Signatures of local adaptive divergence across D. innubila
populations
We downloaded gene ontology groups from Flybase (Gramateset al. 2017). We then used a gene enrichment analysis to identify
enrichments for particular gene categories among genes in the
97.5th percentile and 2.5thpercentile for FST, Tajima’s D and Pairwise Diversity
versus all other genes (Subramanian et al. 2005). Due to
differences on the chromosomes Muller A and B versus other chromosomes
in some cases, we also repeated this analysis chromosome by chromosome,
taking the upper 97.5th percentile of each chromosome.
We next attempted to look for selective sweeps in each population using
Sweepfinder2 (Huber et al. 2016). We
reformatted the polarized VCF file to a folded allele frequency file,
showing allele counts for each base. We then used Sweepfinder2 on the
total called polymorphism in each population to detect selective sweeps
in 1kbp windows (Huber et al. 2016). We reformatted the
results and looked for genes neighboring or overlapping with regions
where selective sweeps have occurred with a high confidence, shown as
peaks above the genomic background. We surveyed for peaks by identifying
1kbp windows in the 97.5th percentile for composite
likelihood ratio per chromosome.
Using the total VCF with outgroup information, we next calculated Dxy
per SNP for all pairwise population comparisons (Nei and Miller
1990), as well as within population pairwise diversity and dS from the
outgroups, using a custom python script. We then found the average Dxy
and dS per gene and looked for gene enrichments in the upper
97.5th percentile, versus all other genes.