Biomedical subjects

A Reverter

Publications and source records attributed to A Reverter.

8 recordsLinked to original sources

PubMed1997

Assessing the efficiency of multiplicative mixed model equations to account for heterogeneous variance across herds in carcass scan traits from beef cattle.

Data (n = 2,658) from live animal ultrasonic measures from 17 Angus herds were used to evaluate a multiplicative mixed model that incorporates scaling factors to correct for across-herd heterogeneity of variance. Traits included were ribeye muscle area (EMA), surface fat at the P8 site (P8), surface fat between the 12th and 13th ribs (RIB12), and weight at scanning (WEIGHT). Cattle ranged in age from 501 to 698 d and represented 291 contemporary groups. Data were initially analyzed using single-trait, animal model, Method R procedures to estimate variance components and heritabilities (h2). These estimates were incorporated into a multiplicative mixed model that simultaneously estimates breeding values (EBV) and heterogeneity factors. Re-estimation of h2 after scaling the data with the correction factors was explored to obtain a measure of the improvement in the genetic evaluation and to detect changes in ranking of individuals and herds. Initial h2 estimates for EMA, P8, RIB12, and WEIGHT were .36, .39, .29, and .48, respectively. Scaling factors ranged from .25 for P8 in a herd with eight records to 1.96 for RIB12 in a herd with 86 individuals. Re-estimates of h2 increased by an average of 4.2% for all the traits as a result of correcting for heterogeneity. Deviations of new scaling factors were within expectations. Correlations between EBV with and without heterogeneity correction were greater than .97 for all the traits. However, some substantial re-rankings of herds were observed for some traits in the smaller herds.

Analysis of Variance↗

PubMed1997

Approximate confidence intervals for heritability from method R estimates.

Method R estimates of heritability (h2) and associated confidence intervals (CI) were obtained from simulated data using a single trait, direct effects, full animal model, with 50% subsampling. Five hundred data sets were simulated for each of five levels of h2 (.10, .20, .30, .40, and .50) and two types of pedigree structure (random pedigree structure [N = 2,000] that varied over simulations, or the pedigree structure from a real data set [N = 2,644] that was constant for all simulations). The first 10, 20, and all 50 h2 estimates were used to obtain 80, 90, 95, and 99% CI for each data set. The variance of h2 estimates within data sets approximated the sampling variance of the h2 estimates. The Box-Cox transformation was used to normalize the distribution of estimates from each data set. Confidence intervals were computed on the transformed scale as CI = mu +/- (T x sigma), where mu and sigma = the mean and SD of the N transformed h2 estimates, respectively, and T = the critical value from the T distribution for a 1-alpha CI, with df = N-1. Upper and lower CI bounds were converted back to the original scale by reversing the transformation. The percentages of CI containing the true h2 value, pooled across all levels of h2, types of pedigree, and number of estimates used to obtain CI, for 80, 90, 95, and 99% CI were 81.14, 90.96, 95.27, and 98.76%, respectively. These results suggested that Method R h2 estimates can be used to obtain reliable CI.

Analysis of Variance↗

PubMed1997

The role of different pedigree structures on the sampling variance of heritability estimates.

A computer-intensive process was performed to simulate 12,600 data sets each with n = 5,000 individuals from distinct pedigree structures to assess the effect of pedigree information on the sampling variance of heritability (h2) estimates. Pedigree structures were determined by varying the proportion of foundation animals (PF), percentage replacement rates for males (RM) and females (RF), and ratio of females to male (F2M). A 2(3) factorial design was modeled; levels of RM and RF were 10 and 20%, and levels of F2M were 10 and 20. For each of the eight cells, 60 foundation animals were simulated, each with 10 replicates. The required mating seasons (MS) to obtain the number of individuals was simulated based on PF and F2M. A REML algorithm was used to estimate h2 and its associated SE. The effect of all factors was analyzed in a regression model with linear and quadratic components for PF. An alternative model with MS replacing PF was also investigated. There was a non-monotonic association (P < .01) between PF and h2 SE. The minimum h2 SE occurred when PF ranged from 20 to 40%. Here, the proportion of first-generation progeny was near its maximum with rapid increases in the proportion of subsequent descendants. Among the class effects, F2M yielded the highest mean square (P < .001). When considering more than one MS, h2 SE was positively associated (P < .01) with RF and F2M and negatively associated with RM. Results suggest that h2 is most accurately estimated when there is performance information on many animals closely related to foundation animals.

Algorithms↗

PubMed1995

Technical note: assessing the consistency of measurement procedures in animal energetics and nutrition.

Our objective was to assess the consistency of representative digestion and energetics determinations used in animal nutrition. We used distribution theory of quadratic forms that allow for the attainment of width of confidence intervals (WI) for intraclass correlations. Three models commonly used in animal nutrition were analyzed, and their respective programs were coded to obtain the required confidence limits. Data sets were obtained from previous research published by our laboratory. Urinary, CH4, and ME were analyzed assuming a two-factor nested balanced variance component model. Rate of ruminal NDF disappearance (kd) and DM digestibility by an 8-d conventional collection trial were fitted to a two-factor crossed variance component model without interaction with a single observation per cell. Empty BW (EBW), carcass energy, and EBW energy were fitted to a two-factor crossed variance component model with interaction. Widths of confidence intervals varied with the example data set and variable tested. The narrowest WI was that of DM digestibility, less than .07 at a 95% confidence level for all the intraclass correlations, which shows the high consistency of the DM digestibility measurement in the specific study. Medium to large WI were found for kd and EBW; WI estimates were less than .70 at a 95% confidence level. Large WI, from .8 to 1.0 at a 95% confidence level, were found for the remaining variables, indicating the greater variability of these measurements. This methodology allows the assessment of the consistency of a measurement process and provides a method to monitor it each time a determination is made.

Analysis of Variance↗

PubMed1995

Technical note: changes in genetic predictions between subsequent evaluations.

A procedure was developed to compute the proportion (P) of future genetic predictions that would be within 1 SE of previous predictions. The procedure is based on the Central Limit Theorem. Whatever the distribution function, provided only that it has a finite variance, the sample mean will have approximately the normal distribution for large samples. The proportion of new individual genetic predictions being within 1 SE of their previous evaluation is expressed as a function of the change in accuracy (ACC) between the previous and subsequent evaluations. If little additional information is made available since the previous evaluation, the increase in ACC will be almost negligible. As anticipated the vast majority of genetic predictions will be within 1 SE of their previous evaluation. The proportion determined from the results of the analysis can be compared to P. An additional appealing feature of the procedure presented is the ease of implementation with most computer softwares. Finally, application to both simulated and field data is presented.

Analysis of Variance↗

PubMed1994

Technical note: detection of bias in genetic predictions.

The theoretical development of a procedure to detect bias in genetic predictions is presented. The procedure is based on the expectation of three statistics. These statistics detect bias by identifying systematic, unexpected change in subsequent analyses. Expectations of the following statistics were obtained: linear correlation coefficient between subsequent predictions, linear regression of recent (more accurate) on previous (less accurate) genetic prediction, and variance of the genetic prediction difference (recent minus previous genetic prediction). Deviations from these expectations can be used to indicate bias. The covariance between subsequent BLUP of genetic value is shown to equal the variance of the early estimate, implying that the expected value of the regression of recent on previous genetic prediction equals 1 regardless of the distribution of the observations and predictions. Also, the expected value of the linear correlation coefficient between subsequent genetic predictions equals the square root of the ratio of the means of the square of accuracy values. The expected value of the variance of the genetic prediction difference was shown to be equal to the difference between prediction error variances.

Analysis of Variance↗

PubMed1994

Method R variance components procedure: application on the simple breeding value model.

An algorithm for estimating variance components (Method R) based on the linear regression coefficient (R) of recent (more accurate) on previous (less accurate) individual genetic predictions is presented. The previous prediction is obtained by analyzing a subsample of the whole data set. First raw moment of R equals 1 regardless of the distribution of observations and predictions. A condition such as the use of inappropriate variance components ratio (VC) can cause this regression to deviate from its expectation. If the computed R (Rc) is greater than 1, then VC ratio has been underestimated, and if Rc is less than 1, then VC ratio has been overestimated. Several iterations are performed, changing the VC ratio at each iteration, until Rc approximately equal to 1. When an Rc is obtained that is acceptably close to 1 (precision is reached), then the appropriate VC has been used. Method R does not require computation of the inverse of the coefficient matrix and has desirable properties of convergence, precision, and computing feasibility. Additional sampling variance in the estimate of VC is expected due to the requirement of taking a subsample of the entire data set to obtain the lower accuracy predictions. This sampling variance is shown to be small for simulated datasets of size n = 10,000 with no selection.

Algorithms↗

PubMed1992

Effects of growth curve parameters on cow efficiency.

Weight-age data from 50 Retinta beef cows from 8 to 97 mo of age located in southwestern Spain were fitted to von Bertalanffy, Brody, and Richards functions to determine the relationship between growth curve parameters and cow efficiency. Only cows having at least 31 weights were included in the analysis. Von Bertalanffy, Brody, and Richards functions were fitted to weights of each cow. Relevant parameters of the three functions are A and K, associated with the asymptotic mature weight and rate of maturing, respectively. Criteria for comparisons among the three functions were computing difficulty, goodness of fit, and lack of bias of A. Productivity indicators were number of calves weaned during the first five calving seasons (NC), average birth weight (BWT), average weaning weight (WW), and average weaning weight per cow per year (WWY). The von Bertalanffy function was selected as the most appropriate. Least squares means for A and K were 650 +/- 8.17 kg and .038 +/- .001 mo-1, respectively. The values of NC, BWT, WW, and WWY were 4.0 +/- .11 calves, 38.2 +/- .4 kg, 218 +/- 5 kg, and 172 +/- 5 kg, respectively. Regression analysis for A indicated a decrease in NC when mature weight increased (P less than .05). There was a nonsignificant trend for heavier cows (higher A) to have calves with heavier BWT or WW. The value of WWY increased (P less than .05) with increased maturing rate (K) of cows. No significant associations were found between K and BWT or WW.(ABSTRACT TRUNCATED AT 250 WORDS)

Animals↗