title: New approaches to population stratification in genome-wide association studies.
abstract: Genome-wide association (GWA) studies are an effective approach for identifying genetic variants associated with disease risk. GWA studies can be confounded by population stratification--systematic ancestry differences between cases and controls--which has previously been addressed by methods that infer genetic ancestry. Those methods perform well in data sets in which population structure is the only kind of structure present but are inadequate in data sets that also contain family structure or cryptic relatedness. Here, we review recent progress on methods that correct for stratification while accounting for these additional complexities.
authors.affiliation: Program in Medical and Population Genetics, Broad Institute of Harvard and Massachusetts Institute of Technology, Cambridge, Massachusetts 02142, USA. aprice@hsph.harvard.edu
authors.forename: Alkes L
authors.initials: AL
authors.lastname: Price
authors.forename: Noah A
authors.initials: NA
authors.lastname: Zaitlen
authors.forename: David
authors.initials: D
authors.lastname: Reich
authors.forename: Nick
authors.initials: N
authors.lastname: Patterson
mesh_terms: D003198:Computer Simulation; D055106:Genome-Wide Association Study / Q000379:methods*; D006801:Humans; D008957:Models, Genetic; D020641:Polymorphism, Single Nucleotide