scholarly journals Weighted single-step genomic BLUP improves accuracy of genomic breeding values for protein content in French dairy goats: a quantitative trait influenced by a major gene

2018 ◽  
Vol 50 (1) ◽  
Author(s):  
Marc Teissier ◽  
Hélène Larroque ◽  
Christèle Robert-Granié
Keyword(s):  
Protein Content ◽  
Quantitative Trait ◽  
Major Gene ◽  
Single Step ◽  
Dairy Goats ◽  
Genomic Breeding ◽  
Breeding Values

43 Single and Multiple-breed Genomic Predictions for Conformation Traits of Canadian Dairy Goats

2021 ◽  
Vol 99 (Supplement_3) ◽  
pp. 27-28
Author(s):  
Erin Massender ◽  
Luiz F Brito ◽  
Laurence Maignel ◽  
Hinayah R Oliveira ◽  
Mohsen Jafarikia ◽  
...  
Keyword(s):  
Single Step ◽  
Dairy Goats ◽  
Genomic Breeding ◽  
Breeding Values ◽  
Suspensory Ligament ◽  
Full Dataset ◽  
Expected Gain ◽  
Breed Difference ◽  
Estimated Breeding Values ◽  
Theoretical Accuracy

Abstract The use of multiple-breed models can increase the accuracy of estimated breeding values (EBV) when few phenotypes are available for a trait. However, pooling breeds is not always beneficial for genomic evaluations due to the low consistency of gametic phase between individual breeds. The objective of this study was to compare the expected gain in accuracy of single-step genomic breeding values (GEBV) for conformation traits of Canadian Alpine and Saanen goats predicted using single and multiple-breed models. The traits considered were body capacity, dairy character, feet and legs, fore udder, general appearance, rear udder, suspensory ligament, and teats, all recorded by trained classifiers, using a 1 to 9 scale. The full datasets included a total of 7,500 phenotypes for each trait (5,158 Alpine and 2,342 Saanen) and 1,707 50K genotypes (833 Alpine, 874 Saanen). Standard errors of prediction (SEP) were obtained for EBV and GEBV predicted using single-trait animal models on full or validation datasets. Breed difference was accounted for as a fixed effect in the multiple-breed models. Average theoretical accuracies were calculated from the SEP. For Saanen, with fewer records, expected accuracies of EBV and GEBV for the validation animals (selection candidates) were consistently higher for the multiple-breed models. Trait specific gains in theoretical accuracy of GEBV relative to EBV for the selection candidates ranged from 30 to 48% for Alpine and 41 to 61% for Saanen. Averaged across all traits, GEBV predicted from the full dataset were 32 to 38% more accurate than EBV for genotyped animals and the largest gains were found for does without conformation records (49 to 55%) and bucks without daughter records (56 to 82%). Overall, the implementation of genomic selection would substantially increase selection accuracy for young breeding candidates and, consequently, the rate of genetic improvement for conformation traits in Canadian dairy goats.

scholarly journals Estimation of genomic breeding values for milk yield in UK dairy goats

2015 ◽  
Vol 98 (11) ◽  
pp. 8201-8208 ◽  
Author(s):  
S. Mucha ◽  
R. Mrode ◽  
I. MacLaren-Lee ◽  
M. Coffey ◽  
J. Conington
Keyword(s):  
Milk Yield ◽  
Dairy Goats ◽  
Genomic Breeding ◽  
Breeding Values

scholarly journals Alternative methods improve the accuracy of genomic prediction using information from a causal point mutation in a dairy sheep model

BMC Genomics ◽  
2019 ◽  
Vol 20 (1) ◽  
Author(s):  
Claire Oget ◽  
Marc Teissier ◽  
Jean-Michel Astruc ◽  
Gwenola Tosser-Klopp ◽  
Rachel Rupp
Keyword(s):  
Major Gene ◽  
Single Step ◽  
Gene Content ◽  
Alternative Methods ◽  
Dairy Sheep ◽  
Genomic Evaluation ◽  
Multiple Trait ◽  
Sheep Model ◽  
Breeding Values ◽  
Polygenic Component

Abstract Background Genomic evaluation is usually based on a set of markers assumed to be linked with causal mutations. Selection and precise management of major genes and the remaining polygenic component might be improved by including causal polymorphisms in the evaluation models. In this study, various methods involving a known mutation were used to estimate prediction accuracy. The SOCS2 gene, which influences body growth, milk production and somatic cell scores, a proxy for mastitis, was studied as an example in dairy sheep. Methods The data comprised 1,503,148 phenotypes and 9844 54K SNPs genotypes. The SOCS2 SNP was genotyped for 4297 animals and imputed in the above 9844 animals. Breeding values and their accuracies were estimated for each of nine traits by using single-step approaches. Pedigree-based BLUP, single-step genomic BLUP (ssGBLUP) involving the 54K ovine SNPs chip, and four weighted ssGBLUP (WssGBLUP) methods were compared. In WssGBLUP methods, weights are assigned to SNPs depending on their effect on the trait. The ssGBLUP and WssGBLUP methods were again tested after including the SOCS2 causal mutation as a SNP. Finally, the Gene Content approach was tested, which uses a multiple-trait model that considers the SOCS2 genotype as a trait. Results EBV accuracies were increased by 14.03% between the pedigree-based BLUP and ssGBLUP methods and by 3.99% between ssGBLUP and WssGBLUP. Adding the SOCS2 SNP to ssGBLUP methods led to an average gain of 0.26%. Construction of the kinship matrix and estimation of breeding values was generally improved by placing emphasis on SNPs in regions with a strong effect on traits. In the absence of chip data, the Gene Content method, compared to pedigree-based BLUP, efficiently accounted for partial genotyping information on SOCS2 as accuracy was increased by 6.25%. This method also allowed dissociation of the genetic component due to the major gene from the remaining polygenic component. Conclusions Causal mutations with a moderate to strong effect can be captured with conventional SNP chips by applying appropriate genomic evaluation methods. The Gene Content method provides an efficient way to account for causal mutations in populations lacking genome-wide genotyping.

scholarly journals Optimum mating designs for exploiting dominance in genomic selection schemes for aquaculture species

2021 ◽  
Vol 53 (1) ◽  
Author(s):  
Jesús Fernández ◽  
Beatriz Villanueva ◽  
Miguel Angel Toro
Keyword(s):  
Inbreeding Depression ◽  
Mate Selection ◽  
Variable Number ◽  
Single Step ◽  
Commercial Fish ◽  
Genomic Breeding ◽  
Breeding Values ◽  
Commercial Population ◽  
Potential Benefits ◽  
Estimated Breeding Values

Abstract Background In commercial fish, dominance effects could be exploited by predicting production abilities of the offspring that would be generated by different mating pairs and choosing those pairs that maximise the average offspring phenotype. Consequently, matings would be performed to reduce inbreeding depression. This can be achieved by applying mate selection (MS) that combines selection and mating decisions in a single step. An alternative strategy to MS would be to apply minimum coancestry mating (MCM) after selection based on estimated breeding values. The objective of this study was to evaluate, by computer simulations, the potential benefits that can be obtained by implementing MS or MCM based on genomic data for exploiting dominance effects when creating commercial fish populations that are derived from a breeding nucleus. Methods The selected trait was determined by a variable number of loci with additive and dominance effects. The population consisted of 50 full-sib families with 30 offspring each. Males and females with the highest estimated genomic breeding values were selected in the nucleus and paired using the MCM strategy. Both MCM and MS were used to create the commercial population. Results For a moderate number of SNPs, equal or even higher mean phenotypic values are obtained by selecting on genomic breeding values and then applying MCM than by using MS when the trait exhibited substantial inbreeding depression. This could be because MCM leads to high levels of heterozygosity across the whole genome, even for loci affecting the trait that are in linkage equilibrium with the SNPs. In contrast, MS specifically promotes heterozygosity for SNPs for which a dominance effect has been detected. Conclusions In most scenarios, for the management of aquaculture breeding programs it seems advisable to follow the MCM strategy when creating the commercial population, especially for traits with large inbreeding depression. Moreover, MCM has the appealing property of reducing inbreeding levels, with a corresponding reduction in inbreeding depression for traits beyond those included in the selection objective.

scholarly journals Genetic and genomic characterization followed by single-step genomic evaluation of withers height in German Warmblood horses

2022 ◽  
Author(s):  
Sarah Vosgerau ◽  
Nina Krattenmacher ◽  
Clemens Falker-Gieske ◽  
Anita Seidel ◽  
Jens Tetens ◽  
...  
Keyword(s):  
Genome Wide Association Study ◽  
Reference Population ◽  
Single Step ◽  
Relationship Matrix ◽  
Nucleotide Polymorphisms ◽  
Step Method ◽  
Genomic Breeding ◽  
Breeding Values ◽  
A Genome ◽  
Estimation System

Abstract  Reliability of genomic predictions is influenced by the size and genetic composition of the reference population. For German Warmblood horses, compilation of a reference population has been enabled through the cooperation of five German breeding associations. In this study, preliminary data from this joint reference population were used to genetically and genomically characterize withers height and to apply single-step methodology for estimating genomic breeding values for withers height. Using data on 2113 mares and their genomic information considering about 62,000 single nucleotide polymorphisms (SNPs), analysis of the genomic relationship revealed substructures reflecting breed origin and different breeding goals of the contributing breeding associations. A genome-wide association study confirmed a known quantitative trait locus (QTL) for withers height on equine chromosome (ECA) 3 close to LCORL and identified a further significant peak on ECA 1. Using a single-step approach with a combined relationship matrix, the estimated heritability for withers height was 0.31 (SE = 0.08) and the corresponding genomic breeding values ranged from − 2.94 to 2.96 cm. A mean reliability of 0.38 was realized for these breeding values. The analyses of withers height showed that compiling a reference population across breeds is a suitable strategy for German Warmblood horses. The single-step method is an appealing approach for practical genomic prediction in horses, because not many genotypes are available yet and animals without genotypes can by this way directly contribute to the estimation system.

scholarly journals A Shortcut Approach for Large-scale Mixed Model Associations with Binary Traits

2021 ◽  
Author(s):  
Runqing Yang ◽  
Jun Bao ◽  
Runqing Yang ◽  
Yuxin Song ◽  
Zhiyu Hao ◽  
...  
Keyword(s):  
Quantitative Trait ◽  
Multiple Testing ◽  
Statistical Power ◽  
Large Scale ◽  
Mixed Model ◽  
Joint Analysis ◽  
Genomic Breeding ◽  
Breeding Values ◽  
Genomic Control ◽  
Genomic Heritability

Abstract Generalized linear mixed models exhibit computationally intensive and biasness in mapping quantitative trait nucleotides for binary diseases. In genomic logit regression, we consider genomic breeding values estimated in advance as a known predictor, and then correct the deflated association test statistics by using genomic control, thereby successfully extending GRAMMAR-Lambda to analyze binary diseases in a complex structured population. Because there is no need to estimate genomic heritability and genomic breeding values can be estimated by a small number of sampling markers, the generalized mixed-model association analysis has been extremely simplified to handle large-scale data. With almost perfect genomic control, joint analysis for the candidate quantitative trait nucleotides chosen by multiple testing offered a significant improvement in statistical power.

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

2019 ◽  
Vol 64 (No. 4) ◽  
pp. 160-165 ◽  
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.

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

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.

scholarly journals A Shortcut Approach for Large-scale Mixed Model Associations with Binary Traits

2021 ◽  
Author(s):  
Runqing Yang ◽  
Jun Bao ◽  
Runqing Yang ◽  
Yuxin Song ◽  
Zhiyu Hao ◽  
...  
Keyword(s):  
Quantitative Trait ◽  
Multiple Testing ◽  
Statistical Power ◽  
Large Scale ◽  
Mixed Model ◽  
Joint Analysis ◽  
Genomic Breeding ◽  
Breeding Values ◽  
Genomic Control ◽  
Genomic Heritability

Abstract Generalized linear mixed models exhibit computationally intensive and biasness in mapping quantitative trait nucleotides for binary diseases. In genomic logit regression, we consider genomic breeding values estimated in advance as a known predictor, and then correct the deflated association test statistics by using genomic control, thereby successfully extending GRAMMAR-Lambda to analyze binary diseases in a complex structured population. Because there is no need to estimate genomic heritability and genomic breeding values can be estimated by a small number of sampling markers, the generalized mixed-model association analysis has been extremely simplified to handle large-scale data. With almost perfect genomic control, joint analysis for the candidate quantitative trait nucleotides chosen by multiple testing offered a significant improvement in statistical power.

Sign in / Sign up

Export Citation Format

Share Document