BackgroundBreed-specific effects are observed when the same allele of a given genetic marker has a different effect depending on its breed origin, which results in different allele substitution effects across breeds. In such a case, single-breed breeding values may not be the most accurate predictors of crossbred performance. Our aim was to estimate the contribution of alleles from each parental breed to the genetic variance of traits that are measured in crossbred offspring, and to compare the prediction accuracies of estimated direct genomic values (DGV) from a traditional genomic selection model (GS) that are trained on purebred or crossbred data, with accuracies of DGV from a model that accounts for breed-specific effects (BS), trained on purebred or crossbred data. The final dataset was composed of 924 Large White, 924 Landrace and 924 two-way cross (F1) genotyped and phenotyped animals. The traits evaluated were litter size (LS) and gestation length (GL) in pigs.ResultsThe genetic correlation between purebred and crossbred performance was higher than 0.88 for both LS and GL. For both traits, the additive genetic variance was larger for alleles inherited from the Large White breed compared to alleles inherited from the Landrace breed (0.74 and 0.56 for LS, and 0.42 and 0.40 for GL, respectively). The highest prediction accuracies of crossbred performance were obtained when training was done on crossbred data. For LS, prediction accuracies were the same for GS and BS DGV (0.23), while for GL, prediction accuracy for BS DGV was similar to the accuracy of GS DGV (0.53 and 0.52, respectively).ConclusionsIn this study, training on crossbred data resulted in higher prediction accuracy than training on purebred data and evidence of breed-specific effects for LS and GL was demonstrated. However, when training was done on crossbred data, both GS and BS models resulted in similar prediction accuracies. In future studies, traits with a lower genetic correlation between purebred and crossbred performance should be included to further assess the value of the BS model in genomic predictions.
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. Within-population 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 within-population prediction for the purebred validation set. Accuracy of prediction was very trait-dependent.
In pig breeding, as the final product is a cross bred (CB) animal, the goal is to increase the CB performance. This goal requires different strategies for the implementation of genomic selection from what is currently implemented in, for example dairy cattle breeding. A good strategy is to estimate marker effects on the basis of CB performance and subsequently use them to select pure bred (PB) breeding animals. The objective of our study was to assess empirically the predictive ability (accuracy) of direct genomic values of PB for CB performance across two traits using CB and PB genomic and phenotypic data. We studied three scenarios in which genetic merit was predicted within each population, and four scenarios where PB genetic merit for CB performance was predicted based on either CB or a PB training data. Accuracy of prediction of PB genetic merit for CB performance based on CB training data ranged from 0.23 to 0.27 for gestation length (GLE), whereas it ranged from 0.11 to 0.22 for total number of piglets born (TNB). When based on PB training data, it ranged from 0.35 to 0.55 for GLE and from 0.30 to 0.40 for TNB. Our results showed that it is possible to predict PB genetic merit for CB performance using CB training data, but predictive ability was lower than training using PB training data. This result is mainly due to the structure of our data, which had small-to-moderate size of the CB training data set, low relationship between the CB training and the PB validation populations, and a high genetic correlation (0.94 for GLE and 0.90 for TNB) between the studied traits in PB and CB individuals, thus favouring selection on the basis of PB data.
Fine mapping of quantitative trait loci (QTL) from previous linkage studies was performed on pig chromosomes 1, 4, 7, 8, 17, and X which were known to harbor QTL. Traits were divided into: growth performance, carcass, internal organs, cut yields, and meat quality. Fifty families were used of a F2 population produced by crossing local Brazilian Piau boars with commercial sows. The linkage map consisted of 237 SNP and 37 microsatellite markers covering 866 centimorgans. QTL were identified by regression interval mapping using GridQTL. Individual marker effects were estimated by Bayesian LASSO regression using R. In total, 32 QTL affecting the evaluated traits were detected along the chromosomes studied. Seven of the QTL were known from previous studies using our F2 population, and 25 novel QTL resulted from the increased marker coverage. Six of the seven QTL that were significant at the 5% genome-wide level had SNPs within their confidence interval whose effects were among the 5% largest effects. The combined use of microsatellites along with SNP markers increased the saturation of the genome map and led to smaller confidence intervals of the QTL. The results showed that the tested models yield similar improvements in QTL mapping accuracy.
Genomic selection is applied to dairy cattle breeding to improve the genetic progress of purebred (PB) animals, whereas in pigs and poultry the target is a crossbred (CB) animal for which a different strategy appears to be needed. The source of information used to estimate the breeding values, i.e., using phenotypes of CB or PB animals, may affect the accuracy of prediction. The objective of our study was to assess the direct genomic value (DGV) accuracy of CB and PB pigs using different sources of phenotypic information. Data used were from 3 populations: 2,078 Dutch Landrace-based, 2,301 Large White-based, and 497 crossbreds from an F1 cross between the 2 lines. Two female reproduction traits were analyzed: gestation length (GLE) and total number of piglets born (TNB). Phenotypes used in the analyses originated from offspring of genotyped individuals. Phenotypes collected on CB and PB animals were analyzed as separate traits using a single-trait model. Breeding values were estimated separately for each trait in a pedigree BLUP analysis and subsequently deregressed. Deregressed EBV for each trait originating from different sources (CB or PB offspring) were used to study the accuracy of genomic prediction. Accuracy of prediction was computed as the correlation between DGV and the DEBV of the validation population. Accuracy of prediction within PB populations ranged from 0.43 to 0.62 across GLE and TNB. Accuracies to predict genetic merit of CB animals with one PB population in the training set ranged from 0.12 to 0.28, with the exception of using the CB offspring phenotype of the Dutch Landrace that resulted in an accuracy estimate around 0 for both traits. Accuracies to predict genetic merit of CB animals with both parental PB populations in the training set ranged from 0.17 to 0.30. We conclude that prediction within population and trait had good predictive ability regardless of the trait being the PB or CB performance, whereas using PB population(s) to predict genetic merit of CB animals had zero to moderate predictive ability. We observed that the DGV accuracy of CB animals when training on PB data was greater than or equal to training on CB data. However, when results are corrected for the different levels of reliabilities in the PB and CB training data, we showed that training on CB data does outperform PB data for the prediction of CB genetic merit, indicating that more CB animals should be phenotyped to increase the reliability and, consequently, accuracy of DGV for CB genetic merit.
Pig breeding companies keep relatively small populations of pure sire and dam lines that are selected to improve the performance of crossbred animals. This design of the pig breeding industry presents challenges to the implementation of genomic selection, which requires large data sets to obtain highly accurate genomic breeding values. The objective of this study was to evaluate the impact of different reference sets (across population and multipopulation) on the accuracy of genomic breeding values in 3 purebred pig populations and to assess the potential of using crossbreed performance in genomic prediction. Data consisted of phenotypes and genotypes on animals from 3 purebred populations (sire line [SL] 1, = 1,146; SL2, = 682; and SL3, = 1,264) and 3 crossbred pig populations (Terminal cross [TER] 1, = 183; TER2, = 106; and TER3, = 177). Animals were genotyped using the Illumina Porcine SNP60 Beadchip. For each purebred population, within-, across-, and multipopulation predictions were considered. In addition, data from the paternal purebred populations were used as a reference set to predict the performance of crossbred animals. Backfat thickness phenotypes were precorrected for fixed effects and subsequently included in the genomic BLUP model. A genomic relationship matrix that accounted for the differences in allele frequencies between lines was implemented. Accuracies of genomic EBV obtained within the 3 different sire lines varied considerably. For within-population prediction, SL1 showed higher values (0.80) than SL2 (0.61) and SL3 (0.67). Multipopulation predictions had accuracies similar to within-population accuracies for the validation in SL1. For SL2 and SL3, the accuracies of multipopulation prediction were similar to the within-population prediction when the reference set was composed by 900 animals (600 of the target line plus 300 of another line). For across-population predictions, the accuracy was mostly close to zero. The accuracies of predicting crossbreed performance were similar for the 3 different crossbred populations (ranging from 0.25 to 0.29). In summary, the differences in accuracy of the within-population scenarios may be due to line divergences in heritability and genetic architecture of the trait. Within- and multipopulation predictions yield similar accuracies. Across-population prediction accuracy was negligible. The moderate accuracy of prediction of crossbreed performance appears to be a result of the relationship between the crossbreed and its parental lines.
-The objective of this research was to estimate genetic parameters for the traits egg weight, egg production in 189 days and age at first egg in three laying quails and one meat line of quails. Data was analyzed by Bayesian procedures using Gibbs sampling. The heritability estimates for egg weight, egg production in 189 days and age at first egg, were, Key Words: genetic correlation, genetic variance, heritability, overlap probability, quails Caracterização genética da idade ao primeiro ovo, da produção e do peso dos ovos em linhagens de codornas RESUMO -O objetivo neste estudo foi obter estimativas de parâmetros genéticos para as características peso do ovo, produção de ovos em 189 dias de postura e dia do primeiro ovo em codornas de três linhagens de postura e uma de corte. Os dados foram analisados por meio de procedimentos bayesianos usando amostragem de Gibbs. As estimativas de herdabilidade para peso do ovo, produção de ovos em 189 dias de postura e dia do primeiro ovo foram, respectivamente, para a linhagem amarela, 0,31; 0,84 e 0,53; azul, 0,14; 0,82 e 0,60; vermelha, 0,70; 0,96 e 0,75; e de corte, 0,73; 0,96 e 0,72. As correlações genéticas entre peso do ovo e produção de ovos em 189 dias de postura, peso do ovo e dia do primeiro ovo e, produção de ovos em 189 dias de postura e dia do primeiro ovo foram, para amarela, 0,58; -0,77; e -0,90; azul, 0,09; -0,01; e -0,95; vermelha, 0,09; 0,03; e -0,76; e de corte, -0,18; 0,19 e -0,91. A partir das probabilidades de superposição das distribuições posteriories dos parâmetros, as linhagens dividem-se em dois grupos distintos: um com as linhagens amarela e azul e outro com as linhagens vermelha e de corte.Palavras-chave: codornas, correlação genética, herdabilidade, probabilidade de superposição, variância genética Revista Brasileira de Zootecnia
BackgroundAndrostenone is one of the major compounds responsible for boar taint, a pronounced urine-like odor produced when cooking boar meat. Several studies have identified quantitative trait loci (QTL) for androstenone level on Sus scrofa chromosome (SSC) 6. For one of the candidate genes in the region SULT2A1, a difference in expression levels in the testis has been shown at the protein and RNA level.ResultsHaplotypes were predicted for the QTL region and their effects were estimated showing that haplotype 1 was consistently related with a lower level, and haplotype 2 with a higher level of androstenone. A recombinant haplotype allowed us to narrow down the QTL region from 3.75 Mbp to 1.94 Mbp. An RNA-seq analysis of the liver and testis revealed six genes that were differentially expressed between homozygotes of haplotypes 1 and 2. Genomic sequences of these differentially expressed genes were checked for variations within potential regulatory regions. We identified one variant located within a CpG island that could affect expression of SULT2A1 gene. An allele-specific expression analysis in the testis did not show differential expression between the alleles of SULT2A1 located on the different haplotypes in heterozygous animals. However a synonymous mutation C166T (SSC6: 49,117,861 bp in Sscrofa 10.2; C/T) was identified within the exon 2 of SULT2A1 for which the haplotype 2 only had the C allele which was higher expressed than the T allele, indicating haplotype-independent allelic-imbalanced expression between the two alleles. A phylogenetic analysis for the 1.94 Mbp region revealed that haplotype 1, associated with low androstenone level, originated from Asia.ConclusionsDifferential expression could be observed for six genes by RNA-seq analysis. No difference in the ratio of C:T expression of SULT2A1 for the haplotypes was found by the allele-specific expression analysis, however, a difference in expression between the C over T allele was found for a variation within SULT2A1, showing that the difference in androstenone levels between the haplotypes is not caused by the SNP in exon 2.
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