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Modelling the influence of kinship systems on human genetic diversity
- Source :
- Probabilistic Modelling in Genomics, Probabilistic Modelling in Genomics, Robert Davies, Jerome Kelleher, Simon Myers, Daniel Wilson, Mar 2022, Oxford (UK), United Kingdom
- Publication Year :
- 2022
- Publisher :
- HAL CCSD, 2022.
-
Abstract
- International audience; Kinship rules such as descent rules – indicating the group (lineage, clan) to which an individual is affiliated – and post-marital residence rules – determining the place of living of a couple after marriage – vary widely between human populations. In western societies, individuals are generally affiliated to the groups of both their parents (bilateral descent) and choose where to settle after their marriage (neolocality). However, the majority of populations display unilineal descent rules, that is, individuals are affiliated to the group of their father (patrilineal) or to the group of their mother (matrilineal), and are either patrilocal – the wife migrates to her husband’s village – or matrilocal – the husband settles in his wife’s village. Interestingly, human populations are currently mostly patrilineal (~ 40 %) and patrilocal (~ 60 %), but little is known about the evolution of these kinship rules in human history. Hence, we wonder if this overrepresentation of patrilineality and patrilocality always existed and, if not, when they became dominant in human populations. By modelling populations displaying different kinship rules, we evaluate the influence of these cultural practices on genetic diversity and identify relevant diversity estimators that could be applied to ancient DNA, in order to trace back the history of human social organizations in space and time.
Details
- Language :
- English
- Database :
- OpenAIRE
- Journal :
- Probabilistic Modelling in Genomics, Probabilistic Modelling in Genomics, Robert Davies, Jerome Kelleher, Simon Myers, Daniel Wilson, Mar 2022, Oxford (UK), United Kingdom
- Accession number :
- edsair.od.......165..b1b5ce8d6fb84c03eb3a840985cb7674