Aaron P. Ragsdale

The paper investigates the relationship between mutation rates and selective constraint in the human genome, finding higher mutation rates in protein-coding genes compared to non-coding regions.

The paper examines the genetic basis of social structure in Kazak pastoral nomads, finding concordance between genetic and genealogical data, sex-biased admixture, and reduced runs of homozygosity linked to clan exogamy.

The paper shows that mean fitness under stabilizing selection is maximized in small populations when mutation rates exceed a threshold determined by selection strength and environmental variance.

This paper uses causal inference to show that background selection and mutation rate variation jointly shape human genetic diversity, with mutation rate variation having comparable impact in some chromosomes.

This paper develops a method to infer hominin demographic history using two-locus statistics from single unphased genomes, revealing two episodes of gene flow and population structure in ancient Eurasian AMH.

This paper develops a model for background selection in non-equilibrium populations, improving accuracy across parameter space and accommodating changing population sizes.

The paper extends the stdpopsim simulation framework to include realistic selection models, enabling studies of selection's impact on genetic variation across species.

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.

The paper models how stabilizing selection affects introgression patterns around trait loci, showing introgressed-ancestry deserts are enriched around such loci and shared between populations.

This paper extends a path integral method to polygenic selection scenarios, deriving analytic expressions for allele frequency transitions under stabilizing and adaptive selection to improve evolve-and-resequence experiments.

The paper infers demographic models of African population history using genomic data, finding a weakly structured stem model explains polymorphism better than archaic introgression.

The paper expands the stdpopsim species catalog and improves genome simulations by adding non-crossover recombination and species-specific annotations, making simulations more realistic for non-model organisms.

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

The paper introduces msprime 1.0, a high-performance simulation tool for ancestry and mutation in population genetics, showing it is faster and more memory efficient than existing alternatives.

This study shows that assumptions in LDSC models can bias functional enrichment estimates in GWAS, suggesting the need for better calibration.

The paper identifies and discusses two bugs in population genetic models implemented with msprime, highlighting their impact on published analyses and offering recommendations to prevent similar errors.

The paper presents unbiased estimators for linkage disequilibrium statistics from unphased data, improving accuracy across populations and LD scales.

The paper develops a likelihood-based framework to infer demographic history using two-locus statistics, showing that models including archaic admixture better explain genetic diversity patterns.

The paper uses diffusion models and whole-genome sequence data to examine the impact of selection on demographic inference and mutation rate estimates in French Canadian individuals.

This paper develops a composite-likelihood framework using two-locus statistics to infer demographic history, showing increased sensitivity compared to single-locus methods like AFS.

The paper uses genomic data and written records to validate domestication events in Brassica rapa, finding support for specific genetic groups and domestication timelines.