TY - JOUR
T1 - An adaptive test for meta-analysis of rare variant association studies
AU - Yang, Tianzhong
AU - Kim, Junghi
AU - Wu, Chong
AU - Ma, Yiding
AU - Wei, Peng
AU - Pan, Wei
N1 - Publisher Copyright:
© 2019 Wiley Periodicals, Inc.
PY - 2020/1/1
Y1 - 2020/1/1
N2 - 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.
AB - 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.
KW - aSPU
KW - genetic heterogeneity
KW - statistical power
KW - whole-exome sequencing
KW - whole-genome sequencing
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U2 - 10.1002/gepi.22273
DO - 10.1002/gepi.22273
M3 - Article
C2 - 31830326
AN - SCOPUS:85076408663
SN - 0741-0395
VL - 44
SP - 104
EP - 116
JO - Genetic epidemiology
JF - Genetic epidemiology
IS - 1
ER -