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Li W, Li W, Song Z, Gao Z, Xie K, Wang Y, Wang B, Hu J, Zhang Q, Ning C, Wang D, Fan X. Marker Density and Models to Improve the Accuracy of Genomic Selection for Growth and Slaughter Traits in Meat Rabbits. Genes (Basel) 2024; 15:454. [PMID: 38674388 PMCID: PMC11050255 DOI: 10.3390/genes15040454] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/11/2024] [Revised: 03/25/2024] [Accepted: 03/27/2024] [Indexed: 04/28/2024] Open
Abstract
The selection and breeding of good meat rabbit breeds are fundamental to their industrial development, and genomic selection (GS) can employ genomic information to make up for the shortcomings of traditional phenotype-based breeding methods. For the practical implementation of GS in meat rabbit breeding, it is necessary to assess different marker densities and GS models. Here, we obtained low-coverage whole-genome sequencing (lcWGS) data from 1515 meat rabbits (including parent herd and half-sibling offspring). The specific objectives were (1) to derive a baseline for heritability estimates and genomic predictions based on randomly selected marker densities and (2) to assess the accuracy of genomic predictions for single- and multiple-trait linear mixed models. We found that a marker density of 50 K can be used as a baseline for heritability estimation and genomic prediction. For GS, the multi-trait genomic best linear unbiased prediction (GBLUP) model results in more accurate predictions for virtually all traits compared to the single-trait model, with improvements greater than 15% for all of them, which may be attributed to the use of information on genetically related traits. In addition, we discovered a positive correlation between the performance of the multi-trait GBLUP and the genetic correlation between the traits. We anticipate that this approach will provide solutions for GS, as well as optimize breeding programs, in meat rabbits.
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Affiliation(s)
- Wenjie Li
- Department of Animal Genetics and Breeding, Shandong Agricultural University, Taian 271000, China; (W.L.); (W.L.); (Z.S.); (K.X.); (B.W.); (J.H.); (Q.Z.); (C.N.)
- Department of Animal Genetics and Breeding, University of Anhui Agricultural, Hefei 230031, China
| | - Wenqiang Li
- Department of Animal Genetics and Breeding, Shandong Agricultural University, Taian 271000, China; (W.L.); (W.L.); (Z.S.); (K.X.); (B.W.); (J.H.); (Q.Z.); (C.N.)
| | - Zichen Song
- Department of Animal Genetics and Breeding, Shandong Agricultural University, Taian 271000, China; (W.L.); (W.L.); (Z.S.); (K.X.); (B.W.); (J.H.); (Q.Z.); (C.N.)
| | - Zihao Gao
- Department of Animal Genetics and Breeding, Shandong Agricultural University, Taian 271000, China; (W.L.); (W.L.); (Z.S.); (K.X.); (B.W.); (J.H.); (Q.Z.); (C.N.)
| | - Kerui Xie
- Department of Animal Genetics and Breeding, Shandong Agricultural University, Taian 271000, China; (W.L.); (W.L.); (Z.S.); (K.X.); (B.W.); (J.H.); (Q.Z.); (C.N.)
| | - Yubing Wang
- Department of Animal Genetics and Breeding, Shandong Agricultural University, Taian 271000, China; (W.L.); (W.L.); (Z.S.); (K.X.); (B.W.); (J.H.); (Q.Z.); (C.N.)
| | - Bo Wang
- Department of Animal Genetics and Breeding, Shandong Agricultural University, Taian 271000, China; (W.L.); (W.L.); (Z.S.); (K.X.); (B.W.); (J.H.); (Q.Z.); (C.N.)
| | - Jiaqing Hu
- Department of Animal Genetics and Breeding, Shandong Agricultural University, Taian 271000, China; (W.L.); (W.L.); (Z.S.); (K.X.); (B.W.); (J.H.); (Q.Z.); (C.N.)
| | - Qin Zhang
- Department of Animal Genetics and Breeding, Shandong Agricultural University, Taian 271000, China; (W.L.); (W.L.); (Z.S.); (K.X.); (B.W.); (J.H.); (Q.Z.); (C.N.)
| | - Chao Ning
- Department of Animal Genetics and Breeding, Shandong Agricultural University, Taian 271000, China; (W.L.); (W.L.); (Z.S.); (K.X.); (B.W.); (J.H.); (Q.Z.); (C.N.)
| | - Dan Wang
- Key Laboratory of Efficient Utilization of Non-Grain Feed Resources (Co-Construction by Ministry and Province), College of Animal Science and Veterinary Medicine, Shandong Agricultural University, Ministry of Agriculture and Rural Affairs, Taian 271000, China
| | - Xinzhong Fan
- Department of Animal Genetics and Breeding, Shandong Agricultural University, Taian 271000, China; (W.L.); (W.L.); (Z.S.); (K.X.); (B.W.); (J.H.); (Q.Z.); (C.N.)
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van Staaveren N, Rojas de Oliveira H, Houlahan K, Chud TCS, Oliveira GA, Hailemariam D, Kistemaker G, Miglior F, Plastow G, Schenkel FS, Cerri R, Sirard MA, Stothard P, Pryce J, Butty A, Stratz P, Abdalla EAE, Segelke D, Stamer E, Thaller G, Lassen J, Manzanilla-Pech CIV, Stephansen RB, Charfeddine N, García-Rodríguez A, González-Recio O, López-Paredes J, Baldwin R, Burchard J, Parker Gaddis KL, Koltes JE, Peñagaricano F, Santos JEP, Tempelman RJ, VandeHaar M, Weigel K, White H, Baes CF. The Resilient Dairy Genome Project-A general overview of methods and objectives related to feed efficiency and methane emissions. J Dairy Sci 2024; 107:1510-1522. [PMID: 37690718 DOI: 10.3168/jds.2022-22951] [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: 10/26/2022] [Accepted: 08/03/2023] [Indexed: 09/12/2023]
Abstract
The Resilient Dairy Genome Project (RDGP) is an international large-scale applied research project that aims to generate genomic tools to breed more resilient dairy cows. In this context, improving feed efficiency and reducing greenhouse gases from dairy is a high priority. The inclusion of traits related to feed efficiency (e.g., dry matter intake [DMI]) or greenhouse gases (e.g., methane emissions [CH4]) relies on available genotypes as well as high quality phenotypes. Currently, 7 countries (i.e., Australia, Canada, Denmark, Germany, Spain, Switzerland, and United States) contribute with genotypes and phenotypes including DMI and CH4. However, combining data are challenging due to differences in recording protocols, measurement technology, genotyping, and animal management across sources. In this study, we provide an overview of how the RDGP partners address these issues to advance international collaboration to generate genomic tools for resilient dairy. Specifically, we describe the current state of the RDGP database, data collection protocols in each country, and the strategies used for managing the shared data. As of February 2022, the database contains 1,289,593 DMI records from 12,687 cows and 17,403 CH4 records from 3,093 cows and continues to grow as countries upload new data over the coming years. No strong genomic differentiation between the populations was identified in this study, which may be beneficial for eventual across-country genomic predictions. Moreover, our results reinforce the need to account for the heterogeneity in the DMI and CH4 phenotypes in genomic analysis.
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Affiliation(s)
- Nienke van Staaveren
- Centre for Genetic Improvement of Livestock, Department of Animal Biosciences, University of Guelph, Guelph, ON N1G 2W1, Canada
| | - Hinayah Rojas de Oliveira
- Centre for Genetic Improvement of Livestock, Department of Animal Biosciences, University of Guelph, Guelph, ON N1G 2W1, Canada; Lactanet Canada, Guelph, ON N1K 1E5, Canada
| | - Kerry Houlahan
- Centre for Genetic Improvement of Livestock, Department of Animal Biosciences, University of Guelph, Guelph, ON N1G 2W1, Canada
| | - Tatiane C S Chud
- Centre for Genetic Improvement of Livestock, Department of Animal Biosciences, University of Guelph, Guelph, ON N1G 2W1, Canada
| | - Gerson A Oliveira
- Centre for Genetic Improvement of Livestock, Department of Animal Biosciences, University of Guelph, Guelph, ON N1G 2W1, Canada
| | - Dagnachew Hailemariam
- Department of Agricultural, Food and Nutritional Science, University of Alberta, Edmonton, AB T6G 2R3, Canada
| | | | - Filippo Miglior
- Centre for Genetic Improvement of Livestock, Department of Animal Biosciences, University of Guelph, Guelph, ON N1G 2W1, Canada; Lactanet Canada, Guelph, ON N1K 1E5, Canada
| | - Graham Plastow
- Department of Agricultural, Food and Nutritional Science, University of Alberta, Edmonton, AB T6G 2R3, Canada
| | - Flavio S Schenkel
- Centre for Genetic Improvement of Livestock, Department of Animal Biosciences, University of Guelph, Guelph, ON N1G 2W1, Canada
| | - Ronaldo Cerri
- Applied Animal Biology, Faculty of Land and Food Systems, University of British Columbia, Vancouver, BC, Canada V6T 1Z4
| | - Marc Andre Sirard
- Department of Animal Sciences, Laval University, Qubec G1V 0A6, QC, Canada
| | - Paul Stothard
- Department of Agricultural, Food and Nutritional Science, University of Alberta, Edmonton, AB T6G 2R3, Canada
| | - Jennie Pryce
- School of Applied Systems Biology, La Trobe University, Bundoora, Victoria 3083, Australia; Agriculture Victoria Research, LaTrobe University, Bundoora, Victoria 3083, Australia
| | | | | | - Emhimad A E Abdalla
- Vereinigte Informationssysteme Tierhaltung w.V. (vit), Heinrich-Schröder-Weg 1, 27283, Verden, Germany
| | - Dierck Segelke
- Vereinigte Informationssysteme Tierhaltung w.V. (vit), Heinrich-Schröder-Weg 1, 27283, Verden, Germany; Institute of Animal Breeding and Husbandry, Christian-Albrechts-University, 24098, Kiel, Germany
| | | | - Georg Thaller
- Institute of Animal Breeding and Husbandry, Christian-Albrechts-University, 24098, Kiel, Germany
| | - Jan Lassen
- Viking Genetics, Ebeltoftvej 16, 8960 Assentoft, Denmark
| | | | - Rasmus B Stephansen
- Center for Quantitative Genetics and Genomics, Aarhus University, DK-8830 Tjele, Denmark
| | - Noureddine Charfeddine
- Spanish Holstein Association (CONAFE), Ctra. Andalucía km 23600 Valdemoro, 28340 Madrid, Spain
| | - Aser García-Rodríguez
- Department of Animal Production, NEIKER-Basque Institute for Agricultural Research and Development, 01192 Arkaute, Spain
| | - Oscar González-Recio
- Department of Animal Breeding, Instituto Nacional de Investigacion y Tecnologia Agraria y Alimentaria (INIA-CSIC), 28040 Madrid, Spain
| | - Javier López-Paredes
- Federación Española de Criadores de Limusín, C/Infanta Mercedes, 31, 28020 Madrid, Spain
| | - Ransom Baldwin
- Animal Genomics and Improvement Laboratory, Agricultural Research Service, USDA, Beltsville, MD 20705
| | | | | | - James E Koltes
- Department of Animal Science, Iowa State University, Ames, IA 50011
| | - Francisco Peñagaricano
- Department of Animal and Dairy Sciences, University of Wisconsin-Madison, Madison, WI 53706
| | | | - Robert J Tempelman
- Department of Animal Science, Michigan State University, East Lansing, MI 48824
| | - Michael VandeHaar
- Department of Animal Science, Michigan State University, East Lansing, MI 48824
| | - Kent Weigel
- Department of Animal and Dairy Sciences, University of Wisconsin-Madison, Madison, WI 53706
| | - Heather White
- Department of Animal and Dairy Sciences, University of Wisconsin-Madison, Madison, WI 53706
| | - Christine F Baes
- Centre for Genetic Improvement of Livestock, Department of Animal Biosciences, University of Guelph, Guelph, ON N1G 2W1, Canada; Vetsuisse Faculty, Institute of Genetics, University of Bern, 3012 Bern, Switzerland.
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Ning C, Xie K, Huang J, Di Y, Wang Y, Yang A, Hu J, Zhang Q, Wang D, Fan X. Marker density and statistical model designs to increase accuracy of genomic selection for wool traits in Angora rabbits. Front Genet 2022; 13:968712. [PMID: 36118881 PMCID: PMC9478554 DOI: 10.3389/fgene.2022.968712] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/14/2022] [Accepted: 08/17/2022] [Indexed: 11/13/2022] Open
Abstract
The Angora rabbit, a well-known breed for fiber production, has been undergoing traditional breeding programs relying mainly on phenotypes. Genomic selection (GS) uses genomic information and promises to accelerate genetic gain. Practically, to implement GS in Angora rabbit breeding, it is necessary to evaluate different marker densities and GS models to develop suitable strategies for an optimized breeding pipeline. Considering a lack in microarray, low-coverage sequencing combined with genotype imputation was used to boost the number of SNPs across the rabbit genome. Here, in a population of 629 Angora rabbits, a total of 18,577,154 high-quality SNPs were imputed (imputation accuracy above 98%) based on low-coverage sequencing of 3.84X genomic coverage, and wool traits and body weight were measured at 70, 140 and 210 days of age. From the original markers, 0.5K, 1K, 3K, 5K, 10K, 50K, 100K, 500K, 1M and 2M were randomly selected and evaluated, resulting in 50K markers as the baseline for the heritability estimation and genomic prediction. Comparing to the GS performance of single-trait models, the prediction accuracy of nearly all traits could be improved by multi-trait models, which might because multiple-trait models used information from genetically correlated traits. Furthermore, we observed high significant negative correlation between the increased prediction accuracy from single-trait to multiple-trait models and estimated heritability. The results indicated that low-heritability traits could borrow more information from correlated traits and hence achieve higher prediction accuracy. The research first reported heritability estimation in rabbits by using genome-wide markers, and provided 50K as an optimal marker density for further microarray design, genetic evaluation and genomic selection in Angora rabbits. We expect that the work could provide strategies for GS in early selection, and optimize breeding programs in rabbits.
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Affiliation(s)
| | | | | | | | | | | | | | | | - Dan Wang
- *Correspondence: Dan Wang, ; Xinzhong Fan,
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Ning Y, Zhang L, Wang W, Wu S. Effect of genetic variants in the SMAD1 and SMAD5 genes promoter on growth and beef quality traits in cattle. Gene 2022; 819:146220. [PMID: 35093446 DOI: 10.1016/j.gene.2022.146220] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/21/2021] [Revised: 12/15/2021] [Accepted: 01/13/2022] [Indexed: 11/27/2022]
Abstract
The SMAD1 and SMAD5 genes belong to mothers against decapentaplegic proteins family, which participate in the BMP pathway to control skeletal myogenesis and growth. In the present study, we analyzed the associations between polymorphisms of SMAD1 and SMAD5 genes promoter and important economical traits in Qinchuan cattle. Four SNPs in the SMAD1 gene promoter and three SNPs in the SMAD5 promoter were identified by sequencing of 448 Qinchuan cattles. Allelic and frequency analyses of these SNPs resulted in eight haplotypes both in the promoters of the two genes promoter and identified potential cis-regulatory transcription factor (TF) components. In addition, correlation analysis showed that cattle SMAD1 promoter activity of individuals with Hap4 (P < 0.01) was stronger than that of individuals with Hap2. while the transcriptional activity of individuals with Hap3 within SMAD5 gene promoter was significantly (P < 0.01) higher followed by H2. Uniformly, diplotypes H4-H6 of SMAD1 gene and H1-H3 of SMAD5 gene performed significant (P < 0.01) associations with body measurement and improved carcass quality traits. All these results have indicated that polymorphisms in SMAD1 and SMAD5 genes promoter could impact the transcriptional regulation and then affect muscle content in beef cattle. Moreover, both the SMAD1 and SMAD5 genes were expressed ubiquitously in 10 tissues and had higher expression in the longissimus thoracis tissue from 6-month-old and 12-month-old cattle than in cattle of other ages. We can conclude that SMAD1 and SMAD5 genes may play an important role in muscle growth and development, and the variants mapped within SMAD1 and SMAD5 genes can be utilized in molecular marker-assisted selection for cattle carcass quality and body measurement traits in breed improvement programs of Qinchuan cattle.
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Affiliation(s)
- Yue Ning
- College of Chemistry and Chemical Engineering, Xianyang Normal University, Xianyang, Shaanxi 712000, China
| | - Le Zhang
- Institute of Physical Education, Yan'an University, Yan'an 716000, Shaanxi, China.
| | - Wenbo Wang
- College of Chemistry and Chemical Engineering, Xianyang Normal University, Xianyang, Shaanxi 712000, China
| | - Sen Wu
- Qinghai Academy of Animal Science and Veterinary Medicine, Qinghai University, Xining, China
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Neumann GB, Korkuć P, Arends D, Wolf MJ, May K, Reißmann M, Elzaki S, König S, Brockmann GA. Design and performance of a bovine 200 k SNP chip developed for endangered German Black Pied cattle (DSN). BMC Genomics 2021; 22:905. [PMID: 34922441 PMCID: PMC8684242 DOI: 10.1186/s12864-021-08237-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/12/2021] [Accepted: 12/03/2021] [Indexed: 08/26/2023] Open
Abstract
BACKGROUND German Black Pied cattle (DSN) are an endangered dual-purpose breed which was largely replaced by Holstein cattle due to their lower milk yield. DSN cattle are kept as a genetic reserve with a current herd size of around 2500 animals. The ability to track sequence variants specific to DSN could help to support the conservation of DSN's genetic diversity and to provide avenues for genetic improvement. RESULTS Whole-genome sequencing data of 304 DSN cattle were used to design a customized DSN200k SNP chip harboring 182,154 variants (173,569 SNPs and 8585 indels) based on ten selection categories. We included variants of interest to DSN such as DSN unique variants and variants from previous association studies in DSN, but also variants of general interest such as variants with predicted consequences of high, moderate, or low impact on the transcripts and SNPs from the Illumina BovineSNP50 BeadChip. Further, the selection of variants based on haplotype blocks ensured that the whole-genome was uniformly covered with an average variant distance of 14.4 kb on autosomes. Using 300 DSN and 162 animals from other cattle breeds including Holstein, endangered local cattle populations, and also a Bos indicus breed, performance of the SNP chip was evaluated. Altogether, 171,978 (94.31%) of the variants were successfully called in at least one of the analyzed breeds. In DSN, the number of successfully called variants was 166,563 (91.44%) while 156,684 (86.02%) were segregating at a minor allele frequency > 1%. The concordance rate between technical replicates was 99.83 ± 0.19%. CONCLUSION The DSN200k SNP chip was proved useful for DSN and other Bos taurus as well as one Bos indicus breed. It is suitable for genetic diversity management and marker-assisted selection of DSN animals. Moreover, variants that were segregating in other breeds can be used for the design of breed-specific customized SNP chips. This will be of great value in the application of conservation programs for endangered local populations in the future.
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Affiliation(s)
- Guilherme B Neumann
- Animal Breeding Biology and Molecular Genetics, Albrecht Daniel Thaer-Institute for Agricultural and Horticultural Sciences, Humboldt Universität zu Berlin, Berlin, Germany
| | - Paula Korkuć
- Animal Breeding Biology and Molecular Genetics, Albrecht Daniel Thaer-Institute for Agricultural and Horticultural Sciences, Humboldt Universität zu Berlin, Berlin, Germany
| | - Danny Arends
- Animal Breeding Biology and Molecular Genetics, Albrecht Daniel Thaer-Institute for Agricultural and Horticultural Sciences, Humboldt Universität zu Berlin, Berlin, Germany
| | - Manuel J Wolf
- Institute of Animal Breeding and Genetics, Justus-Liebig-Universität, Gießen, Germany
| | - Katharina May
- Institute of Animal Breeding and Genetics, Justus-Liebig-Universität, Gießen, Germany
| | - Monika Reißmann
- Animal Breeding Biology and Molecular Genetics, Albrecht Daniel Thaer-Institute for Agricultural and Horticultural Sciences, Humboldt Universität zu Berlin, Berlin, Germany
| | - Salma Elzaki
- Animal Breeding Biology and Molecular Genetics, Albrecht Daniel Thaer-Institute for Agricultural and Horticultural Sciences, Humboldt Universität zu Berlin, Berlin, Germany.,Department of Genetics and Animal Breeding, Faculty of Animal Production, University of Khartoum, Khartoum North, Sudan
| | - Sven König
- Institute of Animal Breeding and Genetics, Justus-Liebig-Universität, Gießen, Germany
| | - Gudrun A Brockmann
- Animal Breeding Biology and Molecular Genetics, Albrecht Daniel Thaer-Institute for Agricultural and Horticultural Sciences, Humboldt Universität zu Berlin, Berlin, Germany.
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