苹果淫院

Updated: Sun, 10/06/2024 - 10:30

From Saturday, Oct. 5 through Monday, Oct. 7, the Downtown and Macdonald Campuses will be open only to 苹果淫院 students, employees and essential visitors. Many classes will be held online. Remote work required where possible. See Campus Public Safety website for details.


Du samedi 5 octobre au lundi 7 octobre, le campus du centre-ville et le campus Macdonald ne seront accessibles qu鈥檃ux 茅tudiants et aux membres du personnel de l鈥橴niversit茅 苹果淫院, ainsi qu鈥檃ux visiteurs essentiels. De nombreux cours auront lieu en ligne. Le personnel devra travailler 脿 distance, si possible. Voir le site Web de la Direction de la protection et de la pr茅vention pour plus de d茅tails.

Event

Seminar Series in Quantitative Life Sciences and Medicine

Tuesday, February 12, 2019 12:00to13:00
Montreal Neurological Institute deGrandpre Communications Centre, 3801 rue University, Montreal, QC, H3A 2B4, CA

Mapping the structure of genetic risk for common disease in the UK Biobank

Gil McVean (Oxford University)
Tuesday February 12, 12-1pm
Montreal Neurological Institute, deGrandpre Communications Centre

Abstract:聽Genetic risk factors frequently affect multiple common human diseases, providing insight into shared pathophysiological pathways and opportunities for therapeutic development. However, systematic identification of genetic profiles of disease risk is limited by the availability of both comprehensive clinical data on population-scale cohorts and the lack of suitable statistical methodology that can handle the scale of and differential power inherent in multi-phenotype data. We have developed a disease-agnostic approach to cluster genetic risk profiles for 3,025 genome-wide independent loci across 19,155 ICD-10 diagnostic codes from 320,644 participants in the UK Biobank, representing a large and heterogeneous population. We identify several hundred distinct disease association profiles and use multiple approaches to link clusters to underlying biological pathways. We show how clusters can decompose the variance and covariance in risk for disease, thereby identifying underlying biological processes and their impact. We demonstrate the use of clusters in defining disease relationships and informing therapeutic strategies.

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