Zixiang Wen

Genomic consequences of selection and genome-wide association mapping in soybean

BackgroundCrop improvement always involves selection of specific alleles at genes controlling traits of agronomic importance, likely resulting in detectable signatures of selection within the genome of modern soybean (Glycine max L. Merr.). The identification of these signatures of selection is meaningful from the perspective of evolutionary biology and for uncovering the genetic architecture of agronomic traits.ResultsTo this end, two populations of soybean, consisting of 342 landraces and 1062 improved lines, were genotyped with the SoySNP50K Illumina BeadChip containing 52,041 single nucleotide polymorphisms (SNPs), and systematically phenotyped for 9 agronomic traits. A cross-population composite likelihood ratio (XP-CLR) method was used to screen the signals of selective sweeps. A total of 125 candidate selection regions were identified, many of which harbored genes potentially involved in crop improvement. To further investigate whether these candidate regions were in fact enriched for genes affected by selection, genome-wide association studies (GWAS) were conducted on 7 selection traits targeted in soybean breeding (grain yield, plant height, lodging, maturity date, seed coat color, seed protein and oil content) and 2 non-selection traits (pubescence and flower color). Major genomic regions associated with selection traits overlapped with candidate selection regions, whereas no overlap of this kind occurred for the non-selection traits, suggesting that the selection sweeps identified are associated with traits of agronomic importance. Multiple novel loci and refined map locations of known loci related to these traits were also identified.ConclusionsThese findings illustrate that comparative genomic analyses, especially when combined with GWAS, are a promising approach to dissect the genetic architecture of complex traits.

Phenotypic characterization and genetic dissection of nine agronomic traits in Tokachi nagaha and its derived cultivars in soybean (Glycine max (L.) Merr.)

By using the soybean founder parent Tokachi nagaha and its 137 derived cultivars as materials, a genome-wide association analysis was performed to identify the single nucleotide polymorphisms (SNPs) underlying soybean yield and quality related traits at two planting densities. Results of ANOVA showed that genotype, environment, and genotype by environment interaction effects were all significant for each trait. The Tokachi nagaha-derived soybean population could be divided into two subpopulations based on molecular data, and accessions in each subpopulation were almost all from the same Chinese province. Relatedness was detected between pair-wise accessions within the population. Linkage disequilibrium was obvious and the level of intra-chromosome linkage disequilibrium was about 8370kb. A total of 40 SNPs with significant signal were detected and distributed across 18 chromosomes. Some SNP markers were located in or near regions where QTLs have been previously mapped by linkage analysis. Nineteen SNPs were identified both in low- and high- density planting treatments, indicating those loci were common and sTable Sixteen SNPs were co-associated with two or more different traits, suggesting that some of the QTLs/genes underlying those identified SNPs were likely to be pleiotropic.

Fine mapping of the soybean aphid-resistance genes Rag6 and Rag3c from Glycine soja 85-32

Key messageRag6andRag3cwere delimited to a 49-kb interval on chromosome 8 and a 150-kb interval on chromosome 16, respectively. Structural variants in the exons of candidate genes were identified.AbstractThe soybean aphid, an invasive species, has significantly threatened soybean production in North America since 2000. Host-plant resistance is known as an ideal management strategy for aphids. Two novel aphid-resistance loci, Rag6 and Rag3c, from Glycine soja 85-32, were previously detected in a 10.5-cM interval on chromosome 8 and a 7.5-cM interval on chromosome 16, respectively. Defining the exact genomic position of these two genes is critical for improving the effectiveness of marker-assisted selection for aphid resistance and for identification of the functional genes. To pinpoint the locations of Rag6 and Rag3c, four populations segregating for Rag6 and Rag3c were used to fine map these two genes. The availability of the Illumina Infinium SoySNP50K/8K iSelect BeadChip, combined with single-nucleotide polymorphism (SNP) markers discovered through the whole-genome re-sequencing of E12901, facilitated the fine mapping process. Rag6 was refined to a 49-kb interval on chromosome 8 with four candidate genes, including three clustered nucleotide-binding site leucine-rich repeat (NBS–LRR) genes and an amine oxidase encoding gene. Rag3c was refined to a 150-kb interval on chromosome 16 with 11 candidate genes, two of which are a LRR gene and a lipase gene. Moreover, by sequencing the whole-genome exome-capture of the resistant source (E12901), structural variants were identified in the exons of the candidate genes of Rag6 and Rag3c. The closely linked SNP markers and the candidate gene information presented in this study will be significant resources for integrating Rag6 and Rag3c into elite cultivars and for future functional genetics studies.

Phenotypic Characterization and Genetic Dissection of Growth Period Traits in Soybean (Glycine max) Using Association Mapping

The growth period traits are important traits that affect soybean yield. The insights into the genetic basis of growth period traits can provide theoretical basis for cultivated area division, rational distribution, and molecular breeding for soybean varieties. In this study, genome-wide association analysis (GWAS) was exploited to detect the quantitative trait loci (QTL) for number of days to flowering (ETF), number of days from flowering to maturity (FTM), and number of days to maturity (ETM) using 4032 single nucleotide polymorphism (SNP) markers with 146 cultivars mainly from Northeast China. Results showed that abundant phenotypic variation was presented in the population, and variation explained by genotype, environment, and genotype by environment interaction were all significant for each trait. The whole accessions could be clearly clustered into two subpopulations based on their genetic relatedness, and accessions in the same group were almost from the same province. GWAS based on the unified mixed model identified 19 significant SNPs distributed on 11 soybean chromosomes, 12 of which can be consistently detected in both planting densities, and 5 of which were pleotropic QTL. Of 19 SNPs, 7 SNPs located in or close to the previously reported QTL or genes controlling growth period traits. The QTL identified with high resolution in this study will enrich our genomic understanding of growth period traits and could then be explored as genetic markers to be used in genomic applications in soybean breeding.

Integrating GWAS and gene expression data for functional characterization of resistance to white mould in soya bean

Summary White mould of soya bean, caused by Sclerotinia sclerotiorum (Lib.) de Bary, is a necrotrophic fungus capable of infecting a wide range of plants. To dissect the genetic architecture of resistance to white mould, a high‐density customized single nucleotide polymorphism (SNP) array (52 041 SNPs) was used to genotype two soya bean diversity panels. Combined with resistance variation data observed in the field and greenhouse environments, genome‐wide association studies (GWASs) were conducted to identify quantitative trait loci (QTL) controlling resistance against white mould. Results showed that 16 and 11 loci were found significantly associated with resistance in field and greenhouse, respectively. Of these, eight loci localized to previously mapped QTL intervals and one locus had significant associations with resistance across both environments. The expression level changes in genes located in GWAS‐identified loci were assessed between partially resistant and susceptible genotypes through a RNA‐seq analysis of the stem tissue collected at various time points after inoculation. A set of genes with diverse biological functionalities were identified as strong candidates underlying white mould resistance. Moreover, we found that genomic prediction models outperformed predictions based on significant SNPs. Prediction accuracies ranged from 0.48 to 0.64 for disease index measured in field experiments. The integrative methods, including GWAS, RNA‐seq and genomic selection (GS), applied in this study facilitated the identification of causal variants, enhanced our understanding of mechanisms of white mould resistance and provided valuable information regarding breeding for disease resistance through genomic selection in soya bean.

Pyramiding different aphid-resistance genes in elite soybean germplasm to combat dynamic aphid populations

The soybean aphid (Aphis glycines Matsumura), an invasive species, has posed a significant threat to soybean [Glycine max (L.) Merr.] production in North America since 2001. Use of resistant cultivars is an effective tactic to protect soybean yield. However, the variability and dynamics of aphid populations could limit the effectiveness of host-resistance gene(s). Gene pyramiding is a promising way to sustain host-plant resistance. The objectives of this study were to determine the prevalent aphid biotypes in Michigan and to assess the effectiveness of different combinations of aphid-resistance genes. A total of 11 soybean genotypes with known resistance gene(s) were used as indicator lines. Based on their responses, Biotype 3 was a major component of Michigan aphid populations during 2015–2016. The different performance of Rag-“Jackson” and Rag1-“Dowling” along with the breakdown of resistance in plant introductions (PIs) 567301B and 567324 may be explained by Biotype 3 or an unknown virulent biotype establishing in Michigan. With the assistance of flanking markers, 12 advanced breeding lines carrying different aphid-resistance gene(s) were developed and evaluated for effectiveness in five trials across 2015 to 2017. Lines with rag1c, Rag3d, Rag6, Rag3c + Rag6, rag1b + rag3, rag1c + rag4, rag1c + rag3 + rag4, rag1c + Rag2 + rag3 + rag4, and rag1b + rag1c + rag3 + rag4 demonstrated strong and consistent resistance. Due to the variability of virulent aphid populations, different combinations of Rag genes may perform differently across geographies. However, advanced breeding lines pyramided with three or four Rag genes likely will provide broader and more durable resistance to diverse and dynamic aphid populations.

QTL effects and epistatic interaction for flowering time and branch number in a soybean mapping population of Japanese×Chinese cultivars

Abstract Flowering time and branching type are important agronomic traits related to the adaptability and yield of soybean. Molecular bases for major flowering time or maturity loci, E1 to E4 , have been identified. However, more flowering time genes in cultivars with different genetic backgrounds are needed to be mapped and cloned for a better understanding of flowering time regulation in soybean. In this study, we developed a population of Japanese cultivar (Toyomusume)×Chinese cultivar (Suinong 10) to map novel quantitative trait locus (QTL) for flowering time and branch number. A genetic linkage map of a F 2 population was constructed using 1 306 polymorphic single nucleotide polymorphism (SNP) markers using Illumina SoySNP8k iSelect BeadChip containing 7 189 (SNPs). Two major QTLs at E1 and E9, and two minor QTLs at a novel locus, qFT2_1 and at E3 region were mapped. Using other sets of F 2 populations and their derived progenies, the existence of a novel QTL of qFT2_1 was verified. qBR6_1, the major QTL for branch number was mapped to the proximate to the E1 gene, inferring that E1 gene or neighboring genetic factor is significantly contributing to the branch number.

Comparison of Genetic Diversity between Chinese and American Soybean (Glycine max (L.)) Accessions Revealed by High-Density SNPs

Soybean is one of the most important economic crops for both China and the United States (US). The exchange of germplasm between these two countries has long been active. In order to investigate genetic relationships between Chinese and US soybean germplasm, 277 Chinese soybean accessions and 300 US soybean accessions from geographically diverse regions were analyzed using 5,361 SNP markers. The genetic diversity and the polymorphism information content (PIC) of the Chinese accessions was higher than that of the US accessions. Population structure analysis, principal component analysis, and cluster analysis all showed that the genetic basis of Chinese soybeans is distinct from that of the USA. The groupings observed in clustering analysis reflected the geographical origins of the accessions; this conclusion was validated with both genetic distance analysis and relative kinship analysis. FST-based and EigenGWAS statistical analysis revealed high genetic variation between the two subpopulations. Analysis of the 10 loci with the strongest selection signals showed that many loci were located in chromosome regions that have previously been identified as quantitative trait loci (QTL) associated with environmental-adaptation-related and yield-related traits. The pattern of diversity among the American and Chinese accessions should help breeders to select appropriate parental accessions to enhance the performance of future soybean cultivars.

Marker-Assisted Breeding for Disease Resistance in Crop Plants

Breeding disease-resistant crop varieties is a cornerstone of disease management. Marker-assisted selection (MAS) incorporates a plethora of plant genomic resources into the process of breeding disease-resistant crops. Although there are species-specific and disease-specific considerations, much of the procedures and theory behind MAS are conserved. Using molecular markers is most likely to increase the efficiency of the breeding process in cases where disease resistance is controlled by one or few genes, and those genes have a large effect on the resistance phenotype. In cases where disease resistance is controlled by many genes of small effect, genomic selection (GS) may be more efficient than MAS or phenotypic selection. GS is an emerging technology, and many of the statistical principles and procedures are still being developed. This chapter should begin to inform breeders as to the potential and the details to consider if using a marker-assisted breeding tool in their plant breeding program.

Mapping Quantitative Trait Loci for Tolerance to Pythium irregulare in Soybean (Glycine max L.)

Pythium root rot is one of the significant diseases of soybean (Glycine max (L.) Merr.) in the United States. The causal agent of the disease is a soil-borne oomycete pathogen Pythium irregulare, the most prevalent and aggressive species of Pythium in North Central United States. However, few studies have been conducted in soybean for the identification of quantitative trait loci (QTL) for tolerance to P. irregulare. In this study, two recombinant inbred line (RIL) populations (designated as POP1 and POP2) were challenged with P. irregulare (isolate CMISO2-5-14) in a greenhouse assay. POP1 and POP2 were derived from ‘E09014’ × ‘E05226-T’ and ‘E05226-T’ × ‘E09088’, and contained 113 and 79 lines, respectively. Parental tests indicated that ‘E05226-T’ and ‘E09014’ were more tolerant than ‘E09088’, while ‘E09088’ was highly susceptible to the pathogen. The disease indices, root weight of inoculation (RWI) and ratio of root weight (RRW) of both populations showed near normal distributions, with transgressive segregation, suggesting the involvement of multiple QTL from both parents contributed to the tolerance. All the lines were genotyped using Illumina Infinium BARCSoySNP6K iSelect BeadChip and yielded 1373 and 1384 polymorphic markers for POP1 and POP2, respectively. Notably, despite high density, polymorphic markers coverage was incomplete in some genomic regions. As such, 28 and 37 linkage groups were obtained in POP1 and POP2, respectively corresponding to the 20 soybean chromosomes. Using RRW, one QTL was identified in POP1 on Chromosome 20 that explained 12.7–13.3% of phenotypic variation. The desirable allele of this QTL was from ‘E05226-T’. Another QTL was found in POP2 on Chromosome 11. It explained 15.4% of the phenotypic variation and the desirable allele was from ‘E09088’. However, no QTL were identified using RWI in either population. These results supported that RRW was more suitable to be used to evaluate P. irregulare tolerance in soybean.