PMC:3480682 / 21857-22759 JSONTXT

{"project":"2_test","denotations":[{"id":"23105934-18780740-44845831","span":{"begin":191,"end":193},"obj":"18780740"},{"id":"23105934-18780740-44845832","span":{"begin":326,"end":328},"obj":"18780740"}],"text":"Bayesian outlier detections\nWe searched the genome regions that show signals of natural selection by the Bayesian likelihood method implemented via reversible jump MCMC in BayeScan software [27]. For each CNV, BayeScan directly estimates the probability that a CNV is under positive selection, from which the PO are computed [27]. The posterior odds indicate how much more likely the model with selection is compared to the neutral model, and posterior odds of 5.0 was chosen as a threshold in the detection of CNVs under positive selection. This directly allows us to control the FDR, the expected proportion of false positives among outlier CNVs [29]. In our analysis, we chose FDR to be 0.05. In the BayeScan application, first following 10 pilot runs of 5,000 iterations and an additional burn-in of 50,000 iterations, we used 50,000 iterations (sample size of 5,000 and a thinning interval of 10)."}