Detection of Epistatic and Gene-Environment Interactions Underlying Three Quality Traits in Rice Using High-Throughput Genome-Wide Data.
With development of sequencing technology, dense single nucleotide polymorphisms (SNPs) have been available, enabling uncovering genetic architecture of complex traits by genome-wide association study (GWAS). However, the current GWAS strategy usually ignores epistatic and gene-environment interactions due to absence of appropriate methodology and heavy computational burden. This study proposed a new GWAS strategy by combining the graphics processing unit- (GPU-) based generalized multifactor dimensionality reduction (GMDR) algorithm with mixed linear model approach. The reliability and efficiency of the analytical methods were verified through Monte Carlo simulations, suggesting that a population size of nearly 150 recombinant inbred lines (RILs) had a reasonable resolution for the scenarios considered. Further, a GWAS was conducted with the above two-step strategy to investigate the additive, epistatic, and gene-environment associations between 701,867 SNPs and three important quality traits, gelatinization temperature, amylose content, and gel consistency, in a RIL population with 138 individuals derived from super-hybrid rice Xieyou9308 in two environments. Four significant SNPs were identified with additive, epistatic, and gene-environment interaction effects. Our study showed that the mixed linear model approach combining with the GPU-based GMDR algorithm is a feasible strategy for implementing GWAS to uncover genetic architecture of crop complex traits.
Institute of Crop Science and Institute of Bioinformatics, College of Agriculture and Biotechnology, Zhejiang University, Hangzhou 310058, China.
Haiming
H
Xu
Institute of Crop Science and Institute of Bioinformatics, College of Agriculture and Biotechnology, Zhejiang University, Hangzhou 310058, China.
Beibei
B
Jiang
Institute of Crop Science and Institute of Bioinformatics, College of Agriculture and Biotechnology, Zhejiang University, Hangzhou 310058, China.
Yujie
Y
Cao
State Key Laboratory of Rice Biology and Zhejiang Key Laboratory of Super Rice Research, China National Rice Research Institute, Hangzhou 311401, China.
Yingxin
Y
Zhang
State Key Laboratory of Rice Biology and Zhejiang Key Laboratory of Super Rice Research, China National Rice Research Institute, Hangzhou 311401, China.
Xiaodeng
X
Zhan
State Key Laboratory of Rice Biology and Zhejiang Key Laboratory of Super Rice Research, China National Rice Research Institute, Hangzhou 311401, China.
Xihong
X
Shen
State Key Laboratory of Rice Biology and Zhejiang Key Laboratory of Super Rice Research, China National Rice Research Institute, Hangzhou 311401, China.
Shihua
S
Cheng
Department of Biostatistics, University of Alabama at Birmingham, Birmingham, AL 35294, USA.
Xiangyang
X
Lou
State Key Laboratory of Rice Biology and Zhejiang Key Laboratory of Super Rice Research, China National Rice Research Institute, Hangzhou 311401, China.
Liyong
L
Cao
D004843:Epistasis, Genetic; D005786:Gene Expression Regulation; D059647:Gene-Environment Interaction; D055106:Genome-Wide Association Study; D012275:Oryza / Q000235:genetics / Q000378:metabolism; D020641:Polymorphism, Single Nucleotide; D040641:Quantitative Trait Loci