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.

Figure 1. P-P plots for visualization of stratification or other confounders

We display simulated P-P plots for genome-wide scans with no causal markers under three scenarios. (a) No stratification: P-values fit the expected distribution. (b) Stratification without unusually differentiated markers: P-values exhibit modest genome-wide inflation. (c) Stratification with unusually differentiated markers: P-value exhibit modest genome-wide inflation, plus severe inflation at a small number of markers.

Figure 1. P-P plots for visualization of stratification or other confounders

We display simulated P-P plots for genome-wide scans with no causal markers under three scenarios. (a) No stratification: P-values fit the expected distribution. (b) Stratification without unusually differentiated markers: P-values exhibit modest genome-wide inflation. (c) Stratification with unusually differentiated markers: P-value exhibit modest genome-wide inflation, plus severe inflation at a small number of markers.