Vendredi 27 mai 2016


Ingo Ruczinski (Johns Hopkins Bloomberg School of Public Health) 
Titre : Inferring rare disease risk variants based on exact probabilities of sharing among multiple affected relatives

Sequencing DNA in extended multiplex families can help to identify high penetrance disease variants too rare in the population to be detected through tests of association in population based studies, but co-segregate with disease in families. When only few affected subjects per family are sequenced, evidence that a rare variant may be causal can be quantified from the probability of sharing alleles by all affected relatives given it was seen in any one family member under the null hypothesis of complete absence of linkage and association. We present a general framework for calculating such sharing probabilities when two or more affected subjects per family are sequenced, and show how information from multiple families can be combined by calculating a p-value as the sum of the probabilities of sharing events as (or more) extreme. We present case studies from families with multiple members born with oral clefts, interrogating the sharing patterns of nucleotide and structural variants. We also present the implementation of a scalable global test for enrichment of shared rare deletions, and the ongoing implementation of the analysis pipeline as open source software.   




Alexandre Bureau (Université Laval)
Titre : Incorporating genealogies into rare variant sharing analysis

Rare variant sharing probabilities among relatives are based on the assumption that familial relationships are entirely known. These sharing probabilities will be underestimated in presence of substantial cryptic relatedness, leading to an overstatement of the evidence of a link with disease based on observed sharing. To prevent biased inference, I will first explain the approximate adjustment to the sharing probabilities based on empirical estimates of mean kinship among founders proposed by Bureau et al. (2014) Bioinformatics 30(15): 2189-2196, and present an assessment of its accuracy.  Next, I will give an overview of improvements of this approximation that the CANSSI Collaborative Research Team could pursue: computing founder-pair specific kinship coefficients from a known genealogy and/or from coalescent modeling genome-wide. In the last part of the talk, I will present the prospect that coalescent modeling of local haplotypes could lead to inference of IBD, even with incomplete genealogical information. In this way, cryptic relatedness could in fact be exploited to increase power of the rare variant sharing approach.




Laurent Briollais (Lenenfeld-Tanenbaum Research Institute)
Titre : A Bayesian Statistical Approach for Association Studies with Next Generation Sequencing Data

The discovery of rare variants is becoming a major challenge in genetic association studies and could help elucidating the genetic basis of common diseases. Because rare variants occur too infrequently in the general population, single-variant association tests lack power in NGS analyses. We developed several region-based test statistics using a Bayes Factor (BF) approach to assess the evidence of association between a set of rare variants located on same chromosomal region and a cancer outcome. Our simulation studies show the advantages of the Bayesian approach compared to two popular approaches: the burden test and the sequence kernel association test (SKAT). We also discuss various settings of the BF statistic, sensitivity to prior specification and generalization to family data. Finally, we illustrate the interest of the method through an application to a lung cancer study from Toronto.




Christina Nieuwoudt (Simon Fraser University) 
Titre : An investigation of the relationship between study design and ascertained data in the Lymphoid-Cancer Family Study

Motivated by the BC Cancer Foundation's Lymphoid-Cancer Family Study we will examine the relationship between the study design and ascertained familial data on phenotypic traits.  First we will discuss methods we have implemented for simulation studies.  Next we will illustrate several key observations from our simulation studies including patterns in affected family members and age of onset across generations. Time permitting, we will discuss the possibility of incorporating simulation of sequence data and the potential uses of such an extension to validate rare variant methods.