The Precision and Power of Population Branch Statistics in Identifying the Genomic Signatures of Local Adaptation.

Abstract

Population branch statistics, which estimate the degree of genetic differentiation along a focal population's lineage, have been used as an alternative to FST-based genome-wide scans for identifying loci associated with local selective sweeps. Beyond the population branch statistic (PBS), the normalized PBSn1 adjusts focal branch length with respect to outgroup branch lengths at the same locus, whereas population branch excess (PBE) incorporates median branch lengths at other loci. PBSn1 and PBE were proposed to be more specific to local selective sweeps as opposed to geographically ubiquitous selection. However, the accuracy and statistical power of branch statistics have not been systematically assessed. To do so, we simulate genomes in representative large and small populations with varying proportions of sites evolving under genetic drift or (approximated) background selection, with local selective sweeps or geographically parallel selective sweeps. We then assess the probability that local selective sweep loci are correctly identified as outliers by FST and by each of the branch statistics. We find that branch statistics consistently outperform FST at identifying local sweeps. Particularly when parallel sweeps are introduced, PBSn1 and PBE correctly identify local sweeps among their top outliers more frequently than PBS. Additionally, we evaluate versions of these statistics based on maximal site differentiation within a window, finding that site-based PBE and PBSn1 are particularly effective at identifying local soft sweeps. These results validate the greater specificity of the rescaled branch statistics PBE and PBSn1 to detect population-specific positive selection, supporting their use in genomic studies focused on local adaptation.