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Meta‐MultiSKAT: Multiple phenotype meta‐analysis for region‐based association test
Author(s) -
Dutta Diptavo,
Gagliano Taliun Sarah A.,
Weinstock Joshua S.,
Zawistowski Matthew,
Sidore Carlo,
Fritsche Lars G.,
Cucca Francesco,
Schlessinger David,
Abecasis Gonçalo R.,
Brummett Chad M.,
Lee Seunggeun
Publication year - 2019
Publication title -
genetic epidemiology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.301
H-Index - 98
eISSN - 1098-2272
pISSN - 0741-0395
DOI - 10.1002/gepi.22248
Subject(s) - meta analysis , type i and type ii errors , computational biology , genetic association , phenotype , association test , kernel (algebra) , biology , statistical power , sample size determination , exome , statistics , genetics , genotype , mathematics , single nucleotide polymorphism , exome sequencing , medicine , gene , combinatorics
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 −6 . Further simulations under different models of association show that Meta‐MultiSKAT can improve the 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.