Li Bingshan, Liu Dajiang J, Leal Suzanne M
Department of Molecular Physiology and Biophysics, Center for Human Genetics Research, Vanderbilt University, Nashville, Tennessee, USA.
Curr Protoc Hum Genet. 2013 Jul;Chapter 1:Unit 1.26. doi: 10.1002/0471142905.hg0126s78.
Although genome-wide association studies have been successful in detecting associations with common variants, there is currently an increasing interest in identifying low-frequency and rare variants associated with complex traits. Next-generation sequencing technologies make it feasible to survey the full spectrum of genetic variation in coding regions or the entire genome. The association analysis for rare variants is challenging, and traditional methods are ineffective, however, due to the low frequency of rare variants, coupled with allelic heterogeneity. Recently a battery of new statistical methods has been proposed for identifying rare variants associated with complex traits. These methods test for associations by aggregating multiple rare variants across a gene or a genomic region or among a group of variants in the genome. In this unit, we describe key concepts for rare variant association for complex traits, survey some of the recent methods, discuss their statistical power under various scenarios, and provide practical guidance on analyzing next-generation sequencing data for identifying rare variants associated with complex traits.
尽管全基因组关联研究已成功检测到与常见变异的关联,但目前人们对识别与复杂性状相关的低频和罕见变异的兴趣日益浓厚。新一代测序技术使全面检测编码区或整个基因组的遗传变异成为可能。然而,由于罕见变异的频率较低,再加上等位基因异质性,罕见变异的关联分析具有挑战性,传统方法也无效。最近提出了一系列新的统计方法来识别与复杂性状相关的罕见变异。这些方法通过汇总一个基因或一个基因组区域内的多个罕见变异或基因组中的一组变异来检测关联。在本单元中,我们描述了复杂性状罕见变异关联的关键概念,概述了一些最新方法,讨论了它们在各种情况下的统计效力,并为分析新一代测序数据以识别与复杂性状相关的罕见变异提供实用指导。