Phase-free local ancestry inference mitigates the impact of switch errors on phase-based methods

Abstract

Local ancestry inference (LAI) is an indispensable component of a variety of analyses in medical and population genetics, from admixture mapping to characterizing demographic history. However, the accuracy of LAI depends on a number of factors such as phase quality (for phase-based LAI methods), time since admixture of the population under study, and other factors. Here we present an empirical analysis of four LAI methods using simulated individuals of mixed African and European ancestry, examining the impact of variable phase quality and a range of demographic scenarios. We found that regardless of phasing options, calls from LAI methods that operate on unphased genotypes (phase-free LAI) have 2.6-4.6% higher Pearson correlation with the ground truth than methods that operate on phased genotypes (phase-based LAI). Applying the TRACTOR phase-correction algorithm led to modest improvements in phase-based LAI, but despite this, the Pearson correlation of phase-free LAI remained 2.4-3.8% higher than phase-corrected phase-based approaches (considering the best performing methods in each category). Phase-free and phase-based LAI accuracy differences can dramatically impact downstream analyses: estimates of the time since admixture using phase-based LAI tracts are upwardly biased by {approx}10 generations using our highest quality phased data but have virtually no bias using phase-free LAI calls. Our study underscores the strong dependence of phase-based LAI accuracy on phase quality and highlights the merits of LAI approaches that analyze unphased genetic data.