ArticleJournal of the American Statistical Association2019
A Geometric Perspective on the Power of Principal Component Association Tests in Multiple Phenotype Studies.
Article in Journal of the American Statistical Association, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.
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Who cites it
19 citing papers in PubMed.
- Protein-protein interactions shape trans-regulatory impact of genetic variation on protein expression and complex traits.Nature genetics · 2026Article
- Network construction using sparse Gaussian graphical model based on GWAS summary statistics.Scientific reports · 2025Article
- Improved chromatin QTL mapping with CACTI.bioRxiv : the preprint server for biology · 2025Article
- Similarity-based multimodal regression.Biostatistics (Oxford, England) · 2024Article
- A high-dimensional omnibus test for set-based association analysis.Briefings in bioinformatics · 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
- Trans-eQTL mapping in gene sets identifies network effects of genetic variants.Cell genomics · 2024Article
- Multiple phenotype association tests based on sliced inverse regression.BMC bioinformatics · 2024Article
- Differences in set-based tests for sparse alternatives when testing sets of outcomes compared to sets of explanatory factors in genetic association studies.Biostatistics (Oxford, England) · 2023Article
- A copula-based set-variant association test for bivariate continuous, binary or mixed phenotypes.The international journal of biostatistics · 2023Article
- Summary statistics-based association test for identifying the pleiotropic effects with set of genetic variants.Bioinformatics (Oxford, England) · 2023Article
- A clustering linear combination method for multiple phenotype association studies based on GWAS summary statistics.Scientific reports · 2023Article
- A genome-wide association study of mammographic texture variation.Breast cancer research : BCR · 2022Article
- Multivariate genome-wide association study on tissue-sensitive diffusion metrics highlights pathways that shape the human brain.Nature communications · 2022Article
- Recent advances and challenges of rare variant association analysis in the biobank sequencing era.Frontiers in genetics · 2022Review
- InTACT: An adaptive and powerful framework for joint-tissue transcriptome-wide association studies.Genetic epidemiology · 2021Article
- Quantitative disease risk scores from EHR with applications to clinical risk stratification and genetic studies.NPJ digital medicine · 2021Article
- Comparison of adaptive multiple phenotype association tests using summary statistics in genome-wide association studies.Human molecular genetics · 2021Article
- A COMPARISON OF PRINCIPAL COMPONENT METHODS BETWEEN MULTIPLE PHENOTYPE REGRESSION AND MULTIPLE SNP REGRESSION IN GENETIC ASSOCIATION STUDIES.The annals of applied statistics · 2020Article
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2 authors.
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Abstract
Joint analysis of multiple phenotypes can increase statistical power in genetic association studies. Principal component analysis, as a popular dimension reduction method, especially when the number of phenotypes is high-dimensional, has been proposed to analyze multiple correlated phenotypes. It has been empirically observed that the first PC, which summarizes the largest amount of variance, can be less powerful than higher order PCs and other commonly used methods in detecting genetic association signals. In this paper, we investigate the properties of PCA-based multiple phenotype analysis from a geometric perspective by introducing a novel concept called principal angle. A particular PC is powerful if its principal angle is 0° and is powerless if its principal angle is 90°. Without prior knowledge about the true principal angle, each PC can be powerless. We propose linear, non-linear and data-adaptive omnibus tests by combining PCs. We demonstrate that the Wald test is a special quadratic PC-based test. We show that the omnibus PC test is robust and powerful in a wide range of scenarios. We study the properties of the proposed methods using power analysis and eigen-analysis. The subtle differences and close connections between these combined PC methods are illustrated graphically in terms of their rejection boundaries. Our proposed tests have convex acceptance regions and hence are admissible. The
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