Genome-wide association study for flowering time,maturity dates and plant height in early maturing soybean (Glycine max) germplasm

Background

Soybean (Glycine max) is a photoperiod-sensitive and self-pollinated species. Days to flowering (DTF) and maturity (DTM), duration of flowering-to-maturity (DFTM) and plant height (PH) are crucial for soybean adaptability and yield. To dissect the genetic architecture of these agronomically important traits, a population consisting of 309 early maturity soybean germplasm accessions was genotyped with the Illumina Infinium SoySNP50K BeadChip and phenotyped in multiple environments. A genome-wide association study (GWAS) was conducted using a mixed linear model that involves both relative kinship and population structure.

Results

The linkage disequilibrium (LD) decayed slowly in soybean, and a substantial difference in LD pattern was observed between euchromatic and heterochromatic regions. A total of 27, 6, 18 and 27 loci for DTF, DTM, DFTM and PH were detected via GWAS, respectively. The Dt1 gene was identified in the locus strongly associated with both DTM and PH. Ten candidate genes homologous to Arabidopsis flowering genes were identified near the peak single nucleotide polymorphisms (SNPs) associated with DTF. Four of them encode MADS-domain containing proteins. Additionally, a pectin lyase-like gene was also identified in a major-effect locus for PH where LD decayed rapidly.

Conclusions

This study identified multiple new loci and refined chromosomal regions of known loci associated with DTF, DTM, DFTM and/or PH in soybean. It demonstrates that GWAS is powerful in dissecting complex traits and identifying candidate genes although LD decayed slowly in soybean. The loci and trait-associated SNPs identified in this study can be used for soybean genetic improvement, especially the major-effect loci associated with PH could be used to improve soybean yield potential. The candidate genes may serve as promising targets for studies of molecular mechanisms underlying the related traits in soybean.

Electronic supplementary material

The online version of this article (doi:10.1186/s12864-015-1441-4) contains supplementary material, which is available to authorized users.     

Genome-wide association study for soybean cyst nematode resistance in Chinese elite soybean cultivars

Soybean cyst nematode (SCN) (Heterodera glycines Ichinohe) is a highly recalcitrant endoparasite of soybean roots, causing more yield loss than any other pest. To identify quantitative trait loci (QTL) controlling resistance to SCN (HG type 2.5.7, race 1), a genome-wide association study (GWAS) was performed. The association panel, consisting of 120 Chinese soybean cultivars, was genotyped with 7189 single nucleotide polymorphism (SNPs). A total of 6204 SNPs with minor allele frequency >0.05 were used to estimate linkage disequilibrium (LD) and population structure. The mean level of LD measured by r ² declined very rapidly to half its maximum value (0.51) at 220 kb. The overall population structure was approximately coincident with geographic origin. The GWAS results identified 13 SNPs in 7 different genomic regions significantly associated with SCN resistance. Of these, three SNPs were localized in previously mapped QTL intervals, including rhg1 and Rhg4. The GWAS results also detected 10 SNPs in 5 different genomic regions associated with SCN resistance. The identified loci explained an average of 95.5% of the phenotypic variance. The proportion of phenotypic variance was due to additive genetic variance of the validated SNPs. The present study identified multiple new loci and refined chromosomal regions of known loci associated with SCN resistance. The loci and trait-associated SNPs identified in this study can be used for developing soybean cultivars with durable resistance against SCN.     

Multivariate eQTL mapping uncovers functional variation on the X-chromosome associated with complex disease traits

Very few studies have investigated the associations between genetic polymorphisms and gene expression on the X-chromosome. This is a major bottleneck when conducting functional follow-up studies of trait-associated variants, as those identified in genome-wide association studies (GWAS). We used a multivariate approach to test the association between individual single nucleotide polymorphisms (SNPs) and exon expression levels measured in 356 Epstein–Barr virus-transformed lymphoblastoid cell lines (LCLs) from the Geuvadis RNA sequencing project to identify SNPs associated with variation in gene expression on the X-chromosome, which we refer to as eSNPs. At an FDR of 5 %, we discovered 548 independent [linkage disequilibrium (LD) r ² < 0.1] eSNPs on the X-chromosome. Of these, 35 were in LD (r ² > 0.8) with previously published disease- or trait-associated variants identified through GWAS. One of the strongest eSNPs identified was rs35975601, which was associated with F8A1 expression (p value = 3 × 10^?20) and was in LD with a type 1 diabetes risk variant. Additionally, we identified a number of genes for which eSNPs were in LD with multiple diseases or traits, including DNASE1L1 which was mapped to bilirubin levels, type 1 diabetes and schizophrenia. Our results also indicate that multivariate exon-level analysis provides a more powerful approach than univariate gene-level analysis, particularly when SNPs influence the expression of different exons with different magnitude and/or direction of effect. The associations identified in our study may provide new insights into the molecular process by which gene expression may contribute to trait variation or disease risk in humans.     

Genome-Wide Association Studies Identifies Seven Major Regions Responsible for Iron Deficiency Chlorosis in Soybean (Glycine max)

Iron deficiency chlorosis (IDC) is a yield limiting problem in soybean (Glycine max (L.) Merr) production regions with calcareous soils. Genome-wide association study (GWAS) was performed using a high density SNP map to discover significant markers, QTL and candidate genes associated with IDC trait variation. A stepwise regression model included eight markers after considering LD between markers, and identified seven major effect QTL on seven chromosomes. Twelve candidate genes known to be associated with iron metabolism mapped near these QTL supporting the polygenic nature of IDC. A non-synonymous substitution with the highest significance in a major QTL region suggests soybean orthologs of FRE1 on Gm03 is a major gene responsible for trait variation. NAS3, a gene that encodes the enzyme nicotianamine synthase which synthesizes the iron chelator nicotianamine also maps to the same QTL region. Disease resistant genes also map to the major QTL, supporting the hypothesis that pathogens compete with the plant for Fe and increase iron deficiency. The markers and the allelic combinations identified here can be further used for marker assisted selection.     

Linkage Disequilibrium and Genome-Wide Association Mapping in Tetraploid Wheat (Triticum turgidum L.)

Association mapping is a powerful tool for the identification of quantitative trait loci through the exploitation of the differential decay of linkage disequilibrium (LD) between marker loci and genes of interest in natural and domesticated populations. Using a sample of 230 tetraploid wheat lines (Triticum turgidum ssp), which included naked and hulled accessions, we analysed the pattern of LD considering 26 simple sequence repeats and 970 mostly mapped diversity array technology loci. In addition, to validate the potential for association mapping in durum wheat, we evaluated the same genotypes for plant height, heading date, protein content, and thousand-kernel weight. Molecular and phenotypic data were used to: (i) investigate the genetic and phenotypic diversity; (ii) study the dynamics of LD across the durum wheat genome, by investigating the patterns of LD decay; and (iii) test the potential of our panel to identify marker–trait associations through the analysis of four quantitative traits of major agronomic importance. Moreover, we compared and validated the association mapping results with outlier detection analysis based on population divergence. Overall, in tetraploid wheat, the pattern of LD is extremely population dependent and is related to the domestication and breeding history of durum wheat. Comparing our data with several other studies in wheat, we confirm the position of many major genes and quantitative trait loci for the traits considered. Finally, the analysis of the selection signature represents a very useful complement to validate marker–trait associations.     

Integrating comprehensive functional annotations to boost power and accuracy in gene-based association analysis

Gene-based association tests aggregate genotypes across multiple variants for each gene, providing an interpretable gene-level analysis framework for genome-wide association studies (GWAS). Early gene-based test applications often focused on rare coding variants; a more recent wave of gene-based methods, e.g. TWAS, use eQTLs to interrogate regulatory associations. Regulatory variants are expected to be particularly valuable for gene-based analysis, since most GWAS associations to date are non-coding. However, identifying causal genes from regulatory associations remains challenging and contentious. Here, we present a statistical framework and computational tool to integrate heterogeneous annotations with GWAS summary statistics for gene-based analysis, applied with comprehensive coding and tissue-specific regulatory annotations. We compare power and accuracy identifying causal genes across single-annotation, omnibus, and annotation-agnostic gene-based tests in simulation studies and an analysis of 128 traits from the UK Biobank, and find that incorporating heterogeneous annotations in gene-based association analysis increases power and performance identifying causal genes.     

Resequencing 250 Soybean Accessions: New Insights into Genes Associated with Agronomic Traits and Genetic Networks

The limited knowledge of genomic diversity and functional genes associated with the traits of soybean varieties has resulted in slow progress in breeding.In this study,we sequenced the genomes of 250 soybean landraces and cultivars from China,America,and Europe,and investigated their population structure,genetic diversity and architecture,and the selective sweep regions of these accessions.Five novel agronomically important genes were identified,and the effects of functional mutations in respect...     

Genome-Wide Association of Stem Water Soluble Carbohydrates in Bread Wheat

Water soluble carbohydrates (WSC) in stems play an important role in buffering grain yield in wheat against biotic and abiotic stresses; however, knowledge of genes controlling WSC is very limited. We conducted a genome-wide association study (GWAS) using a high-density 90K SNP array to better understand the genetic basis underlying WSC, and to explore marker-based breeding approaches. WSC was evaluated in an association panel comprising 166 Chinese bread wheat cultivars planted in four environments. Fifty two marker-trait associations (MTAs) distributed across 23 loci were identified for phenotypic best linear unbiased estimates (BLUEs), and 11 MTAs were identified in two or more environments. Liner regression showed a clear dependence of WSC BLUE scores on numbers of favorable (increasing WSC content) and unfavorable alleles (decreasing WSC), indicating that genotypes with higher numbers of favorable or lower numbers of unfavorable alleles had higher WSC content. In silico analysis of flanking sequences of trait-associated SNPs revealed eight candidate genes related to WSC content grouped into two categories based on the type of encoding proteins, namely, defense response proteins and proteins triggered by environmental stresses. The identified SNPs and candidate genes related to WSC provide opportunities for breeding higher WSC wheat cultivars.     

Genetic variation, population structure, and linkage disequilibrium in European elite germplasm of perennial ryegrass

Perennial ryegrass (Lolium perenne L.) is a highly valued temperate climate grass species grown as forage crop and for amenity uses. Due to its outbreeding nature and recent domestication, a high degree of genetic diversity is expected among cultivars. The aim of this study was to assess the extent of linkage disequilibrium (LD) within European elite germplasm and to evaluate the appropriate methodology for genetic association mapping in perennial ryegrass. A high level of genetic diversity was observed in a set of 380 perennial ryegrass elite genotypes when genotyped with 40 SSRs and 2 STS markers. A Bayesian structure analysis identified two subpopulations, which were confirmed by principal coordinate analysis (PCoA). One subpopulation consisted mainly of genotypes originating from the UK, while germplasm mostly from Continental Europe was grouped into the second subpopulation. LD (r²) decay was rapid and occurred within 0.4 cM across European varieties, when population structure was taken into consideration. However, an extended LD of up to 6.6 cM was detected within the variety Aberdart. High genetic diversity and rapid LD decay provide means for high resolution association mapping in elite materials of perennial ryegrass. However, different strategies need to be applied depending on the material used. Genome-wide association study (GWAS) with several hundred markers can be applied within synthetic varieties to identify large (up to 10 cM) genomic regions affecting trait variation. A combination of available and novel DNA markers is needed to achieve resolution required for GWAS in elite breeding materials. An even higher marker density of several million SNPs might be needed for GWAS in diverse ecotype collections, potentially resulting in quantitative trait polymorphism (QTP) identification.     

Insights into Colon Cancer Etiology via a Regularized Approach to Gene Set Analysis of GWAS Data

Genome-wide association studies (GWAS) have successfully identified susceptibility loci from marginal association analysis of SNPs. Valuable insight into genetic variation underlying complex diseases will likely be gained by considering functionally related sets of genes simultaneously. One approach is to further develop gene set enrichment analysis methods, which are initiated in gene expression studies, to account for the distinctive features of GWAS data. These features include the large number of SNPs per gene, the modest and sparse SNP associations, and the additional information provided by linkage disequilibrium (LD) patterns within genes. We propose a “gene set ridge regression in association studies (GRASS)” algorithm. GRASS summarizes the genetic structure for each gene as eigenSNPs and uses a novel form of regularized regression technique, termed group ridge regression, to select representative eigenSNPs for each gene and assess their joint association with disease risk. Compared with existing methods, the proposed algorithm greatly reduces the high dimensionality of GWAS data while still accounting for multiple hits and/or LD in the same gene. We show by simulation that this algorithm performs well in situations in which there are a large number of predictors compared to sample size. We applied the GRASS algorithm to a genome-wide association study of colon cancer and identified nicotinate and nicotinamide metabolism and transforming growth factor beta signaling as the top two significantly enriched pathways. Elucidating the role of variation in these pathways may enhance our understanding of colon cancer etiology.     

ezQTL: A Web Platform for Interactive Visualization and Colocalization of QTLs and GWAS Loci

Genome-wide association studies(GWAS) have identified thousands of genomic loci associated with complex diseases and traits, including cancer. The vast majority of common traitassociated variants identified via GWAS fall in non-coding regions of the genome, posing a challenge in elucidating the causal variants, genes, and mechanisms involved. Expression quantitative trait locus(e QTL) and other molecular QTL studies have been valuable resources in identifying candidate causal genes from GWAS loc...     

Haplotype analysis improved evidence for candidate genes for intramuscular fat percentage from a genome wide association study of cattle

In genome wide association studies (GWAS), haplotype analyses of SNP data are neglected in favour of single point analysis of associations. In a recent GWAS, we found that none of the known candidate genes for intramuscular fat (IMF) had been identified. In this study, data from the GWAS for these candidate genes were re-analysed as haplotypes. First, we confirmed that the methodology would find evidence for association between haplotypes in candidate genes of the calpain-calpastatin complex and musculus longissimus lumborum peak force (LLPF), because these genes had been confirmed through single point analysis in the GWAS. Then, for intramuscular fat percent (IMF), we found significant partial haplotype substitution effects for the genes ADIPOQ and CXCR4, as well as suggestive associations to the genes CEBPA, FASN, and CAPN1. Haplotypes for these genes explained 80% more of the phenotypic variance compared to the best single SNP. For some genes the analyses suggested that there was more than one causative mutation in some genes, or confirmed that some causative mutations are limited to particular subgroups of a species. Fitting the SNPs and their interactions simultaneously explained a similar amount of the phenotypic variance compared to haplotype analyses. Haplotype analysis is a neglected part of the suite of tools used to analyse GWAS data, would be a useful method to extract more information from these data sets, and may contribute to reducing the missing heritability problem.