Localizing and Classifying Adaptive Targets with Trend Filtered Regression

Identifying genomic locations of natural selection from sequence data is an ongoing challenge in population genetics. Current methods utilizing information combined from several summary statistics typically assume no correlation of summary statistics regardless of the genomic location from which they are calculated. However, due to linkage disequilibrium, summary statistics calculated at nearby genomic positions are highly correlated. We introduce an approach termed Trendsetter that accounts for the similarity of statistics calculated from adjacent genomic regions through trend filtering, while reducing the effects of multicollinearity through regularization. Our penalized regression framework has high power to detect sweeps, is capable of classifying sweep regions as either hard or soft, and can be applied to other selection scenarios as well. We find that Trendsetter is more robust to missing data than similar current approaches, has comparable power, and is also robust to strong background selection. Moreover, the model learned by Trendsetter is highly interpretable, as it represents a set of curves modeling the spatial distribution of summary statistics in the genome. Application to human genomic data revealed positively-selected regions previously discovered such as LCT in Europeans and EDAR in East Asians. We also identified a number of novel candidates and show that populations with greater relatedness share more sweep signals.