Estimation of genetic parameters for growth, feed consumption, and conformation traits for double-muscled Belgian Blue bulls performance-tested in Belgium

For 1,442 Belgian Blue bulls performance-tested at the Centre de Selection de la Race Blanc-Bleue Belge, nine traits were observed: height at withers at 7 mo, height at withers at 13 mo, weight at 7 mo, weight at 13 mo, average feed consumption of concentrates, average daily gain, average feed consumption of concentrates per average daily gain, average feed consumption of concentrates per mean metabolic weight, and price per kilogram of live weight. This price is based on muscle conformation and is therefore used as muscle conformation score. Restricted maximum Likelihood with a derivative-free algorithm was used to estimate (co)variance components because there were different models and missing values per trait. Estimates of heritabilities were above .50 except for average feed consumption per average daily gain (.16) and average feed consumption per mean metabolic weight (.33). Estimates of genetic and phenotypic correlations between height at withers and weight traits were positive and moderate to high. Average daily gain showed a negative genetic correlation with weight at 7 mo (- .68) but had positive correlations with height at withers at 13 mo and weight at 13 mo (.22 and .43). Muscle conformation expressed as price per kilogram of live weight was related to low average feed consumption per average daily gain. Average feed consumption showed high correlations with weight at 7 mo and weight at 13 mo. Average feed consumption per average daily gain had a high negative genetic correlation with average daily gain (- .89)

Influence of dominance relationships on the estimation of dominance variance with sire-dam subclass effects

Two data sets from the USDA Livestock and Range Research Laboratory mere analyzed to study dominance variance and the influence of dominance relationships. The first consisted of 4,155 birth weight (3,884 weaning weight) records of inbred USDA Line 1 Herefords. The second consisted of 8,065 birth weight (7,380 weaning weight) records from a line-cross experiment with five lines. Two models were used. Both included fixed effects of year-sex of calf and age of dam, and covariates for calving date, inbreeding of animal, and inbreeding of dam. For the second set, additional covariates were line composition and heterozygosity coefficients. Random effects were direct and maternal additive genetic, maternal permanent environment, sire-dam subclass, and residual. Model 1 considered sire-dam subclasses unrelated. Model 2 related sire-dam subclasses with a parental dominance relationship matrix. Variance components were estimated using REML. Differences between estimates with Model 1 and 2 were unimportant except for dominance variance. For the first data set, estimates with Model 2 of relative genetic direct and maternal variances, direct-maternal correlation, permanent environment, and dominance variances for birth weight were .35, .13, -.02, .03, and .25, respectively, and they were .39, .11, .04, .06 and .14 for the second data set. For weaning weight, the first data set estimates were .20, .15, -.37, .19, and .11, respectively, and they were .16, .20, -.07, .18, and .18 for the second data set. Changes, decreases and increases, in estimates of dominance variances may be due to increased information from relationships and family types other than full-sibs. The assumption of unrelated sire-dam subclasses might not be appropriate for estimation of dominance variance in populations with many dominance relationships among siredam classes

Genotype and Variability in Dairy Lactation Records

In quantitative genetics, the variation of individuals within genetic groups is commonly attributed to environment. The assumption is then made that the environmental variances are similar from one group to another. Complications arise in the application of selection index theory when this assumption is not made. The results reported here indicate that in a population of dairy records, the environmental variance is not similar from one genetic group to another. Johnson (1945) and Wadell, Van Vleck and Henderson (1960) found similar results with fewer data

Various persistency measures and relationships with total, partial and peak milk yields

peer reviewedSeven persistency measures and their relationships with total, partial and peak yields were studied. Heritabilities, genetic and phenotypic correlations were estimated using mixed model equations with multiple trait restricted maximum likelihood (REML). Data were from 31,482 first lactation Belgian Holstein-Friesian cows. Differences in direction and size of the correlations between measures of persistency and total yield were found. Two types of measures were used. Persistency measures based on ratio showed positive correlations to total yield. Measures based on standard deviations of test, days or partial yields were correlated negatively with total yield. Correlations with partial yields also showed a similar pattern. Genetic correlations between ratio methods and peak yield were positive. Ratio methods not only describe persistency, but are highly influenced by total yield. Methods using standard deviations of test day yields showed a similar problem, but the influence of the level of total production was negative. Heritabilities of several persistency measures were above 0.10. After considering those results, a newly defined method, based on the standard deviation of partial yields, emerged as the best choice to describe persistency and for use in genetic evaluation of persistency

Genetic parameters of milk, fat, and protein yields in the first three lactations, using an animal model and restricted maximum likelihood

Milk, fat, and protein yields of Holstein cows from the States of New York and California in the United States were used to estimate (co)variances among yields in the first three lactations, using an animal model and a derivative-free restricted maximum likelihood (REML) algorithm, and to verify if yields in different lactations are the same trait. The data were split in 20 samples, 10 from each state, with means of 5463 and 5543 cows per sample from California and New York. Mean heritability estimates for milk, fat, and protein yields for California data were, respectively, 0.34, 0.35, and 0.40 for first; 0.31, 0.33, and 0.39 for second; and 0.28, 0.31, and 0.37 for third lactations. For New York data, estimates were 0.35, 0.40, and 0.34 for first; 0.34, 0.44, and 0.38 for second; and 0.32, 0.43, and 0.38 for third lactations. Means of estimates of genetic correlations between first and second, first and third, and second and third lactations for California data were 0.86, 0.77, and 0.96 for milk; 0.89, 0.84, and 0.97 for fat; and 0.90, 0.84, and 0.97 for protein yields. Mean estimates for New York data were 0.87, 0.81, and 0.97 for milk; 0.91, 0.86, and 0.98 for fat; and 0.88, 0.82, and 0.98 for protein yields. Environmental correlations varied from 0.30 to 0.50 and were larger between second and third lactations. Phenotypic correlations were similar for both states and varied from 0.52 to 0.66 for milk, fat and protein yields. These estimates are consistent with previous estimates obtained with animal models. Yields in different lactations are not statistically the same trait but for selection programs such yields can be modelled as the same trait because of the high genetic correlations