A study of Genomic Prediction across Generations of Two Korean Pig Populations

Genomic models that incorporate dense marker information have been widely used for predicting genomic breeding values since they were first introduced, and it is known that the relationship between individuals in the reference population and selection candidates affects the prediction accuracy. When genomic evaluation is performed over generations of the same population, prediction accuracy is expected to decay if the reference population is not updated. Therefore, the reference population must be updated in each generation, but little is known about the optimal way to do it. This study presents an empirical assessment of the prediction accuracy of genomic breeding values of production traits, across five generations in two Korean pig breeds. We verified the decay in prediction accuracy over time when the reference population was not updated. Additionally we compared the prediction accuracy using only the previous generation as the reference population, as opposed to using all previous generations as the reference population. Overall, the results suggested that, although there is a clear need to continuously update the reference population, it may not be necessary to keep all ancestral genotypes. Finally, comprehending how the accuracy of genomic prediction evolves over generations within a population adds relevant information to improve the performance of genomic selection.

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Calibration and validation of predicted genomic breeding values in an advanced cycle maize population

Theoretical and Applied Genetics ◽

10.1007/s00122-021-03880-5 ◽

2021 ◽

Author(s):

Hans-Jürgen Auinger ◽

Christina Lehermeier ◽

Daniel Gianola ◽

Manfred Mayer ◽

Albrecht E. Melchinger ◽

...

Keyword(s):

Sample Size ◽

Genomic Prediction ◽

Prediction Accuracy ◽

Dry Matter ◽

Dry Matter Content ◽

Genomic Breeding ◽

Breeding Values ◽

Average Maximum ◽

Calibration Sets ◽

Model Training

Abstract Key message Model training on data from all selection cycles yielded the highest prediction accuracy by attenuating specific effects of individual cycles. Expected reliability was a robust predictor of accuracies obtained with different calibration sets. Abstract The transition from phenotypic to genome-based selection requires a profound understanding of factors that determine genomic prediction accuracy. We analysed experimental data from a commercial maize breeding programme to investigate if genomic measures can assist in identifying optimal calibration sets for model training. The data set consisted of six contiguous selection cycles comprising testcrosses of 5968 doubled haploid lines genotyped with a minimum of 12,000 SNP markers. We evaluated genomic prediction accuracies in two independent prediction sets in combination with calibration sets differing in sample size and genomic measures (effective sample size, average maximum kinship, expected reliability, number of common polymorphic SNPs and linkage phase similarity). Our results indicate that across selection cycles prediction accuracies were as high as 0.57 for grain dry matter yield and 0.76 for grain dry matter content. Including data from all selection cycles in model training yielded the best results because interactions between calibration and prediction sets as well as the effects of different testers and specific years were attenuated. Among genomic measures, the expected reliability of genomic breeding values was the best predictor of empirical accuracies obtained with different calibration sets. For grain yield, a large difference between expected and empirical reliability was observed in one prediction set. We propose to use this difference as guidance for determining the weight phenotypic data of a given selection cycle should receive in model retraining and for selection when both genomic breeding values and phenotypes are available.

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Estimation of genetic parameters and accuracy of genomic prediction for production traits in Duroc pigs

Czech Journal of Animal Science ◽

10.17221/150/2018-cjas ◽

2019 ◽

Vol 64 (No. 4) ◽

pp. 160-165 ◽

Cited By ~ 1

Author(s):

Bryan Irvine Lopez ◽

Vanessa Viterbo ◽

Choul Won Song ◽

Kang Seok Seo

Keyword(s):

Genomic Prediction ◽

Genetic Parameters ◽

Average Daily Gain ◽

Genetic Correlations ◽

Single Step ◽

Production Traits ◽

Muscle Area ◽

Genomic Breeding ◽

Breeding Values ◽

Heritability Estimates

Abstract: Genetic parameters and accuracy of genomic prediction for production traits in a Duroc population were estimated. Data were on 24 828 purebred Duroc pigs born in 2000–2016. After quality control procedures, 30 263 single nucleotide polymorphism markers and 560 animals remained that were used to predict the genomic breeding values of individuals. Accuracies of predicted breeding values for average daily gain (ADG), backfat thickness (BF), loin muscle area (LMA), lean percentage (LP) and age at 90 kg (D90) between pedigree-based and single-step methods were compared. Analyses were carried out with a multivariate animal model to estimate genetic parameters for production traits while univariate analyses were performed to predict the genomic breeding values of individuals. Heritability estimates from pedigree analysis were moderate to high. Heritability estimates and standard error for ADG, BF, LMA, LP and D90 were 0.35 ± 0.01, 0.35 ± 0.11, 0.24 ± 0.04, 0.42 ± 0.11 and 0.37 ± 0.03, respectively. Genetic correlations of ADG with BF and LP were low and negative. Genetic correlations of LMA with ADG, BF, LP and D90 were –0.37, –0.27, 0.48 and 0.31, respectively. High correlations were observed between ADG and D90 (–0.98), and also between BF and LP (–0.93). Accuracies of genomic breeding values for ADG, BF, LMA, LP and D90 were 0.30, 0.33, 0.38, 0.40 and 0.28, respectively. Corresponding accuracies using pedigree-based method were 0.29, 0.32, 0.38, 0.39 and 0.27, respectively. The results showed that the single-step method did not show significant advantage compared to the pedigree-based method.

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Accuracies of direct genomic breeding values for birth and weaning weights of registered Charolais cattle in Mexico

Animal Production Science ◽

10.1071/an18363 ◽

2020 ◽

Vol 60 (6) ◽

pp. 772

Author(s):

Francisco J. Jahuey-Martínez ◽

Gaspar M. Parra-Bracamonte ◽

Dorian J. Garrick ◽

Nicolás López-Villalobos ◽

Juan C. Martínez-González ◽

...

Keyword(s):

Genomic Prediction ◽

Reference Population ◽

Single Step ◽

Bayesian Regression ◽

Nucleotide Polymorphisms ◽

Linear Unbiased Prediction ◽

Genomic Breeding ◽

Breeding Values ◽

Charolais Cattle ◽

Best Linear Unbiased

Context Genomic prediction is now routinely used in many livestock species to rank individuals based on genomic breeding values (GEBV). Aims This study reports the first assessment aimed to evaluate the accuracy of direct GEBV for birth (BW) and weaning (WW) weights of registered Charolais cattle in Mexico. Methods The population assessed included 823 animals genotyped with an array of 77000 single nucleotide polymorphisms. Genomic prediction used genomic best linear unbiased prediction (GBLUP), Bayes C (BC), and single-step Bayesian regression (SSBR) methods in comparison with a pedigree-based BLUP method. Key results Our results show that the genomic prediction methods provided low and similar accuracies to BLUP. The prediction accuracy of GBLUP and BC were identical at 0.31 for BW and 0.29 for WW, similar to BLUP. Prediction accuracies of SSBR for BW and WW were up to 4% higher than those by BLUP. Conclusions Genomic prediction is feasible under current conditions, and provides a slight improvement using SSBR. Implications Some limitations on reference population size and structure were identified and need to be addressed to obtain more accurate predictions in liveweight traits under the prevalent cattle breeding conditions of Mexico.

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GPS Coordinates for Modelling Correlated Herd Effects in Genomic Prediction Models Applied to Hanwoo Beef Cattle

Animals ◽

10.3390/ani11072050 ◽

2021 ◽

Vol 11 (7) ◽

pp. 2050

Author(s):

Beatriz Castro Dias Cuyabano ◽

Gabriel Rovere ◽

Dajeong Lim ◽

Tae Hun Kim ◽

Hak Kyo Lee ◽

...

Keyword(s):

Environmental Factors ◽

Genomic Prediction ◽

Prediction Models ◽

Phenotypic Expression ◽

Genetic Evaluation ◽

Genomic Breeding ◽

Breeding Values ◽

Korean Cattle ◽

Evaluation Programs ◽

The Impact

It is widely known that the environment influences phenotypic expression and that its effects must be accounted for in genetic evaluation programs. The most used method to account for environmental effects is to add herd and contemporary group to the model. Although generally informative, the herd effect treats different farms as independent units. However, if two farms are located physically close to each other, they potentially share correlated environmental factors. We introduce a method to model herd effects that uses the physical distances between farms based on the Global Positioning System (GPS) coordinates as a proxy for the correlation matrix of these effects that aims to account for similarities and differences between farms due to environmental factors. A population of Hanwoo Korean cattle was used to evaluate the impact of modelling herd effects as correlated, in comparison to assuming the farms as completely independent units, on the variance components and genomic prediction. The main result was an increase in the reliabilities of the predicted genomic breeding values compared to reliabilities obtained with traditional models (across four traits evaluated, reliabilities of prediction presented increases that ranged from 0.05 ± 0.01 to 0.33 ± 0.03), suggesting that these models may overestimate heritabilities. Although little to no significant gain was obtained in phenotypic prediction, the increased reliability of the predicted genomic breeding values is of practical relevance for genetic evaluation programs.

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PSXII-22 Genomic prediction accuracy for feed efficiency related traits using different pseudo-phenotypes, prediction and validation methods in Nellore cattle

Journal of Animal Science ◽

10.1093/jas/skaa278.446 ◽

2020 ◽

Vol 98 (Supplement_4) ◽

pp. 245-246

Author(s):

Cláudio U Magnabosco ◽

Fernando Lopes ◽

Valentina Magnabosco ◽

Raysildo Lobo ◽

Leticia Pereira ◽

...

Keyword(s):

Body Weight ◽

Weight Gain ◽

Genomic Prediction ◽

Feed Efficiency ◽

Prediction Accuracy ◽

Body Weight Gain ◽

Prediction Methods ◽

Genomic Breeding ◽

Validation Population ◽

Nellore Cattle

Abstract The aim of the study was to evaluate prediction methods, validation approaches and pseudo-phenotypes for the prediction of the genomic breeding values of feed efficiency related traits in Nellore cattle. It used the phenotypic and genotypic information of 4,329 and 3,594 animals, respectively, which were tested for residual feed intake (RFI), dry matter intake (DMI), feed efficiency (FE), feed conversion ratio (FCR), residual body weight gain (RG), and residual intake and body weight gain (RIG). Six prediction methods were used: ssGBLUP, BayesA, BayesB, BayesCπ, BLASSO, and BayesR. Three validation approaches were used: 1) random: where the data was randomly divided into ten subsets and the validation was done in each subset at a time; 2) age: the division into the training (2010 to 2016) and validation population (2017) were based on the year of birth; 3) genetic breeding value (EBV) accuracy: the data was split in the training population being animals with accuracy above 0.45; and validation population those below 0.45. We checked the accuracy and bias of genomic value (GEBV). The results showed that the GEBV accuracy was the highest when the prediction is obtained with ssGBLUP (0.05 to 0.31) (Figure 1). The low heritability obtained, mainly for FE (0.07 ± 0.03) and FCR (0.09 ± 0.03), limited the GEBVs accuracy, which ranged from low to moderate. The regression coefficient estimates were close to 1, and similar between the prediction methods, validation approaches, and pseudo-phenotypes. The cross-validation presented the most accurate predictions ranging from 0.07 to 0.037. The prediction accuracy was higher for phenotype adjusted for fixed effects than for EBV and EBV deregressed (30.0 and 34.3%, respectively). Genomic prediction can provide a reliable estimate of genomic breeding values for RFI, DMI, RG and RGI, as to even say that those traits may have higher genetic gain than FE and FCR.

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Genetic architecture and genomic prediction accuracy of apple quantitative traits across environments

10.1101/2021.11.29.470309 ◽

2021 ◽

Author(s):

Michaela Jung ◽

Beat Keller ◽

Morgane Roth ◽

Maria Jose Aranzana ◽

Annemarie Auwerkerken ◽

...

Keyword(s):

Genomic Prediction ◽

Prediction Accuracy ◽

Genetic Architecture ◽

Quantitative Traits ◽

Prediction Models ◽

Phenotypic Variability ◽

Reference Population ◽

Genomic Study ◽

Genomic Tools ◽

Breeding Efficiency

Implementation of genomic tools is desirable to increase the efficiency of apple breeding. The apple reference population (apple REFPOP) proved useful for rediscovering loci, estimating genomic prediction accuracy, and studying genotype by environment interactions (GxE). Here we show contrasting genetic architecture and genomic prediction accuracies for 30 quantitative traits across up to six European locations using the apple REFPOP. A total of 59 stable and 277 location-specific associations were found using GWAS, 69.2% of which are novel when compared with 41 reviewed publications. Average genomic prediction accuracies of 0.18-0.88 were estimated using single-environment univariate, single-environment multivariate, multi-environment univariate, and multi-environment multivariate models. The GxE accounted for up to 24% of the phenotypic variability. This most comprehensive genomic study in apple in terms of trait-environment combinations provided knowledge of trait biology and prediction models that can be readily applied for marker-assisted or genomic selection, thus facilitating increased breeding efficiency.

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Effect of predictor traits on accuracy of genomic breeding values for feed intake based on a limited cow reference population

animal ◽

10.1017/s175173111300150x ◽

2013 ◽

Vol 7 (11) ◽

pp. 1759-1768 ◽

Cited By ~ 35

Author(s):

M. Pszczola ◽

R.F. Veerkamp ◽

Y. de Haas ◽

E. Wall ◽

T. Strabel ◽

...

Keyword(s):

Feed Intake ◽

Reference Population ◽

Genomic Breeding ◽

Breeding Values

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Genomic Prediction of Two Complex Orthopedic Traits Across Multiple Pure and Mixed Breed Dogs

Frontiers in Genetics ◽

10.3389/fgene.2021.666740 ◽

2021 ◽

Vol 12 ◽

Author(s):

Liping Jiang ◽

Zhuo Li ◽

Jessica J. Hayward ◽

Kei Hayashi ◽

Ursula Krotscheck ◽

...

Keyword(s):

Genomic Prediction ◽

Prediction Accuracy ◽

Cruciate Ligament ◽

Pearson Correlation ◽

Snp Array ◽

Reference Population ◽

Genotype Data ◽

Dna Array ◽

Cranial Cruciate Ligament ◽

Validation Population

Canine hip dysplasia (CHD) and rupture of the cranial cruciate ligament (RCCL) are two complex inherited orthopedic traits of dogs. These two traits may occur concurrently in the same dog. Genomic prediction of these two diseases would benefit veterinary medicine, the dog’s owner, and dog breeders because of their high prevalence, and because both traits result in painful debilitating osteoarthritis in affected joints. In this study, 842 unique dogs from 6 breeds with hip and stifle phenotypes were genotyped on a customized Illumina high density 183 k single nucleotide polymorphism (SNP) array and also analyzed using an imputed dataset of 20,487,155 SNPs. To implement genomic prediction, two different statistical methods were employed: Genomic Best Linear Unbiased Prediction (GBLUP) and a Bayesian method called BayesC. The cross-validation results showed that the two methods gave similar prediction accuracy (r = 0.3–0.4) for CHD (measured as Norberg angle) and RCCL in the multi-breed population. For CHD, the average correlation of the AUC was 0.71 (BayesC) and 0.70 (GBLUP), which is a medium level of prediction accuracy and consistent with Pearson correlation results. For RCCL, the correlation of the AUC was slightly higher. The prediction accuracy of GBLUP from the imputed genotype data was similar to the accuracy from DNA array data. We demonstrated that the genomic prediction of CHD and RCCL with DNA array genotype data is feasible in a multiple breed population if there is a genetic connection, such as breed, between the reference population and the validation population. Albeit these traits have heritability of about one-third, higher accuracy is needed to implement in a natural population and predicting a complex phenotype will require much larger number of dogs within a breed and across breeds. It is possible that with higher accuracy, genomic prediction of these orthopedic traits could be implemented in a clinical setting for early diagnosis and treatment, and the selection of dogs for breeding. These results need continuous improvement in model prediction through ongoing genotyping and data sharing. When genomic prediction indicates that a dog is susceptible to one of these orthopedic traits, it should be accompanied by clinical and radiographic screening at an acceptable age with appropriate follow-up.

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Improving Genomic Prediction of Crossbred and Purebred Dairy Cattle

Frontiers in Genetics ◽

10.3389/fgene.2020.598580 ◽

2020 ◽

Vol 11 ◽

Author(s):

Majid Khansefid ◽

Michael E. Goddard ◽

Mekonnen Haile-Mariam ◽

Kon V. Konstantinov ◽

Chris Schrooten ◽

...

Keyword(s):

Genomic Prediction ◽

Milk Fat ◽

Prediction Accuracy ◽

Reference Population ◽

High Density ◽

Crossbred Cows ◽

Mixed Breed ◽

Best Linear Unbiased ◽

Custom Panel ◽

Accuracy Of Prediction

This study assessed the accuracy and bias of genomic prediction (GP) in purebred Holstein (H) and Jersey (J) as well as crossbred (H and J) validation cows using different reference sets and prediction strategies. The reference sets were made up of different combinations of 36,695 H and J purebreds and crossbreds. Additionally, the effect of using different sets of marker genotypes on GP was studied (conventional panel: 50k, custom panel enriched with, or close to, causal mutations: XT_50k, and conventional high-density with a limited custom set: pruned HDnGBS). We also compared the use of genomic best linear unbiased prediction (GBLUP) and Bayesian (emBayesR) models, and the traits tested were milk, fat, and protein yields. On average, by including crossbred cows in the reference population, the prediction accuracies increased by 0.01–0.08 and were less biased (regression coefficient closer to 1 by 0.02–0.16), and the benefit was greater for crossbreds compared to purebreds. The accuracy of prediction increased by 0.02 using XT_50k compared to 50k genotypes without affecting the bias. Although using pruned HDnGBS instead of 50k also increased the prediction accuracy by about 0.02, it increased the bias for purebred predictions in emBayesR models. Generally, emBayesR outperformed GBLUP for prediction accuracy when using 50k or pruned HDnGBS genotypes, but the benefits diminished with XT_50k genotypes. Crossbred predictions derived from a joint pure H and J reference were similar in accuracy to crossbred predictions derived from the two separate purebred reference sets and combined proportional to breed composition. However, the latter approach was less biased by 0.13. Most interestingly, using an equalized breed reference instead of an H-dominated reference, on average, reduced the bias of prediction by 0.16–0.19 and increased the accuracy by 0.04 for crossbred and J cows, with a little change in the H accuracy. In conclusion, we observed improved genomic predictions for both crossbreds and purebreds by equalizing breed contributions in a mixed breed reference that included crossbred cows. Furthermore, we demonstrate, that compared to the conventional 50k or high-density panels, our customized set of 50k sequence markers improved or matched the prediction accuracy and reduced bias with both GBLUP and Bayesian models.

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