scholarly journals Investigation of reliability of genomic predictions in the admixed Nordic Red dairy cattle

2012 ◽  
pp. 1-5
Author(s):  
Mahlako L. Makgahlela ◽  
E. A. Mäntysaari ◽  
I. Strandén ◽  
M. Koivula ◽  
U.S. Nielsen ◽  
...  
Keyword(s):  
Dairy Cattle ◽  
Specific Model ◽  
Snp Markers ◽  
Homogeneous Population ◽  
Genomic Breeding ◽  
Breeding Values ◽  
Specific Effects ◽  
Estimated Breeding Values ◽  
Genomic Predictions ◽  
Uniform Population

The success of genomic selection (GS) in small breeds which are likely to have admixed structures has been minimal. This is because accuracy of GS depends on the extent of linkage disequilibrium (LD) between markers and quantitative trait loci (QTL) and LD depends on the genetic structure of the population and marker density. In the current study, we evaluate reliability of genomic predictions in young unproven bulls, when interactions between marker effects and breed of origin are accounted for in the Nordic Red dairy cattle (RDC). The population structure of the RDC is admixed. Data consisted of animal breed proportions calculated from the full pedigree, deregressed proofs (DRP) of published estimated breeding values (EBV) for yield traits and genotypic data for 37,595 SNP markers. Direct genomic breeding values (DGV) were estimated using 2 models, one accounting for breed-specific effects and other assuming uniform population. Validation reliabilities were calculated as the squared correlation between DRP and DGV (r2DRP, DGV), corrected by the mean reliability ofDRP. Using the breed-specific model increased the reliability of DGV by 2% and 3% for milk and protein, respectively, when compared to homogeneous population GBLUP model. The exception was for fat, where there was no gain in reliability. Estimated validation reliabilities were low for milk (0.32) and protein (0.32) and slightly higher (0.42) for fat.

scholarly journals Genomic Analysis Using Bayesian Methods under Different Genotyping Platforms in Korean Duroc Pigs

Animals ◽  
2020 ◽  
Vol 10 (5) ◽  
pp. 752 ◽  
Author(s):  
Jungjae Lee ◽  
Yongmin Kim ◽  
Eunseok Cho ◽  
Kyuho Cho ◽  
Soojin Sa ◽  
...  
Keyword(s):  
Genomic Prediction ◽  
Genome Wide Association Study ◽  
Genomic Analysis ◽  
Snp Markers ◽  
Snp Genotyping ◽  
Muscle Area ◽  
Bayesian Approaches ◽  
Breeding Values ◽  
Estimated Breeding Values ◽  
Response Variables

Genomic evaluation has been widely applied to several species using commercial single nucleotide polymorphism (SNP) genotyping platforms. This study investigated the informative genomic regions and the efficiency of genomic prediction by using two Bayesian approaches (BayesB and BayesC) under two moderate-density SNP genotyping panels in Korean Duroc pigs. Growth and production records of 1026 individuals were genotyped using two medium-density, SNP genotyping platforms: Illumina60K and GeneSeek80K. These platforms consisted of 61,565 and 68,528 SNP markers, respectively. The deregressed estimated breeding values (DEBVs) derived from estimated breeding values (EBVs) and their reliabilities were taken as response variables. Two Bayesian approaches were implemented to perform the genome-wide association study (GWAS) and genomic prediction. Multiple significant regions for days to 90 kg (DAYS), lean muscle area (LMA), and lean percent (PCL) were detected. The most significant SNP marker, located near the MC4R gene, was detected using GeneSeek80K. Accuracy of genomic predictions was higher using the GeneSeek80K SNP panel for DAYS (Δ2%) and LMA (Δ2–3%) with two response variables, with no gains in accuracy by the Bayesian approaches in four growth and production-related traits. Genomic prediction is best derived from DEBVs including parental information as a response variable between two DEBVs regardless of the genotyping platform and the Bayesian method for genomic prediction accuracy in Korean Duroc pig breeding.

scholarly journals Designing dairy cattle breeding schemes under genomic selection: a review of international research

2012 ◽  
Vol 52 (3) ◽  
pp. 107 ◽  
Author(s):  
J. E. Pryce ◽  
H. D. Daetwyler
Keyword(s):  
Dairy Cattle ◽  
Genomic Selection ◽  
Genetic Gain ◽  
Reproductive Technologies ◽  
Progeny Testing ◽  
Genomic Breeding ◽  
Breeding Values ◽  
Scheme Design ◽  
Dairy Cattle Breeding ◽  
Breeding Schemes

High rates of genetic gain can be achieved through (1) accurate predictions of breeding values (2) high intensities of selection and (3) shorter generation intervals. Reliabilities of ~60% are currently achievable using genomic selection in dairy cattle. This breakthrough means that selection of animals can happen at a very early age (i.e. as soon as a DNA sample is available) and has opened opportunities to radically redesign breeding schemes. Most research over the past decade has focussed on the feasibility of genomic selection, especially how to increase the accuracy of genomic breeding values. More recently, how to apply genomic technology to breeding schemes has generated a lot of interest. Some of this research remains the intellectual property of breeding companies, but there are examples in the public domain. Here we review published research into breeding scheme design using genomic selection and evaluate which designs appear to be promising (in terms of rates of genetic gain) and those that may have unfavourable side-effects (i.e. increasing the rate of inbreeding). The schemes range from fairly conservative designs where bulls are screened genomically to reduce numbers entering progeny testing, to schemes where very large numbers of bull calves are screened and used as sires as soon as they reach sexual maturity. More radical schemes that incorporate the use of reproductive technologies (in juveniles) and genomic selection in nucleus herds are also described. The models used are either deterministic and more recently tend to be stochastic, simulating populations of cattle. A key driver of the rate of genetic gain is the generation interval, which could range from being similar to that in conventional testing (~5 years), down to as little as 1.5 years. Generally, the rate of genetic gain is between 12% and 100% more than in conventional progeny testing, while the rate of inbreeding tends to be lower per generation than in progeny testing because Mendelian sampling terms can be estimated more accurately. However, short generation intervals can lead to higher rates of inbreeding per year in genomic breeding programs.

scholarly journals Genomic Predictions for Fillet Yield and Firmness in Rainbow Trout Using Reduced-density SNP Panels

2020 ◽  
Author(s):  
Rafet Al-Tobasei ◽  
Ali R. Ali ◽  
Andre L. S. Garcia ◽  
Daniela Lourenco ◽  
Tim Leeds ◽  
...  
Keyword(s):  
Rainbow Trout ◽  
Predictive Ability ◽  
Study Data ◽  
Single Step ◽  
Breeding Values ◽  
Snp Chip ◽  
Estimated Breeding Values ◽  
Snp Panels ◽  
Fillet Yield ◽  
Genomic Predictions

Abstract BackgroundOne of the most important goals for the rainbow trout aquaculture industry is to improve fillet yield and fillet quality. Previously, we showed that a 50K transcribed-SNP chip can be used to detect quantitative trait loci (QTL) associated with fillet yield and fillet firmness. In this study, data from 1,568 fish genotyped for the 50K transcribed-SNP chip and ~774 fish phenotyped for fillet yield and fillet firmness were used in a single-step genomic BLUP (ssGBLUP) model to compute the genomic estimated breeding values (GEBV). In addition, pedigree-based best linear unbiased prediction (PBLUP) was used to calculate traditional, family-based estimated breeding values (EBV). ResultsThe genomic predictions outperformed the traditional EBV by 35% for fillet yield and 42% for fillet firmness. The predictive ability for fillet yield and fillet firmness was 0.19 - 0.20 with PBLUP, and 0.27 with ssGBLUP. Additionally, reducing SNP panel densities indicated that using 500 – 800 SNPs in genomic predictions still provides predictive abilities higher than PBLUP. ConclusionThese results suggest that genomic evaluation is a feasible strategy to identify and select fish with superior genetic merit within rainbow trout families, even with low-density SNP panels.

scholarly journals Genomic Predictions for Muscle Yield and Fillet Firmness in Rainbow Trout using Reduced-Density SNP Panels

2020 ◽  
Author(s):  
Rafet Al-Tobasei ◽  
Ali R. Ali ◽  
Andre L. S. Garcia ◽  
Daniela Lourenco ◽  
Tim Leeds ◽  
...  
Keyword(s):  
Rainbow Trout ◽  
Predictive Ability ◽  
Study Data ◽  
Single Step ◽  
Breeding Values ◽  
Snp Chip ◽  
Estimated Breeding Values ◽  
Snp Panels ◽  
Family Based ◽  
Genomic Predictions

Abstract Background One of the most important goals for the rainbow trout aquaculture industry is to improve muscle yield and fillet quality. Previously, we showed that a 50K transcribed-SNP chip can be used to detect quantitative trait loci (QTL) associated with muscle yield and fillet firmness. In this study, data from 1,568 fish genotyped for the 50K transcribed-SNP chip and ~774 fish phenotyped for muscle yield and fillet firmness were used in a single-step genomic BLUP (ssGBLUP) model to compute the genomic estimated breeding values (GEBV). In addition, pedigree-based best linear unbiased prediction (PBLUP) was used to calculate traditional, family-based estimated breeding values (EBV). Results The genomic predictions outperformed the traditional EBV by 35% for muscle yield and 42% for fillet firmness. The predictive ability for muscle yield and fillet firmness was 0.19 - 0.20 with PBLUP, and 0.27 with ssGBLUP. Additionally, reducing SNP panel densities indicated that using 500 – 800 SNPs in genomic predictions still provides predictive abilities higher than PBLUP. Conclusion These results suggest that genomic evaluation is a feasible strategy to identify and select fish with superior genetic merit within rainbow trout families, even with low-density SNP panels.

scholarly journals Level-biases in estimated breeding values due to the use of different SNP panels over time in ssGBLUP

2019 ◽  
Vol 51 (1) ◽  
Author(s):  
Øyvind Nordbø ◽  
Arne B. Gjuvsland ◽  
Leiv Sigbjørn Eikje ◽  
Theo Meuwissen
Keyword(s):  
Value Added ◽  
Single Step ◽  
Fine Tuning ◽  
Genomic Relationship Matrix ◽  
Relationship Matrix ◽  
Optimal Selection ◽  
Breeding Values ◽  
Estimated Breeding Values ◽  
Snp Panels ◽  
Genomic Predictions

Abstract Background The main aim of single-step genomic predictions was to facilitate optimal selection in populations consisting of both genotyped and non-genotyped individuals. However, in spite of intensive research, biases still occur, which make it difficult to perform optimal selection across groups of animals. The objective of this study was to investigate whether incomplete genotype datasets with errors could be a potential source of level-bias between genotyped and non-genotyped animals and between animals genotyped on different single nucleotide polymorphism (SNP) panels in single-step genomic predictions. Results Incomplete and erroneous genotypes of young animals caused biases in breeding values between groups of animals. Systematic noise or missing data for less than 1% of the SNPs in the genotype data had substantial effects on the differences in breeding values between genotyped and non-genotyped animals, and between animals genotyped on different chips. The breeding values of young genotyped individuals were biased upward, and the magnitude was up to 0.8 genetic standard deviations, compared with breeding values of non-genotyped individuals. Similarly, the magnitude of a small value added to the diagonal of the genomic relationship matrix affected the level of average breeding values between groups of genotyped and non-genotyped animals. Cross-validation accuracies and regression coefficients were not sensitive to these factors. Conclusions Because, historically, different SNP chips have been used for genotyping different parts of a population, fine-tuning of imputation within and across SNP chips and handling of missing genotypes are crucial for reducing bias. Although all the SNPs used for estimating breeding values are present on the chip used for genotyping young animals, incompleteness and some genotype errors might lead to level-biases in breeding values.

scholarly journals Genomic predictions for fillet yield and firmness in rainbow trout using reduced-density SNP panels

BMC Genomics ◽  
2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Rafet Al-Tobasei ◽  
Ali Ali ◽  
Andre L. S. Garcia ◽  
Daniela Lourenco ◽  
Tim Leeds ◽  
...  
Keyword(s):  
Rainbow Trout ◽  
Predictive Ability ◽  
Study Data ◽  
Single Step ◽  
Breeding Values ◽  
Snp Chip ◽  
Estimated Breeding Values ◽  
Snp Panels ◽  
Fillet Yield ◽  
Genomic Predictions

Abstract Background One of the most important goals for the rainbow trout aquaculture industry is to improve fillet yield and fillet quality. Previously, we showed that a 50 K transcribed-SNP chip can be used to detect quantitative trait loci (QTL) associated with fillet yield and fillet firmness. In this study, data from 1568 fish genotyped for the 50 K transcribed-SNP chip and ~ 774 fish phenotyped for fillet yield and fillet firmness were used in a single-step genomic BLUP (ssGBLUP) model to compute the genomic estimated breeding values (GEBV). In addition, pedigree-based best linear unbiased prediction (PBLUP) was used to calculate traditional, family-based estimated breeding values (EBV). Results The genomic predictions outperformed the traditional EBV by 35% for fillet yield and 42% for fillet firmness. The predictive ability for fillet yield and fillet firmness was 0.19–0.20 with PBLUP, and 0.27 with ssGBLUP. Additionally, reducing SNP panel densities indicated that using 500–800 SNPs in genomic predictions still provides predictive abilities higher than PBLUP. Conclusion These results suggest that genomic evaluation is a feasible strategy to identify and select fish with superior genetic merit within rainbow trout families, even with low-density SNP panels.

EVALUATION OF DAIRY CATTLE FOR FINAL CLASS USING ANIMAL MODELS

1988 ◽  
Vol 68 (3) ◽  
pp. 639-645 ◽  
Author(s):  
J. JAMROZIK ◽  
L. R. SCHAEFFER
Keyword(s):  
Animal Models ◽  
Dairy Cattle ◽  
Fixed Effects ◽  
Genetic Relationships ◽  
Residual Effects ◽  
Breeding Values ◽  
Stage Of Lactation ◽  
Key Words Animal ◽  
Estimated Breeding Values ◽  
Genetic Evaluations

Estimated breeding values for final class of 364 868 Canadian Holstein Friesian cows and 10 186 bulls from three different animal models were compared. FIRST lactation, first classifications were described by a model with fixed effects of herd-round-classifier, linear and quadratic effects of age at calving and stage of lactation at classification, and random effects of additive genetic effects of cows, and residual effects. All additive genetic relationships among animals were included. A second model used the LATEST classification on each cow rather than the first and these observations were pre-adjusted for age and stage. The third model used ALL classifications on each cow, and these were also pre-adjusted for age and stage effects. Correlations among estimated breeding values between methods ranged from 0.92 to 0.95. Estimated breeding values from LATEST were most highly correlated to sire proofs from the currently official sire model which also used the latest classification of each cow. Correlations of estimated breeding values between sires and their sons showed that results from LATEST were more accurate than results from the other two models. A model similar to that for LATEST is proposed for official genetic evaluations for conformation in the Canadian Holstein population. Key words: Animal model, conformation, dairy cattle

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.

A comprehensive comparison between single- and two-step GBLUP methods in a simulated beef cattle population

2018 ◽  
Vol 98 (3) ◽  
pp. 565-575 ◽  
Author(s):  
Mario L. Piccoli ◽  
Luiz F. Brito ◽  
José Braccini ◽  
Fernanda V. Brito ◽  
Fernando F. Cardoso ◽  
...  
Keyword(s):  
Beef Cattle ◽  
Production Systems ◽  
Simulated Data ◽  
Single Step ◽  
Breeding Values ◽  
True Breeding ◽  
Blending Method ◽  
Estimated Breeding Values ◽  
Genetic Evaluations ◽  
Genomic Predictions

The statistical methods used in the genetic evaluations are a key component of the process and can be best compared by using simulated data. The latter is especially true in grazing beef cattle production systems, where the number of proven bulls with highly reliable estimated breeding values is limited to allow for a trustworthy validation of genomic predictions. Therefore, we simulated data for 4980 beef cattle aiming to compare single-step genomic best linear unbiased prediction (ssGBLUP), which simultaneously incorporates pedigree, phenotypic, and genomic data into genomic evaluations, and two-step GBLUP (tsGBLUP) procedures and genomic estimated breeding values (GEBVs) blending methods. The greatest increases in GEBV accuracies compared with the parents’ average estimated breeding values (EBVPA) were 0.364 and 0.341 for ssGBLUP and tsGBLUP, respectively. Direct genomic value and GEBV accuracies when using ssGBLUP and tsGBLUP procedures were similar, except for the GEBV accuracies using Hayes’ blending method in tsGBLUP. There was no significant or slight bias in genomic predictions from ssGBLUP or tsGBLUP (using VanRaden’s blending method), indicating that these predictions are on the same scale compared with the true breeding values. Overall, genetic evaluations including genomic information resulted in gains in accuracy >100% compared with the EBVPA. In addition, there were no significant differences between the selected animals (10% males and 50% females) by using ssGBLUP or tsGBLUP.

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