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Shi R, Chen Z, Su G, Luo H, Liu L, Guo G, Wang Y. Genomic prediction of service sire effect on female reproductive performance in Holstein cattle: A comparison between different methods, validation population and marker densities. J Anim Breed Genet 2023. [PMID: 36843354 DOI: 10.1111/jbg.12763] [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: 07/19/2022] [Accepted: 01/31/2023] [Indexed: 02/28/2023]
Abstract
Reproductive traits of dairy cattle are bound to the actual efficiency of farm operation, which therefore show great economic importance. Among them, some traits were deemed to be simultaneously affected by service sire and mating cow. Service sires are proved to play an important role in reproduction process of cows. However, limited study explored the genetic effect of service sire (GESS), let alone the genomic prediction of this effect. In the present study, 2244 genotyped bulls together with phenotypic records were used to predict the GESS on conception rate, 56-day non-return rate, calving ease, stillbirth and gestation length. The feasibilities of multi-step genomic best linear unbiased predictor (msGBLUP) and single-step genomic best linear unbiased predictor (ssGBLUP) were investigated under different scenarios, that is, different marker densities and validation population. The predictive accuracies and unbiasedness for GESS ranged from 0.159 to 0.647 and from 0.202 to 2.018, respectively, when validated on young bulls, while the accuracies and unbiasedness ranged from 0.409 to 0.802 and 0.333 to 1.146 when validated on random split data sets. It is feasible to predict GESS on reproductive traits by using a linear mixed model and genomic data, and high-density marker panel had limited contribution to the prediction. This research investigated the potential factors that influence the genomic prediction of GESS on reproductive traits and indicated the possibility of genomic selection on GESS, both in ideal and practical circumstances.
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Affiliation(s)
- Rui Shi
- Key Laboratory of Animal Genetics, Breeding and Reproduction, MARA, National Engineering Laboratory of Animal Breeding, College of Animal Science and Technology, China Agricultural University, Beijing, China.,Wageningen University & Research Animal Breeding and Genomics, Wageningen, the Netherlands.,Animal Production Systems Group, Wageningen University & Research, Wageningen, the Netherlands
| | - Ziwei Chen
- Key Laboratory of Animal Genetics, Breeding and Reproduction, MARA, National Engineering Laboratory of Animal Breeding, College of Animal Science and Technology, China Agricultural University, Beijing, China
| | - Guosheng Su
- Center for Quantitative Genetics and Genomics, Aarhus University, Tjele, Denmark
| | - Hanpeng Luo
- Key Laboratory of Animal Genetics, Breeding and Reproduction, MARA, National Engineering Laboratory of Animal Breeding, College of Animal Science and Technology, China Agricultural University, Beijing, China
| | - Lin Liu
- Beijing Dairy Cattle Center, Beijing, China
| | - Gang Guo
- Beijing Sunlon Livestock Development Co. Ltd, Beijing, China
| | - Yachun Wang
- Key Laboratory of Animal Genetics, Breeding and Reproduction, MARA, National Engineering Laboratory of Animal Breeding, College of Animal Science and Technology, China Agricultural University, Beijing, China
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Jurcic EJ, Villalba PV, Pathauer PS, Palazzini DA, Oberschelp GPJ, Harrand L, Garcia MN, Aguirre NC, Acuña CV, Martínez MC, Rivas JG, Cisneros EF, López JA, Poltri SNM, Munilla S, Cappa EP. Single-step genomic prediction of Eucalyptus dunnii using different identity-by-descent and identity-by-state relationship matrices. Heredity (Edinb) 2021; 127:176-189. [PMID: 34145424 PMCID: PMC8322403 DOI: 10.1038/s41437-021-00450-9] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/12/2021] [Revised: 06/07/2021] [Accepted: 06/07/2021] [Indexed: 02/05/2023] Open
Abstract
Genomic selection based on the single-step genomic best linear unbiased prediction (ssGBLUP) approach is becoming an important tool in forest tree breeding. The quality of the variance components and the predictive ability of the estimated breeding values (GEBV) depends on how well marker-based genomic relationships describe the actual genetic relationships at unobserved causal loci. We investigated the performance of GEBV obtained when fitting models with genomic covariance matrices based on two identity-by-descent (IBD) and two identity-by-state (IBS) relationship measures. Multiple-trait multiple-site ssGBLUP models were fitted to diameter and stem straightness in five open-pollinated progeny trials of Eucalyptus dunnii, genotyped using the EUChip60K. We also fitted the conventional ABLUP model with a pedigree-based covariance matrix. Estimated relationships from the IBD estimators displayed consistently lower standard deviations than those from the IBS approaches. Although ssGBLUP based in IBS estimators resulted in higher trait-site heritabilities, the gain in accuracy of the relationships using IBD estimators has resulted in higher predictive ability and lower bias of GEBV, especially for low-heritability trait-site. ssGBLUP based on IBS and IBD approaches performed considerably better than the traditional ABLUP. In summary, our results advocate the use of the ssGBLUP approach jointly with the IBD relationship matrix in open-pollinated forest tree evaluation.
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Affiliation(s)
- Esteban J Jurcic
- Instituto Nacional de Tecnología Agropecuaria (INTA), Instituto de Recursos Biológicos, Centro de Investigación en Recursos Naturales, Buenos Aires, Argentina.
- Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), Buenos Aires, Argentina.
| | - Pamela V Villalba
- Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), Buenos Aires, Argentina
- Instituto de Agrobiotecnología y Biología Molecular (IABiMo), INTA-CONICET, Buenos Aires, Argentina
| | - Pablo S Pathauer
- Instituto Nacional de Tecnología Agropecuaria (INTA), Instituto de Recursos Biológicos, Centro de Investigación en Recursos Naturales, Buenos Aires, Argentina
| | - Dino A Palazzini
- Instituto Nacional de Tecnología Agropecuaria (INTA), Instituto de Recursos Biológicos, Centro de Investigación en Recursos Naturales, Buenos Aires, Argentina
| | - Gustavo P J Oberschelp
- Instituto Nacional de Tecnología Agropecuaria (INTA), Estación Experimental Agropecuaria Concordia, Entre Ríos, Argentina
| | - Leonel Harrand
- Instituto Nacional de Tecnología Agropecuaria (INTA), Estación Experimental Agropecuaria Concordia, Entre Ríos, Argentina
| | - Martín N Garcia
- Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), Buenos Aires, Argentina
- Instituto de Agrobiotecnología y Biología Molecular (IABiMo), INTA-CONICET, Buenos Aires, Argentina
| | - Natalia C Aguirre
- Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), Buenos Aires, Argentina
- Instituto de Agrobiotecnología y Biología Molecular (IABiMo), INTA-CONICET, Buenos Aires, Argentina
| | - Cintia V Acuña
- Instituto de Agrobiotecnología y Biología Molecular (IABiMo), INTA-CONICET, Buenos Aires, Argentina
| | - María C Martínez
- Instituto de Agrobiotecnología y Biología Molecular (IABiMo), INTA-CONICET, Buenos Aires, Argentina
| | - Juan G Rivas
- Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), Buenos Aires, Argentina
- Instituto de Agrobiotecnología y Biología Molecular (IABiMo), INTA-CONICET, Buenos Aires, Argentina
| | - Esteban F Cisneros
- Facultad de Ciencias Forestales, Universidad Nacional de Santiago del Estero, Santiago del Estero, Argentina
| | - Juan A López
- Instituto Nacional de Tecnología Agropecuaria (INTA), Estación Experimental Agropecuaria Bella Vista, Corrientes, Argentina
| | - Susana N Marcucci Poltri
- Instituto de Agrobiotecnología y Biología Molecular (IABiMo), INTA-CONICET, Buenos Aires, Argentina
| | - Sebastián Munilla
- Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), Buenos Aires, Argentina
- Departamento de Producción Animal, Facultad de Agronomía, Universidad de Buenos Aires, Buenos Aires, Argentina
| | - Eduardo P Cappa
- Instituto Nacional de Tecnología Agropecuaria (INTA), Instituto de Recursos Biológicos, Centro de Investigación en Recursos Naturales, Buenos Aires, Argentina
- Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), Buenos Aires, Argentina
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Cappa EP, de Lima BM, da Silva-Junior OB, Garcia CC, Mansfield SD, Grattapaglia D. Improving genomic prediction of growth and wood traits in Eucalyptus using phenotypes from non-genotyped trees by single-step GBLUP. PLANT SCIENCE : AN INTERNATIONAL JOURNAL OF EXPERIMENTAL PLANT BIOLOGY 2019; 284:9-15. [PMID: 31084883 DOI: 10.1016/j.plantsci.2019.03.017] [Citation(s) in RCA: 24] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/12/2018] [Revised: 01/14/2019] [Accepted: 03/22/2019] [Indexed: 05/10/2023]
Abstract
Genomic Best Linear Unbiased Prediction (GBLUP) in tree breeding typically only uses information from genotyped trees. However, information from phenotyped but non-genotyped trees can also be highly valuable. The single-step GBLUP approach (ssGBLUP) allows genomic prediction to take into account both genotyped and non-genotyped trees simultaneously in a single evaluation. In this study, we investigated the advantage, in terms of breeding value accuracy and bias, of including phenotypic observation from non-genotyped trees in a standard tree GBLUP evaluation. We compared the efficiency of the conventional pedigree-based (ABLUP), GBLUP and ssGBLUP approaches to evaluate eight growth and wood quality traits in a Eucalyptus hybrid population, genotyped with 33,398 single nucleotide polymorphisms (SNPs) using the EucHIP60k. Theoretical accuracies, predictive ability and bias were calculated by ten-fold cross validation on all traits. The use of additional phenotypic information from non-genotyped trees by means of ssGBLUP provided higher predictive ability (from 37% to 75%) and lower prediction bias (from 21% to 73%) for the genetic component of non-phenotyped but genotyped trees when compared to GBLUP. The increase (decrease) in the prediction accuracy (bias) became stronger as trait heritability decreased. We concluded that ssGBLUP is a promising breeding tool to improve accuracies and bias over classical GBLUP for genomic evaluation in Eucalyptus breeding practice.
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Affiliation(s)
- Eduardo P Cappa
- Instituto Nacional de Tecnología Agropecuaria (INTA), Instituto de Recursos Biológicos, Centro de Investigación en Recursos Naturales, De Los Reseros y Dr. Nicolás Repetto s/n, 1686, Hurlingham, Buenos Aires, Argentina; Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), Argentina.
| | | | | | - Carla C Garcia
- International Paper of Brazil, Rodovia SP 340 KM 171, 13840-970, Mogi Guaçu, SP, Brazil
| | - Shawn D Mansfield
- University of British Columbia, Department of Wood Science, Faculty of Forestry, 2424 Main Mall, Vancouver, BC, V6T 1Z4, Canada
| | - Dario Grattapaglia
- EMBRAPA Genetic Resources and Biotechnology, EPQB Final W5 Norte, 70770-917, Brasilia, DF, Brazil; Genomic Sciences Program, Universidade Católica de Brasília, SGAN 916, Brasilia, DF, Brazil
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