Genome-wide patterns of identity-by-descent sharing in the French Canadian founder population

Abstract

In genetics the ability to accurately describe the familial relationships among a group of individuals can be very useful. Recent statistical tools succeeded in assessing the degree of relatedness up to 6–7 generations with good power using dense genome-wide single-nucleotide polymorphism data to estimate the extent of identity-by-descent (IBD) sharing. It is therefore important to describe genome-wide patterns of IBD sharing for more remote and complex relatedness between individuals, such as that observed in a founder population like Quebec, Canada. Taking advantage of the extended genealogical records of the French Canadian founder population, we first compared different tools to identify regions of IBD in order to best describe genome-wide IBD sharing and its correlation with genealogical characteristics. Results showed that the extent of IBD sharing identified with FastIBD correlates best with relatedness measured using genealogical data. Total length of IBD sharing explained 85% of the genealogical kinship's variance. In addition, we observed significantly higher sharing in pairs of individuals with at least one inbred ancestor compared with those without any. Furthermore, patterns of IBD sharing and average sharing were different across regional populations, consistent with the settlement history of Quebec. Our results suggest that, as expected, the complex relatedness present in founder populations is reflected in patterns of IBD sharing. Using these patterns, it is thus possible to gain insight on the types of distant relationships in a sample from a founder population like Quebec.

Whole-genome sequencing in French Canadians from Quebec

“…1). This rapid expansion and migration gave rise to regional populations with genetic features that can be explained by founder effects (Bherer et al 2011;Gauvin et al 2014;Moreau et al 2011a;Roy-Gagnon et al 2011). Geneticists have taken advantage of this genetic homogeneity in the French-Canadian founder population of Quebec to map genes involved in rare and common human diseases (Chami et al 2014;Chetaille et al 2014;Laprise 2014;Scriver 2001).…”

Deep genealogical analysis of a large cohort of participants in the CARTaGENE project (Quebec, Canada)

“…It is worth nothing that results obtained by Roy-Gagnon et al (2011) and Gauvin et al (2014Gauvin et al ( , 2015, which included genomic material along with genealogical data, showed a good agreement between the two types of data. This concordance could be relevant for researchers who plan to use data from the CARTaGENE project, since the present genealogies were constructed with the same resources available at BALSAC.…”

“…e Roy-Gagnon et al (2011). The genealogical sample used in the Roy-Gagnon study was also used by Gauvin et al (2014Gauvin et al ( , 2015. f Not available.…”

“…Approximately 25% of participants agreed to take part in this genealogical survey, adding a unique dimension to the CARTaGENE project. Indeed, genealogies have proven to be a useful tool to better understand the genetic structure of a population (Abney, 2008;Bherer et al, 2011;Colonna et al, 2009;Gauvin et al, 2014;Larmuseau et al, 2013;Newman et al, 2001;Rojas et al, 2013;Roy-Gagnon et al, 2011;Zaitlen et al, 2013). Considering that the CARTaGENE participants came from four distinct geographical regions of the province of Quebec, a detailed analysis of their genealogical characteristics may bring new insights about the specificities of each regional population.…”

Rapidly Registering Identity-by-Descent Across Ancestral Recombination Graphs

“…6 for detailed explanation of the models and the parameters). The latter model is expressive enough to be consistent with some populations that went through a sharp decline in population size in recent history Gusev et al, 2012;Carmi et al, 2014a;Lim et al, 2014;Gauvin et al, 2014). Figure 7 plots the statistic we use for inference, average total IBD-sharing across the cohort, for several constant population sizes across different cohort sizes and cutoffs m for IBD length.…”