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There are no estimates of the heritability of phenotypic udder traits in suckler sheep, which produce meat lambs, and whether these are associated with resilience to mastitis. Mastitis is a common disease which damages the mammary gland and reduces productivity. The aims of this study were to investigate the feasibility of collecting udder phenotypes, their heritability and their association with mastitis in suckler ewes. Udder and teat conformation, teat lesions, intramammary masses (IMM) and litter size were recorded from 10 Texel flocks in Great Britain between 2012 and 2014; 968 records were collected. Pedigree data were obtained from an online pedigree recording system. Univariate quantitative genetic parameters were estimated using animal and sire models. Linear mixed models were used to analyse continuous traits and generalised linear mixed models were used to analyse binary traits. Continuous traits had higher heritabilities than binary with teat placement and teat length heritability (h 2) highest at 0.35 (SD 0.04) and 0.42 (SD 0.04), respectively. Udder width, drop and separation heritabilities were lower and varied with udder volume. The heritabilities of IMM and teat lesions (sire model) were 0.18 (SD 0.12) and 0.17 (SD 0.11), respectively. All heritabilities were sufficiently high to be in a selection programme to increase resilience to mastitis in the population of Texel sheep. Further studies are required to investigate genetic relationships between traits and to determine whether udder traits predict IMM, and the potential benefits from including traits in a selection programme to increase resilience to chronic mastitis.

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In recent years, it has been shown that not only is the phenotype under genetic control, but also the environmental variance. Very little, however, is known about the genetic architecture of environmental variance. The main objective of this study was to unravel the genetic architecture of the mean and environmental variance of somatic cell score (SCS) by identifying genome-wide associations for mean and environmental variance of SCS in dairy cows and by quantifying the accuracy of genome-wide breeding values. Somatic cell score was used because previous research has shown that the environmental variance of SCS is partly under genetic control and reduction of the variance of SCS by selection is desirable. In this study, we used 37,590 single nucleotide polymorphism (SNP) genotypes and 46,353 test-day records of 1,642 cows at experimental research farms in 4 countries in Europe. We used a genomic relationship matrix in a double hierarchical generalized linear model to estimate genome-wide breeding values and genetic parameters. The estimated mean and environmental variance per cow was used in a Bayesian multi-locus model to identify SNP associated with either the mean or the environmental variance of SCS. Based on the obtained accuracy of genome-wide breeding values, 985 and 541 independent chromosome segments affecting the mean and environmental variance of SCS, respectively, were identified. Using a genomic relationship matrix increased the accuracy of breeding values relative to using a pedigree relationship matrix. In total, 43 SNP were significantly associated with either the mean (22) or the environmental variance of SCS (21). The SNP with the highest Bayes factor was on chromosome 9 (Hapmap31053-BTA-111664) explaining approximately 3% of the genetic variance of the environmental variance of SCS. Other significant SNP explained less than 1% of the genetic variance. It can be concluded that fewer genomic regions affect the environmental variance of SCS than the mean of SCS, but genes with large effects seem to be absent for both traits.

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A reduction in boar taint, an unpleasant odor arising in pork from some intact males, is desirable if routine castration of piglets needs to be stopped. Commercial slaughter pigs are typically crosses between sire lines mainly selected for finishing traits and dam lines mainly selected for reproduction traits. Previous studies suggest the possibility of reducing boar taint in sire lines due to favorable genetic correlations between boar taint and finishing traits. However, there are indications of unfavorable genetic associations between boar taint and female reproduction traits, but a lack of genetic correlation estimates remain a major roadblock in reducing boar taint in dam lines. This study was conducted to estimate genetic correlations between boar taint traits and female reproduction traits, investigate differences in these genetic relationships among sire and dam lines, and evaluate possible consequences of selection against boar taint in dam lines. The data consisted of 32,549 reproduction records from a Landrace dam line, 23,874 records from a Yorkshire dam line, and 3,745 records from a Pietrain sire line. Androstenone, skatole, and indole were measured on 1,896 carcasses, and human nose scores were recorded on 7,742 carcass samples. In general, the level of boar taint was significantly greater (P < 0.05) in the two dam lines than in the sire line. A majority of genetic correlations of boar taint compounds with reproduction traits were either low or nonsignificant, except for those of skatole and indole, with age at first insemination in dam lines that were -0.32 and -0.46, respectively. Genetic correlations also differed (P < 0.05) between sire and dam lines. The consequences of selection against boar taint in dam lines were evaluated, using selection indexes based on reproduction traits only, boar taint traits only, and both boar taint and reproduction traits. Selection on an index of only reproduction traits increased the number of carcasses with boar taint from 4 to 7.3% in 5 generations. Selection on a combined index reduced carcasses with boar taint from 4 to <0.1% in 19 generations at the cost of a 10% less economic gain in reproduction traits. In markets for intact males, overall economic gain is 78% greater than with selection for reproduction only. Breeding programs for boar taint in commercial production should include boar taint in breeding goals of dam lines as the levels of boar taint and the risk of a further increase are greater.

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The purpose of this study was to evaluate measures of boar (Sus scrofa) taint as potential selection criteria to reduce boar taint so that castration of piglets will become unnecessary. Therefore, genetic parameters of boar taint measures and their genetic correlations with finishing traits were estimated. In particular, the usefulness of a human panel assessing boar taint (human nose score) was compared with chemical assessment of boar taint compounds, androstenone, skatole, and indole. Heritability estimates for androstenone, skatole, and indole were 0.54, 0.41, and 0.33, respectively. The heritability for the human nose score using multiple panelists was 0.12, and ranged from 0.12 to 0.19 for individual panelists. Genetic correlations between scores of panelists were generally high up to unity. The genetic correlations between human nose scores and the boar taint compounds ranged from 0.64 to 0.999. The boar taint compounds and human nose scores had low or favorable genetic correlations with finishing traits. Selection index estimates indicated that the effectiveness of a breeding program based on human nose scores can be comparable to a breeding program based on the boar taint compounds themselves. Human nose scores can thus be used as a cheap and fast alternative for the costly determination of boar taint compounds, needed in breeding pigs without boar taint.

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Two novel methods for genome wide selection (GWS) were examined for predicting the genetic merit of animals using SNP information alone. A panel of 1,546 dairy bulls with reliable EBVs was genotyped for 15,380 SNPs that spanned the whole bovine genome. Two complexity reduction methods were used, partial least squares (PLS) and regression using a genetic algorithm (GAR), to find optimal solutions of EBVs against SNP information. Extensive internal cross-validation was used tofind the best predictive models followed by external validation (without direct use of the pedigree or SNP location). Both PLS and GAR provided both accurate fit to the training data set for somatic cell count (SCC) (max r = 0.83) and fertility (max r = 0.88) and showed an accuracy of prediction of r = 0.47 for SCC, and r = 0.72 for fertility. This is the first empirical demonstration that genome wide selection can account for a very high proportion of additive genetic variation in fitness traits whilst exploiting only a small percentage of available SNP information, without use of pedigree or QTL mapping. PLS was computationally more efficient than GAR.

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The use of exact and approximate algorithms to calculate prediction error variances using sparse matrix methods are demonstrated for an individual animal effect including maternal effects. One exact algorithm is substantially faster than two others. An approximation of the best exact method gave an acceptable level of reliabilities and reduced the computation by a factor of approximately fifty compared with the exact computation and is routine in national beef evaluation in Britain.

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