Fragmentation of the Qu bec population genetic pool (Canada)

Evidence from the genetic contribution of founders per region in the 17th and 18th centuries

Bibliographic Data

ID8315880
AuthorsAlain Gagnon (0000-0002-6758-9716, Université de Montréal, corresponding author), Evelyne Heyer (0000-0002-0266-3196, Musée de l'Homme)
Year2001
Volume114
Issue1
Pages30-41
Publication date2001-01-01
Peer ReviewedYes
Open AccessYes
TypeARTICLE
VenueAmerican Journal of Physical Anthropology (JOURNAL)
Journal identifiersISSN: 0002-9483 • E-ISSN: 1096-8644
PublisherWiley (PUBLISHER • GB)
DOI10.1002/1096-8644(200101)114:1<30::aid-ajpa1003>3.0.co;2-l
PMID11150050
OpenAlexW1974817964
LanguageEN
Citations received10
References cited10

The 6 million French-Canadians of Québec derive from a relatively small number of founders. Consequently, some hereditary diseases, which may or may not present a worldwide distribution, have been detected in high frequency in this population. Several studies, however, indicate a nonuniform distribution of these diseases through the population, suggesting that the French-Canadian founder effect has been geographically stratified. Here we explore this stratification by using a demographic database, the Population Register of Early Québec, that contains almost all birth, marriage, and death certificates (>712,000) recorded in parish registers between 1608-1800. In this database, every genealogical link has been traced back to the founders of the population, so that we can compute the genetic contribution of founder per region, and then account for the early events that have shaped the distribution of diseases. Ten regions, comprising varying numbers of parishes, have been selected. We first describe each region in terms of homogeneity and concentration of its gene pool. For this purpose, a new concept is introduced, the founders' uniform contribution number (FUN), i.e., the number of founders a population would have if all its founders had an equal contribution. Second, we estimate genetic similarity between regions on the basis of differential genetic contribution. To classify the regions, we use principal component and cluster analysis. Our results show a tripartite clustering of the population, and invite us to reconsider the results obtained from biomolecular and clinical studies, which show a bipartite clustering.

Allele · Biology · Cluster (spacecraft) · Cluster analysis · Distribution (mathematics) · Evolutionary biology · Founder effect · Gene · Genealogy · Genetic drift · Genetic variation · Genotype · Geography · Haplotype · Population · Population stratification · Sociology · Statistics · Computer Science · Demography · Forensic and Genetic Research · Genetics · History · Mathematics

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