QTL MAPPING OF PHYTOPHTHORA ROOT ROT RESISTANCE AND APHID RESISTANCE IN SOYBEAN By Zhongnan Zhang A DISSERTATION Submitted to Michigan State University in partial fulfillment of the requirements for the degree of DOCTOR OF PHILOSOPHY Plant Breeding, Genetics, and Biotechnology—Crop and Soil Sciences 2012 ABSTRACT QTL MAPPING OF PHYTOPHTHORA ROOT ROT RESISTANCE AND APHID RESISTANCE IN SOYBEAN By Zhongnan Zhang Soybean [Glycine max (L.) Merr.] is one of the most important crops worldwide. As the largest soybean producer, the United States produces $40.2 billion US dollar worth soybeans in 2011. Biotic stresses such as soybean cyst nematode (SCN), Phytophthora root rot (PRR), sudden death syndromes (SDS) and soybean aphids are threatening soybean production and plant health. Three independent projects were performed to identify genetic resistance against PRR, caused by Phytophthora sojae; to identify genetic resistance against soybean aphid, Aphis glycines Matsumura; and to develop breeder-friendly simple sequence repeat (SSR) and single nucleotide polymorphism (SNP) markers to facilitate marker-assisted selection (MAS) in breeding for PRR resistance and aphid resistance. In the first project, a major locus Rps1 was identified for resistance to PRR in Michigan elite soybean E00003 (Glycine max) through a genetic mapping approach. A bi-parental mapping population was evaluated in the greenhouse for PRR resistance against P. sojae races 1, 4, and 7, in 2009 and 2010 using a modified rice grain inoculation method. The heritability of the PRR resistance ranged from 83% to 94%. A major locus contributing 50% to 76% of the phenotypic variation was detected within a 3 cM interval in the Rps1 region. Based on the specific responses to the tested races, this locus was determined to be Rps1k. Two SSRs and three SNPs were found located within the predicted functional gene and are highly associated with PRR resistance in the mapping population. In the second project, two major quantitative trait loci (QTLs) for soybean aphid resistance were detected and confirmed in E08934, derived from a wild type soybean, Glycine soja. Aphid resistance phenotyping was conducted one season in the greenhouse and three seasons in field cages. The broad-sense heritability of aphid resistance from field trials was 0.84. After genotyping with SNPs and SSRs, two major QTLs were detected (P < 0.001) in intervals of 13.5 cM between Sat_382 and Satt455 on Chr. 8, and 3.5 cM between Satt693 and Sat_370 on Chr. 16. The locus on Chr. 8 explained 40.8% of the phenotypic variation in the greenhouse trial, and 46.4, 19.5 and 39.1% of the 2009, 2010 and 2011 field trials, respectively. The locus on Chr. 16 explained 12.5 to 22.9% of the phenotypic variation. Further, these two loci were both confirmed in a validation population. The no-choice test indicated both loci confer antibiotic resistant to aphids. The novel locus on Chr. 8 (LG A2) is denoted as Rag6, since it showed significant partially dominant effect in the validation population (P < 0.05). In the third project, nearly 4,000 F2 plants from bi-parental populations were employed to fine map partially dominant locus Rag3 derived from PI 567543C. A major locus explaining 44.7% of the phenotype variance was detected in QTL analysis of 376 F2 lines and confirmed as Rag3 between markers MSUSNP16-10 (Gm16_6262227) and MSUSNP16-12 (Gm16_6423098) on chr.16. After genotyping all 3802 F2 progeny and 983 F3 progeny of 102 F2 recombinants, two F3 recombinants with crossovers between 6.26 and 6.47 Mbp were identified and confirmed according to their F4 progeny. Rag3 was fine mapped to 207 Kbp between MSUSNP16-10 (Gm16_6262227) and SNP at Gm16_6469551. ACKNOWLEDGMENTS I sincerely thank my major professor, Dr. Dechun Wang, for his guidance, financial support, and kindly patience. I am very grateful to my committee members, Dr. James Kelly, Dr. Amy Iezzoni and Dr. Jianjun Hao, for valuable advice on my research projects, thoughtful instruction on my course study and thorough revisions on my dissertation. In the Phytophthora root rot project, I thank Dr. Hao for his kind support through use of laboratory equipment and in the pathogen inoculum preparations. I would like to thank Dr. Anna Dorrance from the Ohio State University for the pathogen isolates of Phytophthora sojae. Special thanks to the Dr. Leo W. Mericle and Dr. Rae Phelps Mericle Memorial Scholarship Committee for the generous financial support during the first year of my study in the program, as well as the funding support from Michigan Soybean Check-off and United Soybean Board. I would like to express my gratitude to Cherry Gu, Carmille Bales, Desmi Chandrasena, Jiazheng Yuan, Zixiang Wen, Ruijuan Tan, John Boyse and Xiaohong Lu for assisting in soybean planting, harvesting and various other laboratory activities throughout my research projects. Also grateful appreciations to Amy Lasley for her assistance with dissertation revisions. Last but not least, special thanks from the bottom of my heart to my husband, Ming, for his love and care. iv TABLE OF CONTENTS LIST OF TABLES……………………………………………..…………………………….......vii LIST OF FIGURES………………………………………………………………….…………...ix CHAPTER 1: LITERATURE REVIEW……………………………………………….…………1 SOYBEAN PRODUCTION………………………………………………………………………2 BIOTIC STRESSES-DISEASES AND INSECTS………………………………………..……...3 MANAGEMENT OF PHYTOPHTHORA ROOT ROT AND SOYBEAN APHIDS…………...5 QTL MAPPING, FINE MAPPING AND MARKER-ASSISTED SELECTION………………..8 RESISTANCE LOCI FOR PHYTOPHTHORA AND APHIDS AS WELL AS OBJECTIVES OF THE RESEARCH CONDUCTED IN THIS DISSERTATION…………………………….10 CHAPTER 2: PHYTOPHTHORA ROOT ROT RESISTANCE IN SOYBEAN E00003……...14 ABSTRACT……………………………………………………………………………………...15 INTRODUCTION……………………………………………………………………………….16 MATERIALS AND METHODS………………………………………………………………..18 Mapping population……………………………………………………………………...18 Evaluation for Phytophthora sojae resistance………...…………………………………18 Genetics analysis…………………………………………………………………………20 Data analysis……………………………………………………………………………..21 RESULTS AND DISCUSSION…………………………………………………………………22 Phenotype distribution…………………………………………………………………...22 PRR resistance locus mapping…………………………………………………………...23 Analysis with SNP markers……………………………………………………………...24 CHAPTER 3: APHID RESISTANCE IN WILD SOYBEAN GLYCINE SOJA………………...36 ABSTRACT……………………………………………………………………………………...37 INTRODUCTION……………………………………………………………………………….39 MATERIALS AND METHODS………………………………………………………………...42 Aphid resistance loci mapping…………………………………………………………...42 Population development and phenotype evaluation……………………………..42 DNA preparation and marker genotyping………………………………………..43 Statistical analysis and mapping analysis………………………………………..44 Validation of aphid resistance loci………………………………………………………44 No-choice test…………………………………………………………………………....45 RESULTS………………………………………………………………………………………..46 Phenotype distribution of the mapping population………………………………………46 Aphid resistance locus mapping…………………………………………………………46 Aphid resistance loci validation………………………………………………………….48 Gene action in validation population…………………………………………………….49 Combination of aphid resistance loci in validation population………………………….50 v No-choice test in the mapping population……………..………………………………...51 DISCUSSION……………………………………………………………………………………52 CHAPTER 4: FINE MAPPING OF APHID RESISTANCE GENE RAG3 IN SOYBEAN PI 567543C WITH BI-PARENTAL F2 POPULATION……………………………………...…....76 ABSTRACT………………………………………………………………………………….......77 INTRODUCTION……………………………………………………………………………….78 MATERIALS AND METHODS…………………………………………………………..….....81 Plant materials………………………………………………………………………...….81 Aphid resistance phenotype evaluation………………………………………………….81 DNA preparation and marker genotyping……………………………………………......82 Data analysis……………………………………………………………………………..83 RESULTS AND DISCUSSION…………………………………………………………………84 Phenotype of soybean aphid resistance…………………………………………………..84 Validation of Rag3 in F2 population……………………………………………………..84 Fine mapping Rag3 with recombinant lines…………………………………………......85 Association of SNPs MSUSNP16-10 (Gm16_6262227), MSUSNP16-11 (Gm16_6413214) and MSUSNP16-12 (Gm16_6423098) between genotypes and the phenotypes……………………………………………………………………………………….86 APPENDIX: GENOTYPING AND GENETIC ANALYSIS OF SUDDEN DEATH SYNDROMES IN SOYBEAN…………………………………………………………………..93 PLANT MATERIALS AND SUMMARY OF PHENOTYPES………………………………...94 SELECTIVE GENOTYPING…………………………………………………………………...95 GENETIC ANALYSIS…………………………………………………………………………..95 REFERENCES…………………………………………………………………………………104 vi LIST OF TABLES Table 1.1. Quantitative resistance loci conferring race-specific resistance to Phytophthora root rot according to soybean consensus map (Song et al., 2004). Table contents were updated according to Cornelius et al. (2005)………………………………………………........………...12 Table 1.2. Quantitative resistance loci to soybean aphids based on soybean consensus map (Song et al., 2004)…………………………………………………………………..…………………..13 Table 2.1. Rps loci and QTLs underlying resistance to Phytophthora root rot and their locations on soybean composite map. Linkage group names, marker names, and marker positions are updated as shown on soybean composite map (Song et al., 2004). Table contents updated according to Cornelius et al. (2005)………………..………………………………...…………..28 Table 2.2. Survival index (mean ±SE) of the mapping population with 240 F4 derived lines and the parents E00003 and PI 567543C in the six P. sojae inoculated greenhouse trials conducted in 2009 and 2010. Survival index = the number of surviving plants / total number of seeds planted × 100%..……………….…………………………………………………………………………29 Table 2.3. Phytophthora root rot (PRR) resistance locus detected in the mapping population E00003 × PI 567543C using composite interval mapping (CIM) method. Chromosome/linkage group 03/N is based on Soybase (Grant et al., 2010).……………………………….…………...30 Table 2.4. Average survival index for different genotypes of the three single nucleotide polymorphism (SNP) markers in the mapping population E00003 × PI 567543C. Numbers followed by different letters within the same row are significantly different at size of 0.05. The three SNP markers are MSUSNP03-1 (Gm03_4487138), MSUSNP03-2 (Gm03_4563499), and MSUSNP03-3 (Gm03_4610670)……………..……....…………………………………………31 Table 3.1. Aphid damage index (DI) for two parents of the mapping population, E08934 and E00003, and 140 F3-derived lines, in the greenhouse and field trials…………….……...……...54 Table 3.2. Aphid resistance loci identified in the mapping population E00003 × E08934 and validation population E08929 × E08934 with composite interval mapping method with 1000 permutations. MSUSNP08-1 (Gm08_23293155), and MSUSNP08-2 (Gm08_40320904) are single nucleotide polymorphism (SNP) markers derived from 6K SNP chip; MSUSNP16-11 (Gm16_6413214) and MSUSNP16-12 (Gm08_6423098) are derived from 52K SNP chip……………………....................55 Table 3.3. Summary of average damage index for genotypic groups of markers near LOD peak from validation population E08929 × E08934……………………………...…………………...56 vii Table 3.4. Summary of aphid damage index (DI) from no-choice test with selected F3-derived lines with resistance genes from mapping population……………………...……………………57 Table 4.1. Single nucleotide polymorphism (SNP) genotypes and phenotypes of selected F3 and F4 lines on 52K SNP Beadchip……………………………..………………………..…………..88 Table 4.2. Correlations of Single nucleotide polymorphism (SNP) with phenotypes in 376 F2 lines from cross E07048 × E10902 and all 983 F3 progeny of 102 F2 recombinants. MSUSNP1610 (Gm16_6262227), MSUSNP16-11 (Gm16_6413214) and MSUSNP16-12 (Gm16_6423098) were developed from 52K SNP Beadchip (Song et al., 2011).……………………………..……90 Table A.1. Broad sense heritability of SDS phenotypic with 90% confidence intervals, August 2 disease severity (Aug2DS), August 2 disease index (Aug2DX), August 15 disease severity (Aug15DS), August 15 disease index (Aug15DX), August 22 disease index (Aug22DX) and August 22 average disease index (Aug22AvDX) of the two mapping populations from cross GD2422 × LD01-5907 and GD2422 × Skylla…………………………………………………...97 Table A.2. Normal distribution table for selective genotyping in mapping population GD2422 × LD01-5907……………………………….………………………………………………………98 Table A.3. Summary of polymorphic single nucleotide polymorphism (SNP) markers on each chromosome between the two parents of mapping population GD2422 × LD01-5907………....99 Table A.4. The chromosome labels in QTL Cartographer 2.5 software and the corresponding chromosomes according to soybean consensus map (Qijian Song personal communication)…100 viii LIST OF FIGURES Figure 2.1. Phenotypic distribution of survival index (SI) of the mapping population among 240 F4 derived lines from PI 567543C × E00003. Parent SIs were indicated by arrows. SI was calculated as: SIij = [(the number of plants surviving of line j in the inoculated trial i) /12] × 100………………………………………………………………………………………………..32 Figure 2.2. Location of Phytophthora root rot resistance locus in E00003 as mapped with the composite interval mapping method. Linkage map of the SSR markers and the SNP markers was constructed with the genotypic data from the mapping population. Bars show intervals of loci mapped with different trials….……………………………………………………….………….35 Figure 3.1. Phenotypic distribution of soybean aphid damage index of mapping population with140 F3-derived lines by E00003 × E08934 (A-D), and of validation population with 252 F2derived lines from E08929 × E08934 (E-F). Parents are indicated by arrows. y-axis is the count of plants that fall into each category. A 2010 greenhouse trial; B-D 2009, 2010 and 2011 caged field trials; E-F validation population in 2010 and 2011 field trials……………..………………58 Figure 3.2. Location of soybean aphid-resistant loci detected in the mapping population E00003 × E08934 and validation population E08929 × E08934 with the composite interval mapping method with 1000 permutations. 1-LOD and 2-LOD intervals of each locus are indicated by thick and thin bars. Threshold line was drawn with P = 0.01 from 1000 permutations. A) Map of linkage group A2 (Chromosome 8) with the aphid resistance locus on the right from four trials in the mapping population, with LOD score plot on the right; B) The soybean consensus map of linkage group A2 (Chromosome 8) (Song et al., 2004); C) Map of linkage group A2 (Chromosome 8) with the aphid resistance locus on the right from two field trials in the validation population, with LOD score plot on the right; D) Map of linkage group J (Chromosome 16) with the aphid resistance locus on the right from four trials in the mapping population, with LOD score plot on the right; E) The soybean consensus map of linkage group J (Chromosome 16) (Song et al., 2004); F) Map of linkage group J (Chromosome 16) with the aphid resistance locus on the right from two field trials in the validation population, with LOD score plot on the right; G) Map of linkage group M (Chromosome 7) with the aphid resistance locus on the right from two field trials in the validation population, with LOD score plot on the right; H soybean consensus map of linkage group M (Chromosome 7) (Song et al., 2004) MSUSNP08-1 (Gm08_23293155), MSUSNP08-2 (Gm08_40320904), MSUSNP08-3 (Gm08_41114696) and MSUSNP08-4 (Gm08_45189358) derived from 38.8 to 45.2 Mbp of LG A2 on 6K SNP Beadchip, MSUSNP16-10 (Gm16_6262227), MSUSNP16-11 (Gm16_6413214), MSUSNP16-12 (Gm16_6423098) and MSUSNP16-15 (Gm16_8051585) derived from 4.8 to 11.3 Mbp region of LG J on 52K SNP Beadchip..........................................................................61 Figure 3.3. Average aphid damage index (DI) of progeny in three genotypic groups for each marker close to the aphid resistance locus detected in the validation population E08929 × E08934, in 2010 and 2011 field trials. The homozygous group with allele derived from parent ix E08934 was denoted as AA. The homozygous group with allele came from parent E08929 was denoted as BB. The heterozygous group with both alleles was denoted as AB. one-way analysis of variance was conducted at a significance level of 0.05. Error bars indicate 95% confidence interval of average DI. A) The average DI of genotypic group AA, AB and BB of marker Satt540 (close to rag1), three bars on the left are from field trial 2010, and bars on the right are from field trial 2011; B) The average DI of genotypic group AA, AB and BB of marker Satt209 (close to Rag6), three bars on the left are from field trial 2010, and bars on the right are from field trial 2011; C) The average DI of genotypic group AA, AB and BB of marker BARCSOYSSR_16_0371 (close to Rag3(?)), three bars on the left are from field trial 2010, and bars on the right are from field trial 2011……………………..…………………………………70 Figure 3.4. Average aphid damage index (DI) for progeny that have no resistance genes, one locus, two loci and all three loci from the validation population E08929 × E08934 in 2010 field trial…………………………………………………………………………………………….…74 Figure 3.5. Average damage index (DI) from aphid no-choice test of selected lines from the mapping population E00003 × E08934, line 24 with Rag3(?), line 131 with Rag6 and line 19 with both loci. E00003 was used as susceptible check, and PI 567598B was antibiosis resistance check. One-way analysis of variance was conducted at significant level of 0.05. Error bars indicate 95% confidence interval of average DI………………..………………………………..75 Figure 4.1. Phenotype histogram of soybean aphid rating score of F2 plants from bi-parental cross E07048 × E10902, where E07048 is susceptible to aphid and E10902 is resistant. A. phenotype distribution of all 1889 F2 plants with y-axis being the percentage of total plants for each score; B. phenotypes of a subset of 376 F2 plants from the same population with y-axis being the number of F2 plants for each score………………………………………………..…..91 Figure 4.2. Validation Rag3 location on Chromosome 16 using a subset of 376 F2 plants from cross E07048 × E10902, with E07048 and E10902 being susceptible and resistant parent. QTL analysis was conducted with both composite interval mapping and multiple interval mapping methods with P < 0.0001. MSUSNP16-14 (Gm16_6164774), MSUSNP16-10 (Gm16_6262227), MSUSNP16-11 (Gm16_6413214), MSUSNP16-12 (Gm16_6423098) and MSUSNP16-15 (Gm16_8051585) are SNP markers with physical position indicated in the middle of the marker names. The black bar next to the linkage map indicates the location of Rag3 with composite interval mapping method; the grey bar on the right shows that with multiple-interval mapping method……………………………………………………………………………………………92 Figure A.1. Phenotype distributions of line 1-129 from cross GD2422 × LD01-5907. August 2 disease severity (Aug2DS), August 2 disease index (Aug2DX), August 15 disease severity (Aug15DS), August 15 disease index (Aug15DX), August 22 disease index (Aug22DX) and August 22 average disease index (Aug22AvDX). Y-axis is the count of number of plants that fall into each category.………………………………………………………………..…………….101 Figure A.2. Phenotype distributions of line 130-243 of GD2422 × Skylla. August 2 disease severity (Aug2DS), August 2 disease index (Aug2DX), August 15 disease severity (Aug15DS), x August 15 disease index (Aug15DX), August 22 disease index (Aug22DX) and August 22 average disease index (Aug22AvDX). Y-axis is the count of number of plants that fall into each category.……………………………..………………………………..……………………...…102 Figure A.3. Phenotypic distribution of the selected subset of population GD2422 × LD01-5907. August 15 disease index (Aug15DX) and August 22 average disease index (Aug22AvDX)….103 xi CHAPTER 1 LITERATURE REVIEW 1 SOYBEAN PRODUCTION Soybean (Glycine max) is a legume species native to East Asia (Hymowitz, 2008). Ever since the cultivation of soybean, its food products: bean curd (tofu), soybean milk, soy sauce and soybean paste have always been immensely popular among East Asian countries, such as China, Japan and Korea. As the origin of soybean cultivation, China was the largest soybean producer th and exporter before mid-20 century (Qiu and Chang, 2010). In 1765, soybean was first introduced to the US by a sailor Samuel Bowen (Brachfeld and Mary, 2007). In the 1950s, the United States became and still is the largest soybean-producing country in the world, followed by Brazil, Argentina, China, India and other countries (FAO, 2008). In 2010, 258.9 million metric tons of soybeans were produced worldwide. Ninety-one million metric tons were produced in the US, which is 35% of the world soybean production, followed by Brazil 27%, Argentina 19% and China 6% (SoyStats, 2011). Soybean has become one of the most valuable crops worldwide not only for human consumptions, but also for its protein and oil content for livestock feed and biofuel (Masuda and Goldsmith, 2009). The rapidly growing demand of animal-based diet in developing countries leads to increased demand of soybean production as livestock feed. As an oilseed, soybean can be used to extract methyl esters of fatty acids (FAMEs) to produce biodiesel with basic catalyst (Fabbri et al., 2007). Biodiesel is recommended to help reduce the emissions of greenhouse gases, and soybean has a smaller environmental impact for biofuel production than sunflower seed (Requena et al., 2011). 2 BIOTIC STRESSES-DISEASES AND INSECTS Like many other field crops, soybean production is always under the threats of biotic stresses, such as diseases and insects (Graham and Vance, 2003). Up to 30% of the world crop yield is reduced by these factors, and the number may be even larger in developing countries due to virus transmission by insect pests (Christou and Twyman, 2004). At least 10% of the global food production loss is caused by plant diseases (FAO, 2000). For example, common bean -1 (Phaseolus vulgaris) losses to root rot are estimated as $4 million year in the State of Minnesota alone (Graham and Vance, 2003). The yield loss in soybean production to diseases increased from 11% worldwide in 1994 to 23% in 2003 (Hartman et al., 1999; Oerke, 2006). Eleven percent of the loss was due to parasitic bacteria and fungi, 1% to viruses and 11% to animal pests including nematodes (Hartman and Hill, 2010). According to Wrather and Koenning (2011), a total of 168 million t of soybean yield was lost due to diseases from 1996 to 2010. In the report, soybean cyst nematode (SCN, Heterodera glycines Ichinohe) ranks number one in most devastating diseases, causing more than 61.5 million t soybean yield loss from 1996 to 2010. Phytophthora root rot (PRR, Phytophthora sojae), seedling diseases and sudden death syndrome (SDS, Fusarium solani f. sp. glycines) ranked second to fourth in total yield reduction from 1996 to 2010. The yield loss caused by Phytophthora root rot was estimated as much as 18 million t. Seedling disease caused 14 million t of yield loss. Weather conditions played a critical role in seedling diseases reducing yield in Illinois, Kansas, Minnesota, North Dakota, and Ohio (Wrather and Koenning, 2006). SDS affected soybean production most in Arkansas, Iowa, Illinois, Indiana, Minnesota, and Tennessee, 3 and was especially severe in Illinois and Indiana. The total yield loss caused by SDS was over 12 million t. Damage caused by Charcoal root rot (Macrophomina phaseolina) and Sclerotinia stem rot (Sclerotinia sclerotiorum) varied significantly between years, due to changes in weather and pathogens. Rust (Phakopsora pachyrhizi) was first confirmed on soybean in nine states in 2004: Alabama, Arkansas, Florida, Georgia, Louisiana, Missouri, Mississippi, Tennessee, and South Carolina (Wrather and Koenning, 2006). Insect pests are another category threatening soybean production. The ecology and management of insect pests in soybean has been reviewed in literatures since 1970 (Altieri and Whitcomb, 1980). Based on their feeding guild, soybean insect pests are categorized into three classes: defoliators (Lepidoptera, Coleoptera, Orthoptera), phloem feeders (Hemiptera) and seed feeders (Coleoptera, Diptera) (O'Neal and Johnson, 2010). Defoliators have chewing mouthparts that can either remove leaf area or destroy the leaf surface, such as grasshoppers (Orthoptera), beetles (Coleoptera), larva or caterpillars of moths and butterflies (Lepidoptera) (Singh, 2010). In spite of yield reduction, insects can pass diseases to their plant host through their feeding habits, for example, grasshoppers is known to transmit Tobacco ring-spot virus into soybeans (Dunleavy, 1957). The phloem feeders, such as the three-cornered alfalfa hopper (Spissistilus festinus), sweet-potato whitefly (Bemisia tabaci) and soybean aphid (Aphis glycines) from the order Hemiptera significantly impact on soybean production (O'Neal and Johnson, 2010). These pests have piercing-sucking mouthparts that enter vascular tissue, consume phloem and excrete sugar content. Besides, these phloem feeders can also transmit viruses to the plant, such as Tobacco ring-spot virus, soybean mosaic virus and soybean dwarf virus (Clark and Perry, 2002; Kloepper et al., 2004; Morales and Anderson, 2001; Pedersen et al., 2007). 4 Other insect pests in the category of seed, pod, stem and root feeders, can cause significant yield loss as well. The pentatomid stinkbugs such as Nezara viridula also have piercing-sucking mouthparts that can cause seed decay and severe abortion of pods leading to severe late-growth stage damage and reduction in soybean yield (O'Neal and Johnson, 2010). Just like other insect pests, stinkbugs can transmit diseases like yeast spot disease (Kimura et al., 2008a; Kimura et al., 2008b). MANAGEMENT OF PHYTOPHTHORA ROOT ROT AND SOYBEAN APHIDS Phytophthora root rot caused by the oomycete Phytophthora sojae can occur at any soybean growth stage. It is the second most devastating disease in soybean production. Yield loss caused by this disease in the US was estimated at 18 million t from 1996 to 2010. Severe damage of soybean yield occurred mostly in the North Central states, such as Illinois, Indiana, Minnesota, Missouri and Ohio, and southern states like North Carolina, Louisiana, Kentucky and Oklahoma (Wrather and Koenning, 2006; Wrather and Koenning, 2011). As a soil-borne pathogen, Phytophthora sojae infects host plants through the root system when the soil moisture favors the disease development (Ho, 1969). For disease management of the monocyclic soil-borne disease caused by Phytophthora sojae, avoiding or reducing the amount or efficacy of primary inoculum is of considerable importance (Appiah et al., 2004; Smilde et al., 1996). Knowing the history of the soil and planting with clean certificated seeds on well-drained or raised beds in the field with clean irrigation systems and sanitation equipment are good practices to reduce disease incidence (Cadle-Davidson and Gray, 2006). Using fungicides 5 at the first appearance of disease and rotating the types of fungicides used could be effective controlling PRR but with increased input cost (Ando and Grumet, 2006; Barbetti et al., 2007; Olanya et al., 2006). Crop rotation with non-host crops is also crucial for soil-borne disease management (Ruppel et al., 1988). The management of PRR has been studied when the pathogen is present (Dorrance et al., 2009; Rehm and Stienstra, 1993; Zhang and Xue, 2010). In Rehm and Stienstra’s study (1993), they investigated whether management practices such as variety selection, fungicide use and potash fertilization could reduce damage in areas where PRR was known to be a problem. In conclusion, they found that yield loss was most severe when a susceptible soybean cultivar was grown, and alternatively using a combination of the chemical Ridomil and a resistant soybean variety can reduce the PRR severity (Rehm and Stienstra, 1993). Application of chemical fungicides can be quite expensive. The residuals of harmful chemicals may pollute natural environments (Vanderborght et al., 2011) and kill beneficial insects (Meikle et al., 2012) or animals (Engelhaupt, 2008). In addition, there is a risk of “super-pesticideresistant” pathogen development induced by high fungicide pressure (Cheatham et al., 2009; Gressel, 2009). Selecting genetically resistant cultivars has always been suggested as the most prominent strategy to manage PRR (Baker and Cook, 1974; Dorrance et al., 2009; Plank, 1963). The soybean aphid (Aphis glycines Matsumura) is considered an invasive species into the Americas from Asia (Kogan and Turnipseed, 1987). As mentioned earlier, Aphis glycines Matsumura is classified in the order Hemiptera with typical piercing-sucking mouthparts that feed on plant sap leading to severe water and nutrient deficiency (O'Neal and Johnson, 2010). Since the first found in Wisconsin in July of 2000, soybean aphids have made a significant impact on soybean production in the United States. Before the aphid was a problem, few insecticides had been needed in the twelve north-central states (Fernandez-Cornejo and Jans, 6 1999; Ragsdale et al., 2011). With the presence and rapid outbreak of soybean aphid across these soybean growing states, the insecticide application has increased from 4 to 14 M ha year -1 (O'Neal and Johnson, 2010). It is challenging to control aphids with effective management practices and at the same time minimizing the risk of insecticide resistance (Lawrence, 2009; O'Neal and Johnson, 2010). As the first commercial insecticide, neonicotinoids have been widely used against phloem and leaf feeding insect pests, such as aphids, whiteflies and potato beetles (Jeschke and Nauen, 2008). The compounds can be used as seed treatments or a foliar formulation (Buchholz and Nauen, 2002; Elzen, 2001; Mukherjee and Gopal, 2000; Weichel and Nauen, 2004). However, a recent study on insecticidal seed treatments for soybean aphids suggested that insecticidal seed treatments hardly benefit soybean production of the Northern Great Plains (Seagraves and Lundgren, 2012). Even with multiple spray treatments, the insecticide application timing is crucial to control soybean aphids. When population density is high, it is most effective to apply at the R2 and R3 stages (Myers et al., 2005). In addition to limited improvement on aphid management and the massive cost of practical inputs, insecticides can leave traceable residues that pollute the environment and harm beneficial insects and natural enemies (Derksen et al., 2008; Ohnesorg et al., 2009; Southwick et al., 2004). Therefore, integrated pest management (IPM) has been suggested as the best solution to control soybean aphids (Hodgson et al., 2004; Irwin, 1999; Johnson et al., 2009; Ragsdale et al., 2011; Rosenheim et al., 2011; Song and Swinton, 2009; Wiarda et al., 2012). To developing robust and sustainable IPM practices, breeding and releasing host plant resistant cultivars can be a remarkably effective (Diaz-Silveira and Herrera, 1998; Kaster and Gray, 2005; Smith and Clement, 2012; Soleimannejad et al., 2010; Vieira et al., 2011; Wiarda et al., 2012). 7 QTL MAPPING, FINE MAPPING AND MARKER-ASSISTED SELECTION Genomic regions that associate with quantitative traits are known as quantitative trait loci (QTLs). Crop yield, seed quality, oil or protein content and many disease resistance traits are superb examples of quantitative traits with continuous distribution of trait values. QTL mapping studies have made remarkable progress by taking advantage of molecular marker development since the 1980s. The marker types include: amplified fragment length polymorphism (AFLP) markers, simple sequence repeat (SSR) markers and single nucleotide polymorphism (SNP) markers and so on (Collard et al., 2005; Gupta et al., 2001; Hollis and Hindley, 1988; Lander and Botstein, 1989; Miles and Wayne, 2008; Paterson et al., 1988; St Clair, 2010; Williams et al., 1991; Young, 1996). There are two main strategies for QTL mapping, linkage-based mapping and association mapping (Donnelly and Wiuf, 1999; Lander and Botstein, 1989; Manly and Olson, 1999; March, 1999; Shifman and Darvasi, 2005; Sterken et al., 2012). Linkage-based mapping utilizes a genetic linkage map as the backbone for QTL analysis (Gardiner et al., 1993). It estimates the map distance between marker and QTL by the recombination fraction (Lander and Botstein, 1989; Wu et al., 2002). In a typical linkage-based QTL mapping experiment, a mapping population developed by crossing two inbred lines is genotyped to construct a genetic linkage map. The position of quantitative trait loci can be located on the genetic linkage map (Lander and Botstein, 1989). Further, epistasis between QTLs and interactions between QTL and environmental factors can be addressed and better understood (Doerge, 2002; Mao and Da, 2004; Purcell and Sham, 2000). However, due to the limited 8 recombination events in meiosis during population development, traditional linkage mapping has lower resolution of QTLs (McMullen et al., 2009). Association mapping, commonly referred as “linkage disequilibrium mapping”, does not require any linkage map. Instead, it takes advantage of historical recombination events between the marker and QTL since such nonrandom co-segregation (linkage disequilibrium) was ever introduced into a population. With more opportunities for recombination, association mapping provides higher resolution of QTLs (Rabinowitz, 1997; Risch and Merikangas, 1996; Wu et al., 2002; Xiong and Guo, 1997). However, association mapping has several bottleneck issues, such as high false discovery rate, population structure, rare variants with low allele frequency and difficulty in distinguishing strong disequilibrium and loose linkage from weak disequilibrium and tight linkage (Brachi et al., 2010; Donnelly and Wiuf, 1999; Hewitt, 2008; March, 1999; Mezmouk et al., 2011; Qin et al., 2010; Sorkheh et al., 2008; Sterken et al., 2012; Weir, 2008; Whittaker et al., 2000). Therefore, linkage-based mapping still plays a pivotal role in genetic mapping of high-heritability disease and insect resistance loci with higher power and low false discovery rate (Duan et al., 2007; Grisi et al., 2007; Sabatti et al., 2003; Stoeckli et al., 2009). Some particularly stimulating research has been proposed to combine the two methods. Wu et al. (2002) proposed a new mapping strategy combining the strength of both methods in the EMimplemented maximum-likelihood framework for an outcross population. More recently, Yu et al. (2008) developed a nested association mapping population in maize to utilize the advantages for both methods to decode the maize genome. The variety, B73 was crossed to 25 diverse founders to develop 5,000 immortal genotypes from genome reshuffling (Yu et al., 2008). Prosperous progress in DNA-based molecular markers and QTL mapping is making marker-assisted selection (MAS) more and more popular and effective (Bernardo, 2008; Chaisan 9 et al., 2012; Collard et al., 2005). For example, MAS has been used for development of disease resistance in bean and cowpea (Kelly et al., 2003), resistance for soybean cyst nematode (Diers, 2005), and other disease resistance and agronomic traits (Ha et al., 2007; Kumar et al., 2011; Maroof et al., 2008; Sebastian et al., 2010; Zhu and Sun, 2006). However, these successful examples are far fewer than the number of QTLs that have been identified in the literature (Bernardo, 2008). A potential pitfall for MAS can be undesired recombination between markers and functional genes. Therefore, to better facilitate MAS, fine mapping of QTL is needed to provide precise location of QTL with high resolution, closely linked molecular markers, and even markers that locate exactly within functional genes. There are usually two approaches for fine mapping, traditional linkage mapping with a large set of progeny from bi-parental crosses, and association mapping. RESISTANCE LOCI FOR PHYTOPHTHORA AND APHIDS AS WELL AS OBJECTIVES OF THE RESEARCH CONDUCTED IN THIS DISSERTATION To date, nine loci with 15 alleles (Rps1a, b, c, d, k, Rps2, Rps3a, b, c, Rps4, Rps5, Rps6, Rps7, Rps8 and RpsYu25) have been reported confer race-specific resistance to Phytophthora sojae (Anderson and Buzzell, 1992; Athow and Laviolette, 1982; Athow et al., 1980; Burnham et al., 2003b; Buzzell and Anderson, 1992; Demirbas et al., 2001; Diers et al., 1992; Gao and Bhattacharyya, 2008; Gordon et al., 2006; Kilen et al., 1974; Mueller et al., 1978; Sandhu et al., 2005; Sun et al., 2011; Weng et al., 2001). The locations of these loci were updated according to Cornelius et al. (2005) and summarized in Table 1.1. In Chapter 2, the research objectives were 10 to identify PRR resistance in an advanced breeding line E00003 from Michigan State University and to develop breeder-friendly molecular markers to facilitate MAS for PRR. For soybean aphids, five quantitative resistance loci with seven alleles have been published (Rag1, rag1, Rag2, Rag3, rag3, rag4 and Rag5) underlying aphid resistance (Hill et al., 2006a; Hill et al., 2006b; Hill et al., 2009; Jun et al., 2012; Li et al., 2007; Mian et al., 2008a; Mian et al., 2008b; Zhang et al., 2009; Zhang et al., 2010). Their locations are summarized in Table 1.2. The objectives of the study in Chapter 3 were to identify aphid resistance loci of E08934 and develop molecular markers for MAS. In Chapter 4, the research aims were to fine map the partially dominant allele Rag3, seeking tightly linked SNP markers for use in MAS and for further genetic studies such as cloning and function studies of this locus. 11 Table 1.1. Quantitative resistance loci conferring race-specific resistance to Phytophthora root rot according to soybean consensus map (Song et al., 2004). Table contents were updated according to Cornelius et al. (2005). † Rps loci Chr. /Linkage Group Flanking markers (cM) Reference Rps1 3/N Satt159─Satt009 (27.1 ─ 28.5) Weng et al. 2001 Rps2 16/J A233_1─A724_1 (83.2 ─ 84.9) Diers et al. 1992; Demirbas et al. 2001 Rps3 13/F A757_1─R045_1 (63.1 ─ 70.1) Diers et al. 1992; Demirbas et al. 2002 Rps4 18/G A586_2 (111.2) Diers et al. 1992; Demirbas et al. 2003 Rps5 18/G T005_2 (81.5) Diers et al. 1992; Demirbas et al. 2004 Rps6 18/G Not defined Demirbas et al. 2001 Rps7 3/N Satt009─Satt125 (28.5 ─ 40.6) Weng et al. 2001 Rps8 8/A2 Sat_040─Satt228 (118.6 ─ 154.1) Burnham et al. 2003b RpsYu25 3/N Sat_186─Satt530 (30.1 ─ 32.8) Sun et al. 2011 † †† Chromosome; †† Possibly (Diers et al. 1992; Demirbas et al. 2004). 12 Table 1.2. Quantitative resistance loci to soybean aphids based on soybean consensus map (Song et al., 2004). Aphid resistance genes Chr. Rag1/rag1 † Flanking markers (cM) Reference 7/M Satt540─Satt435 (35.9─38.9) Hill et al., 2006a; Hill et al., 2006b; Li et al., 2007 Rag2 13/F Satt114─Satt510 (63.7─71.4) Mian et al., 2008b; Hill et al., 2009 Rag3/rag3 16/J Sat_339─Satt414 (28.0─37.0) Zhang et al., 2010; unpublished data rag4 13/F Satt649─Satt348 (5.4─15.3) Zhang et al., 2009 Rag5 13/F † /Linkage Group BARC-060107†† 16382 Chromosome; †† Single nucleotide polymorphism (SNP) marker. 13 Jun et al., 2012 CHAPTER 2 PHYTOPHTHORA ROOT ROT RESISTANCE IN SOYBEAN E00003 14 ABSTRACT Phytophthora root rot (PRR), caused by the oomycete Phytophthora sojae, is a devastating disease in soybean production. Using resistant cultivars has been suggested as the best solution for disease management. Michigan elite soybean E00003 is resistant to P. sojae and has been used as a PRR resistance source in breeding of new cultivars. However, the genetic control of PRR resistance in E00003 is unknown. To facilitate marker-assisted selection (MAS), the PRR resistance loci in E00003 and their map locations need to be determined. In this study, a genetic mapping approach was employed to identify major PRR resistant loci in E00003. The mapping population consists of 240 F4-derived lines developed by crossing E00003 with the P. sojae susceptible line PI 567543C. In 2009 and 2010, the mapping population was evaluated in the greenhouse for PRR resistance against P. sojae races 1, 4, and 7, using a modified rice grain inoculation method. The population was genotyped with single sequence repeat (SSR) and single nucleotide polymorphism (SNP) markers. The heritability of resistance in the population ranged from 83% to 94%. A major locus, contributing 50% to 76% of the phenotypic variation, was mapped within a 3 cM interval in the Rps1 region. Based on the specific responses to the tested races, this locus was determined to be Rps1k. To pinpoint the functional gene, the interval was ® further saturated with more BARCSOY SSRs and SNPs with TaqMan assays. Two SSRs and three SNPs were found located within the predicted functional gene; these are highly associated with PRR resistance in the mapping population. These breeder-friendly molecular markers can be used to improve MAS for PRR resistance. 15 INTRODUCTION Phytophthora root rot (PRR), caused by Phytophthora sojae, is the second most destructive disease of soybean [Glycine max (L.) Merr.] in the United States (Wrather and Koenning, 2006). Significant disease epidemics have occurred in the North Central states, such as Illinois, Indiana, Minnesota, Missouri and Ohio, and also in the southern states such as North Carolina, Louisiana, Kentucky and Oklahoma (Wrather and Koenning, 2006). The total estimated loss in U. S. soybean yield due to the disease was over 18 million t from 1996 to 2010 (Wrather and Koenning, 2011), equivalent to 7 to 8 billion U. S. dollars (O'Brien, 2010). Yield loss can vary with weather conditions. In Ohio, for instance, yield losses averaged 11% in years with rainy springs and 8% in years with normal spring precipitation (Dorrance and Mills, 2009). Phytophthora sojae infects soybeans at any growth stage primarily via the root system. High soil moisture favors disease development since P. sojae zoospores are only produced in saturated soil (Ho, 1969). The disease can only be partially managed using methods such as cultural practices and seed treatments with fungicides (Dorrance and Dennis, 2009; Schmitthenner, 1985). Integrated management strategies that combine host resistance with seed treatments were studied by Dorrance et al. (2009), who concluded that selecting resistant cultivars of the greatest importance. Based on the results of six decades of research, both partial and race-specific resistances to P. sojae have been identified (Burnham et al., 2003a; Dorrance et al., 2004). Nine loci, with a total of 15 alleles, have been reported responsible for race-specific resistance (Anderson and Buzzell, 1992; Athow and Laviolette, 1982; Athow et al., 1980; Burnham et al., 2003b; Buzzell and Anderson, 1992; Demirbas et al., 2001; Diers et al., 1992; Gao and Bhattacharyya, 2008; 16 Gordon et al., 2006; Kilen et al., 1974; Mueller et al., 1978; Sandhu et al., 2005; Sun et al., 2011; Weng et al., 2001). A high yielding Michigan soybean line, E00003, was found resistant to P. sojae races 4 and 7 in the Uniform Soybean Tests in Northern States 2002 (Crochet, 2002). There is insufficient evidence to trace the ancestor that contributed the PRR resistance to this line, thus it is unknown whether it carries any new PRR resistance genes or some specific alleles at existing resistance loci. The objective of this study was to characterize PRR resistance in E00003, and to develop breeder-friendly markers to facilitate marker-assisted selection. 17 MATERIALS AND METHODS Genetic mapping strategy was applied to identify PRR resistance loci in E00003 genome. Phenotypic data were obtained from greenhouse trials, and genotypic data were obtained with SSRs and SNPs. Resistance loci suggested in the literature were first tested with SSRs in those regions. Mapping population The genetic mapping population consists of 240 F4-derived lines using the single seed descent method (Roy, 1973) from the cross E00003 × PI 567543C. E00003 is resistant to P. sojae, whereas PI 567543C (Mensah et al., 2005), from Shandong Province in China, and is susceptible to P. sojae. Evaluation for Phytophthora sojae resistance Three P. sojae races, 1, 4, and 7, were used to evaluate the mapping population and its two parents in the MSU Plant Science Greenhouses in a total of six trials in 2009 and 2010. In each pathogen-inoculated trial, 12 seeds from each inbred line as one replicate, plus 36 seeds ® from each parent as three replicates, were planted in Baccto soil mix (Michigan Peat Company, Houston, Taxes). The pathogen isolates were kindly provided by Dr. Anna Dorrance from the Department of Plant Pathology at Ohio State University. The greenhouse was maintained at 26/15° day/night temperature, and sodium vapor lights were used to supplement light intensity C during the day (14 h). 18 For a higher throughput evaluation, the rice grain inoculation method was adapted, originally developed for evaluation of tree species for phytophthora resistance (Holmes and Benson, 1994). As used by Holmes and Benson, the rice inoculum were mixed well with sand, incorporated into a peat-vermiculite medium and sprinkled on the surface of a tray containing tree seeds. The method was modified by burying three rice grains together with one soybean seed in soil, providing the most appropriate disease pressure to differentiate PRR resistance level in our preliminary tests. This method allows the pathogen to penetrate the plant root naturally, mimicking natural infection for this soil-borne disease. In a 250-mL flask, 25 g white long-grain rice and 20 mL distilled water were mixed thoroughly. The flask was covered with doublelayered aluminum foil and autoclaved for 40 min twice in 24 h. After cooling, the rice grains in the flask were inoculated with three disks (5 mm diam.) of P. sojae mycelia obtained from the margin of an actively growing culture for each race. The flask was shaken daily to prevent the grains from compacting and ensure that all grains were uniformly colonized. After about 12 to 14 d of incubation, the rice grains were sampled and assayed for fungal colonization on V8 medium (Miller, 1955) 2 d prior to use as inoculum. Each soybean seed was buried in the soil with three grains of inoculated rice at a depth of 2.5 cm in 12.5-cm-deep square pots (700051C, T.O. Plastics, Clearwater, Minnesota). The pots were watered every other day for 14 d, and the numbers of live seedlings were counted. For the six trials, survival index (SI) of line j for trial i was calculated as follows: SIij = [(the number of plants of line j surviving in the trial i) /12] × 100. SI values of the parents, E00003 and PI 567543C, were calculated as SI = [(the number of plants surviving in the trial i) /36] × 100. The SI ranges from 0% for the most susceptible lines to 100% for the most resistant lines. 19 Genetic analysis Genotyping of the mapping population with SSR markers was carried out as described by Zhang et al. (2010). All genomic regions that had been suggested in the literature were considered as potential PRR resistance loci (Table 2.1). Bulked segregant analysis (BSA), as proposed by Michelmore et al. (1991), was used to obtain possible genomic regions of major PRR resistance. For each inoculation trial, 15 resistant lines with the largest SI, and 15 susceptible lines with the smallest SI, were selected to form one resistant bulk pool and one susceptible bulk pool. A total of 132 SSR markers covering the genomic regions with reported PRR resistance loci were first selected to test the bulks. Genomic regions potentially associated with PRR resistance were saturated further with more SSR markers and additional BARCSOY SSR markers (Song et al., 2010). Then, a subpopulation of 94 individual lines, as well as the two parents, was genotyped with polymorphic and trait-associated markers identified by BSA. The remaining lines of the entire mapping population were then genotyped with the markers that showed association with PRR resistance in the first 94 lines. Ten DNA samples including the susceptible and resistant parents, two segregating bulks, and six resistant inbred lines from the mapping population were genotyped with Illumina ® BeadChip (Illumina, Inc., San Diego, California) containing over 52,000 soybean SNP markers (Song et al., 2011). Three polymorphic SNPs associated with PRR resistance among the 10 ® samples were selected as candidates for TaqMan SNP allele-specific genotyping assays for the ® population. The sequences were subjected to the Customer Taqman Assay Design Tools of Applied Biosystems (ABI, Foster City, California) to obtain allele-specific primers and probes, which were synthesized by ABI. The entire mapping population was genotyped with allele- 20 ® specific SNP Assays. Taqman probe-based PCR reactions were carried out in 384-well plates ® with a total volume of 3 uL/well on a LightCycler 480 (Roche Applied Science, Indianapolis, Indiana). The PCR reaction mixture for the Taqman ® assay consisted of 20 ng of genomic DNA, ® 0.15 uL of 10X Taqman Assay, and 1.5 uL of 2X ABI Genotyping Master mix containing a modified Taq DNA polymerase, reaction buffer, MgCl2, and dNTPs (ABI). After 10 min preincubation at 95 º 45 PCR cycles were conducted with 10 sec denaturation at 95 º 30 sec C, C, annealing at 60 º and 10 sec extension at 72 º A final melting cycle for nonspecific amplicon C, C. screening was performed by raising the temperature to 95 º for 10 sec, lowering the C temperature to 40 º for 30 sec, then increasing the temperature to 83 º with continuous C C ® fluorescent acquisition followed by cooling to 40 º on the LightCycler 480. Data were C analyzed by the Roche Applied Science software version 1.5.0. Data analysis SI from the six inoculated trials were calculated and analyzed by the statistical software R. 2 The broad-sense heritability (h ) (Fehr, 1987) of PRR resistance to three races was calculated as 2 follows: h  2 σg 2 2 σ g  σ2 / r , where σg is standard deviation of genotype factor, σe is standard deviation from error term and r is the number of years. The two years were treated as two replications, with r = 2. Correlation coefficients for SI of the same pathogen race in 2009 and 2010 were estimated. Tukey's procedure was used for multiple line comparisons. A linkage map was constructed using 21 JoinMap 3.0 with the Kosambi function and a LOD score of 3 (Van Ooijen and Voorrips, 2001). Composite interval mapping (CIM) was performed using QTL Cartographer V2.5 (Wang et al., 2008) to locate PRR resistance loci with LOD threshold determined by 1000 permutations. The plots of LOD scores and the locus positions were generated using MapChart (Voorrips, 2002). RESULTS AND DISCUSSION Phenotype distribution Phytophthora sojae races 1, 4 and 7 were used to screen the mapping population for resistance to PRR. Race 25 was only tested with the two parents of the mapping population, since both parents are partially resistant to race 25. SI of the susceptible parent PI 567543C varied from 0.0 to 5.6%, while that of the resistant parent E00003 ranged from 41.7 to 91.7% (Table 2.2). E00003 and the population showed lower SI means in trials inoculated with race 4 in both years, which indicates that E00003 is less resistant to race 4. The SIs of three replicates of each parent were very closely correlated (P < 0.0001), implying minor experimental error in the trial. Therefore, one replication was sufficient for each trial. The broad sense heritability ranged from 83 to 94%. The correlations of race-specific SI between the two years were all significant for all three P. sojae races, 89.1, 77.2 and 78.3% respectively. Continuous phenotypic distribution of SI with major peaks was observed for each trial, with SI ranging from 0 to 100% (Fig. 2.1). 22 PRR resistance locus mapping The resistant and susceptible bulks were first genotyped with previous markers from the regions listed in Table 2.1 to investigate whether the Rps loci in E00003 was among those reported in the literature. From the bulk segregant analysis (BSA), a cluster of SSR markers on Chromosome 3 (linkage group N) were polymorphic and associated with PRR phenotypic data in the two bulks. These polymorphic SSR markers from BSA were further tested with 94 lines from the mapping population. SSR markers Satt631, Satt675, Satt485, Satt584 and Satt624 appeared to be associated with the phenotypic data, and were confirmed by genotyping the rest of the population. The five SSR markers formed one linkage group in the linkage analysis with JoinMap (Van Ooijen and Voorrips, 2001), and a major QTL was located between Satt631 and Satt675 within a 20 cM interval when the data were analyzed using the CIM method in WinQTLCart 2.5 (Wang et al., 2008) (data not shown). The interval was associated with the Rps1 region (Gardner et al., 2001). Though Rps1 is close to Rps7, Rps7 was eliminated because it is susceptible to races 1, 4, 7 and 25 (Dorrance et al., 2004). According to criteria on responses to P. sojae races (Dorrance et al., 2004) , the PRR resistance allele in E00003 was determined to be Rps1k, as it showed partial resistance to race 25 (data not shown). Within the flanking region by Satt631 and Satt675, there is another recently identified Rps locus, RpsYu25 (Sun et al., 2011). The authors claimed that RpsYu25 is a novel locus based on different responses to a set of various pathogen isolates, and developed a flow chart as a dichotomous key to differentiate Rps loci. The set of pathogens used in that study was not comparable with pathogen isolates that we used; thus, it was not obvious to eliminate RpsYu25as a candidate. However, we were able to exclude RpsYu25 after we further pinpointed the gene with SNP markers (discussed in analysis with SNP markers below). 23 With the goal of developing breeder-friendly SSRs that locate within a functional region, a total of 220 BARCSOY SSRs (Song et al., 2010) between Satt631 and Satt675 were screened with the bulks, and polymorphic BARCSOY SSRs were further genotyped with the entire population. Two BARCSOY markers, BARCSOYSSR_03_0249 and BARCSOYSSR_03_0250, were determined to be strongly associated with Rps1k. These two markers are located within the candidate genes for resistance to P. sojae identified by Gao et al. (2005). To further identify the region containing the resistance locus in E00003, a new linkage map was constructed by adding the two BARCSOY SSRs to the five SSRs mapped earlier (data not shown). The marker order followed the same order as was observed in consensus map and a latest map integrated with BARCSOY SSRs (Song et al., 2004; Song et al., 2010) and was highly comparable to that region of soybean genomic map. The interval containing the resistant locus was less than 2 cM between marker BARCSOYSSR_03_250 and Satt675. The LOD score was estimated at 28.9 to 53.3. The 2 2 R ranged from 0.46 to 0.67 with additive effect from 18.8 to 33.4%. Since the R only partially explained the total genetic variance as estimated by heritability, we investigated whether other PRR resistance loci existed. A total of 1328 SSRs evenly spread through the entire genome were screened with the DNA bulks, and only 15 SSRs were found to be polymorphic. However, none showed association with PRR resistance in the entire mapping population. Therefore, no other major PRR resistance loci were detected in E00003. Analysis with SNP Markers Of the 52,041 SNPs on the soybean BeadChip, 17,830 (34.3%) were polymorphic between the two parents. Among these SNPs, 200 (1.1%) showed consistent genotypes between resistant parent and resistant bulk, and susceptible parent and susceptible bulk. Among the 24 genotypes of the six resistant lines for these 200 SNPs, 76 SNPs were found to be associated with the PRR resistance. All these 76 SNPs are located on Chromosome 3 (linkage group N), confirming that no other major PRR resistance loci reside on other chromosomes. Of the 76 SNPs spreading from the physical position of 2,025,790 bp to 22,595,547 bp, 56 SNPs (73.7%) are clustered within the region 3,613,821bp to 5,948,099 bp, covering the Rps1k candidate gene region, (4,457,810 bp to 4,641,921 bp) (Gao and Bhattacharyya, 2008). This candidate gene family encoding coiled coil-nucleotide binding-leucine rich repeat (CC-NB-LRR)-type proteins could be involved in a hypersensitive response, producing pathogen recognition and defense response initiation (Gao and Bhattacharyya, 2008). Six SNP markers were designed for ® TaqMan SNP allele-specific genotyping assays at the following positions, Gm03_4487138, Gm03_4563499, Gm03_4567214, Gm03_4576493, Gm03_4578272, Gm03_4610670 (The numeric string indicates SNP position). MSUSNP03-1 (Gm03_4487138), MSUSNP03-2 (Gm03_4563499), and MSUSNP03-3 (Gm03_4610670) were selected to genotype the entire ® mapping population using TaqMan SNP allele-specific genotyping assays. A new linkage map was constructed while adding the three SNPs (Fig. 2.2), and QTL analysis was performed using this new map. The QTL positions on the new map are shown in 2 Fig. 2.2, and LOD scores and R are given in Table 2.3. The LOD scores were estimated to be 2 between 32.3 and 62.6. The R ranged from 0.50 to 0.76, with additive effects from 19.2 to 33.0%. These results indicated that about 50 to 76% of the phenotypic variation can be explained by Rps1k. The remaining heritability was due to some other minor effect QTL or experimental errors. 25 The positions of LOD peaks in Table 2.3 were estimated at 24.4 to 26.3 cM based on the position of markers Satt631 and Satt675 from soybean consensus map (Song et al., 2004). Considering physical positions, this candidate gene region is located within interval 4,475,877 to 4,563,799 bp (Fig. 2.2), within the Rps1k candidate interval, 4,457,810 bp to 4,641,921 bp (Gao and Bhattacharyya, 2008). RpsYu25 is located between 3,338,620 and 3,465,436 bp by converting the flanking markers Satt152 and Sat_186 from interval 30.1 to 32.8 cM (Song et al., 2010; Sun et al., 2011). Therefore, Rps1k is a different locus than RpsYu25. To validate the gene action of Rps1k, SI for each genotype of the three polymorphic allele-specific SNP markers was estimated using the combined SI of two years’ data for the same race (Table 2.4). Strong evidence of additive gene action was detected, since the average SI for the heterozygous type was significantly smaller than that for the homozygous resistant type, significantly larger than for the homozygous susceptible type, and not significantly different from the average of the two homozygous types at size of 0.05. In spite of advances in quantitative molecular genetics, due to genomics, computation, and statistics, the bottleneck in genetic analysis is now phenotyping, rather than genotyping (Walsh, 2009). The detection of Rps1k in the Michigan elite line E00003 has shown that the rice grain inoculation method is an effective high-throughput phenotyping approach to detect Rps loci with major effects. The method provides the opportunity for the soil-borne pathogen to interact with its host, mimicking natural infection of soybean, and avoid injury to epidermal tissues, which occurs in the hypocotyl splitting method. The rice grain method saves labor and time of splitting soybean seedlings. Within 14 days, thousands of rice grains can be prepared in a single patch as pathogen inoculum, and they are easier to handle. Compared with the vermiculite layer test (Thomison et al., 1991), growing the pathogen on one layer of agar in a petri dish for each 26 single pot is unnecessary. However, to detect partial resistance to PRR, lesion length measurement using a slant board test (Burnham et al., 2003a) has proven effective (Tucker et al., 2010). Although there might be some confounding effect with germination rate or soil factors, the validity of our findings was not affected. A separate germination test with non-pathogeninoculated seeds was conducted in the greenhouse in 2010. In the entire mapping population, 90% of the lines had a germination rate above 87.5%. The germination rate was calculated and applied as a phenotypic trait in the QTL analysis, where no QTL with LOD more than 1.0 were detected within the region of interest (data not shown). Further, we adjusted SI with germination rate to ΔSI as follows: ΔSIij = the number of surviving seedlings of line j in the non-pathogen germination test minus the number of plants surviving of line j in the original trial. There was a 2 only slight improvement on heritability and R in QTL analysis over the unadjusted SI (data not shown). Thus, we used unadjusted SI for simplicity. PRR resistance locus Rps1k was detected and confirmed in E00003 successfully. Additionally, two SSRs and three SNP allele-specific endpoint genotyping markers, MSUSNP03-1 (Gm03_4487138), MSUSNP03-2 (Gm03_4563499), and MSUSNP03-3 (Gm03_4610670) were identified and developed within the candidate gene region for the resistant line E00003. With these breeder-friendly SSR and SNP markers, marker-assisted selection can be performed efficiently and effectively, by using E00003 or other soybeans containing Rps1k as source for PRR resistance. 27 Table 2.1. Rps loci and QTLs underlying resistance to Phytophthora root rot and their locations on the soybean composite map. Linkage group names, marker names, and marker positions are updated as shown on soybean composite map (Song et al., 2004). Table contents updated according to Cornelius et al. (2005). Rps loci or QTL Rps1 Linkage Group N Rps2 J Rps3 F Rps4 G Rps5 G Rps6 Rps7 G N † Flanking markers (cM) Reference Satt159─Satt009 (27.1 ─ 28.5) A233_1─A724_1 (83.2 ─ 84.9) A757_1─R045_1 (63.1 ─ 70.1) Weng et al. 2001 Diers et al. 1992; Demirbas et al. 2001 Diers et al. 1992; Demirbas et al. 2002 Diers et al. 1992; Demirbas et al. 2003 Diers et al. 1992; Demirbas et al. 2004 Demirbas et al. 2001 Weng et al. 2001 A586_2 (111.2) T005_2 (81.5) Not defined Satt009─Satt125 (28.5 ─ 40.6) Sat_040─Satt228 (118.6 ─ Rps8 A2 154.1) QTL F Satt252─Satt423 (16.1 ─ 20.6) QTL D1b Satt266─Satt579 (59.6 ─ 75.9) Sat_186─Satt530 (30.1 ─ RpsYu25 N 32.8) † possibly (Diers et al. 1992; Demirbas et al. 2004). 28 Burnham et al. 2003b Burnham et al. 2003a Burnham et al. 2003a Sun et al. 2011 Table 2.2. Survival index (mean ±SE) of the mapping population with 240 F4 derived lines and the parents E00003 and PI 567543C in the six P. sojae inoculated greenhouse trials conducted in 2009 and 2010. Survival index = the number of surviving plants / total number of seeds planted × 100%. Trial 1 2 3 4 5 6 † Year 2009 2010 P. sojae race 1 4 7 1 4 7 Survival index Parents F4-derived lines PI E00003 Population † 567543C mean± SE 0.0± 0.0 91.7± 0.0 48.7± 36.2 0.0± 0.1 44.4± 25.5 26.8± 27.0 5.6± 4.8 69.4± 29.3 46.7± 34.2 0.0± 0.0 61.1± 17.3 51.2± 39.7 0.0± 0.1 41.7± 25.0 36.8± 32.7 2.8± 4.8 80.6± 12.7 50.7± 39.1 SE, standard error. 29 Range 0.0 – 100.0 0.0 – 100.0 0.0 – 100.0 0.0 – 100.0 0.0 – 100.0 0.0 – 100.0 Table 2.3. Phytophthora root rot (PRR) resistance locus detected in the mapping population E00003 × PI 567543C using composite interval mapping (CIM) method. Chromosome/linkage group 03/N is based on Soybase (Grant et al., 2010). Trial 1 Year P. sojae race Peak Pos. (cM) 1 24.4 LOD Marker near the peak † 2 (threshold) R †† BARCSOYSSR_03_0250 62.6(3.7) MSUSNP03-1 2 2009 4 26.3 32.3(2.5) (Gm03_4487138)§ 3 7 24.4 BARCSOYSSR_03_0250 32.1(2.4) 4 1 25.4 BARCSOYSSR_03_0250 56.01(2.8) 5 4 24.4 BARCSOYSSR_03_0250 36.6(3.2) 2010 MSUSNP03-1 6 7 26.2 58.9(3.6) (Gm03_4487138) † Peak position converted according to consensus map (Song et al., 2004); †† Additive effect 0.76 31.6 0.50 19.2 0.59 0.69 0.56 26.8 33.0 24.8 0.70 32.9 LOD score threshold for each trial determined by 1000 permutations in CIM by WinQTLCart (Wang et al., 2008); § Single nucleotide polymorphism (SNP) markers from Song et al. (2011). 30 Table 2.4. Average survival index for different genotypes of the three single nucleotide polymorphism (SNP) markers in the mapping population E00003 × PI 567543C. Numbers followed by different letters within the same row are significantly different at size of 0.05. The three SNP markers are MSUSNP03-1 (Gm03_4487138), MSUSNP03-2 (Gm03_4563499), and MSUSNP03-3 (Gm03_4610670). SNP Marker E00003 type Heterozygous type MSUSNP03-1 MSUSNP03-2 MSUSNP03-3 82.8a 58.7b 82.6a 82.1a 57.5b 57.1b MSUSNP03-1 MSUSNP03-2 MSUSNP03-3 53.9a 53.4a 54.0a 31.1b 29.2b 29.4b MSUSNP03-1 MSUSNP03-2 MSUSNP03-3 79.6a 79.1a 79.1a 59.8b 58.5b 59.6b † † PI 567543C type Race 1 13.6c 14.2c 12.7c Race 4 5.9c 6.5c 7.2c Race 7 14.6c 15.7c 15.5c Average of PI 567543C and E00003 types 48.2b 48.4b 47.4b 29.9b 30.0b 30.6b 47.1b 47.4b 47.3b Allele-specific SNP markers designed from 52K Soybean Bead chip (Song et al., 2011). 31 Figure 2.1. Phenotypic distribution of survival index (SI) of the mapping population among 240 F4 derived lines from PI 567543C × E00003. Parent SIs were indicated by arrows. SI was calculated as: SIij = [(the number of plants surviving of line j in the inoculated trial i) /12] × 100. A 2009 Race 1 80 PI 567543C E00003 Count 60 40 20 0 0 B 25 37.5 50 62.5 75 87.5 100 More 2010 Race 1 80 PI 567543C Count 60 E00003 40 20 0 0 25 37.5 50 62.5 75 Survival index 32 87.5 100 More Figure 2.1. (cont’d) C 2009 Race 4 80 PI 567543C E00003 Count 60 40 20 0 0 25 D 37.5 50 62.5 75 87.5 100 More 37.5 50 62.5 75 Survival index 87.5 100 More 2010 Race 4 80 PI 567543C Count 60 E00003 40 20 0 0 25 33 Figure 2.1. (cont’d) E 2009 Race 7 80 PI 567543C Count 60 E00003 40 20 0 0 F 25 Count 50 62.5 75 87.5 100 More 2010 Race 7 80 60 37.5 PI 567543C E00003 40 20 0 0 25 37.5 50 62.5 75 Survival index 34 87.5 100 More Figure 2.2. Location of Phytophthora root rot resistance locus in E00003 as mapped with the composite interval mapping method. Linkage map of the SSR markers and the SNP markers was constructed with the genotypic data from the mapping population. Bars show intervals of loci mapped with different trials. LG03 62 61 60 59 58 57 56 55 54 53 52 51 50 49 48 47 46 45 44 43 42 41 40 39 38 37 36 35 34 33 32 31 30 29 28 27 26 25 24 23 22 21 20 19 18 17 16 15 14 13 12 11 10 9 8 7 6 5 4 3 2 1 0 15.0 15.2 15.7 MSUSNP03-1(Gm03_4487138) MSUSNP03-2(Gm03_4563499) MSUSNP03-3(Gm03_4610670) 21.9 Satt675 24.5 Satt485 27.3 Satt584 29.1 Satt624 35 Race7_2010 BARCSOYSSR_03-0250 Race7_2009 10.5 Race4_2010 BARCSOYSSR_03-0249 Race4_2009 8.1 Race1_2010 Satt631 Race1_2009 0.0 CHAPTER 3 APHID RESISTANCE IN WILD SOYBEAN GLYCINE SOJA 36 ABSTRACT Soybean aphid can cause significant quality damage and yield loss. Host-resistance is cost-effective and environmentally friendly solution. E08934, a single plant selection from a wild type soybean, Glycine soja accession 85-32, is resistant to aphids. To map the resistance loci in E08934 conferred from G. soja, a mapping population consisting of 140 F3-derived lines was developed by crossing E08934 with the aphid susceptible line E00003 (Glycine max). Aphid resistance phenotyping was conducted one season in the greenhouse and three seasons in the field cages. The broad-sense heritability for aphid resistance from field trials is 0.84. According to the results of genotyping eight resistant and eight susceptible lines on the 6K soybean SNP Beadchip, three genomic regions, 38.8 to 43.9 mega base pair (Mbp) on Chromosome (Chr.) 8 (linkage group (LG) A2), 4.8 to 11.3 Mbp and 24.6 to 28.5 Mbp on Chr. 16 (LG J) were detected with clusters of polymorphic SNPs consistent with the aphid damage index. After genotyping the entire population, using polymorphic SSRs from these three regions integrated with SNP ® markers in TaqMan assays, two major QTLs were detected to be significant (P < 0.001) in intervals of 13.5 cM between Sat_382 and Satt455 on Chr. 8, and 3.5 cM between Satt693 and Sat_370 on Chr. 16, respectively. The locus on Chr. 8 explained 40.8% of the phenotypic variation in the greenhouse trial, and 46.4, 19.5 and 39.1% in the field trials in 2009, 2010 and 2011, respectively. The locus on Chr. 16 explained 12.5 to 22.9% of the phenotypic variation. Further, these two loci were both confirmed in a validation population consisting of 252 F2derived lines from cross E08934 with E08929, an accession from PI 567541B. The no-choice test indicated both loci provide antibiotic resistant to aphids. The novel locus between Sat_382 37 and Satt455 on Chr. 8 (LG A2) is denoted as Rag6, since it showed significant partially dominant effect in the validation population (P < 0.05). A new aphid resistance source is of foremost importance, considering the problem of evolving aphid biotypes that may defeat the existing resistance gene. With closely linked SNPs to these resistance loci, breeding for aphid resistance from wild type soybean can be performed more effectively. 38 INTRODUCTION The soybean aphid (Aphis glycines Matsumura) was found in the Midwest region of the United States in 2000, since then it has dispersed to most soybean-growing states and become a serious destructive insect pest in the USA (Jun et al., 2012; Kim et al., 2008; Mian et al., 2008a; Ragsdale et al., 2004; Zhang et al., 2010). Categorized as phloem feeders, the aphids (Hemiptera) have typical piercing-sucking mouthparts that enter vascular tissue and feed on the plant by sucking phloem sap, causing curling, wilting, yellowing, stunting and plant death under heavy infestations (O'Neal and Johnson, 2010; Zhang et al., 2010). By jeopardizing water and nutrient absorption from leaves and stem, soybean aphids can significantly reduce biomass production and seed oil concentration (Beckendorf et al., 2008). Ostlie (2002) reported Minnesota soybean yield loss to aphids exceeded 50% in 2001. Maximum possible yield loss due to soybean aphid was estimated to be 48 to 75% for infestation stage starting at the V5 stage and the R2 stage (Fehr and Cavinese, 1977) based on study of caged soybean plants (Catangui et al., 2009; Catangui et al., 2010; O'Neal et al., 2010). Other severe threats of virus-transmission and seed quality losses were also reported due to soybean aphid infestation (Clark and Perry, 2002; Davis et al., 2005; Hartman et al., 2001; Hill et al., 2001; Iwaki et al., 1980; Mueller and Grau, 2007; Pedersen et al., 2007). In pursuit of chemical control against phloem feeders such as aphids and white flies, neonicotinoids were the first commercial insecticide used as seed treatments or foliar formulations (Buchholz and Nauen, 2002; Elzen, 2001; Jeschke and Nauen, 2008; Mukherjee and Gopal, 2000; Weichel and Nauen, 2004). However, insecticides significantly increase 39 agriculture input cost and may leave residuals in the environment, harming the natural enemies of pests and beneficial insects (Derksen et al., 2008; Ohnesorg et al., 2009; Southwick et al., 2004). Many studies have suggested that host-resistance is a more environmental friendly solution for control of soybean aphid in comparison with chemical control (DiFonzo, 2009; Hill et al., 2004; Jun et al., 2012; Mensah et al., 2005; Mensah et al., 2008; Mian et al., 2008a; O'Neal and Johnson, 2010; Song and Swinton, 2009; Wiarda et al., 2012; Zhang et al., 2009; Zhang et al., 2010). Plant host resistance can be categorized as antibiosis and antixenosis (Painter, 1951). Antibiosis involves toxic secondary metabolites in host plant that disturb insect biology and reproduction, while antixenosis resistance repels insects with non-preference for specific plants. Endeavors have been made in identifying host resistance sources within these two categories. These include PI 567541B, PI 567598B and PI 230977 with antibiosis, and PI 567543C, PI 567597C, PI 595099, PI 567301B with antixenosis resistant to aphids (Hesler et al., 2007; Jun et al., 2012; Mensah et al., 2005). Genetic studies are of crucial importance to better employ the resistance genes. To date, seven major resistance alleles Rag1, rag1, Rag2, Rag3, rag3, rag4 and Rag5 have been reported for aphid resistance from cultivars and plant introductions. Dominant allele Rag1 (Hill et al., 2006a; Hill et al., 2006b; Li et al., 2007) and recessive gene rag1 (Zhang et al., 2009) were mapped to Chr. 7 (LG M) between marker Satt435 and Satt540. Dominant allele Rag2 (Mian et al., 2008b), Rag5 (Jun et al., 2012) and recessive Rag4 (Zhang et al., 2009) were mapped to Chr. 13 (LG F). Partially dominant allele Rag3 (Zhang et al., 2010) was mapped on Chr. 16 (LG J) between marker Sat_339 and Satt414. Further, commercial aphid resistant soybean germplasm has been released, such as “Sparta” from Michigan State University and aphid tolerant varieties with Rag1 gene from Monsanto. However, it is remaining challenging that aphid resistant genes 40 can be overcome by new aphid biotypes (Hill et al., 2010). Therefore, identifying new sources with new aphid resistance loci is of considerable importance and urgency. Wild type soybean, Glycine soja (G. soja), has been proven resistant to a broad range of disease and insects, such as mosaic virus, soybean cyst nematode and soybean aphid (Diers et al., 2005; Hill et al., 2004; Kabelka et al., 2005; Kabelka et al., 2006; Li and Cao, 2011; Pazdernik et al., 1997; Ram et al., 1984; Wang et al., 2001; Winter et al., 2007). Yang et al. (2004) identified three G. soja accessions, 85-32, 85-39 and 85-1, with aphid resistance. To adapt the aphid resistance genes from G. soja to cultivated background, 85-32 was further crossed with Jiyu 71, an aphid susceptible variety. E08934 is a single F4 plant selection that is resistant to soybean aphids, derived from the cross Jiyu 71 × 85-32. The objective of this study was to identify aphid resistant loci in E08934, which was derived from G. soja accession 85-32 using linkage-based quantitative trait locus (QTL) mapping with SSR and SNP markers, confirm resistance in a different genetic background and determine resistance type. 41 MATERIALS AND METHODS Linkage-based QTL mapping was employed with bi-parental populations to locate and validate aphid resistance loci. A mapping population consisting of 140 F3-derived lines was used for initial mapping, and a confirmation population of 240 F2-derived lines was used to validate the aphid resistance loci. Aphid resistance loci mapping Population development and phenotype evaluation The mapping population was developed by crossing aphid-resistant line E08934, derived from a G. soja accession, with an aphid-susceptible, advanced breeding line E00003 (Glycine max), and followed with single seed descent. The population was evaluated in choice-test for aphid resistance in the greenhouse 2010, and in the field cages, in 2009, 2010 and 2011. Only the parents were replicated three times in each trial, rather than the entire mapping population, because aphid resistance has been considered as a high heritability trait (about 0.90) according to previous study (Zhang et al., 2009). In the spring of 2010, one greenhouse trial was conducted in the Plant Science Greenhouse at Michigan State University (MSU) in East Lansing, Michigan. In the summers of 2009, 2010 and 2011, field trials were performed independently on the Agronomy Farm of MSU in East Lansing. The greenhouse maintenance, planting strategy in the field cage, aphid infestation, evaluation scale (Mensah et al., 2005; Mensah et al., 2008) were adopted as described by Zhang et al. (2010). The phenotypic data was scored when the susceptible parent E00003 reached the maximum of the rating scale, with more than 800 aphids 42 per plant. An aphid damage index (DI) defined as DI = ∑ (scale value × no. of plants in the category) / (4 × total no. of plants) × 100, ranging from 0 being no infestation to 100 being the most severe damage (Mensah et al., 2005; Mensah et al., 2008) was used as phenotypic indicator in quantitative trait analysis. DNA preparation and marker genotyping Plant tissues of the mapping population and the two parents were collected during the field cage trial in 2009. CTAB (hexadecyltrimethyl ammonium bromide) DNA extraction protocol was adopted from Zhang et al. (2010). Two segregating bulks were formed with 15 most resistant and 15 most susceptible lines for the purpose of a bulk segregant analysis (BSA), proposed by (Michelmore et al., 1991). Firstly, these two bulks, together with the two parents, were genotyped on 52K soybean SNP Beadchip, covering the entire soybean genome with more than 52,000 SNPs (Song et al., 2011). Then, eight lines selected from the resistant pool and eight lines from the susceptible pool were individually genotyped on newly developed 6K soybean SNP Beadchip (Illumina, CA), covering the most gene-abundant genomic regions throughout the soybean genome (Qijian Song et al. unpublished data). After the selective genotyping above, simple sequence repeat (SSR) markers from candidate genomic regions were used to genotype the two segregating pools and the two parents. In association analysis, SSRs correlated with traits were used to genotype the remaining lines of the entire mapping population. ® To achieve better resolution, SNP markers for TaqMan SNP allele-specific genotyping assays were designed with the information from SNPs located within candidate region on 52K and 6K chips. These SNPs were further genotyped with the entire mapping population. Taqman 43 ® ® probe-based PCR protocol using LightCycler 480 was performed as described by Zhang et al. (2012 submitted to Crop Sci.). Statistical analysis and mapping analysis The statistical correlation analysis on aphid damage index data of the four trials was carried out with R software. The broad-sense heritability of DI from the three-year field trial was estimated according to Fehr (1987) as follows: Yij = µ + Genotypei + ɛ. i=1,2,…140; j=2009, 2010 and 2011. Linkage maps were constructed with JoinMap 4.0 using Kosambi’s and LOD of 3.0 in regression method (Van Ooijen, 2006). Composite interval mapping (CIM) in QTL Cartographer Version 2.5 (Wang et al., 2008) with 1000 permutations described by (Zhang et al., 2010) was conducted to determine the locations of the aphid resistance loci from G.soja. The maps with locus positions were visualized by MapChart (Voorrips, 2002). No-choice test data were also analyzed with one-way analysis of variance in R software, at significance level of 0.05. Validation of aphid resistance loci To confirm the location and determine the gene action of the aphid resistance loci identified from E08934, a validation population was developed by crossing E08934 with E08292, an aphid-resistant accession from PI 456741B with recessive alleles rag1 and rag4 (Zhang et al., 2009). In both summer of 2010 and 2011, the entire validation population together with the two parents were evaluated for aphid resistance in field-cage trials similar to the mapping population. DNA extraction was performed using the CTAB method. Polymorphic and associated SSRs and 44 SNPs from the regions identified in the mapping population, along with markers in region rag1 and rag4 were genotyped in the validation population. Linkage and mapping analysis were carried out in the same manner as above in the mapping population. Data of gene action and gene combination in the validation population were analyzed in one-way analysis of variance with Bonferroni correction in R software, at a significance level of 0.05. No-choice test In the spring of 2012, three replications of aphid no-choice test were performed with E00003 (susceptible control), PI 567598B (antibiotic resistant control), line 24 (possessing Rag3(?) on Chr. 16), line 131 (possessing Rag6 on Chr. 8), and line 19 (possessing both Rag3(?) and Rag6) from the mapping population, in the Plant Science Greenhouse at MSU. In each replication, eight seeds from each line were planted in a 105-mm-diameter and 125-mm-deep plastic pot, maintained at 26/15° day/night temperature with sodium vapor lights as supplement. C Each plant was infested with two healthy wingless aphids at the V1 stage (Fehr and Cavinese, 1977) and immediately covered with a mesh-cage from bottom to top (Mensah et al., 2008). Aphid resistance was rated, and the aphid damage index was calculated, as mentioned in the mapping population phenotype evaluation, when the susceptible control E00003 reached the maximum of the rating scale, the most severe damage stage. Data of average damage index were analyzed using one-way analysis of variance in R software. 45 RESULTS Phenotype distribution of the mapping population The aphid damage index (DI) of the two parents, E08934 and E00003, together with the 140 F3-derived lines from the mapping population is summarized in Table 3.1. In all four trials, resistant parent E08934, derived from G. soja accession had significantly lower DI than susceptible parent E00003 (P < 0.001). DI of the F3-derived lines from the mapping population varied significantly (P < 0.001) from 6.3 to 100.0 with standard errors of 19.6 to 27.4. The correlations between the three field trials are 0.71, 0.65 and 0.63 (P < 0.05). The broad-sense heritability for DI of the field trials was estimated as 0.84 (P < 0.001). The distribution of DI from the mapping population in the greenhouse trial and the three year field trials are shown in Fig. 3.1 A-D. The field trials in 2009 and 2010 showed similar patterns that are bimodal with a ratio of 1:1. However, no pattern was observed in the 2009 greenhouse trial and the 2011caged field trial (Fig. 3.1). Aphid resistance locus mapping Overall, 95% of the SNPs from the 52K SNP Beadchip called the genotype successfully for the two parents and the two segregating bulks from the mapping population, with 37.6% polymorphic SNPs between the two parents and 26.4% between the two bulks. Polymorphic SNPs with genotypes consistent with sample phenotypes were distributed throughout all linkage 46 groups. Interestingly, polymorphic SNPs from LG A2 and J clustered more intensively than other groups (data not shown). From the genotypic data of 6K SNP Beadchip with eight resistant and eight susceptible lines selected from the two bulks, three regions were detected with clustered SNPs that significantly correlated with aphid resistance for all four trials (P < 0.001, data not shown). They are 38.8 to 43.9 mega base pair (Mbp) on LG A2, 4.8 to 11.3 Mbp and 24.6 to 28.5 Mbp on LG J. 104 SSRs were tested on LG A2 and J on consensus map (Song et al., 2004). Satt209 and Satt455 on LG A2, Satt693 and Sat_370 on LG J showed highly consistent genotypes with the aphid resistance for the two parents and bulks, and further approved correlations with the entire mapping population. Therefore, to cover ± cM region nearby, a total of six SSRs from LG A2, 20 eight SSRs from LG J, were genotyped with the remaining lines of the entire mapping population. ® Eight TaqMan SNP allele-specific genotyping markers were developed, including MSUSNP08-1 (Gm08_23293155), MSUSNP08-2 (Gm08_40320904), MSUSNP08-3 (Gm08_41114696) and MSUSNP08-4 (Gm08_45189358) derived from 38.8 to 45.2 Mbp of LG A2, MSUSNP16-10 (Gm16_6262227), MSUSNP16-11 (Gm16_6413214), MSUSNP16-12 (Gm16_6423098) and MSUSNP16-15 (Gm16_8051585) derived from 4.8 to 11.3 Mbp region of LG J. With the genotypic data of SSRs and SNPs, nine markers formed one linkage group with 57.7 cM lone (Fig. 3.2A), denoted as Chr. 8/LG A2 and 12 markers formed another linkage group with 36.8 cM in length (Fig. 3.2D) Chr.16/LG J, and are highly comparable with consensus map (Song et al., 2004) (Fig. 3.2-B and E). The linkage maps were used with CIM method in QTL analysis. On LG A2 (Chr. 8), one QTL was consistently detected between SNP MSUSNP08-2 (Gm08_40320904) and Satt209 for all four trials (Table 3.2; Fig. 3.2A), explaining 19.5 to 46.4% of the phenotypic variation. On LG J (Chr. 16), a QTL located within 47 the interval between Satt693 and Sat_370 was identified for 2010 greenhouse trial, 2009 and 2010 field trials, explaining 12.5 to 22.9% of the phenotypic variation. As expected, the resistance allele comes from E08934. However, no significant interaction was detected between these two loci with multiple-interval mapping method in QTL Cartographer (data not shown). Aphid resistance loci validation The validation population contained 252 F2-derived lines from the cross of E08929 × E08934. The aphid damage index of the population was continuously distributed, but skewed to the resistant category in both field trials 2010 and 2011. The correlation between the two trials was 69.7% (P < 0.05). After the entire validation population was genotyped, seven SSRs with three SNPs from LG A2 (Chr. 8) (Song et al., 2010) formed one linkage group (Fig. 3.2C). Ten SSRs with two BARCSOYSSR markers formed LG J (Chr. 16) and six SSRs formed LG M (Chr. 7) in Fig. 3.2F-G. The marker order is highly comparable with consensus map but with inflation (Fig. 3.2B-H). Marker data with phenotypic traits were analyzed with QTL Cartographer in CIM method. On Chr. 8, one QTL was detected MSUSNP08-1 (Gm08_23293155) and Satt455 for both trials (Fig. 3.2C). That interval is equivalent to 13.5 cM onto consensus map between marker Sat_382 and Satt455 (Fig. 3.2B). This confirms the location of the resistance locus identified on Chr. 8 in the mapping population. This locus was denoted as Rag6 for convenience and will be discussed later. For LG J (Chr. 16), one QTL was detected within the same interval as in the mapping population between Satt693 and Sat_370 in the field trial 2011. The interval detected by field trial 2010 between Satt693 and Satt456 is larger, but covering the region identified in the 48 mapping population (Fig. 3.2F). This confirmed the finding of the locus on LG J in the mapping population. As one of the parents of the validation population, E08929 is an accession from PI 567541B, therefore, it theoretically carries aphid resistance loci rag1 and rag4. The QTL detected from LG M (Chr. 7) in both field trials confirms the location of rag1 between Satt540 and Satt435 (Fig. 3.2G-H). These three loci detected in the validation population explained 4.1 to 13.9% of the phenotypic variation individually (Table 3.2). Though they explained just a small proportion of the phenotypic variation, all three loci detected from the two years field trials are significant by 1000 permutations (P < 0.01). The resistance allele of the locus on Chr. 7 comes from the parent E08929, and the other two resistance alleles on Chr.8 and Chr. 16 respectively come from parent E08934. The recessive allele rag4 was not detected in the validation population. Gene action in validation population To determine the gene action of the loci in the validation population, the 252 F2-derived lines were divided into three genotypic groups for the markers that are closest to the peak LOD score, Satt540 for rag1, Satt209 for the locus on LG A2, and BARCSOYSSR_16_0371 for Rag3(?) (Fig. 3.3). Average DI for each genotypic group was calculated and compared in Table 3.3. We first tested whether the average DI significantly differs among the three genotypic groups for each marker by one-way analysis of variance (ANOVA). The results showed the association was significant even after adjusting for multiple testing (Bonferroni correction) (P < 0.0001, Table 3.3). The pairwise differences were calculated among three groups for each 49 marker to investigate gene actions. For marker Satt540, the closest marker to rag1, the average DI of the heterozygous group was significantly (P < 0.05) lower than that of the homozygous group of allele from E08934, but not significantly different from that of the homozygous group of allele from E08929 in both 2010 and 2011 field data (Fig. 3.3A). This result suggests that rag1 from parent E08929 may have a partially dominant effect instead of a recessive effect. For marker Satt209, the closest marker to the locus on LG A2, the average DI of the heterozygous group was significantly (P < 0.05) lower than that of the homozygous group of allele from E08929, but not significantly different from that of the homozygous group of allele from E08934 in both years (Fig. 3.3B), suggesting partially dominant effect of this locus. The location of this novel locus from G. soja is different from that of the locus identified by Jun et al. (2012) on LG A2. Therefore, we denote it as Rag6 using the conventions of the Soybean Genetics Committee. For marker BARCSOYSSR_16_0371 on LG J (Chr. 16), located within the interval of Rag3(?), the average DI of the heterozygous group was neither significantly different from that of the homozygous group of allele from E08929, or from that of the homozygous group of allele from E08934 in both years (Fig. 3.3C) at size of 0.05. However, the average DI of the homozygous group of allele from E08929 was significantly different from that of the homozygous group of allele from E08934 (P <0.05). Therefore, further study is required to determine the gene action of Rag3(?) on LG J from G. soja. Combination of aphid resistance loci in validation population Progeny was separated that possess different aphid resistance genes or without any resistance gene from the validation population. The average DI of different combination of 50 Rag3(?), Rag6 and rag1 from the 2010 field trial was estimated (data not shown) and plotted in Fig 3.4. Average DI was calculated for progeny that have no resistance genes, one resistance gene, two resistance genes combined and all three genes combination of Rag3(?), Rag6 and rag1 (Fig. 3.4). A clear trend of decreasing DI can be observed as more resistance genes are combined. However, the difference of average DI observed was not significant due to the small number of individuals in each combination. This trend may become significant and more meaningful if more progeny were included. No-choice test in the mapping population The DI of E00003, PI 567598B, line 24 (Rag3(?)), line 131(Rag6), and line 19 (Rag3(?) and Rag6) from the mapping population were analyzed and summarized in Table 3.4. Fig. 3.5 shows the bar graph of the DI with 95% confidence interval derived from one-way analysis of variance conducted at size of 0.05. Though the average DI of PI 5767598B is significantly lower than that of line 24 (Rag3(?)), are higher than that of line 131(Rag6), and line 19 (Rag3(?) and Rag6), the average DI of all these lines are significantly lower than that of the susceptible check E00003. The aphid resistance loci in PI 567898B were proven to be antibiosis (Mensah et al., 2005); therefore, we conclude that the locus Rag3(?) and the new locus Rag6 both possess antibiosis aphid resistance. 51 DISCUSSION A novel aphid resistance gene Rag6 from wild type soybean Glycine soja was mapped in an interval between marker Sat_382 and Satt455 on Chromosome 8 in the F3-derived mapping population, equivalent to 13.5 cM on soybean consensus map (Song et al., 2004). It was further confirmed to the same interval in a validation population. To date, Rag5 is the only gene reported, which has been mapped to Chr. 8 and located between Satt437 and Satt327 interval (Jun et al., 2012). The location of Rag6 is different from that of Rag5, because the marker Satt209 closest to the peak of LOD score is 18.6 cM distance away from Satt327, based on soybean consensus map (Song et al., 2004). Therefore, we denote it as Rag6 based on the conventions of the Soybean Genetics Committee. Meng et al. (2011) reported one QTL qRa_1 located near marker Satt470 on Chr. 8, conferring aphid resistance in a high isoflavone content variety “Zhongdou27”. Compared to consensus map (Song et al., 2004), Rag6 is 6.2 cM away from marker Satt470. Due to the lack of information of the exact location of qRa_1, we cannot conclude whether Rag6 is different from qRa_1. In the validation population, the distribution of phenotypic traits and the aphid damage index were skewed to the resistant category, because both parents E08934 and E08929 possess resistance loci. Based on the results in Fig. 3.4, the more resistance genes a progeny has, the more resistant it shows. Therefore, the validation population skewed to the resistant category. Theoretically, E08929 as an accession from PI 567541B, should carry both rag1 and rag4 genes (Zhang et al., 2009). However, rag4 was not detected in the validation population. According to 52 SNP genotypic data from SNP Beadchip, E08929 was heterozygous in the rag4 region based on rag4 fine mapping results (Jiazheng Yuan unpublished data). With Rag6, Rag3(?) and rag1 present, the effect of the rag4 allele from the heterozygous parent E08929 was masked in the validation population. Another possibility is that E08929 did not inherit rag4 from the original plant introduction. More detailed information of sequencing might be helpful in a future study to verify if rag4 is present in E08929. From this study, novel aphid resistance allele and locus with molecular markers were identified from exotic soybean source, Glycine soja. It is of great importance and urgency to identify new aphid resistance genes in new resistant sources. With the detailed information on SSR and SNP markers that are closely linked with the new aphid resistance genes, breeders can effectively introgress the resistance genes from G. soja into aphid susceptible cultivars. 53 Table 3.1. Aphid damage index (DI) for two parents of the mapping population, E08934 and E00003, and 140 F3-derived lines, in the greenhouse and field trials. Trials Parents F3-derived lines E08934 E00003 Mean Greenhouse 2010 † 26.6a 84.4b Range 47.3 14.1-85.9 †† SE 27.4 Field cage 2009 12.5a 87.5b 46.4 12.5-87.5 19.6 2010 12.5a 87.5b 52.3 12.5-93.8 26.1 2011 20.8a 100.0b 57.7 6.3-100.0 23.1 DI = ∑ (scale value × no. of plants in the category) / (4 × total no. of plants) × 100, ranged from 0 being no infestation and 100 being the heaviest infestation (Mensah et al., 2005); † Means followed by different letters in the parent column within the same row are significantly different at significant level of 0.0001; †† SE standard error. 54 Table 3.2. Aphid resistance loci identified in the mapping population E00003 × E08934 and validation population E08929 × E08934 with composite interval mapping method with 1000 permutations. MSUSNP08-1 (Gm08_23293155), and MSUSNP08-2 (Gm08_40320904) are single nucleotide polymorphism (SNP) markers derived from 6K SNP chip; MSUSNP16-11 (Gm16_6413214) and MSUSNP16-12 (Gm08_6423098) are derived from 52K SNP chip. Populatio Trials n E00003 × Greenhouse E08934 2010 Field cage 2009 2010 2011 E08929 × Field cage E08934 2010 2011 Chr/LG† Peak Pos. †† Flanking markers 8/A2 16/J 18.8 25.2 8/A2 16/J 8/A2 16/J 8/A2 R2§ a# MSUSNP08-2-Satt455 19.5 MSUSNP16-1110.6 MSUSNP16-12 40.8 22.9 12.7 9.7 18.8 25.2 18.8 25.2 20.8 MSUSNP08-2-Satt455 Satt693-Sat_370 Satt455-Satt209 Satt693-Sat_370 MSUSNP08-2-Satt455 22.9 10.3 9.1 7.0 16.0 46.4 16.7 19.5 12.5 39.1 19.0 11.9 11.7 10.6 14.7 7/M 8/A2 16/J 11.1 79.8 16.5 7.3 4.6 6.4 8.4 6.4 6.9 8.5 -7.1 -6.1 7/M 8/A2 16/J 11.1 81.8 15.5 Satt540-Satt435 MSUSNP08-1-Satt455 BARCSOYSSR_16_0 383-Satt456 Satt540-Satt435 MSUSNP08-1-Satt455 BARCSOYSSR_16_0 383-Sat_370 10.8 3.4 10.2 13.9 4.1 12.5 10.7 -4.9 -7.8 LOD † Chromosome/linkage group. The chromosome number and linkage group name are according to the SoyBase (Grant et al., 2010); †† Peak position is presented in centi Morgan (cM); § 2 R , percentage of phenotypic variation that can be explained by the locus; # Additive effect. 55 Table 3.3. Summary of average damage index for genotypic groups of markers near LOD peak from validation population E08929 × E08934. Trials E08934 type Heterozygous type E08929 type P values (F-test) Satt540-Chr. 7 (LG. M) 2010 field 2011 field † 45.8a 55.0a 31.8b 36.0b 24.4b 29.4b 8.598E-07 2.226E-10 Satt209-Chr. 8 (LG. A2) 2010 field 2011 field 19.8a 29.8a 27.0a 33.6a 48.3b 48.8b 2.2E-16 5.191E-09 BARCSOYSSR_16_0371-Chr. 16 (LG. J) 2010 field 2011 field † 25.2ab 27.5ab 30.4abc 37.2abc 39.5bc 46.6bc 0.00002309 3.209E-09 Means followed by different letters within the same row are significantly different at significant level of 0.001. 56 Table 3.4. Summary of aphid damage index (DI) from no-choice test with selected F3-derived lines with resistance genes from mapping population. Test lines E00003 PI 567598B Line 24 (Rag3(?)) Line 131 (Rag6) Line 19 (Rag3(?), Rag6) † SE standard error. † Average DI 86.98 11.46 19.27 5.73 SE 1.17 1.17 1.17 1.17 4.46 1.53 57 Figure 3.1. Phenotypic distribution of soybean aphid damage index of mapping population with140 F3-derived lines by E00003 × E08934 (A-D), and of validation population with 252 F2derived lines from E08929 × E08934 (E-F). Parents are indicated by arrows. y-axis is the count of plants that fall into each category. A 2010 greenhouse trial; B-D 2009, 2010 and 2011 caged field trials; E-F validation population in 2010 and 2011 field trials. A E08934 60 Count E00003 40 20 0 25 37.5 50 62.5 75 87.5 100 More B 60 E08934 Count E00003 40 20 0 25 37.5 50 62.5 75 87.5 100 More Aphid damage index (%) 58 Figure 3.1. (cont’d) C Count 60 E08934 E00003 40 20 0 25 37.5 50 62.5 75 87.5 100 More D 60 Count E08934 40 E00003 20 0 25 37.5 50 62.5 75 87.5 100 More Aphid damage index (%) 59 Figure 3.1. (cont’d) E E08929 150 Count E08934 100 50 0 25 37.5 50 62.5 75 87.5 100 More Count F 150 E08929 E08934 100 50 0 25 37.5 50 62.5 75 87.5 100 More Aphid damage index (%) 60 Figure 3.2. Location of soybean aphid-resistant loci detected in the mapping population E00003 × E08934 and validation population E08929 × E08934 with the composite interval mapping method with 1000 permutations. 1-LOD and 2-LOD intervals of each locus are indicated by thick and thin bars. Threshold line was drawn with P = 0.01 from 1000 permutations. A) Map of linkage group A2 (Chromosome 8) with the aphid resistance locus on the right from four trials in the mapping population, with LOD score plot on the right; B) The soybean consensus map of linkage group A2 (Chromosome 8) (Song et al., 2004); C) Map of linkage group A2 (Chromosome 8) with the aphid resistance locus on the right from two field trials in the validation population, with LOD score plot on the right; D) Map of linkage group J (Chromosome 16) with the aphid resistance locus on the right from four trials in the mapping population, with LOD score plot on the right; E) The soybean consensus map of linkage group J (Chromosome 16) (Song et al., 2004); F) Map of linkage group J (Chromosome 16) with the aphid resistance locus on the right from two field trials in the validation population, with LOD score plot on the right; G) Map of linkage group M (Chromosome 7) with the aphid resistance locus on the right from two field trials in the validation population, with LOD score plot on the right; H soybean consensus map of linkage group M (Chromosome 7) (Song et al., 2004) MSUSNP08-1 (Gm08_23293155), MSUSNP08-2 (Gm08_40320904), MSUSNP08-3 (Gm08_41114696) and MSUSNP08-4 (Gm08_45189358) derived from 38.8 to 45.2 Mbp of LG A2 on 6K SNP Beadchip, MSUSNP16-10 (Gm16_6262227), MSUSNP16-11 (Gm16_6413214), MSUSNP16-12 (Gm16_6423098) and MSUSNP16-15 (Gm16_8051585) derived from 4.8 to 11.3 Mbp region of LG J on 52K SNP Beadchip. 61 Figure 3.2. (cont’d) A Chr.8 22 21 20 19 18 17 16 15 14 13 12 11 10 9 8 7 6 5 4 3 2 1 0 0.0 Sat_382 4.3 Sat_377 32.4 Sat_138 49.9 Satt228 57.7 MSUSNP08-4 2011cage Satt455 Satt209 2010cage 24.3 27.3 2010greenhouse MSUSNP08-3 MSUSNP08-2 2009cage 15.9 16.8 62 Figure 3.2. (cont’d) B Chr.8 106.3 111.5 115.9 116.4 116.6 116.7 122.9 123.3 128.4 128.8 129.9 Sat_392 Sat_310 Satt421 Sat_382 Sat_377 Satt470 MSUSNP08-2 Sat_138 Satt209 MSUSNP08-3 Satt455 154.0 154.1 MSUSNP08-4 Satt228 159.6 162.0 Satt538 Satt429 165.7 Satt378 63 Figure 3.2. (cont’d) C Chr.8 10 9 8 7 6 5 4 3 2 1 0 52.7 Sat_382 59.3 MSUSNP08-1 73.1 75.8 MSUSNP08-2 MSUSNP08-3 81.8 Satt209 86.8 Satt455 92.3 Satt538 120.7 Satt429 130.8 Satt378 2011cage Satt421 2010cage 39.5 64 Figure 3.2. (cont’d) D Chr.16 10 9 8 7 6 5 4 3 2 1 0 Satt693 19.3 23.9 25.2 26.9 28.8 30.9 32.0 33.8 35.2 36.8 MSUSNP16-10 MSUSNP16-11 MSUSNP16-12 Satt132 Sat_370 MSUSNP16-15 Satt280 Satt622 Sct_193 Satt686 2010cage 11.4 2010greenhouse Sat_339 2009cage 0.0 65 Figure 3.2. (cont’d) E Chr.16 28.0 Sat_339 33.9 34.2 34.3 34.5 34.6 37.4 38.1 38.2 38.7 39.2 39.6 40.3 40.5 40.8 41.5 42.3 42.5 43.0 44.1 44.5 44.7 Satt693 BARCSOYSSR_16_0371 MSUSNP16-10 BARCSOYSSR_16_0383 MSUSNP16-11 MSUSNP16-12 Sat_370 Satt654 MSUSNP16-15 Satt280 Satt132 Satt596 Sat_259 Satt686 Satt456 Sct_193 Satt622 Satt183 Satt380 Satt215 Sat_361 Sct_001 66 Figure 3.2. (cont’d) F Chr.16 10 9 8 7 6 5 4 3 2 1 0 7.5 BARCSOYSSR_16_0383 16.5 BARCSOYSSR_16_0371 22.7 26.3 Sat_370 Satt654 31.5 35.3 39.9 42.3 47.1 52.0 Satt686 Satt456 Satt183 Satt380 Sat_259 Satt622 62.8 Sct_001 2011cage Satt693 2010cage 0.0 67 Figure 3.2. (cont’d) G Chr.7 10 9 8 7 6 5 4 3 2 1 0 11.1 Satt540 23.6 Satt435 37.2 Sat_253 44.0 Satt245 51.5 Satt220 2011cage Satt567 2010cage 0.0 68 Figure 3.2. (cont’d) H Chr.7 33.5 Satt567 35.9 Satt540 38.9 Satt435 51.6 Sat_253 53.5 Satt245 56.3 Satt220 69 Figure 3.3. Average aphid damage index (DI) of progeny in three genotypic groups for each marker close to the aphid resistance locus detected in the validation population E08929 × E08934, in 2010 and 2011 field trials. The homozygous group with allele derived from parent E08934 was denoted as AA. The homozygous group with allele came from parent E08929 was denoted as BB. The heterozygous group with both alleles was denoted as AB. one-way analysis of variance was conducted at a significance level of 0.05. Error bars indicate 95% confidence interval of average DI. A) The average DI of genotypic group AA, AB and BB of marker Satt540 (close to rag1), three bars on the left are from field trial 2010, and bars on the right are from field trial 2011; B) The average DI of genotypic group AA, AB and BB of marker Satt209 (close to Rag6), three bars on the left are from field trial 2010, and bars on the right are from field trial 2011; C) The average DI of genotypic group AA, AB and BB of marker BARCSOYSSR_16_0371 (close to Rag3(?)), three bars on the left are from field trial 2010, and bars on the right are from field trial 2011. 70 Figure 3.3. (cont’d) 71 Figure 3.3. (cont’d) 72 Figure 3.3. (cont’d) 73 Figure 3.4. Average aphid damage index (DI) for progeny that have no resistance genes, one locus, two loci and all three loci from the validation population E08929 × E08934 in 2010 field trial. 74 Figure 3.5. Average damage index (DI) from aphid no-choice test of selected lines from the mapping population E00003 × E08934, line 24 with Rag3(?), line 131 with Rag6 and line 19 with both loci. E00003 was used as susceptible check, and PI 567598B was antibiosis resistance check. One-way analysis of variance was conducted at a significance level of 0.05. Error bars indicate 95% confidence interval of average DI. 75 CHAPTER 4 FINE MAPPING OF APHID RESISTANCE GENE RAG3 IN SOYBEAN PI 567543C WITH BI-PARENTAL F2 POPULATION 76 ABSTRACT Soybean aphid can cause total yield loss in severe infestation and seed quality loss due to virus-transmission. Chemical controls are not cost-effective and may harm beneficial insects in the environment. Since the first reported finding in Wisconsin, United States, many efforts have been made to identify host plant resistance to aphids. Partially dominant locus Rag3 was identified from PI 567543C and mapped to a 10 cM interval between simple sequence repeat (SSR) markers Sat_339 and Satt414 on chromosome 16 (linkage group J) (Zhang et al., 2010). The objectives of this study were to fine map Rag3 in bi-parental F2 populations to identify tightly linked SNP markers to functional genes for marker-assisted selection. Advanced breeding lines E10902 and E10905 are derived from PI 567543C which is resistant to soybean aphid, while E07048 is susceptible to aphid. 1,889 F2 lines were developed from a cross between E07048 × E10902 and 1,913 F2 lines were also obtained from a cross between E10905 × E07048. All 1,889 F2 lines from E07048 × E10902 were phenotyped for aphid resistance in the ® greenhouse, and a subset of 376 F2 lines were genotyped with TaqMan SNP markers at 6.16, 6.26, 6.41, 6.42 and 8.05 Mbp on chr.16. A major locus explaining 44.7% of the phenotypic variation was detected between markers MSUSNP16-10 (Gm16_6262227) and MSUSNP16-12 (Gm16_6423098) on chr.16 with QTL analysis of these 376 lines. All 3802 F2 were genotyped with SNPs at 6.16 and 8.05 Mbp to select recombinants. 983 F3 progeny of 102 F2 recombinants were planted for aphid resistance evaluation in the greenhouse and genotyped with SNPs at 6.16, 6.26, 6.41, 6.423 and 6.424 Mbp on chr.16. Sixteen susceptible, heterozygous and recombinant 77 ® F3 lines were selected and applied onto Infinium SNP assays. Two F3 recombinants with crossovers between 6.26 and 6.47 Mbp were identified and confirmed according to their F4 progeny phenotypes and genotypes from the SNP Beadchip. In conclusion, Rag3 is fine mapped ® to 207 Kbp between TaqMan SNPs MSUSNP16-10 (Gm16_6262227) and Gm16_6469551_A_C with serine-threonine protein kinase coding genes as candidate functional ® genes. TaqMan SNPs MSUSNP16-10 (Gm16_6262227), MSUSNP16-11 (Gm16_6413214) and MSUSNP16-12 (Gm16_6423098) can be used in MAS for aphid resistance of Rag3 gene. Further genetics studies can be performed based on the candidate information provided in this study for gene cloning and functional analysis. 78 INTRODUCTION Soybean aphid has become the most destructive insect pest in US soybean production since its first reported finding in Wisconsin in 2000 (Ragsdale et al., 2004). Yield losses to soybean aphids were estimated over 50% in Minnesota in 2001 (Ostlie, 2002). Insecticide application among the 12 north-central soybean growing states increased from 4 to 14 M ha per year due to the rapid outbreak of soybean aphids (O'Neal and Johnson, 2010). Additionally, since soybean aphid is a phloem feeder, it can transmit plant virus, such as soybean mosaic virus, Tobacco ring-spot virus to soybean, and Potato virus Y to potato (Kim et al., 2008; Mian et al., 2008a; Pedersen et al., 2007). Aphid resistance has been considered a quantitative trait with a continuous phenotype distribution of damage index (Michel et al., 2009). Since chemical insecticides add more cost and may harm beneficial insects and the environment, breeding host resistant soybean has been proposed in the literature as a key component in effective integrated pest management (IPM) against soybean aphids (O'Neal and Johnson, 2010). Five loci, with seven alleles, Rag1, rag1, Rag2, Rag3, rag3, rag4 and Rag5 have been reported for aphid resistance conducting either antixenosis or antibiosis forms of resistance (Brachfeld and Mary, 2007; Jun et al., 2012; Kang et al., 2008; Li et al., 2007; Mian et al., 2008b; Zhang et al., 2009; Zhang et al., 2010). Among these resistance alleles, partially dominant Rag3 was mapped to a 10 cM interval between SSR markers Sat_339 and Satt414 on chromosome 16 (linkage group J) by Zhang et al. (2010). With the high density of SNP markers, pinpointing genes becomes possible to better facilitate markerassisted selection (MAS) (Bernardo, 2008; Chaisan et al., 2012; Collard et al., 2005). However, to achieve higher mapping resolution, the occurrence of recombination is equivalently important 79 as high density SNPs. In this study, two large F2 populations were developed with nearly 4,000 plants to increase the number of recombinants within the region of interest. The objectives of this study were to fine map partially dominant Rag3 with closely linked SNP markers for MAS, for gene cloning and further genetic studies. 80 MATERIALS AND METHODS Plant materials Two bi-parental F2 populations were developed with 1,889 F2 lines from the cross of E07048 × E10902 and 1,913 F2 lines from E10905 × E07048. E10902 and E10905 are derived from PI 567543C which is resistant to soybean aphid, while E07048 is susceptible to aphid. Aphid resistance phenotype evaluation In total, 3802 F2 plants, together with the parents, E07048, E10902 and E10905, were planted and inoculated with aphids in the spring of 2011, in the Plant Science Greenhouses at Michigan State University (MSU) in East Lansing, Michigan. Eight F2 plants were grown in a 105-mm-diameter and 125-mm-deep plastic pots, maintained at 26/15°С day/night temperature with sodium vapor lights as supplement (Zhang et al., 2010). The parents were replicated three times to serve as susceptible and resistant checks. Two wingless aphids were transferred onto the un-expanded trifoliate at the V1 stage (Fehr and Caviness, 1977). After the susceptible parent E07048 reached severe infestation stage, with more than 600 aphids per plant, all 1,889 F2 plants from cross E07048 × E10902 were rated for phenotype, using a simplified scale modified from -1 Mensah et al. (2005; 2008), with 1 = 0 – 200 aphids/plant and healthy appearance; 2 = 201 – -1 600 aphids/plant and healthy appearance with slight curling; 3 = more than 600 aphids/plant 1 and stunting appearance with cast skins. 81 - After associated the phenotypic data with the genotypic data of flanking SNPs MSUSNP16-14 (Gm16_6164774) and MSUSNP16-15 (Gm16_8051585), F3 progeny of F2 recombinants were planted in the same greenhouse in the fall of 2011. The F3 plants were maintained and evaluated for aphid resistance in the same manner as the F2 plants. Further, F4 progeny of F3 recombinants were phenotyped in the greenhouse in the spring of 2012 to confirm the crossovers within the region of interest in the selected F3 recombinants. DNA preparation and marker genotyping A quick DNA extraction method was adapted to obtain the genomic DNA from all F2 plants. One young non-expanded trifoliate of each plant was collected into 96-deep-well PCR plates and incubated in 94° oven with 100ul TE buffer (10mM Tris-HCl and 0.5M EDTA, pH C ® 7.5) for 30 min. DNA was further diluted 10 times with 0.1 × TE for TaqMan assay in ® ® LightCycler 480 (Roche Applied Science, Indianapolis, IN). A TaqMan assay protocol described by Zhang et al. (unpublished data) was used for SNP allele-specific genotyping ® analysis. In total, six TaqMan SNPs were designed: MSUSNP16-14 (Gm16_6164774), MSUSNP16-10 (Gm16_6262227), MSUSNP16-11 (Gm16_6413214), MSUSNP16-12 (Gm16_6423098), MSUSNP16-13 (Gm16_6424067) and MSUSNP16-15 (Gm16_8051585) (the numeric string is the physical position of the SNP). A subset of 376 F2 plants from the cross E07048 × E10902 was genotyped with five SNPs, MSUSNP16-14 (Gm16_6164774), 82 MSUSNP16-10 (Gm16_6262227), MSUSNP16-11 (Gm16_6413214), MSUSNP16-12 (Gm16_6423098) and MSUSNP16-15 (Gm16_8051585). After QTL analysis, SNPs at 6.16 and 8.05 Mbp were used to determine recombinants for all F2 lines. Genomic DNA F3 and F4 ® recombinants were quickly extracted and genotyped with SNP TaqMan assay in the same manner as F2 plants. All F3 progeny of the F2 recombinants were genotyped with SNPs MSUSNP16-14 (Gm16_6164774), MSUSNP16-10 (Gm16_6262227), MSUSNP16-11 (Gm16_6413214), MSUSNP16-12 (Gm16_6423098) and MSUSNP16-13 (Gm16_6424067). The selected 16 F3 lines including susceptible lines, heterozygous lines and recombinants were ® applied onto Infinium SNP assays with high resolution customer designed soybean Beadchip containing more than 52,000 SNPs (Song et al., 2011). Data analysis ® ® The histograms of phenotypic data were generated in Excel 2010 for Windows . The association between markers and phenotypes were performed in R, at significance level of 0.05. Linkage analysis was performed with the subset of 376 F2 plants from the cross E07048 × E10902. A genetic linkage map was constructed in JoinMap 3.0 with Kosambi’s and LOD = 3.0 (Van Ooijen, 2006) with the five SNPs between 6.16 and 8.05 Mbp. QTL analysis was conducted in QTL Cartographer Version 2.5 (Wang et al., 2008) with composite interval mapping (CIM) and multiple interval mapping (MIM) methods. MapChart (Voorrips, 2002) was used to visualize the position of the QTL. 83 RESULTS AND DISCUSSION Phenotype of soybean aphid resistance The phenotypes of all 1889 F2 plants and its subset of 376 plants from cross E07048 × E10902 were plotted as histograms (Fig. 4.1). The ratio for the three scores among all F2 plants was 5.06: 8.48: 4.72, which was not significantly different from 1:2:1 in Pearson Chi-square test (data not shown). The ratio of the F2 plant subset was 1.24: 1.64: 0.88. F2 plants from cross E10905 × E07048 were not rated due to technical difficulties. Validation of Rag3 in F2 population With the genotypic data of 376 F2 plants from the cross E07048 × E10902, a 18.7 cM long linkage map was constructed with five SNPs, MSUSNP16-14 (Gm16_6164774), MSUSNP16-10 (Gm16_6262227), MSUSNP16-11 (Gm16_6413214), MSUSNP16-12 (Gm16_6423098)and MSUSNP16-15 (Gm16_8051585) (Fig. 4.2). The marker order is consistent with the reported physical positions (Song et al., 2011). The slight inflation of the ® linkage map was caused by genotyping errors from SNP TaqMan assay. One QTL was detected for both CIM and MIM methods between SNPs MSUSNP16-10 (Gm16_6262227) and MSUSNP16-12 (Gm16_6423098) (Fig. 4.2). The resistant parent E10902 was derived from PI 567543C with no other aphid resistance source integrated; therefore, we conclude that this QTL 2 is indeed Rag3. The LOD scores are 38.7 for CIM and 44.2 for MIM. R was estimated 84 approximately as 0.447 in both CIM and MIM, suggesting that Rag3 can explain about 44.7% of the phenotype variance among these 376 F2 plants (data not shown). Fine mapping Rag3 with recombinant lines With the validation of Rag3 between 6.1 and 8.0 Mbp, SNPs MSUSNP16-14 (Gm16_6164774) and MSUSNP16-15 (Gm16_8051585) were used to select recombinants within 6.1 and 8.0 Mbp conservatively. A total of 102 F2 plants were identified as recombinants within this region. After genotyping all 983 F3 progeny with SNPs, MSUSNP16-14 (Gm16_6164774) and MSUSNP16-15 (Gm16_8051585), 16 F3 lines were selected for further genotyping with 52K SNP Beadchip. The genotypes of these 16 F3 plants with 10 polymorphic SNPs between 6.185 to 6.522 Mbp on Chr. 16 are shown in Table 4.1. First three lines with all markers genotypes homozygous susceptible allele showed susceptible phenotype in the greenhouse aphid resistance test. Nine segregating lines of these SNPs showed intermediate resistance and resistance phenotypes, and the following two homozygous lines of the resistance allele showed resistance phenotype. This evidence is consistent with the result from the QTL analysis above, suggesting that Rag3 locates at least within 6.1.85 and 6.522 Mbp on Chr. 16. Most interestingly, two aphid resistant F3 lines, 2-499-2 and 2-277-6 were observed with crossovers within this region (Table 4.1). For line 2-499-2, all SNPs within 6.185 and 6.262 Mbp are homozygous susceptible type and the remaining SNPs to the right are alleles from the resistant parent, indicating the genotype on the left of 6.262 Mbp has no effect on its phenotype. 85 Similarly for line 2-277-6, SNPs on the right of 6.470 Mbp do not affect the resistance phenotype. These two recombinants within 6.262 and 6.470 Mbp region narrowed the Rag3 region down to a 207 Kbp length on Chr. 16. To further confirm this finding, two F4 progeny of 2-499-2 and 14 F4 progeny of 2-277-6 were phenotyped and genotyped with 52K SNP Beadchip. As shown in Table 4.1, the phenotypes and genotypes of all the 16 F4 progeny are highly consistent as their F3 parents, confirming the 207 Kbp region of Rag3. The distance between SNPs of the two crossovers is actually 17 Kbp. To be conservative, one SNP more on each side with a total distance of 207 Kbp is considered to make sure Rag3 gene is covered. The limitation to further narrow this region down is the lack of recombination events and polymorphic SNP markers ® within this interval. Therefore, Illunima Hi-Seq sequencing is ongoing to develop more SNP markers in the region of 6.2 to 6.4 Mbp. Progeny of segregating F3 plants will continue to be planted for phenotyping and genotyping. Association of SNPs MSUSNP16-10 (Gm16_6262227), MSUSNP16-11 (Gm16_6413214) and MSUSNP16-12 (Gm16_6423098) between genotypes and the phenotypes Correlations of SNPs MSUSNP16-10 (Gm16_6262227), MSUSNP16-11 (Gm16_6413214) and MSUSNP16-12 (Gm16_6423098) genotypes and aphid resistance phenotypes in 376 F2 subset of E07048 by E10902 and all F3 progeny of 102 F2 recombinants are summarized in Table 4.2. Among the subset of 376 F2 lines, the correlation coefficients of SNP genotype and aphid resistance phenotype ranged from 0.52 to 0.68 with P < 0.0001. These 86 three SNPs explain 27 to 47% of the phenotypic variation. Among all 983 F3 progeny, the correlation coefficients ranged from 0.60 to 0.66 with P < 0.0001, explaining 36 to 43% of the ® phenotypic variation. Therefore, these three TaqMan SNPs, MSUSNP16-10 (Gm16_6262227), MSUSNP16-11 (Gm16_6413214) and MSUSNP16-12 (Gm16_6423098) can be used as genotypic indicator for aphid resistance in MAS. 87 Table 4.1. Single nucleotide polymorphism (SNP) genotypes and phenotypes of selected F3 and F4 lines on 52K SNP Beadchip. Physical Position (Mbp) F3 plants Line Phenotype † 2-257-2 S 3-386-9 S 4-58-40 S †† rem18-5 Seg. rem19-6 Seg rem19-12 Seg 3-262-8 Seg # 3-84-5 R 2-291-1 R 4-31-4 R 3-386-2 R 4-248-7 R 2-381-30 R 3-38-2 R 2-499-2 R 2-277-6 R F4 plants 2-499-2-1 R 2-499-2-2 R 2-277-6-1 R 2-277-6-2 R 2-277-6-3 R 2-277-6-4 R 6.185 6.262 6.413 6.423 6.424 6.431 6.47 6.484 6.497 6.522 S S S § H H H H H H H H R R R S R S S S H H H H H H H H H R R S R S S S H H H H H H H H H R R R R S S S H H H H H H H H H R R R R S S S H H H H H H H H H R R R R S S S H H H H H H H H H R R R R S S S H H H H H H H H H R R R S S S S H H H H H H H H H R R R S S S S H H H H H H H H H R R R S S S S H H H H H H H H H R R R S S S R R R R S S R R R R R R R R R R R R R R R R R R R R R R R R R R R R R R S S S S R R S S S S R R S S S S R R S S S S 88 Table 4.1. (cont’d) Physical Position (Mbp) F4 plants Line Phenotype 2-277-6-5 R 2-277-6-6 R 2-277-6-7 R 2-277-6-8 R 2-277-6-9 R 2-277-6R 10 2-277-6R 11 2-277-6R 12 2-277-6R 13 2-277-6R 14 † Susceptible; †† 6.185 6.262 6.423 6.424 6.431 6.47 6.484 6.497 6.522 R R R R R R R R R R R R R R R R R R R R R R R R R R R R R R S S S S S S S S S S S S S S S S S S S S R R R R R R S S S S R R R R R R S S S S R R R R R R S S S S R R R R R R S S S S R R R R R R S S S S Segregating; § Heterozygous; # 6.413 Resistant. 89 Table 4.2. Correlations of single nucleotide polymorphism (SNP) with phenotypes in 376 F2 lines from cross E07048 × E10902 and all 983 F3 progeny of 102 F2 recombinants. MSUSNP1610 (Gm16_6262227), MSUSNP16-11 (Gm16_6413214) and MSUSNP16-12 (Gm16_6423098) were developed from 52K SNP Beadchip (Song et al., 2011). 376 F2 lines Cor. Coe. r SNP markers † square MSUSNP16-10 0.27 MSUSNP16-11 † 0.52 0.68 0.47 MSUSNP16-12 0.58 0.33 Correlation coefficient. 90 983 F3 lines p value 1.24E17 3.50E45 1.49E30 Cor. Coe. r square 0.60 0.36 0.66 0.43 0.61 0.38 p value 1.24E70 7.96E96 6.52E81 Figure 4.1. Phenotype histogram of soybean aphid rating score of F2 plants from bi-parental cross E07048 × E10902, where E07048 is susceptible to aphid and E10902 is resistant. A. phenotype distribution of all 1889 F2 plants with y-axis being the percentage of total plants for each score; B. phenotypes of a subset of 376 F2 plants from the same population with y-axis being the number of F2 plants for each score. 91 Figure 4.2. Validation Rag3 location on Chromosome 16 using a subset of 376 F2 plants from cross E07048 × E10902, with E07048 and E10902 being susceptible and resistant parent. QTL analysis was conducted with both composite interval mapping and multiple interval mapping methods with P < 0.0001. MSUSNP16-14 (Gm16_6164774), MSUSNP16-10 (Gm16_6262227), MSUSNP16-11 (Gm16_6413214), MSUSNP16-12 (Gm16_6423098) and MSUSNP16-15 (Gm16_8051585) are SNP markers with physical position indicated in the middle of the marker names. The black bar next to the linkage map indicates the location of Rag3 with composite interval mapping method; the grey bar on the right shows that with multiple-interval mapping method. Chr.16 44 43 42 41 40 39 38 37 36 35 34 33 32 31 30 29 28 27 26 25 24 23 22 21 20 19 18 17 16 15 14 13 12 11 10 9 8 7 6 5 4 3 9.8 MSUSNP16-12 14.7 MSUSNP16-11 17.8 18.7 MSUSNP16-10 MSUSNP16-14 Rag3_MIM MSUSNP16-15 Rag3_CIM 0.0 92 APPENDIX 93 GENOTYPING AND GENETIC ANALYSIS OF SUDDEN DEATH SYNDROMES IN SOYBEAN Plant Materials and summary of phenotypes The objective of this project was to identify new loci in GD2422 for resistance to sudden death syndromes (SDS). A total of 243 lines from two F4-derived mapping populations GD2422 × LD01-5907 and GD2422 × Skylla were developed and phenotyped for SDS resistance with four replications at Decatur, Michigan in summer of 2011. Line 1- 129 derived from GD2422 × LD01-5907, and line 130- 243 are from GD2422 × Skylla. GD2422 and LD01-5907 are both resistant to SDS while Skylla is susceptible. SDS phenotypic data were collected on three different dates, August 2, 15 and 22 of 2011. On the first two visits, a disease index (DX) described by Cathy Schmidt (Southern Illinois University, personal communications) was employed as a phenotypic indicator. DX was defined with two components: combing disease incidence (DI) and disease severity (DS) as followed: DX = DI × DS/9, where DI is the percentage of plants with leaf symptoms, and DS is the most severe damage on each line, ranging from 1 (1-10% of leaf chlorotic) to 9 (premature death). On August 22, DX and average disease index (AvDX) was also collected and estimated. AvDX is modified as follows: AvDX = DI × DS /9. Instead of the worst symptoms of a line, DS is recorded as the average disease severity in the area with disease symptoms. Both DX and AvDX range from 0 to 100 for the most resistant type to the most susceptible type. DS, DX and AvDX of the first mapping population are plotted in Fig. A.1, and those of the second population are 94 plotted in Fig. A.2. The heritability with 90% confidence interval of these traits is summarized in Table A.1. August 15 disease index (Aug15DX) and August 22 average disease index (Aug22AvDX) fit normal distribution and the correlation coefficient is 0.957 (data not shown). These were selected as phenotypic indicators in QTL analysis. In the first mapping population, the estimated heritability was all above 0.80. There are fewer lines in the second mapping population with lower heritability ranging from 0.11 to 0.62. However, the heritability of the traits used, Aug15DX and Aug22AvDX, are 0.43 and 0.58, respectively. Selective genotyping A subset of 93 lines was selected according to Table A.2 for genotyping on 6K SNP chip. Aug22AvDX was divided into eight categories as the first column of Table A.2 and 93 lines were selected from cross GD2422 × LD01-5907 to form a subset with a normal distribution for initial QTL analysis. The phenotypic traits of the subset were plotted in Fig. A.3. Both Aug15DX and Aug22AvDX showed normal distribution in the selected subset. Genetic analysis A genetic map was constructed with JoinMap 4.0 with the maximum likelihood method. Polymorphic SNP markers on each chromosome are summarized in Table A.3. The genetic map of the 20 chromosomes constructed with the selected subset was visualized in MapChart (data not shown). Single marker analysis (SMA) was performed in Windows QTL Cartographer 2.5. In the QTL analysis, there were 27 linkage fragments. The chromosomal labels in QTL 95 Cartographer and their corresponding chromosomes are listed in Table A.4. The result of SMA is not shown here. As preliminary results show, two significant QTLs were detected on Chr. 18 (linkage group G) and Chr. 19 (linkage group L) with trait August 22AvDX. Several loci have been previously identified as resistant to SDS on these two chromosomes (Grant et al., 2010). Further analysis with SNP genotyping assays within potential regions found in SMA and more phenotypic data are required to confirm if they are novel loci for SDS resistance. 96 Table A.1. Broad sense heritability of SDS phenotypic with 90% confidence intervals, August 2 disease severity (Aug2DS), August 2 disease index (Aug2DX), August 15 disease severity (Aug15DS), August 15 disease index (Aug15DX), August 22 disease index (Aug22DX) and August 22 average disease index (Aug22AvDX) of the two mapping populations from cross GD2422 × LD01-5907 and GD2422 × Skylla. Trait 90% CI upper 90% CI lower Heritability limit limit GD2422 × LD01-5907 Aug2DS 0.88 0.91 0.85 Aug2DX 0.84 0.87 0.79 Aug15DS 0.82 0.86 0.76 Aug15DX 0.9 0.92 0.87 Aug22DX 0.9 0.92 0.88 0.91 0.85 Aug22AvDX 0.88 GD2422 × Skylla Aug2DS 0.65 0.73 0.55 Aug2DX 0.58 0.67 0.46 Aug15DS 0.11 0.3 -0.14 Aug15DX 0.62 0.7 0.51 Aug22DX 0.43 0.55 0.26 Aug22AvDX 0.58 0.67 0.46 97 Table A.2. Normal distribution table for selective genotyping in mapping population GD2422 × LD01-5907. Range of AvDX Expected Count Original Count New Count 0 to 10 0.2 21 1 11 to 20 2.0 5 2 21 to 30 12.6 11 13 31 to 40 31.6 21 26 41 to 50 31.6 19 27 51 to 60 12.6 9 16 61 to 70 2.0 4 5 71 to 80 0.2 3 3 98 Table A.3. Summary of polymorphic single nucleotide polymorphism (SNP) markers on each chromosome between the two parents of mapping population GD2422 × LD01-5907. Linkage Group # of polymorphic SNPs LG01 48 LG02 114 LG03 49 LG04 62 LG05 69 LG06 216 LG07 91 LG08 83 LG09 208 LG10 60 LG11 59 LG12 22 LG13 184 LG14 22 LG15 87 LG16 152 LG17 110 LG18 162 LG19 182 LG20 22 Total Poly. 2002 Total SNPs 5361 99 Table A.4. Chromosome labels in QTL Cartographer 2.5 software and the corresponding chromosomes according to soybean consensus map (Qijian Song personal communication). QTL Cartographer Ch-1, Ch-2 Ch-3, Ch-4 Ch-5, Ch-6 Ch-7, Ch-8 Ch-9 Ch-10 Ch-11 Ch-12 Ch-13 Ch-14 Ch-15, Ch-16 Ch-17 Ch-18 Ch-19 Ch-20, Ch-21 Ch-22 Ch-23, Ch-24 Ch-25 Ch-26 Ch-27 Actual Chromosome Chr.1-part1, 2 Chr.2-part1,2 Chr.3-part1, 2 Chr.4-part1,2 Chr.5 Chr.6 Chr.7 Chr.8 Chr.9 Chr.10 Chr.11-part1, 2 Chr.12 Chr.13 Chr.14 Chr.15-part1, 2 Chr.16 Chr.17-part1, 2 Chr.18 Chr.19 Chr.20 100 Figure A.1. Phenotype distributions of soybean line 1-129 from cross GD2422 × LD01-5907. August 2 disease severity (Aug2DS), August 2 disease index (Aug2DX), August 15 disease severity (Aug15DS), August 15 disease index (Aug15DX), August 22 disease index (Aug22DX) and August 22 average disease index (Aug22AvDX). Y-axis is the count of number of plants that fall into each category. 120 100 80 60 40 20 0 140 120 100 80 60 40 20 0 150 100 50 0 0 2 4 6 8 0 Aug2DS 20 40 60 80 Aug2DX 140 120 100 80 60 40 20 0 60 40 20 0 40 60 Aug15DX 80 4 6 8 120 100 80 60 40 20 0 80 20 2 Aug15DS 100 0 0 0 20 40 60 Aug22DX 101 80 100 0 20 40 60 Aug22AvDX 80 Figure A.2. Phenotype distributions of line 130-243 of GD2422 × Skylla. August 2 disease severity (Aug2DS), August 2 disease index (Aug2DX), August 15 disease severity (Aug15DS), August 15 disease index (Aug15DX), August 22 disease index (Aug22DX) and August 22 average disease index (Aug22AvDX). 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