Comparative Research on Breeding Value Prediction for Calving Score in Beef Breed Cattle

Genetic parameters for hoof health traits estimated with linear and threshold models using alternative cohorts

Context Australian sheep breeding values (ASBVs) for the categorical trait of lambing ease are routinely estimated by Sheep Genetics via a threshold model. This has been pedigree-only, and has not utilised genomic information. Aim The present study aimed to update the genetic evaluation model and parameters for lambing ease for terminal sire sheep (dominated by White Suffolk and Poll Dorset breeds). The model includes correlations with birthweight and gestation length. Cross-validation was used to determine the value of the improved models and the inclusion of genomic information. Methods New data-preparation pipelines were developed to accommodate improved data-filtering methods and contemporary group construction. Genetic parameters, including correlations among traits, were estimated using continuous and threshold models, with various combinations of effects in mixed animal models. Cross-validation of breeding values was performed against progeny performance, by using forward prediction. Key results The increased volume of data, improved data preparation steps and enhanced evaluation software now allow a more complex model to be fitted, including maternal, sire by flock-year and genetic group effects, which were significant for all traits, along with the inclusion of multiple sire groups in the pedigree. However, the inclusion of the direct-maternal covariance and sire by flock-year terms resulted in unrealistically inflated estimates of some components, and thus the final covariance matrices required some adjustments. Cross-validation of breeding values was performed against progeny performance using forward prediction. For all traits, the phenotype accuracies and estimated breeding value correlations were higher from the new model without genomics than were those from the current routine evaluation. The benefit from including genomic information based on cross-validation is minimal currently but is expected to improve as the size of the reference population grows. Further work is required to define acceptable data-quality thresholds for the construction of datasets for routine breeding value estimation. Conclusions The new model and parameters resulted in ASBVs with an improved predictive ability, with increased accuracy and reduced bias compared with the current analysis. Furthermore, a small increase in accuracy was observed for all traits from utilising genomic information in the model. Implications The new genetic evaluation procedures and models will be used to update those being applied in the routine Sheep Genetics evaluation system and also support further index development for the terminal sire breeds in Australia.

Linear–threshold animal model for birth weight, gestation length and calving ease in United Kingdom Limousin beef cattle data

Converting estimated breeding values from the observed to probability scale for health traits

Data from 127 539 Hereford and Braford cattle were used to compare estimates of genetic parameters for navel, conformation, precocity, muscling and size visual scores at yearling, using linear and threshold animal models. In a second step, these models were cross-validated using a multinomial logistic regression in order to quantify the association between phenotype and genetic merit for each trait. For navel score, higher heritability was obtained with the threshold model (0.42 ± 0.02) in relation to the linear model (0.22 ± 0.02). However, similar heritability was estimated in both models for conformation, precocity, muscling and size, with values of 0.18 ± 0.01, 0.19 ± 0.01, 0.19 ± 0.01 and 0.26 ± 0.01, respectively, using linear model, and of 0.19 ± 0.01, 0.19 ± 0.01, 0.20 ± 0.01, and 0.29 ± 0.01, respectively, using threshold model. For navel score, Spearman correlations between sires’ breeding values predicted using linear and threshold models ranged from 0.60 (1% of the best sires are selected) to 0.96 (all sires are selected). For conformation, precocity, muscling and size scores, low changes in sires’ rank are expected using these models (Spearman correlations >0.86), regardless of the proportion of sires selected. Except for navel with the linear model, the direction of the associations between phenotype and genetic merit were in accordance with its expectation, as there were increases in the phenotype per unit of change in the breeding value. Thus, the threshold model would be recommended to perform genetic evaluation of navel score in this population. However, linear and threshold models showed similar predictive ability for conformation, precocity, muscling and size scores.

ABSTRACTThe breeding goal of the Swedish Warmblood horse (SWB) is to produce internationally competitive horses in dressage and show jumping. In the current genetic evaluation, breeding values are estimated in multiple‐trait animal models where competition performance is the target trait and results from two different young horse tests serve as indicator traits. However, preselection of horses, both for young horse tests and for competitions, is not considered in the current evaluation. The overall aim of this study was to analyse the all‐or‐none trait start status, in competition and in young horse tests, for possible use in the genetic evaluation for SWB. All starts in young horse tests have been recorded since long (1973), whereas start status in competition is known from the year 2007 and onwards. Therefore, the studied population was restricted to SWB horses born between 2003 and 2018 that had the possibility to compete during the period from 2007 until 2022. Horses were categorised into four disciplines according to their sire's and grandsire's discipline categories, and only horses in the two major categories, dressage and jumping, were included in this study. In total, 23,125 jumping horses and 14,470 dressage horses were studied separately. Information on discipline‐specific start status in show jumping or dressage competitions, young horse test (YHT) and riding horse test (RHT) was available as well as lifetime accumulated competition points, assessed gaits and jumping traits from YHT and RHT. Out of the jumping horses, 31% had participated in YHT, 10% in RHT and 56% in show jumping competition. For dressage horses, the participation rates were 35% for YHT, 11% for RHT and 34% for dressage competition. The genetic analyses were performed with threshold and linear animal models. Horses that had participated in YHT or RHT had competed to a larger extent and had a higher mean of competition points than horses that had not participated in YHT or RHT. The heritability for start status in competition was estimated using a threshold model at 0.48 for show jumping and 0.39 for dressage. Using linear models, the heritability for start status in show jumping was estimated to be 0.30 on the observable 0/1‐scale and 0.47 when transformed to the underlying continuous scale. For start status in dressage, the corresponding heritability estimates were 0.20 and 0.34. Genetic correlations, estimated with linear models, were strong between start status in show jumping and jumping traits at YHT and RHT (0.78–0.93) and moderate to strong between start status in dressage competition and gait traits at YHT and RHT (0.46–0.88). The genetic correlations between start status and accumulated lifetime points in competition were strong, 0.93 for show jumping and 0.86 for dressage. Using linear models, heritability estimates for start status in young horse tests ranged from 0.07 to 0.42 on the observable scale and from 0.11 to 0.71 after transformation to the underlying continuous scale. Inclusion of start status in the breeding value estimation of competition performance affected stallion ranking somewhat and increased the accuracies of the stallions' breeding values. We conclude that start status is a heritable trait that would be possible to include in the genetic evaluation of SWB horses.

Comparison of approximation methods for genomic estimated breeding values from observed to liability scales in dairy cattle health traits.

The advantages of using a univariate threshold animal model (TAM) over the conventional linear animal model (AM) in the development of a genetic evaluation system for feet and leg traits of Angus cattle were explored. The traits were scored on a scale of 1–9 with scores 5 and 6 being the most desirable. The genetic parameters and estimated breeding values for front feet angle (FA), rear feet angle (RA), front feet claw set (FC), rear feet claw set (RC), rear leg hind view (RH) and rear leg side view (RS) were compared from AM and TAM. In order to predict breeding values to identify the animals with intermediate optimum, the scores were categorised to form three groups to differentiate the desirable group (5–6) from the other two groups with less desirable feet and leg appearances (1–4 and 7–9). The AM and TAM were used to estimate genetic parameters for the grouped data as well as the original score data. A TAM using the group data was used to predict the probability and breeding value for the desirable intermediate group. For the original score data, estimated heritabilities on the underlying scale, using TAM, were 0.50, 0.46, 0.35, 0.44, 0.32 and 0.22 for FA, FC, RA, RC, RH and RS, respectively, and were 0.01–0.18 higher than the heritabilities estimated using AM. Genetic correlation between the six traits using a bivariate TAM with all scores ranged from 0.02 to 0.50 with front and rear angles had the highest genetic correlation at 0.50. For all six traits, proportion in the intermediate desirable group was higher than the other two groups combined. The low annual genetic change observed for all six traits over the 10 years of data recording reflected the lack of directional selection to improve the traits in Angus cattle. For genetic evaluation of feet and leg traits with an intermediate optimum, TAM is a preferred method for estimating genetic parameters and predicting breeding values for the desirable category. The TAM has now been implemented for regular estimated breeding value analysis of feet and leg traits of Angus cattle.

Genetic analysis of subclinical mastitis in early lactation of heifers using both linear and threshold models

Genetic Evaluation of Mastitis in Dairy Cattle Using Linear Models, Threshold Models, and Survival Analysis: A Simulation Study

Heifer pregnancy (HP) in beef cattle is a heritable and economically relevant trait but is challenging to predict using best linear unbiased prediction (BLUP) methodology due to the binary nature of the phenotypes. The observed categorical responses (e.g., 1 = pregnant; 0 = non-pregnant) are due to an animal either exceeding a particular threshold for pregnancy or not on an underlying genetic distribution which is why this binary trait is commonly evaluated using a threshold model (TM). However, TM are susceptible to scenarios where only 1 category of observation is predominant. In this study we compare genetic evaluations including “partial” and “whole” data to cross-validate methodologies for the prediction of HP. The objectives of this study were then to evaluate estimators of prediction accuracy, bias, and dispersion. Heifer fertility data were obtained from the Red Angus Association of America. Evaluations for HP were performed using ASREML3.0 and a univariate, BLUP, traditional TM; and a linear continuous BLUP animal model (LM) on both reference (whole) and validation (partial) datasets. Reference data included all animals with HP phenotypes, and validation data had censored the most recent 5 yr of HP phenotypes (~16% of HP phenotypes). Reference and validation estimated breeding values (EBV) were then compared using linear regression (LR) methods. Heritability estimates were 0.05 (± 0.007) and 0.15 (± 0.009) for LM and TM respectively. The average BIF accuracy of censored validation animals (n = 10,175) using a LM on whole and partial datasets were 0.07 and 0.03 respectively, and using a TM on whole and partial datasets were 0.09 and 0.04 respectively. Comparing the mean EBV from the partial and whole datasets can be a good estimator of bias in the model as the difference is expected to be 0. The difference between the mean EBV from partial and mean EBV from whole datasets were –0.003 and –0.016 for LM and TM respectively, suggesting that the TM is a slightly more biased evaluation. The regression of EBV obtained with whole data on EBV estimated with partial data has an expected value of 1 if there is no over/under dispersion which can be a useful tool in determining model correctness and gauge bias in an evaluation. The closer a regression coefficient is to 1, the less biased the evaluation is. The slope of the regression of EBV estimated from whole data on partial data for validation animals were 0.98 (P < 0.0001) and 0.91 (P < 0.0001) for LM and TM respectively. Although both regression coefficients were above 0.90 suggesting both evaluations are performing well, these results suggest that TM models may be a more biased predictor of HP than LM despite a small benefit in prediction accuracy for the trait.

This study aimed to estimate the genetic parameters of stayability (STAY) at different calvings using a single-step genomic best linear unbiased prediction (ssGBLUP) approach, comparing Gaussian-linear and threshold models in Italian Charolais and Limousine beef cattle. It also examined the genetic relationship between STAY and other traits to identify potential indicators of longevity and assessed the impact of STAY selection on economically important traits. STAY, a key trait for farm profitability, is defined as the probability of a cow surviving and remaining productive in the herd until a determined age. We evaluated STAY from the second to third calving and subsequent intervals (e.g., STAY23, STAY78), along with two fertility traits and several conformation traits. Data included 47,362 Limousine cows and 9,174 Charolais cows from 2,471 to 1,774 herds, respectively, born between 1977 and 2023. Analyses were performed fitting univariate threshold and Gaussian-linear animal models to estimate genetic parameters for STAY traits (STAY2 to STAY8) using ssGBLUP. Also, bivariate models were used to estimate genetic correlations between STAY and fertility and conformation traits. Heritabilities for STAY ranged from 0.13 to 0.11 and from 0.21 to 0.14 for Limousine, and from 0.14 to 0.11 and from 0.21 to 0.19 for Charolais, using Gaussian-linear and threshold models, respectively. Significant re-ranking of genotyped sires based on STAY traits was observed, particularly for more distant calvings (STAY8) compared to earlier ones (STAY3), indicating that STAY traits are genetically distinct. Genetic correlations were positive between STAY and conformation traits for Limousine. In Charolais, many traits were uncorrelated, but some conformation traits showed positive correlations, except for rump convexity, which had negative correlations with STAY. In conclusion, the heritability estimates of STAY suggests that genetic improvement for longevity in Limousine and Charolais herds is feasible. Selecting sires with consistently high genomic breeding values for STAY across early and late calvings highlights the importance of long-term longevity. Genetic correlations indicate that selection based on conformation traits could enhance herd survival by improving cow resilience for the Limousine. Instead for the Charolais some conformation traits showed positive correlations with STAY, while rump convexity had negative association, potentially affecting longevity.

The objective of this study was to estimate the genetic parameters, genetic trends and breeding values using linear model (LM) and threshold model (TM) for the development of hip dysplasia (HD) in Labrador Retrievers in the Czech Republic (n = 3151). The right and left hip joints were evaluated separately using the Fédération Cynologique Internationale scoring system. Four linear and four TMs were tested for the correct estimation of genetic parameters. All the tested models utilized fixed effects of sex, assessor, year of birth, regression of age at evaluation, random direct genetic effects and the effect of the animals' permanent environments. The models differed in the inclusion of the following effects: fixed effects of regression of inbreeding coefficient, random maternal effect and random effect of the maternal permanent environment. Compared to the TM, the LM provided lower coefficients of direct (0.25-0.29 versus 0.26-0.35) and maternal heritability (0.01-0.02 versus 0.03-0.05), repeatability (0.76-0.77 versus 0.78-0.83) and of the correlation between direct and maternal effects (-0.55 to -0.21 versus -0.80 to -0.27). In the tested models, no statistical significance was found for fixed regression of inbreeding coefficients or for the random effect of the permanent maternal environment. In spite of the similarity of the LM and TM results, the TM is recommended as the more suitable model for estimating genetic parameters and subsequent breeding values for HD in Labrador Retrievers in the Czech Republic.

Estimation of Genetic Parameters and Evaluation of Breeding Program Designs with a Focus on Dairy Cattle in Low Input Production Systems

Threshold models are often used in genetic analysis of categorical data, such as calving ease. Solutions in the liability scale are easily transformed into probabilities; therefore, estimated breeding values are published as the probability of expressing the category of main interest and are the industry’s gold standard because they are easy to interpret and use for selection. However, because threshold models involve nonlinear equations and probability functions, implementing such a method is complex. Challenges include long computing time and convergence issues, intensified by including genomic data. Linear models are an alternative to overcome those challenges. Estimated breeding values computed using linear models are highly correlated (≥0.96) with those from threshold models; however, the lack of a transformation from the observed to the probability scale limits the use of linear models. The objective of this study was to propose transformations from observed to probability scale analogous to the transformation from liability to probability scale. We assessed computing time, peak memory use, correlations between estimated breeding values, and estimated genetic trends from linear and threshold models. With 11M animals in the pedigree and almost 965k genotyped animals, linear models were 5× faster to converge than threshold models (32 vs. 145 h), and peak memory use was the same (189 GB). The transformations proposed provided highly correlated probabilities from linear and threshold models. Correlations between direct (maternal) estimated breeding values from linear and threshold models and transformed to probabilities were ≥0.99 (0.97) for all animals in the pedigree, sires with/without progeny records, or animals with phenotypic records; therefore, estimated genetic trends were analogous, suggesting no loss of genetic progress in breeding programs that would adopt linear instead of threshold models. Furthermore, linear models reduced computing time by 5-fold compared to the threshold models; this enables weekly genetic evaluations and opens the possibility of using multi-trait models for categorical traits to improve selection effectiveness.