Models For Genetic Evaluation Research Articles

Rapid turnover of sensor data to genetic evaluation for dairy cows in the cloud.

More and more sensor and automation data are available that enable animal breeders to define novel traits. However, sensor and automation data are often frequently measured differently (e.g., milk yield and different milk components are continuously measured during each milking). These differences are challenging animal breeders to define traits and use the most appropriate analytical models for genetic evaluation and breeding values. Traditionally, the process from raw data to breeding value estimations involves several steps: data curation, trait definition, variance component estimation, genetic evaluation, and validation of the estimated breeding values (EBV). All these steps often take many iterations and several research projects to optimize the final genetic evaluations. To make this entire process-from raw data to validated EBV-more efficient, we combined all these steps in a cloud environment that allows for faster processing and a faster data distribution time. We used real data (including 1,782,373,113 daily milk-yield records of 1,120,550 dairy cows) and a real trait (a resilience trait based on the deviations from expected milk yields) to demonstrate the functioning of this cloud environment. The daily milk-yield records were incorporated into our cloud solution, in which we have set up central binary large object storage. Subsequent steps were all performed in the cloud. The data set was preprocessed in approximately 6 h to obtain the resilience indicator for 352,871 cows in the first 3 lactations. Estimation of genetic parameters (heritabilities and genetic correlations) was performed by splitting the data into 5 subsets in ASReml, and prediction of subsequent EBV was performed on the entire data set using MiXBLUP. Together with the validation of breeding values, this process encompassed 16.5 h. By combining the different steps from preprocessing sensor data to genetic evaluation of new traits in one cloud environment, we generated EBV and validation plots in approximately 1 working day. Moreover, our setup is a flexible design and can be adapted easily to test new, longitudinal sensor-driven traits and compare the performance of these new traits to previous ones.

Integration of Interbull's multiple across-country evaluation approach breeding values into the multiple-trait single-step random regression test-day genetic evaluation for yield traits of Australian Red breeds

Interbull's multiple across-country evaluaftion provides national breeding organizations with breeding values for internationally used bulls, which integrate performance data obtained in different breeding populations, environments, and production systems. However, breeding value-based selection decisions on domestic individuals born to foreign sires can only benefit from Interbull breeding values if they are integrated such that their information can contribute to the breeding values of all related domestic animals. For that purpose, several methods have been proposed which either model Interbull breeding values as prior information in a Bayesian approach, as additional pseudo data points, or as correlated traits, where these methods also differ in their software and parameterization requirements. Further, the complexity of integration also depends on the traits and genetic evaluation models. Especially random regression models require attention because of the dimensionality discrepancy between the number of Interbull breeding values and the number of modeled genetic effects. This paper presents the results from integrating 16,063 Interbull breeding values into the domestic single-step random regression test-day model for milk, fat, and protein yield for Australian Red dairy cattle breeds. Interbull breeding values were modeled as pseudo data points with data point-specific residual variances derived within animal across traits, ignoring relationships between integrated animals. Results suggest that the integration was successful with regard to alignment of Interbull breeding values with their domestic equivalent as well as with regard to the individual and population-wide increase in reliabilities. Depending on the relationship structure between integration candidates, further work is required to account for those relationships in a computationally feasible manner. Other traits with separate parity effects nationally could use a similar approach, even if not modeled with a test-day model.

Comparison of different models for the estimation of genetic parameters in tropical goats.

This study aimed to estimate the variance components and genetic parameters for body weight in tropical goats testing different models using Bayesian approach and investigate the effectiveness of fitting the effects of maternal genetic, permanent environmental, and covariance between direct and maternal effects. Records from 1980 to 2010 of 1453 Anglo-Nubian goats' herd were used. Six performance growth traits: birth weight (BW, kg), at 28 (W28, kg), 56 (W56, kg), 112 (W112, kg), 140 (W140, kg), and 196 (W196; kg) days of age, were evaluated. There was a negative covariance between direct genetic effects and maternal additive for all weights. The effect of maternal permanent environment is an important source of variation for performance characteristics in goats until the 196days, and must be considered in genetic evaluation models in order to obtain accurate predictions of breeding values of individuals. The importance of inclusion of the additive maternal effect appears to be more dependent on the structure of the data set under evaluation. Given the structure of the data, the described management and criteria for choosing the best model (deviance information criterion and the Bayes factor) should make the estimation of parameters for weights at birth and at 28 and 56days using model IV, since that will provide more consistent results than the type I (less complex), without the need of accurate representations of knowledge prior to data collection. Over time, the breeding program will have more data and thereby increase the possibility of building a prior distribution confident that would enable the inference of parameters for more complex models. However, these are preferable components for the estimation of the characteristics and weights to 112 at 140 and at 196days, using model I (less complex).

Extension of the reduced animal model to single-step methods.

For a few decades, animal models (AMs) in the form of best linear unbiased prediction (BLUP) have been used for the genetic evaluation of animals. An equation system is set in the order of all the effects in the model, including all the animals in the pedigree. Solving these large equation systems has been a challenge. Reduced AM (RAM) was introduced in 1980, which allowed setting up equations for parents instead of all animals. That greatly reduced the number of equations to be solved. The RAM is followed by a back-solving step, in which progenies' breeding values are obtained conditional on parental breeding values. Initially, pedigree information was utilized to model genetic relationships between animals. With the availability of genomic information, genomic BLUP (GBLUP), single-step GBLUP (ssGBLUP), and single-step marker models were developed. Single-step methods utilize pedigree and genomic information for simultaneous genetic evaluation of genotyped and nongenotyped animals. There has been a shortage of studies on RAM development for genetic evaluation models utilizing genomic information. This study extended the concept of RAM from BLUP to the single-step methods. Using example data, three RAMs were described for ssGBLUP. The order of animal equations was reduced from the total number of animals to (1) genotyped animals and nongenotyped parents, (2) genotyped animals and nongenotyped phenotyped animals, and (3) genotyped animals and nongenotyped parents of phenotyped nongenotyped nonparents. Solutions for the remaining animals are obtained following a back-solving step. All the RAMs produced identical results to the full ssGBLUP. Advances in computational hardware have alleviated many computational limitations, but, on the other hand, the size of data is growing rapidly by the number of animals, traits, phenotypes, genotypes, and genotype density. There is an opportunity for a RAM comeback for the single-step methods to reduce the computational demands by reducing the number of equations.

Application of structural equation models for genetic evaluation of lifetime reproductive traits and age at first lambing in Moghani sheep

The present study aimed to investigate the advantages of structural equation modeling for genetic evaluation of lifetime reproduction traits and age at first lambing in Moghani sheep. Data collected between 1988 and 2011 were retrieved from the Jafarabad Breeding Station of Moghani sheep, located in the north-west of Iran. The investigated traits were age at first lambing (AFL) and lifetime reproductive traits, including overall litter size at birth (OLSB), overall litter size at weaning (OLSW), overall litter weight at birth (OLWB), and overall litter weight at weaning (OLWW). Two multivariate animal models, including standard (SMM) and Inductive Causation algorithm-based structural equation models (IC-SEM), were compared in terms of deviance information criterion (DIC) and predictive ability measures, including the mean square of error (MSE) and Pearson's correlation coefficient between the observed and predicted values (r(y,yˆ)) of records. In general, IC-SEM performed better than SMM in terms of DIC, MSE, and r(y,yˆ) in such a way that the lowest DIC and MSE, and the highest r(y,yˆ) were obtained under IC-SEM. All the structural coefficients estimated by IC-SEM were statistically significant. Heritability estimates for the traits studied were low values of 0.11, 0.03, 0.07, 0.03, and 0.09 for AFL, OLSB, OLSW, OLWB, and OLWW, respectively, implying that the studied traits were mainly controlled by non-additive genetic and environmental effects. AFL had negative and statistically significant (95% highest posterior density (HPD) intervals did not include zero) genetic correlations with other traits studied, ranging from − 0.45 for AFL-OLSB to − 0.69 for AFL-OLWW; meaning that a fast-growing ewe lamb can achieve higher reproductive performance. Genetic correlations among lifetime reproductive traits were positive and statistically significant (99% HPD did not include zero), and varied from 0.36 (OLSB-OLWW) to 0.81 (OLSB-OLSW). According to the causal structure detected by the inductive causation (IC) algorithm, it can be concluded that OLSB, as a parent trait, had indirect causal effects on OLWW, as a composite reproductive trait, mediated via both OLSW and OLWB. AFL had no phenotypic causal impact on the lifetime reproductive traits of Moghani ewes but was negatively associated with these traits in terms of additive genetic effects.

Relative contribution of Imprinting, X chromosome and Litter effects to phenotypic variation in economic traits of sheep.

Data on Zandi sheep were analysed to quantify maternal and paternal imprinting, X chromosome and litter effects' contribution to phenotypic variation in birth weight (BW), weaning weight (WW), growth rate (GR), Kleiber ratio (KR), efficiency of growth (EF) and relative growth rate (RGR). To this end, a two-step approach was adopted. In the first step, each trait was analysed with a series of 16 animal models, which were identical for fixed and autosomal additive genetic effects but differed for combinations of maternal permanent environmental, maternal genetic, X chromosome and litter effects. For each trait, the best model was selected by the Akaike information criterion (AIC) and likelihood ratio tests (LRTs). In the second step, three additional models were fitted by adding maternal imprinting, paternal imprinting or both (models 17, 18 and 19) to the best model selected in the first step. Estimators of bias, dispersion and accuracy of breeding values estimated within 19 models with whole, and partial data were used to evaluate how well were the 19 models in estimating breeding values for the animals when their records were masked. For all traits studied, fitting the litter effect led to a better data fit. Also, it resulted in noticeable decreases in residual variance and other maternal variances. For growth traits, models containing the X-linked effects fitted the data substantially better than corresponding models without the X-linked effects. For BW, WW and GR, estimates of X-linked heritability ( ) ranged between 0.09 (GR) and 0.14 (BW). Ignoring X-linked effects from the genetic evaluation model resulted in significant inflated autosomal additive genetic variance. For BW, WW, EF and RGR, models containing the imprinting effects provided a better fit of the data than otherwise identical models. Imprinting effects contributed significantly to the phenotypic variation of these traits in a range between 5% (RGR) and 8% (BW, WW). A sharp decline was observed in autosomal additive genetic variance following including imprinting effects in the model (27% to 40% depending on the trait). The least bias and dispersion, as well as greater accuracies for breeding values of focal animals, were for a model which included imprinting, X-linked and litter effects. It was concluded that imprinting, X-linked and litter effects need to be included in the genetic evaluation models for growth and efficiency-related traits of Zandi lambs.

Multi-trait and multi-environment Bayesian analysis to predict the G x E interaction in flood-irrigated rice.

The biggest challenge for the reproduction of flood-irrigated rice is to identify superior genotypes that present development of high-yielding varieties with specific grain qualities, resistance to abiotic and biotic stresses in addition to superior adaptation to the target environment. Thus, the objectives of this study were to propose a multi-trait and multi-environment Bayesian model to estimate genetic parameters for the flood-irrigated rice crop. To this end, twenty-five rice genotypes belonging to the flood-irrigated rice breeding program were evaluated. Grain yield and flowering were evaluated in the agricultural year 2017/2018. The experimental design used in all experiments was a randomized block design with three replications. The Markov Chain Monte Carlo algorithm was used to estimate genetic parameters and genetic values. The flowering is highly heritable by the Bayesian credibility interval: h2 = 0.039-0.80, and 0.02-0.91, environment 1 and 2, respectively. The genetic correlation between traits was significantly different from zero in the two environments (environment 1: -0.80 to 0.74; environment 2: -0.82 to 0.86. The relationship of CVe and CVg higher for flowering in the reduced model (CVg/CVe = 5.83 and 13.98, environments 1 and 2, respectively). For the complete model, this trait presented an estimate of the relative variation index of: CVe = 4.28 and 4.21, environments 1 and 2, respectively. In summary, the multi-trait and multi-environment Bayesian model allowed a reliable estimate of the genetic parameter of flood-irrigated rice. Bayesian analyzes provide robust inference of genetic parameters. Therefore, we recommend this model for genetic evaluation of flood-irrigated rice genotypes, and their generalization, in other crops. Precise estimates of genetic parameters bring new perspectives on the application of Bayesian methods to solve modeling problems in the genetic improvement of flood-irrigated rice.

Mitochondrial DNA Variation Contributes to the Aptitude for Dressage and Show Jumping Ability in the Holstein Horse Breed.

Simple SummaryIn the Holstein horse breed, maternal lineages are considered to be of major importance for the breeding success and can be examined through analysis of the maternally inherited mitochondrial DNA (mtDNA). Since mitochondrial genes are involved in energy metabolism, variation might contribute to differences in performance characteristics, as has already been pointed out in humans and racehorses with respect to endurance. No corresponding studies have yet been conducted for the athletic performance of warmblood breeds and, thus, the aim of this study was to investigate the influence of mitochondrial variation on the performance of Holstein mares. The data set used for this study was composed of both sequenced and non-sequenced mares of 75 maternal lineages as a previous study revealed that Holstein mares within a maternal lineage had identical mtDNA haplotypes regarding their non-synonymous variants. Association analyses were performed using estimated breeding values (EBVs) based on information from sport and breeding events. We observed mitochondrial single nucleotide polymorphisms (SNPs) significantly associated with one or more of the examined EBVs and identified mitochondrial haplogroups with a particular aptitude for dressage or show jumping.Maternal lineages are considered an important factor in breeding. Mitochondrial DNA (mtDNA) is maternally inherited and plays an important role in energy metabolism. It has already been associated with energy consumption and performances, e.g., stamina in humans and racehorses. For now, corresponding studies are lacking for sport performance of warmblood breeds. MtDNA sequences were available for 271 Holstein mares from 75 maternal lineages. As all mares within a lineage showed identical haplotypes regarding the non-synonymous variants, we expanded our data set by also including non-sequenced mares and assigning them to the lineage-specific haplotype. This sample consisting of 6334 to 16,447 mares was used to perform mitochondrial association analyses using breeding values (EBVs) estimated on behalf of the Fédération Équestre Nationale (FN) and on behalf of the Holstein Breeding Association (HOL). The association analyses revealed 20 mitochondrial SNPs (mtSNPs) significantly associated with FN-EBVs and partly overlapping 20 mtSNPs associated with HOL-EBVs. The results indicated that mtDNA contributes to performance differences between maternal lineages. Certain mitochondrial haplogroups were associated with special talents for dressage or show jumping. The findings encourage to set up innovative genetic evaluation models that also consider information on maternal lineages.

XSim version 2: simulation of modern breeding programs.

Simulation can be an efficient approach to design, evaluate, and optimize breeding programs. In the era of modern agriculture, breeding programs can benefit from a simulator that integrates various sources of big data and accommodates state-of-the-art statistical models. The initial release of XSim, in which stochastic descendants can be efficiently simulated with a drop-down strategy, has mainly been used to validate genomic selection results. In this article, we present XSim Version 2 that is an open-source tool and has been extensively redesigned with additional features to meet the needs in modern breeding programs. It seamlessly incorporates multiple statistical models for genetic evaluations, such as GBLUP, Bayesian alphabets, and neural networks, and it can effortlessly simulate successive generations of descendants based on complex mating schemes by the aid of its modular design. Case studies are presented to demonstrate the flexibility of XSim Version 2 in simulating crossbreeding in animal and plant populations. Modern biotechnology, including double haploids and embryo transfer, can all be simultaneously integrated into the mating plans that drive the simulation. From a computing perspective, XSim Version 2 is implemented in Julia, which is a computer language that retains the readability of scripting languages (e.g. R and Python) without sacrificing much computational speed compared to compiled languages (e.g. C). This makes XSim Version 2 a simulation tool that is relatively easy for both champions and community members to maintain, modify, or extend in order to improve their breeding programs. Functions and operators are overloaded for a better user interface so they may concatenate, subset, summarize, and organize simulated populations at each breeding step. With the strong and foreseeable demands in the community, XSim Version 2 will serve as a modern simulator bridging the gaps between theories and experiments with its flexibility, extensibility, and friendly interface.

A comparative study of estimated breeding values with Hanwoo cow using genetic evaluation models

A comparative study of estimated breeding values with Hanwoo cow using genetic evaluation models

An attempt of using public ambient temperature data in swine genetic evaluation for litter-size traits at birth in Japan

Context Large-scale genetic evaluation is promising for efficiently improving sow lifetime productivity, and therefore, a reasonable operational model should be pursued to analyse phenotypic data collected from around Japan, where unique seasonality exists. However, the information on establishing the model is currently insufficient. Aims To obtain the fundamental information on using ambient temperature information in developing the model for routine swine genetic evaluation in Japan, by analysing total number born, number born alive, and number stillborn collected at a Japanese farm, together with off-farm ambient temperature measured at the nearest Automated Meteorological Data Acquisition System station. Methods Five repeatability animal models were exploited, considering the effects of farrowing season (Model 1), farrowing month (Model 2), quadratic regressions of daily maximum ambient temperature of farrowing day (Model 3), season and temperature (Model 4), or month and temperature (Model 5). Key results Patterns of the effects of daily maximum temperature of farrowing day estimated using Model 3 were similar to those of farrowing season by Model 1 and those of farrowing month by Model 2. Adding the effect of daily maximum temperature of farrowing day (Models 4 and 5) could explain phenotypic variability better than only considering either of farrowing season or month (Models 1 and 2). Estimated heritability was stable among the models and the rank correlation of predicted breeding values among models was >0.98 for all traits. Conclusions The results indicated the possibility that using public ambient temperature can capture a large part of the phenotypic variability in litter-size traits at birth caused by seasonality in Japan. Implications This study could support the availability of public meteorological data in the development of flexible operational models for future swine genetic evaluation in Japan.

Row–Col and Bayesian approach seeking to improve the predictive capacity and selection of passion fruit

Methods for genetic improvement of semi–perennial species, such as passion fruit, often involve large areas, unbalanced data, and lack of observations. Some strategies can be applied to solve these problems. In this work, different models and approaches were tested to improve the precision of estimates of genetic evaluation models for several characteristics of the passion fruit. A randomized block design (RBD) model was compared to a posteriori correction, adding two factors to the model (post–hoc blocking Row–Col). These models were also combined with the frequentist and Bayesian approaches to identify which combination yields the most accurate results. These approaches are part of a strategic plan in a perennial plant breeding program to select promising genitors of passion to compose the next selection cycle. For Bayesian, we tested two priors, defining different values for the distribution parameters of effect variances of the model. We also performed a cross–validation test to choose a priori values and compare the frequentist and Bayesian approaches using the root mean square error (RMSE) and the correlation between the predicted and observed values, called Predictive capacity of the model (PC). The model with the post–hoc blocking Row–Col design captured the spatial variability for productivity and number of fruits, directly affecting the experimental precision. Both approaches applied to the models showed a similar performance, with predictive capacity and selective efficiency leading to the selection of the same individuals.

Random Regression Model for Genetic Evaluation and Early Selection in the Iranian Holstein Population.

Simple SummaryThe objective of this study was to use a model to predict breeding values for sires and cows at an early stage of the first lactation of cows and progeny groups in the Iranian Holstein population to support the early selection of sires. Our results show that we can select sires according to their daughters’ early lactation performance before they finish first lactation. Cross-validation results show that early selection accuracy can be high, and such an early selection can decrease the generation interval and lead to an increased genetic gain in the Iranian Holstein population.The objective of this study was to use a model to predict breeding values for sires and cows at an early stage of the first lactation of cows and progeny groups in the Iranian Holstein population to enable the early selection of sires. An additional objective was to estimate genetic and phenotypic parameters associated with this model. The accuracy of predicted breeding values was investigated using cross-validation based on sequential genetic evaluations emulating yearly evaluation runs. The data consisted of 2,166,925 test-day records from 456,712 cows calving between 1990 and 2015. (Co)-variance components and breeding values were estimated using a random regression test-day model and the average information (AI) restricted maximum likelihood method (REML). Legendre polynomial functions of order three were chosen to fit the additive genetic and permanent environmental effects, and a homogeneous residual variance was assumed throughout lactation. The lowest heritability of daily milk yield was estimated to be just under 0.14 in early lactation, and the highest heritability of daily milk yield was estimated to be 0.18 in mid-lactation. Cross-validation showed a highly positive correlation of predicted breeding values between consecutive yearly evaluations for both cows and sires. Correlation between predicted breeding values based only on records of early lactation (5–90 days) and records including late lactation (181–305 days) were 0.77–0.87 for cows and 0.81–0.94 for sires. These results show that we can select sires according to their daughters’ early lactation information before they finish the first lactation. This can be used to decrease generation interval and to increase genetic gain in the Iranian Holstein population.

Population genetic diversity and genetic evaluation models reveal the maternal genetic structure and conservation priority characteristics of indigenous cattle in China

The population genetic structure of domestic animals and the protection of genetic resources are major issues of concern in the field of global biology. In this study, the mtDNA D -loop sequences of 1163 Chinese indigenous cattle ( Bos taurus and Bos indicus ) were used to analyze their maternal origin and population genetic structure and to prioritize conservation evaluations based on a population genetic evaluation model. The results revealed three maternal origins or domestication events among Chinese indigenous cattle and the occurrence of secondary expansions but also the phenomenon of gene penetration. The population genetic evaluation model results showed that the genetic diversity of Huangpi cattle (HHP), Mountainous cattle (CMO), Bashan cattle (SBS) and Guanling cattle (GGL)populations was highest and that the Zaosheng cattle (GZN), Qinchuan cattle (SQB) and Pingliang cattle (GPL) populations contributed most to the richness of Chinese indigenous cattle genetic resources, indicating that these populations should be protected first. Our research provides a scientific basis and effective evaluation methods for the protection of genetic resources of indigenous livestock varieties.

Impact of the event effect in genetic evaluation for ranking traits in horses.

In genetic evaluation of horses, the genetic trend does not correspond into a phenotypic trend when using ranking as a phenotype due to its uniform distribution, and some other effects might be absorbing that trend. From a founder population, a further four discrete generations of 100 individuals were simulated under random mating. Then, ten additional discrete generations were simulated by selecting the best 10% of the animals. Likewise, an underlying variable with heritability 0.1 or 0.2, affected by an event environmental influence, generation and permanent environment, was simulated to establish the ranking assignment of 10 random participants or according to the competitive level for each event, in 10 or 100 structured or unstructured events. The ranking trait genetic evaluation model was tested to include or exclude the event effect and the permanent environment effect, depending on the scenario. The results showed that the event effect fitted the different competitive level of each event, leading to a 5% to 23% of selection response improvement for structured competitions. Therefore, the event effect should be included in the genetic evaluation models of horses. The permanent environment fitted or simulated did not significantly improve the selection response. The event effect explained the competition genetic level, by compensating the genetic trend obtained by selection.

ناهمگنی اثر همخونی جزئی حاصل از حیوانات بنیان‌گذار بر صفات وزن تولد و وزن از شیرگیری بره‌های بلوچی

Uneven distribution of recessive alleles among founder animals genomes leads to the heterogeneity of inbreeding depression among their descendants. In this study, pedigree information, birth weight, and weaning weight records of 4032 Baluchi sheep collected between 1989 to 2017 at the Baluchi sheep breeding station were used to assess the heterogeneity of inbreeding depression between founders of the population. The Wright inbreeding coefficient of the inbred animals was decomposed to the partial inbreeding of the four founder animals with the greatest contribution to the inbreeding of the population. Wright inbreeding coefficients and partial inbreeding coefficients of the founder animals were included as a covariate in the animal model, in two separate analyses. The regression coefficients of birth weight and weaning weight from Wright inbreeding were estimated to be -6.4 and -61.9 g, respectively. The effect of partial inbreeding from different founders on the studied traits was heterogeneous. The regression coefficient of birth weight on partial inbreeding of founder B was -79 g, while partial inbreeding from founder D caused an increase of 121 g in this trait. In weaning weight, partial inbreeding of founder D caused an increase of 83 g in this trait, while partial inbreeding of other founders did not affect this trait. The observed heterogeneity in the effects of partial inbreeding of the different animals can confirm the hypothesis that a few recessive alleles with major effects are contributing to the inbreeding depression of these traits. According to the results of this study, the heterogeneity of the effect of inbreeding of the founder animals should be considered in the genetic evaluation model of this breed.

Including milk production, conformation, and functional traits in multivariate models for genetic evaluation of lameness

Lameness is a serious health and welfare issue that can negatively affect the economic performance of cows, especially on pasture-based dairy farms. However, most genetic predictions (GP) of lameness have low accuracy because lameness data are often incomplete as data are collected voluntarily by farmers in countries such as Australia. The objective of this study was to find routinely measured traits that are correlated with lameness and use them in multivariate evaluation models to improve the accuracy of GP for lameness. We used health events and treatments associated with lameness recorded by Australian farmers from 2002 to early 2019. The lameness incidence rates in Holstein and Jersey cows were 3.3% and 4.6%, respectively. We analyzed the records of 36 other traits (milk production, conformation, fertility, and survival traits) to estimate genetic correlations with lameness. The estimated heritability ± standard error (and repeatability ± standard error) for lameness in both Holstein and Jersey breeds were very low: 0.007 ± 0.002 (and 0.029 ± 0.002) and 0.005 ± 0.003 (and 0.027 ± 0.006), respectively, in univariate sire models. For the GP models, we tested including measurements of overall type to prediction models for Holsteins, stature and body length for Jersey, and milk yield and fertility traits for both breeds. The average accuracy of GP, calculated from prediction error variances, were 0.38 and 0.24 for Holstein and Jersey sires, respectively, when estimated using univariate sire models and both increased to 0.43 using multivariate sire models. In conclusion, we found that the accuracy of GP for lameness could be improved by including genetically correlated traits in a multivariate model. However, to further improve the accuracy of predictions of lameness, precise identification and recording incidences of hoof or leg disorder, or large-scale recording of locomotion and claw scores by trained personnel should be considered.

Herd clustering strategies and corresponding genetic evaluations based on social-ecological characteristics for a local endangered cattle breed.

The accuracy of breeding values strongly depends on the population and herd structure, i.e., the number of animals considered in genetic evaluations and the size of contemporary groups (CGs). Local breeds are usually kept in small-sized family farms under alternative husbandry conditions. For such herd structure, consideration of classical herd or herd-test-day effects in CG modeling approaches implies only a few records per effect level. In consequence, the present study aimed on methodological evaluations of different herd clustering strategies, considering social–ecological and herd characteristics. In this regard, we considered 19 herds keeping cows from the small local population of German Black Pied cattle (Deutsches Schwarzbuntes Niederungsrind; DSN), 10 herds keeping Holstein Friesian (HF) cows and one mixed herd with HF and DSN cows. Herds were characterized for 106 variables, reflecting farm conditions, husbandry practices, feeding regime, herd management, herd fertility status, herd health status and breeding strategies as well as social–ecological descriptors. The variables were input data for different clustering approaches including agglomerative hierarchical clustering (AHC), partition around medoids (PAM), fuzzy clustering (FZC) and a clustering of variables combined with agglomerative hierarchical clustering (CoVAHC). The evaluation criterion was the average silhouette width (ASW), suggesting a CoVAHC application and consideration of four herd clusters (HCs) for herd allocation (ASW of 0.510). HC1 comprised the larger, half organic and half conventional DSN family farms, which generate their main income from milk production. HC2 consisted of small organic DSN family farms where cows are kept in tie stables. HC3 included the DSN sub-population from former East Germany, reflecting the large-scale farm types. The specialized HF herds were well separated and allocated to HC4. Generalized linear mixed models with appropriate link functions were applied to compare test-day and female fertility traits of 5538 cows (2341 DSN and 3197 HF) from the first three lactations among the four HCs. Least squares means for milk, fat and protein yield (Mkg, Fkg and Pkg) significantly differed between HC. The significant differences among the four HCs clearly indicate the influence of varying herd conditions on cow traits. The similarities of herds within HC suggested the application of HCs in statistical models for genetic evaluations for DSN. In this regard, we found an increase of accuracies of estimated breeding values of cows and sires and of heritabilities for milk yield when applying models with herd-cluster-test-day or herd-cluster-test-month effects compared to classical herd-test-day models. The identified increase for the number of cows and cow records in CG due to HC effects may be the major explanation for the identified superiority.

Genomic prediction of hybrid crops allows disentangling dominance and epistasis.

We revisited, in a genomic context, the theory of hybrid genetic evaluation models of hybrid crosses of pure lines, as the current practice is largely based on infinitesimal model assumptions. Expressions for covariances between hybrids due to additive substitution effects and dominance and epistatic deviations were analytically derived. Using dense markers in a GBLUP analysis, it is possible to split specific combining ability into dominance and across-groups epistatic deviations, and to split general combining ability (GCA) into within-line additive effects and within-line additive by additive (and higher order) epistatic deviations. We analyzed a publicly available maize data set of Dent × Flint hybrids using our new model (called GCA-model) up to additive by additive epistasis. To model higher order interactions within GCAs, we also fitted “residual genetic” line effects. Our new GCA-model was compared with another genomic model which assumes a uniquely defined effect of genes across origins. Most variation in hybrids is accounted by GCA. Variances due to dominance and epistasis have similar magnitudes. Models based on defining effects either differently or identically across heterotic groups resulted in similar predictive abilities for hybrids. The currently used model inflates the estimated additive genetic variance. This is not important for hybrid predictions but has consequences for the breeding scheme—e.g. overestimation of the genetic gain within heterotic group. Therefore, we recommend using GCA-model, which is appropriate for genomic prediction and variance component estimation in hybrid crops using genomic data, and whose results can be practically interpreted and used for breeding purposes.

Comparison of random regression models, traditional animal model and with the inclusion of molecular markers in the estimation of genetic parameters in Colombian Holstein cattle

The use of molecular markers to identify desirable genes in animal production is known as marker-assisted selection. The traditional genetic evaluation model uses the BLUP methodology; when genetic markers are included in the evaluation model, the methodology is known as M-BLUP. In contrast, random regression models (RRM), unlike the models based on production at 305 days, consider factors that change for each animal from one test to another. The objective of this study was to compare variance components, genetic parameters and breeding values for milk production, protein percentage and somatic cell score in Colombian Holstein cattle using BLUP, M-BLUP and RRM. For the estimation of genetic parameters and values, 2003 lactations corresponding to 1417 cows in 55 herds were used, and effects of the order of delivery, herd, and contemporary group were included. The three traits presented greater heritability under the MBLUP model: 0.44 for protein percentage, 0.27 for milk production and 0.28 for somatic cell score. This was because the genetic variance was greater when M-BLUP was used, which allowed a greater accuracy of the breeding value estimation in the three traits. Therefore, the model that includes information on molecular markers is more suitable for genetic evaluation in Colombian Holstein cattle.