EPDs and Risk

About 30 years ago there was concern in both the beef and dairy industries that too much emphasis was being given to accuracy of genetic evaluation. This article will discuss attempts to reduce emphasis on accuracy and, thus increase emphasis on the predictor of genetic value itself which is commonly known as estimated breeding value (EBV). Accuracy is a key component of more useful measures of risk such as standard error of prediction which can be used to create confidence ranges in units of measurement for true breeding value based on the EBV and the standard error of prediction. The concept of standard error of prediction can be extended to comparison of pairs of EBV. The influence of genomic relationships and Bayesian analyses on accuracies and standard errors of prediction will also be briefly introduced

EPDs and Risk

About 30 years ago there was concern in both the beef and dairy industries that too much emphasis was being given to accuracy of genetic evaluation. This article will discuss attempts to reduce emphasis on accuracy and, thus increase emphasis on the predictor of genetic value itself which is commonly known as estimated breeding value (EBV). Accuracy is a key component of more useful measures of risk such as standard error of prediction which can be used to create confidence ranges in units of measurement for true breeding value based on the EBV and the standard error of prediction. The concept of standard error of prediction can be extended to comparison of pairs of EBV. The influence of genomic relationships and Bayesian analyses on accuracies and standard errors of prediction will also be briefly introduced

PREDICTION OF BREEDING VALUES FOR UNMEASURED TRAITS FROM MEASURED TRAITS

Henderson (1977, 1984) described a method for prediction of breeding values for traits not in the model for records. This method may be practical for animal or sire models for the case when no measurements can be obtained on any animals for some traits to be evaluated. The least squares equations are augmented with A-1⊗GN-1 rather than with A-1⊗G0-1 where A is the numerator relationship and G0and GN are the genetic covariance matrices for measured and for all traits. This method can be used for each unmeasured trait or simultaneously for measured and all unmeasured traits. An option in the MTDFRUN module of the Multiple Trait Derivative Free REML (MTDFREML) program of Boldman et al. (1993) is to obtain solutions for breeding values and their prediction error variances. However, the preparation program (MTDFPREP) must be tricked to set-up equation numbers for breeding values of the unmeasured traits. Adding dummy records for the unmeasured traits but with missing records for the measured traits for dummy animals to the data file of animals with measured traits but with missing unmeasurable traits will result in the needed equations. At least two dummy records are needed to avoid a divide by zero error in calculating the sample standard deviation. The dummy records need to be associated with a level of at least one fixed factor. The dummy animals also must be added to the pedigree file with unknown sires and dams before running the program to obtain the inverse of the numerator relationship matrix (MTDFNRM). In the program to obtain solutions to the multiple trait mixed model equations (MTDFRUN), the full genetic (co)vaiiance matrix, GN, for both measured and unmeasured traits is needed. The residual (co)variance matrix must have zero covariances between pairs of measured and unmeasured traits but the variance of the unmeasured trait must not be zero. This procedure provides direct solutions for breeding values of unmeasured traits based on mixed model predictions of breeding values of the measured traits and also allows calculation of standard errors of prediction for the solutions directly from elements of the inverse of the ugmented coefficient matrix. For example, this procedure can be used to predict breeding values of bulls (which have tenderness measurements) for the correlated trait of tenderness as a steer or heifer which cannot be measured on the bull

DIFFERENCES AMONG APPRAISERS IN THE NEW YORK TYPE APPRAISAL PROGRAM

The purpose of the New York type appraisal program is to attempt to rate type traits objectively. Approximately 34 traits are included, of which 25 are rated by the appraiser and 9 by the herd manager. This paper reports differences among 18 appraisers, including S professional judges, 7 New York Artificial Breeders\u27 Cooperative fieldmen, and 3 other sire selection personnel all of whom rated 38 cows in a single herd. There were statistically significant differences (P ≤ .05) among appraisers for all traits. Average scores of the professionals were different from those of the fieldmen for all except six traits. Differences among the professionals were also large, as were the differences among the fieldmen. The results suggest that the fieldmen as a group appraised with as much consistency as the group of professional judges

DIFFERENCES AMONG APPRAISERS IN THE NEW YORK TYPE APPRAISAL PROGRAM

The purpose of the New York type appraisal program is to attempt to rate type traits objectively. Approximately 34 traits are included, of which 25 are rated by the appraiser and 9 by the herd manager. This paper reports differences among 18 appraisers, including S professional judges, 7 New York Artificial Breeders\u27 Cooperative fieldmen, and 3 other sire selection personnel all of whom rated 38 cows in a single herd. There were statistically significant differences (P ≤ .05) among appraisers for all traits. Average scores of the professionals were different from those of the fieldmen for all except six traits. Differences among the professionals were also large, as were the differences among the fieldmen. The results suggest that the fieldmen as a group appraised with as much consistency as the group of professional judges

Effect of parentage misidentification on estimates of genetic parameters for milk yield in the Mediterranean Italian buffalo population

The objective of this study was to evaluate the effect of parentage misidentification on estimation of genetic parameters for the Italian buffalo population for milk yield from 45,194 lactation records of 23,104 Italian buffalo cows. Animals were grouped into 10 data sets in which sires and dams were DNA identified, or reported from the pedigree, or unknown. A derivativefree restricted maximum likelihood method was used to estimate components of variance with a repeatability model. The model contained age at calving nested within parity and days from calving to conception as linear covariates, herd-year-seasons as fixed effects, and additive genetic, permanent environmental, and temporary environmental effects as random effects. Estimates of heritability (±SE) ranged from 0.00 ± 0.099 (sires and dams as reported in the pedigree) to 0.39 ± 0.094 (sires DNA identified and dams as reported in the pedigree). When identification of sires was as reported in the pedigree, estimates of heritability were close to zero. These small estimates indicate that a large proportion of reported paternity is incorrect. When sires are unknown and dams are DNA identified, the proportion of variance due to sires seems to be captured in the estimate of permanent environmental variance as a fraction of phenotypic variance. Therefore, as heritability decreased, permanent environmental variance increased about the same amount. Data sets with dams identified from pedigree and sires DNA identified showed the largest estimate of heritability (0.39), which was essentially the same as when dams were DNA identified (0.38). This result supports that most dams are correctly reported from the pedigree. Genetic progress should be much greater with bulls DNA identified because of greater heritability, but without artificial insemination and progeny testing, progress would be slow and would depend mostly on selection of sires based on dam estimated breeding values. Implementation of artificial insemination programs and DNA testing to identify sires are the keys for increasing genetic progress in the Italian buffalo population

Estimates of Genetic Selection Differentials and Generation Intervals for Four Paths of Selection

Estimated genetic values from an animal model based on first lactation milk records for 6OOO AI Holstein sires and 1,074,971 Holstein cows born in 1981 or before were used to estimate average genetic selection differentials and generation intervals for the four paths of selection for each year of birth. Selection differentials for paths of sires of bulls, dams of bulls, sires of cows, and dams of cows averaged over all years were 405, 395, 239, and 42 kg and for the most recent 5 yr 884, 598, 235, and 28 kg. Generation intervals averaged for all years were by path 10.2, 6.4.9.3, and 5.1 yr and for the most recent 5 yr 11.0, 6.4, 8.9, and 4.9 yr. Genetic trend based on the average selection differentials and generation intervals would be 34.9 kg/yr, but based on the latest 5-yr periods considering parents of grade cows genetic trend would be 57.2 kg/yr. Estimates of annual trend are considerably less than the potential rate of 96 kg/yr because of longer than necessary generation intervals and smaller selection differentials than theoretically possible

Estimates of genetic parameters and selection strategies to improve the economic efficiency of postweaning growth in lambs

The objectives of this study were to estimate (co)variance components for growth and feed efficiency measures, and to compare selection strategies to improve economic efficiency of gain. Variance components for pre- and postweaning growth, body weight, and measures of feed efficiency were estimated from data collected on 1,047 Targhee lambs over 7 yr. Approximately 21 d after weaning, lambs were group-fed for 4 wk, with ad libitum access to a diet of 37% whole barley grain and 63% pelleted alfalfa hay. Lambs were then individually fed for 6 wk. Lambs were then returned to group feeding for another 4-wk period. The mean feed conversion ratio (gain/intake) for the individual feeding period was 0.11. Mean postweaning ADG for the total 14-wk feeding period was 0.26 kg. (Co)variance components were estimated from single- and two-trait animal models using REML. The selection strategies compared included direct selection, index selection, and restricted index selection. Estimates of (co)variances derived from single- and two-trait models were similar, except for mid-test body weight. Preweaning growth had a low heritability estimate (0.03 ± 0.04) compared with postweaning growth measures (0.25 to 0.39), but all measures of growth were highly correlated (r2 \u3e 0.98). Heritability estimates of measures of gain efficiency were variable (total feed intake = &#;0.39; feed conversion ratio = &#;0.26; residual feed intake = &#;0.26). Total feed intake was strongly correlated genetically with feed conversion ratio (0.79) and residual feed intake (0.77). The estimate of genetic correlation between feed conversion ratio and residual feed intake was low (0.23). Comparison of selection strategies showed the superiority of index selection (ADG, total feed, body weight) for economic improvement compared with other strategies. Economic response to direct selection for ADG was at least twice that for direct selection for feed conversion ratio or against total feed intake, and that for restricted indices (selecting against residual feed, while holding body weight and/or gain constant). Selection for ADG may be a practical approach for indirectly improving efficiency of gain in lambs

Comparison of Heritability Estimates from Daughter on Dam Regression with Three Models to Account for Production Level of Dam

Three models were used to estimate heritabilities for milk yields at different production levels and for different years as twice the regression of daughter residual effects on dam residual effects. The denominator is the residual mean square for dams. The numerator is the difference between the residual term for sum of dam\u27s and daughter\u27s records and sum of residual terms for records of dams and daughters. Model 1 included sire of daughter and herd-year-season of daughters only. Model 2 included sire of daughter, herd-year-season of dam, and herd-year-season of daughter. Model 3 included sire of daughter and herdyear- season of dam and herd-year-season of daughter combination. The weighted mean estimates for each method were, respectively, .35, .38, .38 for milk production and .61, .67, .67 for fat test. Yearly time trends in heritability were slightly positive for both milk production and fat test. Standard errors of heritability estimates from model 1 were 40 to 50% smaller than those from models 2 and 3 due to the smaller number of effects in the model. Estimates for model 2 from low to high production levels averaged .30, .38, .38, and .42 for milk yield and .64, .68, .67, and .71 for fat test

Genetic and Maternal Influence on the First Three Lactations of Holstein Cows

Milk records of up to the first three lactations of artificially sired Holstein daughters and their dams were used to estimate heritability from both daughter-dam regression and paternal half-sib correlation. The within-herd estimates from records expressed as deviations from herd-mate averages were .37, .30, and .24 from daughter-dam regression for the first three lactations ; and the corresponding estimates from half-sib correlation were .24, .21, and .23. These results suggest a large maternal effect in the first lactation, a small amount in the second, and little in the third lactation. Analysis of the records not as deviations gave the same pattern for the daughter-dam regressions, but confounding of sires with year-seasons apparently biased the half-sib correlations upward. The consequences of considering unequal heritability for different lactations if the apparent difference is due to maternal effects is illustrated for selection for first-lactation breeding value from up to three records on the cow herself and on her dam