ArticleGenetic epidemiology2019
Multi-SKAT: General framework to test for rare-variant association with multiple phenotypes.
Article in Genetic epidemiology, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 38 papers, 2 of them syntheses that pooled it.
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Who cites it
38 citing papers in PubMed, 2 syntheses or guidelines pooled it, 70 citations in OpenAlex.
- Kernel-based gene-environment interaction tests for rare variants with multiple quantitative phenotypes.PloS one · 2022Pooled it
- Trans-ethnic meta-analysis of rare variants in sequencing association studies.Biostatistics (Oxford, England) · 2021Pooled it
- Archipelago Method for Variant Set Association Test Statistics.Genetic epidemiology · 2026Article
- Integrative multi-omic analysis identifies key transcription factors and target proteins in renal cell carcinoma and its subtypes.bioRxiv : the preprint server for biology · 2025Article
- A statistical framework for multi-trait rare variant analysis in large-scale whole-genome sequencing studies.Nature computational science · 2025Article
- The goldmine of GWAS summary statistics: a systematic review of methods and tools.BioData mining · 2024Article
- Article
- JASPER: Fast, powerful, multitrait association testing in structured samples gives insight on pleiotropy in gene expression.American journal of human genetics · 2024Article
- Variance-components tests for genetic association with multiple interval-censored outcomes.Statistics in medicine · 2024Article
- Identifying latent genetic interactions in genome-wide association studies using multiple traits.Genome medicine · 2024Article
- Abundant pleiotropy across neuroimaging modalities identified through a multivariate genome-wide association study.Nature communications · 2024Article
- Meta-analysis of set-based multiple phenotype association test based on GWAS summary statistics from different cohorts.Frontiers in genetics · 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
- A copula-based set-variant association test for bivariate continuous, binary or mixed phenotypes.The international journal of biostatistics · 2023Article
- Excalibur: A new ensemble method based on an optimal combination of aggregation tests for rare-variant association testing for sequencing data.PLoS computational biology · 2023Article
- Kernel-based genetic association analysis for microbiome phenotypes identifies host genetic drivers of beta-diversity.Microbiome · 2023Article
- Genetic heterogeneity: Challenges, impacts, and methods through an associative lens.Genetic epidemiology · 2022Review
- An optimal kernel-based multivariate U-statistic to test for associations with multiple phenotypes.Biostatistics (Oxford, England) · 2022Article
- Multivariate, region-based genetic analyses of facets of reproductive aging in White and Black women.Molecular genetics & genomic medicine · 2022Article
Corrections and comments
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Authors and funding
4 authors at 1 institution in 1 country.
Funding
Abstract
In genetic association analysis, a joint test of multiple distinct phenotypes can increase power to identify sets of trait-associated variants within genes or regions of interest. Existing multiphenotype tests for rare variants make specific assumptions about the patterns of association with underlying causal variants, and the violation of these assumptions can reduce power to detect association. Here, we develop a general framework for testing pleiotropic effects of rare variants on multiple continuous phenotypes using multivariate kernel regression (Multi-SKAT). Multi-SKAT models affect sizes of variants on the phenotypes through a kernel matrix and perform a variance component test of association. We show that many existing tests are equivalent to specific choices of kernel matrices with the Multi-SKAT framework. To increase power of detecting association across tests with different kernel matrices, we developed a fast and accurate approximation of the significance of the minimum observed P value across tests. To account for related individuals, our framework uses random effects for the kinship matrix. Using simulated data and amino acid and exome-array data from the METabolic Syndrome In Men (METSIM) study, we show that Multi-SKAT can improve power over single-phenotype SKAT-O test and existing multiple-phenotype tests, while maintaining Type I error rate.
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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.