Dataset Information

Dissecting the Genetic Basis of Local Adaptation in Soybean.

ABSTRACT: 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.

SUBMITTER: Bandillo NB

PROVIDER: S-EPMC5722827 | biostudies-literature | 2017 Dec

REPOSITORIES: biostudies-literature

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Dissecting the Genetic Basis of Local Adaptation in Soybean.

Bandillo Nonoy B NB Anderson Justin E JE Kantar Michael B MB Stupar Robert M RM Specht James E JE Graef George L GL Lorenz Aaron J AJ

Scientific reports 20171208 1

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. W ...[more]

PMID: 29222468

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Project description:Glycine soja is the wild relative species of cultivated soybean. In this study, we investigated the population divergence and genetic basis of the local adaptation of wild soybean in China using genome-wide single-nucleotide polymorphisms (SNPs) of a population of 72 G. soja accessions. Using phylogenetic analysis, we observed that G. soja accessions clustered into three distinct groups, each corresponding to a specific geographic region, the northeastern region (NER), central region (CR), and southern region (SR), consistent with previous studies. Notably, we found a significant positive correlation between genetic and geographic distances. Further population structure analysis revealed each group was associated with an ancestral population and a specific geographic area. By utilizing the genome sequencing data of accessions from 16 different locations, we inferred the population history of these wild soybean groups. Our results indicate that the three groups diverged ~25,000 years ago, coinciding with the time of the last glacial maximum. The effective population size of the SR group expanded first, and subsequently, the NER and CR groups expanded approximately 5000 and 2500 years ago, respectively. Moreover, 83, 104, and 101 significant associated loci (SALs) were identified using genome-wide association analysis for annual mean temperature, annual precipitation, and latitude, respectively. Functional analysis of genes located in SALs highlighted candidate genes related to local adaptation. This study highlights the significant role of geographic isolation and environmental factors in shaping the genetic structure and adaptability of wild soybean populations. Furthermore, it emphasizes the value of wild soybean as a crucial genetic resource for enhancing the adaptability of cultivated soybeans, which have experienced a loss of genetic diversity due to domestication and intensive breeding practices. The insights gained from our research provide valuable information for the protection, conservation, and utilization of this important genetic resource.

Project description:BACKGROUND:Neurodevelopmental disorders (NDDs) such as autism spectrum disorder, intellectual disability, developmental disability, and epilepsy are characterized by abnormal brain development that may affect cognition, learning, behavior, and motor skills. High co-occurrence (comorbidity) of NDDs indicates a shared, underlying biological mechanism. The genetic heterogeneity and overlap observed in NDDs make it difficult to identify the genetic causes of specific clinical symptoms, such as seizures. METHODS:We present a computational method, MAGI-S, to discover modules or groups of highly connected genes that together potentially perform a similar biological function. MAGI-S integrates protein-protein interaction and co-expression networks to form modules centered around the selection of a single "seed" gene, yielding modules consisting of genes that are highly co-expressed with the seed gene. We aim to dissect the epilepsy phenotype from a general NDD phenotype by providing MAGI-S with high confidence NDD seed genes with varying degrees of association with epilepsy, and we assess the enrichment of de novo mutation, NDD-associated genes, and relevant biological function of constructed modules. RESULTS:The newly identified modules account for the increased rate of de novo non-synonymous mutations in autism, intellectual disability, developmental disability, and epilepsy, and enrichment of copy number variations (CNVs) in developmental disability. We also observed that modules seeded with genes strongly associated with epilepsy tend to have a higher association with epilepsy phenotypes than modules seeded at other neurodevelopmental disorder genes. Modules seeded with genes strongly associated with epilepsy (e.g., SCN1A, GABRA1, and KCNB1) are significantly associated with synaptic transmission, long-term potentiation, and calcium signaling pathways. On the other hand, modules found with seed genes that are not associated or weakly associated with epilepsy are mostly involved with RNA regulation and chromatin remodeling. CONCLUSIONS:In summary, our method identifies modules enriched with de novo non-synonymous mutations and can capture specific networks that underlie the epilepsy phenotype and display distinct enrichment in relevant biological processes. MAGI-S is available at https://github.com/jchow32/magi-s .

Dataset Information

Dissecting the Genetic Basis of Local Adaptation in Soybean.

Publications

Dissecting the Genetic Basis of Local Adaptation in Soybean.

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