Relative accuracy of three common methods of parentage analysis in natural populations

Hugo B. Harrison, Pablo Saenz Agudelo, Serge Planes, Geoffrey P. Jones, Michael L. Berumen

Research output: Contribution to journalArticlepeer-review

114 Scopus citations

Abstract

Parentage studies and family reconstructions have become increasingly popular for investigating a range of evolutionary, ecological and behavioural processes in natural populations. However, a number of different assignment methods have emerged in common use and the accuracy of each may differ in relation to the number of loci examined, allelic diversity, incomplete sampling of all candidate parents and the presence of genotyping errors. Here, we examine how these factors affect the accuracy of three popular parentage inference methods (colony, famoz and an exclusion-Bayes' theorem approach by Christie (Molecular Ecology Resources, 2010a, 10, 115) to resolve true parent-offspring pairs using simulated data. Our findings demonstrate that accuracy increases with the number and diversity of loci. These were clearly the most important factors in obtaining accurate assignments explaining 75-90% of variance in overall accuracy across 60 simulated scenarios. Furthermore, the proportion of candidate parents sampled had a small but significant impact on the susceptibility of each method to either false-positive or false-negative assignments. Within the range of values simulated, colony outperformed FaMoz, which outperformed the exclusion-Bayes' theorem method. However, with 20 or more highly polymorphic loci, all methods could be applied with confidence. Our results show that for parentage inference in natural populations, careful consideration of the number and quality of markers will increase the accuracy of assignments and mitigate the effects of incomplete sampling of parental populations. © 2012 Blackwell Publishing Ltd.
Original languageEnglish (US)
Pages (from-to)1158-1170
Number of pages13
JournalMolecular Ecology
Volume22
Issue number4
DOIs
StatePublished - Dec 27 2012

Bibliographical note

KAUST Repository Item: Exported on 2020-10-01
Acknowledgements: We would like to thank Jinliang Wang for kindly sharing the Fortran source code of COLONY2, Samar Aseeri and Dodi Heryadi from the KAUST Supercomputing Laboratory for compiling the program to enable parallel computation on Mac OSX and Wayne Mallett for assistance with the high-performance computing facility at James Cook University. We are grateful for comments from Glenn Almany and would also like to thank Peter Buston and two anonymous reviewers for insightful reviews. HBH was supported by a James Cook University Post-Graduate Research Scholarship in cotutelle with the Ecole Pratique des Hautes Etude, Universite de Perpignan. PSA was supported by a Post-Doctoral Research Fellowship from King-Abdullah University of Science and Technology.

ASJC Scopus subject areas

  • Ecology, Evolution, Behavior and Systematics
  • Genetics

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