Genomic and genealogical investigation of the French Canadian founder population structure

Abstract

Characterizing the genetic structure of worldwide populations is important for understanding human history and is essential to the design and analysis of genetic epidemiological studies. In this study, we examined genetic structure and distant relatedness and their effect on the extent of linkage disequilibrium (LD) and homozygosity in the founder population of Quebec (Canada). In the French Canadian founder population, such analysis can be performed using both genomic and genealogical data. We investigated genetic differences, extent of LD, and homozygosity in 140 individuals from seven sub-populations of Quebec characterized by different demographic histories reflecting complex founder events. Genetic findings from genome-wide single nucleotide polymorphism data were correlated with genealogical information on each of these sub-populations. Our genomic data showed significant population structure and relatedness present in the contemporary Quebec population, also reflected in LD and homozygosity levels. Our extended genealogical data corroborated these findings and indicated that this structure is consistent with the settlement patterns involving several founder events. This provides an independent and complementary validation of genomic-based studies of population structure. Combined genomic and genealogical data in the Quebec founder population provide insights into the effects of the interplay of two important sources of bias in genetic epidemiological studies, unrecognized genetic structure and cryptic relatedness.

Extensive genome-wide autozygosity in the population isolates of Daghestan

“…[14][15][16]18,20,60,61,67,68 This effect appears to predominate particularly among the ND-speaking populations, with non-ND-speaking Daghestanian and non-Daghestanian Caucasus populations showing levels of autozygosity that are comparable to other Eurasian populations. In addition, a high level of genetic structure is observed among NDspeaking populations.…”

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.…”

“…Genealogical data can also provide useful information that cannot be obtained from genomic data, such as details about places and dates of marriage of all ancestors who were married in Quebec (from which migratory and settlement patterns can be measured), geographical origins and genetic contribution of immigrant ancestors (the founders who introduced all genes in the population) and precise levels of kinship and inbreeding by generation (which helps to differentiate close and distant kinship links). Hence, as Roy-Gagnon et al (2011) also noted, genealogical data is undoubtedly a good complement to genomic data for a better understanding of the genetic structure of a population.…”

“…d Bherer et al (2011);Letendre (2007). 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.…”

“…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.…”