Impact of strong selection for the PrP major gene on genetic variability of four French sheep breeds(<i>Open Access publication</i>)

Palhière, Isabelle; BRochard, Mickaeël; Moazami‐Goudarzi, Katayoun; Laloeë, Denis; Amigues, Yves; Bed’Hom, Bertrand; Neuts, Étienne; Leymarie, Cyril; Pantano, T; Cribiu, Edmond; Bibé, Bernard; Verrier, Étienne

doi:10.1051/gse:2008029

Cited by 8 publications

(8 citation statements)

References 15 publications

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“…Using these methods, r GBV is greatest when Ne and genome size are small and the reference population size is big. Published studies on genetic diversity in sheep (Huby et al, 2003;Palhiere et al, 2008;Garcia-Gamez et al, 2012) and goat (Araújo et al, 2006;Ribeiro et al, 2012) breeds have shown that small ruminant populations are heterogeneous and display high effective population sizes compared with most selected cattle breeds. This is positive for the long term selection response but, with the methods used here, negatively affects the genomic prediction accuracies.…”

Section: The Selection Modelmentioning

confidence: 99%

Potential benefits of genomic selection on genetic gain of small ruminant breeding programs1

Shumbusho

Raoul

Astruc

et al. 2013

Journal of Animal Science

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In conventional small ruminant breeding programs, only pedigree and phenotype records are used to make selection decisions but prospects of including genomic information are now under consideration. The objective of this study was to assess the potential benefits of genomic selection on the genetic gain in French sheep and goat breeding designs of today. Traditional and genomic scenarios were modeled with deterministic methods for 3 breeding programs. The models included decisional variables related to male selection candidates, progeny testing capacity, and economic weights that were optimized to maximize annual genetic gain (AGG) of i) a meat sheep breeding program that improved a meat trait of heritability (h(2)) = 0.30 and a maternal trait of h(2) = 0.09 and ii) dairy sheep and goat breeding programs that improved a milk trait of h(2) = 0.30. Values of ±0.20 of genetic correlation between meat and maternal traits were considered to study their effects on AGG. The Bulmer effect was accounted for and the results presented here are the averages of AGG after 10 generations of selection. Results showed that current traditional breeding programs provide an AGG of 0.095 genetic standard deviation (σa) for meat and 0.061 σa for maternal trait in meat breed and 0.147 σa and 0.120 σa in sheep and goat dairy breeds, respectively. By optimizing decisional variables, the AGG with traditional selection methods increased to 0.139 σa for meat and 0.096 σa for maternal traits in meat breeding programs and to 0.174 σa and 0.183 σa in dairy sheep and goat breeding programs, respectively. With a medium-sized reference population (nref) of 2,000 individuals, the best genomic scenarios gave an AGG that was 17.9% greater than with traditional selection methods with optimized values of decisional variables for combined meat and maternal traits in meat sheep, 51.7% in dairy sheep, and 26.2% in dairy goats. The superiority of genomic schemes increased with the size of the reference population and genomic selection gave the best results when nref > 1,000 individuals for dairy breeds and nref > 2,000 individuals for meat breed. Genetic correlation between meat and maternal traits had a large impact on the genetic gain of both traits. Changes in AGG due to correlation were greatest for low heritable maternal traits. As a general rule, AGG was increased both by optimizing selection designs and including genomic information.

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Section: The Selection Modelmentioning

confidence: 99%

Potential benefits of genomic selection on genetic gain of small ruminant breeding programs1

Shumbusho

Raoul

Astruc

et al. 2013

Journal of Animal Science

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“…With the recent development of molecular technologies, genomic selection is now used for several species, and major genes can be identified and sequenced. Selection for specific alleles of several major genes is already implemented, such as the prion protein ( PrP ) gene for scrapie resistance in dairy sheep and goats [ 1 , 2 ]. The genomic best linear unbiased prediction (GBLUP) approach is based on the assumption that many quantitative trait loci (QTL), each with a small effect, contribute to genetic variation [ 3 , 4 ].…”

Section: Introductionmentioning

confidence: 99%

Including α s1 casein gene information in genomic evaluations of French dairy goats

2016

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BackgroundGenomic best linear unbiased prediction methods assume that all markers explain the same fraction of the genetic variance and do not account effectively for genes with major effects such as the αs1casein polymorphism in dairy goats. In this study, we investigated methods to include the available αs1casein genotype effect in genomic evaluations of French dairy goats.MethodsFirst, the αs1casein genotype was included as a fixed effect in genomic evaluation models based only on bucks that were genotyped at the αs1casein locus. Less than 1 % of the females with phenotypes were genotyped at the αs1casein gene. Thus, to incorporate these female phenotypes in the genomic evaluation, two methods that allowed for this large number of missing αs1casein genotypes were investigated. Probabilities for each possible αs1casein genotype were first estimated for each female of unknown genotype based on iterative peeling equations. The second method is based on a multiallelic gene content approach. For each model tested, we used three datasets each divided into a training and a validation set: (1) two-breed population (Alpine + Saanen), (2) Alpine population, and (3) Saanen population.ResultsThe αs1casein genotype had a significant effect on milk yield, fat content and protein content. Including an αs1casein effect in genetic and genomic evaluations based only on male known αs1casein genotypes improved accuracies (from 6 to 27 %). In genomic evaluations based on all female phenotypes, the gene content approach performed better than the other tested methods but the improvement in accuracy was only slightly better (from 1 to 14 %) than that of a genomic model without the αs1casein effect.ConclusionsIncluding the αs1casein effect in a genomic evaluation model for French dairy goats is possible and useful to improve accuracy. Difficulties in predicting the genotypes for ungenotyped animals limited the improvement in accuracy of the obtained estimated breeding values.

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“…This is the reason why several studies proposed different selection regimes in European sheep breeds evaluating their consequences on breed genetic variability. Usually studies are performed using pedigree information, molecular markers data or a combination of both (Álvarez et al, 2007, 2009; Palhiere et al, 2008; Windig et al, 2004, 2007).…”

Section: Discussionmentioning

confidence: 99%

Genetic variability following selection for scrapie resistance in six autochthonous sheep breeds in the province of Bolzano (northern Italy)

Bordin

Dalvit

Caldon

et al. 2020

J Animal Breeding Genetics

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Scrapie is an ovine transmissible spongiform encephalopathy, and its susceptibility is associated with polymorphisms in the prion protein gene (PRNP). Genetic selection is currently the most effective mean for eradication of the susceptible VRQ allele in favour of resistant ARR allele. Maintenance of genetic diversity should be one of the major objectives in breeding programmes, especially in endangered breeds, and genetic information are an excellent alternative to pedigree data where these information are missing. The aim of our study was to determine changes of genetic variability in six native sheep breeds from autonomous province of Bolzano, northern Italy, following simulation of scrapie selection scenarios. A total of 684 rams were investigated for PRNP polymorphisms and for 10 microsatellite loci to estimate genetic variability. Across all loci, a total of 163 alleles were detected with a mean of 10.4 alleles per locus. Average observed (Ho) and unbiased expected (uHe) heterozygosity overall loci were 0.74 and 0.78, respectively, showing a statistically significant deviation from Hardy–Weinberg equilibrium (HWE) in all breeds. This heterozygosity deficit was confirmed by a positive fixation index (Fis), determining a moderate inbreeding in each breed. Simulating a soft selection, where only rams having at least a VRQ allele should be excluded from reproduction, Ho, uHe and Fis values remained almost unchanged, indicating that genetic variability should not be affected by the removal of these individuals. With a mild selection scenario, considering only rams with at least one ARR allele, we observed a decrease in the mean alleles per breed (8.9) and the maintenance of heterozygosity deficiency, except for two breeds, where it was any longer significant. These results showed that selection strategies allowing use of heterozygous as well homozygous ARR rams might be the right compromise to improve resistance to scrapie and to do not dramatically affect genetic variability of these breeds.

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Impact of strong selection for the PrP major gene on genetic variability of four French sheep breeds(Open Access publication)

Cited by 8 publications

References 15 publications

Potential benefits of genomic selection on genetic gain of small ruminant breeding programs1

Potential benefits of genomic selection on genetic gain of small ruminant breeding programs1

Including α s1 casein gene information in genomic evaluations of French dairy goats

Genetic variability following selection for scrapie resistance in six autochthonous sheep breeds in the province of Bolzano (northern Italy)

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