Department of Biostatistics, University of Michigan, Ann Arbor, Michigan, USA.
Graduate School of Data Science, Seoul National University, Seoul, Republic of Korea.
Genet Epidemiol. 2022 Apr;46(3-4):145-158. doi: 10.1002/gepi.22444. Epub 2022 Feb 16.
Large-scale sequencing and genotyping data provide an opportunity to integrate external samples as controls to improve power of association tests. However, due to the systematic differences between genotyped samples from different studies, naively aggregating the controls could lead to inflation in Type I error rates. There has been recent effort to integrate external controls while adjusting for batch effect, such as the integrating External Controls into Association Test (iECAT) and its score-based single variant tests. Building on the original iECAT framework, we propose an iECAT-Score region-based test that increases power for rare-variant tests when integrating external controls. This method assesses the systematic batch effect between internal and external samples at each variant and constructs compound shrinkage score statistics to test for the joint genetic effect within a gene or a region, while adjusting for covariates and population stratification. Through simulation studies, we demonstrate that the proposed method controls for Type I error rates and improves power in rare-variant tests. The application of the proposed method to the association studies of age-related macular degeneration (AMD) from the International AMD Genomics Consortium and UK Biobank revealed novel rare-variant associations in gene DXO. Through the incorporation of external controls, the iECAT methods offer a powerful suite to identify disease-associated genetic variants, further shedding light on future directions to investigate roles of rare variants in human diseases.
大规模测序和基因分型数据为整合外部样本作为对照提供了机会，以提高关联测试的功效。然而，由于不同研究中基因分型样本之间存在系统性差异，简单地将对照样本进行汇总可能会导致Ⅰ型错误率膨胀。最近，人们已经在努力整合外部对照，同时调整批次效应，例如将外部对照整合到关联测试中（iECAT）及其基于分数的单变体测试。基于原始的 iECAT 框架，我们提出了一种 iECAT-Score 基于区域的测试，该测试在整合外部对照时增加了罕见变异测试的功效。该方法在每个变体处评估内部和外部样本之间的系统批次效应，并构建复合收缩分数统计量来测试基因或区域内的联合遗传效应，同时调整协变量和群体分层。通过模拟研究，我们证明了该方法可以控制Ⅰ型错误率并提高罕见变异测试的功效。该方法在国际年龄相关性黄斑变性（AMD）基因组联盟和英国生物银行的 AMD 关联研究中的应用揭示了基因 DXO 中罕见变异的新关联。通过纳入外部对照，iECAT 方法提供了一套强大的工具来识别与疾病相关的遗传变异，进一步为研究罕见变异在人类疾病中的作用指明了未来的方向。
