title: Rare-variant association analysis: study designs and statistical tests.
abstract: 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.
authors.affiliation: Department of Biostatistics, University of Michigan, Ann Arbor, MI 48105, USA.
authors.forename: Seunggeung
authors.initials: S
authors.lastname: Lee
authors.affiliation: Department of Biostatistics, University of Michigan, Ann Arbor, MI 48105, USA.
authors.forename: Gonçalo R
authors.initials: GR
authors.lastname: Abecasis
authors.affiliation: Department of Biostatistics, University of Michigan, Ann Arbor, MI 48105, USA.
authors.forename: Michael
authors.initials: M
authors.lastname: Boehnke
authors.affiliation: Department of Biostatistics, Harvard School of Public Health, Boston, MA 02115, USA. Electronic address: xlin@hsph.harvard.edu.
authors.forename: Xihong
authors.initials: X
authors.lastname: Lin
mesh_terms: D055106:Genome-Wide Association Study; D005838:Genotype; D006801:Humans