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One of the greatest challenges in epidemiology is to understand what risk factors of a disease are truly causal. This is important, as causal associations are more likely to lead to clinical targets and improve patient care. One way to solve this is by using large-scale genetic studies (e.g. genome-wide association studies) and biostatistics methods like Mendelian randomization and genetic colocalization to perform causal inference. This approach has a strong track record of translating to successful treatments.
The Guillaume Butler-Laporte lab is interested in using and developing these methods to solve biomedical problems. While as a clinician I am an infectious diseases specialist, my scientific interest are broader, and I am interested in any research program focused on translating multi-omics and genetics to better patient care. Feel free to reach out!
The Guillaume Butler-Laporte lab uses a wide variety of omic technologies in large-scale biobanks to solve problems that lead to actionable clinical discoveries. To do this, we apply and develop genetic epidemiology methods that use genetic variants as anchors to perform causal inference. Ongoing research programs in the lab include:
Butler-Laporte G, Farjoun J, Nakanishi T, et al. Commun Biol. 2023;6(1):1113. Published 2023 Nov 3. doi:10.1038/s42003-023-05496-5
Butler-Laporte G, Auckland K, Noor Z, et al. Preprint. medRxiv. 2023;2023.06.26.23291885. Published 2023 Jun 29. doi:10.1101/2023.06.26.23291885
Butler-Laporte G, Povysil G, Kosmicki JA, et al. PLoS Genet. 2022;18(11):e1010367. Published 2022 Nov 3. doi:10.1371/journal.pgen.1010367
Huffman JE, Butler-Laporte G, Khan A, et al. Nat Genet. 2022;54(2):125-127. doi:10.1038/s41588-021-00996-8
COVID-19 Host Genetics Initiative. Nature. 2021 Dec;600(7889):472-477. doi: 10.1038/s41586-021-03767-x. Epub 2021 Jul 8. PMID: 34237774; PMCID: PMC8674144.
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