Genetic parameters for litter size in sheep: natural <i>versus</i> 
hormone-induced oestrus

Janssens, Steven; Vandepitte, W.; Bodin, Loýs

doi:10.1051/gse:2004016

Cited by 12 publications

(13 citation statements)

References 19 publications

(42 reference statements)

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“…Genetic parameters were very similar in both breeds. Heritability (h 2 : 0.09) and repeatability (r; 0.10 to 0.14) were low and in full agreement with the classical values of these parameters for this species (Janssens et al ., 2004). A single copy of the FecL L in Noire du Velay increased the mean prolificacy by 0.42 lamb per lambing ( P < 0.001).…”

Section: Resultssupporting

confidence: 80%

Presence of causative mutations affecting prolificacy in the Noire du Velay and Mouton Vendéen sheep breeds

Chantepie

Bodin

Sarry

et al. 2018

Preprint

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11For many decades, prolificacy has been selected in meat sheep breeds as a polygenic 12 trait but with limited genetic gain. However, the discovery of major genes affecting 13 prolificacy has changed the way of selection for some ovine breeds implementing 14 gene-assisted selection as in the French Lacaune and Grivette meat breeds, or in the 15 Spanish Rasa Aragonesa breed. Based on statistical analysis of litter size parameters 16 from 34 French meat sheep populations, we suspected the segregation of a mutation 17 in a major gene affecting prolificacy in the Noire du Velay and in the Mouton Vendéen 18 breeds exhibiting a very high variability of the litter size. After the genotyping of 19 mutations known to be present in French sheep breeds, we discovered the segregation 20 of the FecL L mutation at the B4GALNT2 locus and the FecX Gr mutation at the BMP15 21 locus in Noire du Velay and Mouton Vendéen, respectively. The frequency of ewes 22 carrying FecL L in the Noire du Velay population was estimated at 21.2% and the 23 Mouton Vendéen ewes carrying FecX Gr at 10.3%. The estimated mutated allele effect 24 of FecL L and FecX Gr on litter size at +0.4 and +0.3 lamb per lambing in Noire du Velay 25 and Mouton Vendéen, respectively. Due to the fairly high frequency and the rather 26 strong effect of the FecL L and FecX Gr prolific alleles, specific management programmes 27including genotyping should be implemented for a breeding objective of prolificacy 28 adapted to each of these breeds. 30In ovine breeds raised for meat purposes, numerical productivity represents an 32 important technical and economic lever. The objective is to reach an optimum for the 33 economic profitability of breeding. Improvement of this numerical productivity is 34 achieved by increasing the number of lambs born per ewe at each lambing, i.e. the 35 prolificacy, associated with the improvement of lamb viability as well as the maternal 36 quality. This leads to increased post-natal survival and growth rate of the lambs. For 37 decades, genetic selection efforts have been made particularly on improving prolificacy 38 of sheep breeds. However, prolificacy is a weakly heritable polygenic trait (h 2 = 0.05 -39 0.2) (see the review by Bradford (Bradford, 1985)), allowing limited genetic gain. 40 Nevertheless in some breeds, a very large effect on ovulation rate (OR) and litter size 41 (LS) due to single mutation in fecundity major genes (called Fec genes, reviewed in 42 (Fabre et al., 2006)) has been demonstrated. The first evidence of the segregation of 43 a prolificacy major gene was established in the early 1980's in Australian Booroola 44 Merino. This was implicated by the observation of a large variability of LS and OR in 45 this population and the presence of extremely prolific ewes in this low prolific breed 46 ( Piper and Bindon, 1982; Davis et al., 1982). The causal mutation named FecB B was 47 France particularly, two genetic programmes were implemented to discover and to 57 manage mutations with major effect in order to improve the pro...

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Section: Resultssupporting

confidence: 80%

Presence of causative mutations affecting prolificacy in the Noire du Velay and Mouton Vendéen sheep breeds

Chantepie

Bodin

Sarry

et al. 2018

Preprint

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“…Additive genetic correlation estimates between two continuous traits have been largely unbiased in linear GS analyses of simulated data (Van Tassell & Van Vleck 1996). In both simulated and real data analyses, positive additive genetic correlation estimates have taken larger values under REML than under GS (Van Tassell & Van Vleck 1996;Janssens et al 2004). Our results indicate that regardless of the amount of information used additive genetic correlation estimates have to be interpreted with caution.…”

Section: Discussionmentioning

confidence: 67%

Estimation of genetic parameters and prediction of breeding values for multivariate threshold and continuous data in a simulated horse population using Gibbs sampling and residual maximum likelihood

Stock

Hoeschele

Distl

2007

J Animal Breeding Genetics

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Simulated horse data were used to compare multivariate estimation of genetic parameters and prediction of breeding values (BV) for categorical, continuous and molecular genetic data using linear animal models via residual maximum likelihood (REML) and best linear unbiased prediction (BLUP) and mixed linear-threshold animal models via Gibbs sampling (GS). Simulation included additive genetic values, residuals and fixed effects for one continuous trait, liabilities of four binary traits, and quantitative trait locus (QTL) effects and genetic markers with different recombination rates and polymorphism information content for one of the liabilities. Analysed data sets differed in the number of animals with trait records and availability of genetic marker information. Consideration of genetic marker information in the model resulted in marked overestimation of the heritability of the QTL trait. If information on 10,000 or 5,000 animals was used, bias of heritabilities and additive genetic correlations was mostly smaller, correlation between true and predicted BV was always higher and identification of genetically superior and inferior animals was - with regard to the moderately heritable traits, in many cases - more reliable with GS than with REML/BLUP. If information on only 1,000 animals was used, neither GS nor REML/BLUP produced genetic parameter estimates with relative bias 50% for all traits. Selection decisions for binary traits should rather be based on GS than on REML/BLUP breeding values.

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“…; Janssens et al . ; Arnason & Jónmundsson ; Cottle et al . ), are expressed later in life, are more time consuming to record and are sex limited.…”

Section: Introductionmentioning

confidence: 99%

Genomic prediction of reproduction traits for Merino sheep

et al. 2017

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Economically important reproduction traits in sheep, such as number of lambs weaned and litter size, are expressed only in females and later in life after most selection decisions are made, which makes them ideal candidates for genomic selection. Accurate genomic predictions would lead to greater genetic gain for these traits by enabling accurate selection of young rams with high genetic merit. The aim of this study was to design and evaluate the accuracy of a genomic prediction method for female reproduction in sheep using daughter trait deviations (DTD) for sires and ewe phenotypes (when individual ewes were genotyped) for three reproduction traits: number of lambs born (NLB), litter size (LSIZE) and number of lambs weaned. Genomic best linear unbiased prediction (GBLUP), BayesR and pedigree BLUP analyses of the three reproduction traits measured on 5340 sheep (4503 ewes and 837 sires) with real and imputed genotypes for 510 174 SNPs were performed. The prediction of breeding values using both sire and ewe trait records was validated in Merino sheep. Prediction accuracy was evaluated by across sire family and random cross-validations. Accuracies of genomic estimated breeding values (GEBVs) were assessed as the mean Pearson correlation adjusted by the accuracy of the input phenotypes. The addition of sire DTD into the prediction analysis resulted in higher accuracies compared with using only ewe records in genomic predictions or pedigree BLUP. Using GBLUP, the average accuracy based on the combined records (ewes and sire DTD) was 0.43 across traits, but the accuracies varied by trait and type of cross-validations. The accuracies of GEBVs from random cross-validations (range 0.17-0.61) were higher than were those from sire family cross-validations (range 0.00-0.51). The GEBV accuracies of 0.41-0.54 for NLB and LSIZE based on the combined records were amongst the highest in the study. Although BayesR was not significantly different from GBLUP in prediction accuracy, it identified several candidate genes which are known to be associated with NLB and LSIZE. The approach provides a way to make use of all data available in genomic prediction for traits that have limited recording.

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Genetic parameters for litter size in sheep: natural versus hormone-induced oestrus

Cited by 12 publications

References 19 publications

Presence of causative mutations affecting prolificacy in the Noire du Velay and Mouton Vendéen sheep breeds

Presence of causative mutations affecting prolificacy in the Noire du Velay and Mouton Vendéen sheep breeds

Estimation of genetic parameters and prediction of breeding values for multivariate threshold and continuous data in a simulated horse population using Gibbs sampling and residual maximum likelihood

Genomic prediction of reproduction traits for Merino sheep

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