Biomedical subjects

L A Kriese

Publications and source records attributed to L A Kriese.

4 recordsLinked to original sources

PubMed1992

Estimation of direct and maternal heritability and genetic correlation for backfat and growth rate in swine using data from centrally tested Yorkshire boars.

The purpose of this study was to estimate components of variance and covariance for backfat and growth rate in swine tested in central test stations in the United States. Data were collected from 26 central boar test stations from 1984 through 1990. The traits analyzed were backfat adjusted to a 104.5-kg basis and ADG adjusted to a 36-kg on-test weight. Records from 7,951 purebred Yorkshire boars were analyzed. Contemporary groups were defined as boars of a breed that were tested and sold as a common group in a test station. Variance components were estimated using a pseudo-expectation method with a multiple-trait, sire-maternal grandsire model. Direct heritabilities for backfat and ADG were estimated to be .56 and .24, respectively. Direct maternal effects were significant for both backfat and ADG; they accounted for 11 and 23% of the variance, respectively. The additive genetic correlation between backfat and ADG was approximately zero. Within this population of centrally tested Yorkshire boars, heritability seems to be high for backfat and moderate for ADG, with a significant maternal effect on each trait.

Adipose Tissue↗

PubMed1991

Age adjustment factors, heritabilities and genetic correlations for scrotal circumference and related growth traits in Hereford and Brangus bulls.

Field data records on 10,511 Hereford and 2,522 Brangus bulls between 330 and 430 d of age were analyzed to find age of calf and age of dam adjustment factors for yearling scrotal circumference. Age of calf adjustment factors were .024 cm/d for Hereford bulls and .041 cm/d for Brangus bulls. Sons of Hereford dams were adjusted to a 6- to 8-yr dam age basis by adding .7, .3, .2, .2 or .3 cm for dams 2, 3, 4, 5 or 8 or more years old, respectively. Age of dam adjustment factors for Brangus bulls were .8, .4, .3 and .2 for dams 2, 3, 4 or 8 or more years old, respectively. Variance and covariance components for yearling scrotal circumference and several growth traits were estimated within breed using multiple-trait models and pseudo expectations involving the solutions and the right-hand sides of the mixed-model equations. Additive heritability estimates for yearling scrotal circumference of .53 and .16 were found for Hereford and Brangus bulls, respectively. Maternal heritability estimates of .12 and .10 were found for Hereford and Brangus bulls, respectively. Genetic correlations between yearling scrotal circumference and other growth traits were positive for both sets of data indicating that selection for yearling scrotal circumference should not adversely affect other growth traits in either breed. Environmental correlation estimates between yearling scrotal circumference and adjusted birth weight and between yearling scrotal circumference and adjusted 205-d weight and adjusted 365-d height were positive and moderate in magnitude for both breeds.

Age Factors↗

PubMed1991

Genetic and environmental growth trait parameter estimates for Brahman and Brahman-derivative cattle.

Beefmaster, Brahman, Brangus, and Santa Gertrudis field data records were used to determine genetic and environmental parameter estimates using a multiple-trait, pseudo-expectation approach. Adjusted birth weight, 205-d weight, and postweaning gain records were analyzed for each breed. Also, Brangus weaning sheath and navel scores were both analyzed using a single-trait, pseudo-expectation method to determine genetic parameter estimates. Additive birth weight heritability (h2A) estimates ranged from .22 to .37 and maternal birth weight heritability (h2M) estimates ranged from .12 to .55. Estimates for 205-d weight h2A for the four breeds varied from .21 to .25, and 205-d weight h2M estimates ranged from .15 to .21. Postweaning gain h2A estimates ranged from .16 to .56. The genetic correlation between direct and maternal portions of birth weight was negative for all breeds. This was also true for the genetic correlation between direct and maternal portions of 205-d weight, except in Brahman cattle, for which it was .15. The genetic correlation between additive portions of birth weight and 205-d weight was large and positive in all breeds. A moderately positive correlation between 205-d weight and postweaning gain was found for all breeds except Santa Gertrudis, whereas the environmental correlation between these two traits was a small to moderately negative estimate in all breeds. Brangus weaning sheath and navel score heritabilities indicated that genetic change for the size and shape of the sheath and navel area is possible.

Analysis of Variance↗

PubMed1990

Two methods for parameter estimation using multiple-trait models and beef cattle field data.

Two methods are presented for estimating variances and covariances from beef cattle field data using multiple-trait sire models. Both methods require that the first trait have no missing records and that the contemporary groups for the second trait be subsets of the contemporary groups for the first trait; however, the second trait may have missing records. One method uses pseudo expectations involving quadratics composed of the solutions and the right-hand sides of the mixed model equations. The other method is an extension of Henderson's Simple Method to the multiple trait case. Neither of these methods requires any inversions of large matrices in the computation of the parameters; therefore, both methods can handle very large sets of data. Four simulated data sets were generated to evaluate the methods. In general, both methods estimated genetic correlations and heritabilities that were close to the Restricted Maximum Likelihood estimates and the true data set values, even when selection within contemporary groups was practiced. The estimates of residual correlations by both methods, however, were biased by selection. These two methods can be useful in estimating variances and covariances from multiple-trait models in large populations that have undergone a minimal amount of selection within contemporary groups.

Analysis of Variance↗