PCA-Based Multiple-Trait GWAS Analysis: A Powerful Model for Exploring Pleiotropy
Simple SummaryIn biological processes, it is common that a single gene controls two or more traits, leading to a high genetically correlation between many traits in human beings and livestock. Genome-wide association study (GWAS) is a popular method for mapping causal genes or regions related to studied traits. Taking the advantage of genetically correlation among traits, a combined analysis of two or more traits can improve the power of detection in GWAS analysis. In this study, we prove the improvement of multiple-traits GWAS through theoretical derivation, simulated dataset and real dataset, respectively. In addition, using this approach, we successfully identified a candidate gene for presoma muscle development in cattle that were not be found in the average association analysis. In summary, we conclude that multiple-trait GWAS is an effective method to explore genetic factors of traits, which have high correlations.
AbstractPrincipal component analysis (PCA) is a potential approach that can be applied in multiple-trait genome-wide association studies (GWAS) to explore pleiotropy, as well as increase the power of quantitative trait loci (QTL) detection. In this study, the relationship of test single nucleotide polymorphisms (SNPs) was determined between single-trait GWAS and PCA-based GWAS. We found that the estimated pleiotropic quantitative trait nucleotides (QTNs)
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Zhang, W.; Gao, X.; Shi, X.; Zhu, B.; Wang, Z.; Gao, H.; Xu, L.; Zhang, L.; Li, J.; Chen, Y. PCA-Based Multiple-Trait GWAS Analysis: A Powerful Model for Exploring Pleiotropy. Animals 2018, 8, 239.
Zhang W, Gao X, Shi X, Zhu B, Wang Z, Gao H, Xu L, Zhang L, Li J, Chen Y. PCA-Based Multiple-Trait GWAS Analysis: A Powerful Model for Exploring Pleiotropy. Animals. 2018; 8(12):239.Chicago/Turabian Style
Zhang, Wengang; Gao, Xue; Shi, Xinping; Zhu, Bo; Wang, Zezhao; Gao, Huijiang; Xu, Lingyang; Zhang, Lupei; Li, Junya; Chen, Yan. 2018. "PCA-Based Multiple-Trait GWAS Analysis: A Powerful Model for Exploring Pleiotropy." Animals 8, no. 12: 239.
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