Genomic selection strategies to improve maternal traits in Norwegian White Sheep

Lillehammer, Marie; Sonesson, Anna K.; Klemetsdal, G.; Blichfeldt, Thor; Meuwissen, T.H.E.

doi:10.1111/jbg.12475

Cited by 10 publications

(5 citation statements)

References 28 publications

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“…Extra genetic gain from genomic selection compared to pedigree-based selection resulted from an increase in prediction accuracy for non-phenotyped queens and a larger number of candidates. Similar results were found for maternal traits in sheep [46], pigs [47], and Atlantic salmon [48]. However, genomic selection is often most efficient when young animals are selected based on genomic EBV and the generation interval is shortened [49].…”

Section: Optimal Breeding Schemesupporting

confidence: 65%

“…Furthermore, the intensity of selection should be carefully considered. Shortening the generation interval also requires changes to the structure of a breeding program, which can subtantially increase the rate of inbreeding per generation [46,51,52]. We suggested GPS as an additional selection step, which is unlikely to alter the rate of inbreeding significantly.…”

Section: Optimal Breeding Schemementioning

confidence: 99%

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Simulation studies to optimize genomic selection in honey bees

Bernstein

Hoppe

et al. 2021

Genet Sel Evol

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Background With the completion of a single nucleotide polymorphism (SNP) chip for honey bees, the technical basis of genomic selection is laid. However, for its application in practice, methods to estimate genomic breeding values need to be adapted to the specificities of the genetics and breeding infrastructure of this species. Drone-producing queens (DPQ) are used for mating control, and usually, they head non-phenotyped colonies that will be placed on mating stations. Breeding queens (BQ) head colonies that are intended to be phenotyped and used to produce new queens. Our aim was to evaluate different breeding program designs for the initiation of genomic selection in honey bees. Methods Stochastic simulations were conducted to evaluate the quality of the estimated breeding values. We developed a variation of the genomic relationship matrix to include genotypes of DPQ and tested different sizes of the reference population. The results were used to estimate genetic gain in the initial selection cycle of a genomic breeding program. This program was run over six years, and different numbers of genotyped queens per year were considered. Resources could be allocated to increase the reference population, or to perform genomic preselection of BQ and/or DPQ. Results Including the genotypes of 5000 phenotyped BQ increased the accuracy of predictions of breeding values by up to 173%, depending on the size of the reference population and the trait considered. To initiate a breeding program, genotyping a minimum number of 1000 queens per year is required. In this case, genetic gain was highest when genomic preselection of DPQ was coupled with the genotyping of 10–20% of the phenotyped BQ. For maximum genetic gain per used genotype, more than 2500 genotyped queens per year and preselection of all BQ and DPQ are required. Conclusions This study shows that the first priority in a breeding program is to genotype phenotyped BQ to obtain a sufficiently large reference population, which allows successful genomic preselection of queens. To maximize genetic gain, DPQ should be preselected, and their genotypes included in the genomic relationship matrix. We suggest, that the developed methods for genomic prediction are suitable for implementation in genomic honey bee breeding programs.

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Section: Optimal Breeding Schemesupporting

confidence: 65%

Section: Optimal Breeding Schemementioning

confidence: 99%

Simulation studies to optimize genomic selection in honey bees

Bernstein

Hoppe

et al. 2021

Genet Sel Evol

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“…These records could also be utilized as phenotypic information for sires in breeding programs. Further refinements should be evaluated in future studies to fully address all mechanisms of a breeding program, e.g., evaluating the proportion of lambs in progeny testing schemes, similar to Grill et al [26], as well as differences caused by selecting animals based on conventional EBVs and different selection strategies based on genomic EBVs, similar to Lillehammer et al [31].…”

Section: Discussionmentioning

confidence: 99%

Optimization Strategies to Adapt Sheep Breeding Programs to Pasture-Based Production Environments: A Simulation Study

Martin,

Pook,

Bennewitz

et al. 2023

Animals

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Strong differences between the selection (indoor fattening) and production environment (pasture fattening) are expected to reduce genetic gain due to possible genotype-by-environment interactions (G × E). To investigate how to adapt a sheep breeding program to a pasture-based production environment, different scenarios were simulated for the German Merino sheep population using the R package Modular Breeding Program Simulator (MoBPS). All relevant selection steps and a multivariate pedigree-based BLUP breeding value estimation were included. The reference scenario included progeny testing at stations to evaluate the fattening performance and carcass traits. It was compared to alternative scenarios varying in the progeny testing scheme for fattening traits (station and/or field). The total merit index (TMI) set pasture-based lamb fattening as a breeding goal, i.e., field fattening traits were weighted. Regarding the TMI, the scenario with progeny testing both in the field and on station led to a significant increase in genetic gain compared with the reference scenario. Regarding fattening traits, genetic gain was significantly increased in the alternative scenarios in which field progeny testing was performed. In the presence of G × E, the study showed that the selection environment should match the production environment (pasture) to avoid losses in genetic gain. As most breeding goals also contain traits not recordable in field testing, the combination of both field and station testing is required to maximize genetic gain.

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“…In reindeer, due to intermediate accuracy and delays over several generations in the evaluation of maternal genetic effects, an early selection for their improvement would benefit from the use of genomic information in parent diagnostics and ultimately in genomic selection. Lillehammer et al (2020) used simulation in analyzing genomic selection for maternal traits in sheep with extant large scale recording scheme. In reindeer for these purposes, we need to develop a cheap SNP panel based possibly on the recent genome sequence information (Weldenegodguad et al 2020).…”

Section: Implications For Selection Schemesmentioning

confidence: 99%

The efficiency of multi-generation selection on maternal traits, with implications for reindeer

Pietarinen

Mäki‐Tanila

2020

AFSci

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Maternally affected traits, such as juvenile growth and survival, provide resilience in mammal species, in particular for reindeer living in extreme northern habitat. The genetic variation in such traits is caused by direct and maternal genetic effects (DGE and MGE, respectively). We used Willham’s variance-component approach and extended a family index with the focal individual and its full- and half-sibs to an approximated BLUP (pseudo-BLUP) by including the parents’ estimated breeding values. Most of the deviations of the predicted responses from the simulated ones were 4.1% for DGE and 5.3% for MGE. The benefits of index and BLUP selection are high in the case of negative correlation, large full-sib family and in particular, when maternal half-sibs are available. Higher economic value for MGE than for DGE is needed, since with equal heritabilities and economic weights for the effects the maternal response is 40 to 70% of the direct one. With negative correlation, records on collateral relatives beyond sibs are possibly needed. They would support also the prediction of MGE in uniparous reindeer lacking full-sib information.

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Genomic selection strategies to improve maternal traits in Norwegian White Sheep

Cited by 10 publications

References 28 publications

Simulation studies to optimize genomic selection in honey bees

Simulation studies to optimize genomic selection in honey bees

Optimization Strategies to Adapt Sheep Breeding Programs to Pasture-Based Production Environments: A Simulation Study

The efficiency of multi-generation selection on maternal traits, with implications for reindeer

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