Rare-Variant Association Analysis: Study Designs and Statistical Tests

被引:702
作者
Lee, Seunggeung [1 ]
Abecasis, Goncalo R. [1 ]
Boehnke, Michael [1 ]
Lin, Xihong [2 ]
机构
[1] Univ Michigan, Dept Biostat, Ann Arbor, MI 48105 USA
[2] Harvard Univ, Sch Publ Hlth, Dept Biostat, Boston, MA 02115 USA
关键词
GENOME-WIDE ASSOCIATION; LINEAR MIXED MODELS; FAST-LMM-SELECT; POPULATION STRATIFICATION; MISSING HERITABILITY; GENOTYPE IMPUTATION; QUANTITATIVE TRAITS; LOW-FREQUENCY; PRINCIPAL-COMPONENTS; NONCODING SEQUENCE;
D O I
10.1016/j.ajhg.2014.06.009
中图分类号
Q3 [遗传学];
学科分类号
071007 ; 090102 ;
摘要
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.
引用
收藏
页码:5 / 23
页数:19
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