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Obšteter J, Jenko J, Pocrnic I, Gorjanc G. Investigating the benefits and perils of importing genetic material in small cattle breeding programs via simulation. J Dairy Sci 2023; 106:S0022-0302(23)00385-5. [PMID: 37474361 DOI: 10.3168/jds.2022-23132] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/09/2022] [Accepted: 02/28/2023] [Indexed: 07/22/2023]
Abstract
Small breeding programs are limited in achieving competitive genetic gain and prone to high rates of inbreeding. Thus, they often import genetic material to increase genetic gain and to limit the loss of genetic variability. However, the benefit of import depends on the strength of genotype-by-environment interaction. Import also diminishes the relevance of domestic selection and the use of domestic breeding animals. Introduction of genomic selection has potentially exacerbated this issue, but is also opening the potential for smaller breeding programs. The aim of this paper was to determine when and to what extent small breeding programs benefit from importing genetic material by quantifying the genetic gain as well as the sources of genetic gain. We simulated 2 cattle breeding programs of the same breed that represented a large foreign and a small domestic breeding program. The programs differed in selection parameters of sire selection, and in the initial genetic mean and annual genetic gain. We evaluated a control scenario without the use of foreign sires in the domestic breeding program and 24 scenarios that varied the percentage of domestic dams mated with foreign sires, the genetic correlation between the breeding programs (0.8 or 0.9), and the time of implementing genomic selection in the domestic compared with the foreign breeding program (concurrently or with a 10-yr delay). We compared the scenarios based on the genetic gain and genic standard deviation. Finally, we partitioned breeding values and genetic trends of the scenarios to quantify the contribution of domestic selection and import to the domestic genetic gain. The simulation revealed that when both breeding programs implemented genomic selection simultaneously, the use of foreign sires increased domestic genetic gain only when genetic correlation was 0.9 (10%-18% increase). In contrast, when the domestic breeding program implemented genomic selection with a 10-yr delay, import increased genetic gain at both tested correlations, 0.8 (5%-23% increase) and 0.9 (15%-53% increase). The increase was significant when we mated at least 10% or 25% domestic females with foreign sires and increased with the increasing use of foreign sires, but with a diminishing return. The partitioning analysis revealed that the contribution of import expectedly increased with the increased use of foreign sires. However, the increase did not depend on the genetic correlation and was not proportional to the increase in domestic genetic gain. This represents a peril for small breeding programs because they could be overly relying on import with diminishing returns for the genetic gain, marginal benefit for the genetic variability, and large loss of the domestic germplasm. The benefit and peril of import depends on an interplay of genetic correlation, extent of using foreign sires, and a breeding scheme. It is therefore crucial that small breeding programs assess the possible benefits of import beyond domestic selection. The benefit of import should be weighed against the perils of decreased use of domestic sires and decreased contribution and value of domestic selection.
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Affiliation(s)
- J Obšteter
- Department of Animal Science, Agricultural Institute of Slovenia, 1000, Ljubljana, Slovenia.
| | - J Jenko
- Geno Breeding and A.I. Association, N-2317, Hamar, Norway
| | - I Pocrnic
- The Roslin Institute and Royal (Dick) School of Veterinary Studies, University of Edinburgh, EH25 9RG, Edinburgh, United Kingdom
| | - G Gorjanc
- The Roslin Institute and Royal (Dick) School of Veterinary Studies, University of Edinburgh, EH25 9RG, Edinburgh, United Kingdom; Biotechnical Faculty, University of Ljubljana, 1000, Ljubljana, Slovenia
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Brzáková M, Bauer J, Steyn Y, Šplíchal J, Fulínová D. The prediction accuracies of linear-type traits in Czech Holstein cattle when using ssGBLUP or wssGBLUP. J Anim Sci 2022; 100:skac369. [PMID: 36334266 PMCID: PMC9746800 DOI: 10.1093/jas/skac369] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/09/2022] [Accepted: 11/04/2022] [Indexed: 11/07/2022] Open
Abstract
The aim of this study was to assess the contribution of the weighted single-step genomic best linear unbiased prediction (wssGBLUP) method compared to the single-step genomic best linear unbiased prediction (ssGBLUP) method for genomic evaluation of 25 linear-type traits in the Czech Holstein cattle population. The nationwide database of linear-type traits with 6,99,681 records combined with deregressed proofs from Interbull (MACE method) was used as the input data. Genomic breeding values (GEBVs) were predicted based on these phenotypes using ssGBLUP and wssGBLUP methods using the BLUPF90 software. The bull validation test was employed which was based on comparing GEBVs of young bulls (N = 334) with no progeny in 2016. A minimum of 50 daughters with their own performance in 2020 was chosen to verify the contribution to the GEBV prediction, GEBV reliability, validation reliabilities (R2), and regression coefficients (b1). The results showed that the differences between the two methods were negligible. The low benefit of wssGBLUP may be due to the inclusion of a small number of SNPs; therefore, most predictions rely on polygenic relationships between animals. Nevertheless, the benefits of wssGBLUP analysis should be assessed with respect to specific population structures and given traits.
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Affiliation(s)
- Michaela Brzáková
- Department of Genetics and Breeding of Farm Animals, Institute of Animal Science, Prague-Uhříněves 104 00, Czech Republic
| | - Jiří Bauer
- Czech-Moravian Breeders’ Corporation, Hradištko 252 09, Czech Republic
| | - Yvette Steyn
- Department of Animal and Dairy Science, University of Georgia, Athens, GA, USA
| | - Jiří Šplíchal
- Czech-Moravian Breeders’ Corporation, Hradištko 252 09, Czech Republic
| | - Daniela Fulínová
- Czech-Moravian Breeders’ Corporation, Hradištko 252 09, Czech Republic
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Determining Heat Stress Effects of Multiple Genetic Traits in Tropical Dairy Cattle Using Single-Step Genomic BLUP. Vet Sci 2022; 9:vetsci9020066. [PMID: 35202319 PMCID: PMC8877667 DOI: 10.3390/vetsci9020066] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2021] [Revised: 01/28/2022] [Accepted: 02/02/2022] [Indexed: 12/04/2022] Open
Abstract
Heat stress is becoming a significant problem in dairy farming, especially in tropical countries, making accurate genetic selection for heat tolerance a priority. This study investigated the effect of heat stress manifestation on genetics for milk yield, milk quality, and dairy health traits with and without genomic information using single-step genomic best linear unbiased prediction (ssGBLUP) and BLUP in Thai−Holstein crossbred cows. The dataset contained 104,150 test-day records from the first lactation of 15,380 Thai−Holstein crossbred cows. A multiple-trait random regression test-day model on a temperature−humidity index (THI) function was used to estimate the genetic parameters and genetic values. Heat stress started at a THI of 76, and the heritability estimates ranged from moderate to low. The genetic correlation between those traits and heat stress in both BLUP methods was negative. The accuracy of genomic predictions in the ssGBLUP method was higher than the BLUP method. In conclusion, heat stress negatively impacted milk production, increased the somatic cell score, and disrupted the energy balance. Therefore, in dairy cattle genetic improvement programs, heat tolerance is an important trait. The new genetic evaluation method (ssGBLUP) should replace the traditional method (BLUP) for more accurate genetic selection.
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Campos GS, Cardoso FF, Gomes CCG, Domingues R, de Almeida Regitano LC, de Sena Oliveira MC, de Oliveira HN, Carvalheiro R, Albuquerque LG, Miller S, Misztal I, Lourenco D. Development of genomic predictions for Angus cattle in Brazil incorporating genotypes from related American sires. J Anim Sci 2022; 100:6507787. [PMID: 35031806 PMCID: PMC8867558 DOI: 10.1093/jas/skac009] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/06/2021] [Accepted: 01/12/2022] [Indexed: 11/24/2022] Open
Abstract
Genomic prediction has become the new standard for genetic improvement programs, and currently, there is a desire to implement this technology for the evaluation of Angus cattle in Brazil. Thus, the main objective of this study was to assess the feasibility of evaluating young Brazilian Angus (BA) bulls and heifers for 12 routinely recorded traits using single-step genomic BLUP (ssGBLUP) with and without genotypes from American Angus (AA) sires. The second objective was to obtain estimates of effective population size (Ne) and linkage disequilibrium (LD) in the Brazilian Angus population. The dataset contained phenotypic information for up to 277,661 animals belonging to the Promebo breeding program, pedigree for 362,900, of which 1,386 were genotyped for 50k, 77k, and 150k single nucleotide polymorphism (SNP) panels. After imputation and quality control, 61,666 SNPs were available for the analyses. In addition, genotypes from 332 American Angus (AA) sires widely used in Brazil were retrieved from the AA Association database to be used for genomic predictions. Bivariate animal models were used to estimate variance components, traditional EBV, and genomic EBV (GEBV). Validation was carried out with the linear regression method (LR) using young-genotyped animals born between 2013 and 2015 without phenotypes in the reduced dataset and with records in the complete dataset. Validation animals were further split into progeny of BA and AA sires to evaluate if their progenies would benefit by including genotypes from AA sires. The Ne was 254 based on pedigree and 197 based on LD, and the average LD (±SD) and distance between adjacent single nucleotide polymorphisms (SNPs) across all chromosomes were 0.27 (±0.27) and 40743.68 bp, respectively. Prediction accuracies with ssGBLUP outperformed BLUP for all traits, improving accuracies by, on average, 16% for BA young bulls and heifers. The GEBV prediction accuracies ranged from 0.37 (total maternal for weaning weight and tick count) to 0.54 (yearling precocity) across all traits, and dispersion (LR coefficients) fluctuated between 0.92 and 1.06. Inclusion of genotyped sires from the AA improved GEBV accuracies by 2%, on average, compared to using only the BA reference population. Our study indicated that genomic information could help us to improve GEBV accuracies and hence genetic progress in the Brazilian Angus population. The inclusion of genotypes from American Angus sires heavily used in Brazil just marginally increased the GEBV accuracies for selection candidates. There was a desire to implement genomic selection for Angus cattle in Brazil since the technology has been proved to increase genetic gain in animal breeding programs. Single-step genomic best linear unbiased prediction (ssGBLUP), which simultaneously combines pedigree and genomic information, was used to estimate individuals’ genomic breeding values (GEBV) or genetic merit. Genomic selection can accelerate genetic progress by increasing accuracy, especially in young animals without progeny. The accuracy of GEBV can also be improved by combing data from other countries to increase the reference population (i.e., genotyped and phenotyped animals) in small, genotyped populations. Thus, the main objective of this study was to evaluate the accuracy of GEBV for young Brazilian Angus (BA) bulls and heifers with ssGBLUP, including or not the genotypes from American Angus sires. The accuracies with ssGBLUP were higher than those from traditional BLUP (EBV calculated from pedigree), improving accuracies by, on average, 16% for young bulls and heifers. Including genotypes from American Angus sires heavily used in Brazil just marginally increased the GEBV accuracies for selection candidates.
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Affiliation(s)
- Gabriel Soares Campos
- Department of Animal and Dairy Science, University of Georgia, 30602, Athens, GA, USA
| | | | | | | | | | | | - Henrique Nunes de Oliveira
- Departamento de Zootecnia, Faculdade de Ciências Agrárias e Veterinárias, Universidade Estadual Paulista, 14884-900, Jaboticabal, SP, Brazil
| | - Roberto Carvalheiro
- Departamento de Zootecnia, Faculdade de Ciências Agrárias e Veterinárias, Universidade Estadual Paulista, 14884-900, Jaboticabal, SP, Brazil
| | - Lucia Galvão Albuquerque
- Departamento de Zootecnia, Faculdade de Ciências Agrárias e Veterinárias, Universidade Estadual Paulista, 14884-900, Jaboticabal, SP, Brazil
| | | | - Ignacy Misztal
- Department of Animal and Dairy Science, University of Georgia, 30602, Athens, GA, USA
| | - Daniela Lourenco
- Department of Animal and Dairy Science, University of Georgia, 30602, Athens, GA, USA
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Vu SV, Knibb W, Gondro C, Subramanian S, Nguyen NTH, Alam M, Dove M, Gilmour AR, Vu IV, Bhyan S, Tearle R, Khuong LD, Le TS, O'Connor W. Genomic Prediction for Whole Weight, Body Shape, Meat Yield, and Color Traits in the Portuguese Oyster Crassostrea angulata. Front Genet 2021; 12:661276. [PMID: 34306010 PMCID: PMC8298027 DOI: 10.3389/fgene.2021.661276] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2021] [Accepted: 06/07/2021] [Indexed: 11/30/2022] Open
Abstract
Genetic improvement for quality traits, especially color and meat yield, has been limited in aquaculture because the assessment of these traits requires that the animals be slaughtered first. Genotyping technologies do, however, provide an opportunity to improve the selection efficiency for these traits. The main purpose of this study is to assess the potential for using genomic information to improve meat yield (soft tissue weight and condition index), body shape (cup and fan ratios), color (shell and mantle), and whole weight traits at harvest in the Portuguese oyster, Crassostrea angulata. The study consisted of 647 oysters: 188 oysters from 57 full-sib families from the first generation and 459 oysters from 33 full-sib families from the second generation. The number per family ranged from two to eight oysters for the first and 12–15 oysters for the second generation. After quality control, a set of 13,048 markers were analyzed to estimate the genetic parameters (heritability and genetic correlation) and predictive accuracy of the genomic selection for these traits. The multi-locus mixed model analysis indicated high estimates of heritability for meat yield traits: 0.43 for soft tissue weight and 0.77 for condition index. The estimated genomic heritabilities were 0.45 for whole weight, 0.24 for cup ratio, and 0.33 for fan ratio and ranged from 0.14 to 0.54 for color traits. The genetic correlations among whole weight, meat yield, and body shape traits were favorably positive, suggesting that the selection for whole weight would have beneficial effects on meat yield and body shape traits. Of paramount importance is the fact that the genomic prediction showed moderate to high accuracy for the traits studied (0.38–0.92). Therefore, there are good prospects to improve whole weight, meat yield, body shape, and color traits using genomic information. A multi-trait selection program using the genomic information can boost the genetic gain and minimize inbreeding in the long-term for this population.
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Affiliation(s)
- Sang V Vu
- GeneCology Research Centre, University of the Sunshine Coast, Sippy Downs, QLD, Australia.,School of Science, Technology and Engineering, University of the Sunshine Coast, Sippy Downs, QLD, Australia.,Northern National Broodstock Center for Mariculture, Research Institute for Aquaculture Number 1, Hai Phong, Vietnam
| | - Wayne Knibb
- GeneCology Research Centre, University of the Sunshine Coast, Sippy Downs, QLD, Australia
| | - Cedric Gondro
- Department of Animal Science, College of Agriculture and Natural Resources, Michigan State University, East Lansing, MI, United States
| | - Sankar Subramanian
- GeneCology Research Centre, University of the Sunshine Coast, Sippy Downs, QLD, Australia.,School of Science, Technology and Engineering, University of the Sunshine Coast, Sippy Downs, QLD, Australia
| | - Ngoc T H Nguyen
- Northern National Broodstock Center for Mariculture, Research Institute for Aquaculture Number 1, Hai Phong, Vietnam
| | - Mobashwer Alam
- Queensland Alliance for Agriculture and Food Innovation, The University of Queensland, Saint Lucia, QLD, Australia
| | - Michael Dove
- NSW Department of Primary Industries, Port Stephens Fisheries Institute, Taylors Beach, NSW, Australia
| | | | - In Van Vu
- Northern National Broodstock Center for Mariculture, Research Institute for Aquaculture Number 1, Hai Phong, Vietnam
| | - Salma Bhyan
- GeneCology Research Centre, University of the Sunshine Coast, Sippy Downs, QLD, Australia.,School of Science, Technology and Engineering, University of the Sunshine Coast, Sippy Downs, QLD, Australia
| | - Rick Tearle
- School of Animal and Veterinary Science, The University of Adelaide, Adelaide, SA, Australia
| | - Le Duy Khuong
- Faculty of Environment, Ha Long University, Uong Bi, Vietnam
| | - Tuan Son Le
- Research Institute for Marine Fisheries, Ngo Quyen, Hai Phong, Vietnam
| | - Wayne O'Connor
- GeneCology Research Centre, University of the Sunshine Coast, Sippy Downs, QLD, Australia.,School of Science, Technology and Engineering, University of the Sunshine Coast, Sippy Downs, QLD, Australia.,NSW Department of Primary Industries, Port Stephens Fisheries Institute, Taylors Beach, NSW, Australia
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Luštrek B, Vandenplas J, Gorjanc G, Potočnik K. Genomic evaluation of Brown Swiss dairy cattle with limited national genotype data and integrated external information. J Dairy Sci 2021; 104:5738-5754. [PMID: 33685705 DOI: 10.3168/jds.2020-19493] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/18/2020] [Accepted: 01/07/2021] [Indexed: 11/19/2022]
Abstract
This study demonstrated the feasibility of a genomic evaluation for the dairy cattle population for which the small national training population can be complemented with foreign information from international evaluations. National test-day milk yield data records for the Slovenian Brown Swiss cattle population were analyzed. Genomic evaluation was carried out using the single-step genomic best linear unbiased prediction method (ssGBLUP), resulting in genomic estimated breeding values (GEBV). The predominantly female group of genotyped animals, representing the national training population in the single-step genomic evaluation, was further augmented with 7,024 genotypes of foreign progeny-tested sires from an international Brown Swiss InterGenomics genomic evaluation (https://interbull.org/ib/whole_cop). Additionally, the estimated breeding values for the altogether 7,246 genotyped domestic and foreign sires from the 2019 sire multiple across-country evaluation (MACE), were added to the ssGBLUP as external pseudophenotypic information. The ssGBLUP method, with integration of MACE information by avoiding double counting, was then performed, resulting in MACE-enhanced GEBV (GEBVM). The methods were empirically validated with forward prediction. The validation group consisted of 315 domestic males and 1,041 domestic females born after 2012. Increase, inflation, and bias of the GEBV(M) reliability (REL) were assessed for the validation group with a focus on females. All individuals in the validation benefited from genomic evaluations using both methods, but the GEBV(M) REL increased most for the youngest selection candidates. Up to 35 points of GEBV REL could be assigned to national genomic information, and up to 17 points of GEBVM REL could additionally be attributed to the integration of foreign sire genomic and MACE information. Results indicated that the combined foreign progeny-tested sire genomic and external MACE information can be used in the single-step genomic evaluation as an equivalent replacement for domestic phenotypic information. Thus, an equal or slightly higher genomic breeding value REL was obtained sooner than the pedigree-based breeding value REL for the female selection candidates. When the abundant foreign progeny-tested sire genomic and MACE information was used to complement available national genomic and phenotypic information in single-step genomic evaluation, the genomic breeding value REL for young-female selection candidates increased approximately 10 points. Use of international information provides the possibility to upgrade small national training populations and obtain satisfying reliability of genomic breeding values even for the youngest female selection candidates, which will help to increase selection efficiency in the future.
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Affiliation(s)
- B Luštrek
- Biotechnical Faculty, University of Ljubljana, 1000 Ljubljana, Slovenia.
| | - J Vandenplas
- Animal Breeding and Genomics, Wageningen University and Research, 6700 AH, Wageningen, the Netherlands
| | - G Gorjanc
- The Roslin Institute and Royal (Dick) School of Veterinary Studies, The University of Edinburgh, Easter Bush, Midlothian EH25 9RG, Scotland, United Kingdom; Biotechnical Faculty, University of Ljubljana, 1000 Ljubljana, Slovenia
| | - K Potočnik
- Biotechnical Faculty, University of Ljubljana, 1000 Ljubljana, Slovenia
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Chu TT, Bastiaansen JWM, Berg P, Romé H, Marois D, Henshall J, Jensen J. Use of genomic information to exploit genotype-by-environment interactions for body weight of broiler chicken in bio-secure and production environments. Genet Sel Evol 2019; 51:50. [PMID: 31533614 PMCID: PMC6751605 DOI: 10.1186/s12711-019-0493-3] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2019] [Accepted: 09/05/2019] [Indexed: 01/17/2023] Open
Abstract
BACKGROUND The increase in accuracy of prediction by using genomic information has been well-documented. However, benefits of the use of genomic information and methodology for genetic evaluations are missing when genotype-by-environment interactions (G × E) exist between bio-secure breeding (B) environments and commercial production (C) environments. In this study, we explored (1) G × E interactions for broiler body weight (BW) at weeks 5 and 6, and (2) the benefits of using genomic information for prediction of BW traits when selection candidates were raised and tested in a B environment and close relatives were tested in a C environment. METHODS A pedigree-based best linear unbiased prediction (BLUP) multivariate model was used to estimate variance components and predict breeding values (EBV) of BW traits at weeks 5 and 6 measured in B and C environments. A single-step genomic BLUP (ssGBLUP) model that combined pedigree and genomic information was used to predict EBV. Cross-validations were based on correlation, mean difference and regression slope statistics for EBV that were estimated from full and reduced datasets. These statistics are indicators of population accuracy, bias and dispersion of prediction for EBV of traits measured in B and C environments. Validation animals were genotyped and non-genotyped birds in the B environment only. RESULTS Several indications of G × E interactions due to environmental differences were found for BW traits including significant re-ranking, heterogeneous variances and different heritabilities for BW measured in environments B and C. The genetic correlations between BW traits measured in environments B and C ranged from 0.48 to 0.54. The use of combined pedigree and genomic information increased population accuracy of EBV, and reduced bias of EBV prediction for genotyped birds compared to the use of pedigree information only. A slight increase in accuracy of EBV was also observed for non-genotyped birds, but the bias of EBV prediction increased for non-genotyped birds. CONCLUSIONS The G × E interaction was strong for BW traits of broilers measured in environments B and C. The use of combined pedigree and genomic information increased population accuracy of EBV substantially for genotyped birds in the B environment compared to the use of pedigree information only.
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Affiliation(s)
- Thinh T. Chu
- Center for Quantitative Genetics and Genomics, Department of Molecular Biology and Genetics, Aarhus University, 8830 Tjele, Denmark
- Wageningen University & Research, Animal Breeding and Genomics, 6709 PG Wageningen, The Netherlands
- Faculty of Animal Science, Vietnam National University of Agriculture, Gia Lam, Hanoi, Vietnam
| | - John W. M. Bastiaansen
- Wageningen University & Research, Animal Breeding and Genomics, 6709 PG Wageningen, The Netherlands
| | - Peer Berg
- Center for Quantitative Genetics and Genomics, Department of Molecular Biology and Genetics, Aarhus University, 8830 Tjele, Denmark
- Department of Animal and Aquacultural Sciences, Norwegian University of Life Sciences, 1432 Ås, Norway
| | - Hélène Romé
- Center for Quantitative Genetics and Genomics, Department of Molecular Biology and Genetics, Aarhus University, 8830 Tjele, Denmark
| | - Danye Marois
- Cobb-Vantress Inc, Siloam Springs, AR 72761-1030 USA
| | - John Henshall
- Cobb-Vantress Inc, Siloam Springs, AR 72761-1030 USA
| | - Just Jensen
- Center for Quantitative Genetics and Genomics, Department of Molecular Biology and Genetics, Aarhus University, 8830 Tjele, Denmark
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Guarini A, Lourenco D, Brito L, Sargolzaei M, Baes C, Miglior F, Tsuruta S, Misztal I, Schenkel F. Use of a single-step approach for integrating foreign information into national genomic evaluation in Holstein cattle. J Dairy Sci 2019; 102:8175-8183. [DOI: 10.3168/jds.2018-15819] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/09/2018] [Accepted: 03/09/2019] [Indexed: 11/19/2022]
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Petrini J, Souza Iung LH, Petersen Rodriguez MA, Salvian M, Alberto Rovadoscki G, Colonia SRR, Cassoli LD, Lehmann Coutinho L, Fernando Machado P, Wiggans G, Mourão GB. Assessing the accuracy of prediction for milk fatty acids by using a small reference population of tropical Holstein cows. J Anim Breed Genet 2019; 136:453-463. [DOI: 10.1111/jbg.12434] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/18/2019] [Revised: 08/01/2019] [Accepted: 08/05/2019] [Indexed: 11/28/2022]
Affiliation(s)
- Juliana Petrini
- Department of Animal Science University of São Paulo Piracicaba Brazil
- Department of Statistics, Institute of Exact Sciences Federal University of Alfenas Alfenas Brazil
| | | | | | - Mayara Salvian
- Department of Animal Science University of São Paulo Piracicaba Brazil
| | | | | | | | | | | | - George Wiggans
- Animal Genomics and Improvement Laboratory, Agricultural Research Service USDA Beltsville Maryland
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Teissier M, Larroque H, Robert-Granie C. Accuracy of genomic evaluation with weighted single-step genomic best linear unbiased prediction for milk production traits, udder type traits, and somatic cell scores in French dairy goats. J Dairy Sci 2019; 102:3142-3154. [PMID: 30712939 DOI: 10.3168/jds.2018-15650] [Citation(s) in RCA: 24] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/05/2018] [Accepted: 12/05/2018] [Indexed: 01/17/2023]
Abstract
Genomic evaluation of French dairy goats is routinely conducted using the single-step genomic BLUP (ssGBLUP) method. This method has the advantage of simultaneously using all phenotypes, pedigrees, and genotypes. However, ssGBLUP assumes that all SNP explain the same amount of genetic variance, which is unlikely in the case of traits whose major genes or QTL are segregating. In this study, we investigated the effect of weighted ssGBLUP and its alternatives, which give more weight to SNP associated with the trait, on the accuracy of genomic evaluation of milk production, udder type traits, and somatic cell scores. The data set included 2,955 genotyped animals and 2,543,680 pedigree animals. The number of phenotypes varied with the trait. The accuracy of genomic evaluation was assessed on 205 genotyped Alpine and 146 genotyped Saanen goats born between 2009 and 2012. For traits with unknown QTL, weighted ssGBLUP was less accurate than, or as accurate as, ssGBLUP. For traits with identified QTL (i.e., QTL only present in the Saanen breed), weighted ssGBLUP outperformed ssGBLUP by between 2 and 14%.
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Affiliation(s)
- M Teissier
- GenPhySE, Université de Toulouse, INRA, INPT, ENVT, 31326 Castanet-Tolosan, France.
| | - H Larroque
- GenPhySE, Université de Toulouse, INRA, INPT, ENVT, 31326 Castanet-Tolosan, France
| | - C Robert-Granie
- GenPhySE, Université de Toulouse, INRA, INPT, ENVT, 31326 Castanet-Tolosan, France
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Zenger KR, Khatkar MS, Jones DB, Khalilisamani N, Jerry DR, Raadsma HW. Genomic Selection in Aquaculture: Application, Limitations and Opportunities With Special Reference to Marine Shrimp and Pearl Oysters. Front Genet 2019; 9:693. [PMID: 30728827 PMCID: PMC6351666 DOI: 10.3389/fgene.2018.00693] [Citation(s) in RCA: 61] [Impact Index Per Article: 12.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2018] [Accepted: 12/11/2018] [Indexed: 11/20/2022] Open
Abstract
Within aquaculture industries, selection based on genomic information (genomic selection) has the profound potential to change genetic improvement programs and production systems. Genomic selection exploits the use of realized genomic relationships among individuals and information from genome-wide markers in close linkage disequilibrium with genes of biological and economic importance. We discuss the technical advances, practical requirements, and commercial applications that have made genomic selection feasible in a range of aquaculture industries, with a particular focus on molluscs (pearl oysters, Pinctada maxima) and marine shrimp (Litopenaeus vannamei and Penaeus monodon). The use of low-cost genome sequencing has enabled cost-effective genotyping on a large scale and is of particular value for species without a reference genome or access to commercial genotyping arrays. We highlight the pitfalls and offer the solutions to the genotyping by sequencing approach and the building of appropriate genetic resources to undertake genomic selection from first-hand experience. We describe the potential to capture large-scale commercial phenotypes based on image analysis and artificial intelligence through machine learning, as inputs for calculation of genomic breeding values. The application of genomic selection over traditional aquatic breeding programs offers significant advantages through being able to accurately predict complex polygenic traits including disease resistance; increasing rates of genetic gain; minimizing inbreeding; and negating potential limiting effects of genotype by environment interactions. Further practical advantages of genomic selection through the use of large-scale communal mating and rearing systems are highlighted, as well as presenting rate-limiting steps that impact on attaining maximum benefits from adopting genomic selection. Genomic selection is now at the tipping point where commercial applications can be readily adopted and offer significant short- and long-term solutions to sustainable and profitable aquaculture industries.
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Affiliation(s)
- Kyall R Zenger
- Centre for Sustainable Tropical Fisheries and Aquaculture, College of Science and Engineering, James Cook University, Townsville, QLD, Australia.,ARC Research Hub for Advanced Prawn Breeding, James Cook University, Townsville, QLD, Australia
| | - Mehar S Khatkar
- ARC Research Hub for Advanced Prawn Breeding, James Cook University, Townsville, QLD, Australia.,Sydney School of Veterinary Science, Faculty of Science, The University of Sydney, Camden, NSW, Australia
| | - David B Jones
- Centre for Sustainable Tropical Fisheries and Aquaculture, College of Science and Engineering, James Cook University, Townsville, QLD, Australia
| | - Nima Khalilisamani
- ARC Research Hub for Advanced Prawn Breeding, James Cook University, Townsville, QLD, Australia.,Sydney School of Veterinary Science, Faculty of Science, The University of Sydney, Camden, NSW, Australia
| | - Dean R Jerry
- Centre for Sustainable Tropical Fisheries and Aquaculture, College of Science and Engineering, James Cook University, Townsville, QLD, Australia.,ARC Research Hub for Advanced Prawn Breeding, James Cook University, Townsville, QLD, Australia.,Tropical Futures Institute, James Cook University Singapore, Singapore, Singapore
| | - Herman W Raadsma
- ARC Research Hub for Advanced Prawn Breeding, James Cook University, Townsville, QLD, Australia.,Sydney School of Veterinary Science, Faculty of Science, The University of Sydney, Camden, NSW, Australia
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Perez BC, Balieiro JCC, Carvalheiro R, Tirelo F, Oliveira Junior GA, Dementshuk JM, Eler JP, Ferraz JBS, Ventura RV. Accounting for population structure in selective cow genotyping strategies. J Anim Breed Genet 2018; 136:23-39. [DOI: 10.1111/jbg.12369] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/15/2018] [Revised: 11/09/2018] [Accepted: 11/12/2018] [Indexed: 11/26/2022]
Affiliation(s)
- Bruno C. Perez
- Faculdade de Zootecnia e Engenharia de Alimentos; Universidade de São Paulo; Pirassununga Brasil
| | - Julio C. C. Balieiro
- Faculdade de Medicina Veterinária e Zootecnia; Universidade de São Paulo; Pirassununga Brasil
| | - Roberto Carvalheiro
- Departamento de Zootecnia; Universidade Estadual Paulista Julio de Mesquita Filho; Jaboticabal Brasil
| | | | - Gerson A. Oliveira Junior
- Centre for Genetic Improvement of Livestock, Department of Animal Biosciences; University of Guelph; Guelph ON Canada
| | - Juliana M. Dementshuk
- Departamento de Zootecnia; Universidade Federal do Rio Grande do Sul; Porto Alegre Brasil
| | - Joanir P. Eler
- Grupo de Melhoramento Animal e Biotecnologia, Departmento de Ciências Veterinárias, Faculdade de Zootecnia e Engenharia de Alimentos; Universidade de São Paulo (GMAB-FZEA/USP); Pirassununga Brasil
| | - José B. S. Ferraz
- Grupo de Melhoramento Animal e Biotecnologia, Departmento de Ciências Veterinárias, Faculdade de Zootecnia e Engenharia de Alimentos; Universidade de São Paulo (GMAB-FZEA/USP); Pirassununga Brasil
| | - Ricardo V. Ventura
- Faculdade de Medicina Veterinária e Zootecnia; Universidade de São Paulo; Pirassununga Brasil
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Teissier M, Larroque H, Robert-Granié C. 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. Genet Sel Evol 2018; 50:31. [PMID: 29907084 PMCID: PMC6003172 DOI: 10.1186/s12711-018-0400-3] [Citation(s) in RCA: 39] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2017] [Accepted: 05/30/2018] [Indexed: 12/21/2022] Open
Abstract
Background In 2017, genomic selection was implemented in French dairy goats using the single-step genomic best linear unbiased prediction (ssGBLUP) method, which assumes that all single nucleotide polymorphisms explain the same fraction of genetic variance. However, ssGBLUP is not suitable for protein content, which is controlled by a major gene, i.e. αs1casein. This gene explains about 40% of the genetic variation in protein content. In this study, we evaluated the accuracy of genomic prediction using different genomic methods to include the effect of the αs1casein gene. Methods Genomic evaluation for protein content was performed with data from the official genetic evaluation on 2955 animals genotyped with the Illumina goat SNP50 BeadChip, 7202 animals genotyped at the αs1casein gene and 6,767,490 phenotyped females. Pedigree-based BLUP was compared with regular unweighted ssGBLUP and with three weighted ssGBLUP methods (WssGBLUP, WssGBLUPMax and WssGBLUPSum), which give weights to SNPs according to their effect on protein content. Two other methods were also used: trait-specific marker-derived relationship matrix (TABLUP) using pre-selected SNPs associated with protein content and gene content based on a multiple-trait genomic model that includes αs1casein genotypes. We estimated accuracies of predicted genomic estimated breeding values (GEBV) in two populations of goats (Alpine and Saanen). Results Accuracies of GEBV with ssGBLUP improved by + 5 to + 7 percent points over accuracies from the pedigree-based BLUP model. With the WssGBLUP methods, SNPs that are located close to the αs1casein gene had the biggest weights and contributed substantially to the capture of signals from quantitative trait loci. Improvement in accuracy of genomic predictions using the three weighted ssGBLUP methods delivered up to + 6 percent points of accuracy over ssGBLUP. A similar accuracy was obtained for ssGBLUP and TABLUP considering the 20,000 most important SNPs. Incorporating information on the αs1casein genotypes based on the gene content method gave similar results as ssGBLUP. Conclusions The three weighted ssGBLUP methods were efficient for detecting SNPs associated with protein content and for a better prediction of genomic breeding values than ssGBLUP. They also combined fast computing, simplicity and required ssGBLUP to be run only twice.
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Affiliation(s)
- Marc Teissier
- GenPhySE, INRA, INPT, ENVT, Université de Toulouse, 31326, Castanet-Tolosan, France.
| | - Hélène Larroque
- GenPhySE, INRA, INPT, ENVT, Université de Toulouse, 31326, Castanet-Tolosan, France
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Shabalina T, Pimentel E, Edel C, Plieschke L, Emmerling R, Götz KU. Short communication: The role of genotypes from animals without phenotypes in single-step genomic evaluations. J Dairy Sci 2017; 100:8277-8281. [DOI: 10.3168/jds.2017-12734] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2017] [Accepted: 06/15/2017] [Indexed: 12/31/2022]
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