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ARS Home » Northeast Area » Beltsville, Maryland (BARC) » Beltsville Agricultural Research Center » Soybean Genomics & Improvement Laboratory » Research » Publications at this Location » Publication #316485

Research Project: Defining the Genetic Diversity and Structure of the Soybean Genome and Applications to Gene Discovery in Soybean, Wheat and Common Bean Germplasm

Location: Soybean Genomics & Improvement Laboratory

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

Author
item WEN, ZIXIANG - Michigan State University
item BOYSE, JOHN - Michigan State University
item Song, Qijian
item CREGAN, PERRY - Retired ARS Employee
item WANG, DECHUN - Michigan State University

Submitted to: Biomed Central (BMC) Genomics
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 8/21/2015
Publication Date: 9/3/2015
Publication URL: http://handle.nal.usda.gov/10113/61461
Citation: Wen, Z., Boyse, J.F., Song, Q., Cregan, P., Wang, D. 2015. Genomic consequences of selection and genome-wide association mapping in soybean. Biomed Central (BMC) Genomics. 16:671.

Interpretive Summary: Domestication and subsequent breeding improvement has likely left signatures of selection or changes within the DNA of the chromosomes of modern soybean varieties. For instance, the elite soybean varieties being grown by soybean farmers have a more uniform erect appearance, stem architecture with reduced branching, and large seed. The identification of these signatures of selection will facilitate the identification of the genes that control traits of agronomic importance for soybean improvement. In this study, we assayed more than one thousand soybean landraces from Asia and a set of improved soybean varieties resulting from soybean breeding with more than 50,000 single nucleotide polymorphism (SNP) DNA markers distributed across the 20 sets of soybean chromosomes. Using this genome-wide set of SNP DNA markers we observed strong signals of selection in the genomic regions that contained genes that are associated with improved plant characteristics such as resistance to the soybean cyst nematode, improved seed quality traits and etc. The research provides soybean breeders at universities and private seed companies valuable information for soybean improvement through the use of DNA marker-assisted breeding to create new genetically improved soybean varieties.

Technical Abstract: Crop improvement always involves selection of specific alleles at genes controlling traits of agronomic importance, likely resulting in detectable signatures within the genome of modern soybean. The identification of these signatures is meaningful from the perspective of evolutionary biology, and for uncovering the genetic architecture of agronomic traits. In this research, two soybean populations including 342 landraces and 1062 improved lines, were genotyped with the SoySNP50K BeadChip containing 52,041 single nucleotide polymorphisms (SNPs), and systematically phenotyped for 9 agronomic traits. Analysis using cross-population composite likelihood ratio (XP-CLR) method identified a total of 125 candidate selection regions, many of which are involved in crop improvement. To further investigate whether these candidate regions are in fact enriched for genes (or quantitative trait loci) affected by selection, a genome-wide association study (GWAS) was performed on 7 selection-targeted traits (grain yield, plant height, lodging, maturity date, seed coat color, protein and oil content) and 2 non-selection-targeted traits (pubescence and flower color) of soybean breeding. Major genomic regions associated with selection-targeted traits overlap with candidate selection regions, whereas no overlap of this kind occurred for the non-selection-target traits, suggesting that the signals of selective sweeps we 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. These results illustrate comparative population genomic analyses, especially when combined with trait-based mapping approaches, are a promising way to dissect improvement related agronomic traits.