title: Towards unbiased parentage assignment: combining genetic, behavioural and spatial data in a Bayesian framework.
abstract: Inferring the parentage of a sample of individuals is often a prerequisite for many types of analysis in molecular ecology, evolutionary biology and quantitative genetics. In all but a few cases, the method of parentage assignment is divorced from the methods used to estimate the parameters of primary interest, such as mate choice or heritability. Here we present a Bayesian approach that simultaneously estimates the parentage of a sample of individuals and a wide range of population-level parameters in which we are interested. We show that joint estimation of parentage and population-level parameters increases the power of parentage assignment, reduces bias in parameter estimation, and accurately evaluates uncertainty in both. We illustrate the method by analysing a number of simulated test data sets, and through a re-analysis of parentage in the Seychelles warbler, Acrocephalus sechellensis. A combination of behavioural, spatial and genetic data are used in the analyses and, importantly, the method does not require strong prior information about the relationship between nongenetic data and parentage.
authors.affiliation: Department of Animal and Plant Sciences, University of Sheffield, Western Bank, Sheffield S10 2TN, UK. j.hadfield@shef.ac.uk
authors.forename: J D
authors.initials: JD
authors.lastname: Hadfield
authors.forename: D S
authors.initials: DS
authors.lastname: Richardson
authors.forename: T
authors.initials: T
authors.lastname: Burke
mesh_terms: D000818:Animals; D001499:Bayes Theorem; D003198:Computer Simulation; D005828:Genetics, Population; D010375:Pedigree; D012726:Sexual Behavior, Animal; D020308:Songbirds / Q000235:genetics* / Q000502:physiology