Recommendations for improving statistical inference in population genomics

Journal article

AbstractEvaluating population genetic inference methods is challenging due to the complexity of evolutionary histories, potential model misspecification, and unconscious biases in self-assessment. The Genomic History Inference Strategies Tournament (GHIST) is a community-driven competition designed to evaluate methods for inferring evolutionary history from population genomic data. The inaugural Genomic History Inference Strategies Tournament competition ran from July to November 2024 and featured four demographic history inference challenges of varying complexity: a bottleneck model, a split with isolation model, a secondary contact model with demographic complexity, and an archaic admixture model. Data were provided as error-free VCF files, and participants submitted numerical parameter estimates that were scored by relative root-mean-squared error. Approximately 60 participants competed, using diverse approaches. Results revealed the current dominance of methods based on site frequency spectra, while highlighting the advantages of flexible model-building approaches for complex demographic histories. We discuss insights regarding the competition and outline the next iteration, which is ongoing with expanded challenge diversity. By providing standardized benchmarks and highlighting areas for improvement, Genomic History Inference Strategies Tournament represents a substantial step toward more reliable inference of evolutionary history from genomic data

Authors: Johri, P; Aquadro, CF; Beaumont, M; Charlesworth, B; Excoffier, L

• Publication status: Published
• Peer review status: Peer reviewed
• Publisher: Public Library of Science
• Journal: PLoS Biology
• Volume: 20
• Issue: 5
• Pages: e3001669-e3001669
• Publication date: 2022-05-31
• DOI: 10.1371/journal.pbio.3001669
• EISSN: 1545-7885
• ISSN: 1544-9173
• Language: English
• Keywords: Genome; Population genomics; Genomics; Scrutiny; Genetics; Computer science; Statistical inference; Biology; Artificial intelligence; Data science; Inference; Population