Article

Identification of single nucleotide polymorphisms and haplotypes associated with yield and yield components in soybean (Glycine max) landraces across multiple environments.

National Center for Soybean Improvement, National Key Laboratory of Crop Genetics and Germplasm Enhancement, Nanjing Agricultural University, 210095, Nanjing, China.
Theoretical and Applied Genetics (impact factor: 3.3). 02/2012; 124(3):447-58. DOI:10.1007/s00122-011-1719-0
Source: PubMed

ABSTRACT Genome-wide association analysis is a powerful approach to identify the causal genetic polymorphisms underlying complex traits. In this study, we evaluated a population of 191 soybean landraces in five environments to detect molecular markers associated with soybean yield and its components using 1,536 single-nucleotide polymorphisms (SNPs) and 209 haplotypes. The analysis revealed that abundant phenotypic and genetic diversity existed in the studied population. This soybean population could be divided into two subpopulations and no or weak relatedness was detected between pair-wise landraces. The level of intra-chromosomal linkage disequilibrium was about 500 kb. Genome-wide association analysis based on the unified mixed model identified 19 SNPs and 5 haplotypes associated with soybean yield and yield components in three or more environments. Nine markers were found co-associated with two or more traits. Many markers were located in or close to previously reported quantitative trait loci mapped by linkage analysis. The SNPs and haplotypes identified in this study will help to further understand the genetic basis of soybean yield and its components, and may facilitate future high-yield breeding by marker-assisted selection in soybean.

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Keywords

1,536 single-nucleotide polymorphisms
 
191 soybean landraces
 
209 haplotypes
 
causal genetic polymorphisms
 
complex traits
 
future high-yield
 
genetic basis
 
Genome-wide association analysis
 
intra-chromosomal linkage disequilibrium
 
linkage analysis
 
marker-assisted selection
 
molecular markers
 
powerful approach
 
quantitative trait loci mapped
 
soybean population
 
soybean yield
 
studied population
 
unified mixed model
 
weak relatedness
 
yield components