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Meta-MultiSKAT: Multiple phenotype meta-analysis for region-based association test

By Diptavo Dutta, Sarah A Gagliano Taliun, Joshua S. Weinstock, Matthew Zawistowski, Carlo Sidore, Lars G Fritsche, Francesco Cucca, David Schlessinger, Gonçalo R. Abecasis, Chad M. Brummett, Seunggeun Lee

Posted 30 Mar 2019
bioRxiv DOI: 10.1101/593814 (published DOI: 10.1002/gepi.22248)

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. Additionally, 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 type-I error rate at exome-wide level of 2.5x10-6. Further simulations under different models of association show that Meta-MultiSKAT can improve power of detection from 23% to 38% on average over single phenotype-based meta-analysis approaches. We demonstrate the utility and improved power of Meta-MultiSKAT in the meta-analyses of four white blood cell subtype traits from the Michigan Genomics Initiative (MGI) and SardiNIA studies.

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