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Results for “Breeding value”

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At least 127 records · Page 7Linked to original sources

PubMed1991

Direct response in yield and correlated response in components accompanying selection for milk yield in Jerseys.

In 1967, the Jersey herd at the Dairy Experiment Station, Lewisburg, TN was divided into two groups on the basis of ancestry, type, and breeding value for milk as part of a project to determine effects of single trait selection for high milk yield on yield and correlated traits. Control group was mated randomly to 20 unproven young sires selected randomly from those available from breeding studs in 1967. Selection group was mated to sires selected solely on the basis of their high transmitting ability for milk. Selection sires were selected at intervals and used for 4 yr. By the end of the project (1984), lactation information was available on 672 daughters (520 selection and 152 control) of 37 bulls (17 selection and 20 control). Differences in breeding values for milk, fat, and fat test as calculated from the PTA reported in the July 1989 USDA genetic evaluations and differences in first lactation mature equivalent production of milk, fat, fat test, and 4% FCM were examined. Linear mixed models were used for all analyses and contained the fixed effects group, generation within group, and year. Sires were random, nested within group, and used to test for group differences. Groups differed for all traits. Selection was superior to control in breeding value for milk and fat (828 and 31 kg) and for production of milk, fat, and 4% FCM (1066, 42, and 1061 kg). Control was superior to selection in breeding value and production fat test (.15 and .12%). Group differences existed within generation class for all yield traits but not for fat percentage. Realized response closely matched or exceeded expected response as estimated from pedigree information.

Animals↗

PubMed2006

Strategy for applying genome-wide selection in dairy cattle.

Animals can be genotyped for thousands of single nucleotide polymorphisms (SNPs) at one time, where the SNPs are located at roughly 1-cM intervals throughout the genome. For each contiguous pair of SNPs there are four possible haplotypes that could be inherited from the sire. The effects of each interval on a trait can be estimated for all intervals simultaneously in a model where interval effects are random factors. Given the estimated effects of each haplotype for every interval in the genome, and given an animal's genotype, a 'genomic' estimated breeding value is obtained by summing the estimated effects for that genotype. The accuracy of that estimator of breeding values is around 80%. Because the genomic estimated breeding values can be calculated at birth, and because it has a high accuracy, a strategy that utilizes these advantages was compared with a traditional progeny testing strategy under a typical Canadian-like dairy cattle situation. Costs of proving bulls were reduced by 92% and genetic change was increased by a factor of 2. Genome-wide selection may become a popular tool for genetic improvement in livestock.

Animals↗

PubMed1994

Selection response for growth in a Spanish Merino flock.

Weight and growth data on 5,136 lambs, from 104 sires and 1,552 dams were collected during 5 yr (13 lambing seasons) of selection for high weaning weight in a single flock under range conditions. Ewes were culled based on fertility and replaced by ewe lambs selected for high weaning weight. Heritabilities of birth weight, weaning weight (30 d), preweaning daily gain, postweaning daily gain, and weight at 90 d, respectively, were .13, .09, .03, .15, and .11. Genetic correlations were generally high. Genetic and phenotypic improvements of weaning weight were 22 +/- 3.0 and 198 +/- 11.3 g per lambing season, respectively, with three lambing seasons per year. At the end of the experiment, phenotypic mean weaning weight was 2.4 kg higher than the initial values. Estimated and theoretical responses were similar in the sire population. At the 13th lambing season, average breeding value of 30-d weight of the ram population was 600 g higher than the mean initial breeding value in the base population. Projected average breeding value of the lamb population under random mating at the 23rd lambing season was equal to 404 g.

Animals↗

PubMed2004

Genetic correlations among production, body size, udder, and productive life traits over time in Holsteins.

Genetic correlations among milk, fat, and protein yields; body size composite (BSC); udder composite (UDC); and productive life (PL) in Holsteins were investigated over time. The data set contained 25,280 records of cows born in Wisconsin between 1979 and 1993. The multiple trait random regression (MT-RR) animal model included registration status, herd-year, age group, and stage of lactation as fixed effects; additive genetic effects with random regressions (RR) on year of birth using the first-order Legendre polynomial; and residual effects. Heterogeneous residual variances were considered in the model. Estimates of variance components and genetic correlations among traits from MT-RR were compared with those estimated with a multiple trait interval (MT-I) model, which assumed that every 3-yr interval was a separate trait and included the same effects as in the MT-RR model except for the RR. Genetic correlations estimated with MT-RR and MT-I models over time among all traits were compared with correlations among breeding values predicted with the single trait (ST) model without RR. Correlations among breeding values predicted with MT-RR, ST, and MT models were also calculated. Additive genetic and residual variances for all traits except PL increased over time; those for PL were constant. As a result, heritability estimates had no significant changes during the 15 yr. Genetic correlations of PL with milk, fat, protein, and BSC declined to zero or negative; those with UDC remained positive. Correlations among breeding values predicted with ST, MT, and MT-RR models were relatively high for all traits except PL. Genetic correlations between PL and other traits varied over time, with some correlations changing sign. For accurate indirect prediction of PL from other traits, the genetic correlations among the traits need to be re-estimated periodically.

Animals↗

PubMed2003

Evaluation models and genetic parameters for calving difficulty in beef cattle.

Calving difficulty was analyzed under threshold and linear models considering either a fixed or random herd-year effect. The aim of the study was to compare models for predicting breeding values according to the size of herd-year groups. When simulating data sets with small herds, in order to obtain an unbiased evaluation under a nonrandom and negative association of sire and herd effects, the best model for a practical evaluation was the fixed linear model. Field data included 246,576 records of the largest Charolais herds in France. Models were compared using the correlations of estimated breeding values between the different models. Although the best model from a theoretical point of view was a threshold model with a fixed herd-year effect, a linear model with a fixed herd-year effect was the best choice from a practical point of view for predicting direct effects for calving difficulty in beef cattle and was a sufficient choice for predicting the associated maternal effects for data set with large herds. Correlations between direct estimated breeding values under the reference model and the fixed linear model and the random threshold model were 0.94 and 0.91, respectively. Correlations between the corresponding maternal estimated breeding values were 0.94 and 0.98. Heritabilities of direct effects were 0.27 and 0.14 under fixed threshold and fixed linear models, respectively. The corresponding heritabilities of maternal effects were 0.18 and 0.13, and the genetic correlation between direct and maternal effects were -0.36 and -0.34, respectively.

Animal Husbandry↗

PubMed2003

Undesirable evolutionary consequences of trophy hunting.

Phenotype-based selective harvests, including trophy hunting, can have important implications for sustainable wildlife management if they target heritable traits. Here we show that in an evolutionary response to sport hunting of bighorn trophy rams (Ovis canadensis) body weight and horn size have declined significantly over time. We used quantitative genetic analyses, based on a partly genetically reconstructed pedigree from a 30-year study of a wild population in which trophy hunting targeted rams with rapidly growing horns, to explore the evolutionary response to hunter selection on ram weight and horn size. Both traits were highly heritable, and trophy-harvested rams were of significantly higher genetic 'breeding value' for weight and horn size than rams that were not harvested. Rams of high breeding value were also shot at an early age, and thus did not achieve high reproductive success. Declines in mean breeding values for weight and horn size therefore occurred in response to unrestricted trophy hunting, resulting in the production of smaller-horned, lighter rams, and fewer trophies.

Alberta↗

PubMed1998

Associations between milk-protein production and reproduction, health, and culling.

Associations between protein production and individual-cow reproductive performance, health, and culling were investigated in a 2-year observational study involving a convenience sample of 75 Ontario, 5 Alberta, and 3 Nova Scotia dairy farms. Protein production was defined by 305-day lactation protein yields and by estimated breeding values for protein yield. After controlling for the level of milk production, herd, parity, breed, and season of calving, there were no significant associations between either measure of protein production and days open or days to first breeding. The only associations between protein production and disease were small positive associations between the estimated breeding value for protein yield and cystic ovaries and mean lactation somatic cell count. The risk of culling, after controlling for the level of milk production, was negatively associated with previous-lactation 305-day protein yield for parity three animals only. The estimated breeding value for protein yield had a small negative association with the overall risk of culling, although the associations were not significant for individual lactations.

Alberta↗

PubMed2005

Genetic analyses of mastitis data using animal threshold models and genetic correlation with production traits.

In the present study, 6 different mastitis data sets of 3 dairy herds with an overall herd size of 3200 German Holstein cows were analyzed. Data collection periods included the first 50, 100, or 300 d of lactation. The 3 data collection periods were analyzed with a lactation model and a test-day model. All models were animal threshold models. Mastitis frequencies in the lactation model data sets varied between 29 and 45%, and varied between 3 and 6% in the test-day model data sets. Depending on the period of data collection, heritabilities of liability to mastitis in the lactation models were 0.05 (50 d), 0.06 (100 d), and 0.07 (300 d). In the test-day models, heritabilities were slightly higher with values of 0.09 (50 and 100 d), and 0.06 (300 d). Between lactation models, the rank correlations between the relative breeding values were high and varied between 0.86 and 0.94. Rank correlations between the relative breeding values of the test-day models ranged from 0.68 to 0.87. The rank correlations between the relative breeding values of lactation models and test-day models varied from 0.51 and 0.80. Genetic correlations between mastitis and milk production traits were estimated with a linear animal test-day model. The correlations with mastitis were 0.29 (milk yield), 0.30 (fat yield), 0.20 (fat content), 0.34 (protein yield), and 0.20 (protein content). The estimated genetic correlation between mastitis and somatic cell score was 0.84.

Animals↗

PubMed2002

Joint evaluation of purebreds and crossbreds in swine.

Data from two purebred swine lines A (n = 6,022) and B (n = 24,170), and their reciprocal, cross C (n = 6,135), were used to examine gains in reliability of combined purebred and crossbred evaluation over conventional within-line evaluations using crossbred and pureline models. Random effects in the pureline model included additive, parental dominance, and litter. In the crossbred model, effects were as in the pureline model except traits of each line were treated as separate traits and two additive effects were present. The approximate model was the same as the pureline except it was used for all lines disregarding breed differences. The traits in the evaluation were lifetime daily gain (LDG) and backfat. When separate line evaluations were replaced by evaluations with crossbreds, mean reliabilities of predicted breeding values increased by 2 to 9% for purebreds and by 21 to 72% for crossbreds. Rank correlations between these breeding values were > 0.99 for purebreds but 0.85 to 0.87 for crossbreds. Rank correlations between predicted breeding values obtained from crossbred and approximate models were 0.98 to 0.99 for purebreds and 0.96 to 0.98 for crossbreds. When the number of crossbreds was small in comparison to purebreds, the increase in reliability by using the crossbred data and the crossbred model as opposed to purebred models was small for purebreds but large for crossbreds. The approximate model provided very similar rankings to the crossbred model for purebreds but rankings were less consistent for crossbreds.

Animals↗

PubMed1991

Animal model evaluations for Mexican Holsteins.

Genetic evaluations for milk yield of Holsteins in Mexico were computed from lactation and pedigree information from Holstein de México. The US animal model system for national evaluations was adapted for Mexico. The primary change was in defining unknown-parent groups. Paths of unknown parents of bulls and sires of cows were combined and separate unknown-parent groups defined for parents of US, Canadian, and Mexican registration. Records with fewer than 305 d were expanded around management group mean; a lower limit of 50% of management group mean was imposed on these records. Based on 123,397 lactation records by 50,538 cows, evaluations were computed for 68,020 cows (including those without lactation records) and 4573 bulls. Estimate of breeding value improvement in 1986 from a quadratic curve was 87 kg milk. Animal model estimates of breeding value by birth year were similar to Modified Contemporary Comparison estimates. Correlations with previous evaluations were .90 for bulls and .85 for cows. Differences resulted from added data as well as changes in evaluation method. Cows born in 1985 with US sires had predicted breeding values for milk 380 kg higher than those with Canadian sires and 336 kg higher than those with Mexican sires. Equations were developed to convert milk PTA between Mexico and the US. No sire by country interaction was found; correlations of US and Mexican PTA were .90 (expected) and .91 (actual).

Animals↗

PubMed2000

Comparison of plasma FSH concentration in boars and gilts from lines selected for ovulation rate and embryonal survival, and litter size and estimation of (co)variance components for FSH and ovulation rate.

The objective of this research was to determine whether plasma concentration of FSH was genetically correlated with ovulation rate and thus was a useful trait for indirect selection. Blood samples were collected from 619 animals from five lines of pigs. Line I was selected for increased index of ovulation rate and embryonal survival, and Line C was its randomly selected control. Pigs sampled from Lines I and C were from generations 12 and 13. Pigs from three additional lines that were derived from eighth-generation pigs of Lines I and C also were used. These lines were Line C2, a randomly selected control derived from Line C, Line COL, derived from Line C, and Line IOL, derived from Line I; each of these lines was selected an additional five generations for increased ovulation rate and increased litter size. A single blood sample was collected from each pig between 46 to 63 (d 58), 86 to 98 (d 90), 110 to 133 (d 124), and 147 to 153 (d 150) d of age. The heritability of ovulation rate was .28 and heritabilities of plasma concentration of FSH at d 58, 90, 124, and 150 were .41, .25, .12, and 0, respectively. Genetic correlations between ovulation rate and d-58, d-90, and d-124 plasma concentration of FSH were .31, .23, and 0, respectively. Line I gilts had greater estimated breeding values for plasma concentration of FSH at d 58 and 90 than Line C gilts (P < .01). Line COL gilts had greater estimated breeding values for plasma concentration of FSH at d 58 than Line C2 gilts (P < .01). Line I boars had greater estimated breeding values for plasma concentration of FSH at d 90 than Line C boars (P < .05). Even though genetic correlations were low, selection for increased plasma concentration of FSH was estimated to be 93% as effective in changing ovulation rate as direct selection because selection for FSH can be practiced in both sexes. Thus, selection for increased plasma concentration of FSH seems to be a practical method for increasing ovulation rate in pig breeding programs without using laparoscopy.

Animals↗

PubMed2003

Risk-adjusted expected return for selection decisions.

The results of genetic evaluation are predictions of breeding values for the selection candidates, and these involve uncertainty with regard to future returns from the use of those selected individuals. This uncertainty is due to differential variability in BLUP of breeding values and can be translated into risk: High fluctuations mean greater risk, which is not taken into account by just looking at expected return. In this research, the methodology of value at risk (VaR) and expected shortfall is introduced for animal breeding decisions as a means to adjust the expected return for the cost of uncertainty in prediction of breeding values. This methodology has recently received a great deal of attention from financial institutions. Given a specified probability alpha, VaR is the alpha-quantile of the distribution of economic returns. The conditional value at risk (CVaR), or expected shortfall, is the expectation of the economic returns, which are less or equal to the VaR. By subtracting the CVaR from the predicted aggregate genotype (muR), a risk-adjusted expected return (RAER) measure was obtained. The measures muR, VaR, and RAER were calculated for a data set with progeny of 161 Polled Hereford bulls belonging to a beef cattle company. The Pearson and Spearman correlations between muR and RAER were 0.89 (P < 0.001) and 0.90 (P < 0.001), respectively. Even though the latter correlation was high, some bulls ranked differently for muR compared with RAER. The Pearson correlation between muR and VaR was low (0.124) and nonsignificant (P > 0.05), whereas the correlation between VaR and RAER was -0.31 (P < 0.0001). The results indicate the need to take into account the adjustment for risk in expected return in order to alleviate the effects of possible losses when overrated animals are selected.

Algorithms↗

PubMed2005

Longitudinal multiple-trait versus cumulative single-trait analysis of male and female fertility and hatchability in chickens.

The objective of the current study was to compare the performances of a longitudinal multipletrait (MT) and cumulative single-trait (ST) best linear unbiased prediction (BLUP) analyses for eggs set (E), percentage fertility (F), and percentage hatched of fertile eggs (H) based on rank correlations of hens and roosters (ROO). Longitudinal data consisted of weekly records, and the ST analysis used cumulative records of the 3 traits. Comparisons were carried out using simulated data, in which true values assigned during the simulation were compared with estimates obtained from MT and ST analyses. Two methods were used to calculate breeding values and ROO effects. Weekly E records equal to zero were assumed as valid records, and missing weekly F and H records were predicted in MT analysis. However, missing F and H were not included in the cumulative record. Weekly E, F, and H records were converted to a cumulative record following a procedure used by a commercial broiler company. Further, ST models were developed to evaluate hens and ROO separately as currently practiced in the industry. There was virtually no difference in hen rank correlations between true and predicted breeding values obtained from MT (0.64) and ST (0.62) analyses for E, thus suggesting that hens would rank similarly when weekly or cumulative data were used for genetic evaluation of E. With ST analysis, rank correlations between true and estimated hen breeding values decreased, compared with MT analysis, by 74 and 75% for F and H, respectively. For F (H), the rank correlation between true and estimated ROO effects were 0.64 (0.67) and 0.17 (0.12) with MT and ST analyses, respectively. The results suggested that longitudinal MT BLUP, which handles missing records, was better at estimating true hen and ROO effects than ST BLUP. Therefore, it was recommended that the MT BLUP method be used for genetic evaluation of hens and ROO for E, F, and H.

Animals↗

PubMed2005

Associations among descriptors of herd management and phenotypic and genetic levels of health and fertility.

The objective of this paper was to investigate the association of descriptors of herd environment with phenotypic levels and breeding values of fertility and health traits. Analyses were performed for 82,080 first-lactation heifers and 173,787 multiparous cows. Fourteen environmental parameters were defined that described herd environment, such as average protein production, average somatic cell score (SCS), average calving interval, and average body condition score (BCS). Herds with lower average SCS had, in general, more desirable values for almost all analyzed traits (i.e., days to first service was 7 d shorter), as did herds with lower average calving interval (i.e., 2.8% lower incidence of predicted mastitis). Herds with higher average protein production had slightly poorer fertility but more desirable values for all other analyzed traits (i.e., 5.1% less predicted mastitis, 0.4 lower SCS, and 0.6 higher BCS). Variance components and breeding values of sires were estimated by applying a random regression on the environmental parameters. In general, genetic variances varied generally only slightly across environments. However, based on data exclusively for heifers, the genetic variance for number of inseminations was 4.1 times higher in herds with a higher number of inseminations, 1.9 times higher for survival in herds with higher fat to protein ratio, and 1.7 times higher for predicted mastitis in herds with higher number of inseminations. Based on the heifer data, the lowest estimated genetic correlation across environments was 0.76 (SE 0.21) for first-service conception between herds with differing average BCS. The minimum based on the cow data was 0.65 (SE 0.10) for survival between herds with differing average ages at calving. The relative importance of some fertility traits compared with yield traits doubled across environments. Possible reranking of individual animals within a population and the changes in genetic variance across environments suggests that environment-specific breeding values should be estimated for use in customized selection indices.

Animals↗

PubMed2000

Additive genetic parameter estimates for heifer pregnancy and subsequent reproduction in Angus females.

A primary objective of this study was to determine whether the binary traits heifer pregnancy (HP) and subsequent rebreeding (SR) were heritable in an experimental population of Angus cattle. A second objective was to determine the nature of the additive genetic relationships among HP, SR, and stayability (S(5/1)) in the same population. Heifer pregnancy was defined as the observation of a heifer conceiving and remaining pregnant to palpation at 120 d, given exposure during the breeding season. Subsequent rebreeding was defined as the observation of a 2-yr-old conceiving and remaining pregnant to palpation at 105 d, given pregnancy as a yearling and exposure during the breeding season. Stayability was defined as the probability of a female having at least five calves, given she becomes a dam as a 2 yr old. Data were analyzed using a maximum a posteriori probit threshold model to predict breeding values on the liability scale and Method R procedures to estimate variance components in the determination of heritability (h2). Additive genetic groups were used in determining the additive genetic relationships among these fertility traits. Additive genetic groups were formed on one trait's breeding values and used in the prediction of another trait's breeding values. Analyses yielded h2 estimates that were out of the parameter space 8.5 and 46.3% for HP and SR, respectively, and 5.9% for the reestimation of S(5/1). The majority of point estimates outside the parameter space for SR converged toward 0, whereas those for HP and S(5/1) primarily converged toward 1. From the subsamples producing h2 estimates within the parameter space, average h2 for HP, SR, and S(5/1) were .21, .19, and .15, with standard deviations of .12, .14, and .08, respectively. The estimates of h2 indicate that HP and S(5/1) were heritable and should respond favorably to selection; however, SR did not appear heritable due to the large number of subsamples producing h2 estimates out of the parameter space. Fixed effect estimates for age of dam were significant for HP. From the analyses using additive genetic groups, the relationship among HP and S(5/1) appeared to be nonlinear. This potential nonlinear relationship seen between HP and S(5/1) indicates that selection for improved female fertility would be most effective by having predictions on both traits.

Animals↗

PubMed2006

Adjustments for heterogeneous herd-year variances in a random regression model for genetic evaluations of Polish Black-and-White cattle.

The study investigated the existence of heterogeneous variance in first-lactation daily milk yield of Polish Black-and-White cows across herds in different years. Bayesian Information Criterion was used to show that the model with unequal residual variances for different herd-years was more plausible than the model assuming equal variances. A method of adjusting phenotypic records was developed to account for unequal variability in herd-years. Factors used for the data adjustment considered variation of general residuals and residuals for specific herd-years. The size of herd-year was also taken into account. Varied power of corrections was used to analyze the effect of adjustment on estimated breeding values. The method was applied to daily milk records of 817,165 primiparous cows. The effectiveness of the data adjustment was evaluated by the analysis of differences between each bull's breeding value and its parental index. Data correction reduced the average difference and variance of differences between breeding values and parental indices. Accounting for the size of herd-year classes in correction factors improved the efficiency of heterogeneous variance adjustment.

Analysis of Variance↗

PubMed2006

Selection for sow longevity.

Sow longevity plays an important role in economically efficient piglet production because sow longevity is related to the number of piglets produced during its productive lifetime; however, selection for sow longevity is not commonly practiced in any pig breeding program. There is relatively little scientific literature concerning the genetic parameters (genetic variation and genetic correlations) or methods available for breeding value estimation for effective selection for sow longevity. This paper summarizes the current knowledge about the genetics of sow longevity and discusses the available breeding value estimation methods for sow longevity traits. The studies in the literature clearly indicate that sow longevity is a complex trait, and even the definition of sow longevity is variable depending on the researcher and research objective. In general, the measures and analyses of sow longevity can be divided into 1) continuous traits (e.g., productive lifetime) analyzed with proportional hazard models; and 2) more simple binary traits such as stayability until some predetermined fixed parity. Most studies have concluded that sufficient genetic variation exists for effective selection on sow longevity, and heritability estimates have ranged between 0.02 and 0.25. Moreover, sow longevity has shown to be genetically associated with prolificacy and leg conformation traits. Variable results from previous research have led to a lack of consensus among swine breeders concerning the valid methodology of estimating breeding values for longevity traits. One can not deny the superiority of survival analysis in the modeling approach of longevity data; however, multiple-trait analyses are not possible using currently available survival analysis software. Less sophisticated approaches have the advantage of evaluating multiple traits simultaneously, and thus, can use the genetic associations between sow longevity and other traits. Additional research is needed to identify the most efficient selection methods for sow longevity. Future research needs to concentrate on multiple trait analysis of sow longevity traits. Moreover, because longevity is a fitness trait, the nonadditive genetic effects (e.g., dominance) may play important role in the inheritance of sow longevity. Currently, not a single estimate for dominance variance of sow longevity could be identified from the scientific literature.

Animal Husbandry↗

PubMed2025

A single-locus quantitative genetic model incorporating DNA methylation.

We describe a single-locus quantitative genetic model that incorporates effects due to DNA methylation. Extending Fisher's decomposition of the genotypic value, we distinguish two quantities to predict an individual's phenotypic or genetic values: the "basic genetic value" and the "expressed genetic value". We show how these quantities relate to the concept of breeding value and derive their corresponding formulas, along with those for phenotypic variance and covariance between relatives. The resulting parameters are influenced by several factors, including the population distribution of DNA methylation levels, the functional relationship between methylation and phenotype, the magnitudes of genetic and methylation effects, and allele frequencies. We show that under the conditions modeled, the presence of DNA methylation does not bias estimated breeding values.

DNA Methylation↗