Meta-MultiSKAT: Multiple phenotype meta-analysis for region-based association test.
Genet Epidemiol
; 43(7): 800-814, 2019 10.
Article
in En
| MEDLINE
| ID: mdl-31433078
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.
Key words
Full text:
1
Collection:
01-internacional
Database:
MEDLINE
Main subject:
Meta-Analysis as Topic
/
Genetic Association Studies
Type of study:
Risk_factors_studies
/
Systematic_reviews
Limits:
Humans
Country/Region as subject:
Europa
Language:
En
Journal:
Genet Epidemiol
Journal subject:
EPIDEMIOLOGIA
/
GENETICA MEDICA
Year:
2019
Type:
Article