ArticleBiometrics2018
Multiple phenotype association tests using summary statistics in genome-wide association studies.
Article in Biometrics, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 34 papers.
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Who cites it
34 citing papers in PubMed.
- Multivariate Genome-Wide Association Analysis Identifies Genetic Susceptibility Loci for Metabolic Syndrome in the Korean Genome and Epidemiology Study.International journal of molecular sciences · 2026Article
- Divided-and-combined association test for pleiotropic effects with GWAS summary statistics.BMC genomics · 2026Article
- Constructing genotype and phenotype network helps reveal disease heritability and phenome-wide association studies.BMC genomics · 2025Article
- A statistical framework for multi-trait rare variant analysis in large-scale whole-genome sequencing studies.Nature computational science · 2025Article
- Testing a Large Number of Composite Null Hypotheses Using Conditionally Symmetric Multidimensional Gaussian Mixtures in Genome-Wide Studies.Journal of the American Statistical Association · 2025Article
- Bivariate genome-wide association study of circulating fibrinogen and C-reactive protein levels.Journal of thrombosis and haemostasis : JTH · 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
- Subset scanning for multi-trait analysis using GWAS summary statistics.Bioinformatics (Oxford, England) · 2024Article
- Meta-analysis of set-based multiple phenotype association test based on GWAS summary statistics from different cohorts.Frontiers in genetics · 2024Article
- A statistical framework for powerful multi-trait rare variant analysis in large-scale whole-genome sequencing studies.bioRxiv : the preprint server for biology · 2023Article
- New statistical selection method for pleiotropic variants associated with both quantitative and qualitative traits.BMC bioinformatics · 2023Article
- Summary statistics-based association test for identifying the pleiotropic effects with set of genetic variants.Bioinformatics (Oxford, England) · 2023Article
- An adaptive test based on principal components for detecting multiple phenotype associations using GWAS summary data.Genetica · 2023Article
- Minimax Powerful Functional Analysis of Covariance Tests: with Application to Longitudinal Genome-Wide Association Studies.Scandinavian journal of statistics, theory and applications · 2023Article
- Leveraging pleiotropy for joint analysis of genome-wide association studies with per trait interpretations.PLoS genetics · 2022Article
- A computationally efficient clustering linear combination approach to jointly analyze multiple phenotypes for GWAS.PloS one · 2022Article
- Recent advances and challenges of rare variant association analysis in the biobank sequencing era.Frontiers in genetics · 2022Review
- Wavelet Screening: a novel approach to analyzing GWAS data.BMC bioinformatics · 2021Article
- Multitrait GWAS to connect disease variants and biological mechanisms.PLoS genetics · 2021Article
- Comparison of adaptive multiple phenotype association tests using summary statistics in genome-wide association studies.Human molecular genetics · 2021Article
Corrections and comments
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2 authors.
Funding
Abstract
We study in this article jointly testing the associations of a genetic variant with correlated multiple phenotypes using the summary statistics of individual phenotype analysis from Genome-Wide Association Studies (GWASs). We estimated the between-phenotype correlation matrix using the summary statistics of individual phenotype GWAS analyses, and developed genetic association tests for multiple phenotypes by accounting for between-phenotype correlation without the need to access individual-level data. Since genetic variants often affect multiple phenotypes differently across the genome and the between-phenotype correlation can be arbitrary, we proposed robust and powerful multiple phenotype testing procedures by jointly testing a common mean and a variance component in linear mixed models for summary statistics. We computed the p-values of the proposed tests analytically. This computational advantage makes our methods practically appealing in large-scale GWASs. We performed simulation studies to show that the proposed tests maintained correct type I error rates, and to compare their powers in various settings with the existing methods. We applied the proposed tests to a GWAS Global Lipids Genetics Consortium summary statistics data set and identified additional genetic variants that were missed by the original single-trait analysis.
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