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Araujo AC, Carneiro PLS, Oliveira HR, Schenkel FS, Veroneze R, Lourenco DAL, Brito LF. A Comprehensive Comparison of Haplotype-Based Single-Step Genomic Predictions in Livestock Populations With Different Genetic Diversity Levels: A Simulation Study. Front Genet 2021; 12:729867. [PMID: 34721524 PMCID: PMC8551834 DOI: 10.3389/fgene.2021.729867] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/23/2021] [Accepted: 09/07/2021] [Indexed: 11/13/2022] Open
Abstract
The level of genetic diversity in a population is inversely proportional to the linkage disequilibrium (LD) between individual single nucleotide polymorphisms (SNPs) and quantitative trait loci (QTLs), leading to lower predictive ability of genomic breeding values (GEBVs) in high genetically diverse populations. Haplotype-based predictions could outperform individual SNP predictions by better capturing the LD between SNP and QTL. Therefore, we aimed to evaluate the accuracy and bias of individual-SNP- and haplotype-based genomic predictions under the single-step-genomic best linear unbiased prediction (ssGBLUP) approach in genetically diverse populations. We simulated purebred and composite sheep populations using literature parameters for moderate and low heritability traits. The haplotypes were created based on LD thresholds of 0.1, 0.3, and 0.6. Pseudo-SNPs from unique haplotype alleles were used to create the genomic relationship matrix ( G ) in the ssGBLUP analyses. Alternative scenarios were compared in which the pseudo-SNPs were combined with non-LD clustered SNPs, only pseudo-SNPs, or haplotypes fitted in a second G (two relationship matrices). The GEBV accuracies for the moderate heritability-trait scenarios fitting individual SNPs ranged from 0.41 to 0.55 and with haplotypes from 0.17 to 0.54 in the most (Ne ≅ 450) and less (Ne < 200) genetically diverse populations, respectively, and the bias fitting individual SNPs or haplotypes ranged between -0.14 and -0.08 and from -0.62 to -0.08, respectively. For the low heritability-trait scenarios, the GEBV accuracies fitting individual SNPs ranged from 0.24 to 0.32, and for fitting haplotypes, it ranged from 0.11 to 0.32 in the more (Ne ≅ 250) and less (Ne ≅ 100) genetically diverse populations, respectively, and the bias ranged between -0.36 and -0.32 and from -0.78 to -0.33 fitting individual SNPs or haplotypes, respectively. The lowest accuracies and largest biases were observed fitting only pseudo-SNPs from blocks constructed with an LD threshold of 0.3 (p < 0.05), whereas the best results were obtained using only SNPs or the combination of independent SNPs and pseudo-SNPs in one or two G matrices, in both heritability levels and all populations regardless of the level of genetic diversity. In summary, haplotype-based models did not improve the performance of genomic predictions in genetically diverse populations.
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Affiliation(s)
- Andre C Araujo
- Postgraduate Program in Animal Sciences, State University of Southwestern Bahia, Itapetinga, Brazil.,Department of Animal Sciences, Purdue University, West Lafayette, IN, United States
| | - Paulo L S Carneiro
- Department of Biology, State University of Southwestern Bahia, Jequié, Brazil
| | - Hinayah R Oliveira
- Department of Animal Sciences, Purdue University, West Lafayette, IN, United States.,Centre for Genetic Improvement of Livestock, Department of Animal Biosciences, University of Guelph, Guelph, ON, Canada
| | - Flavio S Schenkel
- Centre for Genetic Improvement of Livestock, Department of Animal Biosciences, University of Guelph, Guelph, ON, Canada
| | - Renata Veroneze
- Department of Animal Sciences, Federal University of Viçosa, Viçosa, Brazil
| | - Daniela A L Lourenco
- Department of Animal and Dairy Science, University of Georgia, Athens, GA, United States
| | - Luiz F Brito
- Department of Animal Sciences, Purdue University, West Lafayette, IN, United States
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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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Granado-Tajada I, Varona L, Ugarte E. Genotyping strategies for maximizing genomic information in evaluations of the Latxa dairy sheep breed. J Dairy Sci 2021; 104:6861-6872. [PMID: 33773777 DOI: 10.3168/jds.2020-19978] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2020] [Accepted: 02/12/2021] [Indexed: 12/12/2022]
Abstract
Genomic selection has been implemented over the years in several livestock species, due to the achievable higher genetic progress. The use of genomic information in evaluations provides better prediction accuracy than do pedigree-based evaluations, and the makeup of the genotyped population is a decisive point. The aim of this work is to compare the effect of different genotyping strategies (number and type of animals) on the prediction accuracy for dairy sheep Latxa breeds. A simulation study was designed based on the real data structure of each population, and the phenotypic and genotypic data obtained were used in genetic (BLUP) and genomic (single-step genomic BLUP) evaluations of different genotyping strategies. The genotyping of males was beneficial when they were genetically connected individuals and if they had daughters with phenotypic records. Genotyping females with their own lactation records increased prediction accuracy, and the connection level has less relevance. The differences in genotyping females were independent of their estimated breeding value. The combined genotyping of males and females provided intermediate accuracy results regardless of the female selection strategy. Therefore, assuming that genotyping rams is interesting, the incorporation of genotyped females would be beneficial and worthwhile. The benefits of genotyping individuals from various generations were highlighted, although it was also possible to gain prediction accuracy when historic individuals were not considered. Greater genotyped population sizes resulted in more accuracy, even if the increase seems to reach a plateau.
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Affiliation(s)
- I Granado-Tajada
- Department of Animal Production, NEIKER-BRTA Basque Institute of Agricultural Research and Development, Agrifood Campus of Arkaute s/n, E-01080 Arkaute, Spain.
| | - L Varona
- Departamento de Anatomía Embriología y Genética Animal, Instituto Agroalimentario de Aragón (IA2), Universidad de Zaragoza, 50013 Zaragoza, Spain
| | - E Ugarte
- Department of Animal Production, NEIKER-BRTA Basque Institute of Agricultural Research and Development, Agrifood Campus of Arkaute s/n, E-01080 Arkaute, Spain
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