title: Statistical perspectives for genome-wide association studies (GWAS).
abstract: In this chapter we consider some key elements in conducting a successful genome-wide association study or GWAS. The first step is to design the study well (Subheading 3.1), paying particular attention to case and control selection and achieving adequate sample size to deal with the large burden of multiple testing. Second, we focus on the crucial step of applying stringent quality control (Subheading 3.2) to genotyping results. The most crucial potential confounding factor in GWAS is population stratification, and we describe methods for accounting for this in study design and analysis (Subheading 3.3). The primary association analysis is relatively straightforward, and we describe the main approaches to this, including evaluation of results (Subheading 3.4). More comprehensive coverage of the genome can be achieved by using an external reference panel to estimate genotypes at untyped variants using imputation (Subheading 3.5), which we consider in some detail. We finish with some observations on following up a GWAS (Subheading 3.6).
authors.affiliation: Section of Epidemiology and Biostatistics, Leeds Institute of Cancer and Pathology, University of Leeds, St James's University Hospital, Beckett St., Leeds, LS9 7TF, UK, j.h.barrett@leeds.ac.uk.
authors.forename: Jennifer H
authors.initials: JH
authors.lastname: Barrett
authors.forename: John C
authors.initials: JC
authors.lastname: Taylor
authors.forename: Mark M
authors.initials: MM
authors.lastname: Iles
mesh_terms: D015894:Genome, Human; D055106:Genome-Wide Association Study / Q000379:methods*; D005838:Genotype; D006801:Humans; D015810:Linkage Disequilibrium; D015233:Models, Statistical; D018401:Sample Size