ArticleAmerican journal of human genetics2014
Maximizing the power of principal-component analysis of correlated phenotypes in genome-wide association studies.
Article in American journal of human genetics, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 116 papers, 3 of them syntheses that pooled it.
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Who cites it
116 citing papers in PubMed, 3 syntheses or guidelines pooled it.
- Multivariate genome-wide analyses of the well-being spectrum.Nature genetics · 2019Pooled it
- Genomewide meta-analysis identifies loci associated with IGF-I and IGFBP-3 levels with impact on age-related traits.Aging cell · 2016Pooled it
- Meta-analysis of correlated traits via summary statistics from GWASs with an application in hypertension.American journal of human genetics · 2015Pooled it
- Epigenome-wide profiling identifies distinct DNA methylation architecture underlying ME/CFS and fibromyalgia symptom burden.Journal of translational medicine · 2026Article
- Optimized phenotype definitions boost GWAS power.PLoS computational biology · 2026Article
- Regionally specific and highly pleiotropic loci together mediate skeletal evolution in tropical and temperate house mice.Genetics · 2026Article
- GAMMA-RAY: A Fully Automated and Rapid System for High-Dimensional Multi-Phenotype Analysis Considering Population Structure.Biology · 2026Article
- Large deletions in the DNA primase large subunit PRIM2 are associated with NADP-malate dehydrogenase activity in a porcine FAnimal genetics · 2026Article
- Evaluation of mango (Mangifera indica L.) germplasm for phenotypic diversity and breeding potential in Pakistan.Scientific reports · 2026Article
- Network construction using sparse Gaussian graphical model based on GWAS summary statistics.Scientific reports · 2025Article
- A powerful framework for differential co-expression analysis of general risk factors.Bioinformatics (Oxford, England) · 2025Article
- Applying multimodal AI to physiological waveforms improves genetic prediction of cardiovascular traits.American journal of human genetics · 2025Article
- Uncovering covariance patterns across energy balance traits enables the discovery of new obesity-related genes.Obesity (Silver Spring, Md.) · 2025Article
- New composite phenotypes enhance chronic kidney disease classification and genetic associations.PLoS genetics · 2025Article
- The Use of AI for Phenotype-Genotype Mapping.Methods in molecular biology (Clifton, N.J.) · 2025Article
- Principal component analysis revisited: fast multitrait genetic evaluations with smooth convergence.G3 (Bethesda, Md.) · 2024Article
- Genetic architecture of telomere length in 462,666 UK Biobank whole-genome sequences.Nature genetics · 2024Article
- Unsupervised representation learning on high-dimensional clinical data improves genomic discovery and prediction.Nature genetics · 2024Article
- An adaptive and robust method for multi-trait analysis of genome-wide association studies using summary statistics.European journal of human genetics : EJHG · 2024Article
- A novel method for multiple phenotype association studies based on genotype and phenotype network.PLoS genetics · 2024Article
56 more citing papers are in PubMed but not listed here.
Corrections and comments
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Authors and funding
6 authors.
Funding
Abstract
Many human traits are highly correlated. This correlation can be leveraged to improve the power of genetic association tests to identify markers associated with one or more of the traits. Principal component analysis (PCA) is a useful tool that has been widely used for the multivariate analysis of correlated variables. PCA is usually applied as a dimension reduction method: the few top principal components (PCs) explaining most of total trait variance are tested for association with a predictor of interest, and the remaining components are not analyzed. In this study we review the theoretical basis of PCA and describe the behavior of PCA when testing for association between a SNP and correlated traits. We then use simulation to compare the power of various PCA-based strategies when analyzing up to 100 correlated traits. We show that contrary to widespread practice, testing only the top PCs often has low power, whereas combining signal across all PCs can have greater power. This power gain is primarily due to increased power to detect genetic variants with opposite effects on positively correlated traits and variants that are exclusively associated with a single trait. Relative to other methods, the combined-PC approach has close to optimal power in all scenarios considered while offering more flexibility and more robustness to potential confounders. Finally, we apply the proposed PCA strategy to the genome-wide association study of five correlated coagulation traits where we identify two candidate SNPs that were not found by the standard approach.
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