ArticleGenetic epidemiology2019
Meta-MultiSKAT: Multiple phenotype meta-analysis for region-based association test.
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 8 papers.
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Who cites it
8 citing papers in PubMed.
- Noncoding rare variant associations with blood traits in 166,740 UK Biobank genomes.Nature genetics · 2025Article
- A Case-Control Study Supports Genetic Contribution of theAntioxidants (Basel, Switzerland) · 2024Article
- Meta-analysis of set-based multiple phenotype association test based on GWAS summary statistics from different cohorts.Frontiers in genetics · 2024Article
- Bayesian model comparison for rare-variant association studies.American journal of human genetics · 2021Article
- Resequencing of candidate genes for Keratoconus reveals a role for Ehlers-Danlos Syndrome genes.European journal of human genetics : EJHG · 2021Article
- Comparison of adaptive multiple phenotype association tests using summary statistics in genome-wide association studies.Human molecular genetics · 2021Article
- A powerful subset-based method identifies gene set associations and improves interpretation in UK Biobank.American journal of human genetics · 2021Article
- Using Summary Statistics to Model Multiplicative Combinations of Initially Analyzed Phenotypes With a Flexible Choice of Covariates.Frontiers in genetics · 2021Review
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Authors and funding
11 authors.
Funding
Abstract
The power of genetic association analyses can be increased by jointly meta-analyzing multiple correlated phenotypes. Here, we develop a meta-analysis framework, Meta-MultiSKAT, that uses summary statistics to test for association between multiple continuous phenotypes and variants in a region of interest. Our approach models the heterogeneity of effects between studies through a kernel matrix and performs a variance component test for association. Using a genotype kernel, our approach can test for rare-variants and the combined effects of both common and rare-variants. To achieve robust power, within Meta-MultiSKAT, we developed fast and accurate omnibus tests combining different models of genetic effects, functional genomic annotations, multiple correlated phenotypes, and heterogeneity across studies. In addition, Meta-MultiSKAT accommodates situations where studies do not share exactly the same set of phenotypes or have differing correlation patterns among the phenotypes. Simulation studies confirm that Meta-MultiSKAT can maintain the type-I error rate at the exome-wide level of 2.5 × 10
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