Principal component approach in variance component estimation for international sire evaluation

<p>Abstract</p> <p>Background</p> <p>The dairy cattle breeding industry is a highly globalized business, which needs internationally comparable and reliable breeding values of sires. The international Bull Evaluation Service, Interbull, was established in 1983 to respond to this need. Currently, Interbull performs multiple-trait across country evaluations (MACE) for several traits and breeds in dairy cattle and provides international breeding values to its member countries. Estimating parameters for MACE is challenging since the structure of datasets and conventional use of multiple-trait models easily result in over-parameterized genetic covariance matrices. The number of parameters to be estimated can be reduced by taking into account only the leading principal components of the traits considered. For MACE, this is readily implemented in a random regression model.</p> <p>Methods</p> <p>This article compares two principal component approaches to estimate variance components for MACE using real datasets. The methods tested were a REML approach that directly estimates the genetic principal components (direct PC) and the so-called bottom-up REML approach (bottom-up PC), in which traits are sequentially added to the analysis and the statistically significant genetic principal components are retained. Furthermore, this article evaluates the utility of the bottom-up PC approach to determine the appropriate rank of the (co)variance matrix.</p> <p>Results</p> <p>Our study demonstrates the usefulness of both approaches and shows that they can be applied to large multi-country models considering all concerned countries simultaneously. These strategies can thus replace the current practice of estimating the covariance components required through a series of analyses involving selected subsets of traits. Our results support the importance of using the appropriate rank in the genetic (co)variance matrix. Using too low a rank resulted in biased parameter estimates, whereas too high a rank did not result in bias, but increased standard errors of the estimates and notably the computing time.</p> <p>Conclusions</p> <p>In terms of estimation's accuracy, both principal component approaches performed equally well and permitted the use of more parsimonious models through random regression MACE. The advantage of the bottom-up PC approach is that it does not need any previous knowledge on the rank. However, with a predetermined rank, the direct PC approach needs less computing time than the bottom-up PC.</p

Principal component and factor analytic models in international sire evaluation

<p>Abstract</p> <p>Background</p> <p>Interbull is a non-profit organization that provides internationally comparable breeding values for globalized dairy cattle breeding programmes. Due to different trait definitions and models for genetic evaluation between countries, each biological trait is treated as a different trait in each of the participating countries. This yields a genetic covariance matrix of dimension equal to the number of countries which typically involves high genetic correlations between countries. This gives rise to several problems such as over-parameterized models and increased sampling variances, if genetic (co)variance matrices are considered to be unstructured.</p> <p>Methods</p> <p>Principal component (PC) and factor analytic (FA) models allow highly parsimonious representations of the (co)variance matrix compared to the standard multi-trait model and have, therefore, attracted considerable interest for their potential to ease the burden of the estimation process for multiple-trait across country evaluation (MACE). This study evaluated the utility of PC and FA models to estimate variance components and to predict breeding values for MACE for protein yield. This was tested using a dataset comprising Holstein bull evaluations obtained in 2007 from 25 countries.</p> <p>Results</p> <p>In total, 19 principal components or nine factors were needed to explain the genetic variation in the test dataset. Estimates of the genetic parameters under the optimal fit were almost identical for the two approaches. Furthermore, the results were in a good agreement with those obtained from the full rank model and with those provided by Interbull. The estimation time was shortest for models fitting the optimal number of parameters and prolonged when under- or over-parameterized models were applied. Correlations between estimated breeding values (EBV) from the PC19 and PC25 were unity. With few exceptions, correlations between EBV obtained using FA and PC approaches under the optimal fit were ≥ 0.99. For both approaches, EBV correlations decreased when the optimal model and models fitting too few parameters were compared.</p> <p>Conclusions</p> <p>Genetic parameters from the PC and FA approaches were very similar when the optimal number of principal components or factors was fitted. Over-fitting increased estimation time and standard errors of the estimates but did not affect the estimates of genetic correlations or the predictions of breeding values, whereas fitting too few parameters affected bull rankings in different countries.</p

The same ELA class II risk factors confer equine insect bite hypersensitivity in two distinct populations

Insect bite hypersensitivity (IBH) is a chronic allergic dermatitis common in horses. Affected horses mainly react against antigens present in the saliva from the biting midges, Culicoides ssp, and occasionally black flies, Simulium ssp. Because of this insect dependency, the disease is clearly seasonal and prevalence varies between geographical locations. For two distinct horse breeds, we genotyped four microsatellite markers positioned within the MHC class II region and sequenced the highly polymorphic exons two from DRA and DRB3, respectively. Initially, 94 IBH-affected and 93 unaffected Swedish born Icelandic horses were tested for genetic association. These horses had previously been genotyped on the Illumina Equine SNP50 BeadChip, which made it possible to ensure that our study did not suffer from the effects of stratification. The second population consisted of 106 unaffected and 80 IBH-affected Exmoor ponies. We show that variants in the MHC class II region are associated with disease susceptibility (praw = 2.34 × 10−5), with the same allele (COR112:274) associated in two separate populations. In addition, we combined microsatellite and sequencing data in order to investigate the pattern of homozygosity and show that homozygosity across the entire MHC class II region is associated with a higher risk of developing IBH (p = 0.0013). To our knowledge this is the first time in any atopic dermatitis suffering species, including man, where the same risk allele has been identified in two distinct populations

Estimation of genetic variance for macro- and micro-environmental sensitivity using double hierarchical generalized linear models

Background: Genetic variation for environmental sensitivity indicates that animals are genetically different in their response to environmental factors. Environmental factors are either identifiable (e.g. temperature) and called macro-environmental or unknown and called micro-environmental. The objectives of this study were to develop a statistical method to estimate genetic parameters for macro- and micro-environmental sensitivities simultaneously, to investigate bias and precision of resulting estimates of genetic parameters and to develop and evaluate use of Akaike’s information criterion using h-likelihood to select the best fitting model. Methods: We assumed that genetic variation in macro- and micro-environmental sensitivities is expressed as genetic variance in the slope of a linear reaction norm and environmental variance, respectively. A reaction norm model to estimate genetic variance for macro-environmental sensitivity was combined with a structural model for residual variance to estimate genetic variance for micro-environmental sensitivity using a double hierarchical generalized linear model in ASReml. Akaike’s information criterion was constructed as model selection criterion using approximated h-likelihood. Populations of sires with large half-sib offspring groups were simulated to investigate bias and precision of estimated genetic parameters. Results: Designs with 100 sires, each with at least 100 offspring, are required to have standard deviations of estimated variances lower than 50% of the true value. When the number of offspring increased, standard deviations of estimates across replicates decreased substantially, especially for genetic variances of macro- and micro-environmental sensitivities. Standard deviations of estimated genetic correlations across replicates were quite large (between 0.1 and 0.4), especially when sires had few offspring. Practically, no bias was observed for estimates of any of the parameters. Using Akaike’s information criterion the true genetic model was selected as the best statistical model in at least 90% of 100 replicates when the number of offspring per sire was 100. Application of the model to lactation milk yield in dairy cattle showed that genetic variance for micro- and macro-environmental sensitivities existed. Conclusion: The algorithm and model selection criterion presented here can contribute to better understand genetic control of macro- and micro-environmental sensitivities. Designs or datasets should have at least 100 sires each with 100 offspring.Open Access</p

Validation of consistency of Mendelian sampling variance

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Carcass characteristics of Nordic native cattle breeds

Funding Information: This work was the outcome of a Nordic Joint Committee for Agricultural and Food Research (NKJ) network – ’Nordic Native Meat’ – running from 2018–2021. The authors wish to acknowledge the support from NKJ for network activities. Publisher Copyright: © Copyright 2023 the Authors.Native livestock breeds are part of the history of the Nordic people and comprise a resource for future food production. In this study, net gain and carcass characteristics of two Danish, three Finnish, one Icelandic, six Norwegian and five Swedish native cattle breeds were retrieved and compared to commercial breeds: two beef breeds and two dairy breeds. Breed data were collected from national databases and sorted into six animal categories: young bull, bull, steer, heifer, young cow and cow, for which means and standard deviations were calculated within each country. The native breeds ranged from small-sized milking type breeds with low net gain, carcass weights and EUROP classification to larger multipurpose breeds with high net gains, carcass weights and EUROP classification. All Finnish and most of the Norwegian and Swedish native breeds had lower net gain and carcass weight than the dairy breeds in the same category and country, but with similar carcass conformation and fatness scores. The two Danish native breeds had higher net gain, carcass weight and conformation class than the reference dairy breed, but lower than the reference beef breeds. The net gain and carcass traits of the Icelandic native breed were similar to the smallest-sized native breeds from the other countries. The carcass traits of the native breeds indicate that they have comparative advantages in an extensive production system based on forage and marginal grasslands. They may also succeed better in the value-added markets than in mainstream beef production.Peer reviewe