Phenotypic Characterization and Genetic Dissection of Growth Period Traits in Soybean (Glycine max) Using Association Mapping

Abstract

The growth period traits are important traits that affect soybean yield. The insights into the genetic basis of growth period traits can provide theoretical basis for cultivated area division, rational distribution, and molecular breeding for soybean varieties. In this study, genome-wide association analysis (GWAS) was exploited to detect the quantitative trait loci (QTL) for number of days to flowering (ETF), number of days from flowering to maturity (FTM), and number of days to maturity (ETM) using 4032 single nucleotide polymorphism (SNP) markers with 146 cultivars mainly from Northeast China. Results showed that abundant phenotypic variation was presented in the population, and variation explained by genotype, environment, and genotype by environment interaction were all significant for each trait. The whole accessions could be clearly clustered into two subpopulations based on their genetic relatedness, and accessions in the same group were almost from the same province. GWAS based on the unified mixed model identified 19 significant SNPs distributed on 11 soybean chromosomes, 12 of which can be consistently detected in both planting densities, and 5 of which were pleotropic QTL. Of 19 SNPs, 7 SNPs located in or close to the previously reported QTL or genes controlling growth period traits. The QTL identified with high resolution in this study will enrich our genomic understanding of growth period traits and could then be explored as genetic markers to be used in genomic applications in soybean breeding.

Genotyping of Soybean Cultivars With Medium-Density Array Reveals the Population Structure and QTNs Underlying Maturity and Seed Traits

“…In this study, a relatively complex population structure (K = 7) was revealed in comparison of previous reports in which population structures (K = 2, 4, 9) were disclosed (Sonah et al, 2015 ; Liu Z. X. et al, 2016 ; Fang et al, 2017 ). After eliminating batch specific or biased markers, the set of 4471 markers might represents the core markers for this set of germplasm (Data Sheet 1 , ftp://159.226.208.134/public/SNP_data.zip ).…”

Identification of mega‐environments in Europe and effect of allelic variation at maturity E loci on adaptation of European soybean

“…For a better comparison, the correlations among the three traits were calculated for each MG within each MEV, but the results have to be treated with caution because the analysis was based on only 15 genotypes. The reason for the moderately positive correlations between flowering and maturity observed here and also by Jia et al (2014), as compared to the high correlations reported by Alliprandini et al (2009) andLiu et al (2016), is mainly due to the low variation within each MG. Especially flowering is less variable among very early material because of its photoperiod insensitivity, which was also observed by Zhai et al (2014).…”

“…() and Liu et al . (), is mainly due to the low variation within each MG. Especially flowering is less variable among very early material because of its photoperiod insensitivity, which was also observed by Zhai et al .…”

Integrating GWAS , linkage mapping and gene expression analyses reveals the genetic control of growth period traits in rapeseed (Brassica napus L.)

“…This nding further con rmed that co-mapping of QTLs caused by single gene pleiotropy. The co-mapping of QTLs can help breeders identifying favorable alleles for multiple traits simultaneously in marker-assisted breeding [39].…”