{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,2]],"date-time":"2026-10-02T13:21:32Z","timestamp":1790947292285,"version":"4.1.0"},"reference-count":20,"publisher":"Oxford University Press (OUP)","issue":"3","license":[{"start":{"date-parts":[[2017,8,30]],"date-time":"2017-08-30T00:00:00Z","timestamp":1504051200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/academic.oup.com\/journals\/pages\/open_access\/funder_policies\/chorus\/standard_publication_model"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2017,11,1]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:p>Many gene-based association tests have been proposed for genomewide association studies (GWAS). However, the power of existing gene-based tests is...<\/jats:p><jats:p>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.<\/jats:p>","DOI":"10.1534\/genetics.117.300257","type":"journal-article","created":{"date-parts":[[2017,9,7]],"date-time":"2017-09-07T04:30:34Z","timestamp":1504758634000},"page":"883-891","source":"Crossref","is-referenced-by-count":63,"title":["COMBAT: A Combined Association Test for Genes Using Summary Statistics"],"prefix":"10.1093","volume":"207","author":[{"given":"Minghui","family":"Wang","sequence":"first","affiliation":[{"name":"Department of Genetics and Genomic Sciences , Icahn Institute of Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, New York, New York 10029"},{"name":"Department of Genetics and Genomic Sciences , Icahn Institute of Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, New York, New York 10029"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jianfei","family":"Huang","sequence":"additional","affiliation":[{"name":"Department of Psychiatry , University of Iowa, Iowa City, Iowa 52242"},{"name":"College of Mathematical Sciences , Yangzhou University, 225002, China"},{"name":"Department of Genetics and Genomic Sciences , Icahn Institute of Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, New York, New York 10029"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yiyuan","family":"Liu","sequence":"additional","affiliation":[{"name":"Joan & Sanford I. Weill Department of Medicine , Sandra and Edward Meyer Cancer Center, Weill Cornell Medicine, New York, New York 10021"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Li","family":"Ma","sequence":"additional","affiliation":[{"name":"Department of Animal and Avian Sciences , University of Maryland, College Park, Maryland 20742"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"James B","family":"Potash","sequence":"additional","affiliation":[{"name":"Department of Psychiatry , University of Iowa, Iowa City, Iowa 52242"},{"name":"Interdisciplinary Graduate Program in Genetics , University of Iowa, Iowa City, Iowa 52242"},{"name":"Department of Psychiatry and Behavioral Sciences , Johns Hopkins School of Medicine, Baltimore, Maryland 21287"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shizhong","family":"Han","sequence":"additional","affiliation":[{"name":"Department of Psychiatry , University of Iowa, Iowa City, Iowa 52242"},{"name":"Interdisciplinary Graduate Program in Genetics , University of Iowa, Iowa City, Iowa 52242"},{"name":"Department of Psychiatry and Behavioral Sciences , Johns Hopkins School of Medicine, Baltimore, Maryland 21287"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"286","published-online":{"date-parts":[[2017,8,30]]},"reference":[{"key":"2023030913460077500_","doi-asserted-by":"crossref","first-page":"687","DOI":"10.1093\/oxfordjournals.aje.a115184","article-title":"The Atherosclerosis Risk in Communities (ARIC) study: design and objectives. 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