scholarly journals Application of genomic selection in agriculture

2018 ◽  
Vol 33 (4) ◽  
Author(s):  
Neeraj Budhlakoti ◽  
D. C. Mishra ◽  
Devendra Arora ◽  
Rajeev Ranjan Kumar
Keyword(s):  
Genomic Selection ◽  
Marker Assisted Selection ◽  
Genetic Improvement ◽  
Breeding Value ◽  
Slow Time ◽  
Phenotypic Data ◽  
Breeding Values ◽  
Sequencing Technologies ◽  
Breeding Technique ◽  
Density Marker

Traditional breeding technique for genetic improvement of crops are based on, information on phenotypes and pedigrees to predict breeding values, has been found very successful. But, genetic gain through this technique is found to be very slow, time consuming. However, Due to availability of latest DNA sequencing technologies, now it is possible to estimate breeding values more accurately by using information on variation in DNA sequence. Lots of research has been done in direction of marker assisted selection, still it has some limitation on its implementation. Genomic selection (GS) is proposed to overcome such limitation. GS is a form of marker-assisted selection in which genetic markers covering the whole genome are used. GS predicts breeding value using information available on phenotype and high density marker. Several techniques has been developed for selection and prediction of genotype, these techniques are based on analysis of genotypic and phenotypic data.

scholarly journals Genomic selection for white spot syndrome virus resistance in whiteleg shrimp boosts survival under an experimental challenge test

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Marie Lillehammer ◽  
Rama Bangera ◽  
Marcela Salazar ◽  
Sergio Vela ◽  
Edna C. Erazo ◽  
...  
Keyword(s):  
Genomic Selection ◽  
Genetic Improvement ◽  
White Spot Syndrome Virus ◽  
Random Mating ◽  
Challenge Test ◽  
White Spot ◽  
Breeding Value ◽  
Genomic Breeding ◽  
Breeding Values ◽  
White Spot Syndrome

AbstractWhite spot syndrome virus (WSSV) causes major worldwide losses in shrimp aquaculture. The development of resistant shrimp populations is an attractive option for management of the disease. However, heritability for WSSV resistance is generally low and genetic improvement by conventional selection has been slow. This study was designed to determine the power and accuracy of genomic selection to improve WSSV resistance in Litopenaeus vannamei. Shrimp were experimentally challenged with WSSV and resistance was evaluated as dead or alive (DOA) 23 days after infestation. All shrimp in the challenge test were genotyped for 18,643 single nucleotide polymorphisms. Breeding candidates (G0) were ranked on genomic breeding values for WSSV resistance. Two G1 populations were produced, one from G0 breeders with high and the other with low estimated breeding values. A third population was produced from “random” mating of parent stock. The average survival was 25% in the low, 38% in the random and 51% in the high-genomic breeding value groups. Genomic heritability for DOA (0.41 in G1) was high for this type of trait. The realised genetic gain and high heritability clearly demonstrates large potential for further genetic improvement of WSSV resistance in the evaluated L. vannamei population using genomic selection.

scholarly journals Evaluation of Breeding Value Based on Scores Achieved in the Polish Championships for Young Horses

2015 ◽  
Vol 15 (4) ◽  
pp. 879-887
Author(s):  
Tomasz Próchniak ◽  
Iwona Rozempolska-Rucińska ◽  
Grzegorz Zięba ◽  
Marek Łukaszewicz
Keyword(s):  
Genetic Improvement ◽  
Genetic Parameters ◽  
Performance Test ◽  
Breeding Value ◽  
Genetic Trend ◽  
Breeding Values ◽  
Physical Traits ◽  
Young Horses ◽  
Show Jumping ◽  
Detailed Picture

Abstract Genetic improvement of show jumping horses is problematic, given the multitude of physical traits that determine sport usability and the specific mental predispositions required during training and competitions. The Polish Championships for Young Horses (PCYH) provide an opportunity to evaluate usability traits in Polish horses, which, however, is not a basis for evaluation of the breeding value. The aim of the study was to propose a model for evaluation of the breeding value of horses taking part in the Championships. In total, 1232 starts of 894 4-, 5-, 6-, and 7-year-old horses were analysed. Indices of BLUP breeding values were calculated based on 7 traits with known genetic parameters (ranking in the championship, style rating on days 1, 2, and 3, and penalty points on days 1, 2, and 3). A low and irregular genetic trend, significant only in the case of penalties scored on days 1 and 2 of the championships, was shown. Compatibility of the evaluation of the breeding value estimated on the basis of scores achieved in the Polish Championships for Young Horses with the scores of the performance test carried out in Training Centres was shown. It was also demonstrated that the “sum penalty” and “sum style” measured during the three days of the Championships is sufficient for evaluation of the BLUP breeding value. It was suggested that the evaluation combined with the results achieved at the PCYH (in four age categories) would provide a more detailed picture of the genetic predispositions of jumping horses.

scholarly journals Comparison of BLUP and Bayesian methods for different sizes of training population in genomic selection

2020 ◽  
Vol 44 (5) ◽  
pp. 994-1002
Author(s):  
Samet Hasan ABACI ◽  
Hasan ÖNDER
Keyword(s):  
Genomic Selection ◽  
Milk Yield ◽  
Predictive Ability ◽  
Breeding Value ◽  
Training Population ◽  
Breeding Values ◽  
Value Prediction ◽  
The Usa ◽  
Population Sizes ◽  
Estimated Breeding Values

This study aims to compare the accuracy of pedigree-based and genomic-based breeding value prediction for different training population sizes. In this study, Bayes (A, B, C, Cpi) and GBLUP methods for genomic selection and BLUP method for pedigree-based selection were used. Genomic and pedigree-based breeding values were estimated for partial milk yield (158 days) of Holstein cows (400 individuals) from a private enterprise in the USA. For this aim, populations were created for indirect breeding value estimates as training (322–360) and test (78–40) populations. In animals genotyped with a 54k SNP, the marker file was encoded as –10, 0, and 10 for AA, AB, and BB marker genotypes, respectively. Bayes and GBLUP methods were performed using GenSel 4.55 software. A total of 50,000 iterations were used, with the first 5000 excluded as the burn-in. Pedigree-based breeding values were estimated by REML using MTDFREML software employing an animal model. Correlations between partial milk yield and estimated breeding values were used to assess the predictive ability for methods. Bayes B method gave the highest accuracy for the indirect estimate of breeding value.

scholarly journals Genomic Selection—Considerations for Successful Implementation in Wheat Breeding Programs

Agronomy ◽  
2019 ◽  
Vol 9 (9) ◽  
pp. 479 ◽  
Author(s):  
Larkin ◽  
Lozada ◽  
Mason
Keyword(s):  
Genomic Selection ◽  
Wheat Yield ◽  
Wheat Breeding ◽  
Breeding Value ◽  
Selection Marker ◽  
Successful Implementation ◽  
Head Blight ◽  
Phenotypic Data ◽  
Breeding Programs ◽  
A Genome

In order to meet the goal of doubling wheat yield by 2050, breeders must work to improve breeding program efficiency while also implementing new and improved technologies in order to increase genetic gain. Genomic selection (GS) is an expansion of marker assisted selection which uses a statistical model to estimate all marker effects for an individual simultaneously to determine a genome estimated breeding value (GEBV). Breeders are thus able to select for performance based on GEBVs in the absence of phenotypic data. In wheat, genomic selection has been successfully implemented for a number of key traits including grain yield, grain quality and quantitative disease resistance, such as that for Fusarium head blight. For this review, we focused on the ways to modify genomic selection to maximize prediction accuracy, including prediction model selection, marker density, trait heritability, linkage disequilibrium, the relationship between training and validation sets, population structure, and training set optimization methods. Altogether, the effects of these different factors on the accuracy of predictions should be thoroughly considered for the successful implementation of GS strategies in wheat breeding programs.

scholarly journals A review of how dairy farmers can use and profit from genomic technologies

2012 ◽  
Vol 52 (3) ◽  
pp. 180 ◽  
Author(s):  
Jennie Pryce ◽  
Ben Hayes
Keyword(s):  
Energy Balance ◽  
Genomic Selection ◽  
Genetic Gain ◽  
Genetic Diseases ◽  
Breeding Value ◽  
Feed Conversion ◽  
Breeding Values ◽  
Genomic Technologies ◽  
Net Profit ◽  
Estimated Breeding Value

New genomic technologies can help farmers to (1) achieve higher annual rates of genetic gain through using genomically tested bulls in their herds, (2) select for ‘difficult’ to measure traits, such as feed conversion efficiency, methane emissions and energy balance, (3) select the best heifers to become herd replacements, (4) sell pedigree heifers at a premium, (5) use mating plans to optimise rates of genetic gain while controlling inbreeding, (6) achieve certainty in parentage of individual cows and (7) avoid genetic defects that could arise from mating cows to bulls that are known carriers of genetic diseases that are the result of a single lethal mutation. The first use does not require genotyping females and could approximately double the net income per cow that arises due to genetic improvement, mainly through a reduction in generation interval. On the basis of current rates of genetic gain, the net profit from using genotyped bulls could be worth AU$20/cow per year and is permanent and cumulative. One of the most powerful uses of genomic selection is to select for economically important, yet difficult- or expensive-to-measure traits, such as residual feed intake or energy balance. Provided the accuracy of genomic breeding values is high enough (i.e. correlation between the true and estimated breeding values), these traits lend themselves well to genomic selection. For selecting replacement heifers, if genotyping costs are AU$50/cow, the net profit of genotyping 40 heifers to select the top 20 as replacements (per 100 cows) would be worth approximately AU$41 per cow. However, using parent average estimated breeding-value information is free and can already be used to select replacement heifers. So, genotyping costs would need to be very low to be more profitable than selecting on parent average estimated breeding value. However, extra value from genotyping can also be captured by using other strategies. For example, mating plans that use genomic relationships rather than pedigree relationships to capture inbreeding are superior in terms of reducing progeny inbreeding at a desired level of genetic gain, although pedigree does an adequate job. So, again, the benefits of genotyping are small (<AU$10). Ascertainment of pedigree is an additional use of genotyping and is potentially worth ~AU$30 per cow. Avoidance of genetic diseases and selling of pedigree heifers have a value that should be estimated case-by-case. Because genotyping costs continue to fall, it may become increasingly popular to capture the extra value from genotyping females.

scholarly journals The Accuracy of Genomic Selection in Norwegian Red Cattle Assessed by Cross-Validation

Genetics ◽  
2009 ◽  
Vol 183 (3) ◽  
pp. 1119-1126 ◽  
Author(s):  
Tu Luan ◽  
John A. Woolliams ◽  
Sigbjørn Lien ◽  
Matthew Kent ◽  
Morten Svendsen ◽  
...  
Keyword(s):  
Genomic Selection ◽  
Cross Validation ◽  
Strong Relationship ◽  
Breeding Value ◽  
Production Traits ◽  
Phenotypic Data ◽  
Mixture Model Approach ◽  
High Heritability ◽  
Best Linear Unbiased ◽  
Similar Accuracy

Genomic Selection (GS) is a newly developed tool for the estimation of breeding values for quantitative traits through the use of dense markers covering the whole genome. For a successful application of GS, accuracy of the prediction of genomewide breeding value (GW-EBV) is a key issue to consider. Here we investigated the accuracy and possible bias of GW-EBV prediction, using real bovine SNP genotyping (18,991 SNPs) and phenotypic data of 500 Norwegian Red bulls. The study was performed on milk yield, fat yield, protein yield, first lactation mastitis traits, and calving ease. Three methods, best linear unbiased prediction (G-BLUP), Bayesian statistics (BayesB), and a mixture model approach (MIXTURE), were used to estimate marker effects, and their accuracy and bias were estimated by using cross-validation. The accuracies of the GW-EBV prediction were found to vary widely between 0.12 and 0.62. G-BLUP gave overall the highest accuracy. We observed a strong relationship between the accuracy of the prediction and the heritability of the trait. GW-EBV prediction for production traits with high heritability achieved higher accuracy and also lower bias than health traits with low heritability. To achieve a similar accuracy for the health traits probably more records will be needed.

scholarly journals Genotype Imputation to Improve the Cost-Efficiency of Genomic Selection in Rabbits

Animals ◽  
2021 ◽  
Vol 11 (3) ◽  
pp. 803
Author(s):  
Enrico Mancin ◽  
Bolívar Samuel Sosa-Madrid ◽  
Agustín Blasco ◽  
Noelia Ibáñez-Escriche
Keyword(s):  
Genomic Selection ◽  
Litter Size ◽  
Genomic Prediction ◽  
Selection Process ◽  
Imputation Accuracy ◽  
Genotype Imputation ◽  
Breeding Value ◽  
Response To Selection ◽  
Trade Off ◽  
Breeding Values

Genomic selection uses genetic marker information to predict genomic breeding values (gEBVs), and can be a suitable tool for selecting low-hereditability traits such as litter size in rabbits. However, genotyping costs in rabbits are still too high to enable genomic prediction in selective breeding programs. One method for decreasing genotyping costs is the genotype imputation, where parents are genotyped at high SNP-density (HD) and the progeny are genotyped at lower SNP-density, followed by imputation to HD. The aim of this study was to disentangle the best imputation strategies with a trade-off between genotyping costs and the accuracy of breeding values for litter size. A selection process, mimicking a commercial breeding rabbit selection program for litter size, was simulated. Two different Quantitative Trait Nucleotide (QTN) models (QTN_5 and QTN_44) were generated 36 times each. From these simulations, seven different scenarios (S1–S7) and a further replicate of the third scenario (S3_A) were created. Scenarios consist of a different combination of genotyping strategies. In these scenarios, ancestors and progeny were genotyped with a mix of three different platforms, containing 200,000, 60,000, and 600 SNPs under a cost of EUR 100, 50 and 11 per animal, respectively. Imputation accuracy (IA) was measured as a Pearson’s correlation between true genotype and imputed genotype, whilst the accuracy of gEBVs was the correlation between true breeding value and the estimated one. The relationships between IA, the accuracy of gEBVs, genotyping costs, and response to selection were examined under each QTN model. QTN_44 presented better performance, according to the results of genomic prediction, but the same ranks between scenarios remained in both QTN models. The highest IA (0.99) and the accuracy of gEBVs (0.26; QTN_44, and 0.228; QTN_5) were observed in S1 where all ancestors were genotyped at HD and progeny at medium SNP-density (MD). Nevertheless, this was the most expensive scenario compared to the others in which the progenies were genotyped at low SNP-density (LD). Scenarios with low average costs presented low IA, particularly when female ancestors were genotyped at LD (S5) or non-genotyped (S7). The S3_A, imputing whole-genomes, had the lowest accuracy of gEBVs (0.09), even worse than Best Linear Unbiased Prediction (BLUP). The best trade-off between genotyping costs and the accuracy of gEBVs (0.234; QTN_44 and 0.199) was in S6, in which dams were genotyped with MD whilst grand-dams were non-genotyped. However, this relationship would depend mainly on the distribution of QTN and SNP across the genome, suggesting further studies on the characterization of the rabbit genome in the Spanish lines. In summary, genomic selection with genotype imputation is feasible in the rabbit industry, considering only genotyping strategies with suitable IA, accuracy of gEBVs, genotyping costs, and response to selection.

scholarly journals 38 Using pooled data for single-step genomic prediction: Impact of within-pool variance and size

2020 ◽  
Vol 98 (Supplement_4) ◽  
pp. 9-9
Author(s):  
Johnna L Baller ◽  
Stephen D Kachman ◽  
Larry A Kuehn ◽  
Matthew L Spangler
Keyword(s):  
Phenotypic Variation ◽  
Equal Opportunity ◽  
Cost Effective ◽  
Single Step ◽  
Pool Size ◽  
Individual Data ◽  
Phenotypic Data ◽  
Breeding Values ◽  
Mean Values ◽  
Genetic Evaluations

Abstract Economically relevant traits (ERT) are routinely collected within commercial segments of the beef industry but are rarely included in genetic evaluations because of unknown pedigrees. Individual relationships could be resurrected with genomics, which would be costly; pooling DNA and phenotypic data provides a cost-effective solution. A simulated beef cattle population consisting of 15 generations was genotyped with approximately 50k markers (841 quantitative trait loci were located across the genome) and phenotyped for a moderately heritable trait. Individuals from generation 15 were included in pools (observed genotype and phenotype were mean values of a group). Estimated breeding values (EBV) were generated from a single-step GBLUP model. The effects of pooling strategy (random and minimizing or uniformly maximizing phenotypic variation), pool size (1, 2, 10, 20, 50, 100, or no data from generation 15), and generational gaps of genotyping on EBV accuracy (correlation of EBV with true breeding values) were quantified. Greatest EBV accuracies of sires and dams were observed when no gap between genotyped parents and pooled offspring occurred. The EBV accuracies resulting from pools were greater than no data from generation 15 regardless of sire or dam genotyping. Minimizing phenotypic variation increased EBV accuracy by 8% and 9% over random pooling and uniformly maximizing phenotypic variation, respectively. Pool size of 2 was the only scenario that did not significantly decrease EBV accuracy compared to individual data when pools were formed randomly or by uniformly maximizing phenotypic variation (P &gt; 0.05). Pool sizes of 2, 10, 20, or 50 did not generally lead to EBV accuracies that were statistically different than individual data when pools were constructed to minimize phenotypic variation (P &gt; 0.05). Pooled genotyping to garner commercial-level phenotypes for genetic evaluations seems plausible, although differences exist depending on pool size and pool formation strategy. The USDA is an equal opportunity employer.

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