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

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.

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Optimizing Low-Cost Genotyping and Imputation Strategies for Genomic Selection in Atlantic Salmon

G3 Genes|Genome|Genetics ◽

10.1534/g3.119.400800 ◽

2019 ◽

Vol 10 (2) ◽

pp. 581-590 ◽

Cited By ~ 4

Author(s):

Smaragda Tsairidou ◽

Alastair Hamilton ◽

Diego Robledo ◽

James E. Bron ◽

Ross D. Houston

Keyword(s):

Atlantic Salmon ◽

Genomic Selection ◽

Environmental Sustainability ◽

Genomic Prediction ◽

Prediction Accuracy ◽

Imputation Accuracy ◽

Cost Effective ◽

High Density ◽

Genotype Imputation ◽

Breeding Programs

Genomic selection enables cumulative genetic gains in key production traits such as disease resistance, playing an important role in the economic and environmental sustainability of aquaculture production. However, it requires genome-wide genetic marker data on large populations, which can be prohibitively expensive. Genotype imputation is a cost-effective method for obtaining high-density genotypes, but its value in aquaculture breeding programs which are characterized by large full-sibling families has yet to be fully assessed. The aim of this study was to optimize the use of low-density genotypes and evaluate genotype imputation strategies for cost-effective genomic prediction. Phenotypes and genotypes (78,362 SNPs) were obtained for 610 individuals from a Scottish Atlantic salmon breeding program population (Landcatch, UK) challenged with sea lice, Lepeophtheirus salmonis. The genomic prediction accuracy of genomic selection was calculated using GBLUP approaches and compared across SNP panels of varying densities and composition, with and without imputation. Imputation was tested when parents were genotyped for the optimal SNP panel, and offspring were genotyped for a range of lower density imputation panels. Reducing SNP density had little impact on prediction accuracy until 5,000 SNPs, below which the accuracy dropped. Imputation accuracy increased with increasing imputation panel density. Genomic prediction accuracy when offspring were genotyped for just 200 SNPs, and parents for 5,000 SNPs, was 0.53. This accuracy was similar to the full high density and optimal density dataset, and markedly higher than using 200 SNPs without imputation. These results suggest that imputation from very low to medium density can be a cost-effective tool for genomic selection in Atlantic salmon breeding programs.

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

Scientific Reports ◽

10.1038/s41598-020-77580-3 ◽

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.

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Selection of parents and estimation of genetic parameters using BLUP and molecular methods for lentil (Lens culinaris Medik.) breeding program in Argentina

Experimental Agriculture ◽

10.1017/s0014479719000061 ◽

2019 ◽

Vol 56 (1) ◽

pp. 12-25

Author(s):

Carolina Bermejo ◽

Federico Cazzola ◽

Fernando Maglia ◽

Enrique Cointry

Keyword(s):

Quantitative Traits ◽

Genetic Parameters ◽

Selection Process ◽

Recombinant Inbred Lines ◽

Correlation Coefficients ◽

Cost Effective ◽

Molecular Data ◽

Breeding Value ◽

Breeding Values ◽

High Yields

AbstractThe most important objective of lentil breeding programs is to develop new genotypes that are genetically more productive. Besides, it is necessary that the varieties obtained have short flowering cycles to allow the later sowing of summer crops. Selection is based through phenotypic means; however, we argue it should be based on genetic or breeding values because quantitative traits are often influenced by environments and genotype–environment interactions. The objectives of this study were to: (i) identify genotypes with the highest merit; (ii) estimate genetic parameters to know the genetic control of morphological traits in macrosperma and microsperma lentil types using best linear unbiased prediction (BLUP). Twenty-five recombinant inbred lines (RILs) from six F4 families selected on the basis of precocity and high yields were tested in four environments for important quantitative traits. The analysis of variance showed significant differences between genotypes, environments, and genotype–environment interactions for all the traits. Seven macrosperma- and two microsperma-type RILs were selected. Based on average ranking from breeding values and molecular data obtained with sequence-related amplified polymorphism (SRAP), the same genotypes were selected. Genotypic coefficients of variation, heritability across and by environment, and genetic correlation coefficients using BLUP were obtained. According to our results BLUP could replace molecular analysis methods because the selection process was simpler, more cost-effective, and more accurate. The breeding value of parents would give a better ranking of their genetic value than would their phenotypic value; therefore, the selection efficiency would be enhanced and the genetic gain would be more predictable. The selected genotypes could become potential commercial varieties or be used as parental lines in future hybridization programs.

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

TURKISH JOURNAL OF VETERINARY AND ANIMAL SCIENCES ◽

10.3906/vet-2001-52 ◽

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.

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Optimizing Genomic Selection in Dezhou Donkey Using Low Coverage Whole Genome Sequencing

10.21203/rs.3.rs-607740/v1 ◽

2021 ◽

Author(s):

Changheng Zhao ◽

Jun Teng ◽

Xinhao Zhang ◽

Dan Wang ◽

Xinyi Zhang ◽

...

Keyword(s):

Whole Genome Sequencing ◽

Genomic Selection ◽

Genome Sequencing ◽

Sequence Data ◽

Low Cost ◽

Imputation Accuracy ◽

Genotype Imputation ◽

Whole Genome Sequence ◽

Whole Genome ◽

Low Coverage

Abstract Background Low coverage whole genome sequencing is a low-cost genotyping technology. Combining with genotype imputation approaches, it is likely to become a critical component of cost-efficient genomic selection programs in agricultural livestock. Here, we used the low-coverage sequence data of 617 Dezhou donkeys to investigate the performance of genotype imputation for low coverage whole genome sequence data and genomic selection based on the imputed genotype data. The specific aims were: (i) to measure the accuracy of genotype imputation under different sequencing depths, sample sizes, MAFs, and imputation pipelines; and (ii) to assess the accuracy of genomic selection under different marker densities derived from the imputed sequence data, different strategies for constructing the genomic relationship matrixes, and single- vs multi-trait models. Results We found that a high imputation accuracy (> 0.95) can be achieved for sequence data with sequencing depth as low as 1x and the number of sequenced individuals equal to 400. For genomic selection, the best performance was obtained by using a marker density of 410K and a G matrix constructed using marker dosage information. Multi-trait GBLUP performed better than single-trait GBLUP. Conclusions Our study demonstrates that low coverage whole genome sequencing would be a cost-effective method for genomic selection in Dezhou Donkey.

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Application of genomic selection in agriculture

Bhartiya Krishi Anusandhan Patrika ◽

10.18805/bkap138 ◽

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.

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Identification of superior parental lines for biparental crossing via genomic prediction

PLoS ONE ◽

10.1371/journal.pone.0243159 ◽

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.

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

10.1101/443267 ◽

2018 ◽

Cited By ~ 1

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.

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

Animal Production Science ◽

10.1071/an11172 ◽

2012 ◽

Vol 52 (3) ◽

pp. 180 ◽

Cited By ~ 21

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.

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Genomic selection strategies for clonally propagated crops

10.1101/2020.06.15.152017 ◽

2020 ◽

Cited By ~ 1

Author(s):

Christian R. Werner ◽

R. Chris Gaynor ◽

Daniel J. Sargent ◽

Alessandra Lillo ◽

Gregor Gorjanc ◽

...

Keyword(s):

Genomic Selection ◽

Genetic Gain ◽

Genomic Prediction ◽

Breeding Program ◽

Selection Methods ◽

Breeding Programs ◽

Breeding Values ◽

Parent Selection ◽

Estimated Breeding Values ◽

Selection Of

AbstractFor genomic selection in clonal breeding programs to be effective, crossing parents should be selected based on genomic predicted cross performance unless dominance is negligible. Genomic prediction of cross performance enables a balanced exploitation of the additive and dominance value simultaneously. Here, we compared different strategies for the implementation of genomic selection in clonal plant breeding programs. We used stochastic simulations to evaluate six combinations of three breeding programs and two parent selection methods. The three breeding programs included i) a breeding program that introduced genomic selection in the first clonal testing stage, and ii) two variations of a two-part breeding program with one and three crossing cycles per year, respectively. The two parent selection methods were i) selection of parents based on genomic estimated breeding values, and ii) selection of parents based on genomic predicted cross performance. Selection of parents based on genomic predicted cross performance produced faster genetic gain than selection of parents based on genomic estimated breeding values because it substantially reduced inbreeding when the dominance degree increased. The two-part breeding programs with one and three crossing cycles per year using genomic prediction of cross performance always produced the most genetic gain unless dominance was negligible. We conclude that i) in clonal breeding programs with genomic selection, parents should be selected based on genomic predicted cross performance, and ii) a two-part breeding program with parent selection based on genomic predicted cross performance to rapidly drive population improvement has great potential to improve breeding clonally propagated crops.

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