Collaborative Research Team Project: 2016-2019
The advent of high-throughput DNA sequencing has opened the possibility of detecting rare genetic mutations that may be involved in complex diseases. Family samples are better suited to establish involvement of rare mutations in complex traits than samples of unrelated subjects because in a family, multiple affected members may carry the same rare mutation, from the basic principles of inheritance from parents to children. A common theme to the various settings considered in the proposed research is the need to account for various forms of dependence structures in familial DNA sequence data. One source of dependence is the relationships among family members, either known or unknown to the investigators. Another is the association among mutations located at nearby genomic regions, which is detectable through DNA-sequence familial and population patterns. Yet another is the dependence among multiple traits. This project brings together statisticians, genetic epidemiologists and complex trait experts to better integrate and model the various forms of dependence in more general and adapted statistical inference approaches than the few statistical methods currently applicable to these data, with gains in power and validity.
The team leaders are Alexandre Bureau from Université Laval and Karim Oualkacha from Université du Québec à Montréal. Their collaborators from the fields of statistics and genetic epidemiology are Marie-Hélène Roy-Gagnon and Kelly Burkett from University of Ottawa, Fabrice Larribe from Université du Québec à Montréal, Aurélie Labbe from HEC Montréal, Jinko Graham from Simon Fraser University, Celia Greenwood from McGill University, M’Hamed Lajmi Lakhal Chaieb from Université Laval, Ingo Ruczinski from the Johns Hopkins Bloomberg School of Public Health in Baltimore, USA and Eleftheria Zeggini from the Wellcome Trust Sanger Institute in Cambridge, UK. In addition, five Canadian experts contribute data and insights on the genetics of complex traits.