Abstract
Single genome-wide studies may be underpowered to detect trait-associated rare variants with moderate or weak effect sizes. As a viable alternative, meta-analysis is widely used to increase power by combining different studies. The power of meta-analysis critically depends on the underlying association patterns and heterogeneity levels, which are unknown and vary from locus to locus. However, existing methods mainly focus on one or only a few combinations of the association pattern and heterogeneity level, thus may lose power in many situations. To address this issue, we propose a general and unified framework by combining a class of tests including and beyond some existing ones, leading to high power across a wide range of scenarios. We demonstrate that the proposed test is more powerful than some existing methods in simulation studies, then show their performance with the NHLBI Exome-Sequencing Project (ESP) data. One gene (B4GALNT2) was found by our proposed test, but not by others, to be statistically significantly associated with plasma triglyceride. The signal was driven by African-ancestry subjects but it was previously reported to be associated with coronary artery disease among European-ancestry subjects. We implemented our method in an R package aSPUmeta, publicly available at https://github.com/ytzhong/metaRV and will be on CRAN soon.
Original language | English (US) |
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Pages (from-to) | 104-116 |
Number of pages | 13 |
Journal | Genetic epidemiology |
Volume | 44 |
Issue number | 1 |
DOIs | |
State | Published - Jan 1 2020 |
Bibliographical note
Funding Information:The authors thank the reviewers for many helpful comments and suggestions. This study was supported by the National Institutes of Health grants R21AG057038, R01HL116720, R01GM113250, R01GM126002, and R01HL105397. We thank the Minnesota Supercomputing Institute and Texas Advanced Computing Center for providing computing resources.
Funding Information:
The authors thank the reviewers for many helpful comments and suggestions. This study was supported by the National Institutes of Health grants R21AG057038, R01HL116720, R01GM113250, R01GM126002, and R01HL105397. We thank the Minnesota Supercomputing Institute and Texas Advanced Computing Center for providing computing resources.
Publisher Copyright:
© 2019 Wiley Periodicals, Inc.
Keywords
- aSPU
- genetic heterogeneity
- statistical power
- whole-exome sequencing
- whole-genome sequencing