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A historical-genetic reconstruction of human extra-pair paternity

Citation

Larmuseau, Maarten H.D. et al. (2019), A historical-genetic reconstruction of human extra-pair paternity, Dryad, Dataset, https://doi.org/10.5061/dryad.w6m905qk6

Abstract

Paternity testing using genetic markers has shown that extra-pair paternity (EPP) is common in many pair-bonded species. Evolutionary theory and empirical data show that extra-pair copulations can increase the fitness of males as well as females. This can carry a significant fitness cost for the social father, who then invests in rearing offspring that biologically are not his own. In human populations, the incidence and correlates of extra-pair paternity remain highly contentious. Here, we use a population-level genetic genealogy approach to reconstruct spatiotemporal patterns in human EPP rates. Using patrilineal genealogies from the Low Countries spanning a period of over 500 years and Y-chromosomal genotyping of living descendants, our analysis reveals that historical EPP rates, while low overall, were strongly impacted by socioeconomic and demographic factors. Specifically, we observe that estimated EPP rates among married couples varied by more than an order of magnitude, from 0.4% to 5.9%, and peaked among families with a low socioeconomic background living in densely populated cities of the late 19th century. Our results support theoretical predictions that social context can strongly affect the outcomes of sexual conflict in human populations by modulating the incentives and opportunities for engaging in extra-pair relationships. These findings show how contemporary genetic data combined with in-depth genealogies open up a new window on the sexual behaviour of our ancestors.

Usage Notes

* 'Rscript_analysis_EPP_rate' = Script used for the estimation of historical extra-pair paternity rates in a Western human population in function of different socio-demographic factors.

 

* 'data_analysis_EPP_rate' = 

Full dataset for the estimation of historical extra-pair paternity rates in a Western human population with following variables:

‘PAIR’: genealogical pair of presumed patrilineally related men that were Y-chromosome genotyped

‘PSEUDOPAIR’: genealogical pseudopair; genealogical pairs were split up in pseudopairs if additional genetic evidence was available that allowed us to infer that EPP events were absent in given parts of the genealogy

‘SURNAME’: surname, anonymyzed to conform to privacy regulations

‘MISMATCH’: if there was a Y chromosome mismatch within a given pseudopair or not

‘NPERPAIR’: total number of meioses that separate a given genealogical pair

‘NPERPSEUDOPAIR’: total number of meioses that separate a given genealogical pseudopair

‘EPP’: the observed probability for an EPP event to have happened, encoded as a fractionate Bernouilli event (=MISMATCH/NPERPSEUDOPAIR)

‘Maxn’: theoretical maximum number of EPP events that could have occurred within a given pseudopair

‘YEAR’: year of birth, rounded off to nearest decade to conform to privacy regulations

‘SOCIAL_CLASS’: socioeconomic class based on occupation of father (Farmers, Low income, Medium or high income or missing)

‘DENSITY’: density (inhabitants/km^2) in the cite of birth at birth

‘URBAN_RURAL’: RURAL or URBAN, i.e. whether the city of birth could be considered to lie in an urban region or not; is in the Infra Clio database, cities that had more than 5,000 inhabitants in 1850 were considered urban

‘COUNTRY’: country of birth ("Belgie", "Nederland" or "outside_studyarea")

‘PROVINCE’: province where city of birth was located ("Antwerpen", "Brabant", "Gelderland", "Limburg", "Nederlands Limburg", "Noord-Brabant", "Noord-Holland", "Oost-Vlaanderen", "Utrecht", "West-Vlaanderen", "Zeeland", "Zuid-Holland", "outside_studyarea")

‘SAMPLING_CAMPAIGN’: which of the two sampling campaigns the data were derived from.

Important note: ‘YEAR’ was rounded to nearest decade, ‘SURNAME’ was anonymised and ‘AGE_FATHER & AGE_MOTHER & associated models were removed to conform to privacy regulations. Due to the rounding off of year of birth the output of the analyses below may differ very slightly from the ones in the paper; the analysis outputs given below as comments were obtained using the original data that was not rounded off.

 

*'data_densities_cities' =

Historical density estimates for the larger urbanised cities for each year between 1750 and 1950 individually with following variables:

‘YEAR’: year (between 1750-1950)

‘COUNTRY’: country ("Belgie" or "Nederland")

‘PROVINCE’: province where city was located ("Antwerpen", "Brabant", "Gelderland", "Limburg", "Nederlands Limburg", "Noord-Brabant", "Noord-Holland", "Oost-Vlaanderen", "Utrecht", "West-Vlaanderen", "Zeeland", "Zuid-Holland")

‘CITY’: city within the Low-Countries

‘LONGITUDE’: longitude of the city

‘LATITUDE’: latitude of the city

‘DENSITY’: historical estimates of the population density for the urbanised city

‘NRINHABITANTS’: historical estimates of the number of inhabitants in the urbanised city

 

 

*'data_densities_provinces' =

Historical density estimates for the rural area in the provinces between 1750 and 1950 individually with following variables:

‘YEAR’: year (between 1750-1950)

‘PROVINCE’: defined province including "Antwerpen", "Brabant", "Gelderland", "Limburg", "Nederlands Limburg", "Noord-Brabant", "Noord-Holland", "Oost-Vlaanderen", "Utrecht", "West-Vlaanderen", "Zeeland" and "Zuid-Holland".

‘DENSITY_RURAL_PROV’: historical estimates for the rural area in the provinces.

‘DENSITY_PROV’: historical estimates for the provinces.

Funding

KU Leuven, Award: BOF-C1 C12/15/013

Fund for Scientific Research – Flanders, Award: 1503216N

Agencia Estatal de Investigación and Fondo Europeo de Desarollo Regional, Award: CGL2016-75389-P

Agència de Gestió d’Ajuts Universitaris i de la Recerca, Award: 2017 SGR00702

Unidad de Excelencia María de Maeztu, Award: MDM-2014-0370