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Beuningen, Netherlands

Bosse M.,Wageningen University | Lopes M.S.,Wageningen University | Lopes M.S.,Topigs Norsvin Research Center | Madsen O.,Wageningen University | And 6 more authors.
Proceedings of the Royal Society B: Biological Sciences | Year: 2015

Early pig farmers in Europe importedAsian pigs to cross with their local breeds in order to improve traits of commercial interest. Current genomics techniques enabled genome-wide identification of these Asian introgressed haplotypes in modern European pig breeds.We propose that the Asian variants are still present because they affect phenotypes thatwere important for ancient traditional, as well as recent, commercial pig breeding. Genome-wide introgression levels were only weakly correlated with gene content and recombination frequency. However, regions with an excess or absence of Asian haplotypes (AS) contained genes that were previously identified as phenotypically important such as FASN, ME1, and KIT. Therefore, the Asian alleles are thought to have an effect on phenotypes thatwere historically under selection.We aimed to estimate the effect of AS in introgressed regions in Large White pigs on the traits of backfat (BF) and litter size. The majority of regions we tested that retained Asian deoxyribonucleic acid (DNA) showed significantly increased BF from the Asian alleles. Our results suggest that the introgression in Large White pigs has been strongly determined by the selective pressure acting upon the introgressed AS. We therefore conclude that human-driven hybridization and selection contributed to the genomic architecture of these commercial pigs. © 2015 The Author(s) Published by the Royal Society. All rights reserved. Source


Hidalgo A.M.,Wageningen University | Hidalgo A.M.,Swedish University of Agricultural Sciences | Bastiaansen J.W.M.,Wageningen University | Lopes M.S.,Wageningen University | And 4 more authors.
G3: Genes, Genomes, Genetics | Year: 2015

Genomic selection has been widely implemented in dairy cattle breeding when the aim is to improve performance of purebred animals. In pigs, however, the final product is a crossbred animal. This may affect the efficiency of methods that are currently implemented for dairy cattle. Therefore, the objective of this study was to determine the accuracy of predicted breeding values in crossbred pigs using purebred genomic and phenotypic data. A second objective was to compare the predictive ability of SNPs when training is done in either single or multiple populations for four traits: age at first insemination (AFI); total number of piglets born (TNB); litter birth weight (LBW); and litter variation (LVR). We performed marker-based and pedigree-based predictions. Withinpopulation predictions for the four traits ranged from 0.21 to 0.72. Multi-population prediction yielded accuracies ranging from 0.18 to 0.67. Predictions across purebred populations as well as predicting genetic merit of crossbreds from their purebred parental lines for AFI performed poorly (not significantly different from zero). In contrast, accuracies of across-population predictions and accuracies of purebred to crossbred predictions for LBW and LVR ranged from 0.08 to 0.31 and 0.11 to 0.31, respectively. Accuracy for TNB was zero for across-population prediction, whereas for purebred to crossbred prediction it ranged from 0.08 to 0.22. In general, marker-based outperformed pedigree-based prediction across populations and traits. However, in some cases pedigree-based prediction performed similarly or outperformed marker-based prediction. There was predictive ability when purebred populations were used to predict crossbred genetic merit using an additive model in the populations studied. AFI was the only exception, indicating that predictive ability depends largely on the genetic correlation between PB and CB performance, which was 0.31 for AFI. Multi-population prediction was no better than withinpopulation prediction for the purebred validation set. Accuracy of prediction was very trait-dependent. © 2015 Gorrepati et al. Source


Broekhuijse M.L.,Topigs Norsvin Research Center | Gaustad A.H.,Topigs Norsvin | Gaustad A.H.,Hedmark University College | Bolarin Guillen A.,Topigs Norsvin AIM Iberica | Knol E.F.,Topigs Norsvin Research Center
Reproduction in domestic animals = Zuchthygiene | Year: 2015

Diluting semen from high fertile breeding boars, and by that inseminating many sows, is the core business for artificial insemination (AI) companies worldwide. Knowledge about fertility results is the reason by which an AI company can lower the concentration of a dose. Efficient use of AI boars with high genetic merit by decreasing the number of sperm cells per insemination dose is important to maximize dissemination of the genetic progress made in the breeding nucleus. However, a potential decrease in fertility performance in the field should be weighed against the added value of improved genetics and, in general, is not tolerated in commercial production. This overview provides some important aspects that influence the impact of low-dose AI on fertility: (i) the importance of monitoring field fertility, (ii) the need for accurate and precise semen assessment, (iii) the parameters that are taken into account, (iv) the application of information from genetic and genomic selection and (v) the optimization when using different AI techniques. Efficient semen production, processing and insemination in combination with increasing use of genetic and genomic applications result in maximum impact of genetic trend. © 2015 Blackwell Verlag GmbH. Source


Lopes M.S.,Topigs Norsvin Research Center | Lopes M.S.,Wageningen University | Bastiaansen J.W.M.,Wageningen University | Janss L.,University of Aarhus | And 2 more authors.
G3: Genes, Genomes, Genetics | Year: 2015

Traditionally, exploration of genetic variance in humans, plants, and livestock species has been limited mostly to the use of additive effects estimated using pedigree data. However, with the development of dense panels of single-nucleotide polymorphisms (SNPs), the exploration of genetic variation of complex traits is moving from quantifying the resemblance between family members to the dissection of genetic variation at individual loci. With SNPs, we were able to quantify the contribution of additive, dominance, and imprinting variance to the total genetic variance by using a SNP regression method. The method was validated in simulated data and applied to three traits (number of teats, backfat, and lifetime daily gain) in three purebred pig populations. In simulated data, the estimates of additive, dominance, and imprinting variance were very close to the simulated values. In real data, dominance effects account for a substantial proportion of the total genetic variance (up to 44%) for these traits in these populations. The contribution of imprinting to the total phenotypic variance of the evaluated traits was relatively small (1-3%). Our results indicate a strong relationship between additive variance explained per chromosome and chromosome length, which has been described previously for other traits in other species. We also show that a similar linear relationship exists for dominance and imprinting variance. These novel results improve our understanding of the genetic architecture of the evaluated traits and shows promise to apply the SNP regression method to other traits and species, including human diseases. © 2015 Lopes et al. Source


Veroneze R.,Federal University of Vicosa | Veroneze R.,Wageningen University | Lopes P.S.,Federal University of Vicosa | Lopes M.S.,Wageningen University | And 9 more authors.
Journal of Animal Breeding and Genetics | Year: 2016

We studied the effect of including GWAS results on the accuracy of single- and multipopulation genomic predictions. Phenotypes (backfat thickness) and genotypes of animals from two sire lines (SL1, n = 1146 and SL3, n = 1264) were used in the analyses. First, GWAS were conducted for each line and for a combined data set (both lines together) to estimate the genetic variance explained by each SNP. These estimates were used to build matrices of weights (D), which was incorporated into a GBLUP method. Single population evaluated with traditional GBLUP had accuracies of 0.30 for SL1 and 0.31 for SL3. When weights were employed in GBLUP, the accuracies for both lines increased (0.32 for SL1 and 0.34 for SL3). When a multipopulation reference set was used in GBLUP, the accuracies were higher (0.36 for SL1 and 0.32 for SL3) than in single-population prediction. In addition, putting together the multipopulation reference set and the weights from the combined GWAS provided even higher accuracies (0.37 for SL1, and 0.34 for SL3). The use of multipopulation predictions and weights estimated from a combined GWAS increased the accuracy of genomic predictions. © 2016 Blackwell Verlag GmbH. Source

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