Data from: Properties of different selection signature statistics and a new strategy for combining them

Identifying signatures of recent or ongoing selection is of high relevance
in livestock population genomics. From a statistical perspective,
determining a proper testing procedure and combining various test
statistics is challenging. On the basis of extensive simulations in this
study, we discuss the statistical properties of eight different
established selection signature statistics. In the considered scenario, we
show that a reasonable power to detect selection signatures is achieved
with high marker density (>1 SNP/kb) as obtained from sequencing,
while rather small sample sizes (~15 diploid individuals) appear to be
sufficient. Most selection signature statistics such as composite
likelihood ratio and cross population extended haplotype homozogysity have
the highest power when fixation of the selected allele is reached, while
integrated haplotype score has the highest power when selection is
ongoing. We suggest a novel strategy, called de-correlated composite of
multiple signals (DCMS) to combine different statistics for detecting
selection signatures while accounting for the correlation between the
different selection signature statistics. When examined with simulated
data, DCMS consistently has a higher power than most of the single
statistics and shows a reliable positional resolution. We illustrate the
new statistic to the established selective sweep around the lactase gene
in human HapMap data providing further evidence of the reliability of this
new statistic. Then, we apply it to scan selection signatures in two
chicken samples with diverse skin color. Our analysis suggests that a set
of well-known genes such as BCO2, MC1R, ASIP and TYR were involved in the
divergent selection for this trait.