Auer Paul L, Lettre Guillaume
School of Public Health, University of Wisconsin-Milwaukee, Milwaukee, WI 53201-0413 USA.
Montreal Heart Institute and Université de Montréal, Montreal, Quebec H1T 1C8 Canada.
Genome Med. 2015 Feb 23;7(1):16. doi: 10.1186/s13073-015-0138-2. eCollection 2015.
Genome-wide association studies (GWASs) have successfully uncovered thousands of robust associations between common variants and complex traits and diseases. Despite these successes, much of the heritability of these traits remains unexplained. Because low-frequency and rare variants are not tagged by conventional genome-wide genotyping arrays, they may represent an important and understudied component of complex trait genetics. In contrast to common variant GWASs, there are many different types of study designs, assays and analytic techniques that can be utilized for rare variant association studies (RVASs). In this review, we briefly present the different technologies available to identify rare genetic variants, including novel exome arrays. We also compare the different study designs for RVASs and argue that the best design will likely be phenotype-dependent. We discuss the main analytical issues relevant to RVASs, including the different statistical methods that can be used to test genetic associations with rare variants and the various bioinformatic approaches to predicting in silico biological functions for variants. Finally, we describe recent rare variant association findings, highlighting the unexpected conclusion that most rare variants have modest-to-small effect sizes on phenotypic variation. This observation has major implications for our understanding of the genetic architecture of complex traits in the context of the unexplained heritability challenge.
全基因组关联研究(GWAS)已成功发现了数千个常见变异与复杂性状及疾病之间的可靠关联。尽管取得了这些成功,但这些性状的大部分遗传性仍无法得到解释。由于低频和罕见变异未被传统的全基因组基因分型阵列所标记,它们可能是复杂性状遗传学中一个重要但尚未得到充分研究的组成部分。与常见变异GWAS不同,有许多不同类型的研究设计、检测方法和分析技术可用于罕见变异关联研究(RVAS)。在本综述中,我们简要介绍了可用于识别罕见遗传变异的不同技术,包括新型外显子阵列。我们还比较了RVAS的不同研究设计,并认为最佳设计可能取决于表型。我们讨论了与RVAS相关的主要分析问题,包括可用于测试罕见变异与遗传关联的不同统计方法,以及预测变异计算机生物学功能的各种生物信息学方法。最后,我们描述了最近的罕见变异关联研究结果,强调了一个意想不到的结论,即大多数罕见变异对表型变异的影响大小适中至较小。这一观察结果对我们在未解释的遗传性挑战背景下理解复杂性状的遗传结构具有重大意义。