Heterogeneity of variance amongst herds for milk production

Brotherstone, S.; Hill, William G.

doi:10.1017/s0003356100018067

Cited by 84 publications

(41 citation statements)

References 4 publications

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“…heterogeneity of variance between herds). Heterogeneity of variance between herds is commonly found for milk production of dairy cattle [4], for example, and can be accounted for in standard breeding value estimation e.g. [28].…”

Section: Methodology and Resultsmentioning

confidence: 99%

Selection for uniformity in livestock by exploiting genetic heterogeneity of residual variance

2007

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-In some situations, it is worthwhile to change not only the mean, but also the variability of traits by selection. Genetic variation in residual variance may be utilised to improve uniformity in livestock populations by selection. The objective was to investigate the effects of genetic parameters, breeding goal, number of progeny per sire and breeding scheme on selection responses in mean and variance when applying index selection. Genetic parameters were obtained from the literature. Economic values for the mean and variance were derived for some standard non-linear profit equations, e.g. for traits with an intermediate optimum. The economic value of variance was in most situations negative, indicating that selection for reduced variance increases profit. Predicted responses in residual variance after one generation of selection were large, in some cases when the number of progeny per sire was at least 50, by more than 10% of the current residual variance. Progeny testing schemes were more efficient than sib-testing schemes in decreasing residual variance. With optimum traits, selection pressure shifts gradually from the mean to the variance when approaching the optimum. Genetic improvement of uniformity is particularly interesting for traits where the current population mean is near an intermediate optimum.heterogeneity of variance / index selection / uniformity / economic value / optimum trait

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Section: Methodology and Resultsmentioning

confidence: 99%

Selection for uniformity in livestock by exploiting genetic heterogeneity of residual variance

2007

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“…Under the infinitesimal model, the genetic variance within families is the same in each family, so assuming that the residual variance is homogeneous, is equivalent to assuming that the environmental variance and the genetic variance within families are homogeneous. There can, however, be substantial heterogeneity of phenotypic variances amongst environmental groupings such as milk yield in dairy herds [1,19]. In a few studies, the objective has been to quantify heterogeneity of variance and explore the extent to which heterogeneity of environmental or residual variance plays a role [4,5,12,13,21].…”

Section: Introductionmentioning

confidence: 99%

“…This method is transparent, albeit less efficient than a full analysis via model fitting, and requiring some assumptions, particularly of homogeneity of genetic segregation variance. It is basically an extension of an earlier application to detect heterogeneity in phenotypic variance amongst dairy herds [1]. The data analysed in this report are highly suitable for such a study.…”

Section: Introductionmentioning

confidence: 99%

Genetic heterogeneity of residual variance in broiler chickens

et al. 2006

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-Aims were to estimate the extent of genetic heterogeneity in environmental variance. Data comprised 99 535 records of 35-day body weights from broiler chickens reared in a controlled environment. Residual variance within dam families was estimated using ASREML, after fitting fixed effects such as genetic groups and hatches, for each of 377 genetically contemporary sires with a large number of progeny (>100 males or females each). Residual variance was computed separately for male and female offspring, and after correction for sampling, strong evidence for heterogeneity was found, the standard deviation between sires in within variance amounting to 15-18% of its mean. Reanalysis using log-transformed data gave similar results, and elimination of 2-3% of outlier data reduced the heterogeneity but it was still over 10%. The correlation between estimates for males and females was low, however. The correlation between sire effects on progeny mean and residual variance for body weight was small and negative (−0.1). Using a data set bigger than any yet presented and on a trait measurable in both sexes, this study has shown evidence for heterogeneity in the residual variance, which could not be explained by segregation of major genes unless very few determined the trait.broiler chickens / body weight / genetic variance / environmental variance / heterogeneity of variance

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“…Research on 305-d lactation data (Brotherstone and Hill, 1986) showed that the most important factor that affected variance was yield level of the herd. A general positive correlation of yield level with estimated genetic and nongenetic variances was reported.…”

Section: Introductionmentioning

confidence: 99%

Estimated Heterogeneity of Phenotypic Variance of Test-Day Yield with a Structural Variance Model

Gengler

Wiggans

Gillon

2004

Journal of Dairy Science

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First-lactation test-day milk, fat, and protein yields from New York, Wisconsin, and California herds from 1990 through 2000 were adjusted additively for age and lactation stage. A random regression model with thirdorder Legendre polynomials for permanent environmental and genetic effects was used. The model included a random effect with the same polynomial regressions for 2 yr of calvings within herd (herd-time effect) to provide herd-specific lactation curves that can change every 2 yr. (Co)variance components were estimated using expectation-maximization REML simultaneously with phenotypic variances that were modeled using a structural variance model. Maximum heritability for test-day milk yield was estimated to be ∼20% around 200 to 250 d in milk; heritabilities were slightly lower for test-day fat and protein yields. Herd-time effects explained 12 to 20% of phenotypic variance and had the greatest impact at start of lactation. Variances of test-day yields increased with time, subclass size, and milking frequency. Test month had limited influence on variance. Variance increased for cows in herds with low and high milk yields and for early and late lactation stages. Repeatabilities of variances observed for a given class of herd, test-day, and milking frequency were 14 to 17% across nested variance subclasses based on lactation stage. (Key words: heterogeneity of variance, (co)variance structure, test-day yield) Abbreviation key: EM = expectation maximization.

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Heterogeneity of variance amongst herds for milk production

Cited by 84 publications

References 4 publications

Selection for uniformity in livestock by exploiting genetic heterogeneity of residual variance

Selection for uniformity in livestock by exploiting genetic heterogeneity of residual variance

Genetic heterogeneity of residual variance in broiler chickens

Estimated Heterogeneity of Phenotypic Variance of Test-Day Yield with a Structural Variance Model

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