Genome-wide association and epistatic interactions of flowering time in soybean cultivar

• Published in: PloS one Vol. 15; no. 1; p. e0228114
• Main Authors: Kim, Kyoung Hyoun, Kim, Jae-Yoon, Lim, Won-Jun, Jeong, Seongmun, Lee, Ho-Yeon, Cho, Youngbum, Moon, Jung-Kyung, Kim, Namshin
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 22.01.2020; Public Library of Science (PLoS)
• Subjects: Barley; Bioinformatics; Biology and Life Sciences; Biotechnology; Chromosome Mapping - methods; Cultivars; Epistasis; Flowering; Flowers - genetics; Genes; Genetic polymorphisms; Genome, Plant - genetics; Genome-wide association studies; Genome-Wide Association Study - methods; Genomes; Genomics; Glycine max; Glycine max - genetics; Linkage Disequilibrium; Markers; Nucleotides; Phylogenetics; Plant breeding; Polymorphism; Polymorphism, Single Nucleotide; Population structure; Principal components analysis; Quantitative Trait Loci; Research and Analysis Methods; Rural development; Single nucleotide polymorphisms; Single-nucleotide polymorphism; Soybeans; South Korea; China; Japan
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0228114

Genome-wide association studies (GWAS) have enabled the discovery of candidate markers that play significant roles in various complex traits in plants. Recently, with increased interest in the search for candidate markers, studies on epistatic interactions between single nucleotide polymorphism (SNP) markers have also increased, thus enabling the identification of more candidate markers along with GWAS on single-variant-additive-effect. Here, we focused on the identification of candidate markers associated with flowering time in soybean (Glycine max). A large population of 2,662 cultivated soybean accessions was genotyped using the 180k Axiom® SoyaSNP array, and the genomic architecture of these accessions was investigated to confirm the population structure. Then, GWAS was conducted to evaluate the association between SNP markers and flowering time. A total of 93 significant SNP markers were detected within 59 significant genes, including E1 and E3, which are the main determinants of flowering time. Based on the GWAS results, multilocus epistatic interactions were examined between the significant and non-significant SNP markers. Two significant and 16 non-significant SNP markers were discovered as candidate markers affecting flowering time via interactions with each other. These 18 candidate SNP markers mapped to 18 candidate genes including E1 and E3, and the 18 candidate genes were involved in six major flowering pathways. Although further biological validation is needed, our results provide additional information on the existing flowering time markers and present another option to marker-assisted breeding programs for regulating flowering time of soybean.