Genome scan for adaptive differentiation

SNPs subjected to adaptive differentiation were formally inferred through the XtX differentiation measure⁶⁴. The XtX statistic is analogous to F_ST, but explicitly accounts for the relationship among populations and sampling noise in pooled samples (variation in sequencing depth across populations and SNPs)⁶⁴. The XtX genetic differentiation value for each SNP was estimated under the core model implemented in Baypass v.2.1³⁶. To run the Baypass core model, we used the alternative and reference read count data for the ten pools and the haploid pool sizes as inputs, with the –d0yij option set at 8 for Pool-Seq mode and MCMC options as 25 short pilot runs (1,000 iterations each) to adjust the proposal distributions for each model parameter. Subsequently, an 100,000 burn‐in period and an 100,000 updating steps were performed with a thinning interval of 40 steps. A pseudo‐observed data set (POD) was simulated considering the same parameters as those estimated in the original data using the R function simulate.baypass() available in the BayPass software. The POD was further analyzed under the core model with the same parameters to estimate the posterior predictive distribution of the XtX statistics under neutrality. We compared the posterior estimates of Ω in the simulated data against the original data using FMD distance⁶⁵ to assess the precision and robustness of the simulated data. The 99.9^th percentile of this empirical distribution was used to calibrate the original XtX values, i.e., the POD analysis provided the 0.1% threshold XtX value as a decision criterion for discriminating between selection and neutrality, and detect overly differentiated SNPs³⁶.