Genetic evaluation of tropical climate-adapted sheep for carcass traits including genomic information
•Moderate genomic parameters for ultrasound-based carcass traits were obtained for hair sheep adapted to harsh environments.•Carcass traits evaluated in vivo are feasible for selection in Santa Inês sheep.•Genetic evaluation for carcass traits is favored by adding genomic information.•Single-step GB...
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Published in: | Small ruminant research Vol. 188; p. 106120 |
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Abstract | •Moderate genomic parameters for ultrasound-based carcass traits were obtained for hair sheep adapted to harsh environments.•Carcass traits evaluated in vivo are feasible for selection in Santa Inês sheep.•Genetic evaluation for carcass traits is favored by adding genomic information.•Single-step GBLUP outperforms BLUP based on the accuracy of genomic breeding values.
Santa Inês is a locally adapted hair sheep breed that is under large expansion in Brazil and other Tropical countries. This study aimed to evaluate the effect of different relationship matrices (A or H) on the estimation of genetic parameters and accuracy of (genomic) breeding values [(G)EBVs] for carcass traits measured in vivo in Santa Inês sheep. A total of 977, 890, 894, and 882 records for loin eye area (LEA), marbling score (MLE), subcutaneous fat thickness (SFT), and leg circumference (LEC), respectively, were included in this study. There were 1637 animals in the numerator relationship matrix (A). A total of 389 animals with phenotypic information were genotyped using the OvineSNP50 BeadChip. After the genotyping quality control, 388 samples and 42,748 SNPs remained in the dataset. The (co)variance components were estimated via Bayesian inference through single- and multi-trait analyses using the animal model with either the A or H matrix. The theoretical accuracies of (G)EBVs, Pearson correlation between breeding values estimated including or not genomic information, and Spearman rank correlation were used to evaluate the feasibility of incorporating genomic information in the analyses. Heritability estimates ranged from 0.12 ± 0.06 (SFT, using the A matrix) to 0.29 ± 0.07 (LEA, using the H matrix), in the single-trait analyses, and from 0.17 ± 0.05 (SFT, using A) to 0.33 ± 0.07 (LEA, using H), in a multi-trait setting. Direct genetic additive variance and heritability estimates were higher when including genomic information. Unfavorable and high genetic correlation was observed between LEA and SFT. The estimates of theoretical accuracy of (G)EBVs were higher in all the scenarios when genomic information was incorporated in the model. Gains in accuracy ranged from 0.013 to 0.039 units when genomic information was included in the analyses. Thus, if available, genomic information should be incorporated in the estimation of genetic parameters and breeding values for carcass traits measured in vivo in Santa Inês sheep. |
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AbstractList | •Moderate genomic parameters for ultrasound-based carcass traits were obtained for hair sheep adapted to harsh environments.•Carcass traits evaluated in vivo are feasible for selection in Santa Inês sheep.•Genetic evaluation for carcass traits is favored by adding genomic information.•Single-step GBLUP outperforms BLUP based on the accuracy of genomic breeding values.
Santa Inês is a locally adapted hair sheep breed that is under large expansion in Brazil and other Tropical countries. This study aimed to evaluate the effect of different relationship matrices (A or H) on the estimation of genetic parameters and accuracy of (genomic) breeding values [(G)EBVs] for carcass traits measured in vivo in Santa Inês sheep. A total of 977, 890, 894, and 882 records for loin eye area (LEA), marbling score (MLE), subcutaneous fat thickness (SFT), and leg circumference (LEC), respectively, were included in this study. There were 1637 animals in the numerator relationship matrix (A). A total of 389 animals with phenotypic information were genotyped using the OvineSNP50 BeadChip. After the genotyping quality control, 388 samples and 42,748 SNPs remained in the dataset. The (co)variance components were estimated via Bayesian inference through single- and multi-trait analyses using the animal model with either the A or H matrix. The theoretical accuracies of (G)EBVs, Pearson correlation between breeding values estimated including or not genomic information, and Spearman rank correlation were used to evaluate the feasibility of incorporating genomic information in the analyses. Heritability estimates ranged from 0.12 ± 0.06 (SFT, using the A matrix) to 0.29 ± 0.07 (LEA, using the H matrix), in the single-trait analyses, and from 0.17 ± 0.05 (SFT, using A) to 0.33 ± 0.07 (LEA, using H), in a multi-trait setting. Direct genetic additive variance and heritability estimates were higher when including genomic information. Unfavorable and high genetic correlation was observed between LEA and SFT. The estimates of theoretical accuracy of (G)EBVs were higher in all the scenarios when genomic information was incorporated in the model. Gains in accuracy ranged from 0.013 to 0.039 units when genomic information was included in the analyses. Thus, if available, genomic information should be incorporated in the estimation of genetic parameters and breeding values for carcass traits measured in vivo in Santa Inês sheep. |
ArticleNumber | 106120 |
Author | Sousa Júnior, Antônio de Brito, Luiz Fernando Santos, Gleyson Vieira dos Sarmento, José Lindenberg Rocha Santos, Natanael Pereira da Silva Figueiredo Filho, Luiz Antonio Silva Britto, Fábio Barros Sena, Luciano Silva |
Author_xml | – sequence: 1 givenname: Luciano Silva orcidid: 0000-0003-0054-6655 surname: Sena fullname: Sena, Luciano Silva organization: Graduate Program in Animal Science, Agrarian Sciences Center (CCA), Federal University of Piauí (UFPI), Campus Universitário Ministro Petrônio Portella, Teresina, PI, 64049-550, Brazil – sequence: 2 givenname: Luiz Antonio Silva orcidid: 0000-0003-3204-1109 surname: Figueiredo Filho fullname: Figueiredo Filho, Luiz Antonio Silva organization: Federal Institute of Education, Science and Technology of Maranhão (IFMA), MA 340, Km 2, Caxias, MA, 65609-899, Brazil – sequence: 3 givenname: Gleyson Vieira dos surname: Santos fullname: Santos, Gleyson Vieira dos organization: Balsas Superior Studies Center (CESBA), Maranhão State University (UEMA), Praça Gonçalves Dias, s/n, Balsas, MA 65800-000, Brazil – sequence: 4 givenname: Antônio de surname: Sousa Júnior fullname: Sousa Júnior, Antônio de organization: Technical School of Teresina, Agrarian Sciences Center, UFPI, Campus Universitário Ministro Petrônio Portella, Teresina, PI, 64049-550, Brazil – sequence: 5 givenname: Natanael Pereira da Silva surname: Santos fullname: Santos, Natanael Pereira da Silva organization: Department of Agronomy, UFPI, Campus Professora Cinobelina Elvas, BR 135, Km 3, Bom Jesus, PI, 64900-000, Brazil – sequence: 6 givenname: Fábio Barros orcidid: 0000-0002-5705-5374 surname: Britto fullname: Britto, Fábio Barros organization: Department of Biology, UFPI, Campus Universitário Ministro Petrônio Portella, Teresina, PI, 64049-550, Brazil – sequence: 7 givenname: José Lindenberg Rocha orcidid: 0000-0002-4215-1515 surname: Sarmento fullname: Sarmento, José Lindenberg Rocha email: sarmento@ufpi.edu.br organization: Department of Animal Science, Agrarian Sciences Center, UFPI, Campus Universitário Ministro Petrônio Portella, Teresina, PI, 64049-550, Brazil – sequence: 8 givenname: Luiz Fernando surname: Brito fullname: Brito, Luiz Fernando organization: Department of Animal Sciences, College of Agriculture, Purdue University, 270 S. Russell Street, West Lafayette, IN 47907, USA |
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CitedBy_id | crossref_primary_10_1016_j_smallrumres_2022_106655 crossref_primary_10_1590_0103_8478cr20230576 crossref_primary_10_3389_fvets_2022_896241 crossref_primary_10_3389_fanim_2023_1195680 |
Cites_doi | 10.1016/j.smallrumres.2015.12.030 10.1590/S0102-09352006000400021 10.3168/jds.2007-0980 10.3168/jds.2009-2730 10.1016/j.smallrumres.2017.07.011 10.1186/s12863-017-0476-8 10.1111/1541-4337.12149 10.1111/jbg.12396 10.2527/jas.2015-0134 10.1016/j.livprodsci.2004.09.003 10.1590/s0100-204x2018000600008 10.1111/jbg.12029 10.1016/j.smallrumres.2018.12.007 10.1016/j.meatsci.2013.09.007 10.1007/s11250-015-0921-5 10.3168/jds.S0022-0302(84)81479-4 |
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