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Ajasa AA, Boison SA, Gjøen HM, Lillehammer M. Accuracy of genomic prediction using multiple Atlantic salmon populations. Genet Sel Evol 2024; 56:38. [PMID: 38750427 PMCID: PMC11094890 DOI: 10.1186/s12711-024-00907-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/10/2023] [Accepted: 05/06/2024] [Indexed: 05/19/2024] Open
Abstract
BACKGROUND The accuracy of genomic prediction is partly determined by the size of the reference population. In Atlantic salmon breeding programs, four parallel populations often exist, thus offering the opportunity to increase the size of the reference set by combining these populations. By allowing a reduction in the number of records per population, multi-population prediction can potentially reduce cost and welfare issues related to the recording of traits, particularly for diseases. In this study, we evaluated the accuracy of multi- and across-population prediction of breeding values for resistance to amoebic gill disease (AGD) using all single nucleotide polymorphisms (SNPs) on a 55K chip or a selected subset of SNPs based on the signs of allele substitution effect estimates across populations, using both linear and nonlinear genomic prediction (GP) models in Atlantic salmon populations. In addition, we investigated genetic distance, genetic correlation estimated based on genomic relationships, and persistency of linkage disequilibrium (LD) phase across these populations. RESULTS The genetic distance between populations ranged from 0.03 to 0.07, while the genetic correlation ranged from 0.19 to 0.99. Nonetheless, compared to within-population prediction, there was limited or no impact of combining populations for multi-population prediction across the various models used or when using the selected subset of SNPs. The estimates of across-population prediction accuracy were low and to some extent proportional to the genetic correlation estimates. The persistency of LD phase between adjacent markers across populations using all SNP data ranged from 0.51 to 0.65, indicating that LD is poorly conserved across the studied populations. CONCLUSIONS Our results show that a high genetic correlation and a high genetic relationship between populations do not guarantee a higher prediction accuracy from multi-population genomic prediction in Atlantic salmon.
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Affiliation(s)
- Afees A Ajasa
- Nofima (Norwegian Institute of Food, Fisheries and Aquaculture Research), PO Box 210, 1431, Ås, Norway.
- Department of Animal and Aquacultural Sciences, Norwegian University of Life Sciences, 1430, Ås, Norway.
| | | | - Hans M Gjøen
- Department of Animal and Aquacultural Sciences, Norwegian University of Life Sciences, 1430, Ås, Norway
| | - Marie Lillehammer
- Nofima (Norwegian Institute of Food, Fisheries and Aquaculture Research), PO Box 210, 1431, Ås, Norway
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Negro A, Cesarani A, Cortellari M, Bionda A, Fresi P, Macciotta NPP, Grande S, Biffani S, Crepaldi P. A comparison of genetic and genomic breeding values in Saanen and Alpine goats. Animal 2024; 18:101118. [PMID: 38508133 DOI: 10.1016/j.animal.2024.101118] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2023] [Revised: 02/17/2024] [Accepted: 02/20/2024] [Indexed: 03/22/2024] Open
Abstract
Nowadays, several countries are developing or adopting genomic selection in the dairy goat sector. The most used method to estimate breeding values is Single-Step Genomic Best Linear Unbiased Prediction (ssGBLUP) which offers several advantages in terms of computational process and accuracy of the estimated breeding values (EBVs). Saanen and Alpine are the predominant dairy goat breeds in Italy, and both have similar breeding programs where EBVs for productive traits are currently calculated using BLUP. This work describes the implementation of genomic selection for these two breeds in Italy, aligning with the selection practices already carried out in the international landscape. The available dataset included 3 611 genotyped animals, 11 470 lactation records, five traits (milk, protein and fat yields, and fat and protein percentages), and three-generation pedigrees. EBVs were estimated using BLUP, GBLUP, and ssGBLUP both with single and multiple trait approaches. The methods were compared in terms of correlation between EBVs and genetic trends. Results were also validated with the linear regression method excluding part of the phenotypic data. In both breeds, EBVs and GEBVs were strongly correlated and the trend of each trait was similar comparing the three methods. The average increase in accuracy across traits and methods amounted to +13 and +10% from BLUP to ssGBLUP for Alpine and Saanen breeds, respectively. Results indicated higher prediction accuracy and correlation for GBLUP and ssGBLUP compared to BLUP, implying that the use of genotypes increases the accuracy of EBVs, particularly in the absence of phenotypic data. Therefore, ssGBLUP is likely to be the most effective method to enhance genetic gain in Italian Saanen and Alpine goats.
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Affiliation(s)
- A Negro
- Ufficio Studi, Associazione Nazionale della Pastorizia, 00187 Rome, Italy; Dipartimento di Scienze Agrarie e alimentari, Università degli studi di Milano, 20133 Milan, Italy
| | - A Cesarani
- Dipartimento di Scienze Agrarie, Università degli Studi di Sassari, 07100 Sassari, Italy; Department of Animal and Dairy Science, University of Georgia, Athens, GA, USA
| | - M Cortellari
- Dipartimento di Scienze Agrarie e alimentari, Università degli studi di Milano, 20133 Milan, Italy
| | - A Bionda
- Dipartimento di Scienze Agrarie e alimentari, Università degli studi di Milano, 20133 Milan, Italy.
| | - P Fresi
- Ufficio Studi, Associazione Nazionale della Pastorizia, 00187 Rome, Italy
| | - N P P Macciotta
- Dipartimento di Scienze Agrarie, Università degli Studi di Sassari, 07100 Sassari, Italy
| | - S Grande
- Ufficio Studi, Associazione Nazionale della Pastorizia, 00187 Rome, Italy
| | - S Biffani
- Istituto di Biologia e Biotecnologia, Consiglio Nazionale delle Ricerche, 20133 Milan, Italy
| | - P Crepaldi
- Dipartimento di Scienze Agrarie e alimentari, Università degli studi di Milano, 20133 Milan, Italy
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Teissier M, Brito LF, Schenkel FS, Bruni G, Fresi P, Bapst B, Robert-Granie C, Larroque H. Genetic parameters for milk production and type traits in North American and European Alpine and Saanen dairy goat populations. JDS COMMUNICATIONS 2024; 5:28-32. [PMID: 38223387 PMCID: PMC10785233 DOI: 10.3168/jdsc.2023-0389] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 03/15/2023] [Accepted: 08/18/2023] [Indexed: 01/16/2024]
Abstract
The development of an across-country genomic evaluation scheme is a promising alternative for enlarging reference populations and successfully implementing genomic selection in small ruminant populations. However, the feasibility of such evaluations depends on the genetic similarity among the populations, and therefore, high connectedness and high genetic correlations between the traits recorded in different countries or populations are needed. In this study, we evaluated the feasibility of performing an across-country genomic evaluation for milk production and type traits in Alpine and Saanen goats from Canada, France, Italy, and Switzerland. Variance components and genetic parameters, including genetic correlations between traits recorded in different countries, were calculated using combined phenotypes, genotypes, and pedigree datasets. The (co)variance component analyses were performed within breed, either based only on pedigree information or also incorporating genomic information. Across-country genetic parameters were calculated for 3 representative traits (i.e., milk yield, fat content, and rear udder attachment). The heritability estimates ranged from 0.10 to 0.50, which are consistent with previous estimates reported in the literature. The genetic correlations for rear udder attachment ranged from 0.75 (between France and Italy, for the Alpine breed without genomic information) to 0.95 (between Canada and France, for the Saanen breed with genomic information), whereas for fat content, between France and Italy, they ranged from 0.75 in the Alpine breed without genomic information to 0.78 in the Alpine breed with genomic information. However, genetic correlations for milk yield were only estimable between France and Italy, with a moderate value of 0.45 for the Alpine breed with or without genomic information, and of 0.22 and 0.26 in the Saanen breed with and without genomic information, respectively. These low genetic correlations for milk yield could be due to several factors, including the trait definition in each country and genotype-by-environment interactions (GxE). The high genetic correlations found for fat content and rear udder attachment indicate that these traits might be more standardized across countries and less affected by GxE effects. Thus, an international genomic evaluation for these traits might be feasible. Further studies should be performed to understand the surprisingly lower genetic correlations between milk yield across countries. Furthermore, additional efforts should be made to increase the genetic connection among the Alpine and Saanen goat populations in the 4 countries included in the analyses.
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Affiliation(s)
- Marc Teissier
- GenPhySE, Université de Toulouse, INRAE, ENVT, F-31326, Castanet-Tolosan, France
| | - Luiz F. Brito
- Department of Animal Sciences, Purdue University, West Lafayette, IN 47907
- Centre for Genetic Improvement of Livestock, Department of Animal Biosciences, University of Guelph, Guelph, ON, Canada, N1G-2W1
| | - Flavio S. Schenkel
- Centre for Genetic Improvement of Livestock, Department of Animal Biosciences, University of Guelph, Guelph, ON, Canada, N1G-2W1
| | | | | | | | | | - Hélène Larroque
- GenPhySE, Université de Toulouse, INRAE, ENVT, F-31326, Castanet-Tolosan, France
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4
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Teissier M, Brito LF, Schenkel FS, Bruni G, Fresi P, Bapst B, Robert-Granie C, Larroque H. Genetic Characterization and Population Connectedness of North American and European Dairy Goats. Front Genet 2022; 13:862838. [PMID: 35783257 PMCID: PMC9247305 DOI: 10.3389/fgene.2022.862838] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/26/2022] [Accepted: 05/03/2022] [Indexed: 12/26/2022] Open
Abstract
Genomic prediction of breeding values is routinely performed in several livestock breeding programs around the world, but the size of the training populations and the genetic structure of populations evaluated have, in many instances, limited the increase in the accuracy of genomic estimated breeding values. Combining phenotypic, pedigree, and genomic data from genetically related populations can be a feasible strategy to overcome this limitation. However, the success of across-population genetic evaluations depends on the pedigree connectedness and genetic relationship among individuals from different populations. In this context, this study aimed to evaluate the genetic connectedness and population structure of Alpine and Saanen dairy goats from four countries involved in the European project SMARTER (SMAll RuminanTs Breeding for Efficiency and Resilience), including Canada, France, Italy, and Switzerland. These analyses are paramount for assessing the potential feasibility of an across-country genomic evaluation in dairy goats. Approximately, 9,855 genotyped individuals (with 51% French genotyped animals) and 6,435,189 animals included in the pedigree files were available across all four populations. The pedigree analyses indicated that the exchange of breeding animals was mainly unilateral with flows from France to the other three countries. Italy has also imported breeding animals from Switzerland. Principal component analyses (PCAs), genetic admixture analysis, and consistency of the gametic phase revealed that French and Italian populations are more genetically related than the other dairy goat population pairs. Canadian dairy goats showed the largest within-breed heterogeneity and genetic differences with the European populations. The genetic diversity and population connectedness between the studied populations indicated that an international genomic evaluation may be more feasible, especially for French and Italian goats. Further studies will investigate the accuracy of genomic breeding values when combining the datasets from these four populations.
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Affiliation(s)
- Marc Teissier
- GenPhySE, Université de Toulouse, Toulouse, France
- *Correspondence: Marc Teissier,
| | - Luiz F. Brito
- Department of Animal Sciences, Purdue University, West Lafayette, IN, United States
- Department of Animal Biosciences, Centre for Genetic Improvement of Livestock, University of Guelph, Guelph, ON, Canada
| | - Flavio S. Schenkel
- Department of Animal Biosciences, Centre for Genetic Improvement of Livestock, University of Guelph, Guelph, ON, Canada
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Massender E, Brito LF, Maignel L, Oliveira HR, Jafarikia M, Baes CF, Sullivan B, Schenkel FS. Single- and multiple-breed genomic evaluations for conformation traits in Canadian Alpine and Saanen dairy goats. J Dairy Sci 2022; 105:5985-6000. [DOI: 10.3168/jds.2021-21713] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2021] [Accepted: 03/10/2022] [Indexed: 11/19/2022]
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Massender E, Brito LF, Maignel L, Oliveira HR, Jafarikia M, Baes CF, Sullivan B, Schenkel FS. Single-step genomic evaluation of milk production traits in Canadian Alpine and Saanen dairy goats. J Dairy Sci 2022; 105:2393-2407. [PMID: 34998569 DOI: 10.3168/jds.2021-20558] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2021] [Accepted: 11/09/2021] [Indexed: 12/11/2022]
Abstract
Genomic evaluations are routine in most plant and livestock breeding programs but are used infrequently in dairy goat breeding schemes. In this context, the purpose of this study was to investigate the use of the single-step genomic BLUP method for predicting genomic breeding values for milk production traits (milk, protein, and fat yields; protein and fat percentages) in Canadian Alpine and Saanen dairy goats. There were 6,409 and 12,236 Alpine records and 3,434 and 5,008 Saanen records for each trait in first and later lactations, respectively, and a total of 1,707 genotyped animals (833 Alpine and 874 Saanen). Two validation approaches were used, forward validation (i.e., animals born after 2013 with an average estimated breeding value accuracy from the full data set ≥0.50) and forward cross-validation (i.e., subsets of all animals included in the forward validation were used in successive replications). The forward cross-validation approach resulted in similar validation accuracies (0.55 to 0.66 versus 0.54 to 0.61) and biases (-0.01 to -0.07 versus -0.03 to 0.11) to the forward validation when averaged across traits. Additionally, both single and multiple-breed analyses were compared, and similar average accuracies and biases were observed across traits. However, there was a small gain in accuracy from the use of multiple-breed models for the Saanen breed. A small gain in validation accuracy for genomically enhanced estimated breeding values (GEBV) relative to pedigree-based estimated breeding values (EBV) was observed across traits for the Alpine breed, but not for the Saanen breed, possibly due to limitations in the validation design, heritability of the traits evaluated, and size of the training populations. Trait-specific gains in theoretical accuracy of GEBV relative to EBV for the validation animals ranged from 17 to 31% in Alpine and 35 to 55% in Saanen, using the cross-validation approach. The GEBV predicted from the full data set were 12 to 16% more accurate than EBV for genotyped animals, but no gains were observed for nongenotyped animals. The largest gains were found for does without lactation records (35-41%) and bucks without daughter records (46-54%), and consequently, the implementation of genomic selection in the Canadian dairy goat population would be expected to increase selection accuracy for young breeding candidates. Overall, this study represents the first step toward implementation of genomic selection in Canadian dairy goat populations.
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Affiliation(s)
- Erin Massender
- Centre for Genetic Improvement of Livestock, Department of Animal Biosciences, University of Guelph, Guelph, ON, Canada, N1G 2W1.
| | - Luiz F Brito
- Centre for Genetic Improvement of Livestock, Department of Animal Biosciences, University of Guelph, Guelph, ON, Canada, N1G 2W1; Department of Animal Sciences, Purdue University, West Lafayette, IN 47907
| | - Laurence Maignel
- Canadian Centre for Swine Improvement Inc., Ottawa, ON, Canada, K1A 0C6
| | - Hinayah R Oliveira
- Centre for Genetic Improvement of Livestock, Department of Animal Biosciences, University of Guelph, Guelph, ON, Canada, N1G 2W1; Department of Animal Sciences, Purdue University, West Lafayette, IN 47907
| | - Mohsen Jafarikia
- Centre for Genetic Improvement of Livestock, Department of Animal Biosciences, University of Guelph, Guelph, ON, Canada, N1G 2W1; Canadian Centre for Swine Improvement Inc., Ottawa, ON, Canada, K1A 0C6
| | - Christine F Baes
- Centre for Genetic Improvement of Livestock, Department of Animal Biosciences, University of Guelph, Guelph, ON, Canada, N1G 2W1; Institute of Genetics, Vetsuisse Faculty, University of Bern, 3001 Bern, Switzerland
| | - Brian Sullivan
- Canadian Centre for Swine Improvement Inc., Ottawa, ON, Canada, K1A 0C6
| | - Flavio S Schenkel
- Centre for Genetic Improvement of Livestock, Department of Animal Biosciences, University of Guelph, Guelph, ON, Canada, N1G 2W1
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Naserkheil M, Mehrban H, Lee D, Park MN. Evaluation of Genome-Enabled Prediction for Carcass Primal Cut Yields Using Single-Step Genomic Best Linear Unbiased Prediction in Hanwoo Cattle. Genes (Basel) 2021; 12:genes12121886. [PMID: 34946834 PMCID: PMC8701981 DOI: 10.3390/genes12121886] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/12/2021] [Revised: 11/16/2021] [Accepted: 11/24/2021] [Indexed: 11/16/2022] Open
Abstract
There is a growing interest worldwide in genetically selecting high-value cut carcass weights, which allows for increased profitability in the beef cattle industry. Primal cut yields have been proposed as a potential indicator of cutability and overall carcass merit, and it is worthwhile to assess the prediction accuracies of genomic selection for these traits. This study was performed to compare the prediction accuracy obtained from a conventional pedigree-based BLUP (PBLUP) and a single-step genomic BLUP (ssGBLUP) method for 10 primal cut traits-bottom round, brisket, chuck, flank, rib, shank, sirloin, striploin, tenderloin, and top round-in Hanwoo cattle with the estimators of the linear regression method. The dataset comprised 3467 phenotypic observations for the studied traits and 3745 genotyped individuals with 43,987 single-nucleotide polymorphisms. In the partial dataset, the accuracies ranged from 0.22 to 0.30 and from 0.37 to 0.54 as evaluated using the PBLUP and ssGBLUP models, respectively. The accuracies of PBLUP and ssGBLUP with the whole dataset varied from 0.45 to 0.75 (average 0.62) and from 0.52 to 0.83 (average 0.71), respectively. The results demonstrate that ssGBLUP performed better than PBLUP averaged over the 10 traits, in terms of prediction accuracy, regardless of considering a partial or whole dataset. Moreover, ssGBLUP generally showed less biased prediction and a value of dispersion closer to 1 than PBLUP across the studied traits. Thus, the ssGBLUP seems to be more suitable for improving the accuracy of predictions for primal cut yields, which can be considered a starting point in future genomic evaluation for these traits in Hanwoo breeding practice.
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Affiliation(s)
- Masoumeh Naserkheil
- Animal Breeding and Genetics Division, National Institute of Animal Science, Cheonan-si 31000, Chungcheongnam-do, Korea;
| | - Hossein Mehrban
- Department of Animal Science, Shahrekord University, Shahrekord 88186-34141, Iran;
| | - Deukmin Lee
- Department of Animal Life and Environment Sciences, Hankyong National University, Jungang-ro 327, Anseong-si 17579, Gyeonggi-do, Korea
- Correspondence: (D.L.); (M.N.P.); Tel.: +82-31-670-5091 (D.L.); +82-41-580-3355 (M.N.P.)
| | - Mi Na Park
- Animal Breeding and Genetics Division, National Institute of Animal Science, Cheonan-si 31000, Chungcheongnam-do, Korea;
- Correspondence: (D.L.); (M.N.P.); Tel.: +82-31-670-5091 (D.L.); +82-41-580-3355 (M.N.P.)
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9
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Oliveira HRD, McEwan JC, Jakobsen JH, Blichfeldt T, Meuwissen THE, Pickering NK, Clarke SM, Brito LF. Across-country genomic predictions in Norwegian and New Zealand Composite sheep populations with similar development history. J Anim Breed Genet 2021; 139:1-12. [PMID: 34418183 DOI: 10.1111/jbg.12642] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/25/2021] [Revised: 05/29/2021] [Accepted: 08/06/2021] [Indexed: 01/11/2023]
Abstract
The goal of this study was to assess the feasibility of across-country genomic predictions in Norwegian White Sheep (NWS) and New Zealand Composite (NZC) sheep populations with similar development history. Different training populations were evaluated (i.e., including only NWS or NZC, or combining both populations). Predictions were performed using the actual phenotypes (normalized) and the single-step GBLUP via Bayesian inference. Genotyped NWS animals born in 2016 (N = 267) were used to assess the accuracy and bias of genomic estimated breeding values (GEBVs) predicted for birth weight (BW), weaning weight (WW), carcass weight (CW), EUROP carcass classification (EUC), and EUROP fat grading (EUF). The accuracy and bias of GEBVs differed across traits and training population used. For instance, the GEBV accuracies ranged from 0.13 (BW) to 0.44 (EUC) for GEBVs predicted including only NWS, from 0.06 (BW) to 0.15 (CW) when including only NZC, and from 0.10 (BW) to 0.41 (EUC) when including both NWS and NZC animals in the training population. The regression coefficients used to assess the spread of GEBVs (bias) ranged from 0.26 (BW) to 0.64 (EUF) for only NWS, 0.10 (EUC) to 0.52 (CW) for only NZC, and from 0.42 (WW) to 2.23 (EUC) for both NWS and NZC in the training population. Our findings suggest that across-country genomic predictions based on ssGBLUP might be possible for NWS and NZC, especially for novel traits.
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Affiliation(s)
- Hinayah Rojas de Oliveira
- Department of Animal Sciences, Purdue University, West Lafayette, IN, USA.,Department of Animal Biosciences, Centre for Genetic Improvement of Livestock (CGIL), University of Guelph, Guelph, ON, Canada
| | - John C McEwan
- AgResearch Limited, Invermay Agricultural Centre, Mosgiel, New Zealand
| | - Jette H Jakobsen
- The Norwegian Association of Sheep and Goat Breeders, Ås, Norway
| | - Thor Blichfeldt
- The Norwegian Association of Sheep and Goat Breeders, Ås, Norway
| | - Theo H E Meuwissen
- Department of Animal and Aquacultural Sciences, Norwegian University of Life Sciences, Ås, Norway
| | | | - Shannon M Clarke
- AgResearch Limited, Invermay Agricultural Centre, Mosgiel, New Zealand
| | - Luiz F Brito
- Department of Animal Sciences, Purdue University, West Lafayette, IN, USA.,Department of Animal Biosciences, Centre for Genetic Improvement of Livestock (CGIL), University of Guelph, Guelph, ON, Canada
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Mehrban H, Naserkheil M, Lee D, Ibáñez-Escriche N. Multi-Trait Single-Step GBLUP Improves Accuracy of Genomic Prediction for Carcass Traits Using Yearling Weight and Ultrasound Traits in Hanwoo. Front Genet 2021; 12:692356. [PMID: 34394186 PMCID: PMC8363309 DOI: 10.3389/fgene.2021.692356] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/08/2021] [Accepted: 07/02/2021] [Indexed: 11/25/2022] Open
Abstract
There has been a growing interest in the genetic improvement of carcass traits as an important and primary breeding goal in the beef cattle industry over the last few decades. The use of correlated traits and molecular information can aid in obtaining more accurate estimates of breeding values. This study aimed to assess the improvement in the accuracy of genetic predictions for carcass traits by using ultrasound measurements and yearling weight along with genomic information in Hanwoo beef cattle by comparing four evaluation models using the estimators of the recently developed linear regression method. We compared the performance of single-trait pedigree best linear unbiased prediction [ST-BLUP and single-step genomic (ST-ssGBLUP)], as well as multi-trait (MT-BLUP and MT-ssGBLUP) models for the studied traits at birth and yearling date of steers. The data comprised of 15,796 phenotypic records for yearling weight and ultrasound traits as well as 5,622 records for carcass traits (backfat thickness, carcass weight, eye muscle area, and marbling score), resulting in 43,949 single-nucleotide polymorphisms from 4,284 steers and 2,332 bulls. Our results indicated that averaged across all traits, the accuracy of ssGBLUP models (0.52) was higher than that of pedigree-based BLUP (0.34), regardless of the use of single- or multi-trait models. On average, the accuracy of prediction can be further improved by implementing yearling weight and ultrasound data in the MT-ssGBLUP model (0.56) for the corresponding carcass traits compared to the ST-ssGBLUP model (0.49). Moreover, this study has shown the impact of genomic information and correlated traits on predictions at the yearling date (0.61) using MT-ssGBLUP models, which was advantageous compared to predictions at birth date (0.51) in terms of accuracy. Thus, using genomic information and high genetically correlated traits in the multi-trait model is a promising approach for practical genomic selection in Hanwoo cattle, especially for traits that are difficult to measure.
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Affiliation(s)
- Hossein Mehrban
- Department of Animal Science, Shahrekord University, Shahrekord, Iran
| | - Masoumeh Naserkheil
- Department of Animal Science, University College of Agriculture and Natural Resources, University of Tehran, Karaj, Iran.,Department of Animal Life and Environment Sciences, Hankyong National University, Gyeonggi-do, South Korea
| | - Deukhwan Lee
- Department of Animal Life and Environment Sciences, Hankyong National University, Gyeonggi-do, South Korea
| | - Noelia Ibáñez-Escriche
- Institute for Animal Science and Technology, Universitat Politècnica de València, València, Spain
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de Sousa DR, do Nascimento AV, Lôbo RNB. Prediction of genomic breeding values of milk traits in Brazilian Saanen goats. J Anim Breed Genet 2021; 138:541-551. [PMID: 33861884 DOI: 10.1111/jbg.12550] [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: 11/18/2020] [Revised: 03/17/2021] [Accepted: 03/22/2021] [Indexed: 11/28/2022]
Abstract
The study's objective was to compare the genomic prediction ability methods for the traits milk yield, milk composition and somatic cell count of Saanen Brazilian goats. Nine hundred forty goats, genotyped with an Axiom_OviCap (Caprine) panel, Affimetrix customized array with 62,557 single nucleotide polymorphisms (SNPs), were used for the genomic selection analyses. The genomic methods studied to estimate the effects of SNPs and direct genomic values (DGV) were as follows: (a) genomic BLUP (GBLUP), (b) Bayes Cπ and (c) Bayesian Lasso (BLASSO). Estimated breeding values (EBV) and deregressed estimated breeding values (dEBV) were used as response variables for the genomic predictions. The prediction ability was assessed by Pearson's correlation between DGV and response variables (EBV and dEBV). Regression coefficients of the response variables on the DGV were obtained to verify if the genomic predictions were biased. In addition, the mean square error of prediction (MSE) was used as a measure of verification of model fit to the data. The means of prediction accuracy, when EBV was used as a response variable, were 0.68, 0.68 and 0.67 for GBLUP, Bayes Cπ and BLASSO, respectively. With dEBV, the mean prediction accuracy was 0.50 for all models. The averages of the EBV regression coefficients on DGV were 1.08 for all models (GBLUP, Bayes Cπ and BLASSO), higher than those obtained for the regression coefficient of dEBV on DGV, which presented values of 1.05, 1.05 and 1.08 for GBLUP, Bayes Cπ and BLASSO, respectively. None of the methods stood out in terms of prediction ability; however, the GBLUP method was the most appropriate for estimating the DGV, in a slightly more reliable and less biased way, besides presenting the lowest computational cost. In the context of the present study, EBV was the preferred response variables considering the genomic prediction accuracy despite dEBV also presented lower bias.
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Affiliation(s)
| | - André Vieira do Nascimento
- Faculty of Agricultural and Veterinary Sciences of Jaboticabal. Animal Sciences Department I, São Paulo State University "Júlio de Mesquita Filho", Jaboticabal, Brazil
| | - Raimundo Nonato Braga Lôbo
- Animal Sciences Department, Federal University of Ceará, Fortaleza, Brazil.,Brazilian Agricultural Research Corporation - EMBRAPA, Embrapa Caprinos e Ovinos, Estrada Sobral/Groaíras, Sobral, Brazil.,National Council for Scientific and Technological Development - CNPq, Lago Sul, Brazil
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Duenk P, Bijma P, Wientjes YCJ, Calus MPL. Predicting the purebred-crossbred genetic correlation from the genetic variance components in the parental lines. Genet Sel Evol 2021; 53:10. [PMID: 33541267 PMCID: PMC7860586 DOI: 10.1186/s12711-021-00601-w] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/17/2020] [Accepted: 01/08/2021] [Indexed: 01/24/2023] Open
Abstract
Background The genetic correlation between purebred and crossbred performance (\documentclass[12pt]{minimal}
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\begin{document}$${r}_{pc}$$\end{document}rpc) is an important parameter in pig and poultry breeding, because response to selection in crossbred performance depends on the value of \documentclass[12pt]{minimal}
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\begin{document}$${r}_{pc}$$\end{document}rpc when selection is based on purebred (PB) performance. The value of \documentclass[12pt]{minimal}
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\begin{document}$${r}_{pc}$$\end{document}rpc can be substantially lower than 1, which is partly due to differences in allele frequencies between parental lines when non-additive genetic effects are present. This relationship between \documentclass[12pt]{minimal}
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\begin{document}$${r}_{pc}$$\end{document}rpc and parental allele frequencies suggests that \documentclass[12pt]{minimal}
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\begin{document}$${r}_{pc}$$\end{document}rpc can be expressed as a function of genetic parameters for the trait in the parental lines. In this study, we derived expressions for \documentclass[12pt]{minimal}
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\begin{document}$${r}_{pc}$$\end{document}rpc based on genetic variances within, and the genetic covariance between parental lines. It is important to note that the variance components used in our expressions are not the components that are typically estimated in empirical data. The expressions were derived for a genetic model with additive and dominance effects (D), and additive and epistatic additive-by-additive effects (EAA). We validated our expressions using simulations of purebred parental lines and their crosses, where the parental lines were either selected or not. Finally, using these simulations, we investigated the value of \documentclass[12pt]{minimal}
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\begin{document}$${r}_{pc}$$\end{document}rpc for genetic models with both dominance and epistasis or with other types of epistasis, for which expressions could not be derived. Results Our simulations show that when non-additive effects are present, \documentclass[12pt]{minimal}
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\begin{document}$${r}_{pc}$$\end{document}rpc decreases with increasing differences in allele frequencies between the parental lines. Genetic models that involve dominance result in lower values of \documentclass[12pt]{minimal}
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\begin{document}$${r}_{pc}$$\end{document}rpc than genetic models that involve epistasis only. Using information of parental lines only, our expressions provide exact estimates of \documentclass[12pt]{minimal}
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\begin{document}$${r}_{pc}$$\end{document}rpc for models D and EAA, and accurate upper and lower bounds of \documentclass[12pt]{minimal}
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\begin{document}$${r}_{pc}$$\end{document}rpc for two other genetic models. Conclusion This work lays the foundation to enable estimation of \documentclass[12pt]{minimal}
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\begin{document}$${r}_{pc}$$\end{document}rpc from information collected in PB parental lines only.
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Affiliation(s)
- Pascal Duenk
- Animal Breeding and Genomics, Wageningen University and Research, P.O. Box 338, 6700 AH, Wageningen, The Netherlands.
| | - Piter Bijma
- Animal Breeding and Genomics, Wageningen University and Research, P.O. Box 338, 6700 AH, Wageningen, The Netherlands
| | - Yvonne C J Wientjes
- Animal Breeding and Genomics, Wageningen University and Research, P.O. Box 338, 6700 AH, Wageningen, The Netherlands
| | - Mario P L Calus
- Animal Breeding and Genomics, Wageningen University and Research, P.O. Box 338, 6700 AH, Wageningen, The Netherlands
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Advantage of including Genomic Information to Predict Breeding Values for Lactation Yields of Milk, Fat, and Protein or Somatic Cell Score in a New Zealand Dairy Goat Herd. Animals (Basel) 2020; 11:ani11010024. [PMID: 33375575 PMCID: PMC7823755 DOI: 10.3390/ani11010024] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2020] [Revised: 12/18/2020] [Accepted: 12/18/2020] [Indexed: 11/18/2022] Open
Abstract
Simple Summary The objective of this study was to quantify the benefit from the inclusion of genomic information in the estimation of breeding values for lactation yields of milk, fat, and protein or somatic cell score in a New Zealand dairy goat herd. The dataset included lactation yields of milk, fat, and protein and average somatic cell score of 839 does and genotypes from 388 does. A prototype single-step BayesC model was developed to predict genomic breeding values and demonstrated that including genomic information into the evaluation can increase the accuracy of predictions compared with the traditional best linear unbiased prediction methods based on pedigrees alone, which is currently implemented in the New Zealand dairy goat industry. Abstract Selection on genomic breeding values (GBVs) is now readily available for ranking candidates in improvement schemes. Our objective was to quantify benefits in terms of accuracy of prediction from including genomic information in the single-trait estimation of breeding values (BVs) for a New Zealand mixed breed dairy goat herd. The dataset comprised phenotypic and pedigree records of 839 does. The phenotypes comprised estimates of 305-day lactation yields of milk, fat, and protein and average somatic cell score from the 2016 production season. Only 388 of the goats were genotyped with a Caprine 50K SNP chip and 41,981 of the single nucleotide polymorphisms (SNPs) passed quality control. Pedigree-based best linear unbiased prediction (PBLUP) was used to obtain across-breed breeding values (EBVs), whereas a single-step BayesC model (ssBC) was used to estimate across-breed GBVs. The average prediction accuracies ranged from 0.20 to 0.22 for EBVs and 0.34 to 0.43 for GBVs. Accuracies of GBVs were up to 103% greater than EBVs. Breed effects were more reliably estimated in the ssBC model compared with the PBLUP model. The greatest benefit of genomic prediction was for individuals with no pedigree or phenotypic records. Including genomic information improved the prediction accuracy of BVs compared with the current pedigree-based BLUP method currently implemented in the New Zealand dairy goat population.
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Naserkheil M, Lee DH, Mehrban H. Improving the accuracy of genomic evaluation for linear body measurement traits using single-step genomic best linear unbiased prediction in Hanwoo beef cattle. BMC Genet 2020; 21:144. [PMID: 33267771 PMCID: PMC7709290 DOI: 10.1186/s12863-020-00928-1] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/13/2020] [Accepted: 10/27/2020] [Indexed: 12/20/2022] Open
Abstract
BACKGROUND Recently, there has been a growing interest in the genetic improvement of body measurement traits in farm animals. They are widely used as predictors of performance, longevity, and production traits, and it is worthwhile to investigate the prediction accuracies of genomic selection for these traits. In genomic prediction, the single-step genomic best linear unbiased prediction (ssGBLUP) method allows the inclusion of information from genotyped and non-genotyped relatives in the analysis. Hence, we aimed to compare the prediction accuracy obtained from a pedigree-based BLUP only on genotyped animals (PBLUP-G), a traditional pedigree-based BLUP (PBLUP), a genomic BLUP (GBLUP), and a single-step genomic BLUP (ssGBLUP) method for the following 10 body measurement traits at yearling age of Hanwoo cattle: body height (BH), body length (BL), chest depth (CD), chest girth (CG), chest width (CW), hip height (HH), hip width (HW), rump length (RL), rump width (RW), and thurl width (TW). The data set comprised 13,067 phenotypic records for body measurement traits and 1523 genotyped animals with 34,460 single-nucleotide polymorphisms. The accuracy for each trait and model was estimated only for genotyped animals using five-fold cross-validations. RESULTS The accuracies ranged from 0.02 to 0.19, 0.22 to 0.42, 0.21 to 0.44, and from 0.36 to 0.55 as assessed using the PBLUP-G, PBLUP, GBLUP, and ssGBLUP methods, respectively. The average predictive accuracies across traits were 0.13 for PBLUP-G, 0.34 for PBLUP, 0.33 for GBLUP, and 0.45 for ssGBLUP methods. Our results demonstrated that averaged across all traits, ssGBLUP outperformed PBLUP and GBLUP by 33 and 43%, respectively, in terms of prediction accuracy. Moreover, the least root of mean square error was obtained by ssGBLUP method. CONCLUSIONS Our findings suggest that considering the ssGBLUP model may be a promising way to ensure acceptable accuracy of predictions for body measurement traits, especially for improving the prediction accuracy of selection candidates in ongoing Hanwoo breeding programs.
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Affiliation(s)
- Masoumeh Naserkheil
- Department of Animal Science, University College of Agriculture and Natural Resources, University of Tehran, P.O. Box: 4111, Karaj, 77871-31587 Iran
| | - Deuk Hwan Lee
- Department of Animal Life and Environment Sciences, Hankyong National University, Jungang-ro 327, Anseong-si, Gyeonggi-do South Korea
| | - Hossein Mehrban
- Department of Animal Science, Shahrekord University, P.O. Box: 115, Shahrekord, 88186-34141 Iran
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Teissier M, Larroque H, Brito LF, Rupp R, Schenkel FS, Robert-Granié C. Genomic predictions based on haplotypes fitted as pseudo-SNP for milk production and udder type traits and SCS in French dairy goats. J Dairy Sci 2020; 103:11559-11573. [PMID: 33041034 DOI: 10.3168/jds.2020-18662] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/06/2020] [Accepted: 07/27/2020] [Indexed: 12/18/2022]
Abstract
The development of statistical methods aiming to improve the accuracy of genomic predictions is of utmost value for dairy goat breeding programs. In this context, the use of haplotypes, instead of individual SNP, could improve the accuracy of genomic predictions by better capturing the effect of causal variants, instead of relying solely on linkage disequilibrium with individual SNP. Haplotypes can be included in genomic evaluation models in various ways, such as fitting them as pseudo-SNP (i.e., haplotypes converted into biallelic SNP format). This can be easily incorporated in the software already available for single-step genomic predictions (ssGBLUP). Therefore, the aim of this study was to compare the predictive performances of ssGBLUP and weighted ssGBLUP (WssGBLUP) based on individual SNP or on haplotypes fitted as pseudo-SNP. Performance was compared in terms of accuracy, bias, and weights for SNP versus pseudo-SNP. Genomic predictions were performed on 5 milk production traits, 5 udder type traits, and somatic cell score (SCS). The training population was formed by 307 Alpine and 247 Saanen progeny-tested bucks, genotyped using the Illumina Goat SNP50 BeadChip (Illumina, San Diego, CA). The validation population included 205 Alpine and 146 Saanen young bucks. The accuracy of genomic predictions was evaluated in the validation population as the Pearson correlation between genomic estimated breeding values (GEBV), predicted based on various methods, and daughter deviation (DD) based on the official genetic evaluation of January 2016. Haplotype-based models were shown to improve the performance of genomic predictions for some traits. Gains in accuracy of up to +19% (0.310 to 0.368 for fat yield) in Alpine and up to +3% (0.361 to 0.373 for udder shape) in Saanen were observed with ssGBLUP. The ssGBLUP accuracies averaged across all traits and methods were equal to 0.467 (SNP) versus 0.471 (pseudo-SNP) in Alpine and 0.528 (SNP) versus 0.523 (pseudo-SNP) in Saanen. With WssGBLUP, gains in accuracy of up to 24% (0.298 to 0.370 for fat yield) in Alpine and 14% (0.431 to 0.490 for SCS) in Saanen were observed with WssGBLUP. Accuracies of WssGBLUP averaged across all traits and methods were equal to 0.455 (SNP and pseudo-SNP) in Alpine and 0.542 (SNP) versus 0.528 (pseudo-SNP) in Saanen. The average (±SD) slope of the regression of DD on GEBV for the validation animals, across all breeds, traits and scenarios, were equal to 0.82 ± 0.20 (SNP) and 0.83 ± 0.18 (pseudo-SNP) for ssGBLUP and 0.67 ± 0.16 (SNP) and 0.65 ± 0.16 (pseudo-SNP) for WssGBLUP, which suggest that haplotype-based models and ssGBLUPSNP were similarly biased. However, WssGBLUP was more biased than ssGBLUP, and its gains in accuracies were limited to milk production traits. Despite the fact that genomic predictions based on haplotypes require additional steps and time, the observed gains in GEBV predictive performance indicate that haplotype-based methods could be recommended for some traits.
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Affiliation(s)
- Marc Teissier
- GenPhySE, Université de Toulouse, INRAE, ENVT, F-31326 Castanet-Tolosan, France.
| | - Hélène Larroque
- GenPhySE, Université de Toulouse, INRAE, ENVT, F-31326 Castanet-Tolosan, France
| | - Luiz F Brito
- Centre for Genetic Improvement of Livestock, Department of Animal Biosciences, University of Guelph, Guelph, Ontario, N1G 2W1, Canada; Department of Animal Sciences, Purdue University, West Lafayette, IN 47907
| | - Rachel Rupp
- GenPhySE, Université de Toulouse, INRAE, ENVT, F-31326 Castanet-Tolosan, France
| | - Flavio S Schenkel
- Centre for Genetic Improvement of Livestock, Department of Animal Biosciences, University of Guelph, Guelph, Ontario, N1G 2W1, Canada
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Aliakbari A, Delpuech E, Labrune Y, Riquet J, Gilbert H. The impact of training on data from genetically-related lines on the accuracy of genomic predictions for feed efficiency traits in pigs. Genet Sel Evol 2020; 52:57. [PMID: 33028194 PMCID: PMC7539441 DOI: 10.1186/s12711-020-00576-0] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/23/2019] [Accepted: 09/21/2020] [Indexed: 01/08/2023] Open
Abstract
Background Most genomic predictions use a unique population that is split into a training and a validation set. However, genomic prediction using genetically heterogeneous training sets could provide more flexibility when constructing the training sets in small populations. The aim of our study was to investigate the potential of genomic prediction of feed efficiency related traits using training sets that combine animals from two different, but genetically-related lines. We compared realized prediction accuracy and prediction bias for different training set compositions for five production traits. Results Genomic breeding values (GEBV) were predicted using the single-step genomic best linear unbiased prediction method in six scenarios applied iteratively to two genetically-related lines (i.e. 12 scenarios). The objective for all scenarios was to predict GEBV of pigs in the last three generations (~ 400 pigs, G7 to G9) of a given line. For each line, a control scenario was set up with a training set that included only animals from that line (target line). For all traits, adding more animals from the other line to the training set did not increase prediction accuracy compared to the control scenario. A small decrease in prediction accuracies was found for average daily gain, backfat thickness, and daily feed intake as the number of animals from the target line decreased in the training set. Including more animals from the other line did not decrease prediction accuracy for feed conversion ratio and residual feed intake, which were both highly affected by selection within lines. However, prediction biases were systematic for these cases and might be reduced with bivariate analyses. Conclusions Our results show that genomic prediction using a training set that includes animals from genetically-related lines can be as accurate as genomic prediction using a training set from the target population. With combined reference sets, accuracy increased for traits that were highly affected by selection. Our results provide insights into the design of reference populations, especially to initiate genomic selection in small-sized lines, for which the number of historical samples is small and that are developed simultaneously. This applies especially to poultry and pig breeding and to other crossbreeding schemes.
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Affiliation(s)
- Amir Aliakbari
- GenPhySE, Université de Toulouse, INRAE, 31326, Castanet-Tolosan, France.
| | - Emilie Delpuech
- GenPhySE, Université de Toulouse, INRAE, 31326, Castanet-Tolosan, France
| | - Yann Labrune
- GenPhySE, Université de Toulouse, INRAE, 31326, Castanet-Tolosan, France
| | - Juliette Riquet
- GenPhySE, Université de Toulouse, INRAE, 31326, Castanet-Tolosan, France
| | - Hélène Gilbert
- GenPhySE, Université de Toulouse, INRAE, 31326, Castanet-Tolosan, France
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Rio S, Moreau L, Charcosset A, Mary-Huard T. Accounting for Group-Specific Allele Effects and Admixture in Genomic Predictions: Theory and Experimental Evaluation in Maize. Genetics 2020; 216:27-41. [PMID: 32680885 PMCID: PMC7463286 DOI: 10.1534/genetics.120.303278] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/19/2020] [Accepted: 07/10/2020] [Indexed: 02/01/2023] Open
Abstract
Populations structured into genetic groups may display group-specific linkage disequilibrium, mutations, and/or interactions between quantitative trait loci and the genetic background. These factors lead to heterogeneous marker effects affecting the efficiency of genomic prediction, especially for admixed individuals. Such individuals have a genome that is a mosaic of chromosome blocks from different origins, and may be of interest to combine favorable group-specific characteristics. We developed two genomic prediction models adapted to the prediction of admixed individuals in presence of heterogeneous marker effects: multigroup admixed genomic best linear unbiased prediction random individual (MAGBLUP-RI), modeling the ancestry of alleles; and multigroup admixed genomic best linear unbiased prediction random allele effect (MAGBLUP-RAE), modeling group-specific distributions of allele effects. MAGBLUP-RI can estimate the segregation variance generated by admixture while MAGBLUP-RAE can disentangle the variability that is due to main allele effects from the variability that is due to group-specific deviation allele effects. Both models were evaluated for their genomic prediction accuracy using a maize panel including lines from the Dent and Flint groups, along with admixed individuals. Based on simulated traits, both models proved their efficiency to improve genomic prediction accuracy compared to standard GBLUP models. For real traits, a clear gain was observed at low marker densities whereas it became limited at high marker densities. The interest of including admixed individuals in multigroup training sets was confirmed using simulated traits, but was variable using real traits. Both MAGBLUP models and admixed individuals are of interest whenever group-specific SNP allele effects exist.
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Affiliation(s)
- Simon Rio
- Université Paris-Saclay, INRAE, CNRS, AgroParisTech, GQE-Le Moulon, 91190 Gif-sur-Yvette, France
| | - Laurence Moreau
- Université Paris-Saclay, INRAE, CNRS, AgroParisTech, GQE-Le Moulon, 91190 Gif-sur-Yvette, France
| | - Alain Charcosset
- Université Paris-Saclay, INRAE, CNRS, AgroParisTech, GQE-Le Moulon, 91190 Gif-sur-Yvette, France
| | - Tristan Mary-Huard
- Université Paris-Saclay, INRAE, CNRS, AgroParisTech, GQE-Le Moulon, 91190 Gif-sur-Yvette, France
- MIA, INRAE, AgroParisTech, Université Paris-Saclay, 75005 Paris, France
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Macedo FL, Christensen OF, Astruc JM, Aguilar I, Masuda Y, Legarra A. Bias and accuracy of dairy sheep evaluations using BLUP and SSGBLUP with metafounders and unknown parent groups. Genet Sel Evol 2020; 52:47. [PMID: 32787772 PMCID: PMC7425573 DOI: 10.1186/s12711-020-00567-1] [Citation(s) in RCA: 36] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2020] [Accepted: 08/04/2020] [Indexed: 11/29/2022] Open
Abstract
Background Bias has been reported in genetic or genomic evaluations of several species. Common biases are systematic differences between averages of estimated and true breeding values, and their over- or under-dispersion. In addition, comparing accuracies of pedigree versus genomic predictions is a difficult task. This work proposes to analyse biases and accuracies in the genetic evaluation of milk yield in Manech Tête Rousse dairy sheep, over several years, by testing five models and using the estimators of the linear regression method. We tested models with and without genomic information [best linear unbiased prediction (BLUP) and single-step genomic BLUP (SSGBLUP)] and using three strategies to handle missing pedigree [unknown parent groups (UPG), UPG with QP transformation in the \documentclass[12pt]{minimal}
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\begin{document}$${\mathbf{H}}$$\end{document}H matrix (EUPG) and metafounders (MF)]. Methods We compared estimated breeding values (EBV) of selected rams at birth with the EBV of the same rams obtained each year from the first daughters with phenotypes up to 2017. We compared within and across models. Finally, we compared EBV at birth of the rams with and without genomic information. Results Within models, bias and over-dispersion were small (bias: 0.20 to 0.40 genetic standard deviations; slope of the dispersion: 0.95 to 0.99) except for model SSGBLUP-EUPG that presented an important over-dispersion (0.87). The estimates of accuracies confirm that the addition of genomic information increases the accuracy of EBV in young rams. The smallest bias was observed with BLUP-MF and SSGBLUP-MF. When we estimated dispersion by comparing a model with no markers to models with markers, SSGBLUP-MF showed a value close to 1, indicating that there was no problem in dispersion, whereas SSGBLUP-EUPG and SSGBLUP-UPG showed a significant under-dispersion. Another important observation was the heterogeneous behaviour of the estimates over time, which suggests that a single check could be insufficient to make a good analysis of genetic/genomic evaluations. Conclusions The addition of genomic information increases the accuracy of EBV of young rams in Manech Tête Rousse. In this population that has missing pedigrees, the use of UPG and EUPG in SSGBLUP produced bias, whereas MF yielded unbiased estimates, and we recommend its use. We also recommend assessing biases and accuracies using multiple truncation points, since these statistics are subject to random variation across years.
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Affiliation(s)
- Fernando L Macedo
- GenPhySE, INRAE, 31326, Castanet Tolosan, France. .,Facultad de Veterinaria, UdelaR, A. Lasplaces 1620, Montevideo, Uruguay.
| | - Ole F Christensen
- Center for Quantitative Genetics and Genomics, Blichers Allé 20, 8830, Tjele, Denmark
| | | | - Ignacio Aguilar
- Instituto Nacional de Investigación Agropecuaria, Montevideo, Uruguay
| | - Yutaka Masuda
- Department of Animal and Dairy Science, University of Georgia, Athens, GA, USA
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Park MN, Alam M, Kim S, Park B, Lee SH, Lee SS. Genomic selection through single-step genomic best linear unbiased prediction improves the accuracy of evaluation in Hanwoo cattle. ASIAN-AUSTRALASIAN JOURNAL OF ANIMAL SCIENCES 2020; 33:1544-1557. [PMID: 32054201 PMCID: PMC7463086 DOI: 10.5713/ajas.18.0936] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/10/2018] [Accepted: 10/30/2019] [Indexed: 11/27/2022]
Abstract
Objective Genomic selection (GS) is becoming popular in animals’ genetic development. We, therefore, investigated the single-step genomic best linear unbiased prediction (ssGBLUP) as tool for GS, and compared its efficacy with the traditional pedigree BLUP (pedBLUP) method. Methods A total of 9,952 males born between 1997 and 2018 under Hanwoo proven-bull selection program was studied. We analyzed body weight at 12 months and carcass weight (kg), backfat thickness, eye muscle area, and marbling score traits. About 7,387 bulls were genotyped using Illumina 50K BeadChip Arrays. Multiple-trait animal model analyses were performed using BLUPF90 software programs. Breeding value accuracy was calculated using two methods: i) Pearson’s correlation of genomic estimated breeding value (GEBV) with EBV of all animals (rM1) and ii) correlation using inverse of coefficient matrix from the mixed-model equations (rM2). Then, we compared these accuracies by overall population, info-type (PHEN, phenotyped-only; GEN, genotyped-only; and PH+GEN, phenotyped and genotyped), and bull-types (YBULL, young male calves; CBULL, young candidate bulls; and PBULL, proven bulls). Results The rM1 estimates in the study were between 0.90 and 0.96 among five traits. The rM1 estimates varied slightly by population and info-type, but noticeably by bull-type for traits. Generally average rM2 estimates were much smaller than rM1 (pedBLUP, 0.40 to 0.44; ssGBLUP, 0.41 to 0.45) at population level. However, rM2 from both BLUP models varied noticeably across info-types and bull-types. The ssGBLUP estimates of rM2 in PHEN, GEN, and PH+ GEN ranged between 0.51 and 0.63, 0.66 and 0.70, and 0.68 and 0.73, respectively. In YBULL, CBULL, and PBULL, the rM2 estimates ranged between 0.54 and 0.57, 0.55 and 0.62, and 0.70 and 0.74, respectively. The pedBLUP based rM2 estimates were also relatively lower than ssGBLUP estimates. At the population level, we found an increase in accuracy by 2.0% to 4.5% among traits. Traits in PHEN were least influenced by ssGBLUP (0% to 2.0%), whereas the highest positive changes were in GEN (8.1% to 10.7%). PH+GEN also showed 6.5% to 8.5% increase in accuracy by ssGBLUP. However, the highest improvements were found in bull-types (YBULL, 21% to 35.7%; CBULL, 3.3% to 9.3%; PBULL, 2.8% to 6.1%). Conclusion A noticeable improvement by ssGBLUP was observed in this study. Findings of differential responses to ssGBLUP by various bulls could assist in better selection decision making as well. We, therefore, suggest that ssGBLUP could be used for GS in Hanwoo proven-bull evaluation program.
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Affiliation(s)
- Mi Na Park
- Animal Breeding and Genetics Division, National Institute of Animal Science, Rural Development Administration, Cheonan 31000, Korea
| | - Mahboob Alam
- Animal Breeding and Genetics Division, National Institute of Animal Science, Rural Development Administration, Cheonan 31000, Korea
| | - Sidong Kim
- Animal Breeding and Genetics Division, National Institute of Animal Science, Rural Development Administration, Cheonan 31000, Korea
| | - Byoungho Park
- Poultry Research Institute, National Institute of Animal Science, Rural Development Administration, Pyeongchang 25342, Korea
| | - Seung Hwan Lee
- Division of Animal and Dairy Science, Chungnam National University, Daejeon 34134, Korea
| | - Sung Soo Lee
- Hanwoo Genetic Improvement Center, NongHyup Agribusiness Group Inc, Seosan 31948, Korea
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Oget C, Teissier M, Astruc JM, Tosser-Klopp G, Rupp R. Alternative methods improve the accuracy of genomic prediction using information from a causal point mutation in a dairy sheep model. BMC Genomics 2019; 20:719. [PMID: 31533617 PMCID: PMC6751880 DOI: 10.1186/s12864-019-6068-4] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2019] [Accepted: 08/29/2019] [Indexed: 11/13/2022] Open
Abstract
Background Genomic evaluation is usually based on a set of markers assumed to be linked with causal mutations. Selection and precise management of major genes and the remaining polygenic component might be improved by including causal polymorphisms in the evaluation models. In this study, various methods involving a known mutation were used to estimate prediction accuracy. The SOCS2 gene, which influences body growth, milk production and somatic cell scores, a proxy for mastitis, was studied as an example in dairy sheep. Methods The data comprised 1,503,148 phenotypes and 9844 54K SNPs genotypes. The SOCS2 SNP was genotyped for 4297 animals and imputed in the above 9844 animals. Breeding values and their accuracies were estimated for each of nine traits by using single-step approaches. Pedigree-based BLUP, single-step genomic BLUP (ssGBLUP) involving the 54K ovine SNPs chip, and four weighted ssGBLUP (WssGBLUP) methods were compared. In WssGBLUP methods, weights are assigned to SNPs depending on their effect on the trait. The ssGBLUP and WssGBLUP methods were again tested after including the SOCS2 causal mutation as a SNP. Finally, the Gene Content approach was tested, which uses a multiple-trait model that considers the SOCS2 genotype as a trait. Results EBV accuracies were increased by 14.03% between the pedigree-based BLUP and ssGBLUP methods and by 3.99% between ssGBLUP and WssGBLUP. Adding the SOCS2 SNP to ssGBLUP methods led to an average gain of 0.26%. Construction of the kinship matrix and estimation of breeding values was generally improved by placing emphasis on SNPs in regions with a strong effect on traits. In the absence of chip data, the Gene Content method, compared to pedigree-based BLUP, efficiently accounted for partial genotyping information on SOCS2 as accuracy was increased by 6.25%. This method also allowed dissociation of the genetic component due to the major gene from the remaining polygenic component. Conclusions Causal mutations with a moderate to strong effect can be captured with conventional SNP chips by applying appropriate genomic evaluation methods. The Gene Content method provides an efficient way to account for causal mutations in populations lacking genome-wide genotyping. Electronic supplementary material The online version of this article (10.1186/s12864-019-6068-4) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Claire Oget
- GenPhySE, Université de Toulouse, INRA, ENVT, Castanet-Tolosan, France.
| | - Marc Teissier
- GenPhySE, Université de Toulouse, INRA, ENVT, Castanet-Tolosan, France
| | | | | | - Rachel Rupp
- GenPhySE, Université de Toulouse, INRA, ENVT, Castanet-Tolosan, France
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Gipson TA. Recent advances in breeding and genetics for dairy goats. ASIAN-AUSTRALASIAN JOURNAL OF ANIMAL SCIENCES 2019; 32:1275-1283. [PMID: 31357268 PMCID: PMC6668855 DOI: 10.5713/ajas.19.0381] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Received: 05/06/2019] [Accepted: 07/03/2019] [Indexed: 12/27/2022]
Abstract
Goats (Capra hircus) were domesticated during the late Neolithic, approximately 10,500 years ago, and humans exerted minor selection pressure until fairly recently. Probably the largest genetic change occurring over the millennia happened via natural selection and random genetic drift, the latter causing genes to be fixed in small and isolated populations. Recent human-influenced genetic changes have occurred through biometrics and genomics. For the most part, biometrics has concentrated upon the refining of estimates of heritabilities and genetic correlations. Heritabilities are instrumental in the calculation of estimated breeding values and genetic correlations are necessary in the construction of selection indices that account for changes in multiple traits under selection at one time. Early genomic studies focused upon microsatellite markers, which are short tandem repeats of nucleic acids and which are detected using polymerase chain reaction primers flanking the microsatellite. Microsatellite markers have been very important in parentage verification, which can impact genetic progress. Additionally, microsatellite markers have been a useful tool in assessing genetic diversity between and among breeds, which is important in the conservation of minor breeds. Single nucleotide polymorphisms are a new genomic tool that have refined classical BLUP methodology (biometric) to provide more accurate genomic estimated breeding values, provided a large reference population is available.
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Affiliation(s)
- Terry A Gipson
- American Institute for Goat Research, Langston University, Langston, OK 73050, USA
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Ramstein GP, Casler MD. Extensions of BLUP Models for Genomic Prediction in Heterogeneous Populations: Application in a Diverse Switchgrass Sample. G3 (BETHESDA, MD.) 2019; 9:789-805. [PMID: 30651285 PMCID: PMC6404615 DOI: 10.1534/g3.118.200969] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/13/2018] [Accepted: 01/10/2019] [Indexed: 11/18/2022]
Abstract
Genomic prediction is a useful tool to accelerate genetic gain in selection using DNA marker information. However, this technology typically relies on standard prediction procedures, such as genomic BLUP, that are not designed to accommodate population heterogeneity resulting from differences in marker effects across populations. In this study, we assayed different prediction procedures to capture marker-by-population interactions in genomic prediction models. Prediction procedures included genomic BLUP and two kernel-based extensions of genomic BLUP which explicitly accounted for population heterogeneity. To model population heterogeneity, dissemblance between populations was either depicted by a unique coefficient (as previously reported), or a more flexible function of genetic distance between populations (proposed herein). Models under investigation were applied in a diverse switchgrass sample under two validation schemes: whole-sample calibration, where all individuals except selection candidates are included in the calibration set, and cross-population calibration, where the target population is entirely excluded from the calibration set. First, we showed that using fixed effects, from principal components or putative population groups, appeared detrimental to prediction accuracy, especially in cross-population calibration. Then we showed that modeling population heterogeneity by our proposed procedure resulted in highly significant improvements in model fit. In such cases, gains in accuracy were often positive. These results suggest that population heterogeneity may be parsimoniously captured by kernel methods. However, in cases where improvement in model fit by our proposed procedure is null-to-moderate, ignoring heterogeneity should probably be preferred due to the robustness and simplicity of the standard genomic BLUP model.
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Affiliation(s)
| | - Michael D Casler
- Department of Agronomy, University of Wisconsin-Madison, Madison, WI 53706
- Agricultural Research Service, United States Department of Agriculture, Madison, WI 53706
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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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Rio S, Mary-Huard T, Moreau L, Charcosset A. Genomic selection efficiency and a priori estimation of accuracy in a structured dent maize panel. TAG. THEORETICAL AND APPLIED GENETICS. THEORETISCHE UND ANGEWANDTE GENETIK 2019; 132:81-96. [PMID: 30288553 DOI: 10.1007/s00122-018-3196-1] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/25/2018] [Accepted: 09/22/2018] [Indexed: 06/08/2023]
Abstract
Population structure affects genomic selection efficiency as well as the ability to forecast accuracy using standard GBLUP. Genomic prediction models usually assume that the individuals used for calibration belong to the same population as those to be predicted. Most of the a priori indicators of precision, such as the coefficient of determination (CD), were derived from those same models. But genetic structure is a common feature in plant species, and it may impact genomic selection efficiency and the ability to forecast prediction accuracy. We investigated the impact of genetic structure in a dent maize panel ("Amaizing Dent") using different scenarios including within- or across-group predictions. For a given training set size, the best accuracies were achieved when predicting individuals using a model calibrated on the same genetic group. Nevertheless, a diverse training set representing all the groups had a certain predictive efficiency for all the validation sets, and adding extra-group individuals was almost always beneficial. It underlines the potential of such a generic training set for dent maize genomic selection applications. Alternative prediction models, taking genetic structure explicitly into account, did not improve the prediction accuracy compared to GBLUP. We also investigated the ability of different indicators of precision to forecast accuracy in the within- or across-group scenarios. There was a global encouraging trend of the CD to differentiate scenarios, although there were specific combinations of target populations and traits where the efficiency of this indicator proved to be null. One hypothesis to explain such erratic performances is the impact of genetic structure through group-specific allele diversity at QTLs rather than group-specific allele effects.
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Affiliation(s)
- Simon Rio
- GQE - Le Moulon, INRA, Univ. Paris-Sud, CNRS, AgroParisTech, Université Paris-Saclay, 91190, Gif-sur-Yvette, France
| | - Tristan Mary-Huard
- GQE - Le Moulon, INRA, Univ. Paris-Sud, CNRS, AgroParisTech, Université Paris-Saclay, 91190, Gif-sur-Yvette, France
- MIA, INRA, AgroParisTech, Université Paris-Saclay, 75005, Paris, France
| | - Laurence Moreau
- GQE - Le Moulon, INRA, Univ. Paris-Sud, CNRS, AgroParisTech, Université Paris-Saclay, 91190, Gif-sur-Yvette, France
| | - Alain Charcosset
- GQE - Le Moulon, INRA, Univ. Paris-Sud, CNRS, AgroParisTech, Université Paris-Saclay, 91190, Gif-sur-Yvette, France.
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Wientjes YCJ, Calus MPL, Duenk P, Bijma P. Required properties for markers used to calculate unbiased estimates of the genetic correlation between populations. Genet Sel Evol 2018; 50:65. [PMID: 30547748 PMCID: PMC6295113 DOI: 10.1186/s12711-018-0434-6] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/28/2018] [Accepted: 11/28/2018] [Indexed: 11/27/2022] Open
Abstract
BACKGROUND Generally, populations differ in terms of environmental and genetic factors, which can create differences in allele substitution effects between populations. Therefore, a single genotype may have different additive genetic values in different populations. The correlation between the two additive genetic values of a single genotype in two populations is known as the additive genetic correlation between populations and thus, can differ from 1. Our objective was to investigate whether differences in linkage disequilibrium (LD) and allele frequencies of markers and causal loci between populations affect the bias of the estimated genetic correlation. We simulated two populations that were separated by 50 generations and differed in LD pattern between markers and causal loci, as measured by the LD-statistic r. We used a high marker density to represent a high consistency of LD between populations, and lower marker densities to represent situations with a lower consistency of LD between populations. Markers and causal loci were selected to have either similar or different allele frequencies in the two populations. RESULTS Our results show that genetic correlations were underestimated only slightly when the difference in allele frequencies between the two populations was similar for the markers and the causal loci. A lower marker density, representing a lower consistency of LD between populations, had only a minor effect on the underestimation of the genetic correlation. When the difference in allele frequencies between the two populations was not similar for markers and causal loci, genetic correlations were severely underestimated. This bias occurred because the markers did not predict accurately the relationships at causal loci. CONCLUSIONS For an unbiased estimation of the genetic correlation between populations, the markers should accurately predict the relationships at the causal loci. To achieve this, it is essential that the difference in allele frequencies between populations is similar for markers and causal loci. Our results show that differences in LD phase between causal loci and markers across populations have little effect on the estimated genetic correlation.
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Affiliation(s)
- Yvonne C. J. Wientjes
- Animal Breeding and Genomics, Wageningen University and Research, 6700 AH Wageningen, The Netherlands
| | - Mario P. L. Calus
- Animal Breeding and Genomics, Wageningen University and Research, 6700 AH Wageningen, The Netherlands
| | - Pascal Duenk
- Animal Breeding and Genomics, Wageningen University and Research, 6700 AH Wageningen, The Netherlands
| | - Piter Bijma
- Animal Breeding and Genomics, Wageningen University and Research, 6700 AH Wageningen, The Netherlands
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Piccoli ML, Brito LF, Braccini J, Brito FV, Cardoso FF, Cobuci JA, Sargolzaei M, Schenkel FS. A comprehensive comparison between single- and two-step GBLUP methods in a simulated beef cattle population. CANADIAN JOURNAL OF ANIMAL SCIENCE 2018. [DOI: 10.1139/cjas-2017-0176] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/22/2022]
Abstract
The statistical methods used in the genetic evaluations are a key component of the process and can be best compared by using simulated data. The latter is especially true in grazing beef cattle production systems, where the number of proven bulls with highly reliable estimated breeding values is limited to allow for a trustworthy validation of genomic predictions. Therefore, we simulated data for 4980 beef cattle aiming to compare single-step genomic best linear unbiased prediction (ssGBLUP), which simultaneously incorporates pedigree, phenotypic, and genomic data into genomic evaluations, and two-step GBLUP (tsGBLUP) procedures and genomic estimated breeding values (GEBVs) blending methods. The greatest increases in GEBV accuracies compared with the parents’ average estimated breeding values (EBVPA) were 0.364 and 0.341 for ssGBLUP and tsGBLUP, respectively. Direct genomic value and GEBV accuracies when using ssGBLUP and tsGBLUP procedures were similar, except for the GEBV accuracies using Hayes’ blending method in tsGBLUP. There was no significant or slight bias in genomic predictions from ssGBLUP or tsGBLUP (using VanRaden’s blending method), indicating that these predictions are on the same scale compared with the true breeding values. Overall, genetic evaluations including genomic information resulted in gains in accuracy >100% compared with the EBVPA. In addition, there were no significant differences between the selected animals (10% males and 50% females) by using ssGBLUP or tsGBLUP.
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Affiliation(s)
- Mario L. Piccoli
- Departamento de Zootecnia, Universidade Federal do Rio Grande do Sul, Porto Alegre, RS 91540-000, Brazil
- GenSys Consultores Associados S/S, Porto Alegre, RS 90460-060, Brazil
- Centre for Genetic Improvement of Livestock, Department of Animal Biosciences, University of Guelph, Guelph, ON N1G 2W1, Canada
| | - Luiz F. Brito
- Centre for Genetic Improvement of Livestock, Department of Animal Biosciences, University of Guelph, Guelph, ON N1G 2W1, Canada
| | - José Braccini
- Departamento de Zootecnia, Universidade Federal do Rio Grande do Sul, Porto Alegre, RS 91540-000, Brazil
- Conselho Nacional de Desenvolvimento Científico e Tecnológico, Brasília, DF 71605-001, Brazil
| | - Fernanda V. Brito
- GenSys Consultores Associados S/S, Porto Alegre, RS 90460-060, Brazil
| | - Fernando F. Cardoso
- Conselho Nacional de Desenvolvimento Científico e Tecnológico, Brasília, DF 71605-001, Brazil
- Embrapa Pecuária Sul, Bagé, RS 96401-970, Brazil
| | - Jaime A. Cobuci
- Departamento de Zootecnia, Universidade Federal do Rio Grande do Sul, Porto Alegre, RS 91540-000, Brazil
| | - Mehdi Sargolzaei
- Centre for Genetic Improvement of Livestock, Department of Animal Biosciences, University of Guelph, Guelph, ON N1G 2W1, Canada
- The Semex Alliance, Guelph, ON N1H 6J2, Canada
| | - Flávio S. Schenkel
- Centre for Genetic Improvement of Livestock, Department of Animal Biosciences, University of Guelph, Guelph, ON N1G 2W1, Canada
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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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Cappa EP, El-Kassaby YA, Muñoz F, Garcia MN, Villalba PV, Klápště J, Marcucci Poltri SN. Genomic-based multiple-trait evaluation in Eucalyptus grandis using dominant DArT markers. PLANT SCIENCE : AN INTERNATIONAL JOURNAL OF EXPERIMENTAL PLANT BIOLOGY 2018; 271:27-33. [PMID: 29650154 DOI: 10.1016/j.plantsci.2018.03.014] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/15/2017] [Revised: 02/05/2018] [Accepted: 03/12/2018] [Indexed: 05/04/2023]
Abstract
We investigated the impact of combining the pedigree- and genomic-based relationship matrices in a multiple-trait individual-tree mixed model (a.k.a., multiple-trait combined approach) on the estimates of heritability and on the genomic correlations between growth and stem straightness in an open-pollinated Eucalyptus grandis population. Additionally, the added advantage of incorporating genomic information on the theoretical accuracies of parents and offspring breeding values was evaluated. Our results suggested that the use of the combined approach for estimating heritabilities and additive genetic correlations in multiple-trait evaluations is advantageous and including genomic information increases the expected accuracy of breeding values. Furthermore, the multiple-trait combined approach was proven to be superior to the single-trait combined approach in predicting breeding values, in particular for low-heritability traits. Finally, our results advocate the use of the combined approach in forest tree progeny testing trials, specifically when a multiple-trait individual-tree mixed model is considered.
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Affiliation(s)
- Eduardo P Cappa
- Instituto de Recursos Biológicos (IRB), Centro de Investigación en Recursos Naturales (CIRN), Instituto Nacional de Tecnología Agropecuaria (INTA), De Los Reseros y Dr. Nicolás Repetto s/n, 1686, Hurlingham, Buenos Aires, Argentina; Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), Argentina.
| | - Yousry A El-Kassaby
- Department of Forest and Conservation Sciences, Faculty of Forestry, The University of British Columbia, 2424 Main Mall, Vancouver, B.C., V6T 1Z4, Canada
| | | | - Martín N Garcia
- Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), Argentina; Instituto de Biotecnología (IB), Centro de Investigación en Ciencias Veterinarias y Agronómicas (CICVyA), Instituto Nacional de Tecnología Agropecuaria (INTA), De Los Reseros y Dr. Nicolás Repetto s/n, 1686, Hurlingham, Buenos Aires, Argentina
| | - Pamela V Villalba
- Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), Argentina; Instituto de Biotecnología (IB), Centro de Investigación en Ciencias Veterinarias y Agronómicas (CICVyA), Instituto Nacional de Tecnología Agropecuaria (INTA), De Los Reseros y Dr. Nicolás Repetto s/n, 1686, Hurlingham, Buenos Aires, Argentina
| | - Jaroslav Klápště
- Department of Forest and Conservation Sciences, Faculty of Forestry, The University of British Columbia, 2424 Main Mall, Vancouver, B.C., V6T 1Z4, Canada; Department of Genetics and Physiology of Forest Trees, Faculty of Forestry and Wood Sciences, Czech University of Life Sciences Prague, Kamycka 129, 165 21 Praha 6, Czech Republic
| | - Susana N Marcucci Poltri
- Instituto de Biotecnología (IB), Centro de Investigación en Ciencias Veterinarias y Agronómicas (CICVyA), Instituto Nacional de Tecnología Agropecuaria (INTA), De Los Reseros y Dr. Nicolás Repetto s/n, 1686, Hurlingham, Buenos Aires, Argentina
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Molina A, Muñoz E, Díaz C, Menéndez-Buxadera A, Ramón M, Sánchez M, Carabaño MJ, Serradilla JM. Goat genomic selection: Impact of the integration of genomic information in the genetic evaluations of the Spanish Florida goats. Small Rumin Res 2018. [DOI: 10.1016/j.smallrumres.2017.12.010] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
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Invited review: Genomic selection for small ruminants in developed countries: how applicable for the rest of the world? Animal 2018; 12:1333-1340. [PMID: 29343308 DOI: 10.1017/s1751731117003688] [Citation(s) in RCA: 21] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/05/2023] Open
Abstract
Improved management and use of estimated breeding values in breeding programmes, have resulted in rapid genetic progress for small ruminants (SR) in Europe and other developed countries. The development of single nucleotide polymorphisms chips opened opportunities for genomic selection (GS) in SR in these countries. Initially focused on production traits (growth and milk), GS has been extended to functional traits (reproductive performance, disease resistance and meat quality). The GS systems have been characterized by smaller reference populations compared with those of dairy cattle and consisting mostly of cross- or multi-breed populations. Molecular information has resulted in gains in accuracy of between 0.05 and 0.27 and proved useful in parentage verification and the identification of QTLs for economically important traits. Except for a few established breeds with some degree of infrastructure, the basic building blocks to support conventional breeding programmes in small holder systems are lacking in most developing countries. In these systems, molecular data could offer quick wins in undertaking parentage verification and genetic evaluations using G matrix, and determination of breed composition. The development of next-generation molecular tools has prompted investigations on genome-wide signatures of selection for mainly adaptive and reproduction traits in SR in developing countries. Here, the relevance of the developments and application of GS and other molecular tools in developed countries to developing countries context is examined. Worth noting is that in the latter, the application of GS in SR will not be a 'one-size fits all' scenario. For breeds with some degree of conventional genetic improvement, classical GS may be feasible. In small holder systems, where production is key, community-based breeding programmes can provide the framework to implement GS. However, in fragile growth systems, for example those found in marginal environments, innovative GS to maximize adaptive diversity will be required. A cost-benefit analysis should accompany any strategy of implementing GS in these systems.
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Amills M, Capote J, Tosser-Klopp G. Goat domestication and breeding: a jigsaw of historical, biological and molecular data with missing pieces. Anim Genet 2017; 48:631-644. [PMID: 28872195 DOI: 10.1111/age.12598] [Citation(s) in RCA: 41] [Impact Index Per Article: 5.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 07/14/2017] [Indexed: 12/23/2022]
Abstract
Domestic goats (Capra hircus) are spread across the five continents with a census of 1 billion individuals. The worldwide population of goats descends from a limited number of bezoars (Capra aegagrus) domesticated 10 000 YBP (years before the present) in the Fertile Crescent. The extraordinary adaptability and hardiness of goats favoured their rapid spread over the Old World, reaching the Iberian Peninsula and Southern Africa 7000 YBP and 2000 YBP respectively. Molecular studies have revealed one major mitochondrial haplogroup A and five less frequent haplogroups B, C, D, F and G. Moreover, the analysis of autosomal and Y-chromosome markers has evidenced an appreciable geographic differentiation. The implementation of new molecular technologies, such as whole-genome sequencing and genome-wide genotyping, allows for the exploration of caprine diversity at an unprecedented scale, thus providing new insights into the evolutionary history of goats. In spite of a number of pitfalls, the characterization of the functional elements of the goat genome is expected to play a key role in understanding the genetic determination of economically relevant traits. Genomic selection and genome editing also hold great potential, particularly for improving traits that cannot be modified easily by traditional selection.
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Affiliation(s)
- M Amills
- Department of Animal Genetics, Centre for Research in Agricultural Genomics (CRAG), CSIC-IRTA-UAB-UB, Campus Universitat Autònoma de Barcelona, Bellaterra, 08193, Spain
| | - J Capote
- Instituto Canario de Investigaciones Agrarias, La Laguna, 38108, Tenerife, Spain
| | - G Tosser-Klopp
- INRA-GenPhySE-Génétique, Physiologie et Systèmes d'Elevage-UMR1388, 24 Chemin de Borde Rouge-Auzeville CS 52627, 31326, Castanet Tolosan Cedex, France
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Castañeda-Bustos V, Montaldo H, Valencia-Posadas M, Shepard L, Pérez-Elizalde S, Hernández-Mendo O, Torres-Hernández G. Linear and nonlinear genetic relationships between type traits and productive life in US dairy goats. J Dairy Sci 2017; 100:1232-1245. [DOI: 10.3168/jds.2016-11313] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/13/2016] [Accepted: 09/28/2016] [Indexed: 11/19/2022]
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Raoul J, Palhière I, Astruc JM, Elsen JM. Genetic and economic effects of the increase in female paternal filiations by parentage assignment in sheep and goat breeding programs1. J Anim Sci 2016; 94:3663-3683. [DOI: 10.2527/jas.2015-0165] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/17/2023] Open
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Carillier-Jacquin C, Larroque H, Robert-Granié C. Including α s1 casein gene information in genomic evaluations of French dairy goats. Genet Sel Evol 2016; 48:54. [PMID: 27491470 PMCID: PMC4973374 DOI: 10.1186/s12711-016-0233-x] [Citation(s) in RCA: 24] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2015] [Accepted: 07/27/2016] [Indexed: 02/01/2023] Open
Abstract
BACKGROUND Genomic best linear unbiased prediction methods assume that all markers explain the same fraction of the genetic variance and do not account effectively for genes with major effects such as the α s1 casein polymorphism in dairy goats. In this study, we investigated methods to include the available α s1 casein genotype effect in genomic evaluations of French dairy goats. METHODS First, the α s1 casein genotype was included as a fixed effect in genomic evaluation models based only on bucks that were genotyped at the α s1 casein locus. Less than 1 % of the females with phenotypes were genotyped at the α s1 casein gene. Thus, to incorporate these female phenotypes in the genomic evaluation, two methods that allowed for this large number of missing α s1 casein genotypes were investigated. Probabilities for each possible α s1 casein genotype were first estimated for each female of unknown genotype based on iterative peeling equations. The second method is based on a multiallelic gene content approach. For each model tested, we used three datasets each divided into a training and a validation set: (1) two-breed population (Alpine + Saanen), (2) Alpine population, and (3) Saanen population. RESULTS The α s1 casein genotype had a significant effect on milk yield, fat content and protein content. Including an α s1 casein effect in genetic and genomic evaluations based only on male known α s1 casein genotypes improved accuracies (from 6 to 27 %). In genomic evaluations based on all female phenotypes, the gene content approach performed better than the other tested methods but the improvement in accuracy was only slightly better (from 1 to 14 %) than that of a genomic model without the α s1 casein effect. CONCLUSIONS Including the α s1 casein effect in a genomic evaluation model for French dairy goats is possible and useful to improve accuracy. Difficulties in predicting the genotypes for ungenotyped animals limited the improvement in accuracy of the obtained estimated breeding values.
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Affiliation(s)
| | - Hélène Larroque
- GenPhySE, INRA, INPT, ENVT, Université de Toulouse, 31326, Castanet-Tolosan, France
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Burren A, Neuditschko M, Signer-Hasler H, Frischknecht M, Reber I, Menzi F, Drögemüller C, Flury C. Genetic diversity analyses reveal first insights into breed-specific selection signatures within Swiss goat breeds. Anim Genet 2016; 47:727-739. [PMID: 27436146 DOI: 10.1111/age.12476] [Citation(s) in RCA: 39] [Impact Index Per Article: 4.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 06/16/2016] [Indexed: 01/03/2023]
Abstract
We used genotype data from the caprine 50k Illumina BeadChip for the assessment of genetic diversity within and between 10 local Swiss goat breeds. Three different cluster methods allowed the goat samples to be assigned to the respective breed groups, whilst the samples of Nera Verzasca and Tessin Grey goats could not be differentiated from each other. The results of the different genetic diversity measures show that Appenzell, Toggenburg, Valais and Booted goats should be prioritized in future conservation activities. Furthermore, we examined runs of homozygosity (ROH) and compared genomic inbreeding coefficients based on ROH (FROH ) with pedigree-based inbreeding coefficients (FPED ). The linear relationship between FROH and FPED was confirmed for goats by including samples from the three main breeds (Saanen, Chamois and Toggenburg goats). FROH appears to be a suitable measure for describing levels of inbreeding in goat breeds with missing pedigree information. Finally, we derived selection signatures between the breeds. We report a total of 384 putative selection signals. The 25 most significant windows contained genes known for traits such as: coat color variation (MITF, KIT, ASIP), growth (IGF2, IGF2R, HRAS, FGFR3) and milk composition (PITX2). Several other putative genes involved in the formation of populations, which might have been selected for adaptation to the alpine environment, are highlighted. The results provide a contemporary background for the management of genetic diversity in local Swiss goat breeds.
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Affiliation(s)
- A Burren
- School of Agricultural, Forest and Food Sciences HAFL, Bern University of Applied Sciences, Länggasse 85, 3052, Zollikofen, Switzerland.
| | - M Neuditschko
- Swiss National Stud Farm, Agroscope Research Station, Les Longs-Prés, 1580, Avenches, Switzerland
| | - H Signer-Hasler
- School of Agricultural, Forest and Food Sciences HAFL, Bern University of Applied Sciences, Länggasse 85, 3052, Zollikofen, Switzerland
| | - M Frischknecht
- School of Agricultural, Forest and Food Sciences HAFL, Bern University of Applied Sciences, Länggasse 85, 3052, Zollikofen, Switzerland
| | - I Reber
- Institute of Genetics, Vetsuisse Faculty, University of Bern, Bremgartenstrasse 109, 3001, Bern, Switzerland
| | - F Menzi
- Institute of Genetics, Vetsuisse Faculty, University of Bern, Bremgartenstrasse 109, 3001, Bern, Switzerland
| | - C Drögemüller
- Institute of Genetics, Vetsuisse Faculty, University of Bern, Bremgartenstrasse 109, 3001, Bern, Switzerland
| | - C Flury
- School of Agricultural, Forest and Food Sciences HAFL, Bern University of Applied Sciences, Länggasse 85, 3052, Zollikofen, Switzerland
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Mdladla K, Dzomba EF, Huson HJ, Muchadeyi FC. Population genomic structure and linkage disequilibrium analysis of South African goat breeds using genome-wide SNP data. Anim Genet 2016; 47:471-82. [PMID: 27306145 DOI: 10.1111/age.12442] [Citation(s) in RCA: 37] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 03/08/2016] [Indexed: 02/03/2023]
Abstract
The sustainability of goat farming in marginal areas of southern Africa depends on local breeds that are adapted to specific agro-ecological conditions. Unimproved non-descript goats are the main genetic resources used for the development of commercial meat-type breeds of South Africa. Little is known about genetic diversity and the genetics of adaptation of these indigenous goat populations. This study investigated the genetic diversity, population structure and breed relations, linkage disequilibrium, effective population size and persistence of gametic phase in goat populations of South Africa. Three locally developed meat-type breeds of the Boer (n = 33), Savanna (n = 31), Kalahari Red (n = 40), a feral breed of Tankwa (n = 25) and unimproved non-descript village ecotypes (n = 110) from four goat-producing provinces of the Eastern Cape, KwaZulu-Natal, Limpopo and North West were assessed using the Illumina Goat 50K SNP Bead Chip assay. The proportion of SNPs with minor allele frequencies >0.05 ranged from 84.22% in the Tankwa to 97.58% in the Xhosa ecotype, with a mean of 0.32 ± 0.13 across populations. Principal components analysis, admixture and pairwise FST identified Tankwa as a genetically distinct population and supported clustering of the populations according to their historical origins. Genome-wide FST identified 101 markers potentially under positive selection in the Tankwa. Average linkage disequilibrium was highest in the Tankwa (r(2) = 0.25 ± 0.26) and lowest in the village ecotypes (r(2) range = 0.09 ± 0.12 to 0.11 ± 0.14). We observed an effective population size of <150 for all populations 13 generations ago. The estimated correlations for all breed pairs were lower than 0.80 at marker distances >100 kb with the exception of those in Savanna and Tswana populations. This study highlights the high level of genetic diversity in South African indigenous goats as well as the utility of the genome-wide SNP marker panels in genetic studies of these populations.
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Affiliation(s)
- K Mdladla
- Agricultural Research Council, Biotechnology Platform, Private Bag X5, Onderstepoort, 0110, South Africa.,Discipline of Genetics, School of Life Sciences, University of KwaZulu-Natal, Private Bag X01, Scottsville, 3209, South Africa
| | - E F Dzomba
- Discipline of Genetics, School of Life Sciences, University of KwaZulu-Natal, Private Bag X01, Scottsville, 3209, South Africa
| | - H J Huson
- Department of Animal Science, Cornell University, 201 Morrison Hall, 507 Tower Road, Ithaca, NY, 14853, USA
| | - F C Muchadeyi
- Agricultural Research Council, Biotechnology Platform, Private Bag X5, Onderstepoort, 0110, South Africa
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Rupp R, Mucha S, Larroque H, McEwan J, Conington J. Genomic application in sheep and goat breeding. Anim Front 2016. [DOI: 10.2527/af.2016-0006] [Citation(s) in RCA: 44] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022] Open
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Economic evaluation of genomic selection in small ruminants: a sheep meat breeding program. Animal 2016; 10:1033-41. [DOI: 10.1017/s1751731115002049] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022] Open
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Mucha S, Mrode R, MacLaren-Lee I, Coffey M, Conington J. Estimation of genomic breeding values for milk yield in UK dairy goats. J Dairy Sci 2015; 98:8201-8. [DOI: 10.3168/jds.2015-9682] [Citation(s) in RCA: 31] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/08/2015] [Accepted: 07/15/2015] [Indexed: 11/19/2022]
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