Rare variants association testing for a binary outcome when pooling individual level data from heterogeneous studies
Rare variants association testing for a binary outcome when pooling individual level data from heterogeneous studies
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DOI：
10.1002/gepi.22359
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发表时间：
2020-10-22
影响因子：
2.1
通讯作者：
Guo, Na
中科院分区：
文献类型：
--
作者：
Sofer, Tamar;Guo, Na
全基因组测序（WGS）和全外显子组测序研究用于测试罕见遗传变异与健康特征的关联。许多现有的WGS工作现在汇总了来自不同群体的数据，例如，将欧洲和非洲血统的个人组合在一起。我们在这里调查的统计意义罕见的变异关联测试与二元性状结合在一起时，异质性研究，定义为研究潜在的不同疾病的比例和不同频率的变异载体。我们在模拟中研究和比较了1型错误控制和幼稚分数测试的功效，分数测试的鞍点近似，以及BinomiRare测试在一系列设置中，专注于低数量的变异携带者。我们发现，1型错误控制和功率模式依赖于两个罕见等位基因的携带者的数量和疾病的患病率在每个研究。我们为罕见遗传变异的关联分析提出了建议。(1)当样本中的病例比例为50%时，首选Score检验。(2)不要为了平衡病例对照比而减少对照样本，因为这会降低功效。相反，使用控制类型1错误的测试。(3)分层分析与合并分析并行进行。当分层之间的变量效应量不同时，汇总检验可能具有较低的功效。
Whole genome sequencing (WGS) and whole exome sequencing studies are used to test the association of rare genetic variants with health traits. Many existing WGS efforts now aggregate data from heterogeneous groups, for example, combining sets of individuals of European and African ancestries. We here investigate the statistical implications on rare variant association testing with a binary trait when combining together heterogeneous studies, defined as studies with potentially different disease proportion and different frequency of variant carriers. We study and compare in simulations the Type 1 error control and power of the naive score test, the saddlepoint approximation to the score test, and the BinomiRare test in a range of settings, focusing on low numbers of variant carriers. We show that Type 1 error control and power patterns depend on both the number of carriers of the rare allele and on disease prevalence in each of the studies. We develop recommendations for association analysis of rare genetic variants. (1) The Score test is preferred when the case proportion in the sample is 50%. (2) Do not down-sample controls to balance case-control ratio, because it reduces power. Rather, use a test that controls the Type 1 error. (3) Conduct stratified analysis in parallel with combined analysis. Aggregated testing may have lower power when the variant effect size differs between strata.