European Journal of Human GeneticsGeneticsPeer-reviewed journal article
The Genetic History of France
Aude Saint PierreiD, Joanna GiemzaiD, Isabel AlvesiD, Matilde KarakachoffiD, Marinna GaudiniD, Philippe AmouyeliD, Jean‐François DartiguesiD, Christophe TzourioiD, Martial MonteiliD, P. GalaniD, Serge HerçbergiD, Iain MathiesoniD, Richard RedoniD, Emmanuelle GéniniD, Christian Dina CiD
Abstract
The study of the genetic structure of different countries within Europe has provided significant insights into their demographic history and their actual stratification. Although France occupies a particular location at the end of the European peninsula and at the crossroads of migration routes, few population genetic studies have been conducted so far with genome-wide data. In this study, we analyzed SNP-chip genetic data from 2 184 individuals born in France who were enrolled in two independent population cohorts. Using FineStructure, six different genetic clusters of individuals were found that were very consistent between the two cohorts. These clusters match extremely well the geography and overlap with historical and linguistic divisions of France. By modeling the relationship between genetics and geography using EEMS software, we were able to detect gene flow barriers that are similar in the two cohorts and corresponds to major French rivers or mountains. Estimations of effective population sizes using IBDNe program also revealed very similar patterns in both cohorts with a rapid increase of effective population sizes over the last 150 generations similar to what was observed in other European countries. A marked bottleneck is also consistently seen in the two datasets starting in the fourteenth century when the Black Death raged in Europe. In conclusion, by performing the first exhaustive study of the genetic structure of France, we fill a gap in the genetic studies in Europe that would be useful to medical geneticists but also historians and archeologists.
- organism: human
- assay: whole-genome sequencing
- research focus: clustering
- research focus: gene regulatory network inference
- approach: statistical model
- Citations
- 27
- FWCI
- 2.20
- Field percentile
- top 13%
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