Department of Biostatistics, University of Michigan, Ann Arbor, MI 48105, USA.
Department of Biostatistics, Harvard School of Public Health, Boston, MA 02115, USA.
Am J Hum Genet. 2014 Jul 3;95(1):5-23. doi: 10.1016/j.ajhg.2014.06.009.
Despite the extensive discovery of trait- and disease-associated common variants, much of the genetic contribution to complex traits remains unexplained. Rare variants can explain additional disease risk or trait variability. An increasing number of studies are underway to identify trait- and disease-associated rare variants. In this review, we provide an overview of statistical issues in rare-variant association studies with a focus on study designs and statistical tests. We present the design and analysis pipeline of rare-variant studies and review cost-effective sequencing designs and genotyping platforms. We compare various gene- or region-based association tests, including burden tests, variance-component tests, and combined omnibus tests, in terms of their assumptions and performance. Also discussed are the related topics of meta-analysis, population-stratification adjustment, genotype imputation, follow-up studies, and heritability due to rare variants. We provide guidelines for analysis and discuss some of the challenges inherent in these studies and future research directions.
尽管已经广泛发现了与性状和疾病相关的常见变异,但复杂性状的大部分遗传贡献仍未得到解释。稀有变异可以解释额外的疾病风险或性状变异性。越来越多的研究正在进行中,以确定与性状和疾病相关的稀有变异。在这篇综述中,我们提供了稀有变异关联研究中统计问题的概述,重点介绍了研究设计和统计检验。我们展示了稀有变异研究的设计和分析流程,并回顾了具有成本效益的测序设计和基因分型平台。我们比较了各种基于基因或基于区域的关联检验,包括负担检验、方差分量检验和综合汇总检验,讨论了它们的假设和性能。还讨论了荟萃分析、群体分层调整、基因型推断、随访研究以及稀有变异引起的遗传率等相关主题。我们提供了分析指南,并讨论了这些研究中固有的一些挑战和未来的研究方向。