Effects of data structure on the estimation of covariance functions to describe genotype by environment interactions in a reaction norm model

Covariance functions have been proposed to predict breeding values and genetic (co)variances as a function of phenotypic within herd-year averages (environmental parameters) to include genotype by environment interaction. The objective of this paper was to investigate the influence of definition of environmental parameters and non-random use of sires on expected breeding values and estimated genetic variances across environments. Breeding values were simulated as a linear function of simulated herd effects. The definition of environmental parameters hardly influenced the results. In situations with random use of sires, estimated genetic correlations between the trait expressed in different environments were 0.93, 0.93 and 0.97 while simulated at 0.89 and estimated genetic variances deviated up to 30% from the simulated values. Non random use of sires, poor genetic connectedness and small herd size had a large impact on the estimated covariance functions, expected breeding values and calculated environmental parameters. Estimated genetic correlations between a trait expressed in different environments were biased upwards and breeding values were more biased when genetic connectedness became poorer and herd composition more diverse. The best possible solution at this stage is to use environmental parameters combining large numbers of animals per herd, while losing some information on genotype by environment interaction in the data.     

Analysis of genetic correlations of hip height with selection indices and mature weight in Nelore cattle

Body size is directly related to the productive and reproductive performance of beef cattle raised under free-range conditions. In an attempt to better plan selection criteria, avoiding extremes in body size, this study estimated the heritabilities and genetic correlations of yearling hip height (YH) and mature hip height (MH) with selection indices obtained at weaning (WI) and yearling (YI) and mature weight (MW). Data from 102,373 Nelore animals born between 1984 and 2010, which belong to 263 farms that participate in genetic evaluation programmes of beef cattle conducted in Brazil and Paraguay, were used. The (co)variance components and genetic parameters were estimated by Bayesian inference in multi-trait analysis using an animal model. The mean heritabilities for YH, MH and MW were 0.56?±?0.06, 0.47?±?0.02 and 0.42?±?0.02, respectively. The genetic correlation of YH with WI (0.13?±?0.01) and YI (0.11?±?0.01) was practically zero, whereas a higher correlation was observed with MW (0.22?±?0.03). Positive genetic correlations of medium magnitude were estimated between MH and WI and YI (0.23?±?0.01 and 0.43?±?0.02, respectively). On the other hand, a high genetic correlation (0.68?±?0.03) was observed between the indicator traits of mature body size (MH and MW). Considering the top 20 % of sire (896 sires) in terms of breeding values for the yearling index, the rank sire correlations between breeding values for MH and MW was 0.62. In general, the results indicate that selection based on WI and YI should not lead to important changes in YH. However, an undesired correlated response in mature cow height is expected, particularly when selection is performed using YI. Therefore, changes in the body structure of Nelore females can be obtained when MH and MW is used as a selection criterion for cows.     

Accuracies of genomic breeding values in American Angus beef cattle using K-means clustering for cross-validation

Background

Genomic selection is a recently developed technology that is beginning to revolutionize animal breeding. The objective of this study was to estimate marker effects to derive prediction equations for direct genomic values for 16 routinely recorded traits of American Angus beef cattle and quantify corresponding accuracies of prediction.

Methods

Deregressed estimated breeding values were used as observations in a weighted analysis to derive direct genomic values for 3570 sires genotyped using the Illumina BovineSNP50 BeadChip. These bulls were clustered into five groups using K-means clustering on pedigree estimates of additive genetic relationships between animals, with the aim of increasing within-group and decreasing between-group relationships. All five combinations of four groups were used for model training, with cross-validation performed in the group not used in training. Bivariate animal models were used for each trait to estimate the genetic correlation between deregressed estimated breeding values and direct genomic values.

Results

Accuracies of direct genomic values ranged from 0.22 to 0.69 for the studied traits, with an average of 0.44. Predictions were more accurate when animals within the validation group were more closely related to animals in the training set. When training and validation sets were formed by random allocation, the accuracies of direct genomic values ranged from 0.38 to 0.85, with an average of 0.65, reflecting the greater relationship between animals in training and validation. The accuracies of direct genomic values obtained from training on older animals and validating in younger animals were intermediate to the accuracies obtained from K-means clustering and random clustering for most traits. The genetic correlation between deregressed estimated breeding values and direct genomic values ranged from 0.15 to 0.80 for the traits studied.

Conclusions

These results suggest that genomic estimates of genetic merit can be produced in beef cattle at a young age but the recurrent inclusion of genotyped sires in retraining analyses will be necessary to routinely produce for the industry the direct genomic values with the highest accuracy.     

Genetic variation in milk urea nitrogen concentration of dairy cattle and its implications for reducing urinary nitrogen excretion

Nitrogen (N) leached into groundwater from urine patches of cattle grazing in situ is an environmental problem in pasture-based dairy industries. One potential mitigation is to breed cattle for lower urinary nitrogen (UN) excretion. Urinary nitrogen is difficult to measure, while milk urea nitrogen concentration (MUN) is relatively easy to measure. For animals fed diets of differing N content in confinement, MUN is moderately heritable and is positively related to UN. However, there is little information on the heritability of MUN, and its relationship with other traits such as milk yield and composition, for animals grazing fresh pasture. Milk urea nitrogen concentration data together with milk yield, fat, protein and lactose composition and somatic cell count was collected from 133 624 Holstein-Friesian (HF), Jersey (J) and HF×J (XBd) cows fed predominantly pasture over three full lactations and one part lactation. Mean MUN was 14.0; and 14.4, 13.2 and 13.9 mg/dl for HF, J and XBd cows, respectively. Estimates of heritability of MUN were 0.22 using a repeatability model that fitted year-of-lactation by month-of-lactation by cow-age with days-in-milk within month-of-lactation and cow-age, and 0.28 using a test-day model analysis with Gibbs sampling methods. Sire breeding values (BVs) ranged from −2.8 to +3.2 indicating that MUN could be changed by selection. The genetic correlation between MUN and percent true protein in milk was −0.22; −0.29 for J cows and −0.16 for HF cows. Should the relationship between MUN and UN observed in dietary manipulation studies hold similarly when MUN is manipulated by genetic selection, UN excretion could be reduced by 6.6 kg/cow per year in one generation of selection using sires with low MUN BVs. Although J cows had lower MUN than HF, total herd UN excretion may be similar for the same fixed feed supply because more J cows are required to utilise the available feed. The close relationship between blood plasma urea N concentration and MUN may enable early selection of bulls to breed progeny that excrete less UN.     

Genotype by production environment interaction for birth and weaning weights in a population of composite beef cattle

The objectives of the present study were: (1) to evaluate the importance of genotype×production environment interaction for the genetic evaluation of birth weight (BW) and weaning weight (WW) in a population of composite beef cattle in Brazil, and (2) to investigate the importance of sire×contemporary group interaction (S×CG) to model G×E and improve the accuracy of prediction in routine genetic evaluations of this population. Analyses were performed with one, two (favorable and unfavorable) or three (favorable, intermediate, unfavorable) different definitions of production environments. Thus, BW and WW records of animals in a favorable environment were assigned to either trait 1, in an intermediate environment to trait 2 or in an unfavorable environment to trait 3. The (co)variance components were estimated using Gibbs sampling in single-, bi- or three-trait animal models according to the definition of number of production environments. In general, the estimates of genetic parameters for BW and WW were similar between environments. The additive genetic correlations between production environments were close to unity for BW; however, when examining the highest posterior density intervals, the correlation between favorable and unfavorable environments reached a value of only 0.70, a fact that may lead to changes in the ranking of sires across environments. The posterior mean genetic correlation between direct effects was 0.63 in favorable and unfavorable environments for WW. When S×CG was included in two- or three-trait analyses, all direct genetic correlations were close to unity, suggesting that there was no evidence of a genotype×production environment interaction. Furthermore, the model including S×CG contributed to prevent overestimation of the accuracy of breeding values of sires, provided a lower error of prediction for both direct and maternal breeding values, lower squared bias, residual variance and deviance information criterion than the model omitting S×CG. Thus, the model that included S×CG can therefore be considered the best model on the basis of these criteria. The genotype×production environment interaction should not be neglected in the genetic evaluation of BW and WW in the present population of beef cattle. The inclusion of S×CG in the model is a feasible and plausible alternative to model the effects of G×E in the genetic evaluations.     

Association of an insulin-like growth factor 1 gene microsatellite with phenotypic variation and estimated breeding values of growth traits in Canchim cattle

A population of 1398 Canchim (CA) cattle was genotyped to assess the association of an insulin-like growth factor 1 (IGF1) gene microsatellite with phenotypic variation and estimated breeding values of pre-weaning, weaning and post-weaning growth traits. After an initial analysis, the IGF1 genotype only had a significant effect (P < 0.05) on birth weight (BW) and weaning weight adjusted to 240 days (WW240). For these two traits, direct and maternal breeding values were estimated using the restricted maximum likelihood (reml). Two analyses were carried out. In the first (Model I), all fixed effects were fitted. In the second (Model II), the fixed effect of the IGF1 genotype was omitted. The estimated genetic and phenotypic components of variance were similar for every trait in both models. For Model I, estimated direct and maternal heritabilities were 0.26 and 0.16 for BW and 0.23 and 0.14 for WW240 respectively. The genetic and phenotypic correlations between BW and WW240 were 0.38 and 0.38 (Model I) and 0.19 and 0.38 (Model II) respectively. Fifty animals were classified according to their direct and maternal breeding values for both traits. Spearman rank-order correlation between animal rankings in the two models was used to assess the effect of including the IGF1 genotype in the model. Non-significant values from this correlation were indicative of a difference in breeding value rankings between the two approaches. The IGF1 gene was found to be associated with phenotypic variation and breeding values in the early phase of growth.     

Accuracy of direct genomic breeding values for nationally evaluated traits in US Limousin and Simmental beef cattle

Background

In national evaluations, direct genomic breeding values can be considered as correlated traits to those for which phenotypes are available for traditional estimation of breeding values. For this purpose, estimates of the accuracy of direct genomic breeding values expressed as genetic correlations between traits and their respective direct genomic breeding values are required.

Methods

We derived direct genomic breeding values for 2239 registered Limousin and 2703 registered Simmental beef cattle genotyped with either the Illumina BovineSNP50 BeadChip or the Illumina BovineHD BeadChip. For the 264 Simmental animals that were genotyped with the BovineHD BeadChip, genotypes for markers present on the BovineSNP50 BeadChip were extracted. Deregressed estimated breeding values were used as observations in weighted analyses that estimated marker effects to derive direct genomic breeding values for each breed. For each breed, genotyped individuals were clustered into five groups using K-means clustering, with the aim of increasing within-group and decreasing between-group pedigree relationships. Cross-validation was performed five times for each breed, using four groups for training and the fifth group for validation. For each trait, we then applied a weighted bivariate analysis of the direct genomic breeding values of genotyped animals from all five validation sets and their corresponding deregressed estimated breeding values to estimate variance and covariance components.

Results

After minimizing relationships between training and validation groups, estimated genetic correlations between each trait and its direct genomic breeding values ranged from 0.39 to 0.76 in Limousin and from 0.29 to 0.65 in Simmental. The efficiency of selection based on direct genomic breeding values relative to selection based on parent average information ranged from 0.68 to 1.28 in genotyped Limousin and from 0.51 to 1.44 in genotyped Simmental animals. The efficiencies were higher for 323 non-genotyped young Simmental animals, born after January 2012, and ranged from 0.60 to 2.04.

Conclusions

Direct genomic breeding values show promise for routine use by Limousin and Simmental breeders to improve the accuracy of predicted genetic merit of their animals at a young age and increase response to selection. Benefits from selecting on direct genomic breeding values are greater for breeders who use natural mating sires in their herds than for those who use artificial insemination sires. Producers with unregistered commercial Limousin and Simmental cattle could also benefit from being able to identify genetically superior animals in their herds, an opportunity that has in the past been limited to seed stock animals.     

Bayesian prediction of breeding values for multivariate binary and continuous traits in simulated horse populations using threshold-linear models with Gibbs sampling

Simulated data were used to determine the properties of multivariate prediction of breeding values for categorical and continuous traits using phenotypic, molecular genetic and pedigree information by mixed linear-threshold animal models via Gibbs sampling. Simulation parameters were chosen such that the data resembled situations encountered in Warmblood horse populations. Genetic evaluation was performed in the context of the radiographic findings in the equine limbs. The simulated pedigree comprised seven generations and 40 000 animals per generation. The simulated data included additive genetic values, residuals and fixed effects for one continuous trait and liabilities of four binary traits. For one of the binary traits, quantitative trait locus (QTL) effects and genetic markers were simulated, with three different scenarios with respect to recombination rate (r) between genetic markers and QTL and polymorphism information content (PIC) of genetic markers being studied: r = 0.00 and PIC = 0.90 (r0p9), r = 0.01 and PIC = 0.90 (r1p9), and r = 0.00 and PIC = 0.70 (r0p7). For each scenario, 10 replicates were sampled from the simulated horse population, and six different data sets were generated per replicate. Data sets differed in number and distribution of animals with trait records and the availability of genetic marker information. Breeding values were predicted via Gibbs sampling using a Bayesian mixed linear-threshold animal model with residual covariances fixed to zero and a proper prior for the genetic covariance matrix. Relative breeding values were used to investigate expected response to multi- and single-trait selection. In the sires with 10 or more offspring with trait information, correlations between true and predicted breeding values ranged between 0.89 and 0.94 for the continuous traits and between 0.39 and 0.77 for the binary traits. Proportions of successful identification of sires of average, favourable and unfavourable genetic value were 81% to 86% for the continuous trait and 57% to 74% for the binary traits in these sires. Expected decrease of prevalence of the QTL trait was 3% to 12% after multi-trait selection for all binary traits and 9% to 17% after single-trait selection for the QTL trait. The combined use of phenotype and genotype data was superior to the use of phenotype data alone. It was concluded that information on phenotypes and highly informative genetic markers should be used for prediction of breeding values in mixed linear-threshold animal models via Gibbs sampling to achieve maximum reduction in prevalences of binary traits.     

Importance of adaptation and genotype × environment interactions in tropical beef breeding systems

This paper examines the relative importance of productive and adaptive traits in beef breeding systems based on Bos taurus and tropically adapted breeds across temperate and (sub)tropical environments. In the (sub)tropics, differences that exist between breeds in temperate environments are masked by the effects of environmental stressors. Hence in tropical environments, breeds are best categorised into breed types to compare their performance across environments. Because of the presence of environmental stressors, there are more sources of genetic variation in tropical breeding programmes. It is therefore necessary to examine the genetic basis of productive and adaptive traits for breeding programmes in those environments. This paper reviews the heritabilities and genetic relationships between economically important productive and adaptive traits relevant to (sub)tropical breeding programmes. It is concluded that it is possible to simultaneously genetically improve productive and adaptive traits in tropically adapted breeds of beef cattle grazed in tropical environments without serious detrimental consequences for either adaptation or production. However, breed-specific parameters are required for genetic evaluations. The paper also reviews the magnitude of genotype × environment (G × E) interactions impacting on production and adaptation of cattle, where 'genotype' is defined as breed (within a crossbreeding system), sire within breed (in a within-breed selection programme) or associations between economically important traits and single nucleotide polymorphisms (SNPs - within a marker-assisted selection programme). It is concluded that re-ranking of breeds across environments is best managed by the use of the breed type(s) best suited to the particular production environment. Re-ranking of sires across environments is apparent in poorly adapted breed types across extreme tropical and temperate environments or where breeding animals are selected in a temperate environment for use in the (sub)tropics. However, G × E interactions are unlikely to be of major importance in tropically adapted beef cattle grazed in either temperate or (sub)tropical environments, although sex × environment interactions may provide new opportunities for differentially selecting to simultaneously improve steer performance in benign environments and female performance in harsher environments. Early evidence suggests that re-ranking of SNPs occurs across temperate and tropical environments, although their magnitude is still to be confirmed in well-designed experiments. The major limitation to genetic improvement of beef cattle over the next decade is likely to be a deficiency of large numbers of accurately recorded phenotypes for most productive and adaptive traits and, in particular, for difficult-to-measure adaptive traits such as resistance to disease and environmental stressors.     

Genotype × region and genotype × production level interactions in Holstein cows

The identification of the presence of genotype by environment interaction effects on important traits in Holstein cattle allows for the use of international genetic evaluations and a more efficient design of regional genetic evaluation programmes. The aim of this study was to determine the genotype × environment interaction effects in Chilean Holstein dairy cattle through the analysis of records corresponding to calvings between 1998 and 2015. Herds were classified in the central and southern regions of Chile based on herd location as well as by high and low levels of production environments based on the fat plus protein yield averages per herd within each region. The central region has a Mediterranean climate and a confined production system while the southern region has a humid temperate climate and a production system based on grazing with supplementation. Traits studied were milk yield (MY), fat yield (FY), protein yield (PY), fat content (FC) and protein content (PC) by lactation, age at first calving (AFC) and calving interval (CI). Several four-trait mixed animal models were applied to environmental category data as different traits, which included herd-year-calving season (herd-year-birth season for AFC) and lactation number as fixed effects, and animal additive genetic, sire-herd, permanent environment and residual effects as random effects. Genetic correlations (r_g) for MY, FY, FC, PC and CI were found to decrease as differences between environmental categories increased. The r_g between the most extreme environmental categories considered in this study for AFC (0.26) was the only one found statistically lower than 0.60. Genetic correlation values statistically lower than 0.80 (P < 0.05) were observed for AFC, CI, MY, FY and PY between some environmental categories. If separate genetic evaluations are adopted as practical criteria when the value of r_g is lower than 0.60, the consequence of improving a multi-trait economic breeding objective in this population is likely to be small unless extreme environmental categories are considered. However, a moderate decrease in selection response and re-ranking of selection candidates is expected for AFC, CI and yield traits when selection is performed in different environmental conditions. Genotype × environment interaction effects involving production systems in a Mediterranean climate and confinement vs. Temperate Oceanic climate and grazing with supplementation, and between two fat plus protein yield level categories within each environment, were at most moderate for the studied traits.     

Impact of bovine somatotropin on ranking for genetic value of dairy sires for milk yield traits and somatic cell score

Records of Holstein cows were used to examine how different models account for the effect of bovine somatotropin (bST) treatment on genetic evaluation of dairy sires for yield traits and somatic cell score. Data set 1 included 65,720 first-lactation records. Set 2 included 50,644 second-lactation records. Set 3 included 45,505 records for lactations three, four and five. Estimated breeding values (EBV) of sires were with three different animal models. With Model 1, bST administration was ignored. With Model 2, bST administration was used as a fixed effect. With Model 3, administration of bST was used to define the contemporary group (herd-year-month of calving-bST). Correlations for EBV of 1,366 sires with treated daughters between pairs of the three models were calculated for milk, fat and protein yields and somatic cell score for the three data sets. Correlations for EBV of sires between pairs of models for all traits ranged from 0.971 to 0.999. Fractions of sires with bST-treated progeny selected in common (top 10 to 15%) were 0.94 and usually greater for all pairs of models for all traits and parities. For this study, the method of statistical adjustment for bST treatment resulted in a negligible effect on genetic evaluations of sires when some daughters were treated with bST and suggests that selection of sires to produce the next generation of sires and cows might not be significantly affected by how the effect of bST is modeled for prediction of breeding values for milk, fat and protein yields and somatic cell score.     

Genetic analysis of the temperament of Nellore cattle using linear and threshold models

Temperament is an important trait for the management and welfare of animals and for reducing accidents involving people who work with cattle. The present study aimed to estimate the genetic parameters related to the temperament score (T) and weaning weight (WW) of Nellore cattle, reared in a beef cattle breeding program in Brazil. Data were analyzed using two different two-trait statistical models, both considering WW and T: (1) a linear-linear model in which variance components (VCs) were estimated using restricted maximum likelihood; and (2) a linear-threshold model in which VCs were estimated via Bayesian inference. WW was included in the analyses of T to minimize any possible effects of sequential selection and to allow for estimation of the genetic correlation between these two traits. The heritability estimates for T were 0.21±0.003 (model 1) and 0.26 (model 2, with a 95% credibility interval (95% CI) of 0.21 to 0.32). The estimated genetic correlations between WW and T were of a moderate magnitude (−0.33±0.01 (model 1) and −0.34 (95% CI: −0.40, −0.28, model 2). The genetic correlations between the estimated breeding values (EBVs) obtained for the animals based on the two models were high (>0.92). The use of different models had little influence on the classification of animals based on EBVs or the accuracy of the EBVs.     

Genotype by environment interaction for 450-day weight of Nelore cattle analyzed by reaction norm models

Genotype by environment interactions (GEI) have attracted increasing attention in tropical breeding programs because of the variety of production systems involved. In this work, we assessed GEI in 450-day adjusted weight (W450) Nelore cattle from 366 Brazilian herds by comparing traditional univariate single-environment model analysis (UM) and random regression first order reaction norm models for six environmental variables: standard deviations of herd-year (RRMw) and herd-year-season-management (RRMw-m) groups for mean W450, standard deviations of herd-year (RRMg) and herd-year-season-management (RRMg-m) groups adjusted for 365-450 days weight gain (G450) averages, and two iterative algorithms using herd-year-season-management group solution estimates from a first RRMw-m and RRMg-m analysis (RRMITw-m and RRMITg-m, respectively). The RRM results showed similar tendencies in the variance components and heritability estimates along environmental gradient. Some of the variation among RRM estimates may have been related to the precision of the predictor and to correlations between environmental variables and the likely components of the weight trait. GEI, which was assessed by estimating the genetic correlation surfaces, had values < 0.5 between extreme environments in all models. Regression analyses showed that the correlation between the expected progeny differences for UM and the corresponding differences estimated by RRM was higher in intermediate and favorable environments than in unfavorable environments (p < 0.0001).     

Evaluation of longevity modeling censored records in Nellore

The aim of the present study was to evaluate the prediction ability of models that cope with longevity phenotypic expression as uncensored and censored in Nellore cattle. Longevity was defined as the difference between the dates of last weaned calf and cow birth. There were information of 77 353 females, being 61 097 cows with uncensored phenotypic information and 16 256 cows with censored records. These data were analyzed considering three different models: (1) Gaussian linear model (LM), in which only uncensored records were considered; and two models that consider both uncensored and censored records: (2) Censored Gaussian linear model (CLM); and (3) Weibull frailty hazard model (WM). For the model prediction ability comparisons, the data set was randomly divided into training and validation sets, containing 80% and 20% of the records, respectively. There were considered 10 repetitions applying the following restrictions: (a) at least three animals per contemporary group in the training set; and (b) sires with more than 10 progenies with uncensored records (352 sires) should have daughters in the training and validation sets. The variance components estimated using the whole data set in each model were used as true values in the prediction of breeding values of the animals in the training set. The WM model showed the best prediction ability, providing the lowest χ² average and the highest number of sets in which a model had the smallest value of χ² statistics. The CLM and LM models showed prediction abilities 2.6% and 3.7% less efficient than WM, respectively. In addition, the accuracies of sire breeding values for LM and CLM were lower than those obtained for WM. The percentages of bulls in common, considering only 10% of sires with the highest breeding values, were around 75% and 54%, respectively, between LM–CLM and LM–WM models, considering all sires, and 75% between LM–CLM and LM–WM, when only sires with more than 10 progenies with uncensored records were taken into account. These results are indicative of reranking of animals in terms of genetic merit between LM, CLM and WM. The model in which censored records of longevity were excluded from the analysis showed the lowest prediction ability. The WM provides the best predictive performance, therefore this model would be recommended to perform genetic evaluation of longevity in this population.     

Measuring connectedness among herds in mixed linear models: From theory to practice in large-sized genetic evaluations

A procedure to measure connectedness among groups in large-sized genetic evaluations is presented. It consists of two steps: (a) computing coefficients of determination (CD) of comparisons among groups of animals; and (b) building sets of connected groups. The CD of comparisons were estimated using a sampling-based method that estimates empirical variances of true and predicted breeding values from a simulated n-sample. A clustering method that may handle a large number of comparisons and build compact clusters of connected groups was developed. An aggregation criterion (Caco) that reflects the level of connectedness of each herd was computed. This procedure was validated using a small beef data set. It was applied to the French genetic evaluation of the beef breed with most records and to the genetic evaluation of goats. Caco was more related to the type of service of sires used in the herds than to herd size. It was very sensitive to the percentage of missing sires. Disconnected herds were reliably identified by low values of Caco. In France, this procedure is the reference method for evaluating connectedness among the herds involved in on-farm genetic evaluation of beef cattle (IBOVAL) since 2002 and for genetic evaluation of goats from 2007 onwards.     

Economic implications of using Japanese Black sires carrying recessive genes associated with genetic defects

The objective of this study was to calculate cumulative discounted expressions (CDE) for Japanese Black sires carrying a single defective allele in a herd by applying the gene-flow method to investigate the expression pattern of homozygous recessive genotype and to evaluate the monetary loss of using these sires. A single biallelic locus was considered with A representing the dominant allele and a representing the recessive allele. The gene-flow method was modified to consider the fitness of homozygous recessive genotype. Input parameters representing a typical situation in a Japanese Black cattle herd were used to calculate the CDE and the loss of using carrier sires. The effects of initial allele frequency and fitness on the CDE were determined for Aa and AA sires. The CDE of Aa sires were larger than those of AA sires under all initial allele frequencies and fitness. The difference in the CDE between using Aa and AA sires was largest when fitness was 0 (lethal recessive condition). The differences in the loss between Aa and AA sires were larger with increasing initial allele frequencies in lethal recessive condition. Applying the method used in this study to defects reported in Japanese Black cattle and with a population size of 628 000, the difference in the loss between using Aa and AA sires was US$48 575 800 and US$74 418 000 in the case of Band-3 and Claudin-16 deficiencies, respectively. The approach used in this study could be applied to other genetic defects in different breeds and species.     

Effect of pregnancy on the genetic evaluation of dairy cattle

We investigated the effect of stage of pregnancy on estimates of breeding values for milk yield and milk persistency in Gyr and Holstein dairy cattle in Brazil. Test-day milk yield records were analyzed using random regression models with or without the effect of pregnancy. Models were compared using residual variances, heritabilities, rank correlations of estimated breeding values of bulls and cows, and number of nonpregnant cows in the top 200 for milk yield and milk persistency. The estimates of residual variance and heritabilities obtained with the models with or without the effect of pregnancy were similar for the two breeds. Inclusion of the effect of pregnancy in genetic evaluation models for these populations did not affect the ranking of cows and sires based on their predicted breeding values for 305-day cumulative milk yield. In contrast, when we examined persistency of milk yield, lack of adjustment for the effect of pregnancy overestimated breeding values of nonpregnant cows and cows with a long days open period and underestimated breeding values of cows with a short days open period. We recommend that models include the effect of days of pregnancy for estimation of adjustment factors for the effect of pregnancy in genetic evaluations of Dairy Gyr and Holstein cattle.     

Integrating genomic selection into dairy cattle breeding programmes: a review

Extensive genetic progress has been achieved in dairy cattle populations on many traits of economic importance because of efficient breeding programmes. Success of these programmes has relied on progeny testing of the best young males to accurately assess their genetic merit and hence their potential for breeding. Over the last few years, the integration of dense genomic information into statistical tools used to make selection decisions, commonly referred to as genomic selection, has enabled gains in predicting accuracy of breeding values for young animals without own performance. The possibility to select animals at an early stage allows defining new breeding strategies aimed at boosting genetic progress while reducing costs. The first objective of this article was to review methods used to model and optimize breeding schemes integrating genomic selection and to discuss their relative advantages and limitations. The second objective was to summarize the main results and perspectives on the use of genomic selection in practical breeding schemes, on the basis of the example of dairy cattle populations. Two main designs of breeding programmes integrating genomic selection were studied in dairy cattle. Genomic selection can be used either for pre-selecting males to be progeny tested or for selecting males to be used as active sires in the population. The first option produces moderate genetic gains without changing the structure of breeding programmes. The second option leads to large genetic gains, up to double those of conventional schemes because of a major reduction in the mean generation interval, but it requires greater changes in breeding programme structure. The literature suggests that genomic selection becomes more attractive when it is coupled with embryo transfer technologies to further increase selection intensity on the dam-to-sire pathway. The use of genomic information also offers new opportunities to improve preservation of genetic variation. However, recent simulation studies have shown that putting constraints on genomic inbreeding rates for defining optimal contributions of breeding animals could significantly reduce achievable genetic gain. Finally, the article summarizes the potential of genomic selection to include new traits in the breeding goal to meet societal demands regarding animal health and environmental efficiency in animal production.     

Quantitative genetics of ovariole number in Drosophila melanogaster. II. Mutational variation and genotype-environment interaction.

The rare alleles model of mutation-selection balance (MSB) hypothesis for the maintenance of genetic variation was evaluated for two quantitative traits, ovariole number and body size. Mutational variances (VM) for these traits, estimated from mutation accumulation lines, were 4.75 and 1.97 x 10(-4) times the environmental variance (VE), respectively. The mutation accumulation lines were studied in three environments to test for genotype x environment interaction (GEI) of new mutations; significant mutational GEI was found for both traits. Mutations for ovariole number have a quadratic relationship with competitive fitness, suggesting stabilizing selection for the trait; there is no significant correlation between mutations for body size and competitive fitness. Under MSB, the ratio of segregating genetic variance, VG, to mutational variance, VM, estimates the inverse of the selection coefficient against a heterozygote for a new mutation. Estimates of VG/VM for ovariole number and body size were both approximately 1.1 x 10(4). Thus, MSB can explain the level of variation, if mutations affecting these traits are under very weak selection, which is inconsistent with the empirical observation of stabilizing selection, or if the estimate of VM is biased downward by two orders of magnitude. GEI is a possible alternative explanation.     

Gene flow in a national cross-breeding beef population

Future progress in genetic improvement and the monitoring of genetic resources in beef cattle requires a detailed understanding of the population under selection. This study examines the gene flow in the UK beef population with an uncommon breeding structure involving interaction between the beef and dairy populations. British Cattle Movement Service records were used as the primary source of information, and these data were triangulated with UK government statistics, other industry information sources and existing literature to build up a profile of the UK beef industry. Estimates were made of the breed composition of suckler cows, breeding bulls and the prime slaughter population. Cross-bred animals made up 85% and 94%, respectively, of the commercial beef breeding cow and prime slaughter populations. Holstein/Friesian (through cross-breeding) made up the largest proportion of genes in both these populations with 33% and 28%, respectively. The next five most popular breeds were specialist beef breeds: Limousin (22% and 18%), Charolais (11% and 6%), Simmental (9% and 11%), Angus (7% and 8%) and Belgian Blue (6% and 6%). Combined, the top seven beef breeds accounted for 94% of beef genetics in the prime slaughter population, and 80% of this came from non-native breeds. The influence of dairy breeds in the commercial beef breeding population was highlighted by the fact that 44% of replacement commercial beef breeding females were sourced from beef-sired crosses in the dairy herd, and in total 74% of all maternal grand dams of prime slaughter animals were Holstein/Friesian. The use of selection index technology was also investigated by analysing breeding bull sale results, with the correlation between the terminal sire index and sale price of young breeding bulls being generally moderate but significant, ranging from 0.21 to 0.38 across the major beef breeds. The most influential source of genetics in the commercial suckler beef herd was natural service breeding bulls. These were mostly sourced from pedigree breeders, and accounted for 47.8% of the genetics in the prime beef population. Artificial insemination sires were responsible for 16.6% of prime beef genetics, with the remaining 35.6% coming from dairy breeds, 95% of which was Holstein/Friesian.