Staff Publications

Staff Publications

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    'Staff publications' is the digital repository of Wageningen University & Research

    'Staff publications' contains references to publications authored by Wageningen University staff from 1976 onward.

    Publications authored by the staff of the Research Institutes are available from 1995 onwards.

    Full text documents are added when available. The database is updated daily and currently holds about 240,000 items, of which 72,000 in open access.

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Record number 531933
Title Which individuals to choose to update the reference population? Minimizing the loss of genetic diversity in animal genomic selection programs
Author(s) Eynard, Sonia E.; Croiseau, Pascal; Laloë, Denis; Fritz, Sebastien; Calus, Mario P.L.; Restoux, Gwendal
Source G3 : Genes Genomes Genetics 8 (2018)1. - ISSN 2160-1836 - p. 113 - 121.
DOI https://doi.org/10.1534/g3.117.1117
Department(s) Animal Breeding and Genetics
WIAS
Publication type Refereed Article in a scientific journal
Publication year 2018
Keyword(s) Genetic diversity - Genomic selection - GenPred - Optimal contribution - Reference population - Shared data resources
Abstract Genomic selection (GS) is commonly used in livestock and increasingly in plant breeding. Relying on phenotypes and genotypes of a reference population, GS allows performance prediction for young individuals having only genotypes. This is expected to achieve fast high genetic gain but with a potential loss of genetic diversity. Existing methods to conserve genetic diversity depend mostly on the choice of the breeding individuals. In this study, we propose a modification of the reference population composition to mitigate diversity loss. Since the high cost of phenotyping is the limiting factor for GS, our findings are of major economic interest. This study aims to answer the following questions: how would decisions on the reference population affect the breeding population, and how to best select individuals to update the reference population and balance maximizing genetic gain and minimizing loss of genetic diversity? We investigated three updating strategies for the reference population: random, truncation, and optimal contribution (OC) strategies. OC maximizes genetic merit for a fixed loss of genetic diversity. A French Montbéliarde dairy cattle population with 50K SNP chip genotypes and simulations over 10 generations were used to compare these different strategies using milk production as the trait of interest. Candidates were selected to update the reference population. Prediction bias and both genetic merit and diversity were measured. Changes in the reference population composition slightly affected the breeding population. Optimal contribution strategy appeared to be an acceptable compromise to maintain both genetic gain and diversity in the reference and the breeding populations.
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