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.
Department of Psychiatry, University of Iowa, Iowa City, Iowa 52242.
Genetics. 2017 Nov;207(3):883-891. doi: 10.1534/genetics.117.300257. Epub 2017 Sep 6.
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.
全基因组关联研究（GWAS）已被广泛用于识别与复杂疾病相关的常见变异。传统的 GWAS 分析通常一次检查一个标记，通常是单核苷酸多态性（SNP），以识别与疾病相关的个体变异。然而，由于常见变异的效应大小较小，检测个体风险变异的能力通常较低。作为 SNP 水平分析的补充方法，已经提出了多种基于基因的关联测试。然而，现有基于基因的测试的功效通常取决于潜在的遗传模型，并且不知道哪种测试是最佳的。在这里，我们提出了一种基于基因的关联测试（COMBAT），它结合了现有基于基因的测试的优势，并显示出比任何单个测试更高的整体性能。我们的方法不需要原始基因型或表型数据，只需要来自匹配样本的 SNP 水平 - 值和 SNP 之间的相关性。广泛的模拟表明，COMBAT 具有适当的 I 型错误率，在广泛的遗传模型范围内保持更高的功效，并且比任何单个基于基因的测试更稳健。我们通过重新分析双相情感障碍 GWAS 的荟萃分析结果，进一步证明了 COMBAT 优于其他几种基于基因的测试。我们的方法允许更强大的基于基因的分析应用于复杂疾病，鉴于 GWAS 汇总结果越来越多地公开可用，该方法将具有广泛的用途。
