@article{95190c7aae8a4204823d0b2d0873ee58,
title = "Combat: A combined association test for genes using summary statistics",
abstract = "Genome-wide association studies (GWAS) have been widely used for identifying common variants associated with complex diseases. Traditional analysis of GWAS typically examines one marker at a time, usually single nucleotide polymorphisms (SNPs), to identify individual variants associated with a disease. However, due to the small effect sizes of common variants, the power to detect individual risk variants is generally low. As a complementary approach to SNP-level analysis, a variety of gene-based association tests have been proposed. However, the power of existing gene-based tests is often dependent on the underlying genetic models, and it is not known a priori which test is optimal. Here we propose a combined association test (COMBAT) for genes, which incorporates strengths from existing gene-based tests and shows higher overall performance than any individual test. Our method does not require raw genotype or phenotype data, but needs only SNP-level P-values and correlations between SNPs from ancestry-matched samples. Extensive simulations showed that COMBAT has an appropriate type I error rate, maintains higher power across a wide range of genetic models, and is more robust than any individual gene-based test. We further demonstrated the superior performance of COMBAT over several other gene-based tests through reanalysis of the meta-analytic results of GWAS for bipolar disorder. Our method allows for the more powerful application of gene-based analysis to complex diseases, which will have broad use given that GWAS summary results are increasingly publicly available.",
keywords = "Association, Complex disease, GWAS, Gene-based test, Summary statistics",
author = "Minghui Wang and Jianfei Huang and Yiyuan Liu and Li Ma and Potash, {James B.} and Shizhong Han",
note = "Funding Information: We acknowledge the Psychiatric Genomics Consortium for making the bipolar disorder GWAS results publicly available. The Study of Addiction: Genetics and Environment (SAGE) GWAS data set described in this manuscript was obtained from the Genotypes and Phenotypes (dbGaP) database. This study was supported by National Institutes of Health (NIH) grants R01AA022994 and R01 AA024486. Funding support for SAGE was provided through the NIH Genes, Environment, and Health Initiative (GEI) (U01 HG004422). SAGE is one of the GWAS funded as part of the Gene Environment Association Studies (GENEVA) under GEI. Assistance with phenotype harmonization and genotype cleaning, as well as with general study coordination, was provided by the GENEVA Coordinating Center (U01 HG004446). Assistance with data cleaning was provided by the National Center for Biotechnology Information. Support for collection of data sets and samples was provided by the Collaborative Study on the Genetics of Alcoholism (COGA; U10 AA008401), the Collaborative Genetic Study of Nicotine Dependence (COGEND; P01 CA089392), and the Family Study of Cocaine Dependence (FSCD; R01 DA013423). Funding support for genotyping, which was performed at the Johns Hopkins University Center for Inherited Disease Research, was provided by the NIH GEI (U01HG004438), the National Institute on Alcohol Abuse and Alcoholism, the National Institute on Drug Abuse, and the NIH contract “High throughput genotyping for studying the genetic contributions to human disease” (HHSN268200782096C). Publisher Copyright: {\textcopyright} 2017 by the Genetics Society of America.",
year = "2017",
month = nov,
doi = "10.1534/genetics.117.300257",
language = "English (US)",
volume = "207",
pages = "883--891",
journal = "Genetics",
issn = "0016-6731",
publisher = "Genetics Society of America",
number = "3",
}