Detection of Epistatic and Gene-Environment Interactions Underlying Three Quality Traits in Rice Using High-Throughput Genome-Wide Data
Joint Authors
Xu, Haiming
Jiang, Beibei
Cao, Yujie
Zhang, Yingxin
Zhan, Xiaodeng
Shen, Xihong
Cheng, Shihua
Lou, Xiangyang
Cao, Liyong
Source
Issue
Vol. 2015, Issue 2015 (31 Dec. 2015), pp.1-7, 7 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2015-08-04
Country of Publication
Egypt
No. of Pages
7
Main Subjects
Abstract EN
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.
American Psychological Association (APA)
Xu, Haiming& Jiang, Beibei& Cao, Yujie& Zhang, Yingxin& Zhan, Xiaodeng& Shen, Xihong…[et al.]. 2015. Detection of Epistatic and Gene-Environment Interactions Underlying Three Quality Traits in Rice Using High-Throughput Genome-Wide Data. BioMed Research International،Vol. 2015, no. 2015, pp.1-7.
https://search.emarefa.net/detail/BIM-1054317
Modern Language Association (MLA)
Xu, Haiming…[et al.]. Detection of Epistatic and Gene-Environment Interactions Underlying Three Quality Traits in Rice Using High-Throughput Genome-Wide Data. BioMed Research International No. 2015 (2015), pp.1-7.
https://search.emarefa.net/detail/BIM-1054317
American Medical Association (AMA)
Xu, Haiming& Jiang, Beibei& Cao, Yujie& Zhang, Yingxin& Zhan, Xiaodeng& Shen, Xihong…[et al.]. Detection of Epistatic and Gene-Environment Interactions Underlying Three Quality Traits in Rice Using High-Throughput Genome-Wide Data. BioMed Research International. 2015. Vol. 2015, no. 2015, pp.1-7.
https://search.emarefa.net/detail/BIM-1054317
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1054317