Single-trait Evaluations Research Articles

Prediction of birth weight using body measurements of local caprine population kids raised in a low-input breeding mode under arid environment.

The aim of this study is to establish linear measurements of local goat kids at birth and their factors of variation, as well as their possible correlations with birth weight. Additionally, the study analyses statistical models and barymetric functions to predict birth weight of kids based on their morphometric data. The database includes data on 128 goat kids born to 89 goats and 9 bucks in the experimental goat herd at the El GORDHAB station of IRA. Average BW, withers height (L1), heart girth (L2), rump height (L3), and body length (L4) of all kids were 2.45kg, 32.66cm,30.56cm, 33.41cm, and 31.21cm respectively. Results show that in general, local goat kids are small in size and weight at birth, which varies depending on sex, dam age, and type of birth. Highest and positive correlation coefficient value between birth weight and heart girth were observed (r = 0.95). The coefficient of determination (R2) for heart girth (0.78) was higher than other body measurements in single trait evaluation indicating it as the best trait for the predication of birth weight. The most appropriate combination of body measurements (R2 = 0.82) was observed between height at withers and heart girth for predication of birth weight estimation. Developing a system for recording birth weight based on easily obtainable body measurements could be a useful approach for rural areas. Result join the study objective by conceiving feasible genetic improvement plans for agropastoral herds by establishing individual phenotypes estimation even when the classical animal management does not already allowed.

Consequences of incorrect genetic parameter estimates for single-trait and multi-trait genetic evaluations in honeybees.

Genetic and residual variances of traits are important input parameters for best linear unbiased prediction (BLUP) breeding value estimation. In honeybees, estimates of these variances are often associated with large standard errors, entailing a risk to perform genetic evaluations under wrong premises. The consequences hereof have not been sufficiently studied. In particular, there are no adequate investigations on this topic accounting for multi-trait selection or genetic peculiarities of the honeybee. We performed simulation studies and explored the consequences of selection for honeybee populations with a broad range of true and assumed genetic parameters. We found that in single-trait evaluations, the response to selection was barely compromised by assuming erroneous parameters, so that reductions in genetic progress after 20 years never exceeded 21%. Phenotypic selection appeared inferior to BLUP selection, particularly under low heritabilities. Parameter choices for genetic evaluation had great effects on inbreeding development. By wrongly assuming high heritabilities, inbreeding rates were reduced by up to 74%. When parallel selection was performed for two traits, the right choice of genetic parameters appeared considerably more crucial as several incorrect premises yielded inadvertent negative selection for one of the traits. This phenomenon occurred in multiple constellations in which the selection traits expressed a negative genetic correlation. It was not reflected in the estimated breeding values. Our results indicate that breeding efforts heavily rely on detailed knowledge on genetic parameters, particularly when multi-trait selection is performed. Thus, considerable effort should be invested into precise parameter estimations.

Comparison of single-trait to multi-trait national evaluations for yield, health, and fertility

Flexible software was designed to replace the current animal model programs used for national genetic evaluations. Model improvements included (1) multi-trait processing, (2) multiple fixed class and regression variables, (3) differing models for different traits, (4) random regressions, and (5) foreign data included using pseudo-records. Computational improvements included (6) parallel processing, (7) renumbering class variables to equation numbers within the program so that estimated effects are output with original identification numbers, and (8) reliability computed within the same program. When applied to 3 fertility traits of 27,971,895 cows and heifers, the new model used daughter pregnancy rate as a correlated trait to improve heifer and cow conception rate evaluations for older animals and in herd-years where records are missing, and also added information from crossbreds. When applied to 7 traits and 76,846,327 lactation records of 30,064,300 cows, gains in accuracy were small for yield and somatic cell score, moderate for daughter pregnancy rate, and larger for productive life for recent bulls compared with single-trait evaluations. For very old bulls, multi-trait gains were also large for protein because lactation records were available only for milk and fat. Multi-trait productive life was computed with exact rather than approximate methods; however, correlated information from conformation was excluded, reducing advantages of the new model over the previous software. Estimates of breed differences, inbreeding depression, and heterosis were similar to previous estimates; new estimates were obtained for conception rates. Predictions were compared by truncating 4 yr of data, and genetic trend validation was applied to all breed–trait combinations. The estimates of trend account for increases in inbreeding across time. Incorporation of foreign data gave correlations above 0.98 for new with previous evaluations of foreign Holstein bulls, but lower for other breeds. The 7-trait model required 35 GB of memory and 3 d to converge using 7 processors. The new software was implemented for fertility traits in 2013 and is scheduled for implementation with yield, somatic cell score, and productive life in 2014. Further revision of the models and software may be needed in the near future to account for genomic preselection.

Genetic parameters for production traits and somatic cell score of the SA Dairy Swiss population

The SA Dairy Swiss population could not previously be incorporated into the National Dairy Genetic Evaluation System, owing to inadequate population numbers and data structures. However, in recent years participation in Milk Recording has improved sufficiently to allow for genetic evaluation of the population. The aim of the study was therefore to estimate genetic parameters for the SA Dairy Swiss for production traits and somatic cell score in order to estimate breeding values routinely from the National Dairy Genetic Evaluation System. After editing procedures, a dataset of 25 621 test-day records for milk, butterfat and protein yields and a dataset of 10 438 test-day records for somatic cell count were obtained for further analysis. (Co)variance components for milk (kg), butterfat and protein yields (kg), as well as for somatic cell score, were estimated using a fixed regression multi-lactation animal model, based on test-day records of the first three lactations. Records from the various lactations were included as repeated measures. A multi-trait evaluation was performed for the production traits, while a single trait evaluation was done for somatic cell score, using the software package VCE4. Heritability estimates were 0.19 for milk, 0.16 for butterfat, and 0.16 for protein yields. The heritability estimate for somatic cell score was lower, at 0.07. The genetic correlations between the production traits were lowest for milk and butterfat (0.83), and were similar for milk and protein (0.94) and butterfat and protein yields (0.94). These values compare favourably with those of the other South African dairy breeds, based on the same model. The (co)variances were then implemented for estimation of breeding values for the SA Dairy Swiss population. Genetic trends were favourable for milk production traits with milk yield, butterfat and protein yields increasing by 25.13 kg/year, 0.96 kg/year and 0.79 kg/year, respectively; while the trend for somatic cell score was unfavourable, increasing by 0.86 units/year, since 1983. The SA Dairy Swiss could therefore be incorporated into the National Dairy Genetic Evaluation System of South Africa, using these covariance components and model.

Effect of Number of Lactation Records on the Selection Rates in Holstein Dairy Cattle

This experiment was conducted to investigate the effects of lactation records in Holstein dairy cattle on the selection rates using a total of 341,436 test records from 113, 812 heads of cattle from first to third lactation. Heritabilities for milk, fat, and protein yields were highest at first lactation (0.28, 0.24, and 0.27, respectively), and decreased to 0.14, 0.15, and 0.13 at third lactation. For the milk yields, phenotypic correlations between first and second lactation, first and third lactation, and second and third lactation were low (0.49, 0.39, and 0.47, respectively), while genetic correlations among consecutive lactations and between second and third were above 0.8 and 0.9. In Model I, of the 1,138 heads the top 1% were selected based on first lactation records, only 32.4% (396 heads) were re-selected when the second lactation records were included and the 67.6% (769 heads) were newly selected animals. While in Model II, 85.1% (1,138 heads) of the animals which were selected as the top 1% on the basis of first and second lactation records were included. A multiple trait evaluation method using multiple lactation records is more desirable than a single trait evaluation method using first lactation records only.

Estimates of variance components for postweaning feed intake and growth in Bonsmara bulls and evaluation of alternative measures of feed efficiency

Feed efficiency is of major economic importance in beef production. The objective of this work was to evaluate alternative measures of feed efficiency for use in genetic evaluation. To meet this objective, genetic parameters were estimated for the components of efficiency. These parameters were then used in multiple-trait animal model genetic evaluations and alternative genetic predictors of feed efficiency were derived from the results. Corresponding single-trait evaluations for residual feed intake (RFI), residual daily gain (RDG), and residual intake and gain (RIG) were also conducted. The data contained 3 331 animals with records and an additional 6 322 animals in their pedigree. The alternative measures of feed efficiency were compared using Spearman rank correlations. Heritability estimates for metabolic body weight (MBW), average daily gain (ADG) and average daily feed intake (DFI) were 0.38 ± 0.05, 0.25 ± 0.05 and 0.37 ± 0.05, respectively. Estimates of the genetic correlations among these traits were 0.79 ± 0.07, 0.54 ± 0.08 and 0.66 ± 0.08 for MBW with ADG, MBW with DFI, and ADG with DFI, respectively. The various measures of feed efficiency were highly correlated. However, RFI and RDG did provide different rankings of candidates for selection. The combination of RFI and RDG to form RIG appears to provide an effective harmonization of the former two biological measures of efficiency. It is recommended that genetic evaluation systems include analyses of ADG and DFI, and breeders are advised to base selection decisions on the basis of multiple-trait selection indexes that incorporate feed intake and performance. Keywords: Genetic evaluation, residual feed intake, residual gain

Evidence for three highly significant QTL for meat quality traits in the Finnish Yorkshire pig breed1

Meat quality is important both to consumers and to the meat processing industry. Commonly used measures of porcine meat quality are the pH and color of the meat. The purpose of this study was to identify SNP associated with these meat quality traits in Finnish Yorkshire using the Illumina PorcineSNP60 BeadChip. The association of each SNP with the quality traits was tested with a weighted linear model. The relatedness of samples was accounted for by a random polygenic genetic effect with the accompanying full relationship matrix. The original EBV from single-trait evaluations were deregressed before analysis. The statistical significance of SNP was established using the Bonferroni correction to adjust for multiple testing. Three genomic regions were significant for the meat quality traits. The PRKAG3 region on chromosome 15 was significant for pH measured from loin and ham and for a* (redness) measured from loin. The smallest P-value in the region was obtained for pH measured from loin (ASGA0070634, P-value = 3.8 × 10(-13)). The allele substitution effect (-0.047) of the unfavorable allele A corresponds to 1 SD of the polygenic effect. The second significant region, on chromosome 2 at around 31 megabases (Mb), was associated with pH and L* (lightness) measured from loin. The most significant SNP (ASGA0009814, P-value = 3.89 × 10(-10)) had an allele substitution effect of 0.86, corresponding to 0.7 SD of the polygenic effect of L*. The third region, located on chromosome 6 at around 83 Mb, was significant for a* measured from ham. The P-value of the best SNP (ALGA0035896) was 8.71 × 10(-7) and the allele substitution effect -0.38, corresponding to 0.5 SD of the polygenic effect of a*. The significant association of PRKAG3 with pH was not due to the known AA substitutions. The candidate gene on chromosome 2 associated with color L* is RCN1, which has a high affinity Ca(2+)-binding motif, the EF hand. The significant region on chromosome 6 for color a* contains several genes, so more data are needed to identify the causative gene. Our results indicate that instead of the known AA substitutions of PRKAG3, some yet-unknown AA substitutions are causative for the pH variation in Finnish Yorkshire. Also, a new major QLT for L* was found on chromosome 2. The significant SNP identified in this study can be used in marker-assisted selection.

Udder health and female fertility traits are favourably correlated and support each other in multi-trait evaluations

Genetic parameters were estimated for protein yield (PY), clinical mastitis (CM), somatic cell score, number of inseminations (NI) and days from calving to first insemination (CFI) in first-parity Swedish Red cows by series of tri-variate linear animal models. The heritability of PY was moderate (0.34 ± 0.004), and the heritabilities of the functional traits were all low (0.014 ± 0.001-0.14 ± 0.004). The genetic correlation between CM and CFI (0.38 ± 0.05) was stronger than the correlation between CM and NI (0.05 ± 0.06), perhaps because CM and CFI usually are observed in early lactation when the cow is likely to be in negative energy balance, whereas NI generally is recorded when the cow is not in negative energy balance any more. The genetic correlation between NI and CFI was very close to zero (-0.002 ± 0.05), indicating that these two fertility traits have different genetic backgrounds. All genetic correlations between PY and the functional traits were moderate and unfavourable, ranging from 0.22 ± 0.02 to 0.47 ± 0.03. In addition, the effect of including genetic and phenotypic correlations between the trait groups milk production, udder health and female fertility on the accuracy of the selection index was quantified for a heifer, a cow and a proven bull. The difference between the accuracy obtained by multi-trait and single-trait evaluations was largest for the cow (0.012) and small for the heifer and the bull (0.006 and 0.004) because the phenotype of the cow for one trait could assist in predicting the Mendelian sampling term for a correlated trait.

Connectedness among herds of beef cattle bred under natural service

BackgroundA procedure to measure connectedness among herds was applied to a beef cattle population bred by natural service. It consists of two steps: (a) computing coefficients of determination (CDs) of comparisons among herds; and (b) building sets of connected herds.MethodsThe CDs of comparisons among herds were calculated using a sampling-based method that estimates empirical variances of true and predicted breeding values from a simulated n-sample. Once the CD matrix was estimated, a clustering method that can handle a large number of comparisons was applied to build compact clusters of connected herds of the Bruna dels Pirineus beef cattle. Since in this breed, natural service is predominant and there are almost no links with reference sires, to estimate CDs, an animal model was used taking into consideration all pedigree information and, especially, the connections with dams. A sensitivity analysis was performed to contrast single-trait sire and animal model evaluations with different heritabilities, multiple-trait animal model evaluations with different degrees of genetic correlations and models with maternal effects.ResultsUsing a sire model, the percentage of connected herds was very low even for highly heritable traits whereas with an animal model, most of the herds of the breed were well connected and high CD values were obtained among them, especially for highly heritable traits (the mean of average CD per herd was 0.535 for a simulated heritability of 0.40). For the lowly heritable traits, the average CD increased from 0.310 in the single-trait evaluation to 0.319 and 0.354 in the multi-trait evaluation with moderate and high genetic correlations, respectively. In models with maternal effects, the average CD per herd for the direct effects was similar to that from single-trait evaluations. For the maternal effects, the average CD per herd increased if the maternal effects had a high genetic correlation with the direct effects, but the percentage of connected herds for maternal effects was very low, less than 12%.ConclusionsThe degree of connectedness in a bovine population bred by natural service mating, such as Bruna del Pirineus beef cattle, measured as the CD of comparisons among herds, is high. It is possible to define a pool of animals for which estimated breeding values can be compared after an across-herds genetic evaluation, especially for highly heritable traits.

Test-day models for South African dairy cattle for participation in international evaluations

Variance components and breeding values of production traits and somatic cell score of South African Guernsey, Ayrshire, Holstein and Jersey breeds have been estimated using a multi-lactation repeatability test-day model, including tests of the first three lactations as repeated measures and fitting the permanent environmental effect across lactations. Multitrait evaluations were done for the production traits (milk, butterfat and protein) and single trait evaluations for somatic cell score. Heritability estimates were comparable with yield and somatic cell score estimates obtained by test-day models from other countries (17-24% for milk yield; 10-13% for butterfat yield; 14-19% for protein yield and 6-8% for somatic cell score). Proofs of qualifying sires were sent to the International Bull Evaluation Service (INTERBULL) for participation in the March 2005 test runs. Genetic correlations between South Africa and other participating countries, estimated by INTERBULL, compared well with those amongst the other participating countries. Trend validation tests were successful using this methodology for all traits and breeds except for somatic cell score of the Guernsey breed, due to insufficient data for this trait. South Africa can now participate in routine INTERBULL evaluations to obtain Multiple Across Country Evaluation (MACE) breeding values, using this methodology.

Overview of genetic evaluation in dairy cattle

It is costly and time-consuming to carry out dairy cattle selection on a large experimental scale. For this reason, sire and cow evaluations are almost exclusively based on field data, which are highly affected by a large array of environmental factors. Therefore, it is crucial to adjust for those environmental effects in order to accurately estimate the genetic merits of sires and cows. Index selection is a simple extension of the ordinary least squares under the assumption that the fixed effects are assumed known without error. The mixed-model equations (MME) of Henderson provide a simpler alternative to the generalized least squares procedure, which is computationally difficult to apply to large data sets. Solution to the MME yields the best linear unbiased estimator of the fixed effects and the best linear unbiased predictor (BLUP) of the random effects. In an animal breeding situation, the random effects such as sire or animal represent the animal's estimated breeding value, which provides a basis for selection decision. The BLUP procedure under sire model assumes random mating between sires and dams. The genetic evaluation procedure has progressed a long way from the dam-daughter comparison method to animal model, from single trait to multiple trait analysis, and from lactational to test-day model, to improve accuracy of evaluations. Multiple-trait evaluation appears desirable because it takes into account the genetic and environmental variance-covariance of all traits evaluated. For these reasons, multiple-trait evaluation would reduce bias from selection and achieve a better accuracy of prediction as compared to single-trait evaluation. The number of traits included in multiple-trait evaluation should depend upon the breeding goal. Recent advances in molecular and reproductive technologies have created great potential for quantitative geneticists concerning genetic dissection of quantitative traits, and marker-assisted genetic evaluation and selection.

Principles underlying genetic improvement for high and stable crop yield potential

This paper explores the genetic basis of high and stable crop yield, and delineates the conditions that provide the link between genotypic and phenotypic superiority and, consequently, improve selection efficiency in plant breeding. Response to selection is viewed as the result of the conversion, through intralocus recombination, of two heterotic nonadditive allelic loci into a single super additive locus, and fixation of the latter. This concept connects the important phenomena of vigor, degeneration, and response to selection through a unifying genetic basis. Heterozygosity of nonadditive allelic loci leads to the nonheritable repulsion-phase vigor. Heterozygosity of a super additive locus leads to the partially heritable coupling-phase vigor. Homozygosity of the super additive locus leads to the fully heritable inbred vigor, which is responsible for genetic gain. A reliable explanation of heterosis is the one that considers repulsion- and coupling-phase vigor as being mutually essential and inter-convertible. Response to selection is the outcome of the evolution of hybrid to inbred vigor. The superiority of inbred vigor is due to the improved additive allelic complementation, that at the same time enhances epistatic interactions. The full exploitation of the above concept leads to maximization of homeostatic crop yield and can be successfully achieved using the principles of honeycomb breeding. These include clarification of the negative role of competition on: (1) crop yield, and (2) selection efficiency, and explain why crop yields are maximized when cultivars are monogenotypic. The unit of evaluation and selection becomes the individual plant grown at the critical distance, where the range of phenotypic expression is enlarged, and the negative effect of competition on selection efficiency is eliminated. Honeycomb breeding uncouples the reliable selection for yield and stability from the visual evaluation that predominates during the critical early generations of selection, and offers the transition from single-trait evaluation to whole-genome phenotypic evaluation. The concept of whole-genome phenotypic evaluation recognizes that genes controlling crop yield concern the genome as a whole and belong to three categories: (1) genes that control yield potential per plant and expand the lower limit of the optimal plant density range; (2) genes that confer tolerance to biotic and abiotic stresses and expand the upper limit of the optimal plant density range; (3) genes that control cultivar responsiveness to inputs. The outcome of selection, based on whole-genome phenotypic evaluation during all generations of a breeding program, is high yielding, stable, and density-independent cultivars.

Methods to Combine Estimated Breeding Values Obtained from Separate Sources

Separate estimates of breeding value can be combined using meta-analysis if a combined analysis of all data is not possible or efficient. Computation is fast but not exact if the reliabilities of the separate estimates are approximate, if the extent of overlap of the datasets is unknown, or if selection has occurred across the datasets. Selection index methods were used to combine single-trait evaluations into approximate multitrait evaluations for productive life and to combine single-country rankings into multicountry rankings for yield traits. The same methods are used for males and females. To avoid iteration, parent evaluations were included in the data and combined before progeny evaluations. A little information is lost because foreign progeny contribute to domestic parents but not to domestic grandparents. Exchange of sire and dam evaluations provides a closer connection between national and international evaluations and may be more accurate than the current sire-maternal grandsire model used internationally. Correlations of the two evaluation methods were about 0.99 for 35,414 bulls from eight countries. The estimated breeding value of each bull was adjusted separately for information from foreign parents and foreign progeny. Reliabilities of the animal, its sire, and its dam were used to determine how much information came from the parents of the animal versus from its progeny and records. Multitrait reliabilities for productive life were higher than single-trait reliabilities by a mean of 7% for recent bulls and 3% for recent cows. Selection index methods may allow current multitrait across-country evaluations for bulls to be improved and to be extended to cows.

Combining selection for carcass quality, body weight, and milk traits in dairy cattle.

Alternative selection strategies were evaluated for breeding for carcass quality, body weight, and milk traits in dairy cattle. The efficiency of different alternatives was evaluated by comparing predicted genetic responses in individual traits as well as in the aggregate genotype. Particular interest was paid to the loss of response with indices that partially or totally ignored genetic correlations between traits, reflecting selection on single-trait evaluations. Simplified indices gave almost the same total response as a full multiple-trait index, but relative responses in carcass and milk traits differed between indices. Also studied were the consequences of restricting increase in body weight of dairy cows, while selecting for carcass and milk production traits. Restriction of body weight of cows resulted in considerable loss of predicted total response.

Uni- and bivariate breeding value estimation in a simulated horse population under sequential selection

Single trait evaluation in a population under sequential selection may result in selection bias due to disregarding selection on correlated traits. Furthermore, optimal index weights for single trait breeding values can only be approximated and selection across age classes would not be optimal if animals from different age classes have different amounts of information available, as is typical in horse breeding programs. To study the effect of multiple trait breeding value estimation on genetic gain, a riding horse population under sequential selection was stochastically simulated for station test performance and competition performance of progeny, and genetic evaluation was compared for uni- and bivariate models as well as for sire- and animal models. Genetic response of the univariate animal model was higher than response under the bivariate sire model. However, a bivariate sire model may be preferred when more selection bias is likely to occur, e.g with more intense selection on the correlated trait. The bivariate animal model did not converge when females had no records for the station test. The bivariate animal model converged well with station test information on females and resulted in more genetic improvement and shorter generation intervals.

Comparison of Multitrait and Single-Trait Multiple Parity Evaluations by Monte Carlo Simulation

Three methods of analysis: 1) multiparity analysis with the first three parities analyzed as correlated traits, 2) multiparity analysis with all lactations analyzed as a single trait, and 3) first parity only analysis were compared on simulated data. A selection index was used to compute multiparity evaluations from the separate parity evaluations of the multitrait analyses. Twenty simulated populations, each of 8,500 cows, were generated by an algorithm that approximated the multitrait model. Populations were simulated with both random and yield-based culling of cows after first and second parity. Populations simulated with yield-based culling were analyzed both with the first parity records of all cows included and with an arbitrary one-third of first parity records deleted. First parity records of all cows were included in the analyses of the randomly culled populations. Accuracy of evaluation, estimated by correlations between true effects and evaluations and prediction error variances, was highest by the multitrait analysis and lowest by the first parity only analysis. Evaluations obtained by the two multilactation methods were nearly identical with random culling. Regression of effect on evaluation was close to unity for the multitrait evaluation; was only .94 and .90 for the all lactation single trait evaluation with random and yield culling, respectively; and was .80 for the index of sire effects on the first parity only analysis. Single-trait multilactation method may be preferred, as it is nearly as accurate as the multitrait method and easier computationally.

Sire Evaluations with Parities as Correlated Traits and Comparison with Single-Trait Evaluations

All lactation records are included in sire evaluations for milk components by mixed model methodology by the United States Department of Agriculture. To increase accuracy of these evaluations, procedures were investigated for processing information from different parities as correlated traits rather than as a repeated single trait. The current model for mature equivalent records, which includes fixed group, random sire, fixed herd-year-season, and random cow effects, was modified to include a separate sire effect for each of first three parities. Residual variances for different parities were assumed uncorrelated and of equal variance and, therefore, could be factored out of mixed model equations. Cows and herd-year-seasons were absorbed prior to iteration. Sire equations then were augmented by addition of direct product of the inverse of the relationship matrix and the inverse of the matrix of genetic variance among parities. Sire solutions were obtained by block iteration, with blocks consisting of group equations and three equations for each sire. Evaluations were computed for 591 Ayrshire sires with 13,551 daughter records for milk and fat yields and 447 Guernsey sires with 10,817 daughter records. Computation time for multitrait evaluation was four times that for single trait multi parity evaluation, and convergence was .57 as fast. Correlations between multi parity evaluations and those based only on first lactation records ranged from .85 to .90. Correlations between single trait multi parity and multitrait evaluations ranged from .97 to .99 for sires with more than 10 daughters. Therefore, although inclusion of later lactation records increases accuracy of evaluations, little additional gain is achieved by multitrait evaluation.

Selection Bias and Multiple Trait Evaluation

Potential bias in evaluation of single traits resulting from selection on a correlated trait is considered. Examples of potential bias use simulated data for sequential selection and for a trait showing a correlated response. In both cases, single trait evaluation for the correlated trait does not consider the data on which selection decisions were made. The use of methodology for evaluation of multiple traits to reduce or eliminate bias from selection on correlated traits is demonstrated.

Impact of Culling on Sire Evaluation by Mixed Model Procedures

Records from the North Carolina State University dairy where each of 130 females produced second records regardless of first lactation yield provided data to compare sire evaluations from three mixed model procedures: single trait evaluations of first and second lactations (Model 1); evaluations of both lactations together including a random component for cow effects (Model 2); and a multitrait procedure where first and second lactation evaluations were calculated simultaneously (Model 3). Relationships among the 45 sires were included in all models. Culling was simulated at intensities of 10, 20, and 30% on deviation of first lactation from population means. Variation in Model 2 evaluations was least affected by increases of culling intensity. Evaluations by second lactation from Model 1 were most affected by culling. The effect of culling on variability of sire evaluations by Model 3 was not large but was dependent on the genetic correlation assumed between first and second lactations. Expected values for correlations between sire evaluations on first and second lactations were derived and tested.