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Efficient inference of paternity and sibship inference given known maternity via hierarchical clustering
Author(s) -
Ellis Thomas James,
Field David Luke,
Barton Nicholas H.
Publication year - 2018
Publication title -
molecular ecology resources
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.96
H-Index - 136
eISSN - 1755-0998
pISSN - 1755-098X
DOI - 10.1111/1755-0998.12782
Subject(s) - biology , antirrhinum majus , pedigree chart , cluster analysis , inference , mating , evolutionary biology , statistics , computer science , genetics , artificial intelligence , mathematics , gene
Pedigree and sibship reconstruction are important methods in quantifying relationships and fitness of individuals in natural populations. Current methods employ a Markov chain‐based algorithm to explore plausible possible pedigrees iteratively. This provides accurate results, but is time‐consuming. Here, we develop a method to infer sibship and paternity relationships from half‐sibling arrays of known maternity using hierarchical clustering. Given 50 or more unlinked SNP markers and empirically derived error rates, the method performs as well as the widely used package Colony , but is faster by two orders of magnitude. Using simulations, we show that the method performs well across contrasting mating scenarios, even when samples are large. We then apply the method to open‐pollinated arrays of the snapdragon Antirrhinum majus and find evidence for a high degree of multiple mating. Although we focus on diploid SNP data, the method does not depend on marker type and as such has broad applications in nonmodel systems.