Simon Gravel

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Simon Gravel

Assistant Professor, Department of Human Genetics

Email: simon.gravel@mcgill.ca
Phone: (514) 398-0753

740 Dr Penfield Ave, Room 7206
Montréal, Québec, Canada, H3A 0G1


Simon Gravel obtained his BSc and MSc in Mathematics and Physics from the Université de Montréal and his PhD in Physics from Cornell University in 2009. His research in Genetics began during a short postdoc in the Physics department at the Universität zu Köln and the Kavli Institute for Theoretical Physics in Santa Barbara, and continued in the Genetics department at Stanford University. He joined the Department of Human Genetics at McGill and the Genome Quebec Innovation Centre in 2013.

Dr Gravel’s is interested in learning about biology and evolution through creative analysis of high-throughput biological data. His group develops mathematical and statistical methods that take advantage of diverse data sources to refine our understanding of fundamental parameters of human history and biology. His recent research has focused on how the history of diverse human populations affected patterns of genetic diversity and disease. His group made contributions about the origins of modern humans, the successive waves of migrations that led to the formations of contemporary populations in the Americas, as well as the identification of genetic predispositions for disease.

This research is largely data-driven, and it combines modeling at multiple levels: we first wish to understand the fundamental biology underpinning evolution, such as the processes of mutation, recombination, and selection. To understand human genomes, we also need to understand how recent and ancient human history affected patterns of genetic diversity: ancient population expansions, recent migrations, and marriage patterns all impact genomic diversity, and in many cases we can reconstruct these events through careful modelling. Finally, we need to understand the behavior of cutting edge technology involved in the latest datasets. Dr Gravel’s group has projects focusing on anthropology and history, technology development, biology, and medicine, and is always happy to explore new opportunities involving new technologies and creative mathematical modeling.

Recent Publications

  • Zabad, S, Ragsdale, AP, Sun, R, Li, Y, Gravel, S. Assumptions about frequency-dependent architectures of complex traits bias measures of functional enrichment. Genet Epidemiol. 2021;45 (6):621-632. doi: 10.1002/gepi.22388. PubMed PMID:34157784 .
  • Spear, ML, Diaz-Papkovich, A, Ziv, E, Yracheta, JM, Gravel, S, Torgerson, DG et al.. Recent shifts in the genomic ancestry of Mexican Americans may alter the genetic architecture of biomedical traits. Elife. 2020;9 :. doi: 10.7554/eLife.56029. PubMed PMID:33372659 PubMed Central PMC7771964.
  • Diaz-Papkovich, A, Anderson-Trocmé, L, Gravel, S. A review of UMAP in population genetics. J Hum Genet. 2021;66 (1):85-91. doi: 10.1038/s10038-020-00851-4. PubMed PMID:33057159 PubMed Central PMC7728596.
  • Martin, AR, Gignoux, CR, Walters, RK, Wojcik, GL, Neale, BM, Gravel, S et al.. Human Demographic History Impacts Genetic Risk Prediction across Diverse Populations. Am J Hum Genet. 2020;107 (4):788-789. doi: 10.1016/j.ajhg.2020.08.020. PubMed PMID:33007199 PubMed Central PMC7536609.
  • Ragsdale, AP, Nelson, D, Gravel, S, Kelleher, J. Lessons Learned from Bugs in Models of Human History. Am J Hum Genet. 2020;107 (4):583-588. doi: 10.1016/j.ajhg.2020.08.017. PubMed PMID:33007197 PubMed Central PMC7536610.
  • Ben-Eghan, C, Sun, R, Hleap, JS, Diaz-Papkovich, A, Munter, HM, Grant, AV et al.. Don't ignore genetic data from minority populations. Nature. 2020;585 (7824):184-186. doi: 10.1038/d41586-020-02547-3. PubMed PMID:32901124 .
  • Adrion, JR, Cole, CB, Dukler, N, Galloway, JG, Gladstein, AL, Gower, G et al.. A community-maintained standard library of population genetic models. Elife. 2020;9 :. doi: 10.7554/eLife.54967. PubMed PMID:32573438 PubMed Central PMC7438115.
  • Nelson, D, Kelleher, J, Ragsdale, AP, Moreau, C, McVean, G, Gravel, S et al.. Accounting for long-range correlations in genome-wide simulations of large cohorts. PLoS Genet. 2020;16 (5):e1008619. doi: 10.1371/journal.pgen.1008619. PubMed PMID:32369493 PubMed Central PMC7266353.
  • Ragsdale, AP, Gravel, S. Unbiased Estimation of Linkage Disequilibrium from Unphased Data. Mol Biol Evol. 2020;37 (3):923-932. doi: 10.1093/molbev/msz265. PubMed PMID:31697386 PubMed Central PMC7038669.
  • Diaz-Papkovich, A, Anderson-Trocmé, L, Ben-Eghan, C, Gravel, S. UMAP reveals cryptic population structure and phenotype heterogeneity in large genomic cohorts. PLoS Genet. 2019;15 (11):e1008432. doi: 10.1371/journal.pgen.1008432. PubMed PMID:31675358 PubMed Central PMC6853336.
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