COMBAT: A Combined Association Test for Genes Using Summary Statistics

• Published in: Genetics (Austin) 2017-11, Vol.207 (3), p.883-891
• Main Authors: Wang, Minghui, Huang, Jianfei, Liu, Yiyuan, Ma, Li, Potash, James B, Han, Shizhong
• Format: Article
• Language: English
• Subjects: Advantages; Bipolar disorder; Bipolar Disorder - genetics; Computer simulation; Data processing; Disease; Genes; Genetic Loci; Genetics; Genome-wide association studies; Genome-Wide Association Study - methods; Genomes; Genotype & phenotype; Humans; Investigations; Methods; Nuclear electric power generation; Polymorphism, Single Nucleotide; Power; Single-nucleotide polymorphism; Software; Statistical analysis; Statistical methods; Statistical tests; Statistics; Studies
• ISSN: 1943-2631; 0016-6731; 1943-2631
• DOI: 10.1534/genetics.117.300257

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 which test is optimal. Here we propose a ined ssociation est (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 -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.