DOI: 10.5061/dryad.w6m905qk6
title: A historical-genetic reconstruction of human extra-pair paternity

description: 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.
description: * '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.