ArticleEuropean journal of human genetics : EJHG2016
A method for analyzing multiple continuous phenotypes in rare variant association studies allowing for flexible correlations in variant effects.
Article in European journal of human genetics : EJHG, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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Who cites it
14 citing papers in PubMed.
- A copula-based set-variant association test for bivariate continuous, binary or mixed phenotypes.The international journal of biostatistics · 2023Article
- Statistical Inference for High-Dimensional Pathway Analysis with Multiple Responses.Computational statistics & data analysis · 2022Article
- Associating Multivariate Traits with Genetic Variants Using Collapsing and Kernel Methods with Pedigree- or Population-Based Studies.Computational and mathematical methods in medicine · 2021Article
- A review of kernel methods for genetic association studies.Genetic epidemiology · 2019Review
- Multi-SKAT: General framework to test for rare-variant association with multiple phenotypes.Genetic epidemiology · 2019Article
- Testing cross-phenotype effects of rare variants in longitudinal studies of complex traits.Genetic epidemiology · 2018Article
- Pathway-based approach using hierarchical components of rare variants to analyze multiple phenotypes.BMC bioinformatics · 2018Article
- Multivariate association analysis with somatic mutation data.Biometrics · 2018Article
- Exome-wide rare variant analyses of two bone mineral density phenotypes: the challenges of analyzing rare genetic variation.Scientific reports · 2018Article
- Testing an optimally weighted combination of common and/or rare variants with multiple traits.PloS one · 2018Article
- A novel method to test associations between a weighted combination of phenotypes and genetic variants.PloS one · 2018Article
- A rare-variant test for high-dimensional data.European journal of human genetics : EJHG · 2017Article
- Accelerating Gene Discovery by Phenotyping Whole-Genome Sequenced Multi-mutation Strains and Using the Sequence Kernel Association Test (SKAT).PLoS genetics · 2016Article
- Joint analysis of multiple blood pressure phenotypes in GAW19 data by using a multivariate rare-variant association test.BMC proceedings · 2016Article
Corrections and comments
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Authors and funding
8 authors.
Funding
Abstract
For region-based sequencing data, power to detect genetic associations can be improved through analysis of multiple related phenotypes. With this motivation, we propose a novel test to detect association simultaneously between a set of rare variants, such as those obtained by sequencing in a small genomic region, and multiple continuous phenotypes. We allow arbitrary correlations among the phenotypes and build on a linear mixed model by assuming the effects of the variants follow a multivariate normal distribution with a zero mean and a specific covariance matrix structure. In order to account for the unknown correlation parameter in the covariance matrix of the variant effects, a data-adaptive variance component test based on score-type statistics is derived. As our approach can calculate the P-value analytically, the proposed test procedure is computationally efficient. Broad simulations and an application to the UK10K project show that our proposed multivariate test is generally more powerful than univariate tests, especially when there are pleiotropic effects or highly correlated phenotypes.
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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.