4.3 Article

Implications of methodologies for integrating empirical kinships into ex situ population management using PMx: A case study of Baer's Pochard (Aythya baeri) in North America

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JOURNAL OF HEREDITY
卷 -, 期 -, 页码 -

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OXFORD UNIV PRESS INC
DOI: 10.1093/jhered/esad069

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ddRAD; ex situ breeding; molecular kinship; pedigree; relatedness; SNP

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The integration of empirical kinships into the genetic management of ex situ populations can impact genetic summary statistics and highlights the importance of understanding the scale of molecular kinships. This study focuses on Baer's pochard and provides guidance on incorporating empirical kinships into population management.
The application of molecular tools to population management can improve the long-term genetic viability of ex situ populations. In this study, we aimed to understand the implications of integrating empirical kinships into the genetic management of an ex situ population of the endangered waterfowl, Baer's pochard (Aythya baeri), in North America. Single nucleotide polymorphism data were generated for 141 Baer's pochard using double digest restriction site-associated DNA sequencing and empirical kinships were derived and integrated into the population management software PMx. Analyses suggested 37.7% of pairwise relationships previously assumed to be unrelated were first, second, or third-order relatives. We determined that most genetic summary statistics were impacted through the calculation of the population's mean kinship, which increased from MK over bar =0.0772 to MK over bar =0.2074 after empirical kinships were integrated into our analyses. Our results also revealed the importance of understanding how molecular kinships derived from a particular estimator are scaled, if the scale differs significantly from pedigree-based kinships. We describe the theory behind the genetic metrics impacted and provide general guidance on incorporating empirical kinships into ex situ population management as well as provide suggestions for sampling strategies to minimize the biases inherent in merging two types of kinship estimators.

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