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.

Meta-MultiSKAT: Multiple phenotype meta-analysis for region-based association test / Dutta, D.; Gagliano Taliun, S. A.; Weinstock, J. S.; Zawistowski, M.; Sidore, C.; Fritsche, L. G.; Cucca, F.; Schlessinger, D.; Abecasis, G. R.; Brummett, C. M.; Lee, S.. - In: GENETIC EPIDEMIOLOGY. - ISSN 1098-2272. - 43:7(2019), pp. 800-814. [10.1002/gepi.22248]

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

Sidore C.;Cucca F.;
2019-01-01

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 × 10−6. 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.
2019
Meta-MultiSKAT: Multiple phenotype meta-analysis for region-based association test / Dutta, D.; Gagliano Taliun, S. A.; Weinstock, J. S.; Zawistowski, M.; Sidore, C.; Fritsche, L. G.; Cucca, F.; Schlessinger, D.; Abecasis, G. R.; Brummett, C. M.; Lee, S.. - In: GENETIC EPIDEMIOLOGY. - ISSN 1098-2272. - 43:7(2019), pp. 800-814. [10.1002/gepi.22248]
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11388/232735
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