 2 2 2 2 3 - 2 3 5.8 A 2.3 A - - - - Average Tuber Number 4.2 3.0 5.3 9.2 7.9 3.5 4.3 5.3 4.4 10.9 5.7 2.3 - 1.6 1.2 5.3 B 6.2 5.4 143 (g) 117 114 105 89 58 104 76 109 155 69 112 47 - 58 72 (g) 488 343 553 815 459 365 329 571 687 749 640 105 132 92 85 SPGP Generation 1.079 F1 1.091 F1 1.079 F1 1.066 F1 1.1 F1 1.09 F1 1.075 F1 1.074 F1 1.073 F1 1.068 F1 1.076 F1 1.1 F1 1.101 F1 1.089 F1 1.105 F1 1.086 A 1.099 BC1 1.089 BC1 Table S3. 3. (cont’d) MSEE774_05 1 MSEE774_08 3 MSEE779_05 4 MSEE780_02 5 MSEE781_01 1 MSEE781_03 3 MSEE783_05 4 MSEE790_04 2 MSEE790_05 2 MSEE791_06 4 MSEE791_07 4 MSEE798_02 3 MSEE798_06 5 MSEE798_07 5 MSEE798_08 2 MSEE798_09 3 MSEE798_10 3 MSEE798_14 1 MSEE801_01 3 MSEE805_01 1 MSEE809_01 1 MSEE810_06 5 MSEE815_06 2 MSEE815_07 4 MSEE824_04 1 MSEE824_06 2 MSEE828_01 3 MSEE844_07 5 2.0 2.0 1.5 2.0 - - 2.0 1.5 0.5 - 2.5 0.5 0.5 2.0 - 2.0 0.5 - 1.5 2.0 - - 1.5 - 2.0 2.0 - 2.0 6 6 5 5 - - 5 4 4 7 7 6 - 7 6 6 6 6 5 - 4 8 5 6 6 6 5 6 2 2 2 3 - - 2 2 2 2 3 3 - 2 2 2 2 2 3 - 2 2 2 2 2 2 2 2 7.0 5.0 5.4 6.4 - 4.0 5.6 11.5 9.5 1.0 4.8 4.4 6.2 6.8 8.2 5.2 6.0 7.6 4.0 5.2 4.7 2.0 4.3 8.3 11.3 6.6 5.3 8.8 144 63 79 60 128 0 61 67 34 70 146 107 68 77 55 63 81 86 88 84 73 66 218 102 73 33 58 39 54 439 396 324 819 0 244 374 395 665 146 510 301 478 377 517 422 515 671 335 380 310 435 434 607 377 381 204 471 - 1.078 BC1 1.072 BC1 1.097 BC1 1.088 BC1 BC1 1.087 BC1 1.085 BC1 1.09 BC1 1.09 BC1 1.093 BC1 1.092 BC1 1.083 BC1 1.076 BC1 1.093 BC1 1.08 BC1 1.089 BC1 1.081 BC1 BC1 1.083 BC1 1.081 BC1 1.073 BC1 1.064 BC1 1.084 BC1 1.094 BC1 1.092 BC1 1.079 BC1 1.082 BC1 1.107 BC1 - Table S3. 3. (cont’d) MSEE847_04 2 MSEE847_08 3 MSEE847_09 4 MSEE847_12 3 MSEE849_01 1 MSEE853_04 5 MSEE853_05 5 MSEE853_06 5 MSEE853_07 5 MSEE853_08 5 MSEE853_09 5 MSEE853_11 5 MSEE853_16 4 MSEE853_18 5 MSEE853_27 5 MSEE853_28 4 MSEE853_29 4 MSEE853_30 4 MSEE858_02 2 MSEE872_03 3 Mean/ BC1 MSFF690_01 MSFF690_03 MSFF690_04 MSFF691_01 MSFF691_03 MSFF691_04 MSFF692_02 2 2 2 1 1 3 3 3.1 B 2.0 - - 1.5 - 2.0 2.5 2.0 2.5 2.5 2.0 2.0 1.5 2.5 1.0 2.5 - 1.0 2.5 2.5 1.8 A 0.0 0.0 1.5 1.0 0.0 2.5 0.5 - 6 6 6 5 5 6 5 5 5 6 5 5 5 7 4 - 5 5 5 - 2 2 3 2 3 3 3 2 3 3 2 2 2 2 2 - 3 3 2 5.7 A 2.3 A 6 6 5 4 6 6 6 3 3 3 2 2 3 2 4.4 2.8 2.4 4.4 1.0 2.8 4.8 9.5 4.4 9.2 2.4 8.6 9.0 10.0 7.2 10.8 2.4 4.4 5.4 5.0 5.9 B 5.6 10.3 4.0 4.1 9.0 9.2 7.0 145 45 53 38 85 48 77 69 119 82 58 38 73 75 73 206 89 106 63 34 63 93.0 A 85 72 101 53 72 61 50 198 147 90 372 48 216 332 1130 359 529 90 630 677 725 1480 963 255 276 182 315 457 A 477 742 406 219 647 565 351 1.077 BC1 1.081 BC1 1.09 BC1 1.098 BC1 1.091 BC1 1.1 BC1 1.093 BC1 1.083 BC1 1.083 BC1 1.085 BC1 1.083 BC1 1.093 BC1 1.092 BC1 1.09 BC1 1.088 BC1 1.091 BC1 1.081 BC1 1.079 BC1 1.074 BC1 1.088 BC1 1.086 A 1.081 BC2 1.08 BC2 1.057 BC2 1.075 BC2 1.091 BC2 1.079 BC2 1.081 BC2 Table S3. 3. (cont’d) MSFF694_06 1 MSFF696_01 1 MSFF696_02 1 MSFF696_03 1 MSFF696_04 1 MSFF696_08 1 MSFF703_01 1 MSFF703_03 1 MSFF706_02 1 MSFF713_01 1 MSFF716_01 1 MSFF720_01 1 MSFF725_01 1 MSFF725_02 1 MSFF725_03 1 MSFF725_04 1 MSFF729_03 1 MSFF733_01 1 MSFF733_02 1 MSFF733_03 1 MSFF735_01 4 MSFF735_02 1 MSFF736_01 3 MSFF742_01 1 MSFF744_01 4 MSFF744_02 2 MSFF747_02 1 MSFF749_01 1 0.0 1.0 0.0 0.5 1.5 1.0 0.0 2.5 1.0 2.5 0.0 0.5 1.0 0.5 0.5 0.5 0.0 1.0 1.0 1.0 0.5 0.5 0.5 2.5 - 0.5 3.0 0.0 7 7 6 8 8 5 6 6 5 5 7 5 8 6 6 5 7 5 6 5 6 6 5 7 4 6 5 6 2 2 3 2 2 3 2 2 3 2 2 3 2 3 2 1 2 3 3 3 2 2 3 3 3 2 1 2 12.7 11.7 6.7 11.2 3.6 7.4 5.0 7.3 5.8 4.8 6.0 7.8 7.8 6.0 8.0 5.0 10.2 6.3 7.5 3.5 7.7 5.8 5.6 8.4 3.6 11.3 9.9 4.2 146 50 75 84 69 68 51 87 49 110 67 72 127 85 86 77 115 45 86 76 80 115 62 51 133 167 81 104 55 635 881 561 777 246 378 436 356 638 323 435 987 657 519 616 575 456 544 570 280 887 362 287 1115 602 909 1031 229 1.063 BC2 1.076 BC2 1.068 BC2 1.073 BC2 1.08 BC2 1.075 BC2 1.081 BC2 1.072 BC2 1.055 BC2 1.058 BC2 1.066 BC2 1.086 BC2 1.058 BC2 1.083 BC2 1.066 BC2 1.082 BC2 1.079 BC2 1.068 BC2 1.076 BC2 1.069 BC2 1.073 BC2 1.077 BC2 1.079 BC2 1.061 BC2 1.087 BC2 1.083 BC2 1.082 BC2 1.079 BC2 Table S3. 3. (cont’d) MSFF749_03 4 MSFF749_04 1 MSFF751_01 2 MSFF752_01 2 MSFF752_03 1 MSFF754_02 1 MSFF756_04 4 MSFF760_01 1 MSFF760_02 1 MSFF773_01 1 MSFF773_05 1 MSFF773_08 1 MSFF774_05 3 MSFF775_03 2 MSFF776_02 4 Mean/ BC2 1.8 C * Means with the same letter designation are not significantly different as determined by Student’s t-test a = 0.05. 7 1.0 5 0.5 6 1.0 5 1.0 5 - 6 0.0 8 0.0 6 0.0 5 0.0 5 0.0 5 0.0 6 1.5 6 0.0 7 0.0 0.5 7 0.7 B 5.8 A 1.086 BC2 1.077 BC2 1.079 BC2 1.088 BC2 1.087 BC2 1.076 BC2 1.074 BC2 1.08 BC2 1.069 BC2 1.063 BC2 1.071 BC2 1.066 BC2 1.061 BC2 1.062 BC2 1.071 BC2 10.5 10.0 7.9 8.9 5.8 13.6 2.8 5.0 7.6 7.4 16.0 3.0 3.7 2.5 9.9 7.1 A 2 3 2 2 3 2 3 2 3 3 2 3 2 2 2 2.3 A 49 40 73 108 176 49 95 41 83 133 33 98 95 74 85 512 397 573 959 1022 666 266 206 634 984 521 294 348 185 844 71.1 A 540 A 1.075 B 147 SUPPLEMENTAL FIGURES 148 Figure S3. 1. Neighbor-Joining tree using 6023 SNPs. 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American Journal of Potato Research 86: 417–429. doi:10.1007/s12230-009-9097-8. 154 CHAPTER 4 COMPARISON OF METHOD TO DISTINGUISH DIPLOID AND TETRAPLOID POTATO FOR APPLIED DIPLOID BREEDING [Published in: AJPR] Maher Alsahlany 1,2, Daniel Zarka 1, Joseph Coombs 1, and David S. Douches 1 1 Department of Plant, Soil and Microbial Sciences, Michigan State University, East Lansing, MI 2 Horticulture Dept. College of Agriculture, Al-Qasim Green University, Babylon, Iraq 48824. *Corresponding author (douchesd@msu.edu). 155 Abstract Diploid (2n=2x=24) and tetraploid (2n=4x=48) germplasm are commonly used in potato breeding programs. Potato breeders need to efficiently and inexpensively differentiate between diploid and tetraploid progeny in dihaploid induction crosses as well as 2x-2x crosses where 2n gametes may occur in the parents. In this study, we compared the chloroplast count, genome- wide SNP genotyping and flow cytometry methods to determine ploidy. Twenty-eight clones were used as reference samples (14 diploid lines (2n=2x=24), 14 tetraploids (2n=4x=48) varieties and advanced breeding lines) to compare the three ploidy determination methods. The chloroplast count method was used to determine the ploidy level in the 28 reference samples and 102 potato breeding lines derived from diploid (2x-2x) crosses. The Infinium 12K V2 Potato Single Nucleotide Polymorphism (SNP) Array was used to examine the 28 reference samples and the 102 breeding lines. The results obtained from both chloroplasts counts and SNP genotyping techniques determined that there was a total of 84 diploid lines and 18 tetraploid lines. Flow cytometry was also used to determine ploidy level in a subset of 42 lines (28 reference lines set and 14 breeding lines). All three methods of ploidy determination (chloroplast counting, SNP genotyping and flow cytometry) agreed for all samples evaluated. These results demonstrate the usefulness of chloroplast counting as an efficient and inexpensive method for breeders to differentiate ploidy between diploid and tetraploid potato in applied breeding programs. Introduction Potato (Solanum tuberosum) is a third most important food crop in the world after wheat and rice (Visser et al. 2009; Jansky et al. 2016). Cultivated potato varieties range in ploidy from 156 diploid (2n=2x=24) to pentaploid (2n= 5x=60) (Jackson et al. 1980), with tetraploid potato varieties being the most commonly grown. Ploidy changes may occur in different ways. Some diploid potatoes can produce 2n gametes that increase the possibility of developing tetraploid hybrids from 2x-2x crosses (Chase 1963; Jansky and Peloquin 2006). Doubling chromosome number has been reported by Francis (1939) and Hermsen (1969) using colchicine as an artificial method to induce tetraploids from diploid wild species. Jacobsen (1981) used somatic chromosome doubling to obtain tetraploids from dihaploids of S. tuberosum potato breeding clones through regenerated potato plants from leaf callus tissue. Pehu et al. (1989) reported using chemical and electrical protoplast fusion which led to ploidy manipulation of dihaploid S. tuberosum and diploid S. brevidens. Foliar phenotype can be used sometimes to differentiate between tetraploid and diploid progeny from diploid parent crosses (2x-2x). Determining ploidy can be a challenge when genotypes of differing ploidy have similar phenotypes for traits such as vigor and maturity (Maris 1990; Hutten et al. 1995). In applied breeding programs focused on developing commercial diploid potato varieties with marketable traits, the tetraploid and diploid hybrid lines have similar phenotypes for yield and yield components (i.e., tuber size and number), specific gravity and chip color (Hutten et al. 1995). Having a practical and reliable method that is efficient and inexpensive to determine ploidy level in potato is a high priority for potato breeders. Determining ploidy level in early generations may reduce experimental work load, resources, and time, especially when there is the possibility of 2n gametes occurring in the parents that produce tetraploid plants or when using colchicine or potato regeneration to obtain somatic chromosome doubling. Ploidy level determination has been reported by authors using several methods. The chloroplast count method has been used by Hermsen (1969) to determine the ploidy level of S. chacoense Bitt species after treatment with colchicine to doubled 157 chromosomes. Flow cytometry has been used to measure the DNA content of plant cells since the early 1980s (Doležel et al. 1989). The potato SNP array has recently been used to determine potato ploidy level at the International Potato Center (CIP) genebank (Ellis et al. 2018), but SNP genotyping can be time-consuming and expensive. The chloroplast count method has been used to determine ploidy level for both in vitro and in vivo plants. Counting chloroplasts in a pair of somatic guard cells from potato leaves has been previously proposed as an efficient, fast, accurate, and inexpensive method to identify ploidy level (Veilleux et al. 1985; Singsit and Veilleux 1991; Mattheij et al. 1992; Gebhardt et al. 2006). Studies were conducted by Rasmussen and Rasmussen (1995), Gebhardt et al. (2006), and Ordoñez (2014) using the chloroplast count methods to determine ploidy level in potato by counting the average number of chloroplasts in ten guard cells. They found that diploid potato lines have an average of 6-8 chloroplasts per guard cell, while the tetraploid potato lines have an average of 12–14 chloroplasts per guard cell. Langton (1974), Jacobsen (1981), and Veilleux et al. (1985) used two methods to determine the ploidy level for regenerated potato plants from leaf callus tissue and anther culture, respectively by using chloroplast count in guard cells and root- tip squashes to confirm the ploidy level. The chloroplast counts, flow cytometry, and root-tip squashes were used to determine and confirm the tetraploid potato plants that developed from somatic fusions between cultivated tetraploid (S. tuberosum Grp. Tuberosum L.) and diploid wild potato species S. circaeifolium subsp. circaeifolium Bitter (Mattheij et al. 1992). Flow cytometry is a reliable method that could be used to measure the nuclear DNA content by comparing it with a standard DNA sample, but flow cytometry can be time- consuming and expensive (Doležel et al. 1989; Doležel 1991; Mattheij et al. 1992; Ochatt et al. 2011). Nuclear DNA content analysis using flow cytometry is based on the comparison of DNA- 158 specific fluorochromes and the analysis of the relative fluorochromes intensity of stained nuclei (Doležel 1991). Sari et al. (1999) compared chromosome counting as a direct method with chloroplast counting, DNA content, stomatal size and morphological observation as indirect methods for haploid and diploid watermelon plants. Ramulu and Dijkhuis (1986) and Pijnacker et al. (1989) used flow cytometry to study polysomaty and polyploidization of S. tuberosum and S. phureja callus formed from shoot culture and cell suspensions of greenhouse plants. The flow cytometry method was also used to determine the ploidy level of plantlets that regenerated from heterokaryons of diploid lines of S. tuberosum and S. phureja (Puite et al. 1988). The stomatal size and direct counting of chromosome number to determine the ploidy level of doubled chromosomes of diploid wild potato species via colchicine has been used by Francis (1939). Furthermore, Langton (1974), Kessel and Rowe (1975), and Bamberg and Hanneman (1991) were able to determine the ploidy level of Solanum potato species by measuring the pollen size as a fast, easy and consistent method using a microscope. Altmann et al. (1994) and Zonneveld and Van Iren (2001) were able to determine ploidy level of Arabidopsis and Hosta, respectively, based on pollen grain size. The pollen size method was not tested in this study due to lack of pollen production in some clones, and time necessary to obtain pollen in some clones. A morphological marker such as cell size was used to determine ploidy level in citrus callus (Hao et al. 2002). Counting chromosomes in root-tips for determining the potato ploidy is a definitive but technically challenging method, requiring the correct developmental stage of plant tissue and 2-4 days to obtain the chromosome number (Veilleux et al. 1985; Bamberg and Hanneman 1991; Sari et al. 1999; Ochatt et al. 2011). The purpose of this study is to compare the chloroplast count method with the SNP array genotyping and the flow 159 cytometry methods for distinguishing diploid and tetraploid potatoes. Determining a reliable method like the chloroplast method can save time and money for breeding programs. Materials and Methods Plant Material In this study, 28 reference samples (12 commercial tetraploid varieties (4x), two advanced breeding lines (4x), 14 diploid breeding lines (2x) including M6, DM_1-3_516_r44, and IVP101) were used as ploidy references determined by 1) pedigree, 2) previous sexual crossing and 3) SNP dosage at individual loci (Table 4. 1). In addition, 102 progeny from (2x- 2x) crosses of diploid parents were used from the Michigan State University diploid potato breeding program. The 102 progeny in this study were generated by recurrent selection and backcross breeding strategies. The progeny are a genomic mix of S. berthaultii, S. chacoense, S. microdontum, S. tuberosum Grp. Phureja, and S. tuberosum Grp. Tuberosum (Alsahlany unpublished data). A total of 130 samples were evaluated for ploidy level by counting chloroplasts and SNP genotyping on the Illumina Infinium 12K PotatoV2 array (Table 4. 1). All plants were grown in the greenhouse under 16 h photoperiod at 20-25°C for one month prior to sampling leaf tissue for chloroplast counting and DNA isolation. For the flow cytometry method, we sampled the 28 reference samples and a subset of 14 of the 102 progeny that were maintained in tissue culture. The flow cytometry plant samples were collected from one-month-old in vitro plants grown on MS-medium (Murashige and Skoog 1962) with a 16 h photoperiod at 20-25°C. 160 Chloroplast Counts in Guard Cells Chloroplasts in a pair of guard cells were counted from leaf samples of 130 greenhouse- grown plants (28 reference samples and 102 progeny) following the procedure of Ordoñez (2014) (Table 4. 1). Tweezers were used to peel the lower leaflet epidermis and then place it on a glass slide (one peel from a random leaflet of the fourth true leaf from a one-month-old plant). A buffer solution of propidium iodine-potassium iodide (PIPI) was added to stain the chloroplasts. The PIPI buffer solution was prepared by dissolving 500 mg of propidium iodine and 500 mg of potassium iodide in 50 ml of 70% ethanol and stored in a dark bottle. The epidermis was stained with 1-2 drops of buffer for 1- 2 min and then a coverslip was placed on the epidermis and observed under a light microscope at 400x magnification. Chloroplasts that absorbed the PIPI appear dark black (Figure 4. 1). Ten guard cells were used to calculate the average chloroplast count for each sample. Genome-wide Single Nucleotide Polymorphisms (SNPs) Genotyping DNA was isolated from young leaf tissue of 28 reference samples and 102 progeny using the QIAGEN DNeasy Plant Mini Kit (QIAGEN, Germantown, MD). DNA was quantified using Quant-iT PicoGreen dsDNA Assay Kit (Invitrogen, SanDiego, CA) and normalized to a concentration of 50 ng µl-1. Samples were assayed with the Infinium 12K PotatoV2 array using an Illumina iScan Reader (Illumina, San Diego, CA) according to the manufacturer’s protocol. GenomeStudio software was used to analyze the SNP data (Illumina, San Diego, CA). Tetraploid genotyping was done using a custom Perl script for five clusters genotype calling based on theta value thresholds (Hirsch et al. 2013). The SNP marker data were filtered to 4571 SNPs with tetraploid genotypes. The SNPs that were heterozygous or were not classified by the 161 Genomestudio software (no calls) from double monoploid line (DM_1-3_516_r44) were excluded. Tetraploid SNP genotypes (AAAA, AAAB, AABB, ABBB, and BBBB) for each SNP were summed across all loci for each sample. The genotype summary data were used to classify ploidy as tetraploid or diploid. The observation of simplex and triplex (AAAB and ABBB) SNPs in a sample indicates a heterozygous tetraploid genotype sample (Table 4. 1). The SNP genotyping approach was recently used by Ellis et al. (2018) to classify ploidy levels in the International Potato Center (CIP; Lima, Peru) genebank. Flow Cytometry DNA content was measured using flow cytometry at the Benaroya Research Institute (K. Arumuganathan) at Virginia Mason Medical center, Seattle, WA according to Arumuganathan and Earle (1991) from the lines in the study that were maintained in vitro. A 50 mg sample of leaf tissue was collected from one-month-old in vitro plants, stored on ice, and then shipped overnight to the Benaroya Research Institute. Forty-two samples (28 reference samples and 14 breeding progeny) were used to measure DNA content and determine ploidy level (Table 4. 1). Statistical Analyses The chloroplast counts, SNP genotype calls, and flow cytometry DNA content results were analyzed using one-way analysis of variance (ANOVA, a = 0.05). Means comparisons were conducted with a Tukey-Kramer honest significant difference (HSD) multiple range test (a = 0.05) using JMP software version 14.0.0 (SAS Institute, Cary, NC). A total of 28 samples (12 commercial varieties (4x), two advanced breeding lines (4x), 11 diploid breeding lines (2x), M6, DM_1-3_516_r44, IVP101) were used as references for chloroplast count, SNP genotype calls, 162 and flow cytometry DNA content. Individual samples were compared to reference samples to determine ploidy level. There were significant differences for chloroplast count, SNP genotype calls, and flow cytometry DNA content between tetraploid and diploid reference samples. We compared the 102 progeny from (2x-2x) crosses of diploid parents results to the reference samples of each the three methods. We considered the sample to be diploid when the progeny sample was not significantly different from the mean of the 14 reference diploids for chloroplast counts based upon ANOVA. However, we considered the sample to be tetraploid when the progeny sample was not significantly different from the mean of the 14 tetraploid reference samples. For the SNP genotyping method, the ploidy determination is based on the simplex (AAAB) and triplex (ABBB) call frequency. When the simplex and triplex frequency was close to zero, the sample was considered to be diploid, and when the frequency was over 0.20, then the progeny samples were considered to be tetraploid based upon the reference samples results (Table 10). For flow cytometry, the ploidy determination is based upon the DNA content (pg/2C). The sample was considered to be diploid when the DNA content in the progeny sample was close to 1.8 pg/2C and was not significantly different from the mean of the diploid reference samples. The sample was considered to be tetraploid when the mean of DNA content was close to 3.5 pg/2C and was not significantly different from the mean of the tetraploid reference samples (Table 4. 1). Results and Discussion Our study compared the efficacy of using chloroplast counts, SNP genotyping calls and flow cytometry methods to determine the ploidy level in progeny from 2x-2x crosses (Table 4. 1). The 14 diploid reference samples had 6.8 ± 0.7 chloroplasts per guard cell and the tetraploid 163 reference samples had 12.7 ± 1.1 chloroplasts per guard cell (Table 4. 1, Figure 4. 2a). These diploid reference and tetraploid reference samples were significantly different from each other (ANOVA p-value is < 0.0001). The chloroplast count method was used for 102 breeding lines and their results were compared with the reference lines used in this study. Eighty-four lines were classified as diploid, while 18 samples were classified as tetraploid (Table 4. 1, Figure 4. 2a). Based on one-way ANOVA, the average of chloroplast counts in diploid and tetraploid lines were found to be significantly different (p-value is < 0.0001) (Table 4. 1). All chloroplast count data agreed with previous studies (Rasmussen and Rasmussen 1995; Gebhardt et al. 2006; Ordoñez 2014). The diploid potato lines ranged from 6-8 chloroplasts in the guard cells, while the tetraploid potato lines ranged from 12-14 chloroplasts in the guard cells (Figure 4. 2a). Tetraploid SNP genotyping was used to classify ploidy level in 28 reference samples and 102 breeding lines (Table 4. 1). Tetraploid SNP genotypes (AAAA, AAAB, AABB, ABBB, and BBBB) for each loci were summed across all 4751 filtered SNPs for each sample. The diploid reference samples demonstrating three major genotypic class clusters are visualized in Figure 4. 3 and Table 4. 1 with the two homozygous classes (AAAA, BBBB) and the duplex heterozygous class (AABB) being the most frequent calls. Figure 4. 3 shows that the simplex (AAAB) and triplex (ABBB) class clusters were present but at a low frequency, likely due to genotyping errors. A low frequency of simplex (AAAB) and triplex (ABBB) SNP genotype clusters (2% ± 1) were observed when using the five cluster (tetraploid) SNP genotype calling of the diploid samples (Figure 4. 3). The tetraploid breeding lines and varieties have five major genotypic clusters (Figure 4. 3) due to the heterozygosity observed in cultivated tetraploid potato varieties. The tetraploid lines have a significantly higher percentage of simplex and triplex genotypes 164 (AAAB and ABBB) calls (36% ± 14) (Table 4. 1 and Figure 4. 2b). The SNP genotyping results are summarized in Table 4. 1 and Figures (4. 3b, 4. 3 and 4. 4) along with the ploidy calling of the 102 breeding lines. Based on one-way ANOVA, the frequency of simplex and triplex genotype calls in diploid and tetraploid lines were significantly different (p-value is < 0.0001). The ploidy determination obtained from chloroplast counts and SNP genotyping methods agreed completely for all 130 samples. SNP genotyping has become an important tool in evaluating germplasm in potato breeding and genetics programs (D. Douches et al. 2014) including ploidy determination (Ellis et al. 2018). We suggest that SNP genotyping can be used to confirm ploidy calls from chloroplast counts if there is any ambiguity in the chloroplast numbers or the phenotype of the plant does not match the ploidy call for progeny from sexual crosses. The limitation of the SNP genotyping is the sample cost and the time to process and analyze SNP genotype data. A total of 42 lines were examined for ploidy level by measuring the DNA content using flow cytometry (28 reference samples and 14 breeding lines). The 14 diploid reference samples had an average DNA content of 1.76 ± 0.14 pg/2C and the 12 diploid breeding lines had an average DNA content of 1.65 ± 0.24 pg/2C (Figure 4. 2c, Figure 4. 5). The DNA content for the tetraploid reference samples was 3.47 ± 0.34 pg/2C and there were two lines classified as tetraploid from the breeding lines based on the DNA content of 3.37 pg/2C (Figure 4. 2c, Figure 4. 5). Based on one-way ANOVA, the mean DNA content of the diploid and tetraploid lines were significantly different (p-value is < 0.0001) (Table 4. 2). These results were in agreement with experiments done by Mattheij et al. (1992) using chloroplast count and flow cytometry methods to determine hybrid ploidy level for in vitro shoot cultures generated from somatic 165 fusions between cultivated tetraploid (S. tuberosum Grp. Tuberosum) and diploid wild potato species S. circaeifolium subsp. circaeifolium Bitter. 166 APPENDIX 167 Table 4. 1. Comparison of chloroplast count, SNP genotyping and flow cytometry to determine ploidy level in potato hybrids. Sample ID ATL_M_170 ATL_M_188 DM_1-3_516_r44** DMRH_195 DMRH_558 IVP101 KalDH-16 KalDH-30 M6 MSDD880-03 F1 MSZ219DH-13 VT_SUP_08 VT_SUP_12 VT_SUP_19 Diploid average Atlantic Desiree Jacqueline_Lee Kalkaska Missaukee MSU_Red_Marker_2 SNP Flow cytometry Ploidy Chloroplast count Average number of chloroplasts per guard cell genotype Simplex and triplex frequency DNA content (pg/2C) Diploid reference samples 0.02 b 0.02 b 0.00 b 0.00 b 0.00 b 0.01 b 0.01 b 0.01 b 0.01 b 0.01 b 0.01 b 0.01 b 0.01 b 0.01 b 0.01 ± 0.01 lm 1.8 1.8 lm 1.9 k lm 1.8 1.7 lmno lmn 1.7 lm 1.8 1.8 lm 1.7 lmn 1.8 L 1.8 lm 1.7 mno 1.8 1.7 mno 1.8 ± 0.1 lm 7.0 c 6.7 c 6.8 c 7.5 c 6.3 c 7.2 c 6.9 c 7.1 c 6.2 c 6.5 c 7.2 c 6.1 c 6.4 c 7.5 c 6.8 ± 0.7 Sample background S. tuberosum Grp. Tuberosum S. tuberosum Grp. Tuberosum S. tuberosum Grp. Tuberosum S. tuberosum Grp. Tuberosum S. tuberosum Grp. Tuberosum S. tuberosum Grp. Tuberosum S. tuberosum Grp. Tuberosum S. tuberosum Grp. Tuberosum S. tuberosum Grp. Tuberosum S. chacoense /S. tuberosum S. tuberosum Grp. Tuberosum S. tuberosum Grp. Tuberosum S. tuberosum Grp. Tuberosum S. tuberosum Grp. Tuberosum 2x 2x 2x 2x 2x 2x 2x 2x 2x 2x 2x 2x 2x 2x Tetraploid varieties and advance breeding reference samples 13.8 a 13.2 ab 12.4 ab 12.4 ab 12.7 ab 13.2 ab 3.5 bcde 3.6 ab 3.6 abc 3.5 cdef 3.1 3.5 defg 0.51 a 0.44 a 0.40 a 0.45 a 0.45 a 0.40 a 4x 4x 4x 4x 4x 4x j S. tuberosum Grp. Tuberosum S. tuberosum Grp. Tuberosum S. tuberosum Grp. Tuberosum S. tuberosum Grp. Tuberosum S. tuberosum Grp. Tuberosum S. tuberosum Grp. Tuberosum 168 Table 4. 1. (cont’d) MSZ219-14 Norland Purple_Haze Ranger_Russet Snowden Spunta Superior Yukon_Gold Tetraploid average 12.7 ab 12.5 ab 12.1 ab 12.8 ab 12.7 ab 12.3 ab 12.3 ab 12.7 ab 12.7 ± 1.1 i 3.3 3.6 a 3.6 abc 3.6 abc 3.4 fgh 3.6 abcd 3.4 gh 3.4 rfgh 3.5 ± 0.4 4x 4x 4x 4x 4x 4x 4x 4x S. tuberosum Grp. Tuberosum S. tuberosum Grp. Tuberosum S. tuberosum Grp. Tuberosum S. tuberosum Grp. Tuberosum S. tuberosum Grp. Tuberosum S. tuberosum Grp. Tuberosum S. tuberosum Grp. Tuberosum S. tuberosum Grp. Tuberosum (2x-2x) crosses samples from diploid potato breeding program ND ND ND ND 1.5 qr ND ND lmn 1.7 ND ND ND ND ND ND 1.7 mno ND ND ND ND 2x 2x 2x 2x 2x 2x 2x 2x 2x 2x 2x 2x 2x 2x 2x 2x 2x 2x 2x Backcross *** Backcross Backcross Backcross Backcross Backcross Backcross Backcross Backcross Backcross Backcross Backcross Backcross Backcross Backcross Backcross Backcross Backcross Backcross 169 0.45 a 0.42 a 0.43 a 0.44 a 0.45 a 0.41 a 0.45 a 0.45 a 0.44 ± 0.07 0.01 b 0.02 b 0.01 b 0.02 b 0.02 b 0.02 b 0.02 b 0.02 b 0.02 b 0.02 b 0.02 b 0.02 b 0.02 b 0.02 b 0.02 b 0.02 b 0.02 b 0.01 b 0.02 b 7.1 c 7.3 c 7.4 c 7.0 c 7.8 c 6.5 c 6.5 c 6.5 c 7.4 c 7.3 c 7.3 c 7.2 c 7.5 c 7.0 c 7.8 c 6.9 c 7.3 c 7.3 c 7.1 c MSCC841-04 MSCC842-01 MSCC843-01 MSCC844-01 MSCC845-01 MSCC845-05 MSCC845-06 MSCC846-05 MSCC846-07 MSCC847-06 MSCC848-05 MSCC849-01 MSCC849-04 MSCC851-02 MSCC852-02 MSCC852-08 MSCC853-02 MSCC854-01 MSCC856-01 Table 4. 1. (cont’d) MSCC856-03 MSCC857-05 MSCC858-03 MSCC859-04 MSCC863-08 MSCC863-12 MSCC863-17 MSCC863_25 MSCC863-44 MSCC863-63 MSCC863-75 MSCC863-77 MSCC864-01 MSCC864-17 MSCC864_20 MSCC864-28 MSDD802-01 MSDD802-04 MSDD803-05 MSDD804-06 MSDD804-09 MSDD805-05 MSDD805-08 MSDD807-03 MSDD807-05 MSDD807-06 MSDD808-10 MSDD809-07 MSDD809-09 MSDD812-01 6.9 c 7.4 c 6.1 c 7.0 c 7.2 c 7.1 c 7.9 c 7.8 c 7.8 c 7.1 c 7.6 c 7.3 c 8.0 c 7.0 c 6.5 c 6.9 c 7.0 c 7.5 c 7.9 c 6.1 c 7.9 c 7.1 c 7.1 c 8.0 c 7.3 c 7.5 c 7.1 c 7.4 c 7.9 c 7.8 c 0.02 b 0.02 b 0.01 b 0.02 b 0.02 b 0.02 b 0.02 b 0.02 b 0.02 b 0.02 b 0.02 b 0.02 b 0.02 b 0.01 b 0.01 b 0.02 b 0.01 b 0.01 b 0.01 b 0.01 b 0.01 b 0.01 b 0.01 b 0.02 b 0.02 b 0.02 b 0.02 b 0.02 b 0.02 b 0.02 b ND ND ND ND ND ND ND ND ND ND ND ND ND ND ND ND ND lm 1.8 ND ND ND 1.4 r ND ND ND ND ND 1.7 lmno ND ND 2x 2x 2x 2x 2x 2x 2x 2x 2x 2x 2x 2x 2x 2x 2x 2x 2x 2x 2x 2x 2x 2x 2x 2x 2x 2x 2x 2x 2x 2x Backcross Backcross Backcross Backcross Backcross Backcross Backcross Backcross Backcross Backcross Backcross Backcross Backcross Backcross Backcross Backcross Backcross Recurrent selection **** Recurrent selection Recurrent selection Recurrent selection Recurrent selection Recurrent selection Recurrent selection Recurrent selection Recurrent selection Recurrent selection Recurrent selection Recurrent selection Recurrent selection 170 Table 4. 1. (cont’d) MSDD812-02 MSDD812-03 MSDD812-05 MSDD814-04 MSDD821-09 MSDD821-10 MSDD824-01 MSDD825-01 MSDD829-01 MSDD829-10 MSDD831-01 MSDD837-02 MSDD837-08 MSDD838-01 MSDD838-02 MSDD844-03 MSDD845-02 MSDD845-03 MSDD847-05 MSDD847-06 MSDD848-01 MSDD848-02 MSDD849-06 MSDD849-07 MSDD850-03 MSDD850-06 MSDD851-06 MSDD851-08 MSDD852-04 MSDD853-05 6.0 c 6.6 c 7.3 c 7.3 c 7.1 c 7.9 c 6.8 c 7.4 c 7.7 c 7.7 c 6.7 c 7.0 c 7.6 c 7.6 c 7.6 c 7.1 c 8.0 c 7.0 c 7.0 c 7.2 c 7.5 c 7.8 c 7.2 c 7.6 c 7.6 c 7.5 c 6.6 c 6.9 c 7.9 c 7.5 c 0.01 b 0.01 b 0.02 b 0.01 b 0.01 b 0.02 b 0.02 b 0.01 b 0.01 b 0.01 b 0.01 b 0.01 b 0.01 b 0.01 b 0.01 b 0.01 b 0.01 b 0.01 b 0.01 b 0.01 b 0.01 b 0.01 b 0.02 b 0.01 b 0.01 b 0.01 b 0.01 b 0.02 b 0.01 b 0.01 b lm ND ND 1.6 nop ND ND ND ND 1.7 mno 1.8 ND ND ND ND ND ND ND ND 1.6 op ND ND ND ND ND ND ND 1.7 ND ND ND ND lmn 2x 2x 2x 2x 2x 2x 2x 2x 2x 2x 2x 2x 2x 2x 2x 2x 2x 2x 2x 2x 2x 2x 2x 2x 2x 2x 2x 2x 2x 2x Recurrent selection Recurrent selection Recurrent selection Recurrent selection Recurrent selection Recurrent selection Recurrent selection Recurrent selection Recurrent selection Recurrent selection Recurrent selection Recurrent selection Recurrent selection Recurrent selection Recurrent selection Recurrent selection Recurrent selection Recurrent selection Recurrent selection Recurrent selection Recurrent selection Recurrent selection Recurrent selection Recurrent selection Recurrent selection Recurrent selection Recurrent selection Recurrent selection Recurrent selection Recurrent selection 171 Table 4. 1. (cont’d) MSDD855-01 MSDD855-03 MSDD857-03 MSDD865-02 MSDD865-03 MSCC863-54 MSCC863-61 MSCC864-13 MSCC864-35 MSDD804-03 MSDD804-05 MSDD804-10 MSDD808-01 MSDD808-02 MSDD808-04 MSDD809-06 MSDD809-10 MSDD837-01 MSDD837-10 MSDD844-01 MSDD844-02 MSDD844-05 MSDD848-05 7.6 c 6.7 c 7.6 c 7.2 c 7.9 c 12.3 ab 12.3 ab 12.3 ab 12.1 ab 12.7 ab 12.0 ab 11.7 b 12.1 ab 12.0 ab 13.1 ab 11.4 b 12.0 ab 12.0 ab 12.2 ab 12.5 ab 12.6 ab 12.0 ab 12.2 ab 0.02 b 0.01 b 0.02 b 0.01 b 0.01 b 0.30 a 0.33 a 0.39 a 0.31 a 0.26 a 0.35 a 0.25 a 0.30 a 0.33 a 0.29 a 0.24 a 0.35 a 0.34 a 0.30 a 0.27 a 0.36 a 0.39 a 0.23 a ND ND ND 1.6 pq ND ND ND 3.4 hi ND ND ND ND ND ND ND ND ND ND ND ND 3.4 gh ND ND 2x 2x 2x 2x 2x 4x 4x 4x 4x 4x 4x 4x 4x 4x 4x 4x 4x 4x 4x 4x 4x 4x 4x Recurrent selection Recurrent selection Recurrent selection Recurrent selection Recurrent selection Backcross Backcross Backcross Backcross Recurrent selection Recurrent selection Recurrent selection Recurrent selection Recurrent selection Recurrent selection Recurrent selection Recurrent selection Recurrent selection Recurrent selection Recurrent selection Recurrent selection Recurrent selection Recurrent selection * Means with same letter designation are not significantly different as determined by Tukey-Kramer HSD a = 0.05. ** DM_1-3_516_r44 is the doubled monoploid that used for potato genome sequence and it has only AAAA and BBBB cluster because it is homozygous. 172 Table 4. 1. (cont’d) *** Backcross. Samples were used from germplasm pool that initiated by crossing S. chacoense to S. tuberosum Grp. Tuberosum dihaploids (Alsahlany unpublished data). **** Recurrent selection. Samples were used from germplasm pool that include S. berthaultii, S. chacoense, S. microdontum, S. tuberosum Grp. Phureja, and S. tuberosum Grp. Tuberosum in the genetic background (Alsahlany unpublished data). 173 Table 4. 2. Advantages and disadvantages of the chloroplast count, SNP genotyping, and flow cytometry ploidy determination methods. Chloroplast counts SNP genotyping call One-month Plant age Time required/ sample 3 mins Cost/sample $0.20 Technical skills Minimally-required Required One-month 3 days $50 + $3-5 DNA extraction $23 Flow cytometry One-month 63 mins Required 174 (b) (a) Figure 4. 1. Guard cells of (a) diploid (2n-2x=24) and (b) tetraploid (2n=4x=48) potato leaf sections with chloroplasts stained with PIPI buffer (600x magnification). 175 Figure 4. 2. Comparison of chloroplast count, SNP genotyping and flow cytometry data to determine ploidy level in potato hybrids (reference samples (left), breeding lines (right) in each panel). (a) Average chloroplast counts from ten guard cells per plant. (b) Frequency of simplex (AAAB) and triplex (ABBB) SNP genotype cluster summaries for 4571 SNPs. (c) Comparison of DNA content using flow cytometry. 176 Figure 4. 3. SNP genotype summary examples of tetraploid and diploid reference samples using a tetraploid genotyping model (five cluster calling). The 28 reference samples shown include 14 tetraploid samples (top) and 14 diploid samples (bottom). 177 Figure 4. 4. SNP genotype summary examples of breeding lines using a tetraploid genotyping model (five-cluster calling). The 102 breeding lines shown include 22 tetraploid samples (top) and 84 diploid samples (bottom). 178 Figure 4. 5. Flow cytometry method analysis of DNA content for select tetraploid and diploid reference samples and breeding lines. The DNA content (pg/2C) is shown for 14 tetraploid reference samples (red), two tetraploid breeding lines (grey), 14 diploid reference samples (blue), and 12 diploid breeding lines (green). 179 LITERATURE CITED 180 LITERATURE CITED Altmann, T., B. Damm, W. B. Frommer, T. Martin, P. Morris, D. Schweizer, L. Willmitzer, and R. Schmidt. 1994. Easy determination of ploidy level in Arabidopsis thaliana plants by means of pollen size measurement. Plant Cell Reports 13: 652–656. Arumuganathan, K., and E. D. Earle. 1991. Estimation of nuclear DNA content of plants by flow cytometry. Plant Molecular Biology Reporter 9: 229–241. doi:10.1007/BF02672073. Bamberg, J. B., and R. E. Hanneman. 1991. Rapid ploidy screening of tuber-bearing Solanum (potato) species through pollen diameter measurement. American Potato Journal 68: 279– 285. doi:10.1007/BF02853666. Chase, S. S. 1963. 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These two germplasm pools are adapted to a long-photoperiod temperate growing environment. Additionally, they have several agronomic traits that potato breeders can use for the improvement of tetraploid varieties by using 2n gametes to produce (4x – 2x) crosses as well as producing diploid inbred parental lines for future F1 hybrid potato varieties. Recurrent Selection Germplasm Pool Five cycles of recurrent selection were conducted to introgress and improve self- compatibility of diploid potato germplasm. A multi-species germplasm pool with 83% self- compatibility was generated as well as adapted to tuberize in a long-photoperiod growing season. This germplasm has variation in tuber traits that could be used to produce parental material for implement an inbred/F1 hybrid variety breeding strategy. A neighbor-joining (NJ) clustering and Principal Component (PC) analysis were used to evaluate the population structure showing that cycle 4 selection have maintained genetic variability and improvement in specific traits after five cycles of selections. Cycle 4 selections are clearly distinct from dihaploid S. tuberosum checks. We also observed five defined subpopulations in the cycle 4 population based on the present of parental lines that were used to generate germplasm pool using and ancestry contribution analysis. Using a 1-year cycle selection of progeny from seedling transplants to the field led to the adaptation of the germplasm to a long-photoperiod growing environment. Developing a 184 germplasm pool with several economic traits such as early maturity, common scab resistance, and good tuber traits is important for the production of diploid F1 hybrid varieties. The vine maturity mean was reduced to 1.4 (early maturing) in cycle 4 of RS with approximately 92% of the screened selections having early maturity. Sixty-two percent of the germplasm pool showed resistance for common scab. The tuber appearance and shape were not significantly improved through recurrent selection. Eighty-four percent of cycle 4 selections have acceptable tuber appearance (> 5 on scale 1 – 9) compared with 73% from cycle 0 selections. Moreover, the average tuber number per plant was significantly increased from 8.9 in cycle 0 to 10.9 in cycle 4 tubers. In contrast, the average tuber weight was not significantly difference between cycle 0 (50.4 g) and RS cycle 4 (50.6 g). Average tuber yield per plant significantly increased from 430 g in cycle 0 to 522 g in cycle 4 of RS. The average of tuber specific gravity was significantly increased from 1.066 in cycle 0 to 1.076 in cycle 4 of RS. The specific gravity improved approximately 0.010 in cycle 4 compared with cycle 0 of RS. This germplasm will provide a broad genetic base that could be used for developing parents diploid F1 hybrids. Selections with favorable round tuber shape have been made over the cycles of the RS (Figure 5. 1). Converting to a diploid breeding strategy to create inbred lines in potato is a rational approach for future potato breeding. Developing true diploid potato seeds program will lead to the use of technologies that were developed for the diploid seed-propagated crop. High phytosanitary seed quality, as well as less demanding seed storage conditions, are some of the advantages of using true seed as planting material for commercial potato growers (Simmonds 1997) particularly in tropical regions. We have shown that a recurrent selection strategy can be used to introgress and improve a trait such as self-compatibility while maintaining genetic variability in a diploid multi-species 185 germplasm pool. The five cycles of RS have improved maturity, tuber appearance, average tuber number, average tuber yield, and SPGR. These results provided a strong evidence that this germplasm pool could contribute valuable genes for improving production and tuber related traits, and specific gravity in diploid and tetraploid breeding programs. This germplasm has the potential to change other potato breeders’ perceptions about producing a SC germplasm. This germplasm has shown the potential of diploid breeding by developing selections that have good tuber traits and are adapted to the long-photoperiod growing environment which contrasts with most SC diploid germplasm that has been developed through other breeding strategies. Producing inbred lines from this germplasm could help researchers uncover aspects of potato genetics that have not yet been realized. Therefore, many resources exist for diploid seed- propagated crops and development of inbred lines in potatoes provides opportunities to use new technologies to develop F1 hybrids and improve yield quality. As a future direction, the generation of inbreds from the SC selections can be achived by self-pollinated to develop inbred lines. Currently, inbreeding is progressing, and second self- pollinated (S2) generation developed from F1 hybrid of cycle 0, cycle 1, cycle 2, and cycle 3 and first self-pollinated (S1) generation of cycle 4 of RS have been generated. The one-year cycle of recurrent selection was applied to select for tuber traits and maturity in the field, and favorable selections have been made. Self-pollination will continue to be used in production of inbred lines. The inbred lines will be evaluated in different location and years for use as parental stocks in hybrid development program. 186 Backcross Germplasm Pool Two generations of backcrossing were used to introgress and improve self-compatibility in S. tuberosum dihaploids. A backcross 2 have been generated and SC improved significantly to 33% of the selections compared with 11% in the F1 and 14% in the BC1 generations. Improving SC in the BC2 generation increases the potential of diploid based variety development, increasing the possibility of generating inbred lines for an inbred/F1 hybrid variety breeding strategy. The BC2 generation selections showed significantly improvement in vine maturity compared with F1 and BC1 generations. I also found a significantly increase for common scab resistance in BC2 generation compared with F1 and BC1 generations, having a 63% resistant BC2 selections. I observed an improvement in tuber appearance for BC2; however, it was not significantly different from F1 and BC1 generations, thus 96% of the selections in a BC2 generation have acceptable tuber appearance (> 5 on scale 1 – 9). Moreover, the average tuber number increased significantly in the BC2 generation compared with F1 and BC1 generations. In contrast, average tuber yield was not improved compared with other generations. The specific gravity was more uniform between selections in BC2 generation compared with F1 generation. Crossing to S. tuberosum dihaploids has improved vine maturity, tuber appearance, average tuber number, and tuber yield. These results provided strong evidence that this germplasm pool could be utilized for valuable genes for in improving maturity and tuber related traits for both diploid and tetraploid breeding programs. Converting a potato breeding program from tetraploid to diploid level requires developing SC germplasm adapted to the long-day photoperiod growing season with broad genetic variability. Favorable selections were made across germplasm development (Figure 5. 2). Producing inbred lines with commercial traits at the diploid level provide breeders the 187 opportunity to use the technologies that have helped diploid seed-propagated crops to develop F1 hybrid and improve yield quality. Future directions aim to develop inbreds that have desirable traits from the SC selections of different BC generations. S2 selections from F1 hybrid and S1 selections from BC1, and BC2 generations have been generated. A one-year generation cycle was applied to select for tuber traits and maturity in the field, and good selections have been made and will continue as self- pollination advances toward producing inbred lines. The inbred lines will be evaluated in different location and years for hybrid development program. Different S. tuberosum dihaploids were used to generate BC1, BC2, BC3 generations and NY148 HP# 1 dihaploid is one of them which has a resistant marker (RYSC3) for potato virus Y (PVY) was used to generate some of BC1 families. Eighteen selections from these families with NY148 HP# 1 dihaploid genetic background were screened for RYSC3 marker, and five selections have the benefical alleles (Figure 5. 3). Unfortunately, these five selections are self-incompatible, so crosses with bulk pollen from cycle 4 of the recurrent selection project has been used to introgress SC from adapted multi-species germplasm pool. Seedlings from these crosses were grown in the field in summer 2018, and selections based on maturity and tuber traits have been accomplished in fall. These selections along with the BC2 and BC3 selections with R gene(s) for PVY, PVX, golden nematode, verticillium wilt (VW), potato leafroll virus (PLRV), and late blight (LB) will be screened using available markers. Examine Recurrent Selection and Backcross Germplasm Pools for Future Direction The aim of this dissertation was to develop SC germplasm pools that contained commercial tuber traits and adapted to the long-day growing environment. A total of 4787 SNPs 188 with 44 selections (39 selections cycle 4 and five selections BC2) were used to generate NJ tree and PCA. The recurrent selection and backcross germplasm have a wide variation based on NJ tree clustering, PCA, and commercial tuber trait measurements. Figure 5. 4 shows the NJ tree of cycle 4 of the recurrent selection germplasm and BC2 of the backcross germplasm. The NJ tree cluster shows BC2 selections distinct from the cycle 4 RS selections. Moreover, the PCA supports the NJ tree and Figure 5. 5 shows the PC1 and PC2 the explain 11.4% and 6.4% of the total genetic variance in these two germplasm pools. For future direction, based on the NJ tree, PCA, and commercial tuber trait measurements results show two genetically distant pools that could lead to heterotic pools. To extend the backcross pool, the MSU potato breeding program has started a project to develop larger number of dihaploid lines from chip-processing, table stock, russet potato market classes. These new dihaploid lines have various desirable traits such as R gene(s) for PVY, PVX, golden nematode verticillium wilt (VW), potato leafroll virus (PLRV), and late blight (LB) that will be used for backcrosses SC selections to maximum heterozygosity to optimize heterotic combinations between these two pools. Maximum heterozygosity leads to optimize heterotic combinations (Bradeen and Chittaranjan 2011). Crosses between these two germplasm pools have been generated to identify the best combination to observe hybrid vigor. These hybrids will be evaluated for commercial tuber traits and photoperiod adaptation. Genetic diversity and distance among the two pools will be measured and determine the correlation with hybrid performance that could allow defining heterotic between these two germplasm pools. The specific combining ability (SCA) and general combining ability (GCA) will be test also. 189 Impact of Diploid Inbred SC Potato Diploid Inbred SC potato will be the key for using advantages of genetics and genomics tools to improve selection gain in potato (Jansky et al. 2016). Producing inbred potato lines that could be maintained as true potato seeds (TPS) rather than tuber seeds that accumulate diseases, have limited storage time, and requires large storage space and will benefit potato breeding programs (Simmonds 1997; Jansky et al. 2016). While TPS can be stored at + 5 °C for over 20 years (Howard 1975). Simmonds (1997) reported that TPS does not carry any economically diseases such as fungal or viruses. Potato research programs have the opportunity to share TPS easily that have cumulative phenotypic data and correlated with genotype data. Potato breeding programs are using tissue culture which is costly or seed tubers that may caring tuber-borne pathogens during growing seasons that could negatively impact phenotypes, all these constraints can be excluded by using TPS (Muthoni et al. 2013; S. Jansky et al. 2016). Therefore, TPS will impact seeds production and diseases control (Muthoni et al. 2013). Genome Editing Genome sequences for many crops along with the implementation of targeted mutagenesis using genome editing tools has exposed new alternative breeding strategy for desirable traits (Jaganathan et al. 2018). Genome editing approach by Clustered Regularly Interspaced Short Palindromic Repeat (CRISPR)-associated protein 9 (Cas9) has developed fast as standard tool for genetic engineering in crop species by knock-in-knock-outs of candidate gene(s) related to crops’ agronomic traits (Jaganathan et al. 2018). Alternative method has provided a strategy to introduce or knock-out gene(s) without disrupting genetic background of elite cultivars. MSU potato breeding program has successfully knocked-out SI-related genes to 190 produce SC diploid potato lines. This technique will help to accelerate the SC diploid lines generation and provide the opportunity of converting the SI selections that developed from the two germplasm pools that have desirable traits and are adapted to a long photoperiod growing season. The genome editing approach will be used to introduce SC into 18 SI selections that were generated from RS and BC germplasm pools. Ploidy Determination With greater interest in breeding diploid potatoes for the commercial market, there is a need to use an efficient, accurate, and inexpensive method to reliably check ploidy level. Accurate ploidy determination in the early generation stages of the breeding program becomes essential to improve breeding efficiency and save breeder’s time and reduce work load and resource expenditures. This study examined three methods that are available for potato breeders to differentiate diploid and tetraploid potato: chloroplast counts in guard cells, SNP genotype calls from an array platform, and DNA content using flow cytometry. We found all three methods of calling ploidy comparable in our potato samples. There are advantages and disadvantages to using each method to determine ploidy (Table 17). We recommend chloroplast counting as an inexpensive and time-efficient method that potato breeders can use to reliably identify ploidy level in early generations. Both SNP genotyping and flow cytometry methods can be used to confirm the chloroplast count results. For future direction, chloroplast count method will be compared with other methods such as pollen diameter and cell size, and direct chromosome counting using root tips squash technique. 191 Dissertation Funds The work in this dissertation was funded by USDA NIFA 2014-67013-22434 and the Higher Committee for Education Development in Iraq (HCED). 192 APPENDIX 193 Figure 5. 1. Best selections from five cycles of recurrent selection. 194 Figure 5. 2. Best selections from F1, BC1, and BC2 generations of backcross. 195 a b Figure 5. 3. DNA amplification of 18 BC1 progeny that have NY148 HP #1 dihaploid genetic background. R and S indicate resistance susceptible progeny to PVY, respectively. Sample 12 (MSZ219DH-15) and 13 (NY152) are positive controls. While sample 14 is negative control (water). b: a: 1) 1000bp ladder 2) EE774-8 3) EE780-02 4) EE853-18 5) EE798-14 6) EE853-05 7) EE844-09 8) EE853-08 9) EE798-09 1) 1000bp ladder 2) EE798-02 3) EE853-16 4) EE847-09 5) EE847-04 6) EE853-07 7) EE847-12 8) EE853-11 9) EE798-07 10) EE853-04 11) EE791-07 196 Figure 5. 4. Neighbor-joining tree using 4787 SNPs. Clustering cycle 4 of recurrent selection germplasm (magenta) and BC2 of backcross germplasm (green) (39 selections of RS and five selections of BC2). 197 Figure 5. 5. Principal component analysis. Comparison of cycle 4 of recurrent selection germplasm and BC2 of backcross germplasm (39 selections of RS and five selections of BC2) using 4787 SNPs. 198 LITERATURE CITED 199 LITERATURE CITED Bradeen, J. M., and K. Chittaranjan. 2011. Genetics, genomics and breeding of potato. CRC Press. Howard, H. W. 1975. The prolonged storage of true seeds of potatoes. Potato Research 18: 320– 321. doi:10.1007/BF02361736. Jaganathan, D., K. 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