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 effectiveness of genomic selection for milk production traits of Holstein dairy cattle

2020 ◽  
Vol 33 (3) ◽  
pp. 382-389 ◽  
Author(s):  
Yun-Mi Lee ◽  
Chang-Gwon Dang ◽  
Mohammad Z. Alam ◽  
You-Sam Kim ◽  
Kwang-Hyeon Cho ◽  
...  
Keyword(s):  
Genomic Selection ◽  
Milk Production ◽  
Genetic Gain ◽  
Milk Fat ◽  
Breeding Value ◽  
Production Traits ◽  
Milk Production Traits ◽  
Data Set ◽  
Selection For ◽  
Estimated Breeding Value

Objective: This study was conducted to test the efficiency of genomic selection for milk production traits in a Korean Holstein cattle population.Methods: A total of 506,481 milk production records from 293,855 animals (2,090 heads with single nucleotide polymorphism information) were used to estimate breeding value by single step best linear unbiased prediction.Results: The heritability estimates for milk, fat, and protein yields in the first parity were 0.28, 0.26, and 0.23, respectively. As the parity increased, the heritability decreased for all milk production traits. The estimated generation intervals of sire for the production of bulls (L<sub>SB</sub>) and that for the production of cows (L<sub>SC</sub>) were 7.9 and 8.1 years, respectively, and the estimated generation intervals of dams for the production of bulls (L<sub>DB</sub>) and cows (L<sub>DC</sub>) were 4.9 and 4.2 years, respectively. In the overall data set, the reliability of genomic estimated breeding value (GEBV) increased by 9% on average over that of estimated breeding value (EBV), and increased by 7% in cows with test records, about 4% in bulls with progeny records, and 13% in heifers without test records. The difference in the reliability between GEBV and EBV was especially significant for the data from young bulls, i.e. 17% on average for milk (39% vs 22%), fat (39% vs 22%), and protein (37% vs 22%) yields, respectively. When selected for the milk yield using GEBV, the genetic gain increased about 7.1% over the gain with the EBV in the cows with test records, and by 2.9% in bulls with progeny records, while the genetic gain increased by about 24.2% in heifers without test records and by 35% in young bulls without progeny records.Conclusion: More genetic gains can be expected through the use of GEBV than EBV, and genomic selection was more effective in the selection of young bulls and heifers without test records.

Efficiency of genomic selection to improve meat quality in pigs using ZPLAN+

2018 ◽  
Author(s):  
B. I. Lopez ◽  
C. W. Song ◽  
K. S. Seo
Keyword(s):  
Genomic Selection ◽  
Meat Quality ◽  
Genetic Gain ◽  
Selection Index ◽  
Average Daily Gain ◽  
Breeding Value ◽  
Feed Conversion ◽  
Negative Effect ◽  
Daily Gain ◽  
Positive Effect

The phenotype or genomic enhanced breeding value (GEBV) of ultrasound intramuscular fat (UIMF) was used as the target trait to improve meat quality. The ZPLAN+ software was employed to calculate and compare the genetic gain and accuracy of each selection scenario. The first scenario reflected the current conventional selection program in which the selection index is composed of average daily gain (ADG), feed conversion ratio (FCR) and ultrasound backfat (UBF). In the second scenario, UIMF was added into the basic selection index as an indicator trait for meat quality. In scenario 3, UIMF was also incorporated into index; however, the GEBV was used instead of phenotype. In scenario 4 and 5, selection was based strictly on the GEBV, and UIMF was included in scenario 5. The results showed that the accuracies of scenario 3, 4 and 5, in which GEBV information was used, increased with increasing accuracy of the GEBV. Moreover, the trends of scenario 4 and 5 changed more rapidly relative to scenario 3. The addition of UIMF to the selection index had a positive effect on the genetic gain of ADG and FCR, but a negative effect on UBF. The addition of UIMF to the selection index led to improvement of other traits and to the overall meat quality, especially when genomic selection was applied.

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 Accelerating Genetic Gain in Sugarcane Breeding Using Genomic Selection

Agronomy ◽  
2020 ◽  
Vol 10 (4) ◽  
pp. 585 ◽  
Author(s):  
Seema Yadav ◽  
Phillip Jackson ◽  
Xianming Wei ◽  
Elizabeth M. Ross ◽  
Karen Aitken ◽  
...  
Keyword(s):  
Genomic Selection ◽  
Genetic Gain ◽  
Cycle Length ◽  
Primary Source ◽  
Genetic Effects ◽  
Breeding Cycle ◽  
Narrow Sense Heritability ◽  
Breeding Values ◽  
Sugarcane Breeding ◽  
Additive Genetic Effects

Sugarcane is a major industrial crop cultivated in tropical and subtropical regions of the world. It is the primary source of sugar worldwide, accounting for more than 70% of world sugar consumption. Additionally, sugarcane is emerging as a source of sustainable bioenergy. However, the increase in productivity from sugarcane has been small compared to other major crops, and the rate of genetic gains from current breeding programs tends to be plateauing. In this review, some of the main contributors for the relatively slow rates of genetic gain are discussed, including (i) breeding cycle length and (ii) low narrow-sense heritability for major commercial traits, possibly reflecting strong non-additive genetic effects involved in quantitative trait expression. A general overview of genomic selection (GS), a modern breeding tool that has been very successfully applied in animal and plant breeding, is given. This review discusses key elements of GS and its potential to significantly increase the rate of genetic gain in sugarcane, mainly by (i) reducing the breeding cycle length, (ii) increasing the prediction accuracy for clonal performance, and (iii) increasing the accuracy of breeding values for parent selection. GS approaches that can accurately capture non-additive genetic effects and potentially improve the accuracy of genomic estimated breeding values are particularly promising for the adoption of GS in sugarcane breeding. Finally, different strategies for the efficient incorporation of GS in a practical sugarcane breeding context are presented. These proposed strategies hold the potential to substantially increase the rate of genetic gain in future sugarcane 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 New cycle, same old mistakes? Overlapping vs. discrete generations in long-term recurrent selection

2021 ◽  
Author(s):  
Marlee R. Labroo ◽  
Jessica E. Rutkoski
Keyword(s):  
Genetic Gain ◽  
Overlapping Generations ◽  
Recurrent Selection ◽  
Phenotypic Selection ◽  
Breeding Value ◽  
Breeding Values ◽  
Truncation Selection ◽  
Breeding Cycles ◽  
Error Bias

Background: Recurrent selection is a foundational breeding method for quantitative trait improvement. It typically features rapid breeding cycles that can lead to high rates of genetic gain. In recurrent phenotypic selection, generations do not overlap, which means that breeding candidates are evaluated and considered for selection for only one cycle. With recurrent genomic selection, candidates can be evaluated based on genomic estimated breeding values indefinitely, therefore facilitating overlapping generations. Candidates with true high breeding values that were discarded in one cycle due to underestimation of breeding value could be identified and selected in subsequent cycles. The consequences of allowing generations to overlap in recurrent selection are unknown. We assessed whether maintaining overlapping and discrete generations led to differences in genetic gain for phenotypic, genomic truncation, and genomic optimum contribution recurrent selection by simulation of traits with various heritabilities and genetic architectures across fifty breeding cycles. We also assessed differences of overlapping and discrete generations in a conventional breeding scheme with multiple stages and cohorts. Results: With phenotypic selection, overlapping generations led to decreased genetic gain compared to discrete generations due to increased selection error bias. Selected individuals, which were in the upper tail of the distribution of phenotypic values, tended to also have high absolute error relative to their true breeding value compared to the overall population. Without repeated phenotyping, these erroneously outlying individuals were repeatedly selected across cycles, leading to decreased genetic gain. With genomic truncation selection, overlapping and discrete generations performed similarly as updating breeding values precluded repeatedly selecting individuals with inaccurately high estimates of breeding values in subsequent cycles. Overlapping generations did not outperform discrete generations in the presence of a positive genetic trend with genomic truncation selection, as past generations had lower mean genetic values than the current generation of selection candidates. With genomic optimum contribution selection, overlapping and discrete generations performed similarly, but overlapping generations slightly outperformed discrete generations in the long term if the targeted inbreeding rate was extremely low. Conclusions: Maintaining discrete generations in recurrent phenotypic mass selection leads to increased genetic gain, especially at low heritabilities, by preventing selection error bias. With genomic truncation selection and genomic optimum contribution selection, genetic gain does not differ between discrete and overlapping generations assuming non-genetic effects are not present. Overlapping generations may increase genetic gain in the long term with very low targeted rates of inbreeding in genomic optimum contribution selection.

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 Identification of superior parental lines for biparental crossing via genomic prediction

PLoS ONE ◽  
2020 ◽  
Vol 15 (12) ◽  
pp. e0243159
Author(s):  
Ping-Yuan Chung ◽  
Chen-Tuo Liao
Keyword(s):  
Genetic Gain ◽  
Genomic Prediction ◽  
Genomic Diversity ◽  
Breeding Value ◽  
Selection Strategy ◽  
Parental Selection ◽  
Breeding Values ◽  
Truncation Selection ◽  
Selection Approach ◽  
Parental Lines

A parental selection approach based on genomic prediction has been developed to help plant breeders identify a set of superior parental lines from a candidate population before conducting field trials. A classical parental selection approach based on genomic prediction usually involves truncation selection, i.e., selecting the top fraction of accessions on the basis of their genomic estimated breeding values (GEBVs). However, truncation selection inevitably results in the loss of genomic diversity during the breeding process. To preserve genomic diversity, the selection of closely related accessions should be avoided during parental selection. We thus propose a new index to quantify the genomic diversity for a set of candidate accessions, and analyze two real rice (Oryza sativa L.) genome datasets to compare several selection strategies. Our results showed that the pure truncation selection strategy produced the best starting breeding value but the least genomic diversity in the base population, leading to less genetic gain. On the other hand, strategies that considered only genomic diversity resulted in greater genomic diversity but less favorable starting breeding values, leading to more genetic gain but unsatisfactorily performing recombination inbred lines (RILs) in progeny populations. Among all strategies investigated in this study, compromised strategies, which considered both GEBVs and genomic diversity, produced the best or second-best performing RILs mainly because these strategies balance the starting breeding value with the maintenance of genomic diversity.

scholarly journals The effects of training population design on genomic prediction accuracy in wheat

2018 ◽  
Author(s):  
Stefan McKinnon Edwards ◽  
Jaap B. Buntjer ◽  
Robert Jackson ◽  
Alison R. Bentley ◽  
Jacob Lage ◽  
...  
Keyword(s):  
Genomic Selection ◽  
Genetic Gain ◽  
Genomic Prediction ◽  
Genetic Material ◽  
Breeding Value ◽  
Training Set ◽  
Or Efficiency ◽  
Genomic Breeding Value ◽  
Close Relatives ◽  
Training Sets

AbstractGenomic selection offers several routes for increasing genetic gain or efficiency of plant breeding programs. In various species of livestock there is empirical evidence of increased rates of genetic gain from the use of genomic selection to target different aspects of the breeder’s equation. Accurate predictions of genomic breeding value are central to this and the design of training sets is in turn central to achieving sufficient levels of accuracy. In summary, small numbers of close relatives and very large numbers of distant relatives are expected to enable accurate predictions.To quantify the effect of some of the properties of training sets on the accuracy of genomic selection in crops we performed an extensive field-based winter wheat trial. In summary, this trial involved the construction of 44 F2:4 bi- and triparental populations, from which 2992 lines were grown on four field locations and yield was measured. For each line, genotype data were generated for 25,000 segregating single nucleotide polymorphism markers. The overall heritability of yield was estimated to 0.65, and estimates within individual families ranged between 0.10 and 0.85. Within cross genomic prediction accuracies of yield BLUEs were 0.125 – 0.127 using two different cross-validation approaches, and generally increased with training set size. Using related crosses in training and validation sets generally resulted in higher prediction accuracies than using unrelated crosses. The results of this study emphasize the importance of the training set design in relation to the genetic material to which the resulting prediction model is to be applied.

scholarly journals Effect of errors in pedigree on the accuracy of estimated breeding value for carcass traits in Korean Hanwoo cattle

2020 ◽  
Vol 33 (7) ◽  
pp. 1057-1067 ◽  
Author(s):  
Chiemela Peter Nwogwugwu ◽  
Yeongkuk Kim ◽  
Yun Ji Chung ◽  
Sung Bong Jang ◽  
Seung Hee Roh ◽  
...  
Keyword(s):  
Genetic Gain ◽  
Carcass Traits ◽  
Error Rates ◽  
Breeding Value ◽  
Marbling Score ◽  
Muscle Area ◽  
Data Set ◽  
Eye Muscle ◽  
Estimated Breeding Value ◽  
Hanwoo Cattle

Objective: This study evaluated the effect of pedigree errors (PEs) on the accuracy of estimated breeding value (EBV) and genetic gain for carcass traits in Korean Hanwoo cattle.Methods: The raw data set was based on the pedigree records of Korean Hanwoo cattle. The animals’ information was obtained using Hanwoo registration records from Korean animal improvement association database. The record comprised of 46,704 animals, where the number of the sires used was 1,298 and the dams were 38,366 animals. The traits considered were carcass weight (CWT), eye muscle area (EMA), back fat thickness (BFT), and marbling score (MS). Errors were introduced in the pedigree dataset through randomly assigning sires to all progenies. The error rates substituted were 5%, 10%, 20%, 30%, 40%, 50%, 60%, 70%, and 80%, respectively. A simulation was performed to produce a population of 1,650 animals from the pedigree data. A restricted maximum likelihood based animal model was applied to estimate the EBV, accuracy of the EBV, expected genetic gain, variance components, and heritability (h2) estimates for carcass traits. Correlation of the simulated data under PEs was also estimated using Pearson’s method.Results: The results showed that the carcass traits per slaughter year were not consistent. The average CWT, EMA, BFT, and MS were 342.60 kg, 78.76 cm<sup>2, 8.63 mm, and 3.31, respectively. When errors were introduced in the pedigree, the accuracy of EBV, genetic gain and h2 of carcass traits was reduced in this study. In addition, the correlation of the simulation was slightly affected under PEs.Conclusion: This study reveals the effect of PEs on the accuracy of EBV and genetic parameters for carcass traits, which provides valuable information for further study in Korean Hanwoo cattle.

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