ArticleAmerican journal of human genetics2016
A Statistical Approach for Testing Cross-Phenotype Effects of Rare Variants.
Article in American journal of human genetics, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 49 papers, 2 of them syntheses that pooled it.
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Who cites it
49 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Kernel-based gene-environment interaction tests for rare variants with multiple quantitative phenotypes.PloS one · 2022Pooled it
- Meta-analysis of quantitative pleiotropic traits for next-generation sequencing with multivariate functional linear models.European journal of human genetics : EJHG · 2017Pooled it
- Dissecting pleiotropy to gain mechanistic insights into human disease.Nature reviews. Genetics · 2026Review
- A powerful framework for differential co-expression analysis of general risk factors.Bioinformatics (Oxford, England) · 2025Article
- A statistical framework for multi-trait rare variant analysis in large-scale whole-genome sequencing studies.Nature computational science · 2025Article
- SCAMPI: A scalable statistical framework for genome-wide interaction testing harnessing cross-trait correlations.bioRxiv : the preprint server for biology · 2024Article
- Article
- JASPER: Fast, powerful, multitrait association testing in structured samples gives insight on pleiotropy in gene expression.American journal of human genetics · 2024Article
- Identifying latent genetic interactions in genome-wide association studies using multiple traits.Genome medicine · 2024Article
- Multiple phenotype association tests based on sliced inverse regression.BMC bioinformatics · 2024Article
- JASPER: fast, powerful, multitrait association testing in structured samples gives insight on pleiotropy in gene expression.bioRxiv : the preprint server for biology · 2023Article
- A statistical framework for powerful multi-trait rare variant analysis in large-scale whole-genome sequencing studies.bioRxiv : the preprint server for biology · 2023Article
- Gene-level association analysis of bivariate ordinal traits with functional regressions.Genetic epidemiology · 2023Article
- Using Drosophila to identify naturally occurring genetic modifiers of amyloid beta 42- and tau-induced toxicity.G3 (Bethesda, Md.) · 2023Article
- A maximum kernel-based association test to detect the pleiotropic genetic effects on multiple phenotypes.Bioinformatics (Oxford, England) · 2023Article
- Kernel-based genetic association analysis for microbiome phenotypes identifies host genetic drivers of beta-diversity.Microbiome · 2023Article
- POIROT: a powerful test for parent-of-origin effects in unrelated samples leveraging multiple phenotypes.Bioinformatics (Oxford, England) · 2023Article
- Integrated Single-Trait and Multi-Trait GWASs Reveal the Genetic Architecture of Internal Organ Weight in Pigs.Animals : an open access journal from MDPI · 2023Article
- An optimal kernel-based multivariate U-statistic to test for associations with multiple phenotypes.Biostatistics (Oxford, England) · 2022Article
- Brain DNA Methylation Patterns in CLDN5 Associated With Cognitive Decline.Biological psychiatry · 2022Article
Corrections and comments
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Authors and funding
13 authors.
Funding
Abstract
Increasing empirical evidence suggests that many genetic variants influence multiple distinct phenotypes. When cross-phenotype effects exist, multivariate association methods that consider pleiotropy are often more powerful than univariate methods that model each phenotype separately. Although several statistical approaches exist for testing cross-phenotype effects for common variants, there is a lack of similar tests for gene-based analysis of rare variants. In order to fill this important gap, we introduce a statistical method for cross-phenotype analysis of rare variants using a nonparametric distance-covariance approach that compares similarity in multivariate phenotypes to similarity in rare-variant genotypes across a gene. The approach can accommodate both binary and continuous phenotypes and further can adjust for covariates. Our approach yields a closed-form test whose significance can be evaluated analytically, thereby improving computational efficiency and permitting application on a genome-wide scale. We use simulated data to demonstrate that our method, which we refer to as the Gene Association with Multiple Traits (GAMuT) test, provides increased power over competing approaches. We also illustrate our approach using exome-chip data from the Genetic Epidemiology Network of Arteriopathy.
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Registered trials
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.