Tang Zheng-Zheng, Lin Dan-Yu
Department of Biostatistics, University of North Carolina, Chapel Hill, North Carolina, United States of America.
Genet Epidemiol. 2014 Jul;38(5):389-401. doi: 10.1002/gepi.21798. Epub 2014 May 5.
Recent advances in sequencing technologies have made it possible to explore the influence of rare variants on complex diseases and traits. Meta-analysis is essential to this exploration because large sample sizes are required to detect rare variants. Several methods are available to conduct meta-analysis for rare variants under fixed-effects models, which assume that the genetic effects are the same across all studies. In practice, genetic associations are likely to be heterogeneous among studies because of differences in population composition, environmental factors, phenotype and genotype measurements, or analysis method. We propose random-effects models which allow the genetic effects to vary among studies and develop the corresponding meta-analysis methods for gene-level association tests. Our methods take score statistics, rather than individual participant data, as input and thus can accommodate any study designs and any phenotypes. We produce the random-effects versions of all commonly used gene-level association tests, including burden, variable threshold, and variance-component tests. We demonstrate through extensive simulation studies that our random-effects tests are substantially more powerful than the fixed-effects tests in the presence of moderate and high between-study heterogeneity and achieve similar power to the latter when the heterogeneity is low. The usefulness of the proposed methods is further illustrated with data from National Heart, Lung, and Blood Institute Exome Sequencing Project (NHLBI ESP). The relevant software is freely available.
测序技术的最新进展使得探索罕见变异对复杂疾病和性状的影响成为可能。荟萃分析对于这一探索至关重要，因为检测罕见变异需要大样本量。在固定效应模型下有几种方法可用于对罕见变异进行荟萃分析，该模型假定所有研究中的遗传效应相同。在实际中，由于人群构成、环境因素、表型和基因型测量或分析方法的差异，研究之间的遗传关联可能是异质性的。我们提出了允许遗传效应在研究之间变化的随机效应模型，并开发了用于基因水平关联检验的相应荟萃分析方法。我们的方法以评分统计量而非个体参与者数据作为输入，因此可以适应任何研究设计和任何表型。我们给出了所有常用基因水平关联检验的随机效应版本，包括负担检验、可变阈值检验和方差成分检验。我们通过广泛的模拟研究表明，在存在中度和高度研究间异质性的情况下，我们的随机效应检验比固定效应检验的效能显著更高，而在异质性较低时，其效能与固定效应检验相似。美国国立心肺血液研究所外显子测序项目（NHLBI ESP）的数据进一步说明了所提出方法的实用性。相关软件可免费获取。
