About

Kirk Lohmueller is a professor in the Department of Ecology and Evolutionary Biology at UCLA. His research focuses on population genetics and genomics, developing and implementing computational approaches to interpret genetic variation data to learn about evolution and disease. He is specifically interested in understanding how natural selection has shaped patterns of genetic variation across the genome and in different species, as well as the role that population history has played in this process. Additionally, he uses genetic variation data to learn about population history and explores how population genetic approaches can contribute to identifying genes responsible for complex traits. His group combines the development of new computational methods with the analysis of cutting-edge genomic data, relying heavily on population genetic models.

Research topics

• Artificial Intelligence
• Genetics
• Biology
• Computer Science
• Ecology
• Demography
• Evolutionary biology
• Programming language
• Medicine
• Data science

Selected publications

• Inference of population demographic history captures differing evolutionary signals based on the number of individuals in the dataset

  bioRxiv (Cold Spring Harbor Laboratory) · 2026-04-08

  articleOpen accessSenior authorCorresponding

  Accurate estimation of population demographic history is central to population genetics yet remains challenging due to the sensitivity of inference methods to the number of individuals and the demographic scenario assumed in inference. The site-frequency spectrum (SFS) of neutral variants, a widely used summary statistic of genetic variation, is particularly sensitive to demographic processes, but studies have shown that qualitative results from demographic inference, i.e., population expansion vs. contraction, can depend strongly on the number of individuals in the dataset. Here, we analyzed two simulated datasets and one empirical dataset characterized by an ancient population bottleneck followed by a recent population expansion. Fitting a two-epoch demographic model across a range of sample sizes, we found that inference shifted from signals of ancient population contraction at small sample sizes to signals of recent population expansion at large sample sizes. Other summary statistics, including Tajima's D and the proportion of singletons, also changed with sample size. We found that these changes of inferred evolutionary signals under a two-epoch model can be explained by the epoch which contributes the highest mean proportion of coalescent branch lengths. Our results highlight that demographic inference depends critically on the number of individuals analyzed and suggest that analyzing datasets at multiple sample sizes can reveal complementary aspects of population history.

  PublisherOA PDFDOI

• Inference of the Demographic Histories and Selective Effects of Human Gut Commensal Microbiota Over the Course of Human History

  Molecular Biology and Evolution · 2025-01-20 · 5 citations

  articleOpen access

  Despite the importance of gut commensal microbiota to human health, there is little knowledge about their evolutionary histories, including their demographic histories and distributions of fitness effects (DFEs) of mutations. Here, we infer the demographic histories and DFEs for amino acid-changing mutations of 39 of the most prevalent and abundant commensal gut microbial species found in Westernized individuals over timescales exceeding human generations. Some species display contractions in population size and others expansions, with several of these events coinciding with several key historical moments in human history. DFEs across species vary from highly to mildly deleterious, with differences between accessory and core gene DFEs largely driven by genetic drift. Within genera, DFEs tend to be more congruent, reflective of underlying phylogenetic relationships. Together, these findings suggest that gut microbes have distinct demographic and selective histories.

  PublisherOA PDFDOI

• Additional file 2 of Lab Retriever: a software tool for calculating likelihood ratios incorporating a probability of drop-out for forensic DNA profiles

  Figshare · 2025-01-01

  articleOpen accessSenior author

  Description of the drop-in model used by Lab Retriever. (PDF 90 kb)

  PublisherDOI

• Neutral Evolution, Population Genetic Tests of

  Elsevier eBooks · 2025-01-01

  book-chapterSenior author
  PublisherDOI

• Accessible, Realistic Genome Simulation with Selection Using stdpopsim.

  UNC Libraries · 2025-11-14

  articleOpen access

  Selection is a fundamental evolutionary force that shapes patterns of genetic variation across species. However, simulations incorporating realistic selection along heterogeneous genomes in complex demographic histories are challenging, limiting our ability to benchmark statistical methods aimed at detecting selection and to explore theoretical predictions. stdpopsim is a community-maintained simulation library that already provides an extensive catalog of species-specific population genetic models. Here, we present a major extension to the stdpopsim framework that enables simulation of various modes of selection, including background selection, selective sweeps, and arbitrary distributions of fitness effects (DFE) acting on annotated subsets of the genome (for instance, exons). This extension maintains stdpopsim's core principles of reproducibility and accessibility while adding support for species-specific genomic annotations and published DFE estimates. We demonstrate the utility of this framework by comparing methods for demographic inference, DFE estimation, and selective sweep detection across several species and scenarios. Our results demonstrate the robustness of demographic inference methods to selection on linked sites, reveal the sensitivity of DFE-inference methods to model assumptions, and show how genomic features, like recombination rate and functional sequence density, influence power to detect selective sweeps. This extension to stdpopsim provides a powerful new resource for the population genetics community to explore the interplay between selection and other evolutionary forces in a reproducible, user-friendly framework.

  PublisherDOI

• Genetic rescue of Florida panthers reduced homozygosity but did not swamp ancestral genotypes

  Proceedings of the National Academy of Sciences · 2025-07-28 · 12 citations

  articleOpen accessCorresponding

  ) occupy a vast geographical range spanning from Canada to Argentina. Due to urbanization and unregulated hunting, pumas in Florida, known as panthers, are the only breeding population east of the Mississippi River. In the 1990s, Florida panthers numbered <30 individuals suffering from inbreeding depression. In 1995, eight pumas from Texas were translocated into southern Florida to mitigate the effects of isolation. This translocation reduced inbreeding depression and increased population size. While genetic rescue is often suggested as a means of ameliorating the effects of small population size, the underlying genetic mechanism and its long-term efficacy remain understudied. We sequenced the genomes of posttranslocation Florida panthers (PTFPs) to elucidate the genomic consequences of genetic rescue. We inferred local ancestry across the genomes of PTFPs and found that no regions have been entirely replaced by Texas ancestry, discarding the possibility of genetic swamping. Furthermore, the beneficial effects of the translocation were likely caused by a reduction in homozygosity, alleviating recessive deleterious load, rather than by a reduction in the number of deleterious variants. We did not find evidence that selection has favored replacement of original Florida DNA with Texas DNA in any systematic fashion. Using simulations, we found that heterozygosity increased in the long-term compared to a no translocation scenario; however, the effects on fitness are more transient. Our findings hold significant implications not only for the management of Florida's panther population, but also for informing strategies for genetic rescue in other wild, inbred populations encompassing broader conservation efforts.

  PublisherOA PDFDOI

• Accessible, Realistic Genome Simulation with Selection Using <tt>stdpopsim</tt>

  Molecular Biology and Evolution · 2025-09-25 · 4 citations

  articleOpen access

  Selection is a fundamental evolutionary force that shapes patterns of genetic variation across species. However, simulations incorporating realistic selection along heterogeneous genomes in complex demographic histories are challenging, limiting our ability to benchmark statistical methods aimed at detecting selection and to explore theoretical predictions. stdpopsim is a community-maintained simulation library that already provides an extensive catalog of species-specific population genetic models. Here, we present a major extension to the stdpopsim framework that enables simulation of various modes of selection, including background selection, selective sweeps, and arbitrary distributions of fitness effects (DFE) acting on annotated subsets of the genome (for instance, exons). This extension maintains stdpopsim's core principles of reproducibility and accessibility while adding support for species-specific genomic annotations and published DFE estimates. We demonstrate the utility of this framework by comparing methods for demographic inference, DFE estimation, and selective sweep detection across several species and scenarios. Our results demonstrate the robustness of demographic inference methods to selection on linked sites, reveal the sensitivity of DFE-inference methods to model assumptions, and show how genomic features, like recombination rate and functional sequence density, influence power to detect selective sweeps. This extension to stdpopsim provides a powerful new resource for the population genetics community to explore the interplay between selection and other evolutionary forces in a reproducible, user-friendly framework.

  PublisherOA PDFDOI

• Accessible, realistic genome simulation with selection using stdpopsim

  bioRxiv (Cold Spring Harbor Laboratory) · 2025-03-23 · 7 citations

  preprintOpen access

  Selection is a fundamental evolutionary force that shapes patterns of genetic variation across species. However, simulations incorporating realistic selection along heterogeneous genomes in complex demographic histories are challenging, limiting our ability to benchmark statistical methods aimed at detecting selection and to explore theoretical predictions. stdpopsim is a community-maintained simulation library that already provides an extensive catalog of species-specific population genetic models. Here we present a major extension to the stdpopsim framework that enables simulation of various modes of selection, including background selection, selective sweeps, and arbitrary distributions of fitness effects (DFE) acting on annotated subsets of the genome (for instance, exons). This extension maintains stdpopsim's core principles of reproducibility and accessibility while adding support for species-specific genomic annotations and published DFE estimates. We demonstrate the utility of this framework by comparing methods for demographic inference, DFE estimation, and selective sweep detection across several species and scenarios. Our results demonstrate the robustness of demographic inference methods to selection on linked sites, reveal the sensitivity of DFE-inference methods to model assumptions, and show how genomic features, like recombination rate and functional sequence density, influence power to detect selective sweeps. This extension to stdpopsim provides a powerful new resource for the population genetics community to explore the interplay between selection and other evolutionary forces in a reproducible, user-friendly framework.

  PublisherOA PDFDOI

• Neanderthal introgressed ancestry reveals human genomic regions enriched with recessive deleterious mutations

  bioRxiv (Cold Spring Harbor Laboratory) · 2025-05-07 · 1 citations

  preprintOpen accessSenior author

  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.

  PublisherOA PDFDOI

• Long runs of homozygosity are reliable genomic markers of inbreeding depression

  Trends in Ecology & Evolution · 2025-08-01 · 17 citations

  reviewSenior author
  PublisherDOI

Recent grants

• NIH Grant F32HG005308

  NIH · $23k · 2010

• Population genomics of the selective effects of new mutations

  NIH · $3.4M · 2016–2027

Frequent coauthors

• Robert K. Wayne

  University of California, Los Angeles

  27 shared

• Bernard Kim

  Stanford University

  22 shared

• Christopher C. Kyriazis

  University of California, Los Angeles

  20 shared

• Christian D. Huber

  18 shared

• Elaine A. Ostrander

  National Institutes of Health

  14 shared

• Rasmus Nielsen

  University of Copenhagen

  14 shared

• Jacqueline A. Robinson

  University of California, San Francisco

  14 shared

• Diego Ortega‐Del Vecchyo

  Autonomous University of Queretaro

  11 shared

Education

• Ph.D., Human Genetics

  University of California, Los Angeles

  2003

• M.S., Human Genetics

  University of California, Los Angeles

  1999

• B.S., Human Genetics

  University of California, Los Angeles

  1997