We propose a general statistical framework for meta-analysis of gene- or region-based multimarker rare variant association tests in sequencing association studies. In genome-wide association studies, single-marker meta-analysis has been widely used to increase statistical power by combining results via regression coefficients and standard errors from different studies. In analysis of rare variants in sequencing studies, region-based multimarker tests are often used to increase power. We propose meta-analysis methods for commonly used gene- or region-based rare variants tests, such as burden tests and variance component tests. Because estimation of regression coefficients of individual rare variants is often unstable or not feasible, the proposed method avoids this difficulty by calculating score statistics instead that only require fitting the null model for each study and then aggregating these score statistics across studies. Our proposed meta-analysis rare variant association tests are conducted based on study-specific summary statistics, specifically score statistics for each variant and between-variant covariance-type (linkage disequilibrium) relationship statistics for each gene or region. The proposed methods are able to incorporate different levels of heterogeneity of genetic effects across studies and are applicable to meta-analysis of multiple ancestry groups. We show that the proposed methods are essentially as powerful as joint analysis by directly pooling individual level genotype data. We conduct extensive simulations to evaluate the performance of our methods by varying levels of heterogeneity across studies, and we apply the proposed methods to meta-analysis of rare variant effects in a multicohort study of the genetics of blood lipid levels.

中文翻译：

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测序关联研究中罕见变异荟萃分析的通用框架。

我们提出了一个通用统计框架，用于在测序关联研究中对基于基因或区域的多标记罕见变异关联测试进行荟萃分析。在全基因组关联研究中，单标记荟萃分析已被广泛用于通过回归系数和来自不同研究的标准误差组合结果来提高统计功效。在测序研究中对罕见变异的分析中，通常使用基于区域的多标记测试来提高功效。我们为常用的基于基因或区域的稀有变异测试提出了荟萃分析方法，例如负担测试和方差分量测试。因为对个别稀有变异的回归系数的估计通常不稳定或不可行，所提出的方法通过计算分数统计来避免这种困难，而只需要为每个研究拟合空模型，然后跨研究汇总这些分数统计。我们提出的荟萃分析罕见变异关联测试是基于研究特定的汇总统计数据进行的，特别是每个变异的评分统计和每个基因或区域的变异间协方差类型（连锁不平衡）关系统计。所提出的方法能够在研究中纳入不同水平的遗传效应异质性，并适用于多个祖先群体的荟萃分析。我们表明，通过直接汇集个体水平基因型数据，所提出的方法本质上与联合分析一样强大。