Principal  Component Analysis-based Genome-wide Association Study for Detecting Candidate Genes Associated with Various Traits in Rice

J. K. Lee; S. G. Hwang

doi:10.22698/jales.20200036

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2020 Vol.32, Issue 3 Preview Page Next Page

Research Article

Principal Component Analysis-based Genome-wide Association Study for Detecting Candidate Genes Associated with Various Traits in Rice 다양한 형질 관련 후보유전자 동정을 위한 주성분 분석 기반 GWAS

31 December 2020. pp. 417-424

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Abstract

In this study, we attempted to identify candidate single-nucleotide polymorphisms (SNPs) corresponding to agronomic traits via a genome-wide association study (GWAS) using principal component analysis (PCA). We confirmed the normally distributed traits in each PC, suggesting that the PCs were suitable for GWAS. We found 2,527,855 SNPs and 381,681 indels among 26 rice varieties. The PC1 was showed 22 significant missense SNPs associated with three traits (leaf blight disease, rice stripe disease, and gains per panicle). Especially, three of 22 missense SNPs had the highest significance in GWAS for each trait. The 06g0587200 gene associated with gain per panicle belongs to two QTLs: qtl1-5 corresponding to the length and width of grains and Tgw6.1 corresponding to the thousand-grain weight. The two candidate genes (Os01g0858000 implicated in rice stripe disease and Os03g0165000 implicated in leaf blight disease) were newly detected in this study. However, further studies are required to confirm the exact function of these two genes. Thus, GWAS based on PCA can be used to find a key gene corresponding to various traits.

Keywords

GWAS

Indel

PCA

Rice, SNP

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Information

Publisher :Agriculture and Life Sciences Research Institute, Kangwon National University
Publisher(Ko) :None
Journal Title :Journal of Agricultural, Life and Environmental Sciences
Volume : 32
No :3
Pages :417-424
Received Date : 2020-11-19
Revised Date : 2020-12-10
Accepted Date : 2020-12-16
DOI :https://doi.org/10.22698/jales.20200036

Journal of Agricultural, Life and Environmental Sciences ISSN:2233-8322(Print) 2508-870X(Online)

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