Korean soybean core collection: Genotypic and phenotypic diversity population structure and genome-wide association study

Abstract

A core collection is a subset that represents genetic diversity of the total collection. Soybean (Glycine max (L.) Merr.) is one of major food and feed crops. It is the world’s most cultivated annual herbaceous legume. Constructing a core collection for soybean could play a pivotal role in conserving and utilizing its genetic variability for research and breeding programs. To construct and evaluate a Korean soybean core collection, genotypic and phenotypic data as well as population structure, were analyzed. The Korean soybean core collection consisted of 430 accessions selected from 2,872 collections based on Affymetrix Axiom® 180k SoyaSNP array data. The core collection represented 99% of genotypic diversity of the total collection. Analysis of population structure clustered the core collection into five subpopulations. Accessions from South Korea and North Korea were distributed across five subpopulations. Analysis of molecular variance indicated that only 2.01% of genetic variation could be explained by geographic origins while 16.18% of genetic variation was accounted for by subpopulations. Genome-wide association study (GWAS) for days to flowering, flower color, pubescent color, and growth habit confirmed that the core collection had the same genetic diversity for tested traits as the total collection. The Korean soybean core collection was constructed based on genotypic information of the 180k SNP data. Size and phenotypic diversity of the core collection accounted for approximately 14.9% and 18.1% of the total collection, respectively. GWAS of core and total collections successfully confirmed loci associated with tested traits. Consequently, the present study showed that the Korean soybean core collection could provide fundamental and practical material and information for both soybean genetic research and breeding programs.

GMStool: GWAS-based marker selection tool for genomic prediction from genomic data

“…The rice data for both genotypes and phenotypes were obtained from www.ricediversity.org 26 . The soybean data for genotypes were obtained from www.soybase.org/data/public/Glycine_max/Wm82.gnm2.div.L78C 27 , and its phenotype data was shared from our previous study 28 . In the present study, the rice genotype data consisted of 413 samples with 44,100 SNPs, and the soybean genotype data consisted of 1928 samples with 170,223 SNPs.…”

QTL Mapping and Candidate Gene Analysis for Pod Shattering Tolerance in Soybean (Glycine max)

“…The genomic DNA was extracted using a commercial kit (Exgene Plant SV Miniprep Kit; GeneAll, Seoul, Korea) following the manufacturer’s instruction and 50 µL of AE buffer was used to elute DNA [ 40 ]. The 180K Axiom ® SoyaSNP array [ 41 , 42 , 43 ] was used to genotype the parents and 307 RILs of two populations.…”

Patterns of Genetic Variation in a Soybean Germplasm Collection as Characterized with Genotyping-by-Sequencing

“…Characterizing germplasm using genomic tools has been technically and practically more feasible than before, thanks to the recent development of next generation sequencing technologies and bioinformatics tools, and drastically decreasing costs [ 5 ]. Such characterization can provide high resolution with genome-wide SNP markers to analyze genetic diversity and structure (e.g., see Milner et al [ 3 ]; Sansaloni et al [ 4 ]), group germplasm for the development of core collections (e.g., Jeong et al [ 6 ]), and can support identification of genetic duplicates (e.g., see Ellis et al [ 7 ]; Singh et al [ 8 ]) for better germplasm management. It can also enhance the search for unique germplasm with traits of breeding targets for better varietal development (e.g., see Sansaloni et al [ 4 ]; Mascher et al [ 9 ]).…”

“…Considerable efforts were made using genomic tools to characterize ex situ soybean [ Glycine spp.] germplasm (e.g., see Jeong et al [ 6 ]; Song et al [ 10 ]) and to develop different core subsets of soybean accessions in different genebanks (e.g., see Wang et al [ 11 ]; Cho et al [ 12 ]; Oliveira et al [ 13 ]; Kaga et al [ 14 ]; Priolli et al [ 15 ]). These characterizations not only allow for a better understanding of the genetic variation present in soybean germplasm, but also support enhanced soybean germplasm management and utilization.…”