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Title: The Impact of Genomic and Traditional Selection on the Contribution of Mutational Variance to Long-Term Selection Response and Genetic Variance
Contributor(s): Mulder, Herman A (author); Lee, Sang Hong  (author); Clark, Sam  (author)orcid ; Hayes, Ben J (author); van der Werf, Julius H J  (author)orcid 
Publication Date: 2019-10
DOI: 10.1534/genetics.119.302336
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Abstract: De novo mutations (DNM) create new genetic variance and are an important driver for long-term selection response. We hypothesized that genomic selection exploits mutational variance less than traditional selection methods such as mass selection or selection on pedigree-based breeding values, because DNM in selection candidates are not captured when the selection candidates’ own phenotype is not used in genomic selection, DNM are not on SNP chips and DNM are not in linkage disequilibrium with the SNP on the chip. We tested this hypothesis with Monte Carlo simulation. From whole-genome sequence data, a subset of ∼300,000 variants was used that served as putative markers, quantitative trait loci or DNM. We simulated 20 generations with truncation selection based on breeding values from genomic best linear unbiased prediction without (GBLUP_no_OP) or with own phenotype (GBLUP_OP), pedigree-based BLUP without (BLUP_no_OP) or with own phenotype (BLUP_OP), or directly on phenotype. GBLUP_OP was the best strategy in exploiting mutational variance, while GBLUP_no_OP and BLUP_no_OP were the worst in exploiting mutational variance. The crucial element is that GBLUP_no_OP and BLUP_no_OP puts no selection pressure on DNM in selection candidates. Genetic variance decreased faster with GBLUP_no_OP and GBLUP_OP than with BLUP_no_OP, BLUP_OP or mass selection. The distribution of mutational effects, mutational variance, number of DNM per individual and nonadditivity had a large impact on mutational selection response and mutational genetic variance, but not on ranking of selection strategies. We advocate that more sustainable genomic selection strategies are required to optimize long-term selection response and to maintain genetic diversity.
Publication Type: Journal Article
Source of Publication: Genetics, 213(2), p. 361-378
Publisher: Genetics Society of America
Place of Publication: United States of America
ISSN: 1943-2631
Fields of Research (FoR) 2008: 070201 Animal Breeding
060412 Quantitative Genetics (incl. Disease and Trait Mapping Genetics)
060408 Genomics
Fields of Research (FoR) 2020: 300305 Animal reproduction and breeding
310506 Gene mapping
310509 Genomics
Socio-Economic Objective (SEO) 2008: 830399 Livestock Raising not elsewhere classified
830302 Dairy Cattle
Socio-Economic Objective (SEO) 2020: 100402 Dairy cattle
Peer Reviewed: Yes
HERDC Category Description: C1 Refereed Article in a Scholarly Journal
Description: Supplemental material available at FigShare:
Appears in Collections:Journal Article
School of Environmental and Rural Science

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