Genome-wide association studies (GWAS) have proven effective at identifying genetic variants and genes that are associated with phenotypes in humans, animals, and plants. Since most phenotypes of plant species are complex traits regulated by many genes and their functional interactions, GWAS are increasing in popularity for genetic dissections of plant phenotypes. For the reference plant, Arabidopsis thaliana, detailed information on genetic variations became available with the completion of the 1001 Genomes Project, enabling highly resolved association mapping between chromosomal loci and complex traits. Improvements have been made in the statistical analysis methods for testing the significance of genotype-to-phenotype associations, thereby substantially reducing the confounding effects of population structures. Furthermore, there have been large efforts toward post-GWAS augmentation of signals via integration with other types of information to overcome the limited statistical power of GWAS. This chapter describes the stepwise procedure of GWAS in Arabidopsis, focusing on data analysis processes including preprocessing of genotype and phenotype data, statistical analysis to identify phenotype-associated chromosomal loci, identification of phenotype-associated genes based on the phenotype-associated loci, and finally network-based augmentation of GWAS signals to identify additional candidate genes for the phenotype.
|Title of host publication||Methods in Molecular Biology|
|Publisher||Humana Press Inc.|
|Number of pages||24|
|Publication status||Published - 2021|
|Name||Methods in Molecular Biology|
Bibliographical noteFunding Information:
This work was supported by a National Research Foundation of Korea (NRF) grant funded by the Korean Government (MSIT) (NRF-2018M3C9A5064709, NRF-2018R1A5A2025079) to I.L. We thank Sang-Dong Yoo and Geundon Kim for discussions and sharing Arabidopsis phenotype data.
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All Science Journal Classification (ASJC) codes
- Molecular Biology