Data from: A likelihood approach for uncovering selective sweep signatures from haplotype data

Selective sweeps are frequent and varied signatures in the genomes of
natural populations, and detecting them is consequently important in
understanding mechanisms of adaptation by natural selection. Following a
selective sweep, haplotypic diversity surrounding the site under selection
decreases, and this deviation from the background pattern of variation can
be applied to identify sweeps. Multiple methods exist to locate selective
sweeps in the genome from haplotype data, but none leverage the power of a
model-based approach to make their inference. Here, we propose a
likelihood ratio test statistic T to probe whole genome
polymorphism datasets for selective sweep signatures. Our framework uses a
simple but powerful model of haplotype frequency spectrum distortion to
find sweeps and additionally make an inference on the number of presently
sweeping haplotypes in a population. We found that
the T statistic is suitable for detecting both hard and
soft sweeps across a variety of demographic models, selection strengths,
and ages of the beneficial allele. Accordingly, we applied
the T statistic to variant calls from European and
sub-Saharan African human populations, yielding primarily
literature-supported candidates, including LCT, RSPH3,
and ZNF211 in CEU, SYT1, RGS18,
and NNT in YRI, and HLA genes in both
populations. We also searched for sweep signatures in Drosophila
melanogaster, finding expected candidates at Ace, Uhg1,
and Pimet. Finally, we provide open-source software to compute
the T statistic and the inferred number of presently
sweeping haplotypes from whole-genome data.