Travis J. Struck

GHIST 2024 evaluates population genetic inference methods through four demographic history challenges, revealing the dominance of site frequency spectrum-based methods and the potential of flexible models for complex histories.

This paper introduces donni, a machine learning-based method for demographic history inference that is faster and more efficient than dadi while maintaining similar accuracy.

dadi-cli simplifies dadi usage and enables distributed computing for population genetic model inference from allele frequency spectra.

The paper introduces Demes, a standard format for demographic models in population genetics to improve clarity and ease of use in simulations and analysis.

This study assesses the impact of GWAS on biomedical research publications and finds declining effects on publication rates for newly associated complex disease genes.