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Integrating new technologies with embryology and animal production.

The present review describes a range of selected farm animal embryo technologies used in embryological research and applied in animal breeding and production. Some of the techniques are driven by the breeder's wish to obtain animals with higher breeding values, whereas others are primarily driven by the curiosity of researchers. The interaction between basic research and practical application in these areas is still a characteristic feature for people who contribute to the International Embryo Transfer Society (IETS) and has been an advantage for both researchers and breeders. One example of such an interaction is that detailed structural analyses have described quality differences between embryos of various origins and, following embryo transfer, the pregnancy results have confirmed the correlation between morphology and viability. Another example is that polymerase chain reaction technology has allowed detection of Y-specific sequences in male embryos and has become a tool in animal production today. Data from domestic animal genome sequencing will provide a great deal of new information. A major challenge for the years to come will be using this information in a physiologically meaningful context and to continue the efforts to convert the laboratory experience into use in practise. Finally, it is important to obtain societal acceptance for a wider application of many of the technologies, such as in vitro embryo production and cloning.

Animals↗

Variances of and correlations among progeny tests for reproductive traits of cows sired by AI bulls.

Estimates of daughter fertility were computed using first artificial insemination (AI) breedings reported to the US Dairy Herd Improvement Association (DHIA) from 1995 through 1997. An animal model was used to compute estimated breeding values (EBV) of daughter groups with fixed effects of herd-year-month bred and classes of early lactation energy-corrected milk, days in milk (DIM) when bred, and parity. Standard deviations and ranges of bull EBV for daughter fertility for DIM were 9.1 and -31 to 18; standard deviations and ranges of bull EBV for daughter fertility for nonreturn were 3.8 and -11 to 10. Correlations were computed for EBV for daughter fertility with EBV for mating bull fertility and with predicted transmitting abilities (PTA) for milk, somatic cell score (SCS), and productive life for bulls (213) with minimums of 200 matings and 100 progeny with reproductive traits. None of the correlations among EBV for reproductive traits differed from 0.0. Correlations of EBV for daughter fertility with PTA for productive life were significantly positive. PTA for yield traits were not correlated with EBV for daughter differences in nonreturn or DIM. Very low correlations of EBV for daughter reproductive traits with PTA for yield indicate that, in order to improve daughter fertility, fertility must be incorporated in sire selection decisions.

Animals↗

Effect of selection for maternal and direct genetic effects on genetic improvement of litter size in swine.

Optimum weighting ratios of maternal:direct EBV for litter size using an animal model were examined to achieve maximum genetic improvement (direct plus maternal response). Stochastic simulation of a 120-sow herd over a 10-yr period of selection was used (20 replicates). Directional selection was based on a merit function of maternal and direct EBV for first-parity litter size. Optimum weighting ratios for maternal to direct EBV in pure- and crossbreeding schemes with different genetic correlations between maternal and direct effects were obtained. Genetic gain in maternal and direct effects was more sensitive to change in weighting ratios of maternal to direct estimates of breeding values under an animal model than earlier theoretical studies showed for selection index. In the purebreeding scheme, the weighting ratios of maternal:direct effects of 1:1, 1.25:1, and -.5:1 resulted in the highest overall response of 3.11, 1.73, and .69 pigs after 10 yr of selection with genetic correlations between maternal and direct effects of 0, -.5, and -.9, respectively. In the crossbreeding scheme with a male dam line selected for direct effects only and a female dam line selected for an optimum weighting ratio of maternal and direct effects the overall response was always higher with 3.19, 1.89, and 1.31 pigs/10 yr for the genetic correlations 0, -.5, and -.9, respectively, than in the purebreeding scheme. With a large negative correlation between maternal and direct effects a meaningful overall response of litter size was achieved only in the crossbreeding scheme. The effect of negative weighting of maternal effects to increase direct response with a large negative correlation between maternal and direct effects was also examined as well as the influence of weighting ratios on accuracy of evaluation, additive genetic variance, prediction error variance, and correlation between maternal and direct EBV.

Animals↗

Effect of high-oleic trait and paste storage variables on sensory attribute stability of roasted peanuts.

There has been much interest in the effect of the high-oleic acid trait of peanuts on various quality factors since discovery of high levels of oleic acid in a peanut mutant genotype. The trait provides greater oxidative stability for the high-oleic oil and seed. Several research groups have investigated high-oleic peanut oil and roasted peanut flavor characteristics, which were similar within high-oleic lines compared to Florunner. It was observed that some high-oleic lines derived from the Sunrunner cultivar have consistently higher predicted breeding values for roasted peanut attribute than Sunrunner itself. This study investigated if this apparent effect of the trait was an artifact arising from the handling procedures during processing and storage or from flavor fade. High-oleic lines used were derived by backcrossing the trait into existing cultivars, and the comparison of sensory attribute intensity was with the recurrent parent used in backcrossing. Previous comparisons have been between lines differing in more than just oleate content, that is, with widely different background genotypes that could contribute to the differences observed. Differential rates of change in sensory attributes were found in different background genotypes, suggesting that the comparison of high- and normal-oleic lines should be made in common background genotypes as well as in common production and postharvest environments. There was no measurable change in roasted peanut attribute in samples stored at -20 degrees C over the 63 day duration of this experiment. There were changes in roasted peanut in samples stored at 22 degrees C, confirming that storage at -20 degrees C is sufficient for large studies that require multiple sensory panel sessions over a period of weeks.

Arachis↗

Genetic association between parameters of inmate immunity and measures of mastitis in periparturient Holstein cattle.

Relationships between genetic measures of mastitis (somatic cell score, score for clinical mastitis, and scores for IMI with major or minor pathogens) and immunological parameters (physiological and molecular markers) were examined for periparturient Holstein cows. Physiological markers included 11 in vitro immunological assays. Molecular markers included the second exon of the DRB3 locus of the bovine major histocompatibility complex, the IgG2 isotype genotype, and the CD18 genotype (the locus responsible for bovine leukocyte adhesion deficiency). A gene substitution model was used to estimate the additive genetic effects of alleles of the three molecular markers on estimated breeding value (EBV) for mastitis measures. Pearson correlation coefficients between EBV for immunological assays and EBV for mastitis measures were computed. Molecular markers explained up to 40% of the variation in EBV for measures of mastitis. The presence of allele DRB3.2*16 was associated with higher EBV for SCS. Allele DRB3.2*8 was associated with increased EBV for clinical mastitis, as was the IgG2b allele and the normal CD18 allele. Alleles DRB3.2*11, *23, IgG2a, and the recessive allele for bovine leukocyte adhesion deficiency were associated with decreased clinical mastitis. A positive genetic association was found between allele DRB3.2*24 and EBV for IMI by major pathogens and between DRB3.2*3 and IMI by minor pathogens. Several correlations between EBV for immunological assays and EBV for mastitis measures were significantly different from 0. Cows with low EBV for SCS tended to have neutrophils that had greater functional ability at maximal immunosuppression, low serum IgG1, and high numbers of circulating mononuclear cells. Immunological parameters, including physiological and molecular markers, are useful aids to understand the genetics of resistance to mastitis.

Alleles↗

Genetic analysis of male and female fertility using longitudinal binary data.

A longitudinal Bayesian threshold analysis of insemination events during the first 250 d after calving of first-parity Holsteins was carried out. The outcome of an insemination event was treated as a binary response of either a success (1) or a failure (0). Thus, all breeding information for a cow, including all service sires, was included, thereby allowing for a joint evaluation of male and female fertility. An edited data set of 297,823 insemination records from 151,758 first lactation cows was used. On the liability scale, the model included the systematic effects of herd-year of insemination, technician, month of insemination, and regressions on age of service sire, 3 test days in the first 100 d of lactation (early milk yield), and days in milk at insemination. The random effects in the model were the additive breeding value, the permanent effect of the cow, and the service sire effect. Posterior mean (standard deviation) of the dispersion parameters in the model were 0.034 (0.006), 0.009 (0.001), and 0.171 (0.013) for the additive, service sire, and permanent environmental variances, respectively. The residual variance was fixed at 1, as a result of the nonidentifiability of the threshold model. The posterior mean (standard deviation) of heritability was 0.028 (0.005). This point estimate of heritability is well within the range of available estimates for the trait. Thus, these estimates suggest that some genetic variation exists that can potentially be used to improve reproductive performance or at least avoid its further deterioration. The estimate of the regression coefficient on age of service sire was 0.001, indicating better fertility among older bulls. However, this result has to be interpreted with caution given the preferential use of proven bulls on well-managed cows (as opposed to problem breeders). The estimate of the regression coefficient was negative (-0.005) for early milk yield, as expected, and positive (0.003) for days in milk at insemination. This suggests that high-producing cows are less likely to conceive at the beginning of lactation.

Age Factors↗

Impact of bovine somatotropin on genetic evaluation of dairy sires and cows.

Data for milk yield and bovine somatotropin (bST) injections were obtained for 92,463 lactations of 51,986 Holstein cows in 222 commercial dairy herds that had used bST during the previous 3 yr. The overall rate of bST usage in these herds was 29%, and little difference occurred across lactations. A slightly higher percentage of cows with a low pretreatment phenotypic milk yield received bST, but there was no relationship between bST usage rate and the genetic potential of the cows, as measured by the PTA for milk of the sire. The mean starting date of bST administration was 147 d of lactation. Breeding values were estimated using three different animal models. In the first model, all data regarding bST injection were ignored. The second model included a fixed effect of bST injection (0 or 1 for each lactation). In the third model, data for bST injection were used to define management groups such that cows within a particular herd, year, and season of calving that received bST during the lactation were in a different management group from their contemporaries that did not receive bST. Correlations for EBV between sire and cow either ignoring bST or treating bST as a fixed effect were > 0.999, and approximately 98% of selected individuals were chosen under both models. Use of bST information to define the management groups resulted in correlations > 0.99; the EBV from models that either ignored bST or treated bST as a fixed effect had 92 to 95% of the selected individuals chosen by the other two models. The genetic correlation between lactation milk yield with and without bST treatment, which was estimated using a multiple-trait model, was 0.98, which indicated minimal interaction of genotype and bST. In general, bST treatment had little impact on the genetic selection decisions in this study.

Animals↗

Bayes factor for testing between different structures of random genetic groups: a case study using weaning weight in Bruna dels Pirineus beef cattle.

The implementation of genetic groups in BLUP evaluations accounts for different expectations of breeding values in base animals. Notwithstanding, many feasible structures of genetic groups exist and there are no analytical tools described to compare them easily. In this sense, the recent development of a simple and stable procedure to calculate the Bayes factor between nested competing models allowed us to develop a new approach of that method focused on compared models with different structures of random genetic groups. The procedure is based on a reparameterization of the model in terms of intraclass correlation of genetic groups. The Bayes factor can be easily calculated from the output of a Markov chain Monte Carlo sampling by averaging conditional densities at the null intraclass correlation. It compares two nested models, a model with a given structure of genetic groups against a model without genetic groups. The calculation of the Bayes factor between different structures of genetic groups can be quickly and easily obtained from the Bayes factor between the nested models. We applied this approach to a weaning weight data set of the Bruna dels Pirineus beef cattle, comparing several structures of genetic groups, and the final results showed that the preferable structure was an only group for unknown dams and different groups for unknown sires for each year of calving.

Animals↗

Simulation study of teat score in first-parity Gelbvieh cows: parameter estimation.

Teat scores from 9,598 first-parity Gelbvieh cows were used to investigate the adequacy of grouping approaches to decrease score misclassifications or inconsistencies as well as to simplify the data collection process. The procedure was tested using simulated data and then validated using teat score records of Gelbvieh cattle. First-parity cows were considered to be 4 yr of age or younger at first calving, did not have multiple records within 280 d, and were at least 50% Gelbvieh. Producers scored cows within 24 h of parturition. Teat score, a subjective measure of teat size, ranged from 0 (very large) to 50 (very small). A linear mixed model that included herd-year, month of calving, and age at calving as systematic effects; regression on the percentage of Gelbvieh; and additive breeding values (BV) and residual as random effects was used to generate the data. Simulated data were analyzed using one of three scoring methods: all values (S50), 10 classes (S10), and five classes (S5). The 10 classes were formed by subdividing every five scores into a single class starting at score zero. Similarly, the five classes were formed by combining every 10 scores into one class. The average Pearson correlations, based on five replicates, between the true and estimated BV (systematic effects) were 0.36 (0.85), 0.35 (0.89), and 0.32 (0.87) using S50, S10, and S5, respectively. Average correlations between estimated BV (systematic effects) were 0.97 (0.95), 0.89 (0.92), and 0.92 (0.97) based on S50 and S10, S50 and S5, and S10 and S5, respectively. Field data were used to validate the simulation procedure. The field data were categorized into 10 classes (F10) and five classes (F5) as described for the simulated data. Pearson correlations between estimated BV (systematic effects) were 0.99 (0.93), 0.93 (0.88), and 0.93 (0.96), based on F50 with F10, F50 with F5, and F10 with F5, respectively. The extremely high correlations between predicted BV based on S50, S10, S5, F50, F10, with F5 suggest that a simplified score classification method could be adopted without compromising the expected genetic progress for the trait under consideration. Furthermore, the difference in corresponding Pearson correlations across the field and simulated data might suggest the presence of some inconsistencies or misclassifications of the actual scoring system.

Animals↗

Animal model estimation of genetic parameters and response to selection for litter size and weight, growth, and backfat in closed seedstock populations of large white and Landrace swine.

Records from 2,495 litters and 14,605 Landrace and Large White pigs from two farms, but established from the same base population and run as replicated selection lines, were analyzed. Selection within herd was on estimated breeding values weighted by economic values. Animal models and REML procedures were used to estimate genetic, phenotypic, and environmental parameters for the number of pigs born alive (NBA), litter weight at 21 d (LW), average daily gain form approximately 30 to 104 kg (ADG), and backfat thickness adjusted to 104 kg (BF). Random animal genetic effects (o), permanent (NBA and LW) or litter (ADG and BF) environmental effects, maternal genetic effects (m), and the covariance between o and m were sequentially added to the model. Estimates of total heritability calculated from all data (ht2 = sigma o2 + 1/2 sigma m2 + 3/2 sigma om) ranged from .01 to .14 for NBA, from .18 to .22 for LW, from .23 to .34 for ADG, and from .40 to .50 for BF. Maternal genetic variance was from 2.4 to 3.8% of phenotypic variance in NBA, from 1.2 to 3.6% in LW, from .5 to 1.5% in ADG, and from 1.9 to 3.4% in BF. The correlation between o and m was -.07 for NBA, -.25 for LW, -.34 for ADG, and -.26 for BF. Permanent environmental effects explained from 16 to 17% of total phenotypic variation for NBA and from 1.6 to 5.3% for LW. Approximately 7% of the variation in ADG and 5% in BF was due to litter environmental effects.(ABSTRACT TRUNCATED AT 250 WORDS)

Adipose Tissue↗

The relationship between genetic merit for yield and live weight, condition score, and energy balance of spring calving Holstein Friesian dairy cows on grass based systems of milk production.

The objectives of this study were to estimate the effects of genetic merit for milk yield on energy balance, DM intake (DMI), and fertility for cows managed on three different grass-based feeding systems and to estimate possible interactions between genetic merit and feeding system. Individual animal intake estimates were obtained at pasture on 11 occasions across three grazing seasons. The data set contained 96 first lactation, 96 second lactation, and 72 third lactation cows in 1995, 1996, and 1997, respectively. Half of these cows were of high genetic merit, and half were of medium genetic merit for milk solids production. Genetic effects for the traits of interest were estimated as the contrast between the two genetic groups and by the genetic regression of phenotypic performance on the estimated breeding value for fat and protein yield, based on pedigree index. Significant effects of feeding system were observed on yields, DMI, and energy balance, with no effect on live weight, condition score, or reproductive performance. The interaction between genetic merit and feeding system was not significantly different from zero for any of the traits. Yields, grass DMI, and total DMI were all higher for cows of high genetic merit than for those of medium genetic merit and were positively correlated (P < 0.001) with pedigree index. Furthermore, condition score, conception to first and second services, and pregnancy rate were significantly negatively correlated with pedigree index. While at pasture, energy balance was positively (P < 0.01) correlated with pedigree index, although the contrast between high genetic merit and medium genetic merit was not significantly different from zero. This positive energy balance was unexpected and was probably due to the lactation stage that intake was measured. Condition score changes and energy balance measures on a small subgroup of the animals, while indoors offered a diet of silage and concentrates (n = 33), demonstrated that high genetic merit had a more negative energy balance than did medium genetic merit. The results clearly illustrate the production potential of high genetic merit cows on grass-based systems. The reduced reproductive performance questions the suitability of high yield for seasonal calving systems.

Animal Feed↗

Investigation of three strategies for an international genetic evaluation of beef cattle weaning weight.

Weaning weights from 83,389 Limousin calves born between 1993 and 2002 in France and the Trans-Tasman block (Australia/New Zealand) were analysed to compare different strategies for running an international genetic evaluation for the breed. These records were a subset of the complete data for both countries and comprised a sample of herds that had recorded progeny of sires used across both countries. Genetic and phenotypic parameters for weaning weight were estimated within the countries. The estimates of direct genetic heritabilities were higher in France than in the Trans-Tasman block (0.31 vs. 0.22), while direct-maternal genetic correlations were less negative in the Trans-Tasman block (-0.10) than in France (-0.21). Different strategies for an international evaluation were studied, and the correlations between the estimated breeding values (EBV) of national evaluations and these strategies were derived. The international evaluation strategies were a) an animal model on raw performance data with non unity genetic correlations and heterogeneous residual and genetic variances across countries; b) the same animal model applied to pre-corrected (for fixed effects) performance data; and c) a sire model on de-regressed proofs (MACE). Estimates of the genetic correlations between weaning weight in both countries were 0.86 (0.80) for direct (maternal) genetic effects for the first strategy. Estimation of variance components by MACE appeared to be very sensitive to the sample of bulls and their reliability approximations. Variance component estimates obtained using pre-corrected data were inconsistent with estimates on raw data. However, the EBV predicted using pre-corrected data and parameters estimated from the raw data were similar to those predicted from raw data. Correlations between national and international EBV were always high (> 0.90) for sires, whichever genetic effect (direct or maternal) or international evaluation model was considered. The ranking of the bulls in the top 100 is of primary interest in terms of international genetic evaluation. In this study, some re-ranking of sires was observed for the top 100 bulls between countries and between the three international evaluation models. Thus, the origin of top sires may vary according to the implemented international evaluation strategy.

Algorithms↗

Solving large test-day models by iteration on data and preconditioned conjugate gradient.

A preconditioned conjugate gradient method was implemented into an iteration on a program for data estimation of breeding values, and its convergence characteristics were studied. An algorithm was used as a reference in which one fixed effect was solved by Gauss-Seidel method, and other effects were solved by a second-order Jacobi method. Implementation of the preconditioned conjugate gradient required storing four vectors (size equal to number of unknowns in the mixed model equations) in random access memory and reading the data at each round of iteration. The preconditioner comprised diagonal blocks of the coefficient matrix. Comparison of algorithms was based on solutions of mixed model equations obtained by a single-trait animal model and a single-trait, random regression test-day model. Data sets for both models used milk yield records of primiparous Finnish dairy cows. Animal model data comprised 665,629 lactation milk yields and random regression test-day model data of 6,732,765 test-day milk yields. Both models included pedigree information of 1,099,622 animals. The animal model ¿random regression test-day model¿ required 122 ¿305¿ rounds of iteration to converge with the reference algorithm, but only 88 ¿149¿ were required with the preconditioned conjugate gradient. To solve the random regression test-day model with the preconditioned conjugate gradient required 237 megabytes of random access memory and took 14% of the computation time needed by the reference algorithm.

Algorithms↗

Accuracy of international conversions of elite sires and cows when conversion equations are based on linear regression.

Conversion equations that are based on linear regression are used widely to transform estimated breeding values (EBV) of sires for production, type, health, and management traits from the genetic base, scale, and units of measurement of an exporting country to that of an importing country. One of the major deficiencies of these regression equations is that the accuracy of converted EBV of elite sires and cows, which are of primary interest in genetic selection programs, is lower than that for average animals. In this study, it is shown both mathematically and in practical examples that the standard error (SE) of prediction of elite dairy sires can be much larger than for average sires. When more than 100 sires are used to develop conversion equations, the SE of prediction for elite AI sires is up to 10% larger than for an average sire, and, when 50 sires are used to develop conversion equations, the SE of prediction is up to 25% larger than for an average sire. When fewer than 50 sires are used to develop conversion equations, the accuracy of converted EBV of elite sires is very poor, and SE can be 30 to 60% larger than for an average sire. Based on this study, it is recommended that international sire evaluations based on BLUP methodology (rather than linear regression) be made available as soon as possible for nonproduction traits in all countries and for production traits in countries that currently do not participate in routine INTERBULL (International Bull Evaluation Service) analyses.

Animals↗

Progress of farrowing and early postnatal pig behavior in relation to genetic merit for pig survival.

The objective of this study was to investigate whether pigs with different genetic merit for survival differed in birth weight, progress of farrowing, early postnatal behavior, or rectal temperature within 24 h after birth. On a nucleus farm in Rio Verde, Brazil, information was collected on 280 pigs, originating from 25 litters with known estimated breeding values for pig survival (EBVps). Litters were selected in such a way that a continuous range of EBVps with a maximum genetic contrast was achieved. Birth weight was recorded for all pigs. Indicators for progress of farrowing were birth intervals and duration of farrowing. Behavioral indicators of pig vitality were time until first upright standing (FUS), time until first udder contact (FUC), time until first teat in mouth (FTM), and time until first colostrum uptake (FCU). Rectal temperature was measured within 24 h after birth. Farrowing survival and early postnatal survival (within 3 d after farrowing) were registered. Farrowing survival and early postnatal survival both increased with increasing EBVps (farrowing survival: P = 0.007; early postnatal survival: P = 0.027). Birth weight decreased with increasing EBVps (P = 0.01). Birth intervals tended to increase with increasing EBVps (P = 0.10) and duration of farrowing was not related to EBVps. Time until first teat in mouth increased with increasing EBVps (P = 0.05), but the other behavioral indicators of pig vitality were not related to EBVps. Rectal temperature within 24 h after birth was not related to EBVps. Pigs with a higher genetic merit for survival have a lower birth weight but nevertheless have an increased farrowing survival and early postnatal survival. Their increased survival cannot be explained by differences in progress of farrowing, early postnatal behavior, or rectal temperature within 24 h after birth.

Animals↗

Modifying the lactation curve to improve lactation milk and persistency.

Daily, stage and lactation estimated breeding values (EBV) and the shape of the lactation curve for each cow are controlled by a unique set of random (genetic) regression coefficients under a test day model, thus providing a basis for genetic improvement of these characteristics. Three selection procedures were developed for simultaneous improvement of total lactation milk and persistency: 1) index selection based on daily EBV, 2) index selection based on stage EBV, and 3) index selection based on random regression (RR) coefficients. A numerical example was given to demonstrate the computation of indexes based on stage EBV and based on RR coefficients. A conversion equation was derived to convert between genetic changes in EBV and RR coefficients. Index selection based on daily EBV would require the finding of 305 weighting factors for a lactation period of 305 d, making it impractical to determine the weighting factors on a daily basis. Alternatively, a lactation period was partitioned into a few stages to facilitate the construction of index selection based on stage EBV and index selection based on RR coefficients. These selection procedures make use of the annual genetic gains routinely computed in national genetic evaluations to restrict the genetic gains between different lactation stages to achieve the desired curve. When there is no prior knowledge of annual genetic gains, the proportional restriction of genetic gains between stages may be used. In summary, this study provides a simple means of modifying the lactation curve by manipulating genetic changes in different lactation stages at a pre-specified rate.

Animals↗

Potential gain from optimizing multigeneration selection on an identified quantitative trait locus.

The potential extra response that can be obtained from the optimal use of a known QTL in selection by optimizing weights in an index of breeding value for the QTL and polygenic EBV was investigated for a range of parameters. Optimal strategies were derived for a deterministic model of simultaneous selection on a QTL and polygenic effects using optimal control theory. Responses over 10 generations to the following selection strategies were compared: 1) standard QTL selection, with QTL weights equal to 1, 2) optimal QTL selection, 3) stepwise single-generation optimal QTL selection, and 4) non-QTL selection based on phenotype. Cumulative discounted response with discount rates of 10 or 30% per generation were evaluated and used as objective for optimal selection strategies. Optimal selection balanced the conflict between short- and long-term responses and gave greater cumulative discounted response than standard QTL selection of up to 20%, but less than 5% for most cases. Discount rate had limited impact. For a QTL with an additive effect of one polygenic standard deviation, cumulative discounted response from optimal QTL selection was less than 5% greater than response for non-QTL selection for most cases. Exceptions were traits with low heritability and recessive QTL at low frequency, for which extra response was up to 55% greater. Stepwise optimal selection resulted in less cumulative discounted response than standard QTL selection for QTL with negative dominance. The benefit of optimal over stepwise optimal selection was limited (less than 4%) for most cases, except for overdominant QTL. These results indicate that optimizing selection on an identified QTL can result in greater responses to selection but that extra responses tend to be limited for the situations studied here of single-stage purebred selection on a single QTL for a trait observed on both sexes.

Animals↗

Number of calves born, number of calves weaned, and cumulative weaning weight as measures of lifetime production for Hereford cows.

Genetic parameters for lifetime production for cows with the opportunity to produce from 2 through 7 yr of age, as measured by the number of calves born (NB2, ..., NB7), the number of calves weaned (NW2, ..., NW7), and cumulative weaning weight (CW2, ..., CW7), were estimated using data from 3,064 Hereford cows from a selection experiment with a control line (CTL) and three lines selected for weaning weight (WWL), yearling weight (YWL), and an index of yearling weight and muscle score (IXL). Weaning weights were adjusted to 200 d of age and for sex and age of dam. Estimates of heritability and genetic and environmental correlations were obtained by restricted maximum likelihood with bivariate animal models, with year of birth of the cow as a fixed effect and direct genetic and residual as random effects. Genetic trends were estimated by regressing means of estimated breeding values by year of birth and line on birth year. Estimates of heritability (SE) for opportunity groups of 2 to 7 yr of age ranged from 0.08 (0.03) to 0.16 (0.05) for NB; from 0.05 (0.02) to 0.16 (0.05) for NW; and from 0.06 (0.02) to 0.16 (0.05) for CW. Estimates of genetic correlations (SE) among NB traits ranged from 0.60 (0.14) to 1.00 (0.00), and estimates of environmental correlations (SE) ranged from 0.67 (0.02) to 0.99 (0.00). For NW, estimates of genetic and environmental correlations ranged from 0.98 (0.11) to 1.00 (0.00) and from 0.65 (0.02) to 0.99 (0.00), respectively. Estimates of genetic correlations (SE) among CW traits ranged from 0.94 (0.08) to 1.00 (0.00). Estimates of environmental correlations (SE) ranged from 0.66 (0.02) to 0.99 (0.00). Estimates of genetic correlations for NB2 with all definitions of NW ranged from 0.47 (0.18) to 0.71 (0.12), and with all definitions of CW ranged from 0.55 (0.16) to 0.80 (0.11). Estimates of genetic correlations between NW2 and all definitions for CW ranged from 0.95 (0.02) to 0.99 (0.06). Estimates of annual genetic (SE) change were negligible for NB2, NB6, NW2, and NW6 for all lines. Estimates of annual genetic (SE) change for CW2 were 0.85 (0.11), 0.79 (0.14), 0.51 (0.10), and 0.52 (0.18) kg/yr, and for CW6 were 5.01 (1.25), 2.64 (1.75), 3.67 (1.16), and 3.33 (2.37) kg/yr for WWL, YWL, IXL, and CTL, respectively. Selection for lifetime production as measured by NB, NW, or CW could be effective but would be relatively slow due to low estimates of heritability and to increased generation intervals.

Animals↗