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The advent of high-throughput DNA sequencing has opened the possibility of detecting rare genetic variants that may be involved in complex diseases. Family samples are better suited to establish involvement of rare variants in complex traits than samples of unrelated subjects because in a family, multiple affected members may carry the same rare variant from the basic principles of inheritance. A common theme to these various settings 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 variants located at nearby genomic regions, which is detectable through DNA-sequence familial and population patterns. Yet another is the dependence among multiple traits.

In this context, we have formed a team of statisticians, genetic epidemiologists and complex trait experts coming from various institutions (U. Laval, McGill U., UQÀM, U. of Ottawa, and Simon Fraser U.) 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. This workshop is an opportunity to meet the members of this team and exchange with them about their work.

NOTICE: Registration is free but mandatory.