Data from: Inference of cross-species gene flow using genomic data depends on the methods: Case study of gene flow in Drosophila

Analysis of genomic data in the past two decades has highlighted the
prevalence of introgression as an important evolutionary force in both
plants and animals. The genus Drosophila has received much attention
recently, with an analysis of genomic sequence data detailing widespread
introgression across the species phylogeny for the genus. However, the
methods used in the study are based on data summaries for species triplets
and are unable to infer gene flow between sister lineages or to identify
the direction of gene flow. Hence, we reanalyze a subset of the data using
the Bayesian program bpp, which is a full-likelihood implementation of the
multispecies coalescent (MSC) model and can provide more powerful
inference of gene flow between species, including its direction, timing,
and strength. While our analysis supports the presence of gene flow in the
species group, the results differ from the previous study: we infer gene
flow between sister lineages undetected previously whereas most gene-flow
events inferred in the previous study are rejected in our tests. To verify
our conclusions, we performed simulations to examine the properties of
Bayesian and summary methods. Bpp was found to have high power to detect
gene flow, high accuracy in estimated rates of gene flow, and robustness
under misspecification of the mode of gene flow. In contrast, summary
methods had low power and produced biased estimates of introgression
probability. Our results highlight an urgent need for improving the
statistical properties ofsummary methods and the computational efficiency
of likelihood methods for inferring gene flow using genomic sequence data.