Genomic prediction and genetic correlations estimated for milk production and fatty acid traits in Walloon Holstein cattle using random regression models.

The aims of this study were to: (1) estimate genetic correlation for milk production traits (milk, fat and protein yields and fat and protein contents) and fatty acids (FA: C16:0, C18:1 cis-9, LCFA, SFA, and UFA) over days in milk, (2) investigate the performance of genomic predictions using single-step GBLUP (ssGBLUP) based on random regression models (RRM), and (3) identify the optimal scaling and weighting factors to be used in the construction of the H matrix. A total of 302 684 test-day records of 63.875 first lactation Walloon Holstein cows were used. Positive genetic correlations were found between milk yield and fat and protein yield (rg from 0.46 to 0.85) and between fat yield and milk FA (rg from 0.17 to 0.47). On the other hand, negative correlations were estimated between fat and protein contents (rg from -0.22 to -0.59), between milk yield and milk FA (rg from -0.22 to -0.62), and between protein yield and milk FA (rg from -0.11 to -0.19). The selection for high fat content increases milk FA throughout lactation (rg from 0.61 to 0.98). The test-day ssGBLUP approach showed considerably higher prediction reliability than the parent average for all milk production and FA traits, even when no scaling and weighting factors were used in the H matrix. The highest validation reliabilities (r2 from 0.09 to 0.38) and less biased predictions (b1 from 0.76 to 0.92) were obtained using the optimal parameters (i.e., ω = 0.7 and α = 0.6) for the genomic evaluation of milk production traits. For milk FA, the optimal parameters were ω = 0.6 and α = 0.6. However, biased predictions were still observed (b1 from 0.32 to 0.81). The findings suggest that using ssGBLUP based on RRM is feasible for the genomic prediction of daily milk production and FA traits in Walloon Holstein dairy cattle.

Random regression test-day models to describe milk production and fatty acid traits in first lactation Walloon Holstein cows.

We investigated the use of different Legendre polynomial orders to estimate genetic parameters for milk production and fatty acid (FA) traits in the first lactation Walloon Holstein cows. The data set comprised 302,684 test-day records of milk yield, fat and protein contents, and FAs generated by mid-infrared (MIR) spectroscopy, C16:0 (palmitic acid), C18:1 cis-9 (oleic acid), LCFAs (long-chain FAs), SFAs (saturated FAs) and UFAs (unsaturated FAs) were studied. The models included random regression coefficients for herd-year of calving (h), additive genetic (a) and permanent environment (p) effects. The selection of the best random regression model (RRM) was based on the deviance information criterion (DIC), and genetic parameters were estimated via a Bayesian approach. For all analysed random effects, DIC values decreased as the order of the Legendre polynomials increased. Best-fit models had fifth-order (degree 4) for the p effect and ranged from second- to fifth-order (degree 1-4) for the a and h effects (LEGhap: LEG555 for milk yield and protein content; LEG335 for fat content and SFA; LEG545 for C16:0 and UFA; and LEG535 for C18:1 cis-9 and LCFA). Based on the best-fit models, an effect of overcorrection was observed in early lactation (5-35 days in milk [DIM]). On the contrary, third-order (LEG333; degree 2) models showed flat residual trajectories throughout lactation. In general, the estimates of genetic variance tended to increase over DIM, for all traits. Heritabilities for milk production traits ranged from 0.11 to 0.58. Milk FA heritabilities ranged from low-to-high magnitude (0.03-0.56). High Spearman correlations (>0.90 for all bulls and >0.97 for top 100) were found among breeding values for 155 and 305 DIM between the best RRM and LEG333 model. Therefore, third-order Legendre polynomials seem to be most parsimonious and sufficient to describe milk production and FA traits in Walloon Holstein cows.

The influence of animals from embryo transfer on the genetic evaluation of growth in Simmental beef cattle by using multi-trait models.

The weight records from Simmental beef cattle were used in a genetic evaluation of growth with or without the inclusion of animals obtained by embryo transfer. A multi-trait model in which embryo transfer individuals were excluded (MTM1) contained 29,510 records from 10,659 animals, while another model without exclusion of these animals (MTM2) contained 62,895 weight records from 23,160 animals. The weight records were adjusted for ages of 100, 205, 365, 450, 550 and 730 days. The (co)variance components and genetic parameters were estimated by the restricted maximum likelihood method. The (co)variance components were similar in both models, except for maternal permanent environment variance. Direct heritabilities (h(2) d) in MTM1 were 0.04, 0.11, 0.20, 0.27, 0.31 and 0.42, while in MTM2 they were 0.11, 0.11, 0.17, 0.21, 0.22 and 0.26 for 100, 205, 365, 450, 550 and 730 days of age, respectively. Estimates of h(2) d in MTM1 were higher than in MTM2 for the weight at 365 days of age. Genetic correlations between weights in both models ranged from moderate to high, suggesting that these traits may be determined mainly by the same genes. Animals from embryo transfer may be included in the genetic evaluation of Simmental beef cattle in Brazil; this inclusion may provide potential gains in accuracy and genetic gains by reducing the interval between generations.

Recent genetic advances on boar taint reduction as an alternative to castration: a review.

Boar taint is an unpleasant odor in male pig meat, mainly caused by androstenone, skatole, and indole, which are deposited in the fat tissue. Piglet castration is the most common practice to prevent boar taint. However, castration is likely to be banished in a few years due to animal welfare concerns. Alternatives to castration, such as genetic selection, have been assessed. Androstenone and skatole have moderate to high heritability, which makes it feasible to select against these compounds. This review presents the latest results obtained on genetic selection against boar taint, on correlation with other traits, on differences in breeds, and on candidate genes related to boar taint. QTLs for androstenone and skatole have been reported mainly on chromosomes 6, 7, and 14. These chromosomes were reported to contain genes responsible for synthesis and degradation of androstenone and skatole. A myriad of work has been done to find markers or genes that can be used to select animals with lower boar taint. The selection against boar taint could decrease performance of some reproduction traits. However, a favorable response on production traits has been observed by selecting against boar taint. Selection results have shown that it is possible to reduce boar taint in few generations. In addition, modifications in diet and environment conditions could be associated with genetic selection to reduce boar taint. Nevertheless, costs to measure and select against boar taint should be rewarded with incentives from the market; otherwise, it would be difficult to implement genetic selection.

Multi-Trait analysis of growth traits: fitting reduced rank models using principal components for Simmental beef cattle / Efetividade da redução da dimensão da matriz de covariância do efeito genético direto na avaliação genética do crescimento em bovinos Simental

ABSTRACT: The aim of this research was to evaluate the dimensional reduction of additive direct genetic covariance matrices in genetic evaluations of growth traits (range 100-730 days) in Simmental cattle using principal components, as well as to estimate (co)variance components and genetic parameters. Principal component analyses were conducted for five different models-one full and four reduced-rank models. Models were compared using Akaike information (AIC) and Bayesian information (BIC) criteria. Variance components and genetic parameters were estimated by restricted maximum likelihood (REML). The AIC and BIC values were similar among models. This indicated that parsimonious models could be used in genetic evaluations in Simmental cattle. The first principal component explained more than 96% of total variance in both models. Heritability estimates were higher for advanced ages and varied from 0.05 (100 days) to 0.30 (730 days). Genetic correlation estimates were similar in both models regardless of magnitude and number of principal components. The first principal component was sufficient to explain almost all genetic variance. Furthermore, genetic parameter similarities and lower computational requirements allowed for parsimonious models in genetic evaluations of growth traits in Simmental cattle.

RESUMO: Objetivou-se estudar a efetividade da redução da dimensão da matriz de covariância do efeito genético direto na avaliação genética do crescimento (pesos dos 100 aos 730 dias de idade) de bovinos Simental, por meio da análise de componentes principais, e estimar componentes de (co)variância e parâmetros genéticos. A análise de componentes principais foi realizada ajsutando-se cinco diferentes modelos: um modelo multicaracterístico padrão, de posto completo, e quatro modelos de posto reduzido. Os modelos foram comparados via informação de Akaike (AIC) e informação Bayesiana de Schwarz (BIC). Os componentes de variância e parâmetros genéticos foram obtidos via REML. Os valores de AIC e BIC para os modelos testados foram similares, indicando a possibilidade da escolha de um modelo mais parcimonioso na avaliação genética da raça Simental. O primeiro componente principal explicou mais de 96% de toda variação genética aditiva direta em ambos os modelos. Os valores de herdabilidades foram maiores em idades mais avançadas e variaram de 0,05 (peso aos 100 dias) a 0,30 (peso aos 730 dias). As estimativas de correlações genéticas foram similares em todos os modelos e apresentaram mesma magnitude e comportamento independentemente do número de componentes principais adotado. Diante dos resultados, pode-se afirmar que apenas o primeiro componente principal foi suficiente para explicar quase que na totalidade a variação genética aditiva direta existente. Além disso, a similaridade dos parâmetros genéticos estimados e a menor demanda computacional são indicativos da possibilidade da utilização de modelos mais parcimoniosos na avaliação genética de bovinos Simental.