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Lozada-Soto EA, Parker Gaddis KL, Tiezzi F, Jiang J, Ma L, Toghiani S, VanRaden PM, Maltecca C. Inbreeding depression for producer-recorded udder, metabolic, and reproductive diseases in US dairy cattle. J Dairy Sci 2024; 107:3032-3046. [PMID: 38056567 DOI: 10.3168/jds.2023-23909] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/27/2023] [Accepted: 11/07/2023] [Indexed: 12/08/2023]
Abstract
This study leveraged a growing dataset of producer-recorded phenotypes for mastitis, reproductive diseases (metritis and retained placenta), and metabolic diseases (ketosis, milk fever, and displaced abomasum) to investigate the potential presence of inbreeding depression for these disease traits. Phenotypic, pedigree, and genomic information were obtained for 354,043 and 68,292 US Holstein and Jersey cows, respectively. Total inbreeding coefficients were calculated using both pedigree and genomic information; the latter included inbreeding estimates obtained using a genomic relationship matrix and runs of homozygosity. We also generated inbreeding coefficients based on the generational inbreeding for recent and old pedigree inbreeding, for different run-of-homozygosity length classes, and for recent and old homozygous-by-descent segment-based inbreeding. Estimates on the liability scale revealed significant evidence of inbreeding depression for reproductive-disease traits, with an increase in total pedigree and genomic inbreeding showing a notable effect for recent inbreeding. However, we found inconsistent evidence for inbreeding depression for mastitis or any metabolic diseases. Notably, in Holsteins, the probability of developing displaced abomasum decreased with inbreeding, particularly for older inbreeding. Estimates of disease probability for cows with low, average, and high inbreeding levels did not significantly differ across any inbreeding coefficient and trait combination, indicating that although inbreeding may affect disease incidence, it likely plays a smaller role compared with management and environmental factors.
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Affiliation(s)
| | | | - Francesco Tiezzi
- Department of Agriculture, Food, Environment and Forestry (DAGRI), University of Florence, 50144 Florence, Italy
| | - Jicai Jiang
- Department of Animal Science, North Carolina State University, Raleigh, NC 27607
| | - Li Ma
- Department of Animal and Avian Sciences, University of Maryland, College Park, MD 20742
| | - Sajjad Toghiani
- Animal Genomics and Improvement Laboratory, Henry A. Wallace Beltsville Agricultural Research Service, USDA, Beltsville, MD 20705
| | - Paul M VanRaden
- Animal Genomics and Improvement Laboratory, Henry A. Wallace Beltsville Agricultural Research Service, USDA, Beltsville, MD 20705
| | - Christian Maltecca
- Department of Animal Science, North Carolina State University, Raleigh, NC 27607
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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] [What about the content of this article? (0)] [Affiliation(s)] [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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Novo LC, Parker Gaddis KL, Wu XL, McWhorter TM, Burchard J, Norman HD, Dürr J, Fourdraine R, Peñagaricano F. Genetic Parameters and Genetic Trends for Johne's Disease in U.S. Holsteins: An Updated Study. J Dairy Sci 2024:S0022-0302(24)00515-0. [PMID: 38428495 DOI: 10.3168/jds.2023-23788] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/24/2023] [Accepted: 02/01/2024] [Indexed: 03/03/2024]
Abstract
Johne's disease (JD) is an infectious enteric disease in ruminants, causing substantial economic loss annually worldwide. This work aimed to estimate JD's genetic parameters and the phenotypic and genetic trends by incorporating recent data. It also explores the feasibility of a national genetic evaluation for JD susceptibility in Holstein cattle in the United States (US). The data were extracted from a Johne's disease data repository, maintained at the Council on Dairy Cattle Breeding (CDCB), and initially supplied by 2 dairy records processing centers. The data comprised 365,980 Holstein cows from 1,048 herds participating in a voluntary control program for JD. Two protocol kits, Idexx Paratuberculosis Screening Ab Test (IDX) and Parachek® 2 (PCK), were used to analyze milk samples with the Enzyme-Linked Immunosorbent Assay (ELISA) technique. Test results from the first 5 parities were considered. An animal was considered infected if it had at least one positive outcome. The overall average of JD incidence was 4.72% in these US Holstein cattle. Genotypes of 78,964 SNP markers were used for 25,000 animals randomly selected from the phenotyped population. Variance components and genetic parameters were estimated based on 3 models, namely, a pedigree-only threshold model (THR), a single-step threshold model (ssTHR), and a single-step linear model (ssLR). The posterior heritability estimates of JD susceptibility were low to moderate: 0.11 to 0.16 based on the 2 threshold models and 0.05 to 0.09 based on the linear model. The average reliability of estimated breeding values of JD susceptibility using single-step analysis for animals with or without phenotypes varied from 0.18 (THR) to 0.22 (ssLR) for IDX and from 0.14 (THR) to 0.18 (ssTHR and ssLR) for PCK. Despite no prior direct genetic selection against JD, the estimated genetic trends of JD susceptibility were negative and highly significant (P-value <0.01). The correlations of bulls' predicted transmitting abilities with economically important traits such as milk yield, milk protein, milk fat, somatic cell score, and mastitis were low, indicating a non-overlapping genetic selection process with traits in current genetic evaluations. Our results suggest the feasibility of reducing the JD incidence rate by incorporating it into the national genetic evaluation programs.
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Affiliation(s)
- Larissa C Novo
- Department of Animal and Dairy Sciences, University of Wisconsin, Madison, WI 53706, USA; Council on Dairy Cattle Breeding, Bowie, MD 20716, USA.
| | | | - Xiao-Lin Wu
- Department of Animal and Dairy Sciences, University of Wisconsin, Madison, WI 53706, USA; Council on Dairy Cattle Breeding, Bowie, MD 20716, USA
| | - T M McWhorter
- Council on Dairy Cattle Breeding, Bowie, MD 20716, USA
| | | | | | - João Dürr
- Council on Dairy Cattle Breeding, Bowie, MD 20716, USA
| | | | - Francisco Peñagaricano
- Department of Animal and Dairy Sciences, University of Wisconsin, Madison, WI 53706, USA
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Cavani L, Parker Gaddis KL, Baldwin RL, Santos JEP, Koltes JE, Tempelman RJ, VandeHaar MJ, White HM, Peñagaricano F, Weigel KA. Consistency of dry matter intake in Holstein cows: Heritability estimates and associations with feed efficiency. J Dairy Sci 2024; 107:1054-1067. [PMID: 37769947 DOI: 10.3168/jds.2023-23774] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/22/2023] [Accepted: 08/31/2023] [Indexed: 10/03/2023]
Abstract
Resilience can be defined as the capacity to maintain performance or bounce back to normal functioning after a perturbation, and studying fluctuations in daily feed intake may be an effective way to identify resilient dairy cows. Our goal was to develop new phenotypes based on daily dry matter intake (DMI) consistency in Holstein cows, estimate genetic parameters and genetic correlations with feed efficiency and milk yield consistency, and evaluate their relationships with production, longevity, health, and reproduction traits. Data consisted of 397,334 daily DMI records of 6,238 lactating Holstein cows collected from 2007 to 2022 at 6 research stations across the United States. Consistency phenotypes were calculated based on the deviations from expected daily DMI for individual cows during their respective feeding trials, which ranged from 27 to 151 d in duration. Expected values were derived from different models, including simple average, quadratic and cubic quantile regression with a 0.5 quantile, and locally estimated scatterplot smoothing (LOESS) regression with span parameters 0.5 and 0.7. We then calculated the log of variance (log-Var-DMI) of daily deviations for each model as the consistency phenotype. Consistency of milk yield was also calculated, as a reference, using the same methods (log-Var-Milk). Genetic parameters were estimated using an animal model, including lactation, days in milk and cohort as fixed effects, and animal as random effect. Relationships between log-Var-DMI and traits currently considered in the US national genetic evaluation were evaluated using Spearman's rank correlations between sires' breeding values. Heritability estimates for log-Var-DMI ranged from 0.11 ± 0.02 to 0.14 ± 0.02 across models. Different methods (simple average, quantile regressions, and LOESS regressions) used to calculate log-Var-DMI yielded very similar results, with genetic correlations ranging from 0.94 to 0.99. Estimated genetic correlations between log-Var-DMI and log-Var-Milk ranged from 0.51 to 0.62. Estimated genetic correlations between log-Var-DMI and feed efficiency ranged from 0.55 to 0.60 with secreted milk energy, from 0.59 to 0.63 with metabolic body weight, and from 0.26 to 0.31 with residual feed intake (RFI). Relationships between log-Var-DMI and the traits in the national genetic evaluation were moderate and positive correlations with milk yield (0.20 to 0.21), moderate and negative correlations with female fertility (-0.07 to -0.20), no significant correlations with health and longevity, and favorable correlations with feed efficiency (-0.23 to -0.25 with feed saved and 0.21 to 0.26 with RFI). We concluded that DMI consistency is heritable and may be an indicator of resilience. Cows with lower variation in the difference between actual and expected daily DMI (more consistency) may be more effective in maintaining performance in the face of challenges or perturbations, whereas cows with greater variation in observed versus expected daily DMI (less consistency) are less feed efficient and may be less resilient.
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Affiliation(s)
- Ligia Cavani
- Department of Animal and Dairy Sciences, University of Wisconsin-Madison, Madison, WI 53706.
| | | | - Ransom L Baldwin
- Animal Genomics and Improvement Laboratory, ARS, USDA, Beltsville, MD 20705
| | - José E P Santos
- Department of Animal Sciences, University of Florida, Gainesville, FL 32608
| | - James E Koltes
- Department of Animal Science, Iowa State University, Ames, IA 50011
| | - Robert J Tempelman
- Department of Animal Science, Michigan State University, East Lansing, MI 48824
| | - Michael J VandeHaar
- Department of Animal Science, Michigan State University, East Lansing, MI 48824
| | - Heather M White
- Department of Animal and Dairy Sciences, University of Wisconsin-Madison, Madison, WI 53706
| | - Francisco Peñagaricano
- Department of Animal and Dairy Sciences, University of Wisconsin-Madison, Madison, WI 53706
| | - Kent A Weigel
- Department of Animal and Dairy Sciences, University of Wisconsin-Madison, Madison, WI 53706
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Cavani L, Parker Gaddis KL, Baldwin RL, Santos JE, Koltes JE, Tempelman RJ, VandeHaar MJ, Caputo MJ, White HM, Peñagaricano F, Weigel KA. Impact of parity differences on residual feed intake estimation in Holstein cows. JDS Commun 2023; 4:201-204. [PMID: 37360126 PMCID: PMC10285233 DOI: 10.3168/jdsc.2022-0307] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 07/19/2022] [Accepted: 11/22/2022] [Indexed: 06/28/2023]
Abstract
Residual feed intake (RFI) has been used as a measure of feed efficiency in farm animals. In lactating dairy cattle, RFI is typically obtained as the difference between dry matter intake observations and predictions from regression on known energy sinks, and effects of parity, days in milk, and cohort. The impact of parity (lactation number) on the estimation of RFI is not well understood, so the objectives of this study were to (1) evaluate alternative RFI models in which the energy sinks (metabolic body weight, body weight change, and secreted milk energy) were nested or not nested within parity, and (2) estimate variance components and genetic correlations for RFI across parities. Data consisted of 72,474 weekly RFI records of 5,813 lactating Holstein cows collected from 2007 to 2022 in 5 research stations across the United States. Estimates of heritability, repeatability, and genetic correlations between weekly RFI for parities 1, 2, and 3 were obtained using bivariate repeatability animal models. The nested RFI model showed better goodness of fit than the nonnested model, and some partial regression coefficients of dry matter intake on energy sinks were heterogeneous between parities. However, the Spearman's rank correlation between RFI values calculated from nested and nonnested models was equal to 0.99. Similarly, Spearman's rank correlation between the RFI breeding values from these 2 models was equal to 0.98. Heritability estimates for RFI were equal to 0.16 for parity 1, 0.19 for parity 2, and 0.22 for parity 3. Repeatability estimates for RFI across weeks within parities were high, ranging from 0.51 to 0.57. Spearman's rank correlations of sires' breeding values were 0.99 between parities 1 and 2, 0.91 between parities 1 and 3, and 0.92 between parities 2 and 3. We conclude that nesting energy sinks within parity when computing RFI improves model goodness of fit, but the impact on the estimated breading values appears to be minimal.
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Affiliation(s)
- Ligia Cavani
- Department of Animal and Dairy Sciences, University of Wisconsin, Madison 53706
| | | | - Ransom L. Baldwin
- Animal Genomics and Improvement Laboratory, Agricultural Research Service, USDA, Beltsville, MD 20705
| | - José E.P. Santos
- Department of Animal Sciences, University of Florida, Gainesville 32608
| | - James E. Koltes
- Department of Animal Science, Iowa State University, Ames 50011
| | | | | | - Malia J.M. Caputo
- Department of Animal and Dairy Sciences, University of Wisconsin, Madison 53706
| | - Heather M. White
- Department of Animal and Dairy Sciences, University of Wisconsin, Madison 53706
| | | | - Kent A. Weigel
- Department of Animal and Dairy Sciences, University of Wisconsin, Madison 53706
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Liang Z, Prakapenka D, Parker Gaddis KL, VandeHaar MJ, Weigel KA, Tempelman RJ, Koltes JE, Santos JEP, White HM, Peñagaricano F, Baldwin VI RL, Da Y. Impact of epistasis effects on the accuracy of predicting phenotypic values of residual feed intake in U. S Holstein cows. Front Genet 2022; 13:1017490. [PMID: 36386803 PMCID: PMC9664219 DOI: 10.3389/fgene.2022.1017490] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/12/2022] [Accepted: 09/27/2022] [Indexed: 11/06/2022] Open
Abstract
The impact of genomic epistasis effects on the accuracy of predicting the phenotypic values of residual feed intake (RFI) in U.S. Holstein cows was evaluated using 6215 Holstein cows and 78,964 SNPs. Two SNP models and seven epistasis models were initially evaluated. Heritability estimates and the accuracy of predicting the RFI phenotypic values from 10-fold cross-validation studies identified the model with SNP additive effects and additive × additive (A×A) epistasis effects (A + A×A model) to be the best prediction model. Under the A + A×A model, additive heritability was 0.141, and A×A heritability was 0.263 that consisted of 0.260 inter-chromosome A×A heritability and 0.003 intra-chromosome A×A heritability, showing that inter-chromosome A×A effects were responsible for the accuracy increases due to A×A. Under the SNP additive model (A-only model), the additive heritability was 0.171. In the 10 validation populations, the average accuracy for predicting the RFI phenotypic values was 0.246 (with range 0.197-0.333) under A + A×A model and was 0.231 (with range of 0.188-0.319) under the A-only model. The average increase in the accuracy of predicting the RFI phenotypic values by the A + A×A model over the A-only model was 6.49% (with range of 3.02-14.29%). Results in this study showed A×A epistasis effects had a positive impact on the accuracy of predicting the RFI phenotypic values when combined with additive effects in the prediction model.
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Affiliation(s)
- Zuoxiang Liang
- Department of Animal Science, University of Minnesota, Saint Paul, MN, United States
| | - Dzianis Prakapenka
- Department of Animal Science, University of Minnesota, Saint Paul, MN, United States
| | | | - Michael J. VandeHaar
- Department of Animal Science, Michigan State University, East Lansing, MI, United States
| | - Kent A. Weigel
- Department of Animal and Dairy Sciences, University of Wisconsin, Madison, WI, United States
| | - Robert J. Tempelman
- Department of Animal Science, Michigan State University, East Lansing, MI, United States
| | - James E. Koltes
- Department of Animal Science, Iowa State University, Ames, IA, United States
| | | | - Heather M. White
- Department of Animal and Dairy Sciences, University of Wisconsin, Madison, WI, United States
| | - Francisco Peñagaricano
- Department of Animal and Dairy Sciences, University of Wisconsin, Madison, WI, United States
| | - Ransom L. Baldwin VI
- Animal Genomics and Improvement Laboratory, ARS, USDA, Beltsville, MD, United States
| | - Yang Da
- Department of Animal Science, University of Minnesota, Saint Paul, MN, United States,*Correspondence: Yang Da,
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Cavani L, Brown WE, Parker Gaddis KL, Tempelman RJ, VandeHaar MJ, White HM, Peñagaricano F, Weigel KA. Estimates of genetic parameters for feeding behavior traits and their associations with feed efficiency in Holstein cows. J Dairy Sci 2022; 105:7564-7574. [PMID: 35863925 DOI: 10.3168/jds.2022-22066] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/09/2022] [Accepted: 04/29/2022] [Indexed: 11/19/2022]
Abstract
Residual feed intake (RFI) is commonly used to measure feed efficiency but individual intake recording systems are needed. Feeding behavior may be used as an indicator trait for feed efficiency using less expensive precision livestock farming technologies. Our goal was to estimate genetic parameters for feeding behavior and the genetic correlations with feed efficiency in Holstein cows. Data consisted of 75,877 daily feeding behavior records of 1,328 mid-lactation Holstein cows in 31 experiments conducted from 2009 to 2020 with an automated intake recording system. Feeding behavior traits included number of feeder visits per day, number of meals per day, duration of each feeder visit, duration of each meal, total duration of feeder visits, intake per visit, intake per meal [kg of dry matter (DM)], feeding rate per visit, and feeding rate per meal (kg of DM per min). The meal criterion was estimated as 26.4 min, which means that any pair of feeder visits separated by less than 26.4 min were considered part of the same meal. The statistical model included lactation and days in milk as fixed effects, and experiment-treatment, animal, and permanent environment as random effects. Genetic parameters for feeding behavior traits were estimated using daily records and weekly averages. Estimates of heritability for daily feeding behavior traits ranged from 0.09 ± 0.02 (number of meals; mean ± standard error) to 0.23 ± 0.03 (feeding rate per meal), with repeatability estimates ranging from 0.23 ± 0.01 (number of meals) to 0.52 ± 0.02 (number of feeder visits). Estimates of heritability for weekly averages of feeding behavior traits ranged from 0.19 ± 0.04 (number of meals) to 0.32 ± 0.04 (feeding rate per visit), with repeatability estimates ranging from 0.46 ± 0.02 (duration of each meal) to 0.62 ± 0.02 (feeding rate per visit and per meal). Most of the feeding behavior measures were strongly genetically correlated, showing that with more visits or meals per day, cows spend less time in each feeder visit or meal with lower intake per visit or meal. Weekly averages for feeding behavior traits were analyzed jointly with RFI and its components. Number of meals was genetically correlated with milk energy (0.48), metabolic body weight (-0.27), and RFI (0.19). Duration of each feeder visit and meal were genetically correlated with milk energy (0.43 and 0.44, respectively). Total duration of feeder visits per day was genetically correlated with DM intake (0.29), milk energy (0.62), metabolic body weight (-0.37), and RFI (0.20). Intake per visit and meal were genetically correlated with DM intake (0.63 and 0.87), milk energy (0.47 and 0.69), metabolic body weight (0.47 and 0.68), and RFI (0.31 and 0.65). Feeding rate was genetically correlated with DM intake (0.69), metabolic body weight (0.67), RFI (0.47), and milk energy (0.21). We conclude that measures of feeding behavior could be useful indicators of dairy cow feed efficiency, and individual cows that eat at a slower rate may be more feed efficient.
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Affiliation(s)
- Ligia Cavani
- Department of Animal and Dairy Sciences, University of Wisconsin, Madison 53706.
| | - William E Brown
- Department of Animal and Dairy Sciences, University of Wisconsin, Madison 53706
| | | | - Robert J Tempelman
- Department of Animal Science, Michigan State University, East Lansing 48824
| | | | - Heather M White
- Department of Animal and Dairy Sciences, University of Wisconsin, Madison 53706
| | | | - Kent A Weigel
- Department of Animal and Dairy Sciences, University of Wisconsin, Madison 53706
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Sigdel A, Wu XL, Parker Gaddis KL, Norman HD, Carrillo JA, Burchard J, Peñagaricano F, Dürr J. Genetic Evaluations of Stillbirth for Five United States Dairy Breeds: A Data-Resource Feasibility Study. Front Genet 2022; 13:819678. [PMID: 35480321 PMCID: PMC9035607 DOI: 10.3389/fgene.2022.819678] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2021] [Accepted: 03/23/2022] [Indexed: 11/13/2022] Open
Abstract
Genetic selection has been an effective strategy to improve calving traits including stillbirth in dairy cattle. The primary objectives of the present study were to characterize stillbirth data and determine the feasibility of implementing routine genetic evaluations of stillbirth in five non-Holstein dairy breeds, namely Ayrshire, Guernsey, Milking Shorthorn, Brown Swiss, and Jersey. An updated sire-maternal grandsire threshold model was used to estimate genetic parameters and genetic values for stillbirth. Stillbirth data with the birth years of dams from 1995 to 2018 were extracted from the United States national calving ease database maintained by the Council on Dairy Cattle Breeding. The extracted stillbirth records varied drastically among the five dairy breeds. There were approximately 486K stillbirth records for Jersey and more than 80K stillbirth records for Brown Swiss. The direct and maternal heritability estimates of stillbirth were 6.0% (4.5–7.6%) and 4.7% (3.3–6.1%) in Jersey and 6.8% (3.2–10.5%) and 1.1% (0.6–2.9%) in Brown Swiss. The estimated genetic correlations between direct and maternal genetic effects for stillbirth were −0.15 (−0.38 to −0.08) in Jersey and −0.35 (−0.47 to −0.12) in Brown Swiss. The estimated genetic parameters for stillbirth in these two breeds were within close ranges of previous studies. The reliabilities of predicted transmitting abilities in Jersey and Brown Swiss increased substantially, thanks to the substantial increase in available stillbirth data in the past 10 years. The stillbirth records for Ayrshire, Guernsey, and Milking Shorthorn, which ranged approximately between 3K and 12K, are insufficient to implement reliable routine genetic evaluations of stillbirth in these three dairy breeds. Estimated genetic (co)variances and genetic values deviated considerably from the reported ranges of previous studies, and the reliabilities of predicted transmitting abilities were low in these three breeds. In conclusion, routine genetic evaluations of stillbirth are feasible in Brown Swiss and Jersey. However, reliable genetic evaluations of stillbirth in Ayrshire, Guernsey, and Milking Shorthorn require further data collection on stillbirth.
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Affiliation(s)
- Anil Sigdel
- Council on Dairy Cattle Breeding, Bowie, MD, United States
- Department of Animal and Dairy Sciences, University of Wisconsin-Madison, Madison, WI, United States
- *Correspondence: Anil Sigdel, ; Xiao-Lin Wu,
| | - Xiao-Lin Wu
- Council on Dairy Cattle Breeding, Bowie, MD, United States
- Department of Animal and Dairy Sciences, University of Wisconsin-Madison, Madison, WI, United States
- *Correspondence: Anil Sigdel, ; Xiao-Lin Wu,
| | | | | | | | | | - Francisco Peñagaricano
- Department of Animal and Dairy Sciences, University of Wisconsin-Madison, Madison, WI, United States
| | - João Dürr
- Council on Dairy Cattle Breeding, Bowie, MD, United States
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Wu XL, Parker Gaddis KL, Burchard J, Norman HD, Nicolazzi E, Connor EE, Cole JB, Durr J. An alternative interpretation of residual feed intake by phenotypic recursive relationships in dairy cattle. JDS Commun 2021; 2:371-375. [PMID: 36337099 PMCID: PMC9623681 DOI: 10.3168/jdsc.2021-0080] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/15/2021] [Accepted: 07/18/2021] [Indexed: 06/16/2023]
Abstract
There has been increasing interest in residual feed intake (RFI) as a measure of net feed efficiency in dairy cattle. Residual feed intake phenotypes are obtained as residuals from linear regression encompassing relevant factors (i.e., energy sinks) to account for body tissue mobilization. By rearranging the single-trait linear regression, we showed a causal RFI interpretation underlying the linear regression for RFI. It postulates recursive effects in energy allocation from energy sinks on dry matter intake, but the feedback or simultaneous effects are nonexistent. A Bayesian recursive structural equation model was proposed for directly predicting RFI and energy sinks and estimating relevant genetic parameters simultaneously. A simplified Markov chain Monte Carlo algorithm was described. The recursive model is asymptotically equivalent to one-step linear regression for RFI, yet extends the analytical capacity to multiple-trait analysis.
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Affiliation(s)
- Xiao-Lin Wu
- Council on Dairy Cattle Breeding, Bowie, MD 20716
- Department of Animal and Dairy Sciences, University of Wisconsin, Madison 53706
| | | | | | | | | | - Erin E. Connor
- Department of Animal and Food Sciences, University of Delaware, Newark 19716
| | - John B. Cole
- USDA, Agricultural Research Service, Animal Genomics and Improvement Laboratory, Beltsville, MD 20705-2350
| | - Joao Durr
- Council on Dairy Cattle Breeding, Bowie, MD 20716
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Freebern E, Santos DJA, Fang L, Jiang J, Parker Gaddis KL, Liu GE, VanRaden PM, Maltecca C, Cole JB, Ma L. GWAS and fine-mapping of livability and six disease traits in Holstein cattle. BMC Genomics 2020; 21:41. [PMID: 31931710 PMCID: PMC6958677 DOI: 10.1186/s12864-020-6461-z] [Citation(s) in RCA: 57] [Impact Index Per Article: 14.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/03/2019] [Accepted: 01/07/2020] [Indexed: 12/19/2022] Open
Abstract
BACKGROUND Health traits are of significant economic importance to the dairy industry due to their effects on milk production and associated treatment costs. Genome-wide association studies (GWAS) provide a means to identify associated genomic variants and thus reveal insights into the genetic architecture of complex traits and diseases. The objective of this study is to investigate the genetic basis of seven health traits in dairy cattle and to identify potential candidate genes associated with cattle health using GWAS, fine mapping, and analyses of multi-tissue transcriptome data. RESULTS We studied cow livability and six direct disease traits, mastitis, ketosis, hypocalcemia, displaced abomasum, metritis, and retained placenta, using de-regressed breeding values and more than three million imputed DNA sequence variants. After data edits and filtering on reliability, the number of bulls included in the analyses ranged from 11,880 (hypocalcemia) to 24,699 (livability). GWAS was performed using a mixed-model association test, and a Bayesian fine-mapping procedure was conducted to calculate a posterior probability of causality to each variant and gene in the candidate regions. The GWAS detected a total of eight genome-wide significant associations for three traits, cow livability, ketosis, and hypocalcemia, including the bovine Major Histocompatibility Complex (MHC) region associated with livability. Our fine-mapping of associated regions reported 20 candidate genes with the highest posterior probabilities of causality for cattle health. Combined with transcriptome data across multiple tissues in cattle, we further exploited these candidate genes to identify specific expression patterns in disease-related tissues and relevant biological explanations such as the expression of Group-specific Component (GC) in the liver and association with mastitis as well as the Coiled-Coil Domain Containing 88C (CCDC88C) expression in CD8 cells and association with cow livability. CONCLUSIONS Collectively, our analyses report six significant associations and 20 candidate genes of cattle health. With the integration of multi-tissue transcriptome data, our results provide useful information for future functional studies and better understanding of the biological relationship between genetics and disease susceptibility in cattle.
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Affiliation(s)
- Ellen Freebern
- Department of Animal and Avian Sciences, University of Maryland, College Park, MD 20742 USA
| | - Daniel J. A. Santos
- Department of Animal and Avian Sciences, University of Maryland, College Park, MD 20742 USA
| | - Lingzhao Fang
- Department of Animal and Avian Sciences, University of Maryland, College Park, MD 20742 USA
- Animal Genomics and Improvement Laboratory, Henry A. Wallace Beltsville Agricultural Research Service, USDA, Beltsville, MD 20705 USA
| | - Jicai Jiang
- Department of Animal and Avian Sciences, University of Maryland, College Park, MD 20742 USA
| | | | - George E. Liu
- Animal Genomics and Improvement Laboratory, Henry A. Wallace Beltsville Agricultural Research Service, USDA, Beltsville, MD 20705 USA
| | - Paul M. VanRaden
- Animal Genomics and Improvement Laboratory, Henry A. Wallace Beltsville Agricultural Research Service, USDA, Beltsville, MD 20705 USA
| | - Christian Maltecca
- Department of Animal Science, North Carolina State University, Raleigh, 27695 USA
| | - John B. Cole
- Animal Genomics and Improvement Laboratory, Henry A. Wallace Beltsville Agricultural Research Service, USDA, Beltsville, MD 20705 USA
| | - Li Ma
- Department of Animal and Avian Sciences, University of Maryland, College Park, MD 20742 USA
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Parker Gaddis KL, Tiezzi F, Cole JB, Clay JS, Maltecca C. Genomic prediction of disease occurrence using producer-recorded health data: a comparison of methods. Genet Sel Evol 2015; 47:41. [PMID: 25951822 PMCID: PMC4423125 DOI: 10.1186/s12711-015-0093-9] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/25/2014] [Accepted: 01/14/2015] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Genetic selection has been successful in achieving increased production in dairy cattle; however, corresponding declines in fitness traits have been documented. Selection for fitness traits is more difficult, since they have low heritabilities and are influenced by various non-genetic factors. The objective of this paper was to investigate the predictive ability of two-stage and single-step genomic selection methods applied to health data collected from on-farm computer systems in the U.S. METHODS Implementation of single-trait and two-trait sire models was investigated using BayesA and single-step methods for mastitis and somatic cell score. Variance components were estimated. The complete dataset was divided into training and validation sets to perform model comparison. Estimated sire breeding values were used to estimate the number of daughters expected to develop mastitis. Predictive ability of each model was assessed by the sum of χ(2) values that compared predicted and observed numbers of daughters with mastitis and the proportion of wrong predictions. RESULTS According to the model applied, estimated heritabilities of liability to mastitis ranged from 0.05 (SD=0.02) to 0.11 (SD=0.03) and estimated heritabilities of somatic cell score ranged from 0.08 (SD=0.01) to 0.18 (SD=0.03). Posterior mean of genetic correlation between mastitis and somatic cell score was equal to 0.63 (SD=0.17). The single-step method had the best predictive ability. Conversely, the smallest number of wrong predictions was obtained with the univariate BayesA model. The best model fit was found for single-step and pedigree-based models. Bivariate single-step analysis had a better predictive ability than bivariate BayesA; however, the latter led to the smallest number of wrong predictions. CONCLUSIONS Genomic data improved our ability to predict animal breeding values. Performance of genomic selection methods depends on a multitude of factors. Heritability of traits and reliability of genotyped individuals has a large impact on the performance of genomic evaluation methods. Given the current characteristics of producer-recorded health data, single-step methods have several advantages compared to two-step methods.
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Affiliation(s)
| | | | - John B Cole
- Animal Genomics and Improvement Laboratory, Agricultural Research Service, USDA, Beltsville, 20705-2350, MD, USA.
| | - John S Clay
- Dairy Records Management Systems, Raleigh, 27603, NC, USA.
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