SNP-level FST outperforms window statistics for detecting soft sweeps in local adaptation

Abstract

AbstractLocal adaptation can lead to elevated genetic differentiation at the targeted genetic variant and nearby sites. Selective sweeps come in different forms, and depending on the initial and final frequencies of a favored variant, very different patterns of genetic variation may be produced. If local selection favors an existing variant that had already recombined onto multiple genetic backgrounds, then the width of elevated genetic differentiation (high FST) may be too narrow to detect using a typical windowed genome scan, even if the targeted variant becomes highly differentiated. We therefore used a simulation approach to investigate the power of SNP-level FST (specifically, the maximum SNP FST value within a window) to detect diverse scenarios of local adaptation, and compared it against whole-window FST and the Comparative Haplotype Identity statistic. We found that SNP FST had superior power to detect complete or mostly complete soft sweeps, but lesser power than window-wide statistics to detect partial hard sweeps. To investigate the relative enrichment and nature of SNP FST outliers from real data, we applied the two FST statistics to a panel of Drosophila melanogaster populations. We found that SNP FST had a genome-wide enrichment of outliers compared to demographic expectations, and though it yielded a lesser enrichment than window FST, it detected mostly unique outlier genes and functional categories. Our results suggest that SNP FST is highly complementary to typical window-based approaches for detecting local adaptation, and merits inclusion in future genome scans and methodologies.Significance statementStudies that use genetic variation to search for genes evolving under population-specific natural selection tend to analyze data at the level of genomic windows that may each contain hundreds of variable sites. However, some models of natural selection (e.g. favoring an existing genetic variant) may result in genetic signals of local adaptation that are too narrow to be detected by such approaches. Here we use both simulations and empirical data analysis to show that searching for a site-specific signal of elevated genetic differentiation can find instances of local adaptation that other approaches miss, and therefore the integration of this signal into future studies may significantly improve our understanding of adaptive evolution and its genetic targets.