ArticleGenetic epidemiology2015
An Adaptive Association Test for Multiple Phenotypes with GWAS Summary Statistics.
Article in Genetic epidemiology, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 55 papers, 3 of them syntheses that pooled it.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
55 citing papers in PubMed, 3 syntheses or guidelines pooled it.
- A Bayesian hierarchically structured prior for gene-based association testing with multiple traits in genome-wide association studies.Genetic epidemiology · 2022Pooled it
- Multitrait genome association analysis identifies new susceptibility genes for human anthropometric variation in the GCAT cohort.Journal of medical genetics · 2018Pooled it
- Genome-wide association study and meta-analysis identify loci associated with ventricular and supraventricular ectopy.Scientific reports · 2018Pooled it
- 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
- Network construction using sparse Gaussian graphical model based on GWAS summary statistics.Scientific reports · 2025Article
- Health utility adjusted survival: A composite endpoint for clinical trial designs.Statistical methods in medical research · 2025Article
- Methodological opportunities in genomic data analysis to advance health equity.Nature reviews. Genetics · 2025Review
- Health Utility Survival for Randomized Clinical Trials: Extensions and Statistical Properties.Statistics in medicine · 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
- A novel method for multiple phenotype association studies based on genotype and phenotype network.PLoS genetics · 2024Article
- Conditional transcriptome-wide association study for fine-mapping candidate causal genes.Nature genetics · 2024Article
- Article
- Subset scanning for multi-trait analysis using GWAS summary statistics.Bioinformatics (Oxford, England) · 2024Article
- Joint analysis of multiple phenotypes for extremely unbalanced case-control association studies using multi-layer network.Bioinformatics (Oxford, England) · 2023Article
- Inferring a directed acyclic graph of phenotypes from GWAS summary statistics.bioRxiv : the preprint server for biology · 2023Article
- Two-stage multivariate Mendelian randomization on multiple outcomes with mixed distributions.Statistical methods in medical research · 2023Article
- Combining Mendelian randomization and network deconvolution for inference of causal networks with GWAS summary data.PLoS genetics · 2023Article
- Robust multivariable Mendelian randomization based on constrained maximum likelihood.American journal of human genetics · 2023Article
Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
We study the problem of testing for single marker-multiple phenotype associations based on genome-wide association study (GWAS) summary statistics without access to individual-level genotype and phenotype data. For most published GWASs, because obtaining summary data is substantially easier than accessing individual-level phenotype and genotype data, while often multiple correlated traits have been collected, the problem studied here has become increasingly important. We propose a powerful adaptive test and compare its performance with some existing tests. We illustrate its applications to analyses of a meta-analyzed GWAS dataset with three blood lipid traits and another with sex-stratified anthropometric traits, and further demonstrate its potential power gain over some existing methods through realistic simulation studies. We start from the situation with only one set of (possibly meta-analyzed) genome-wide summary statistics, then extend the method to meta-analysis of multiple sets of genome-wide summary statistics, each from one GWAS. We expect the proposed test to be useful in practice as more powerful than or complementary to existing methods.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.