Transcriptome-Wide Association Supplements Genome-Wide Association in Zea mays.

Modern improvement of complex traits in agricultural species relies on successful associations of heritable molecular variation with observable phenotypes. Historically, this pursuit has primarily been based on easily measurable genetic markers. The recent advent of new technologies allows assaying and quantifying biological intermediates (hereafter endophenotypes) which are now readily measurable at a large scale across diverse individuals. The usefulness of endophenotypes for delineating the regulatory landscape of the genome and genetic dissection of complex trait variation remains underexplored in plants. The work presented here illustrated the utility of a large-scale (299-genotype and seven-tissue) gene expression resource to dissect traits across multiple levels of biological organization. Using single-tissue- and multi-tissue-based transcriptome-wide association studies (TWAS), we revealed that about half of the functional variation acts through altered transcript abundance for maize kernel traits, including 30 grain carotenoid abundance traits, 20 grain tocochromanol abundance traits, and 22 field-measured agronomic traits. Comparing the efficacy of TWAS with genome-wide association studies (GWAS) and an ensemble approach that combines both GWAS and TWAS, we demonstrated that results of TWAS in combination with GWAS increase the power to detect known genes and aid in prioritizing likely causal genes. Using a variance partitioning approach in the largely independent maize Nested Association Mapping (NAM) population, we also showed that the most strongly associated genes identified by combining GWAS and TWAS explain more heritable variance for a majority of traits than the heritability captured by the random genes and the genes identified by GWAS or TWAS alone. This not only improves the ability to link genes to phenotypes, but also highlights the phenotypic consequences of regulatory variation in plants

Genome-wide Association Mapping of Qualitatively Inherited Traits in a Germplasm Collection

Genome-wide association (GWA) has been used as a tool for dissecting the genetic architecture of quantitatively inherited traits. We demonstrate here that GWA can also be highly useful for detecting many major genes governing categorically defined phenotype variants that exist for qualitatively inherited traits in a germplasm collection. Genome-wide association mapping was applied to categorical phenotypic data available for 10 descriptive traits in a collection of ~13,000 soybean [Glycine max (L.) Merr.] accessions that had been genotyped with a 50,000 single nucleotide polymorphism (SNP) chip. A GWA on a panel of accessions of this magnitude can offer substantial statistical power and mapping resolution, and we found that GWA mapping resulted in the identification of strong SNP signals for 24 classical genes as well as several heretofore unknown genes controlling the phenotypic variants in those traits. Because some of these genes had been cloned, we were able to show that the narrow GWA mapping SNP signal regions that we detected for the phenotypic variants had chromosomal bp spans that, with just one exception, overlapped the bp region of the cloned genes, despite local variation in SNP number and nonuniform SNP distribution in the chip set

Dissecting the Genetic Basis of Local Adaptation in Soybean

Soybean (Glycine max) is the most widely grown oilseed in the world and is an important source of protein for both humans and livestock. Soybean is widely adapted to both temperate and tropical regions, but a changing climate demands a better understanding of adaptation to specific environmental conditions. Here, we explore genetic variation in a collection of 3,012 georeferenced, locally adapted landraces from a broad geographical range to help elucidate the genetic basis of local adaptation. We used geographic origin, environmental data and dense genome-wide SNP data to perform an environmental association analysis and discover loci displaying steep gradients in allele frequency across geographical distance and between landrace and modern cultivars. Our combined application of methods in environmental association mapping and detection of selection targets provide a better understanding of how geography and selection may have shaped genetic variation among soybean landraces. Moreover, we identified several important candidate genes related to drought and heat stress, and revealed important genomic regions possibly involved in the geographic divergence of soybean

Deleterious Mutation Burden and Its Association with Complex Traits in Sorghum (Sorghum bicolor)

Sorghum (Sorghum bicolor L.) is a major food cereal for millions of people worldwide. The sorghum genome, like other species, accumulates deleterious mutations, likely impacting its fitness. The lack of recombination, drift, and the coupling with favorable loci impede the removal of deleterious mutations from the genome by selection. To study how deleterious variants impact phenotypes, we identified putative deleterious mutations among ∼5.5 M segregating variants of 229 diverse biomass sorghum lines. We provide the whole-genome estimate of the deleterious burden in sorghum, showing that ∼33% of nonsynonymous substitutions are putatively deleterious. The pattern of mutation burden varies appreciably among racial groups. Across racial groups, the mutation burden correlated negatively with biomass, plant height, specific leaf area (SLA), and tissue starch content (TSC), suggesting that deleterious burden decreases trait fitness. Putatively deleterious variants explain roughly one-half of the genetic variance. However, there is only moderate improvement in total heritable variance explained for biomass (7.6%) and plant height (average of 3.1% across all stages). There is no advantage in total heritable variance for SLA and TSC. The contribution of putatively deleterious variants to phenotypic diversity therefore appears to be dependent on the genetic architecture of traits. Overall, these results suggest that incorporating putatively deleterious variants into genomic models slightly improves prediction accuracy because of extensive linkage. Knowledge of deleterious variants could be leveraged for sorghum breeding through either genome editing and/or conventional breeding that focuses on the selection of progeny with fewer deleterious alleles

Distribution of genomic variation in the USDA Soybean Germplasm Collection and relationship with phenotypic variation

The USDA Soybean Germplasm Collection harbors a large stock of genetic diversity with potential to accelerate soybean cultivar development. The extent and nature of favorable alleles contained in the collection are not well known nor is the distribution of genetic variation and how it relates to phenotypic variation. The genotyping of the entire USDA Soybean Germplasm Collection marked the beginning of a systematic exploration of genetic diversity for genetic research and breeding. In this research, we conducted the first comprehensive analysis of population structure on the collection of ~14,400 soybean accessions [Glycine max (L.) Merr. and G. soja Siebold & Zucc.] that were genotyped using a 50KSNP chip. Accessions originating from Japan and Korea diverged from the Chinese accessions. The ancestry of founders of the American accessions derived mostly from two Chinese subpopulations, which reflects the composition of the American accessions as a whole. A genome-wide association study on ~12,000 accession conducted on seed protein and oil is the largest reported to date in plants and identified strong single nucleotide polymorphisms (SNPs) signals on chromosomes 20 and 15. The haplotype effects of the chromosome 20 region show a strong negative relationship between oil and protein at this locus, indicating negative pleiotropic effects or multiple closely linked loci in repulsion phase linkage. Genome-wide association mapping for ten descriptive traits identified a total of 23 known genes and unknown genes controlling the phenotypic variants. Because some of those genes had been cloned, we were able to show that the narrow SNP signal regions had chromosomal base pair spans that, with few exceptions, bracketed the base pair region of the cloned gene coding sequences, despite variation in SNP distribution of chip SNP set. We also elucidate the genetic basis of local adaptation in soybean by exploring the natural variation available in 3,012 locally adapted landrace accessions from across the geographical range of soybean. Our approach using selection mapping and landscape genomic association methods identified important candidate genes related to abiotic stresses, and revealed important signatures of directional selection that are likely involved on geographic divergence of soybean

Distribution of genomic variation in the USDA Soybean Germplasm Collection and relationship with phenotypic variation

The USDA Soybean Germplasm Collection harbors a large stock of genetic diversity with potential to accelerate soybean cultivar development. The extent and nature of favorable alleles contained in the collection are not well known nor is the distribution of genetic variation and how it relates to phenotypic variation. The genotyping of the entire USDA Soybean Germplasm Collection marked the beginning of a systematic exploration of genetic diversity for genetic research and breeding. In this research, we conducted the first comprehensive analysis of population structure on the collection of ~14,400 soybean accessions [Glycine max (L.) Merr. and G. soja Siebold & Zucc.] that were genotyped using a 50KSNP chip. Accessions originating from Japan and Korea diverged from the Chinese accessions. The ancestry of founders of the American accessions derived mostly from two Chinese subpopulations, which reflects the composition of the American accessions as a whole. A genome-wide association study on ~12,000 accession conducted on seed protein and oil is the largest reported to date in plants and identified strong single nucleotide polymorphisms (SNPs) signals on chromosomes 20 and 15. The haplotype effects of the chromosome 20 region show a strong negative relationship between oil and protein at this locus, indicating negative pleiotropic effects or multiple closely linked loci in repulsion phase linkage. Genome-wide association mapping for ten descriptive traits identified a total of 23 known genes and unknown genes controlling the phenotypic variants. Because some of those genes had been cloned, we were able to show that the narrow SNP signal regions had chromosomal base pair spans that, with few exceptions, bracketed the base pair region of the cloned gene coding sequences, despite variation in SNP distribution of chip SNP set. We also elucidate the genetic basis of local adaptation in soybean by exploring the natural variation available in 3,012 locally adapted landrace accessions from across the geographical range of soybean. Our approach using selection mapping and landscape genomic association methods identified important candidate genes related to abiotic stresses, and revealed important signatures of directional selection that are likely involved on geographic divergence of soybean

Finding Needles in a Haystack: Using Geo-References to Enhance the Selection and Utilization of Landraces in Breeding for Climate-Resilient Cultivars of Upland Cotton (<i>Gossypium hirsutum</i> L.)

The genetic uniformity of cultivated cotton as a consequence of domestication and modern breeding makes it extremely vulnerable to abiotic challenges brought about by major climate shifts. To sustain productivity amidst worsening agro-environments, future breeding objectives need to seriously consider introducing new genetic variation from diverse resources into the current germplasm base of cotton. Landraces are genetically heterogeneous, population complexes that have been primarily selected for their adaptability to specific localized or regional environments. This makes them an invaluable genetic resource of novel allelic diversity that can be exploited to enhance the resilience of crops to marginal environments. The utilization of cotton landraces in breeding programs are constrained by the phenology of the plant and the lack of phenotypic information that can facilitate efficient selection of potential donor parents for breeding. In this review, the genetic value of cotton landraces and the major challenges in their utilization in breeding are discussed. Two strategies namely Focused Identification of Germplasm Strategy and Environmental Association Analysis that have been developed to effectively screen large germplasm collections for accessions with adaptive traits using geo-reference-based, mathematical modelling are highlighted. The potential applications of both approaches in mining available cotton landrace collections are also presented

Dissecting the Genetic Basis of Local Adaptation in Soybean

Soybean (Glycine max) is the most widely grown oilseed in the world and is an important source of protein for both humans and livestock. Soybean is widely adapted to both temperate and tropical regions, but a changing climate demands a better understanding of adaptation to specific environmental conditions. Here, we explore genetic variation in a collection of 3,012 georeferenced, locally adapted landraces from a broad geographical range to help elucidate the genetic basis of local adaptation. We used geographic origin, environmental data and dense genome-wide SNP data to perform an environmental association analysis and discover loci displaying steep gradients in allele frequency across geographical distance and between landrace and modern cultivars. Our combined application of methods in environmental association mapping and detection of selection targets provide a better understanding of how geography and selection may have shaped genetic variation among soybean landraces. Moreover, we identified several important candidate genes related to drought and heat stress, and revealed important genomic regions possibly involved in the geographic divergence of soybean

Genome-wide Association Mapping of Qualitatively Inherited Traits in a Germplasm Collection

Genome-wide association (GWA) has been used as a tool for dissecting the genetic architecture of quantitatively inherited traits. We demonstrate here that GWA can also be highly useful for detecting many major genes governing categorically defined phenotype variants that exist for qualitatively inherited traits in a germplasm collection. Genome-wide association mapping was applied to categorical phenotypic data available for 10 descriptive traits in a collection of ~13,000 soybean [Glycine max (L.) Merr.] accessions that had been genotyped with a 50,000 single nucleotide polymorphism (SNP) chip. A GWA on a panel of accessions of this magnitude can offer substantial statistical power and mapping resolution, and we found that GWA mapping resulted in the identification of strong SNP signals for 24 classical genes as well as several heretofore unknown genes controlling the phenotypic variants in those traits. Because some of these genes had been cloned, we were able to show that the narrow GWA mapping SNP signal regions that we detected for the phenotypic variants had chromosomal bp spans that, with just one exception, overlapped the bp region of the cloned genes, despite local variation in SNP number and nonuniform SNP distribution in the chip set