Statistical Inference Using Identity-by-Descent Segments: Perspectives on Recent Positive Selection

Abstract

Positive selection is suggested to be the primary mechanism of phenotypic adaptation. Selective sweeps are one model of positive selection in which beneficial mutations increase in frequency. Many existing methods to detect positive selection do not adjust for multiple hypothesis tests. Additionally, many approaches to estimate the selection coefficient, a parameter that influences the rate of allele frequency change, lack uncertainty quantification. Here we develop theory and methodology to study recent positive selection with genetic data from the present day. Our methods use long identity-by-descent segments which should be unusually abundant in strong and recent selective sweeps. In our first project, we prove that the rate of detectable identity-by-descent segments around a locus is normally distributed for large sample size and large scaled population size. In our second project, we propose an estimator of the selection coefficient, with confidence intervals, that is an easy-to-interpret one-to-one non-decreasing function of the identity-by-descent rate. Furthermore, we provide methods to analyze selective sweeps regardless of whether the selected allele is known or genotyped. In our third project, we derive a multiple testing correction to control family-wise error rate when scanning for excess identity-by-descent rates. We apply our suite of methods to detect and model selective sweeps in European, African, and South Asian human populations.