Dual-trait genomic analysis in highly stratified Arabidopsis thaliana populations using genome-wide association summary statistics. Feng, X., Zan, Y., Li, T., Yao, Y., Ning, Z., Li, J., Charati, H., Xu, W., Wan, Q., Zeng, D., Zeng, Z., Liu, Y., & Shen, X. Heredity, 133(1):11–20, Nature Publishing Group, July, 2024. ![Dual-trait genomic analysis in highly stratified Arabidopsis thaliana populations using genome-wide association summary statistics [link]](https://bibbase.org/img/filetypes/link.svg "link")
Paper doi abstract bibtex Genome-wide association study (GWAS) is a powerful tool to identify genomic loci underlying complex traits. However, the application in natural populations comes with challenges, especially power loss due to population stratification. Here, we introduce a bivariate analysis approach to a GWAS dataset of Arabidopsis thaliana. We demonstrate the efficiency of dual-phenotype analysis to uncover hidden genetic loci masked by population structure via a series of simulations. In real data analysis, a common allele, strongly confounded with population structure, is discovered to be associated with late flowering and slow maturation of the plant. The discovered genetic effect on flowering time is further replicated in independent datasets. Using Mendelian randomization analysis based on summary statistics from our GWAS and expression QTL scans, we predicted and replicated a candidate gene AT1G11560 that potentially causes this association. Further analysis indicates that this locus is co-selected with flowering-time-related genes. The discovered pleiotropic genotype-phenotype map provides new insights into understanding the genetic correlation of complex traits.
@article{feng_dual-trait_2024,
title = {Dual-trait genomic analysis in highly stratified {Arabidopsis} thaliana populations using genome-wide association summary statistics},
volume = {133},
copyright = {2024 The Author(s), under exclusive licence to The Genetics Society},
issn = {1365-2540},
url = {https://www.nature.com/articles/s41437-024-00688-z},
doi = {10.1038/s41437-024-00688-z},
abstract = {Genome-wide association study (GWAS) is a powerful tool to identify genomic loci underlying complex traits. However, the application in natural populations comes with challenges, especially power loss due to population stratification. Here, we introduce a bivariate analysis approach to a GWAS dataset of Arabidopsis thaliana. We demonstrate the efficiency of dual-phenotype analysis to uncover hidden genetic loci masked by population structure via a series of simulations. In real data analysis, a common allele, strongly confounded with population structure, is discovered to be associated with late flowering and slow maturation of the plant. The discovered genetic effect on flowering time is further replicated in independent datasets. Using Mendelian randomization analysis based on summary statistics from our GWAS and expression QTL scans, we predicted and replicated a candidate gene AT1G11560 that potentially causes this association. Further analysis indicates that this locus is co-selected with flowering-time-related genes. The discovered pleiotropic genotype-phenotype map provides new insights into understanding the genetic correlation of complex traits.},
language = {en},
number = {1},
urldate = {2026-05-19},
journal = {Heredity},
publisher = {Nature Publishing Group},
author = {Feng, Xiao and Zan, Yanjun and Li, Ting and Yao, Yue and Ning, Zheng and Li, Jiabei and Charati, Hadi and Xu, Weilin and Wan, Qianhui and Zeng, Dongyu and Zeng, Ziyi and Liu, Yang and Shen, Xia},
month = jul,
year = {2024},
keywords = {Genome-wide association studies, Population genetics, Quantitative trait},
pages = {11--20},
}
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