Preprint proposes method to integrate bottleneck size into selection tests for genetic diversity data

A preprint posted to bioRxiv (not yet peer-reviewed) introduces a computational framework designed to improve the detection of natural selection in population genetic datasets by explicitly incorporating estimates of population bottleneck size into neutrality tests.

Population bottlenecks — episodes of sharply reduced population size — have a strong influence on patterns of genetic diversity. They reduce effective population size, amplify the effects of genetic drift, and can create signatures in allele frequency data that resemble those produced by positive or purifying selection. Distinguishing genuine selection from stochastic drift in bottlenecked populations is therefore a longstanding challenge in population genetics.

The authors build on existing computational approaches, including beta-binomial modelling frameworks commonly applied to deep-sequencing data, and propose modifications to neutrality tests that take bottleneck size estimates as an explicit input rather than treating them as a nuisance parameter. The framework is designed for variant frequency data of the kind generated by deep sequencing of mixed populations.

As a preprint, this work has not undergone peer review. The methodology and conclusions should be treated as preliminary. The approach may be of interest to researchers working in population genetics, evolutionary genomics, and the design of selection scans in bottlenecked or founder populations.