Genomic predictions in purebreds with a multibreed genomic relationship matrix1

Strategies for genomic predictions of an indicine multi-breed population using single-step GBLUP

Brazilian livestock breeding programmes strive to enhance the genetics of beef cattle, with a strong emphasis on the Nellore breed, which has an extensive database and has achieved significant genetic progress in the last years. There are other indicine breeds that are economically important in Brazil; however, these breeds have more modest sets of phenotypes, pedigree and genotypes, slowing down their genetic progress as their predictions are less accurate. Combining several breeds in a multi-breed evaluation could help enhance predictions for those breeds with less information available. This study aimed to evaluate the feasibility of multi-breed, single-step genomic best linear unbiased predictor genomic evaluations for Nellore, Brahman, Guzerat and Tabapua. Multi-breed evaluations were contrasted to the single-breed ones. Data were sourced from the National Association of Breeders and Researchers of Brazil and included pedigree (4,207,516), phenotypic (328,748), and genomic (63,492) information across all breeds. Phenotypes were available for adjusted weight at 210 and 450 days of age, and scrotal circumference at 365 days of age. Various scenarios were evaluated to ensure pedigree and genomic information compatibility when combining different breeds, including metafounders (MF) or building the genomic relationship matrix with breed-specific allele frequencies. Scenarios were compared using the linear regression method for bias, dispersion and accuracy. The results showed that using multi-breed evaluations significantly improved accuracy, especially for smaller breeds like Guzerat and Tabapua. The validation statistics indicated that the MF approach provided accurate predictions, albeit with some bias. While single-breed evaluations tended to have lower accuracy, merging all breeds in multi-breed evaluations increased accuracy and reduced dispersion. This study demonstrates that multi-breed genomic evaluations are proper for indicine beef cattle breeds. The MF approach may be particularly beneficial for less-represented breeds, addressing limitations related to small reference populations and incompatibilities between G and A22. By leveraging genomic information across breeds, breeders and producers can make more informed selection decisions, ultimately improving genetic gain in these cattle populations.

Genomic prediction based on a joint reference population for the Xinjiang Brown cattle

Introduction: Xinjiang Brown cattle constitute the largest breed of cattle in Xinjiang. Therefore, it is crucial to establish a genomic evaluation system, especially for those with low levels of breed improvement. Methods: This study aimed to establish a cross breed joint reference population by analyzing the genetic structure of 485 Xinjiang Brown cattle and 2,633 Chinese Holstein cattle (Illumina GeneSeek GGP bovine 150 K chip). The Bayes method single-step genome-wide best linear unbiased prediction was used to conduct a genomic evaluation of the joint reference population for the milk traits of Xinjiang Brown cattle. The reference population of Chinese Holstein cattle was randomly divided into groups to construct the joint reference population. By comparing the prediction accuracy, estimation bias, and inflation coefficient of the validation population, the optimal number of joint reference populations was determined. Results and Discussion: The results indicated a distinct genetic structure difference between the two breeds of adult cows, and both breeds should be considered when constructing multi-breed joint reference and validation populations. The reliability range of genome prediction of milk traits in the joint reference population was 0.142-0.465. Initially, it was determined that the inclusion of 600 and 900 Chinese Holstein cattle in the joint reference population positively impacted the genomic prediction of Xinjiang Brown cattle to certain extent. It was feasible to incorporate the Chinese Holstein into Xinjiang Brown cattle population to form a joint reference population for multi-breed genomic evaluation. However, for different Xinjiang Brown cattle populations, a fixed number of Chinese Holstein cattle cannot be directly added during multi-breed genomic selection. Pre-evaluation analysis based on the genetic structure, kinship, and other factors of the current population is required to ensure the authenticity and reliability of genomic predictions and improve estimation accuracy.

Improving Genomic Predictions in Multi-Breed Cattle Populations: A Comparative Analysis of BayesR and GBLUP Models

Numerous studies have shown that combining populations from similar or closely related genetic breeds improves the accuracy of genomic predictions (GP). Extensive experimentation with diverse Bayesian and genomic best linear unbiased prediction (GBLUP) models have been developed to explore multi-breed genomic selection (GS) in livestock, ultimately establishing them as successful approaches for predicting genomic estimated breeding value (GEBV). This study aimed to assess the effectiveness of using BayesR and GBLUP models with linkage disequilibrium (LD)-weighted genomic relationship matrices (GRMs) for genomic prediction in three different beef cattle breeds to identify the best approach for enhancing the accuracy of multi-breed genomic selection in beef cattle. Additionally, a comparison was conducted to evaluate the predictive precision of different marker densities and genetic correlations among the three breeds of beef cattle. The GRM between Yunling cattle (YL) and other breeds demonstrated modest affinity and highlighted a notable genetic concordance of 0.87 between Chinese Wagyu (WG) and Huaxi (HX) cattle. In the within-breed GS, BayesR demonstrated an advantage over GBLUP. The prediction accuracies for HX cattle using the BayesR model were 0.52 with BovineHD BeadChip data (HD) and 0.46 with whole-genome sequencing data (WGS). In comparison to the GBLUP model, the accuracy increased by 26.8% for HD data and 9.5% for WGS data. For WG and YL, BayesR doubled the within-breed prediction accuracy to 14.3% from 7.1%, outperforming GBLUP across both HD and WGS datasets. Moreover, analyzing multiple breeds using genomic selection showed that BayesR consistently outperformed GBLUP in terms of predictive accuracy, especially when using WGS. For instance, in a mixed reference population of HX and WG, BayesR achieved a significant accuracy of 0.53 using WGS for HX, which was a substantial enhancement over the accuracies obtained with GBLUP models. The research further highlights the benefit of including various breeds in the reference group, leading to enhanced accuracy in predictions and emphasizing the importance of comprehensive genomic selection methods. Our research findings indicate that BayesR exhibits superior performance compared to GBLUP in multi-breed genomic prediction accuracy, achieving a maximum improvement of 33.3%, especially in genetically diverse breeds. The improvement can be attributed to the effective utilization of higher single nucleotide polymorphism (SNP) marker density by BayesR, resulting in enhanced prediction accuracy. This evidence conclusively demonstrates the significant impact of BayesR on enhancing genomic predictions in diverse cattle populations, underscoring the crucial role of genetic relatedness in selection methodologies. In parallel, subsequent studies should focus on refining GRM and exploring alternative models for GP.

Fitting Genomic Prediction Models with Different Marker Effects among Prefectures to Carcass Traits in Japanese Black Cattle

We fitted statistical models, which assumed single-nucleotide polymorphism (SNP) marker effects differing across the fattened steers marketed into different prefectures, to the records for cold carcass weight (CW) and marbling score (MS) of 1036, 733, and 279 Japanese Black fattened steers marketed into Tottori, Hiroshima, and Hyogo prefectures in Japan, respectively. Genotype data on 33,059 SNPs was used. Five models that assume only common SNP effects to all the steers (model 1), common effects plus SNP effects differing between the steers marketed into Hyogo prefecture and others (model 2), only the SNP effects differing between Hyogo steers and others (model 3), common effects plus SNP effects specific to each prefecture (model 4), and only the effects specific to each prefecture (model 5) were exploited. For both traits, slightly lower values of residual variance than that of model 1 were estimated when fitting all other models. Estimated genetic correlation among the prefectures in models 2 and 4 ranged to 0.53 to 0.71, all <0.8. These results might support that the SNP effects differ among the prefectures to some degree, although we discussed the necessity of careful consideration to interpret the current results.

Genomic evaluation with multibreed and crossbred data

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We found low accuracy of genomic evaluation of crossbreds based on purebred data.

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We found higher accuracy for crossbreds based on crossbred data.

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Use of putative sequence variants had a small impact on genomic accuracy.

Several types of multibreed genomic evaluation are in use. These include evaluation of crossbreds based on purebred SNP effects, joint evaluation of all purebreds and crossbreds with a single additive effect, and treating each purebred and crossbred group as a separate trait. Additionally, putative quantitative trait nucleotides can be exploited to increase the accuracy of prediction. Existing studies indicate that the prediction of crossbreds based on purebred data has low accuracy, that a joint evaluation can potentially provide accurate evaluations for crossbreds but could lower accuracy for purebreds compared with single-breed evaluations, and that the use of putative quantitative trait nucleotides only marginally increases the accuracy. One hypothesis is that genomic selection is based on estimation of clusters of independent chromosome segments. Subsequently, predicting a particular group type would require a reference population of the same type, and crosses with same breed percentage but different type (F₁ vs. F₂) would, at best, use separate reference populations. The genomic selection of multibreed population is still an active research topic.

Single-step genomic evaluation of milk production traits in Canadian Alpine and Saanen dairy goats

Genomic evaluations are routine in most plant and livestock breeding programs but are used infrequently in dairy goat breeding schemes. In this context, the purpose of this study was to investigate the use of the single-step genomic BLUP method for predicting genomic breeding values for milk production traits (milk, protein, and fat yields; protein and fat percentages) in Canadian Alpine and Saanen dairy goats. There were 6,409 and 12,236 Alpine records and 3,434 and 5,008 Saanen records for each trait in first and later lactations, respectively, and a total of 1,707 genotyped animals (833 Alpine and 874 Saanen). Two validation approaches were used, forward validation (i.e., animals born after 2013 with an average estimated breeding value accuracy from the full data set ≥0.50) and forward cross-validation (i.e., subsets of all animals included in the forward validation were used in successive replications). The forward cross-validation approach resulted in similar validation accuracies (0.55 to 0.66 versus 0.54 to 0.61) and biases (-0.01 to -0.07 versus -0.03 to 0.11) to the forward validation when averaged across traits. Additionally, both single and multiple-breed analyses were compared, and similar average accuracies and biases were observed across traits. However, there was a small gain in accuracy from the use of multiple-breed models for the Saanen breed. A small gain in validation accuracy for genomically enhanced estimated breeding values (GEBV) relative to pedigree-based estimated breeding values (EBV) was observed across traits for the Alpine breed, but not for the Saanen breed, possibly due to limitations in the validation design, heritability of the traits evaluated, and size of the training populations. Trait-specific gains in theoretical accuracy of GEBV relative to EBV for the validation animals ranged from 17 to 31% in Alpine and 35 to 55% in Saanen, using the cross-validation approach. The GEBV predicted from the full data set were 12 to 16% more accurate than EBV for genotyped animals, but no gains were observed for nongenotyped animals. The largest gains were found for does without lactation records (35-41%) and bucks without daughter records (46-54%), and consequently, the implementation of genomic selection in the Canadian dairy goat population would be expected to increase selection accuracy for young breeding candidates. Overall, this study represents the first step toward implementation of genomic selection in Canadian dairy goat populations.

Genomic prediction using a reference population of multiple pure breeds and admixed individuals

BACKGROUND

In dairy cattle populations in which crossbreeding has been used, animals show some level of diversity in their origins. In rotational crossbreeding, for instance, crossbred dams are mated with purebred sires from different pure breeds, and the genetic composition of crossbred animals is an admixture of the breeds included in the rotation. How to use the data of such individuals in genomic evaluations is still an open question. In this study, we aimed at providing methodologies for the use of data from crossbred individuals with an admixed genetic background together with data from multiple pure breeds, for the purpose of genomic evaluations for both purebred and crossbred animals. A three-breed rotational crossbreeding system was mimicked using simulations based on animals genotyped with the 50 K single nucleotide polymorphism (SNP) chip.

RESULTS

For purebred populations, within-breed genomic predictions generally led to higher accuracies than those from multi-breed predictions using combined data of pure breeds. Adding admixed population's (MIX) data to the combined pure breed data considering MIX as a different breed led to higher accuracies. When prediction models were able to account for breed origin of alleles, accuracies were generally higher than those from combining all available data, depending on the correlation of quantitative trait loci (QTL) effects between the breeds. Accuracies varied when using SNP effects from any of the pure breeds to predict the breeding values of MIX. Using those breed-specific SNP effects that were estimated separately in each pure breed, while accounting for breed origin of alleles for the selection candidates of MIX, generally improved the accuracies. Models that are able to accommodate MIX data with the breed origin of alleles approach generally led to higher accuracies than models without breed origin of alleles, depending on the correlation of QTL effects between the breeds.

CONCLUSIONS

Combining all available data, pure breeds' and admixed population's data, in a multi-breed reference population is beneficial for the estimation of breeding values for pure breeds with a small reference population. For MIX, such an approach can lead to higher accuracies than considering breed origin of alleles for the selection candidates, and using breed-specific SNP effects estimated separately in each pure breed. Including MIX data in the reference population of multiple breeds by considering the breed origin of alleles, accuracies can be further improved. Our findings are relevant for breeding programs in which crossbreeding is systematically applied, and also for populations that involve different subpopulations and between which exchange of genetic material is routine practice.

Indirect genomic predictions for milk yield in crossbred Holstein-Jersey dairy cattle

The objective of this study was to predict genomic breeding values for milk yield of crossbred dairy cattle under different scenarios using single-step genomic BLUP (ssGBLUP). The data set included 13,880,217 milk yield measurements on 6,830,415 cows. Genotypes of 89,558 Holstein, 40,769 Jersey, and 22,373 Holstein-Jersey crossbred animals were used, of which all Holstein, 9,313 Jersey, and 1,667 crossbred animals had phenotypic records. Genotypes were imputed to 45K SNP markers. The SNP effects were estimated from single-breed evaluations for Jersey (JE), Holstein (HO) and crossbreds (CROSS), and multibreed evaluations including all Jersey and Holstein (JE_HO) or approximately equal proportions of Jersey, Holstein, and crossbred animals (MIX). Indirect predictions (IP) of the validation animals (358 crossbred animals with phenotypes excluded from evaluations) were calculated using the resulting SNP effects. Additionally, breed proportions (BP) of crossbred animals were applied as a weight when IP were estimated based on each pure breed. The predictive ability of IP was calculated as the Pearson correlation between IP and phenotypes of the validation animals adjusted for fixed effects in the model. Regression of adjusted phenotypes on IP was used to assess the inflation of IP. The predictive ability of IP for CROSS, JE, HO, JE_HO, and MIX scenario was 0.50, 0.50, 0.47, 0.50, and 0.46, respectively. Using BP was the least successful, with a predictive ability of 0.32. The inflation of the IP for crossbred animals using CROSS, JE, HO, JE_HO, MIX, and BP scenarios were 1.17, 0.65, 0.55, 0.78, 1.00, and 0.85, respectively. The IP of crossbred animals can be predicted using single-step GBLUP under a scenario that includes purebred genotypes.

Assessing the genetic background and genomic relatedness of red cattle populations originating from Northern Europe

Background

Local cattle breeds need special attention, as they are valuable reservoirs of genetic diversity. Appropriate breeding decisions and adequate genomic management of numerically smaller populations are required for their conservation. At this point, the analysis of dense genome-wide marker arrays provides encompassing insights into the genomic constitution of livestock populations. We have analyzed the genetic characterization of ten cattle breeds originating from Germany, The Netherlands and Denmark belonging to the group of red dairy breeds in Northern Europe. The results are intended to provide initial evidence on whether joint genomic breeding strategies of these populations will be successful.

Results

Traditional Danish Red and Groningen White-Headed were the most genetically differentiated breeds and their populations showed the highest levels of inbreeding. In contrast, close genetic relationships and shared ancestry were observed for the populations of German Red and White Dual-Purpose, Dutch Meuse-Rhine-Yssel, and Dutch Deep Red breeds, reflecting their common histories. A considerable amount of gene flow from Red Holstein to German Angler and to German Red and White Dual-Purpose was revealed, which is consistent with frequent crossbreeding to improve productivity of these local breeds. In Red Holstein, marked genomic signatures of selection were reported on chromosome 18, suggesting directed selection for important breeding goal traits. Furthermore, tests for signatures of selection between Red Holstein, Red and White Dual-Purpose, and Meuse-Rhine-Yssel uncovered signals for all investigated pairs of populations. The corresponding genomic regions, which were putatively under different selection pressures, harboured various genes which are associated with traits such as milk and beef production, mastitis and female fertility.

Conclusions

This study provides comprehensive knowledge on the genetic constitution and genomic connectedness of divergent red cattle populations in Northern Europe. The results will help to design and optimize breeding strategies. A joint genomic evaluation including some of the breeds studied here seems feasible.

Hierarchical Modelling of Haplotype Effects on a Phylogeny

We introduce a hierarchical model to estimate haplotype effects based on phylogenetic relationships between haplotypes and their association with observed phenotypes. In a population there are many, but not all possible, distinct haplotypes and few observations per haplotype. Further, haplotype frequencies tend to vary substantially. Such data structure challenge estimation of haplotype effects. However, haplotypes often differ only due to few mutations, and leveraging similarities can improve the estimation of effects. We build on extensive literature and develop an autoregressive model of order one that models haplotype effects by leveraging phylogenetic relationships described with a directed acyclic graph. The phylogenetic relationships can be either in a form of a tree or a network, and we refer to the model as the haplotype network model. The model can be included as a component in a phenotype model to estimate associations between haplotypes and phenotypes. Our key contribution is that we obtain a sparse model, and by using hierarchical autoregression, the flow of information between similar haplotypes is estimated from the data. A simulation study shows that the hierarchical model can improve estimates of haplotype effects compared to an independent haplotype model, especially with few observations for a specific haplotype. We also compared it to a mutation model and observed comparable performance, though the haplotype model has the potential to capture background specific effects. We demonstrate the model with a study of mitochondrial haplotype effects on milk yield in cattle. We provide R code to fit the model with the INLA package.

Core-dependent changes in genomic predictions using the Algorithm for Proven and Young in single-step genomic best linear unbiased prediction

Single-step genomic best linear unbiased prediction with the Algorithm for Proven and Young (APY) is a popular method for large-scale genomic evaluations. With the APY algorithm, animals are designated as core or noncore, and the computing resources to create the inverse of the genomic relationship matrix (GRM) are reduced by inverting only a portion of that matrix for core animals. However, using different core sets of the same size causes fluctuations in genomic estimated breeding values (GEBVs) up to one additive standard deviation without affecting prediction accuracy. About 2% of the variation in the GRM is noise. In the recursion formula for APY, the error term modeling the noise is different for every set of core animals, creating changes in breeding values. While average changes are small, and correlations between breeding values estimated with different core animals are close to 1.0, based on the normal distribution theory, outliers can be several times bigger than the average. Tests included commercial datasets from beef and dairy cattle and from pigs. Beyond a certain number of core animals, the prediction accuracy did not improve, but fluctuations decreased with more animals. Fluctuations were much smaller than the possible changes based on prediction error variance. GEBVs change over time even for animals with no new data as genomic relationships ties all the genotyped animals, causing reranking of top animals. In contrast, changes in nongenomic models without new data are small. Also, GEBV can change due to details in the model, such as redefinition of contemporary groups or unknown parent groups. In particular, increasing the fraction of blending of the GRM with a pedigree relationship matrix from 5% to 20% caused changes in GEBV up to 0.45 SD, with a correlation of GEBV > 0.99. Fluctuations in genomic predictions are part of genomic evaluation models and are also present without the APY algorithm when genomic evaluations are computed with updated data. The best approach to reduce the impact of fluctuations in genomic evaluations is to make selection decisions not on individual animals with limited individual accuracy but on groups of animals with high average accuracy.

Current status of genomic evaluation

Early application of genomic selection relied on SNP estimation with phenotypes or de-regressed proofs (DRP). Chips of 50k SNP seemed sufficient for an accurate estimation of SNP effects. Genomic estimated breeding values (GEBV) were composed of an index with parent average, direct genomic value, and deduction of a parental index to eliminate double counting. Use of SNP selection or weighting increased accuracy with small data sets but had minimal to no impact with large data sets. Efforts to include potentially causative SNP derived from sequence data or high-density chips showed limited or no gain in accuracy. After the implementation of genomic selection, EBV by BLUP became biased because of genomic preselection and DRP computed based on EBV required adjustments, and the creation of DRP for females is hard and subject to double counting. Genomic selection was greatly simplified by single-step genomic BLUP (ssGBLUP). This method based on combining genomic and pedigree relationships automatically creates an index with all sources of information, can use any combination of male and female genotypes, and accounts for preselection. To avoid biases, especially under strong selection, ssGBLUP requires that pedigree and genomic relationships are compatible. Because the inversion of the genomic relationship matrix (G) becomes costly with more than 100k genotyped animals, large data computations in ssGBLUP were solved by exploiting limited dimensionality of genomic data due to limited effective population size. With such dimensionality ranging from 4k in chickens to about 15k in cattle, the inverse of G can be created directly (e.g., by the algorithm for proven and young) at a linear cost. Due to its simplicity and accuracy, ssGBLUP is routinely used for genomic selection by the major chicken, pig, and beef industries. Single step can be used to derive SNP effects for indirect prediction and for genome-wide association studies, including computations of the P-values. Alternative single-step formulations exist that use SNP effects for genotyped or for all animals. Although genomics is the new standard in breeding and genetics, there are still some problems that need to be solved. This involves new validation procedures that are unaffected by selection, parameter estimation that accounts for all the genomic data used in selection, and strategies to address reduction in genetic variances after genomic selection was implemented.

Genomic predictions for crossbred dairy cattle

Genomic evaluations are useful for crossbred as well as purebred populations when selection is applied to commercial herds. Dairy farmers had already spent more than $1 million to genotype over 32,000 crossbred animals before US genomic evaluations became available for those animals. Thus, new tools were needed to provide accurate genomic predictions for crossbreds. Genotypes for crossbreds are imputed more accurately when the imputation reference population includes purebreds. Therefore, genotypes of 6,296 crossbred animals were imputed from lower-density chips by including either 3,119 ancestors or 834,367 genotyped animals in the reference population. Crossbreds in the imputation study included 733 Jersey × Holstein F₁ animals, 55 Brown Swiss × Holstein F₁ animals, 2,300 Holstein backcrosses, 2,026 Jersey backcrosses, 27 Brown Swiss backcrosses, and 502 other crossbreds of various breed combinations. Another 653 animals appeared to be purebreds that owners had miscoded as a different breed. Genomic breed composition was estimated from 60,671 markers using the known breed identities for purebred, progeny-tested Holstein, Jersey, Brown Swiss, Ayrshire, and Guernsey bulls as the 5 traits (breed fractions) to be predicted. Estimates of breed composition were adjusted so that no percentages were negative or exceeded 100%, and breed percentages summed to 100%. Another adjustment set percentages above 93.5% equal to 100%, and the resulting value was termed breed base representation (BBR). Larger percentages of missing alleles were imputed by using a crossbred reference population rather than only the closest purebred reference population. Crossbred predictions were averages of genomic predictions computed using marker effects for each pure breed, which were weighted by the animal's BBR. Marker and polygenic effects were estimated separately for each breed on the all-breed scale instead of within-breed scales. For crossbreds, genomic predictions weighted by BBR were more accurate than the average of parents' breeding values and slightly more accurate than predictions using only the predominant breed. For purebreds, single-trait predictions using only within-breed data were as accurate as multi-trait predictions with allele effects in different breeds treated as correlated effects. Crossbred genomic predicted transmitting abilities were implemented by the Council on Dairy Cattle Breeding in April 2019 and will aid producers in managing their breeding programs and selecting replacement heifers.