1
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Worku D. Unraveling the genetic basis of methane emission in dairy cattle: a comprehensive exploration and breeding approach to lower methane emissions. Anim Biotechnol 2024; 35:2362677. [PMID: 38860914 DOI: 10.1080/10495398.2024.2362677] [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] [Indexed: 06/12/2024]
Abstract
Ruminant animals, such as dairy cattle, produce CH4, which contributes to global warming emissions and reduces dietary energy for the cows. While the carbon foot print of milk production varies based on production systems, milk yield and farm management practices, enteric fermentation, and manure management are major contributors togreenhouse gas emissions from dairy cattle. Recent emerging evidence has revealed the existence of genetic variation for CH4 emission traits among dairy cattle, suggests their potential inclusion in breeding goals and genetic selection programs. Advancements in high-throughput sequencing technologies and analytical techniques have enabled the identification of potential metabolic biomarkers, candidate genes, and SNPs linked to methane emissions. Indeed, this review critically examines our current understanding of carbon foot print in milk production, major emission sources, rumen microbial community and enteric fermentation, and the genetic architecture of methane emission traits in dairy cattle. It also emphasizes important implications for breeding strategies aimed at halting methane emissions through selective breeding, microbiome driven breeding, breeding for feed efficiency, and breeding by gene editing.
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Affiliation(s)
- Destaw Worku
- Department of Animal Science, College of Agriculture, Food and Climate Science, Injibara University, Injibara, Ethiopia
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2
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van Breukelen AE, Veerkamp RF, de Haas Y, Aldridge MN. Genetic parameter estimates for methane emission from breath during lactation and potential inaccuracies in reliabilities assuming a repeatability versus random regression model. J Dairy Sci 2024; 107:5853-5868. [PMID: 38490557 DOI: 10.3168/jds.2024-24285] [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: 10/06/2023] [Accepted: 02/13/2024] [Indexed: 03/17/2024]
Abstract
Methane emissions will be added to many national ruminant breeding programs in the coming years. Little is known about the covariance structure of CH4 traits over a lactation, which is important for optimizing recording strategies and establishing optimal genetic evaluation models. Our aim was to study CH4 over a lactation using random regression (RR) models, and to compare the accuracy to a fixed regression repeatability model under different phenotyping strategies. Data were available from repeated measurements of CH4 concentrations (ppm) recorded in the feed bins of milking robots on 52 commercial dairy farms in the Netherlands. In total, 36,370 averaged weekly records were available from 4,664 cows. Genetic parameters were estimated using a fixed regression model, and a RR model with first- to fifth-order Legendre polynomials for the additive genetic and within-lactation permanent environmental effect. The mean heritability (± SE) was 0.17 ± 0.04, and the mean within-lactation repeatability was 0.56 ± 0.03. The genetic correlations between DIM were high and ranged from 0.34 ± 0.36 to 1.00 ± <0.01. Permanent environmental correlations showed large deviations and ranged from -0.73 ± 0.08 to 1.00 ± <0.01. With a large number of full lactation daughter CH4 records per bull, the reliability was not sensitive to using the fixed versus the RR model. However, when shorter periods were recorded at the start and end of the lactation, the fixed regression model resulted in a loss of reliability up to 28% for bulls. Assuming the fixed model when the true (co)variance structure is reflected by the RR model, more than twice as long of a recording from the start of lactation was required to achieve maximum reliability for a bull. Thus, a too simplistic model could result in implementing too little recording, and in lower genetic gains than predicted from the reliability.
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Affiliation(s)
- A E van Breukelen
- Wageningen University & Research, Animal Breeding and Genomics Group, 6700 AH Wageningen, the Netherlands.
| | - R F Veerkamp
- Wageningen University & Research, Animal Breeding and Genomics Group, 6700 AH Wageningen, the Netherlands
| | - Y de Haas
- Wageningen University & Research, Animal Breeding and Genomics Group, 6700 AH Wageningen, the Netherlands
| | - M N Aldridge
- Wageningen University & Research, Animal Breeding and Genomics Group, 6700 AH Wageningen, the Netherlands
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3
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Giagnoni G, Lassen J, Lund P, Foldager L, Johansen M, Weisbjerg MR. Feed intake in housed dairy cows: validation of a three-dimensional camera-based feed intake measurement system. Animal 2024; 18:101178. [PMID: 38823283 DOI: 10.1016/j.animal.2024.101178] [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: 11/09/2023] [Revised: 04/22/2024] [Accepted: 04/22/2024] [Indexed: 06/03/2024] Open
Abstract
Measuring feed intake accurately is crucial to determine feed efficiency and for genetic selection. A system using three-dimensional (3D) cameras and deep learning algorithms can measure the volume of feed intake in dairy cows, but for now, the system has not been validated for feed intake expressed as weight of feed. The aim of this study was to validate the weight of feed intake predicted from the 3D cameras with the actual measured weight. It was hypothesised that diet-specific coefficients are necessary for predicting changes in weight, that the relationship between weight and volume is curvilinear throughout the day, and that manually pushing the feed affects this relationship. Twenty-four lactating Danish Holstein cows were used in a cross-over design with four dietary treatments, 2 × 2 factorial arranged with either grass-clover silage or maize silage as silage factor, and barley or dried beet pulp as concentrate factor. Cows were adapted to the diets for 11 d, and for 3 d to tie-stall housing before camera measurements. Six cameras were used for recording, each mounted over an individual feeding platform equipped with a weight scale. When building the predictive models, four cameras were used for training, and the remaining two for testing the prediction of the models. The most accurate predictions were found for the average feed intake over a period when using the starting density of the feed pile, which resulted in the lowest errors, 6% when expressed as RMSE and 5% expressed as mean absolute error. A model including curvilinear effects of feed volume and the impact of manual feed pushing was used on a dataset including daily time points. When cross-validating, the inclusion of a curvilinear effect and a feed push effect did not improve the accuracy of the model for neither the feed pile nor the feed removed by the cow between consecutive time points. In conclusion, measuring daily feed intake from this 3D camera system in the present experimental setup could be accomplished with an acceptable error (below 8%), but the system should be improved for individual meal intake measurements if these measures were to be implemented.
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Affiliation(s)
- G Giagnoni
- Department of Animal and Veterinary Sciences, AU Viborg - Research Centre Foulum, Aarhus University, Blichers Allé 20, 8830 Tjele, Denmark.
| | - J Lassen
- Center for Quantitative Genetics and Genomics, Aarhus University, C. F. Møllers Allé 3, 8000 Aarhus, Denmark; Viking Genetics, Ebeltoftvej 16, 8960 Randers, Denmark
| | - P Lund
- Department of Animal and Veterinary Sciences, AU Viborg - Research Centre Foulum, Aarhus University, Blichers Allé 20, 8830 Tjele, Denmark
| | - L Foldager
- Department of Animal and Veterinary Sciences, AU Viborg - Research Centre Foulum, Aarhus University, Blichers Allé 20, 8830 Tjele, Denmark; Bioinformatics Research Centre, Aarhus University, Universitetsbyen 81, 8000 Aarhus, Denmark
| | - M Johansen
- Department of Animal and Veterinary Sciences, AU Viborg - Research Centre Foulum, Aarhus University, Blichers Allé 20, 8830 Tjele, Denmark
| | - M R Weisbjerg
- Department of Animal and Veterinary Sciences, AU Viborg - Research Centre Foulum, Aarhus University, Blichers Allé 20, 8830 Tjele, Denmark
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4
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Fresco S, Boichard D, Lefebvre R, Barbey S, Gaborit M, Fritz S, Martin P. Short communication: Correlation of methane production, intensity, and yield with residual feed intake throughout lactation in Holstein cows. Animal 2024; 18:101110. [PMID: 38442541 DOI: 10.1016/j.animal.2024.101110] [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: 10/05/2023] [Revised: 02/06/2024] [Accepted: 02/06/2024] [Indexed: 03/07/2024] Open
Abstract
The environmental impact of dairy production can be reduced in several ways, including increasing feed efficiency and reducing methane (CH4) emissions. There is no consensus on their relationship. This study aimed at estimating the correlations between residual feed intake (RFI) and CH4 emissions expressed in g/d methane production (MeP), g/kg of fat- and protein-corrected milk methane intensity (MeI), or g/kg of DM intake methane yield (MeY) throughout lactation. We collected CH4 data using GreenFeed devices from 107 Holstein cows, as well as production and intake phenotypes. RFI was predicted from DM intake, fat- and protein-corrected milk, BW, and body condition score. Five-trait random regression models were used to estimate the individual variance components of the CH4 and production traits, which were used to calculate the correlations between RFI and CH4 traits throughout lactation. We found positive correlations of RFI with MeP and MeI ranging from 0.05 to 0.47 throughout the lactation. Correlations between RFI and MeY are low and vary from positive to negative, ranging from -0.18 to 0.17. Both MeP and MeI are favorably correlated with RFI, as is MeY during the first half of lactation. These correlations are mostly favorable for genetic selection, but the confirmation of these results is needed with genetic correlations over a larger dataset.
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Affiliation(s)
- S Fresco
- Eliance, 149 rue de Bercy, 75595 Paris, France; Université Paris-Saclay, INRAE, AgroParisTech, GABI, 78350 Jouy-en-Josas, France.
| | - D Boichard
- Université Paris-Saclay, INRAE, AgroParisTech, GABI, 78350 Jouy-en-Josas, France
| | - R Lefebvre
- Université Paris-Saclay, INRAE, AgroParisTech, GABI, 78350 Jouy-en-Josas, France
| | - S Barbey
- INRAE, UE326, Domaine Expérimental du Pin, 61310 Exmes, France
| | - M Gaborit
- INRAE, UE326, Domaine Expérimental du Pin, 61310 Exmes, France
| | - S Fritz
- Eliance, 149 rue de Bercy, 75595 Paris, France; Université Paris-Saclay, INRAE, AgroParisTech, GABI, 78350 Jouy-en-Josas, France
| | - P Martin
- Université Paris-Saclay, INRAE, AgroParisTech, GABI, 78350 Jouy-en-Josas, France
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5
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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: 1] [Impact Index Per Article: 1.0] [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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6
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Martínez-Marín G, Schiavon S, Tagliapietra F, Cecchinato A, Toledo-Alvarado H, Bittante G. Interactions among breed, farm intensiveness and cow productivity on predicted enteric methane emissions at the population level. ITALIAN JOURNAL OF ANIMAL SCIENCE 2023. [DOI: 10.1080/1828051x.2022.2158953] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/04/2023]
Affiliation(s)
- Gustavo Martínez-Marín
- Department of Agronomy, Food, Natural resources, Animals and Environment (DAFNAE), University of Padova (Padua), Legnaro, Italy
| | - Stefano Schiavon
- Department of Agronomy, Food, Natural resources, Animals and Environment (DAFNAE), University of Padova (Padua), Legnaro, Italy
| | - Franco Tagliapietra
- Department of Agronomy, Food, Natural resources, Animals and Environment (DAFNAE), University of Padova (Padua), Legnaro, Italy
| | - Alessio Cecchinato
- Department of Agronomy, Food, Natural resources, Animals and Environment (DAFNAE), University of Padova (Padua), Legnaro, Italy
| | - Hugo Toledo-Alvarado
- Department of Genetics and Biostatistics, Faculty of Veterinary Medicine and Zootechnics, National Autonomous University of Mexico, Mexico City, México
| | - Giovanni Bittante
- Department of Agronomy, Food, Natural resources, Animals and Environment (DAFNAE), University of Padova (Padua), Legnaro, Italy
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7
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Manzanilla-Pech CIV, Stephansen RB, Lassen J. Genetic parameters for feed intake and body weight in dairy cattle using high-throughput 3-dimensional cameras in Danish commercial farms. J Dairy Sci 2023; 106:9006-9015. [PMID: 37641284 DOI: 10.3168/jds.2023-23405] [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: 02/21/2023] [Accepted: 06/08/2023] [Indexed: 08/31/2023]
Abstract
Recording complex phenotypes on a large scale is becoming possible with the incorporation of recently developed new technologies. One of these new technologies is the use of 3-dimensional (3D) cameras on commercial farms to measure feed intake and body weight (BW) daily. Residual feed intake (RFI) has been proposed as a proxy for feed efficiency in several species, including cattle, pigs, and poultry. Dry matter intake (DMI) and BW records are required to calculate RFI, and the use of this new technology will help increase the number of individual records more efficiently. The aim of this study was to estimate genetic parameters (including genetic correlations) for DMI and BW obtained by 3D cameras from 6,000 cows in commercial farms from the breeds Danish Holstein, Jersey, and Nordic Red. Additionally, heritabilities per parity and genetic correlations among parities were estimated for DMI and BW in the 3 breeds. Data included 158,000 weekly records of DMI and BW obtained between 2019 and 2022 on 17 commercial farms. Estimated heritability for DMI ranged from 0.17 to 0.25, whereas for BW they ranged from 0.44 to 0.58. The genetic correlations between DMI and BW were moderately positive (0.58-0.65). Genetic correlations among parities in both traits were highly correlated in the 3 breeds, except for DMI between first parity and late parities in Holstein where they were down to 0.62. Based on these results, we conclude that DMI and BW phenotypes measured by 3D cameras are heritable for the 3 dairy breeds and their heritabilities are comparable to those obtained by traditional methods (scales and feed bins). The high heritabilities and correlations of 3D measurements with the true trait in previous studies demonstrate the potential of this new technology for measuring feed intake and BW in real time. In conclusion, 3D camera technology has the potential to become a valuable tool for automatic and continuous recording of feed intake and BW on commercial farms.
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Affiliation(s)
| | - Rasmus B Stephansen
- Center for Quantitative Genetics and Genomics, Aarhus University, DK-8000 Aarhus, Denmark
| | - Jan Lassen
- Center for Quantitative Genetics and Genomics, Aarhus University, DK-8000 Aarhus, Denmark; Viking Genetics, Assentoft, 8960 Randers, Denmark
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8
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Stephansen RB, Martin P, Manzanilla-Pech CIV, Gredler-Grandl B, Sahana G, Madsen P, Weigel K, Tempelman RJ, Peñagaricano F, Parker Gaddis KL, White HM, Santos JEP, Koltes JE, Schenkel F, Hailemariam D, Plastow G, Abdalla E, VandeHaar M, Veerkamp RF, Baes C, Lassen J. Novel genetic parameters for genetic residual feed intake in dairy cattle using time series data from multiple parities and countries in North America and Europe. J Dairy Sci 2023; 106:9078-9094. [PMID: 37678762 DOI: 10.3168/jds.2023-23330] [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: 02/03/2023] [Accepted: 07/06/2023] [Indexed: 09/09/2023]
Abstract
Residual feed intake is viewed as an important trait in breeding programs that could be used to enhance genetic progress in feed efficiency. In particular, improving feed efficiency could improve both economic and environmental sustainability in the dairy cattle industry. However, data remain sparse, limiting the development of reliable genomic evaluations across lactation and parity for residual feed intake. Here, we estimated novel genetic parameters for genetic residual feed intake (gRFI) across the first, second, and third parity, using a random regression model. Research data on the measured feed intake, milk production, and body weight of 7,379 cows (271,080 records) from 6 countries in 2 continents were shared through the Horizon 2020 project Genomic Management Tools to Optimise Resilience and Efficiency, and the Resilient Dairy Genome Project. The countries included Canada (1,053 cows with 47,130 weekly records), Denmark (1,045 cows with 72,760 weekly records), France (329 cows with 16,888 weekly records), Germany (938 cows with 32,614 weekly records), the Netherlands (2,051 cows with 57,830 weekly records), and United States (1,963 cows with 43,858 weekly records). Each trait had variance components estimated from first to third parity, using a random regression model across countries. Genetic residual feed intake was found to be heritable in all 3 parities, with first parity being predominant (range: 22-34%). Genetic residual feed intake was highly correlated across parities for mid- to late lactation; however, genetic correlation across parities was lower during early lactation, especially when comparing first and third parity. We estimated a genetic correlation of 0.77 ± 0.37 between North America and Europe for dry matter intake at first parity. Published literature on genetic correlations between high input countries/continents for dry matter intake support a high genetic correlation for dry matter intake. In conclusion, our results demonstrate the feasibility of estimating variance components for gRFI across parities, and the value of sharing data on scarce phenotypes across countries. These results can potentially be implemented in genetic evaluations for gRFI in dairy cattle.
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Affiliation(s)
- R B Stephansen
- Center for Quantitative Genetics and Genomics, Aarhus University, C. F. M⊘llers Allé 3, 8000 Aarhus, Denmark.
| | - P Martin
- Université Paris-Saclay, INRAE, AgroParisTech, UMR GABI, 78350 Jouy-en-Josas, France
| | - C I V Manzanilla-Pech
- Center for Quantitative Genetics and Genomics, Aarhus University, C. F. M⊘llers Allé 3, 8000 Aarhus, Denmark
| | - B Gredler-Grandl
- Wageningen University & Research Animal Breeding and Genomics, 6700 AH Wageningen, the Netherlands
| | - G Sahana
- Center for Quantitative Genetics and Genomics, Aarhus University, C. F. M⊘llers Allé 3, 8000 Aarhus, Denmark
| | - P Madsen
- Center for Quantitative Genetics and Genomics, Aarhus University, C. F. M⊘llers Allé 3, 8000 Aarhus, Denmark
| | - K Weigel
- Department of Animal and Dairy Sciences, University of Wisconsin, Madison, WI 53706
| | - R J Tempelman
- Department of Animal Science, Michigan State University, East Lansing, MI 48824-1226
| | - F Peñagaricano
- Department of Animal and Dairy Sciences, University of Wisconsin, Madison, WI 53706
| | | | - H M White
- Department of Animal and Dairy Sciences, University of Wisconsin, Madison, WI 53706
| | - J E P Santos
- Department of Animal Science, University of Florida, Gainesville, FL 32608
| | - J E Koltes
- Department of Animal Science, Iowa State University, Ames, IA 50011
| | - F Schenkel
- Centre for Genetic Improvement of Livestock, Department of Animal Biosciences, University of Guelph, Guelph, ON, N1G 2W1, Canada
| | - D Hailemariam
- Department of Agricultural, Food and Nutritional Science, University of Alberta, Edmonton, AB T6G 2P5, Canada
| | - G Plastow
- Department of Agricultural, Food and Nutritional Science, University of Alberta, Edmonton, AB T6G 2P5, Canada
| | - E Abdalla
- Vereinigte Informationssysteme Tierhaltung w.V. (vit), Heideweg 1, 27283, Verden, Germany
| | - M VandeHaar
- Department of Animal Science, Michigan State University, East Lansing, MI 48824-1226
| | - R F Veerkamp
- Wageningen University & Research Animal Breeding and Genomics, 6700 AH Wageningen, the Netherlands
| | - C Baes
- Centre for Genetic Improvement of Livestock, Department of Animal Biosciences, University of Guelph, Guelph, ON, N1G 2W1, Canada; Department of Clinical Research and Veterinary Public Health, University of Bern, Bern, 3001, Switzerland
| | - J Lassen
- Center for Quantitative Genetics and Genomics, Aarhus University, C. F. M⊘llers Allé 3, 8000 Aarhus, Denmark; Viking Genetics, Ebeltoftvej 16, Assentoft, 8960 Randers, Denmark
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9
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Thorup VM, Munksgaard L, Terré M, Henriksen JCS, Weisbjerg MR, Løvendahl P. The relationship between feed efficiency and behaviour differs between lactating Holstein and Jersey cows. J DAIRY RES 2023; 90:257-260. [PMID: 37615115 DOI: 10.1017/s0022029923000420] [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] [Indexed: 08/25/2023]
Abstract
In dairy production, high feed efficiency (FE) is important to reduce feed costs and negative impacts of milk production on the climate and environment, yet little is known about the relationship between FE, eating behaviour and activity. This research communication describes how cows differing in FE, expressed as daily energy corrected milk production per unit of feed intake, differed in eating behaviour and activity. We used data from a study of 253 lactations obtained from 97 Holstein and 91 Jersey cows milked in an automatic milking system. Automated feed troughs recorded feed intake behaviour and cows wore a sensor that recorded activity from 5 to 200 d in milk (DIM). We used a mixed linear model to estimate random solutions for individual cows for traits of steps, lying and eating behaviour and calculated their correlation with FE during four periods (5-35, 36-75, 76-120 and 121-200 DIM). Separate analyses were performed for each breed and period. We found that individual level correlations between FE and behaviour traits were stronger in Jersey than in Holstein cows. Eating rate correlated weakly negatively to FE in Holstein cows and more strongly so in Jersey cows, such that efficient Jerseys were slower eaters. The physical activity of Jersey cows was weakly and negatively correlated to FE, but this was not the case in Holstein cows. We conclude that eating rate was consistently negatively associated with FE throughout lactation for Jersey cows, but not for Holstein cows.
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Affiliation(s)
- Vivi M Thorup
- Department of Animal and Veterinary Sciences, Aarhus University, Blichers Allé 20, 8830 Tjele, Denmark
| | - Lene Munksgaard
- Department of Animal and Veterinary Sciences, Aarhus University, Blichers Allé 20, 8830 Tjele, Denmark
| | - Marta Terré
- Ruminant Production, IRTA, 08140 Caldes de Montbui, Spain
| | - Julie C S Henriksen
- Department of Animal and Veterinary Sciences, Aarhus University, Blichers Allé 20, 8830 Tjele, Denmark
| | - Martin R Weisbjerg
- Department of Animal and Veterinary Sciences, Aarhus University, Blichers Allé 20, 8830 Tjele, Denmark
| | - Peter Løvendahl
- Center for Quantitative Genetics and Genomics, Aarhus University, Blichers Allé 20, 8830 Tjele, Denmark
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10
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van Breukelen AE, Aldridge MN, Veerkamp RF, Koning L, Sebek LB, de Haas Y. Heritability and genetic correlations between enteric methane production and concentration recorded by GreenFeed and sniffers on dairy cows. J Dairy Sci 2023; 106:4121-4132. [PMID: 37080783 DOI: 10.3168/jds.2022-22735] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/05/2022] [Accepted: 01/05/2023] [Indexed: 04/22/2023]
Abstract
To reduce methane (CH4) emissions of dairy cows by animal breeding, CH4 measurements have to be recorded on thousands of individual cows. Currently, several techniques are used to phenotype cows for CH4, differing in costs and applicability. However, there is uncertainty about the agreement between techniques. To judge the similarity and repeatability between measurements of different recording techniques, the repeatability, heritability, and genetic correlation are useful metrics. Therefore, our objective was to estimate (1) the repeatability and heritability for CH4 and carbon dioxide production recorded by GreenFeed (GF) and for CH4 and carbon dioxide concentration measured by cost-effective but less accurate sniffers, and (2) the genetic correlation between CH4 recorded with these 2 different on farm and high throughput techniques. Data were available from repeated measurements of CH4 production (grams/day) by GF units and of CH4 concentration (ppm) by sniffers, recorded on commercial dairy farms in the Netherlands. The final data comprised 24,284 GF daily means from 822 cows, 170,826 sniffer daily means from 1,800 cows, and 1,786 daily means from 75 cows by both GF and sniffer (in the same period). Additionally, CH4 records were averaged per week. For daily and weekly mean GF CH4 the heritabilities were 0.19 ± 0.02 and 0.33 ± 0.04, and for daily and weekly mean sniffer CH4 the heritabilities were similar and were 0.18 ± 0.01 and 0.32 ± 0.02, respectively. Phenotypic correlations between GF CH4 production and sniffer CH4 concentration were moderate (0.39 ± 0.03 for daily means and 0.37 ± 0.05 for weekly means). However, genetic correlations were high; 0.71 ± 0.13 for daily means and 0.76 ± 0.15 for weekly means. The high genetic correlation indicates that selection on low CH4 concentrations (ppm) recorded by the cost-effective sniffer method, will result in reduced CH4 production (grams/day) as recorded with GF.
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Affiliation(s)
- A E van Breukelen
- Animal Breeding and Genomics Group, Wageningen University & Research, PO Box 338, 6700 AH Wageningen, the Netherlands.
| | - M N Aldridge
- Animal Breeding and Genomics Group, Wageningen University & Research, PO Box 338, 6700 AH Wageningen, the Netherlands
| | - R F Veerkamp
- Animal Breeding and Genomics Group, Wageningen University & Research, PO Box 338, 6700 AH Wageningen, the Netherlands
| | - L Koning
- Animal Nutrition Group, Wageningen University & Research, PO Box 338, 6700 AH Wageningen, the Netherlands
| | - L B Sebek
- Animal Nutrition Group, Wageningen University & Research, PO Box 338, 6700 AH Wageningen, the Netherlands
| | - Y de Haas
- Animal Breeding and Genomics Group, Wageningen University & Research, PO Box 338, 6700 AH Wageningen, the Netherlands
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11
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Nadri S, Sadeghi-Sefidmazgi A, Zamani P, Ghorbani GR, Toghiani S. Implementation of Feed Efficiency in Iranian Holstein Breeding Program. Animals (Basel) 2023; 13:ani13071216. [PMID: 37048472 PMCID: PMC10093623 DOI: 10.3390/ani13071216] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/24/2023] [Revised: 03/25/2023] [Accepted: 03/27/2023] [Indexed: 04/03/2023] Open
Abstract
This study aimed to evaluate the economic impact of improving feed efficiency on breeding objectives for Iranian Holsteins. Production and economic data from seven dairy herds were used to estimate the economic values of different traits, and a meta-analysis was conducted to analyze the genetic relationships between feed efficiency and other traits. Economic weights were calculated for various traits, with mean values per cow and per year across herds estimated at USD 0.34/kg for milk yield, USD 6.93/kg for fat yield, USD 5.53/kg for protein yield, USD −1.68/kg for dry matter intake, USD −1.70/kg for residual feed intake, USD 0.47/month for productive life, and USD −2.71/day for days open. The Iranian selection index was revised to improve feed efficiency, and the feed efficiency sub-index (FE$) introduced by the Holstein Association of the United States of America was adopted to reflect Iran’s economic and production systems. However, there were discrepancies between Iranian and US genetic coefficients in the sub-index, which could be attributed to differences in genetic and phenotypic parameters, as well as the economic value of each trait. More accurate estimates of economic values for each trait in FE$ could be obtained by collecting dry matter intake from Iranian herds and conducting genetic evaluations for residual feed intake.
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Affiliation(s)
- Sara Nadri
- Department of Animal Sciences, College of Agriculture, Isfahan University of Technology, Isfahan 83111-84156, Iran
| | - Ali Sadeghi-Sefidmazgi
- Department of Animal Sciences, College of Agriculture, Isfahan University of Technology, Isfahan 83111-84156, Iran
- Department of Animal Science, University of Tehran, Karaj P.O. Box 3158711167-4111, Iran
| | - Pouya Zamani
- Department of Animal Science, Faculty of Agriculture, Bu-Ali Sina University, Hamedan 65176-58978, Iran
| | - Gholam Reza Ghorbani
- Department of Animal Sciences, College of Agriculture, Isfahan University of Technology, Isfahan 83111-84156, Iran
| | - Sajjad Toghiani
- Animal Genomics and Improvement Laboratory, Agricultural Research Service, USDA, Beltsville, MD 20705-2350, USA
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12
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Ribeiro G, Baldi F, Cesar ASM, Alexandre PA, Peripolli E, Ferraz JBS, Fukumasu H. Detection of potential functional variants based on systems-biology: the case of feed efficiency in beef cattle. BMC Genomics 2022; 23:774. [PMID: 36434498 PMCID: PMC9700932 DOI: 10.1186/s12864-022-08958-y] [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: 08/25/2021] [Accepted: 10/20/2022] [Indexed: 11/26/2022] Open
Abstract
BACKGROUND Potential functional variants (PFVs) can be defined as genetic variants responsible for a given phenotype. Ultimately, these are the best DNA markers for animal breeding and selection, especially for polygenic and complex phenotypes. Herein, we described the identification of PFVs for complex phenotypes (in this case, Feed Efficiency in beef cattle) using a systems-biology driven approach based on RNA-seq data from physiologically relevant organs. RESULTS The systems-biology coupled with deep molecular phenotyping by RNA-seq of liver, muscle, hypothalamus, pituitary, and adrenal glands of animals with high and low feed efficiency (FE) measured by residual feed intake (RFI) identified 2,000,936 uniquely variants. Among them, 9986 variants were significantly associated with FE and only 78 had a high impact on protein expression and were considered as PFVs. A set of 169 significant uniquely variants were expressed in all five organs, however, only 27 variants had a moderate impact and none of them a had high impact on protein expression. These results provide evidence of tissue-specific effects of high-impact PFVs. The PFVs were enriched (FDR < 0.05) for processing and presentation of MHC Class I and II mediated antigens, which are an important part of the adaptive immune response. The experimental validation of these PFVs was demonstrated by the increased prediction accuracy for RFI using the weighted G matrix (ssGBLUP+wG; Acc = 0.10 and b = 0.48) obtained in the ssGWAS in comparison to the unweighted G matrix (ssGBLUP; Acc = 0.29 and b = 1.10). CONCLUSION Here we identified PFVs for FE in beef cattle using a strategy based on systems-biology and deep molecular phenotyping. This approach has great potential to be used in genetic prediction programs, especially for polygenic phenotypes.
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Affiliation(s)
- Gabriela Ribeiro
- grid.11899.380000 0004 1937 0722Department of Veterinary Medicine, Faculty of Animal Science and Food Engineering, University of Sao Paulo, Pirassununga, Sao Paulo, 13635-900 Brazil
| | - Fernando Baldi
- grid.410543.70000 0001 2188 478XDepartment of Animal Science, São Paulo State University (UNESP), Jaboticabal, São Paulo, Brazil
| | - Aline S. M. Cesar
- grid.11899.380000 0004 1937 0722Escola Superior de Agricultura “Luiz de Queiroz”, University of Sao Paulo, Piracicaba, São Paulo, Brazil
| | - Pâmela A. Alexandre
- grid.11899.380000 0004 1937 0722Department of Veterinary Medicine, Faculty of Animal Science and Food Engineering, University of Sao Paulo, Pirassununga, Sao Paulo, 13635-900 Brazil ,CSIRO Agriculture & Food, 306 Carmody Rd., St. Lucia, Brisbane, QLD 4067 Australia
| | - Elisa Peripolli
- grid.11899.380000 0004 1937 0722Department of Veterinary Medicine, Faculty of Animal Science and Food Engineering, University of Sao Paulo, Pirassununga, Sao Paulo, 13635-900 Brazil ,grid.410543.70000 0001 2188 478XDepartment of Animal Science, São Paulo State University (UNESP), Jaboticabal, São Paulo, Brazil
| | - José B. S. Ferraz
- grid.11899.380000 0004 1937 0722Department of Veterinary Medicine, Faculty of Animal Science and Food Engineering, University of Sao Paulo, Pirassununga, Sao Paulo, 13635-900 Brazil
| | - Heidge Fukumasu
- grid.11899.380000 0004 1937 0722Department of Veterinary Medicine, Faculty of Animal Science and Food Engineering, University of Sao Paulo, Pirassununga, Sao Paulo, 13635-900 Brazil
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Manzanilla-Pech C, Difford G, Løvendahl P, Stephansen R, Lassen J. Genetic (co-)variation of methane emissions, efficiency, and production traits in Danish Holstein cattle along and across lactations. J Dairy Sci 2022; 105:9799-9809. [DOI: 10.3168/jds.2022-22121] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/25/2022] [Accepted: 07/24/2022] [Indexed: 11/17/2022]
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Manzanilla-Pech CIV, Stephansen RB, Difford GF, Løvendahl P, Lassen J. Selecting for Feed Efficient Cows Will Help to Reduce Methane Gas Emissions. Front Genet 2022; 13:885932. [PMID: 35692829 PMCID: PMC9178123 DOI: 10.3389/fgene.2022.885932] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2022] [Accepted: 04/19/2022] [Indexed: 11/13/2022] Open
Abstract
In the last decade, several countries have included feed efficiency (as residual feed intake; RFI) in their breeding goal. Recent studies showed that RFI is favorably correlated with methane emissions. Thus, selecting for lower emitting animals indirectly through RFI could be a short-term strategy in order to achieve the intended reduction set by the EU Commission (-55% for 2030). The objectives were to 1) estimate genetic parameters for six methane traits, including genetic correlations between methane traits, production, and feed efficiency traits, 2) evaluate the expected correlated response of methane traits when selecting for feed efficiency with or without including methane, 3) quantify the impact of reducing methane emissions in dairy cattle using the Danish Holstein population as an example. A total of 26,664 CH4 breath records from 647 Danish Holstein cows measured over 7 years in a research farm were analyzed. Records on dry matter intake (DMI), body weight (BW), and energy corrected milk (ECM) were also available. Methane traits were methane concentration (MeC, ppm), methane production (MeP; g/d), methane yield (MeY; g CH4/kg DMI), methane intensity (MeI; g CH4/kg ECM), residual methane concentration (RMeC), residual methane production (RMeP, g/d), and two definitions of residual feed intake with or without including body weight change (RFI1, RFI2). The estimated heritability of MeC was 0.20 ± 0.05 and for MeP, it was 0.21 ± 0.05, whereas heritability estimates for MeY and MeI were 0.22 ± 0.05 and 0.18 ± 0.04, and for the RMeC and RMeP, they were 0.23 ± 0.06 and 0.16 ± 0.02, respectively. Genetic correlations between methane traits ranged from moderate to highly correlated (0.48 ± 0.16–0.98 ± 0.01). Genetic correlations between methane traits and feed efficiency were all positive, ranging from 0.05 ± 0.20 (MeI-RFI2) to 0.76 ± 0.09 (MeP-RFI2). Selection index calculations showed that selecting for feed efficiency has a positive impact on reducing methane emissions’ expected response, independently of the trait used (MeP, RMeP, or MeI). Nevertheless, adding a negative economic value for methane would accelerate the response and help to reach the reduction goal in fewer generations. Therefore, including methane in the breeding goal seems to be a faster way to achieve the desired methane emission reductions in dairy cattle.
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Affiliation(s)
| | | | - Gareth Frank Difford
- Department of Animal and Aquacultural Sciences, Faculty of Biosciences, Norwegian University of Life Sciences, As, Norway
| | - Peter Løvendahl
- Center for Quantitative Genetics and Genomics, Aarhus University, Aarhus, Denmark
| | - Jan Lassen
- Center for Quantitative Genetics and Genomics, Aarhus University, Aarhus, Denmark
- Viking Genetics, Assentoft, Randers, Denmark
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15
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van Breukelen AE, Aldridge MA, Veerkamp RF, de Haas Y. Genetic parameters for repeatedly recorded enteric methane concentrations of dairy cows. J Dairy Sci 2022; 105:4256-4271. [PMID: 35307185 DOI: 10.3168/jds.2021-21420] [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: 10/13/2021] [Accepted: 02/08/2022] [Indexed: 11/19/2022]
Abstract
Animal breeding techniques offer potential to reduce enteric emissions of ruminants to lower the environmental impact of dairy farming. The aim of this study was to estimate the heritability and repeatability of methane (CH4) concentrations, using the largest data set from long-term repeatedly recorded CH4 on cows to date, and to evaluate (1) the accuracy of breeding values for different CH4 traits, including using visits or weekly means, and (2) recording strategies (with varying numbers of records and recorded daughters per sire). The data comprised of long-term recording of CH4 and carbon dioxide (CO2), from 1,746 Holstein Friesian cows, on 14 commercial dairy farms throughout the Netherlands. Emissions were recorded in 10- to 35-s intervals, between 64 and 436 d, depending on farms. From each robot visit, CH4 and CO2 concentrations were summarized into various traits, averaged per visit and per week: mean, median, mean log, and mean CH4/CO2 ratio. Genetic parameters were estimated with animal repeatability models, using a restricted maximum likelihood procedure, and a relationship matrix based on genotypes and pedigree. The heritability was equal for mean and median CH4 per visit (0.13) but lower for logCH4 and CH4/CO2 (0.07 and 0.01, respectively). Phenotypic and genetic correlations were high (≥0.78) between the CH4 traits, apart from the genetic correlations with the CH4/CO2 trait, which were negative. To achieve a minimum reliability of 50% for the estimated breeding value of a bull, 25 records on mean CH4, measured on 10 different daughters, were sufficient. Although the heritability and repeatability were higher for weekly (0.32 and 0.68, respectively) than for visit mean CH4 (0.13 and 0.30, respectively), the reliabilities of estimated breeding values from visit or weekly means were equal; thus, we found no advantage in averaging records to weekly means for genetic evaluations.
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Affiliation(s)
- A E van Breukelen
- Wageningen University & Research Animal Breeding and Genomics, 6700 AH Wageningen, the Netherlands.
| | - M A Aldridge
- Wageningen University & Research Animal Breeding and Genomics, 6700 AH Wageningen, the Netherlands
| | - R F Veerkamp
- Wageningen University & Research Animal Breeding and Genomics, 6700 AH Wageningen, the Netherlands
| | - Y de Haas
- Wageningen University & Research Animal Breeding and Genomics, 6700 AH Wageningen, the Netherlands
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16
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Translational multi-omics microbiome research for strategies to improve cattle production and health. Emerg Top Life Sci 2022; 6:201-213. [PMID: 35311904 DOI: 10.1042/etls20210257] [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: 11/28/2021] [Revised: 02/23/2022] [Accepted: 03/01/2022] [Indexed: 12/27/2022]
Abstract
Cattle microbiome plays a vital role in cattle growth and performance and affects many economically important traits such as feed efficiency, milk/meat yield and quality, methane emission, immunity and health. To date, most cattle microbiome research has focused on metataxonomic and metagenomic characterization to reveal who are there and what they may do, preventing the determination of the active functional dynamics in vivo and their causal relationships with the traits. Therefore, there is an urgent need to combine other advanced omics approaches to improve microbiome analysis to determine their mode of actions and host-microbiome interactions in vivo. This review will critically discuss the current multi-omics microbiome research in beef and dairy cattle, aiming to provide insights on how the information generated can be applied to future strategies to improve production efficiency, health and welfare, and environment-friendliness in cattle production through microbiome manipulations.
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17
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Manzanilla-Pech CIV, Difford GF, Sahana G, Romé H, Løvendahl P, Lassen J. Genome-wide association study for methane emission traits in Danish Holstein cattle. J Dairy Sci 2021; 105:1357-1368. [PMID: 34799107 DOI: 10.3168/jds.2021-20410] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/05/2021] [Accepted: 10/07/2021] [Indexed: 02/04/2023]
Abstract
Selecting for lower methane emitting cows requires insight into the most biologically relevant phenotypes for methane emission, which are close to the breeding goal. Several methane phenotypes have been suggested over the last decade. However, the (dis)similarity of their underlying genetic architecture and correlation structures are poorly understood. Therefore, the objective of this study was to test association of SNP and genomic regions through GWAS on 8 CH4 emission traits in Danish Holstein cattle. The traits studied were methane concentration (MeC; ppm), methane production (MeP ; g/d), 2 definitions of residual methane (RMETc and RMETp: MeC and MeP regressed on metabolic body weight and energy-corrected milk, respectively), 2 definitions of methane intensity (MeI; MeIc = MeC/ECM and MeIp = MeP/ECM); 2 definitions of methane yield per kilogram of dry matter intake (MeY; MeYc = MeC/dry matter intake and MeYp = MeP/dry matter intake). A total of 1,962 cows with genotypes (Illumina BovineSNP50 Chip or Eurogenomic custom SNP chip) and repeated records of the above-mentioned 8 methane traits were analyzed. Strong associations were found with 3 traits (MeC, MeP, and MeYc) on chromosome 13 and with 5 traits (MeC, MeP, MeIp, MeYp, and MeYc) on chromosome 26. For MeIc, MeIp, RMETc, MeYc, and MeYp, some suggestive association signals were identified on chromosome 1. Genomic segments of 1 Mbp (n = 2,525) were tested for their association with these traits, which identified between 33 to 54 significantly associated regions. In a pairwise comparison, MeC and MeP were the traits that shared the highest number of significant segments (17). The same trend was observed when comparing SNP significantly associated with the traits MeC and MeP shared from 23 to 25 SNP (most of which were located in chromosomes 11, 13, and 26). Based on our results on GWAS and genetic correlations, we conclude that MeC is (genetically) more closely linked to MeP than any of the other methane traits analyzed.
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Affiliation(s)
- C I V Manzanilla-Pech
- Center for Quantitative Genetics and Genomics, Aarhus University, Blichers Alle 20, 8830 Tjele, Denmark.
| | - G F Difford
- Department of Breeding and Genetics, Nofima AS, PO Box 210, N-1431 Ås, Norway
| | - G Sahana
- Center for Quantitative Genetics and Genomics, Aarhus University, Blichers Alle 20, 8830 Tjele, Denmark
| | - H Romé
- Center for Quantitative Genetics and Genomics, Aarhus University, Blichers Alle 20, 8830 Tjele, Denmark
| | - P Løvendahl
- Center for Quantitative Genetics and Genomics, Aarhus University, Blichers Alle 20, 8830 Tjele, Denmark
| | - J Lassen
- Viking Genetics, Ebeltoftvej 16, Assentoft, 8960 Randers, Denmark
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18
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Richardson CM, Amer PR, Hely FS, van den Berg I, Pryce JE. Estimating methane coefficients to predict the environmental impact of traits in the Australian dairy breeding program. J Dairy Sci 2021; 104:10979-10990. [PMID: 34334195 DOI: 10.3168/jds.2021-20348] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2021] [Accepted: 06/08/2021] [Indexed: 11/19/2022]
Abstract
The dairy industry has been scrutinized for the environmental impact associated with rearing and maintaining cattle for dairy production. There are 3 possible opportunities to reduce emissions through genetic selection: (1) a direct methane trait, (2) a reduction in replacements, and (3) an increase in productivity. Our aim was to estimate the independent effects of traits in the Australian National Breeding Objective on the gross methane production and methane intensity (EI) of the Australian dairy herd of average genetic potential. Based on similar published research, the traits determined to have an effect on emissions include production, fertility, survival, health, and feed efficiency. The independent effect of each trait on the gross emissions produced per animal due to genetic improvement and change in EI due to genetic improvement (intensity value, IV) were estimated and compared. Based on an average Australian dairy herd, the gross emissions emitted per cow per year were 4,297.86 kg of carbon dioxide equivalents (CO2-eq). The annual product output, expressed in protein equivalents (protein-eq), and EI per cow were 339.39 kg of protein-eq and 12.67 kg of CO2-eq/kg of protein-eq, respectively. Of the traits included in the National Breeding Objective, genetic progress in survival and feed saved were consistently shown to result in a favorable environmental impact. Conversely, production traits had an unfavorable environmental impact when considering gross emissions, and favorable when considering EI. Fertility had minimal impact as its effects were primarily accounted for through survival. Mastitis resistance only affected IV coefficients and to a very limited extent. These coefficients may be used in selection indexes to apply emphasis on traits based on their environmental impact, as well as applied by governments and stakeholders to track trends in industry emissions. Although initiatives are underway to develop breeding values to reduce methane by combining small methane data sets internationally, alternative options to reduce emissions by utilizing selection indexes should be further explored.
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Affiliation(s)
- C M Richardson
- Agriculture Victoria Research, AgriBio, Centre for AgriBioscience, Bundoora, Victoria 3083, Australia; School of Applied Systems Biology, La Trobe University, Bundoora, Victoria 3083, Australia
| | - P R Amer
- AbacusBio Limited, PO Box 5585, Dunedin, New Zealand
| | - F S Hely
- AbacusBio Limited, PO Box 5585, Dunedin, New Zealand
| | - I van den Berg
- Agriculture Victoria Research, AgriBio, Centre for AgriBioscience, Bundoora, Victoria 3083, Australia
| | - J E Pryce
- Agriculture Victoria Research, AgriBio, Centre for AgriBioscience, Bundoora, Victoria 3083, Australia; School of Applied Systems Biology, La Trobe University, Bundoora, Victoria 3083, Australia.
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19
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de Haas Y, Veerkamp RF, de Jong G, Aldridge MN. Selective breeding as a mitigation tool for methane emissions from dairy cattle. Animal 2021; 15 Suppl 1:100294. [PMID: 34246599 DOI: 10.1016/j.animal.2021.100294] [Citation(s) in RCA: 22] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/29/2020] [Revised: 01/21/2021] [Accepted: 01/25/2021] [Indexed: 12/17/2022] Open
Abstract
The global livestock sector, particularly ruminants, contributes substantially to the total anthropogenic greenhouse gases. Management and dietary solutions to reduce enteric methane (CH4) emissions are extensively researched. Animal breeding that exploits natural variation in CH4 emissions is an additional mitigation solution that is cost-effective, permanent, and cumulative. We quantified the effect of including CH4 production in the Dutch breeding goal using selection index theory. The current Dutch national index contains 15 traits, related to milk yield, longevity, health, fertility, conformation and feed efficiency. From the literature, we obtained a heritability of 0.21 for enteric CH4 production, and genetic correlations of 0.4 with milk lactose, protein, fat and DM intake. Correlations between enteric CH4 production and other traits in the breeding goal were set to zero. When including CH4 production in the current breeding goal with a zero economic value, CH4 production increases each year by 1.5 g/d as a correlated response. When extrapolating this, the average daily CH4 production of 392 g/d in 2018 will increase to 442 g/d in 2050 (+13%). However, expressing the CH4 production as CH4 intensity in the same period shows a reduction of 13%. By putting economic weight on CH4 production in the breeding goal, selective breeding can reduce the CH4 intensity even by 24% in 2050. This shows that breeding is a valuable contribution to the whole set of mitigation strategies that could be applied in order to achieve the goals for 2050 set by the EU. If the decision is made to implement animal breeding strategies to reduce enteric CH4 production, and to achieve the expected breeding impact, there needs to be a sufficient reliability of prediction. The only way to achieve that is to have enough animals phenotyped and genotyped. The power calculations offer insights into the difficulties that will be faced in trying to record enough data. Recording CH4 data on 100 farms (with on average 150 cows each) for at least 2 years is required to achieve the desired reliability of 0.40 for the genomic prediction.
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Affiliation(s)
- Y de Haas
- Animal Breeding and Genomics, Wageningen University & Research, 6700 AH Wageningen, the Netherlands.
| | - R F Veerkamp
- Animal Breeding and Genomics, Wageningen University & Research, 6700 AH Wageningen, the Netherlands
| | - G de Jong
- CRV, 6800 AL Arnhem, the Netherlands
| | - M N Aldridge
- Animal Breeding and Genomics, Wageningen University & Research, 6700 AH Wageningen, the Netherlands
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20
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Manzanilla-Pech CIV, L Vendahl P, Mansan Gordo D, Difford GF, Pryce JE, Schenkel F, Wegmann S, Miglior F, Chud TC, Moate PJ, Williams SRO, Richardson CM, Stothard P, Lassen J. Breeding for reduced methane emission and feed-efficient Holstein cows: An international response. J Dairy Sci 2021; 104:8983-9001. [PMID: 34001361 DOI: 10.3168/jds.2020-19889] [Citation(s) in RCA: 33] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2020] [Accepted: 04/14/2021] [Indexed: 01/23/2023]
Abstract
Selecting for lower methane (CH4) emitting animals is one of the best approaches to reduce CH4 given that genetic progress is permanent and cumulative over generations. As genetic selection requires a large number of animals with records and few countries actively record CH4, combining data from different countries could help to expedite accurate genetic parameters for CH4 traits and build a future genomic reference population. Additionally, if we want to include CH4 in the breeding goal, it is important to know the genetic correlations of CH4 traits with other economically important traits. Therefore, the aim of this study was first to estimate genetic parameters of 7 suggested methane traits, as well as genetic correlations between methane traits and production, maintenance, and efficiency traits using a multicountry database. The second aim was to estimate genetic correlations within parities and stages of lactation for CH4. The third aim was to evaluate the expected response of economically important traits by including CH4 traits in the breeding goal. A total of 15,320 methane production (MeP, g/d) records from 2,990 cows belonging to 4 countries (Canada, Australia, Switzerland, and Denmark) were analyzed. Records on dry matter intake (DMI), body weight (BW), body condition score, and milk yield (MY) were also available. Additional traits such as methane yield (MeY; g/kg DMI), methane intensity (MeI; g/kg energy-corrected milk), a genetic standardized methane production, and 3 definitions of residual methane production (g/d), residual feed intake, metabolic BW (MBW), BW change, and energy-corrected milk were calculated. The estimated heritability of MeP was 0.21, whereas heritability estimates for MeY and MeI were 0.30 and 0.38, and for the residual methane traits heritability ranged from 0.13 to 0.16. Genetic correlations between different methane traits were moderate to high (0.41 to 0.97). Genetic correlations between MeP and economically important traits ranged from 0.29 (MY) to 0.65 (BW and MBW), being 0.41 for DMI. Selection index calculations showed that residual methane had the most potential for inclusion in the breeding goal when compared with MeP, MeY, and MeI, as residual methane allows for selection of low methane emitting animals without compromising other economically important traits. Inclusion of residual feed intake in the breeding goal could further reduce methane, as the correlation with residual methane is moderate and elicits a favorable correlated response. Adding a negative economic value for methane could facilitate a substantial reduction in methane emissions while maintaining an increase in milk production.
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Affiliation(s)
- C I V Manzanilla-Pech
- Center for Quantitative Genetics and Genomics, Aarhus University, PO Box 50, DK-8830 Tjele, Denmark.
| | - P L Vendahl
- Center for Quantitative Genetics and Genomics, Aarhus University, PO Box 50, DK-8830 Tjele, Denmark
| | - D Mansan Gordo
- Center for Quantitative Genetics and Genomics, Aarhus University, PO Box 50, DK-8830 Tjele, Denmark
| | - G F Difford
- Center for Quantitative Genetics and Genomics, Aarhus University, PO Box 50, DK-8830 Tjele, Denmark
| | - J E Pryce
- Agriculture Victoria, AgriBio, Centre for AgriBioscience, Bundoora, Victoria 3083, Australia; School of Applied Systems Biology, La Trobe University, Bundoora, Victoria 3083, Australia
| | - F Schenkel
- Centre for Genomic Improvement of Livestock, Department of Animal Biosciences, University of Guelph, Guelph, ON, N1G 2W1, Canada
| | | | - F Miglior
- Centre for Genomic Improvement of Livestock, Department of Animal Biosciences, University of Guelph, Guelph, ON, N1G 2W1, Canada
| | - T C Chud
- Centre for Genomic Improvement of Livestock, Department of Animal Biosciences, University of Guelph, Guelph, ON, N1G 2W1, Canada
| | - P J Moate
- Centre for Agricultural Innovation, School of Agriculture and Food, Faculty of Veterinary and Agricultural Sciences, The University of Melbourne, Victoria 3083, Australia; Agriculture Victoria Research, Ellinbank, Victoria 3820, Australia
| | - S R O Williams
- Agriculture Victoria Research, Ellinbank, Victoria 3820, Australia
| | - C M Richardson
- Agriculture Victoria, AgriBio, Centre for AgriBioscience, Bundoora, Victoria 3083, Australia; School of Applied Systems Biology, La Trobe University, Bundoora, Victoria 3083, Australia
| | - P Stothard
- Faculty of Agricultural, Life and Environmental Science, Agriculture, Food and Nutrition Sciences Department, University of Alberta, Edmonton, AB, T6G 2C8, Canada
| | - J Lassen
- Viking Genetics, Ebeltoftvej 16, Assenstoft, 8960 Randers, Denmark
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21
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Huhtanen P, Bayat A, Lund P, Guinguina A. Residual carbon dioxide as an index of feed efficiency in lactating dairy cows. J Dairy Sci 2021; 104:5332-5344. [PMID: 33663828 DOI: 10.3168/jds.2020-19370] [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: 07/28/2020] [Accepted: 12/11/2020] [Indexed: 11/19/2022]
Abstract
High feed costs make feed conversion efficiency a desirable target for genetic improvement. Residual feed intake (RFI), calculated as the difference between observed and predicted intake, is a commonly used estimate of feed efficiency. However, determination of feed efficiency in dairy herds is challenging due to difficulties in measuring feed intake of individual animals reliably. Using residual CO2 (RCO2) production as an estimate of feed efficiency would allow ranking the cows according to feed efficiency, provided that CO2 production is closely related to heat production and feed intake. The objective of this study was to evaluate the potential of RCO2 as an index of feed efficiency using data from respiration calorimetry studies (289 cow per period observations). Heat production was precisely predicted from CO2 production [root mean square error (RMSE)] adjusted for random effects was 1.5% of observed mean]. Dry matter intake (DMI) was better predicted from energy-corrected milk (ECM) yield and CO2 production than from ECM yield and body weight in the model (adjusted RSME = 0.92 vs. 1.39 kg/d). Residual CO2 production estimated as the difference between actual CO2 production and that predicted from ECM yield, metabolic body weight was closely related to RFI (adjusted RMSE = 0.42) that was calculated as the difference between actual DMI and that predicted from ECM, metabolic body weight, and energy balance (EB). When the cows were categorized in 3 groups of equal sizes on the basis of RCO2 (low, medium, and high), low RCO2 cows had lower DMI, RFI, methane production and intensity (g/kg ECM), and heat production, but higher efficiency of metabolizable energy utilization for lactation than high RCO2 cows. When RFI was predicted from RCO2, the residuals (observed - predicted) were negatively related to EB and digestibility. Predicting RFI with a 2-variable model based on RCO2 and digestibility, adjusted RMSE decreased to 0.23 kg/d, and residuals were not significantly related to EB. The cows in low RCO2 group had a higher energy digestibility than the cows in the high RCO2 group, and differences in EB were observed between the groups. Error of the model predicting residual ECM production from RCO2 was 1.41 kg/d. The residuals were positively related to ECM yield and energy digestibility. Predicting residual ECM from RCO2 and ECM yield decreased adjusted RMSE to 1.07 kg/d, and further to 0.78 kg/d when digestibility was included in the 2-variable model. It is concluded that RCO2 has a potential for ranking individual cows based on feed efficiency.
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Affiliation(s)
- Pekka Huhtanen
- Department of Agricultural Research for Northern Sweden, Swedish University of Agricultural Sciences, SE-901 83 Umeå, Sweden.
| | - Alireza Bayat
- Production Systems, Natural Resources Institute Finland (LUKE), 31600 Jokioinen, Finland
| | - Peter Lund
- Department of Animal Science, Aarhus University, AU Foulum, PO Box 50, 8830 Tjele, Denmark
| | - Abdulai Guinguina
- Department of Agricultural Research for Northern Sweden, Swedish University of Agricultural Sciences, SE-901 83 Umeå, Sweden
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22
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Diets supplemented with corn oil and wheat starch, marine algae, or hydrogenated palm oil modulate methane emissions similarly in dairy goats and cows, but not feeding behavior. Anim Feed Sci Technol 2021. [DOI: 10.1016/j.anifeedsci.2020.114783] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
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23
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Brito LF, Oliveira HR, Houlahan K, Fonseca PA, Lam S, Butty AM, Seymour DJ, Vargas G, Chud TC, Silva FF, Baes CF, Cánovas A, Miglior F, Schenkel FS. Genetic mechanisms underlying feed utilization and implementation of genomic selection for improved feed efficiency in dairy cattle. CANADIAN JOURNAL OF ANIMAL SCIENCE 2020. [DOI: 10.1139/cjas-2019-0193] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
Abstract
The economic importance of genetically improving feed efficiency has been recognized by cattle producers worldwide. It has the potential to considerably reduce costs, minimize environmental impact, optimize land and resource use efficiency, and improve the overall cattle industry’s profitability. Feed efficiency is a genetically complex trait that can be described as units of product output (e.g., milk yield) per unit of feed input. The main objective of this review paper is to present an overview of the main genetic and physiological mechanisms underlying feed utilization in ruminants and the process towards implementation of genomic selection for feed efficiency in dairy cattle. In summary, feed efficiency can be improved via numerous metabolic pathways and biological mechanisms through genetic selection. Various studies have indicated that feed efficiency is heritable, and genomic selection can be successfully implemented in dairy cattle with a large enough training population. In this context, some organizations have worked collaboratively to do research and develop training populations for successful implementation of joint international genomic evaluations. The integration of “-omics” technologies, further investments in high-throughput phenotyping, and identification of novel indicator traits will also be paramount in maximizing the rates of genetic progress for feed efficiency in dairy cattle worldwide.
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Affiliation(s)
- Luiz F. Brito
- Department of Animal Sciences, Purdue University, West Lafayette, IN 47907, USA
- Centre for Genetic Improvement of Livestock, Department of Animal Biosciences, University of Guelph, Guelph, ON N1G 2W1, Canada
| | - Hinayah R. Oliveira
- Department of Animal Sciences, Purdue University, West Lafayette, IN 47907, USA
- Centre for Genetic Improvement of Livestock, Department of Animal Biosciences, University of Guelph, Guelph, ON N1G 2W1, Canada
| | - Kerry Houlahan
- Centre for Genetic Improvement of Livestock, Department of Animal Biosciences, University of Guelph, Guelph, ON N1G 2W1, Canada
| | - Pablo A.S. Fonseca
- Centre for Genetic Improvement of Livestock, Department of Animal Biosciences, University of Guelph, Guelph, ON N1G 2W1, Canada
| | - Stephanie Lam
- Centre for Genetic Improvement of Livestock, Department of Animal Biosciences, University of Guelph, Guelph, ON N1G 2W1, Canada
| | - Adrien M. Butty
- Centre for Genetic Improvement of Livestock, Department of Animal Biosciences, University of Guelph, Guelph, ON N1G 2W1, Canada
| | - Dave J. Seymour
- Centre for Genetic Improvement of Livestock, Department of Animal Biosciences, University of Guelph, Guelph, ON N1G 2W1, Canada
- Centre for Nutrition Modelling, Department of Animal Biosciences, University of Guelph, Guelph, ON N1G 2W1, Canada
| | - Giovana Vargas
- 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
| | - Fabyano F. Silva
- Department of Animal Sciences, Federal University of Viçosa, Viçosa, Minas Gerais 36570-000, Brazil
| | - 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, Bern 3001, Switzerland
| | - Angela Cánovas
- Centre for Genetic Improvement of Livestock, Department of Animal Biosciences, University of Guelph, Guelph, ON N1G 2W1, Canada
| | - Filippo Miglior
- Centre for Genetic Improvement of Livestock, Department of Animal Biosciences, University of Guelph, Guelph, ON N1G 2W1, Canada
| | - Flavio S. Schenkel
- Centre for Genetic Improvement of Livestock, Department of Animal Biosciences, University of Guelph, Guelph, ON N1G 2W1, Canada
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24
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Rovelli G, Ceccobelli S, Perini F, Demir E, Mastrangelo S, Conte G, Abeni F, Marletta D, Ciampolini R, Cassandro M, Bernabucci U, Lasagna E. The genetics of phenotypic plasticity in livestock in the era of climate change: a review. ITALIAN JOURNAL OF ANIMAL SCIENCE 2020. [DOI: 10.1080/1828051x.2020.1809540] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/03/2023]
Affiliation(s)
- Giacomo Rovelli
- Dipartimento di Scienze Agrarie, Alimentari e Ambientali, University of Perugia, Perugia, Italy
| | - Simone Ceccobelli
- Dipartimento di Scienze Agrarie, Alimentari e Ambientali, Università Politecnica delle Marche, Ancona, Italy
| | - Francesco Perini
- Dipartimento di Scienze Agrarie, Alimentari e Ambientali, University of Perugia, Perugia, Italy
| | - Eymen Demir
- Dipartimento di Scienze Agrarie, Alimentari e Ambientali, University of Perugia, Perugia, Italy
- Department of Animal Science, Faculty of Agriculture, Akdeniz University, Antalya, Turkey
| | - Salvatore Mastrangelo
- Dipartimento di Scienze Agrarie, Alimentari e Forestali, University of Palermo, Palermo, Italy
| | - Giuseppe Conte
- Dipartimento di Scienze Agrarie, Alimentari e Agro-Ambientali, University of Pisa, Pisa, Italy
| | - Fabio Abeni
- Centro di ricerca Zootecnia e Acquacoltura, Consiglio per la ricerca in agricoltura e l’analisi dell’economia agraria (CREA), Lodi, Italy
| | - Donata Marletta
- Dipartimento di Agricoltura, Alimentazione e Ambiente, University of Catania, Catania, Italy
| | | | - Martino Cassandro
- Dipartimento di Agronomia, Animali, Alimenti, Risorse naturali e Ambiente, University of Padova, Legnaro, Italy
| | - Umberto Bernabucci
- Dipartimento di Scienze Agrarie e Forestali, Università della Tuscia, Viterbo, Italy
| | - Emiliano Lasagna
- Dipartimento di Scienze Agrarie, Alimentari e Ambientali, University of Perugia, Perugia, Italy
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25
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Manzanilla-Pech CIV, Gordo D, Difford GF, Løvendahl P, Lassen J. Multitrait genomic prediction of methane emissions in Danish Holstein cattle. J Dairy Sci 2020; 103:9195-9206. [PMID: 32747097 DOI: 10.3168/jds.2019-17857] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/06/2019] [Accepted: 05/18/2020] [Indexed: 02/04/2023]
Abstract
In dairy cattle, selecting for lower methane-emitting animals is one of the new challenges of this decade. However, genetic selection requires a large number of animals with records to get accurate estimated breeding values (EBV). Given that CH4 records are scarce, the use of information on routinely recorded and highly correlated traits with CH4 has been suggested to increase the accuracy of genomic EBV (GEBV) through multitrait (genomic) prediction. Therefore, the objective of this study was to evaluate accuracies of prediction of GEBV for CH4 by including or omitting CH4, energy-corrected milk (ECM), and body weight (BW) as well as genotypic information in multitrait analyses across 2 methods: BLUP and single-step genomic BLUP (SSGBLUP). A total of 2,725 cows with CH4 concentration in breath (14,125 records), BW (61,667 records), and ECM (61,610 records) were included in the analyses. Approximately 2,000 of these cows were genotyped or imputed to 50K. Ten cross-validation groups were formed by randomly grouping paternal half-sibs. Five scenarios were performed: (1) base scenario with only CH4 information; (2) without CH4, but with information from BW, ECM, or BW+ECM only in reference population; (3) without CH4, but with information from BW, ECM, or BW+ECM in both validation and reference population; (4) with CH4 information and BW, ECM, or BW+ECM information only in the reference population; and (5) with CH4 information and BW, ECM, or BW+ECM information in both validation and reference population. As a result, for each method (BLUP, SSGBLUP), 13 sub-scenarios were performed, 1 from scenario 1, and 3 for each of the subsequent 4 scenarios. The average accuracy of GEBV for CH4 in the base scenario was 0.32 for BLUP and 0.42 for SSGBLUP, and it ranged from 0.10 in scenario 2 to 0.78 in scenario 5 across methods. In terms of bias, the base scenario 1 was unbiased for SSGBLUP; similar results were achieved with scenario 5. Including information on ECM increased the accuracy of GEBV for CH4 by up to 61%, whereas adding information on both traits (BW and ECM) increased the accuracy by up to 90%. Scenarios that did not include CH4 in the reference population had the lowest correlations (0.17-0.33) with single-trait CH4 GEBV, and scenarios with CH4 in the reference population had the highest correlations (0.41-0.81). Thus, failure to include CH4 in future reference populations results in predicted CH4 GEBV, which cannot be used in practical selection. Therefore, recording CH4 in more animals remains a priority. Finally, multiple-trait genomic prediction using routinely recorded BW and ECM leads to higher prediction accuracies than traditional single-trait genomic prediction for CH4 and is a viable solution for increasing the accuracies of GEBV for scarcely recorded CH4 in practice.
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Affiliation(s)
- C I V Manzanilla-Pech
- Department of Molecular Biology and Genetics, Aarhus University, PO Box 50, DK-8830 Tjele, Denmark.
| | - D Gordo
- Department of Molecular Biology and Genetics, Aarhus University, PO Box 50, DK-8830 Tjele, Denmark
| | - G F Difford
- Department of Molecular Biology and Genetics, Aarhus University, PO Box 50, DK-8830 Tjele, Denmark; Department of Breeding and Genetics, Nofima AS, PO Box 210, N-1431 Ås, Norway
| | - P Løvendahl
- Department of Molecular Biology and Genetics, Aarhus University, PO Box 50, DK-8830 Tjele, Denmark
| | - J Lassen
- Viking Genetics, Ebeltoftvej 16, Assentoft, 8960 Randers, Denmark
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