Estimated Breeding Values Research Articles

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.

Genomic selection accelerates genetic improvement of resistance to Vibriosis in the Pacific oyster, Crassostrea gigas

Due to lack of acquired immune system, the oysters cultured along coasts are subject to frequent pathogen threats, which leads to severe disease outbreaks around the world. It's well recognized that the selection breeding of aquatic animals can be accelerated via the harnessing of genomic tools to increase genetic gain and shorten the breeding time. In this work, we carried out genomic selection breeding in the Pacific oysters (Crassostrea gigas) for genetic improvement of resistance to Vibriosis. The genome-wide variations were genotyped by ddRAD-seq from 295 oysters with contrasted resistance to Vibrio infection. Based on genome-wide SNPs, we performed an estimation of genomic heritability and prediction accuracy for resistance to Vibrio alginolyticus in C. gigas. The genomic heritability of resistance to V. alginolyticus was low to moderate, ranging from 0.1405 to 0.2730. Four genomic selection models including rrBLUP, Bayes A, Bayes B and Bayesian Lasso were evaluated, of which Bayes A showed superior prediction accuracy and computational speed. The genomic estimated breeding value (GEBV) calculated by genomic selection model can effectively distinguish the resistance or susceptibility of oysters to Vibriosis. Selection of individuals with high GEBV as broodstock greatly improved the resistance to Vibriosis of their progeny, resulting in 18.42% increase in relative survival rate and 12.73% increase in relative survival time compared to the control population. For the first time, this work reported the efficiency of genomic selection breeding for genetic improvement for resistance trait to Vibriosis in the C. gigas, which would greatly accelerate the cultivation of Vibriosis resistant oyster strains to support the healthy and sustainable development of aquaculture.

Genetic and phenotypic trends for female fertility traits derived from service records of South African Holstein cattle

The objective of this study was to estimate breeding values (EBV) and genetic and phenotypic trends for female fertility traits derived from artificial insemination (AI) records. Data were collected from automated milk recording systems: 64464 records from 18 South African (SA) Holstein herds. Breeding values were estimated for heifer traits, i.e., age at first service (AFS), age at first calving (AFC), number of services per conception (SPCh); and for cow traits, i.e., calving to first service (CFS), number of days open (DO), number of services per conception (SPC), first service within 80 days post-partum (FS80d), and whether cows were confirmed pregnant within 100 days post-partum (PD100d) or within 200 days post-partum (PD200d) using THRGIBBSF90. Trends were estimated for the period 1984 to 2011. There was no clear trend on average breeding values for heifer traits. However, phenotypic trends for heifer traits showed a desirable downward trend, with a decrease of 0.14 and 0.13 months/year for AFS and AFC, respectively. Phenotypic trends for cow fertility traits appeared to increase at the rate 0.16 and 0.83 days/year for CFS and DO, respectively, with animals requiring 0.02 more services per year for SPC. The average EBV for binary traits FS80d, PD100d, and PD200d, showed no distinct trend. In general, there was no clear genetic and phenotypic trend for all traits defined. The trends that appeared to follow trait improvement could have been coincidental. Thus, efforts have to be made towards the improvement of heifer and cow fertility traits explored in this study

Simulations of multiple breeding strategy scenarios in common bean for assessing genomic selection accuracy and model updating.

The aim of this study was to evaluate the accuracy of the ridge regression best linear unbiased prediction model across different traits, parent population sizes, and breeding strategies when estimating breeding values in common bean (Phaseolus vulgaris). Genomic selection was implemented to make selections within a breeding cycle and compared across five different breeding strategies (single seed descent, mass selection, pedigree method, modified pedigree method, and bulk breeding) following 10 breeding cycles. The model was trained on a simulated population of recombinant inbreds genotyped for 1010 single nucleotide polymorphism markers including 38 known quantitative trait loci identified in the literature. These QTL included 11 for seed yield, eight for white mold disease incidence, and 19 for days to flowering. Simulation results revealed that realized accuracies fluctuate depending on the factors investigated: trait genetic architecture, breeding strategy, and the number of initial parents used to begin the first breeding cycle. Trait architecture and breeding strategy appeared to have a larger impact on accuracy than the initial number of parents. Generally, maximum accuracies (in terms of the correlation between true and estimated breeding value) were consistently achieved under a mass selection strategy, pedigree method, and single seed descent method depending on the simulation parameters being tested. This study also investigated model updating, which involves retraining the prediction model with a new set of genotypes and phenotypes that have a closer relation to the population being tested. While it has been repeatedly shown that model updating generally improves prediction accuracy, it benefited some breeding strategies more than others. For low heritability traits (e.g., yield), conventional phenotype-based selection methods showed consistent rates of genetic gain, but genetic gain under genomic selection reached a plateau after fewer cycles. This plateauing is likely a cause of faster fixation of alleles and a diminishing of genetic variance when selections are made based on estimated breeding value as opposed to phenotype.

Impacts of reference population size and methods on the accuracy of genomic prediction for fleece traits in Inner Mongolia Cashmere Goats.

Inner Mongolia Cashmere Goats (IMCGs) are famous for its cashmere quality and it's a unique genetic resource in China. Therefore, it is necessary to use genomic selection to improve the accuracy of selection for fleece traits in Inner Mongolia cashmere goats. The aim of this study was to determine the effect of methods (GBLUP, BayesA, BayesB, Bayesian LASSO, Bayesian Ridge Region) and the reference population size on accuracy of genomic selection in IMCGs. This study fully utilizes the pedigree and phenotype records of fleece traits in 2255 individuals, genotype of 50794 SNPs after quality control, and environmental data to perform genomic selection of fleece traits. Then GBLUP and Bayes series methods (BayesA, BayesB, Bayesian LASSO, Bayesian Ridge Region) were used to perform estimates of genetic parameter and genomic breeding value. And the accuracy of genomic estimated breeding value (GEBV) is evaluated using the five-fold cross validation method. And the analysis of variance and multiple comparison methods were used to determine the best method for genomic selection in fleece traits of IMCGs. Further the different reference population sizes (500, 1000, 1500, and 2000) was set. Then the best method was applied to estimate genome breeding values, and evaluate the impact of reference population sizes on the accuracy of genome selection for fleece traits in IMCGs. It was found that the genomic prediction accuracy for each fleece trait in IMCGs by GBLUP method is highest, and it is significantly higher than that obtained by Bayesian method. The accuracy of breeding value estimation is 58.52% -68.49%. Also, it was found that the size of the reference population has a significant impact on the accuracy of genome prediction of fleece traits. When the reference population size is 2000, the accuracy of genomic prediction for each fleece trait is significantly higher than other levels, with accuracy of 55.47% -67.87%. This provides a theoretical basis for design a reasonable genome selection plan for Inner Mongolia cashmere goats in the later stag.

'We have 'Tinder' tools to solve the health issues caused by pedigree dog breeding'.

Technology and estimated breeding values are the key to breeding dogs that will live long and have desired behaviour and good health and welfare, argue Aashild Roaldset and Eva Breuer Hustoft.

Modeling missing pedigree with metafounders and validating single-step genomic predictions in a small dairy cattle population with a great influence of foreign genetics

Genetic improvement in small countries rely heavily on foreign genetics. In an importing country such as Uruguay, consideration of unknown parent groups (UPG) for foreign sires is essential. However, the use of UPG in genomic model evaluations may lead to bias in genomic estimated breeding values. The objective of this study was to study different models including UPG or Metafounders (MF) in the Uruguayan Holstein evaluation and to analyze bias, dispersion, and accuracy of (G)EBV predictions in BLUP and ssGBLUP. Gamma matrix (Γ) was estimated either by using base allele population frequencies obtained by bounded linear regression (MFbounded), or by using 2 values to design Γ, i.e., a single value for the diagonal and a different value for the off-diagonal (MFrobust). Both Γ estimators performed well in terms of GEBV predictions, but MFbounded was the best option. There is, however, some bias whose origin was not completely understood. UPG or MF seem to model correctly genetic progress for unknown parents except for the very first groups (earlier time period). As for validation bulls, bias was observed across all models, whereas for validation cows it was only observed with UPG in BLUP. Overdispersion was found in all models, but it was mostly detected in validation bulls. Ratio of accuracies indicated that ssGBLUP gave better predictions than BLUP.

Dissection of a novel major stable QTL on chromosome 7D for grain hardness and its breeding value estimation in bread wheat.

Grain hardness (Gh) is important for wheat processing and end-product quality. Puroindolines polymorphism explains over 60% of Gh variation and the novel genetic factors remain to be exploited. In this study, a total of 153 quantitative trait loci (QTLs), clustered into 12 genomic intervals (C1-C12), for 13 quality-related traits were identified using a recombinant inbred line population derived from the cross of Zhongkemai138 (ZKM138) and Chuanmai44 (CM44). Among them, C7 (harboring eight QTLs for different quality-related traits) and C8 (mainly harboring QGh.cib-5D.1 for Gh) were attributed to the famous genes, Rht-D1 and Pina, respectively, indicating that the correlation of involved traits was supported by the pleotropic or linked genes. Notably, a novel major stable QTL for Gh was detected in the C12, QGh.cib-7D, with ZKM138-derived allele increasing grain hardness, which was simultaneously mapped by the BSE-Seq method. The geographic pattern and transmissibility of this locus revealed that the increasing-Gh allele is highly frequently present in 85.79% of 373 worldwide wheat varieties and presented 99.31% transmissibility in 144 ZKM138-derivatives, indicating the non-negative effect on yield performance and that its indirect passive selection has happened during the actual breeding process. Thus, the contribution of this new Gh-related locus was highlighted in consideration of improving the efficiency and accuracy of the soft/hard material selection in the molecular marker-assisted process. Further, TraesCS7D02G099400, TraesCS7D02G098000, and TraesCS7D02G099500 were initially deduced to be the most potential candidate genes of QGh.cib-7D. Collectively, this study provided valuable information of elucidating the genetic architecture of Gh for wheat quality improvement.

Genomic prediction based on preselected single-nucleotide polymorphisms from genome-wide association study and imputed whole-genome sequence data annotation for growth traits in Duroc pigs.

The use of whole-genome sequence (WGS) data is expected to improve genomic prediction (GP) power of complex traits because it may contain mutations that in strong linkage disequilibrium pattern with causal mutations. However, a few previous studies have shown no or small improvement in prediction accuracy using WGS data. Incorporating prior biological information into GP seems to be an attractive strategy that might improve prediction accuracy. In this study, a total of 6334 pigs were genotyped using 50K chips and subsequently imputed to the WGS level. This cohort includes two prior discovery populations that comprise 294 Landrace pigs and 186 Duroc pigs, as well as two validation populations that consist of 3770 American Duroc pigs and 2084 Canadian Duroc pigs. Then we used annotation information and genome-wide association study (GWAS) from the WGS data to make GP for six growth traits in two Duroc pig populations. Based on variant annotation, we partitioned different genomic classes, such as intron, intergenic, and untranslated regions, for imputed WGS data. Based on GWAS results of WGS data, we obtained trait-associated single-nucleotide polymorphisms (SNPs). We then applied the genomic feature best linear unbiased prediction (GFBLUP) and genomic best linear unbiased prediction (GBLUP) models to estimate the genomic estimated breeding values for growth traits with these different variant panels, including six genomic classes and trait-associated SNPs. Compared with 50K chip data, GBLUP with imputed WGS data had no increase in prediction accuracy. Using only annotations resulted in no increase in prediction accuracy compared to GBLUP with 50K, but adding annotation information into the GFBLUP model with imputed WGS data could improve the prediction accuracy with increases of 0.00%-2.82%. In conclusion, a GFBLUP model that incorporated prior biological information might increase the advantage of using imputed WGS data for GP.

The Effects of Genetic Distance and Genetic Diversity on Genomic Prediction Accuracy for Soybean Quantitative Disease Resistance to Phytophthora sojae

Assessing quantitative disease resistance and other complex traits can be time- and space-consuming, limiting the number of lines that can be evaluated in a breeding program. A potential improvement is the application of genomic prediction models, where genotypic data are used to calculate genomic estimated breeding values, referred to as genomic selection. Genomic prediction uses training datasets, where a germplasm panel is phenotyped and genotyped to calculate genomic estimated breeding values in a validation panel of lines based solely on genotypic data. To develop an initial phenotypic dataset, breeders may consider utilizing previously phenotyped lines in a publicly available dataset, such as plant introductions (PIs) from the USDA Soybean collection. A relevant question is, how effective is genomic prediction across diverse training and prediction panels? To answer this, we used previously collected phenotypic data for quantitative disease resistance toward Phytophthora sojae. Diverse germplasm panels were represented by sets of PIs originating from the United States, the Republic of Korea, and worldwide, for a total collection of 1,768 PIs. The accuracy of the prediction model was significantly influenced by population differentiation between panels, as well as genetic and phenotypic diversity. Low prediction accuracy resulted when the panel used to create the prediction model was highly differentiated from the validation panel. However, the addition of a small number of accessions to the training panel that were more closely related to the validation panel resulted in increased accuracy. [Formula: see text] Copyright © 2024 The Author(s). This is an open access article distributed under the CC BY-NC-ND 4.0 International license .

Genetic evaluation for piglet crushing behaviour in primiparous sows

Stress in farrowing sows is associated with the number of piglets crushed or attacked. Sow’s behaviour is variable and heritable, therefore genetic selection can be a viable approach for improving pig’s welfare. In this report, we used first parity litter records of Yorkshire sows to test a genetic evaluation model for piglet crushing. The data were split into training and validation to check the prediction accuracy of piglet crushing estimated breeding values (EBVs) for young sows. We found that the estimated heritability of piglet crushing was 0.07 ± 0.03. The difference in the EBVs in the validation set was equivalent to 0.15 more piglets crushed in the top 10% group than in the bottom group of sows. These results indicate that the genetic selection may be used to reduce piglet crushing which will improve the welfare of pigs as well as production efficiency. The average reliability of the estimated EBVs across all animals in the pedigree was (0.07; 0.0 to 0.72). More research on evaluation models and the genetics underlying sow stress and behaviour is warranted to improve the reliabilities of modeling and to identify robust genetic markers for animal breeding for the implementation.

MAGE: metafounders-assisted genomic estimation of breeding value, a novel additive-dominance single-step model in crossbreeding systems.

Utilizing both purebred and crossbred data in animal genetics is widely recognized as an optimal strategy for enhancing the predictive accuracy of breeding values. Practically, the different genetic background among several purebred populations and their crossbred offspring populations limits the application of traditional prediction methods. Several studies endeavor to predict the crossbred performance via the partial relationship, which divides the data into distinct sub-populations based on the common genetic background, such as one single purebred population and its corresponding crossbred descendant. However, this strategy makes prediction inaccurate due to ignoring half of the parental information of crossbreed animals. Furthermore, dominance effects, although playing a significant role in crossbreeding systems, cannot be modeled under such a prediction model. To overcome this weakness, we developed a novel multi-breed single-step model using metafounders to assess ancestral relationships across diverse breeds under a unified framework. We proposed to use multi-breed dominance combined relationship matrices to model additive and dominance effects simultaneously. Our method provides a straightforward way to evaluate the heterosis of crossbreeds and the breeding values of purebred parents efficiently and accurately. We performed simulation and real data analyses to verify the potential of our proposed method. Our proposed model improved prediction accuracy under all scenarios considered compared to commonly used methods. The software for implementing our method is available at https://github.com/CAU-TeamLiuJF/MAGE.

Genotyping KIF1C (c.608G>A) Mutant Reveals a Wide Distribution of Progressive Ataxia in German Charolais Cattle

Bovine progressive ataxia in Charolais cattle was first described in the 1970s; then, cases were reported in Charolais worldwide. A homozygous loss-of-function mutation within the KIF1C gene (c.608G>A) was found to be responsible for this neurodegenerative disease. The aim of this study was to determine whether the mutated KIF1C allele segregates in the German Charolais population and whether the estimated breeding values for growth and muscle conformation are associated with the mutated genotypes. Genetic test results of the KIF1C:c.608G>A variant were available for 1315 Charolais cattle from 35 herds located in Germany. In addition, 324 samples from eight other beef cattle breeds were tested for the mutated KIF1C allele. We were able to demonstrate that the KIF1C mutation is common, with a frequency of 11.75% in the German Charolais population. All but two of the eight (2/8 = 25%) homozygous mutated individuals showed clinical signs consistent with progressive ataxia. The estimated breeding values of muscle conformation in 200- and 365-day-old animals indicated a significant superiority for homozygous mutated animals when compared either with heterozygous or homozygous wild-type genotypes; this was also the case for heterozygous genotypes in comparison with homozygous wild-type genotypes. For the estimated breeding values of daily weight gain in 200- and 365-day-old animals, the significant differences between homozygous mutated and heterozygous or wild-type genotypes were in favour of the homozygous mutant animals. There were no differences in the estimated maternal breeding values among all three KIF1C genotypes. For the first time, two German Angus cattle carrying the KIF1C mutation heterozygous were detected. The breeders’ survey highlighted that increased awareness would facilitate increased conviction among breeders of the need for genetic testing in order to eliminate the lethal KIF1C allele.

Genetic parameters, trends, and inbreeding in a honeybee breeding program for royal jelly production and behavioral traits

An economically sustainable production of royal jelly (RJ) requires the use of genetics specifically improved for this very demanding trait. The French Royal Jelly Association has developed since 2010 a selection plan for that purpose, based on a collective data recording system and the initial seedstock of five French beekeepers. The aim of this study was to evaluate the efficiency of this collective breeding program, a decade after its concrete start. We considered three different genetic models to describe the phenotypes, hereafter named the Colony, Worker, and Queen Models. We showed that when the size and the structure of the dataset are insufficient to fit a Colony Model, fitting a Queen Model gives more understandable results for practical use of the estimated breeding values than the Worker Model. Regarding genetic parameters, we estimated moderate to high heritability values for all traits, regardless of the genetic model. Under a Queen Model, heritability estimates of RJ production were 0.19 ± 0.09 and 0.35 ± 0.13 for first and second harvests, respectively; the estimates were 0.76 ± 0.08, 0.29 ± 0.09, and 0.22 ± 0.08, respectively, for the calmness, gentleness, and spring development of the colony. We also proved that the breeding program is efficient, increasing the production at second harvest of 3.5% per year on average. Attention must, however, be paid to the evolution of inbreeding in this very small breeding population. Few genetic correlations were significantly different from zero between the traits, due to large standard errors of our estimates. It is, however, worth to note that the trends were generally favorable between all traits, which appears encouraging to improve production and behavioral traits jointly in the breeding program.

Genomic prediction for root and yield traits of barley under a water availability gradient: a case study comparing different spatial adjustments

BackgroundIn drought periods, water use efficiency depends on the capacity of roots to extract water from deep soil. A semi-field phenotyping facility (RadiMax) was used to investigate above-ground and root traits in spring barley when grown under a water availability gradient. Above-ground traits included grain yield, grain protein concentration, grain nitrogen removal, and thousand kernel weight. Root traits were obtained through digital images measuring the root length at different depths. Two nearest-neighbor adjustments (M1 and M2) to model spatial variation were used for genetic parameter estimation and genomic prediction (GP). M1 and M2 used (co)variance structures and differed in the distance function to calculate between-neighbor correlations. M2 was the most developed adjustment, as accounted by the Euclidean distance between neighbors.ResultsThe estimated heritabilities (h^2\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${\\widehat{h}}^{2}$$\\end{document}) ranged from low to medium for root and above-ground traits. The genetic coefficient of variation (GCV\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$GCV$$\\end{document}) ranged from 3.2 to 7.0% for above-ground and 4.7 to 10.4% for root traits, indicating good breeding potential for the measured traits. The highest GCV\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$GCV$$\\end{document} observed for root traits revealed that significant genetic change in root development can be achieved through selection. We studied the genotype-by-water availability interaction, but no relevant interaction effects were detected. GP was assessed using leave-one-line-out (LOO) cross-validation. The predictive ability (PA) estimated as the correlation between phenotypes corrected by fixed effects and genomic estimated breeding values ranged from 0.33 to 0.49 for above-ground and 0.15 to 0.27 for root traits, and no substantial variance inflation in predicted genetic effects was observed. Significant differences in PA were observed in favor of M2.ConclusionsThe significant GCV\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$GCV$$\\end{document} and the accurate prediction of breeding values for above-ground and root traits revealed that developing genetically superior barley lines with improved root systems is possible. In addition, we found significant spatial variation in the experiment, highlighting the relevance of correctly accounting for spatial effects in statistical models. In this sense, the proposed nearest-neighbor adjustments are flexible approaches in terms of assumptions that can be useful for semi-field or field experiments.

Validation of reaction norm breeding values for robustness in Australian sheep

BackgroundThere can be variation between animals in how stable their genetic merit is across different environments due to genotype-by-environment (G×E) interactions. This variation could be used in breeding programs to select robust genotypes that combine high overall performance with stable genetic ranking across environments. There have been few attempts to validate breeding values for robustness in livestock, although this is a necessary step towards their implementation in selection decisions. The objective of this study was to validate breeding values for the robustness of body weight across different growth environments that were estimated using reaction norm models in sheep data.ResultsUsing threefold cross-validation for the progeny of 337 sires, the average correlation between single-step breeding values for the reaction norm slope and the realised robustness of progeny across different growth environments was 0.21. The correlation between breeding values for the reaction slope estimated independently in two different datasets linked by common sires was close to the expected correlation based on theory.ConclusionsSlope estimated breeding values (EBV) obtained using reaction norm models were predictive of the phenotypic robustness of progeny across different environments and were consistent for sires with progeny in two different datasets. Selection based on reaction norm EBV could be used to increase the robustness of a population to environmental variation.

Exploring definitions of daily enteric methane emission phenotypes for genetic evaluations using a population of indoor-fed multi-breed growing cattle with feed intake data.

Genetic selection has been identified as a promising approach for reducing enteric methane (CH4) emissions; a prerequisite for genetic evaluations; however, these are estimates of the necessary genetic parameters based on a population representative of where the genetic evaluations will be used. The objective of this study was, therefore, to derive genetic parameters for a series of definitions of CH4, carbon dioxide (CO2), and dry matter intake (DMI) as well as genetic correlations between CH4, CO2, and DMI in a bid to address the paucity of studies involving methane emissions measured in beef cattle using GreenFeed systems. Lastly, estimated breeding values (EBV) were generated for nine alternative definitions of CH4 using the derived genetic parameters; the EBV were validated against both phenotypic performance (adjusted for non-genetic effects) and the Legarra and Reverter method comparing EBV generated for a subset of the dataset compared to EBV generated from the entire dataset. Individual animal CH4 and CO2 records were available from a population of 1,508 multi-breed growing beef cattle using 10 GreenFeed Emission Monitoring systems. Nine trait definitions for CH4 and CO2 were derived: individual spot measures, the average of all spot measures within a 3-h, 6-h, 12-h, 1-d, 5-d, 10-d, and 15-d period and the average of all spot measures across the full test period (20 to 114 d on test). Heritability estimates from 1,155 animals, for CH4, increased as the length of the averaging period increased and ranged from 0.09 ± 0.03 for the individual spot measures trait to 0.43 ± 0.11 for the full test average trait; a similar trend existed for CO2 with the estimated heritability ranging from 0.17 ± 0.04 to 0.50 ± 0.11. Enteric CH4 was moderately to strongly genetically correlated with DMI with a genetic correlation of 0.72 ± 0.02 between the spot measures of CH4 and a 1-d average DMI. Correlations, adjusted for heritability, between the adjusted phenotype and (parental average) EBV ranged from 0.56 to 1.14 across CH4 definitions and the slope between the adjusted phenotype and EBV ranged from 0.92 to 1.16 (expectation = 1). Validation results from the Legarra and Reverter regression method revealed a level bias of between -0.81 and -0.45, a dispersion bias of between 0.93 and 1.17, and ratio accuracy (ratio of the partial evaluation accuracies on whole evaluation accuracies) from 0.28 to 0.38. While EBV validation results yielded no consensus, CH4 is a moderately heritable trait, and selection for reduced CH4 is achievable.

Comparison between multiple-trait and random regression models for genetic evaluation of weight traits in Australian meat sheep.

Random regression (RR) models are recommended as an alternative to multiple-trait (MT) models for better capturing the variance-covariance structure over a trajectory and hence more accurate genetic evaluation of traits that are repeatedly measured and genetically change gradually over time. However, a limited number of studies have been done to empirically compare RR over a MT model to determine how much extra benefit could be achieved from one method over another. We compared the prediction accuracy of RR and MT models for growth traits of Australian meat sheep measured from 60 to 525d, using 102,579 weight records from 24,872 animals. Variance components and estimated breeding values (EBVs) estimated at specific ages were compared and validated with forward prediction. The accuracy of EBVs obtained from the MT model was 0.58, 0.51, 0.54, and 0.56 for weaning, postweaning, yearling, and hogget weight stages, respectively. RR model produced accuracy estimates of 0.56, 0.51, 0.54, and 0.54 for equivalent weight stages. Regression of adjusted phenotype on EBVs was very similar between the MT and the RR models (P > 0.05). Although the RR model did not significantly increase the accuracy of predicting future progeny performance, there are other benefits of the model such as no limit to the number of records per animal, estimation of EBVs for early and late growth, no need for age correction. Therefore, RR can be considered a more flexible method for the genetic evaluation of Australian sheep for early and late growth, and no need for age correction.

Genetic parameters for carcass traits of progeny of beef bulls mated to dairy cows.

Beef × dairy crossbred cattle (n = 615) were used to evaluate the effect of preharvest indicator traits and genotypes on the accuracy of estimated breeding values (EBVs) of seedstock candidates for selection. Genotypes for 100,000 single nucleotide polymorphisms were provided by the American Simmental Association of purebred and crossbred seedstock animals (n = 2,632). Five hundred and ninety-five of the 615 beef × dairy cattle had carcass camera and ultrasound data. Phenotypes were not used for any of the seedstock animals even though some may have had performance and ultrasound data. We estimated the genomic relationship matrix among 3,247 animals including both phenotyped and unphenotyped animals. We computed genetic parameters among 37 traits using 666 bivariate restricted maximum likelihood analyses. The accuracy of EBV depends on heritability. For the sake of brevity, we report accuracy for marbling as a proxy for other traits with similar heritability. We focus on accuracy for marbling because marbling is the primary determinant of carcass value. We computed EBV for all 3,247 animals for marbling based on camera data postharvest using best linear unbiased prediction. We report evidence of overlap in causative genes among postharvest carcass traits; marbling, ribeye area, yield grade, fat thickness, and hot carcass weight (HCW) based on genetic correlations. Genetic correlations range from -0.73 to 0.89. Several live animal traits (frame size, body weight and ultrasound fat thickness and ribeye area) were genetically correlated with postharvest traits; including HCW, ribeye area, yield grade, fat thickness, and marbling. Genetic correlations between pre- and postharvest traits ranged from -0.53 to 0.95. Accuracy for marbling ranged from 0.64 to 0.80 for animals with marbling recorded, and from 0.09 to 0.60 for animals without marbling recorded. The accuracy of animals without phenotypes was related to the genomic relationship between animals with phenotype and those without. Live animal traits were useful for predicting economically important carcass traits based on genetic correlations. The accuracy of EBV for seedstock animals that were not phenotyped was low, but this is consistent with theory, and accuracy is expected to increase with the addition of genotypes and carcass data from beef × dairy animals.

Genomic selection for target traits in the Australian lentil breeding program.

Genomic selection (GS) uses associations between markers and phenotypes to predict the breeding values of individuals. It can be applied early in the breeding cycle to reduce the cross-to-cross generation interval and thereby increase genetic gain per unit of time. The development of cost-effective, high-throughput genotyping platforms has revolutionized plant breeding programs by enabling the implementation of GS at the scale required to achieve impact. As a result, GS is becoming routine in plant breeding, even in minor crops such as pulses. Here we examined 2,081 breeding lines from Agriculture Victoria's national lentil breeding program for a range of target traits including grain yield, ascochyta blight resistance, botrytis grey mould resistance, salinity and boron stress tolerance, 100-grain weight, seed size index and protein content. A broad range of narrow-sense heritabilities was observed across these traits (0.24-0.66). Genomic prediction models were developed based on 64,781 genome-wide SNPs using Bayesian methodology and genomic estimated breeding values (GEBVs) were calculated. Forward cross-validation was applied to examine the prediction accuracy of GS for these targeted traits. The accuracy of GEBVs was consistently higher (0.34-0.83) than BLUP estimated breeding values (EBVs) (0.22-0.54), indicating a higher expected rate of genetic gain with GS. GS-led parental selection using early generation breeding materials also resulted in higher genetic gain compared to BLUP-based selection performed using later generation breeding lines. Our results show that implementing GS in lentil breeding will fast track the development of high-yielding cultivars with increased resistance to biotic and abiotic stresses, as well as improved seed quality traits.