Professional Education


  • Bachelor of Science, University of California Los Angeles (2010)
  • Master of Science, University of California Los Angeles (2013)
  • Doctor of Philosophy, University of California Los Angeles (2018)

All Publications


  • A community-maintained standard library of population genetic models. eLife Adrion, J. R., Cole, C. B., Dukler, N., Galloway, J. G., Gladstein, A. L., Gower, G., Kyriazis, C. C., Ragsdale, A. P., Tsambos, G., Baumdicker, F., Carlson, J., Cartwright, R. A., Durvasula, A., Gronau, I., Kim, B. Y., McKenzie, P., Messer, P. W., Noskova, E., Ortega Del Vecchyo, D., Racimo, F., Struck, T. J., Gravel, S., Gutenkunst, R. N., Lohmueller, K. E., Ralph, P. L., Schrider, D. R., Siepel, A., Kelleher, J., Kern, A. D. 2020; 9

    Abstract

    The explosion in population genomic data demands ever more complex modes of analysis, and increasingly these analyses depend on sophisticated simulations. Re-cent advances in population genetic simulation have made it possible to simulate large and complex models, but specifying such models for a particular simulation engine remains a difficult and error-prone task. Computational genetics researchers currently re-implement simulation models independently, leading to inconsistency and duplication of effort. This situation presents a major barrier to empirical researchers seeking to use simulations for power analyses of upcoming studies or sanity checks on existing genomic data. Population genetics, as a field, also lacks standard benchmarks by which new tools for inference might be measured. Here we describe a new resource, stdpopsim, that attempts to rectify this situation. Stdpopsim is a community-driven open source project, which provides easy access to a growing catalog of published simulation models from a range of organisms and supports multiple simulation engine backends. This resource is available as a well-documented python library with a simple command-line interface. We share some examples demonstrating how stdpopsim can be used to systematically compare demographic inference methods, and we encourage a broader community of developers to contribute to this growing resource.

    View details for DOI 10.7554/eLife.54967

    View details for PubMedID 32573438

  • The Impact of Recessive Deleterious Variation on Signals of Adaptive Introgression in Human Populations. Genetics Zhang, X., Kim, B., Lohmueller, K. E., Huerta-Sanchez, E. 2020

    Abstract

    Admixture with archaic hominins has altered the landscape of genomic variation in modern human populations. Several gene regions have been previously identified as candidates of adaptive introgression (AI) that facilitated human adaptation to specific environments. However, simulation-based studies have suggested that population genetic processes other than adaptive mutations, such as heterosis from recessive deleterious variants private to populations before admixture, can also lead to patterns in genomic data that resemble adaptive introgression. The extent to which the presence of deleterious variants affect the false-positive rate and the power of current methods to detect AI has not been fully assessed. Here, we used extensive simulations under parameters relevant for human evolution to show that recessive deleterious mutations can increase the false positive rates of tests for AI compared to models without deleterious variants, especially when the recombination rates are low. We next examined candidates of AI in modern humans identified from previous studies and show that 24 out of 26 candidate regions remain significant even when deleterious variants are included in the null model. However, two AI candidate genes, HYAL2 and HLA, are particularly susceptible to high false positive signals of AI due to recessive deleterious mutations. These genes are located in regions of the human genome with high exon density together with low recombination rate, factors that we show increase the rate of false-positives due to recessive deleterious mutations. Although the combination of such parameters is rare in the human genome, caution is warranted in such regions as well as in other species with more compact genomes and/or lower recombination rates. In sum, our results suggest that recessive deleterious mutations cannot account for the signals of AI in most, but not all, of the top candidates for AI in humans, suggesting they may be genuine signals of adaptation.

    View details for DOI 10.1534/genetics.120.303081

    View details for PubMedID 32487519

  • Population genetic models of GERP scores suggest pervasive turnover of constrained sites across mammalian evolution PLOS GENETICS Huber, C. D., Kim, B. Y., Lohmueller, K. E. 2020; 16 (5)
  • Population genetic models of GERP scores suggest pervasive turnover of constrained sites across mammalian evolution. PLoS genetics Huber, C. D., Kim, B. Y., Lohmueller, K. E. 2020; 16 (5): e1008827

    Abstract

    Comparative genomic approaches have been used to identify sites where mutations are under purifying selection and of functional consequence by searching for sequences that are conserved across distantly related species. However, the performance of these approaches has not been rigorously evaluated under population genetic models. Further, short-lived functional elements may not leave a footprint of sequence conservation across many species. We use simulations to study how one measure of conservation, the Genomic Evolutionary Rate Profiling (GERP) score, relates to the strength of selection (Nes). We show that the GERP score is related to the strength of purifying selection. However, changes in selection coefficients or functional elements over time (i.e. functional turnover) can strongly affect the GERP distribution, leading to unexpected relationships between GERP and Nes. Further, we show that for functional elements that have a high turnover rate, adding more species to the analysis does not necessarily increase statistical power. Finally, we use the distribution of GERP scores across the human genome to compare models with and without turnover of sites where mutations under purifying selection. We show that mutations in 4.51% of the noncoding human genome are under purifying selection and that most of this sequence has likely experienced changes in selection coefficients throughout mammalian evolution. Our work reveals limitations to using comparative genomic approaches to identify deleterious mutations. Commonly used GERP score thresholds miss over half of the noncoding sites in the human genome where mutations are under purifying selection.

    View details for DOI 10.1371/journal.pgen.1008827

    View details for PubMedID 32469868