Genome-Wide Association Studies in Arabidopsis thaliana: Statistical Analysis and Network-Based Augmentation of Signals

Tak Lee, Insuk Lee

Research output: Chapter in Book/Report/Conference proceedingChapter


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.

Original languageEnglish
Title of host publicationMethods in Molecular Biology
PublisherHumana Press Inc.
Number of pages24
Publication statusPublished - 2021

Publication series

NameMethods in Molecular Biology
ISSN (Print)1064-3745
ISSN (Electronic)1940-6029

Bibliographical note

Funding 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.

Publisher Copyright:
© 2021, Springer Science+Business Media, LLC, part of Springer Nature.

All Science Journal Classification (ASJC) codes

  • Molecular Biology
  • Genetics


Dive into the research topics of 'Genome-Wide Association Studies in Arabidopsis thaliana: Statistical Analysis and Network-Based Augmentation of Signals'. Together they form a unique fingerprint.

Cite this