Jazlyn A. Mooney

Negative selection on deleterious mutations plays a key role in shaping human genetic variation, yet the dominance effects of these mutations remain poorly understood because existing statistical methods often cannot distinguish dominance effects from the overall selective effects. In this work, we take a fundamentally different approach by leveraging the distribution of Neanderthal ancestry across the human genome. Simulations show that recessive deleterious mutations can increase archaic introgressed ancestry through heterosis in the absence of positive selection, in contrast to the depletion expected under additive effects. We use this signal to develop DominL , a machine learning classifier trained on simulations of human demography with Neanderthal introgression to identify megabase-scale genomic windows enriched with recessive mutations. DominL demonstrates robust accuracy, with particularly high power in exon-dense regions. Applied to 7 non-African populations from the 1000 Genomes Project, DominL identifies approximately 3-9% of the genome as enriched for recessive mutations, with most high-confidence regions shared across populations. Predicted regions show patterns consistent with expected signatures from recessive mutations, including weakened background selection, depletion of runs of homozygosity, and enrichment of non-additive trait-associated variants. These regions also contain genes associated with metabolic and immune-related functions.

Runs of homozygosity (ROH) are genomic regions that arise when identical haplotypes are inherited from a shared ancestor. In this study, we explored ROH across 556 whole-genome sequences from domesticated and non-domesticated dogs. Then, we leveraged ROH from 466 breed dogs, representing 13 breed groups and 13 phenotypic traits, to investigate associations between genetic diversity and non-disease phenotypes. We identified significant associations between the ROH-based inbreeding coefficient (FROH) and multiple phenotypes. These include three quantitative traits (height, weight, lifespan) and ten morphological and coat-related traits. After correcting for population structure, we identified more than 45 genes associated with quantitative traits that exceeded suggestive or genome-wide significance (GWS) thresholds. We also observed distinct patterns of inbreeding across dog populations, including elevated levels of long ROH in modern breed dogs relative to more ancient breeds, consistent with intensive breeding practices during Victorian-era breed formation. Together, our results demonstrate how domestication, demographic bottlenecks, and selective breeding have shaped patterns of homozygosity and contributed to the genetic architecture of complex traits in dogs, highlighting an important role for non-additive genetic variation and polygenicity.

Complex demographic events have shaped human history, leaving signatures of genetic variation across the genome. Here, we investigate the recent evolutionary dynamics of admixed populations that descend from distinct ancestral sources. We present a discrete, generalizable model of admixture that leverages ancestry switches, which are recombination breakpoints that mark changes in ancestral origin along a chromosome. We derive analytical expectations for the number of ancestry switches within a genomic segment as functions of recombination rate, ancestry heterozygosity, and effective population size. We then extend these expectations to incorporate population-specific recombination maps. Our theoretical predictions are in close agreement with forward-in-time simulations that trace ancestry junction accumulation following an initial admixture event under both constant and variable recombination models. We observe minimal variability in switch counts across ten simulation replicates, underscoring the robustness of the theoretical expectation. Furthermore, model-based switch counts, parameterized using literature-informed demographic values, agree with empirical observations from African-American individuals in the 1000 Genomes Project. For example, when modeling human chromosome 1, we found a mean of approximately six switches per haplotype, which aligns with the theoretical expectation under an initial African ancestry proportion of 0.85 and agrees with published estimates from other African-American cohorts. Overall, the model provides a new route for using ancestry switches to understand how recombination and demography jointly shape ancestry patterns in admixed populations without requiring separation into parental sources.

Complex demographic events have shaped human history and genetic variation across the genome. Here, we investigate the recent evolutionary history of admixed populations that descend from distinct ancestral sources. We present a discrete, generalizable model of admixture that leverages ancestry switches, which are recombination breakpoints that mark changes in ancestral origin along a chromosome. We derive analytical expectations for the number of ancestry switches within a genomic segment as functions of recombination rate, ancestry heterozygosity, and effective population size. We then extend these expectations to incorporate population-specific recombination maps. Our theoretical predictions are in close agreement with forward-in-time simulations that we use to trace ancestry junction accumulation since an initial admixture event with both constant and variable recombination models. We observe minimal variability in switch counts across ten simulation replicates, underscoring the robustness of the theoretical expectation. Furthermore, model-based switch counts, parameterized using literature-informed demographic values, agree with empirical observations from African American individuals in the 1000 Genomes Project. For example, when modeling human chromosome 1, we found a mean of approximately six switches per haplotype, which aligns with the theoretical expectation under an initial African ancestry proportion of 0.85, and agrees with published estimates from other African-American cohorts. Overall, the model provides a new route for using ancestry switches to understand how recombination and demography jointly shape ancestry patterns in admixed populations without requiring separation into parental sources.

In this perspective piece, we review the history of eugenics and its impacts on Indigenous peoples of North America. The perspective outlines historical policies, such as forced sterilization and immigration efforts targeting Indigenous populations. We explore how science is intertwined with eugenics and how eugenic ideologies continue to negatively impact Indigenous communities and science today. The work investigates the legacy of eugenics in shaping genetic studies, including genetic counseling and large-scale initiatives like the Human Genome Diversity Project. Additionally, we consider how institutions and biomedical research settings have committed inexcusable and unethical science that contributed to Indigenous peoples' attitudes toward genetic and biomedical research. The work addresses the challenges genetic testing, including direct-to-consumer testing, poses to Indigenous people's identities, culture, and sovereignty. Acknowledging this devastating history is essential to understanding its continued impact on Indigenous communities today. We conclude by highlighting ongoing efforts and strategies to effectively engage with Indigenous peoples in genetic research, emphasizing the responsibilities of modern researchers to build trust, ensure ethical practices, and contribute to inclusive and respectful science.

Scaling is a common practice in population genetic simulations to increase computational efficiency. However, few studies systematically examine the effects of scaling on diversity estimates and the comparability of scaled results to unscaled simulations and empirical data. We investigate the effects of scaling in two species, modern humans and Drosophila melanogaster. These species have stark differences in population size and generation time, necessitating moderate-to-no scaling for humans and dramatic scaling for Drosophila. We determine how coalescence, runtime, memory, estimates of diversity, the site frequency spectra, and linkage disequilibrium are influenced by scaling. We also examine the impact of simulated segment length and burn-in time on these metrics. Our results demonstrate that while computational efficiency improves with scaling, large scaling factors distort genetic diversity and dynamics between genetic variants, resulting in deviations from the intended model and empirical observations. Specifically, strongly scaled simulations may experience stronger negative selection on deleterious mutations, which amplifies background selection and purges linked mutations, leaving only rare strongly deleterious variants in the final population. We additionally show that a heuristic burn-in length of 10N generations is often insufficient for full coalescence in both models and alters expected linkage disequilibrium patterns. Finally, we provide considerations for conducting scaled simulations and offer potential strategies for the mitigation of scaling effects. For most nonmodel species simulations, we advocate for a bespoke scaling strategy drawn from these use cases.

Negative natural selection on deleterious mutations plays a key role in shaping human genetic variation. Understanding the dominance of deleterious mutations is critical as it can fundamentally impact the rate and efficiency of natural selection, the magnitude of inbreeding depression, and the prevalence and evolution of genetic diseases. Despite its inarguable importance, the dominance effects of mutations remain poorly understood in humans, primarily because existing statistical methods cannot distinguish them from the overall selective effects of mutations. In this work, we take a fundamentally different approach to infer dominance by leveraging the distribution of Neanderthal ancestry across the human genome. We show through simulations that recessive deleterious mutations lead to an increase in archaic introgressed ancestry in the absence of positive selection, contrary to what is expected when deleterious mutations are additive. Leveraging this unique pattern, we develop a machine learning classifier to infer dominance in genomic windows at a megabase resolution, trained on simulations of a human demographic model with Neanderthal introgression using fully recessive or additive mutations. Our method demonstrates robust accuracy at detecting genomic windows containing recessive deleterious mutations, with particularly high power in exon-dense regions. When applied to the non-African populations from the 1000 Genomes Project, we find that approximately 3-9% of the human genome is enriched for recessive mutations with most recessive regions shared across human populations. Furthermore, our method reveals that recessive deleterious mutations are not evenly distributed across the genome: regions enriched for recessive mutations are significantly depleted of haploinsufficient genes and runs of homozygosity, and are enriched with non-additive variants associated with complex traits. Overall, our Neanderthal ancestry-based approach reveals the presence of recessive deleterious mutations in the human genome and suggests that these mutations are found in regions containing genes associated with metabolism and immune-related traits.

Summary: Characterizing genetic variation in natural populations is vital to evolutionary biology, however many non-model species lack genomic resources. Here, we demonstrate that reference bias significantly affects population genomic analyses by mapping whole genome sequence data from gray foxes ( Urocyon cinereoargenteus ) to a conspecific reference and two heterospecific canid genomes (dog and Arctic fox). Mapping to the conspecific genome improved read pairing by ∼5%, detected 26–32% more SNPs, and 33–35% more singletons. Nucleotide diversity estimates increased over 30%, F _ST increased from 0.189 to 0.197, and effective population size estimates were 30-60% higher with the conspecific reference. Recombination rates varied by up to 3-fold at chromosome ends with heterospecific references. Importantly, F _ST outlier detection differed markedly, with heterospecific genomes identifying twice as many unique outlier windows. These findings highlight the impact of reference genome choice and the importance of conspecific genomic resources for accurate evolutionary inference. Graphical Highlights: A species-specific reference genome improves read mapping and variant detection Reference bias underestimates genetic diversity and differentiation Divergent reference genomes distort demographic histories and recombination landscapes Unique F _ST outliers are detected across references, affecting functional interpretations