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Berry DP, Spangler ML. Animal board invited review: Practical applications of genomic information in livestock. Animal 2023; 17:100996. [PMID: 37820404 DOI: 10.1016/j.animal.2023.100996] [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: 08/29/2023] [Revised: 09/08/2023] [Accepted: 09/11/2023] [Indexed: 10/13/2023] Open
Abstract
Access to high-dimensional genomic information in many livestock species is accelerating. This has been greatly aided not only by continual reductions in genotyping costs but also an expansion in the services available that leverage genomic information to create a greater return-on-investment. Genomic information on individual animals has many uses including (1) parentage verification and discovery, (2) traceability, (3) karyotyping, (4) sex determination, (5) reporting and monitoring of mutations conferring major effects or congenital defects, (6) better estimating inbreeding of individuals and coancestry among individuals, (7) mating advice, (8) determining breed composition, (9) enabling precision management, and (10) genomic evaluations; genomic evaluations exploit genome-wide genotype information to improve the accuracy of predicting an animal's (and by extension its progeny's) genetic merit. Genomic data also provide a huge resource for research, albeit the outcome from this research, if successful, should eventually be realised through one of the ten applications already mentioned. The process for generating a genotype all the way from sample procurement to identifying erroneous genotypes is described, as are the steps that should be considered when developing a bespoke genotyping panel for practical application.
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Affiliation(s)
- D P Berry
- Animal & Grassland Research and Innovation Centre, Teagasc, Moorepark, Fermoy, Cork, Ireland.
| | - M L Spangler
- Department of Animal Science, University of Nebraska-Lincoln, Lincoln, NE, United States
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Lu AT, Fei Z, Haghani A, Robeck TR, Zoller JA, Li CZ, Lowe R, Yan Q, Zhang J, Vu H, Ablaeva J, Acosta-Rodriguez VA, Adams DM, Almunia J, Aloysius A, Ardehali R, Arneson A, Baker CS, Banks G, Belov K, Bennett NC, Black P, Blumstein DT, Bors EK, Breeze CE, Brooke RT, Brown JL, Carter GG, Caulton A, Cavin JM, Chakrabarti L, Chatzistamou I, Chen H, Cheng K, Chiavellini P, Choi OW, Clarke SM, Cooper LN, Cossette ML, Day J, DeYoung J, DiRocco S, Dold C, Ehmke EE, Emmons CK, Emmrich S, Erbay E, Erlacher-Reid C, Faulkes CG, Ferguson SH, Finno CJ, Flower JE, Gaillard JM, Garde E, Gerber L, Gladyshev VN, Gorbunova V, Goya RG, Grant MJ, Green CB, Hales EN, Hanson MB, Hart DW, Haulena M, Herrick K, Hogan AN, Hogg CJ, Hore TA, Huang T, Izpisua Belmonte JC, Jasinska AJ, Jones G, Jourdain E, Kashpur O, Katcher H, Katsumata E, Kaza V, Kiaris H, Kobor MS, Kordowitzki P, Koski WR, Krützen M, Kwon SB, Larison B, Lee SG, Lehmann M, Lemaitre JF, Levine AJ, Li C, Li X, Lim AR, Lin DTS, Lindemann DM, Little TJ, Macoretta N, Maddox D, Matkin CO, Mattison JA, McClure M, Mergl J, Meudt JJ, Montano GA, Mozhui K, Munshi-South J, Naderi A, Nagy M, Narayan P, Nathanielsz PW, Nguyen NB, Niehrs C, O'Brien JK, O'Tierney Ginn P, Odom DT, Ophir AG, Osborn S, Ostrander EA, Parsons KM, Paul KC, Pellegrini M, Peters KJ, Pedersen AB, Petersen JL, Pietersen DW, Pinho GM, Plassais J, Poganik JR, Prado NA, Reddy P, Rey B, Ritz BR, Robbins J, Rodriguez M, Russell J, Rydkina E, Sailer LL, Salmon AB, Sanghavi A, Schachtschneider KM, Schmitt D, Schmitt T, Schomacher L, Schook LB, Sears KE, Seifert AW, Seluanov A, Shafer ABA, Shanmuganayagam D, Shindyapina AV, Simmons M, Singh K, Sinha I, Slone J, Snell RG, Soltanmaohammadi E, Spangler ML, Spriggs MC, Staggs L, Stedman N, Steinman KJ, Stewart DT, Sugrue VJ, Szladovits B, Takahashi JS, Takasugi M, Teeling EC, Thompson MJ, Van Bonn B, Vernes SC, Villar D, Vinters HV, Wallingford MC, Wang N, Wayne RK, Wilkinson GS, Williams CK, Williams RW, Yang XW, Yao M, Young BG, Zhang B, Zhang Z, Zhao P, Zhao Y, Zhou W, Zimmermann J, Ernst J, Raj K, Horvath S. Author Correction: Universal DNA methylation age across mammalian tissues. Nat Aging 2023; 3:1462. [PMID: 37674040 PMCID: PMC10645586 DOI: 10.1038/s43587-023-00499-7] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/08/2023]
Affiliation(s)
- A T Lu
- Department of Human Genetics, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA
- Altos Labs, San Diego Institute of Science, San Diego, CA, USA
| | - Z Fei
- Department of Biostatistics, Fielding School of Public Health, University of California, Los Angeles, Los Angeles, CA, USA
- Department of Statistics, University of California, Riverside, Riverside, CA, USA
| | - A Haghani
- Department of Human Genetics, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA
- Altos Labs, San Diego Institute of Science, San Diego, CA, USA
| | - T R Robeck
- Zoological SeaWorld Parks and Entertainment, Orlando, FL, USA
| | - J A Zoller
- Department of Biostatistics, Fielding School of Public Health, University of California, Los Angeles, Los Angeles, CA, USA
| | - C Z Li
- Department of Biostatistics, Fielding School of Public Health, University of California, Los Angeles, Los Angeles, CA, USA
| | - R Lowe
- Altos Labs, Cambridge Institute of Science, Cambridge, UK
| | - Q Yan
- Altos Labs, San Diego Institute of Science, San Diego, CA, USA
| | - J Zhang
- Department of Human Genetics, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA
| | - H Vu
- Bioinformatics Interdepartmental Program, University of California, Los Angeles, CA, USA
- Department of Biological Chemistry, University of California, Los Angeles, Los Angeles, CA, USA
| | - J Ablaeva
- Department of Biology, University of Rochester, Rochester, NY, USA
| | - V A Acosta-Rodriguez
- Department of Neuroscience, Peter O'Donnell Jr. Brain Institute, University of Texas Southwestern Medical Center, Dallas, TX, USA
| | - D M Adams
- Department of Biology, University of Maryland, College Park, MD, USA
| | - J Almunia
- Loro Parque Fundacion, Puerto de la Cruz, Spain
| | - A Aloysius
- Department of Biology, University of Kentucky, Lexington, KY, USA
| | - R Ardehali
- Division of Cardiology, Department of Internal Medicine, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA
| | - A Arneson
- Bioinformatics Interdepartmental Program, University of California, Los Angeles, CA, USA
- Department of Biological Chemistry, University of California, Los Angeles, Los Angeles, CA, USA
| | - C S Baker
- Marine Mammal Institute, Oregon State University, Newport, OR, USA
| | - G Banks
- School of Science and Technology, Clifton Campus, Nottingham Trent University, Nottingham, UK
| | - K Belov
- School of Life and Environmental Sciences, the University of Sydney, Sydney, New South Wales, Australia
| | - N C Bennett
- Department of Zoology and Entomology, University of Pretoria, Hatfield, South Africa
| | - P Black
- Busch Gardens Tampa, Tampa, FL, USA
| | - D T Blumstein
- Department of Ecology and Evolutionary Biology, University of California, Los Angeles, Los Angeles, CA, USA
- Rocky Mountain Biological Laboratory, Crested Butte, CO, USA
| | - E K Bors
- Marine Mammal Institute, Oregon State University, Newport, OR, USA
| | - C E Breeze
- Altius Institute for Biomedical Sciences, Seattle, WA, USA
| | - R T Brooke
- Epigenetic Clock Development Foundation, Los Angeles, CA, USA
| | - J L Brown
- Center for Species Survival, Smithsonian Conservation Biology Institute, Front Royal, VA, USA
| | - G G Carter
- Department of Evolution, Ecology and Organismal Biology, The Ohio State University, Columbus, OH, USA
| | - A Caulton
- AgResearch, Invermay Agricultural Centre, Mosgiel, New Zealand
- Department of Biochemistry, University of Otago, Dunedin, New Zealand
| | - J M Cavin
- Gulf World, Dolphin Company, Panama City Beach, FL, USA
| | - L Chakrabarti
- School of Veterinary Medicine and Science, University of Nottingham, Nottingham, UK
| | - I Chatzistamou
- Department of Pathology, Microbiology and Immunology, School of Medicine, University of South Carolina, Columbia, SC, USA
| | - H Chen
- Department of Pharmacology, Addiction Science and Toxicology, the University of Tennessee Health Science Center, Memphis, TN, USA
| | - K Cheng
- Medical Informatics, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA
| | - P Chiavellini
- Biochemistry Research Institute of La Plata, Histology and Pathology, School of Medicine, University of La Plata, La Plata, Argentina
| | - O W Choi
- Center for Neurobehavioral Genetics, Semel Institute for Neuroscience and Human Behavior, Department of Psychiatry and Biobehavioral Sciences, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA
| | - S M Clarke
- AgResearch, Invermay Agricultural Centre, Mosgiel, New Zealand
| | - L N Cooper
- Department of Anatomy and Neurobiology, Northeast Ohio Medical University, Rootstown, OH, USA
| | - M L Cossette
- Department of Environmental and Life Sciences, Trent University, Peterborough, Ontario, Canada
| | - J Day
- Taronga Institute of Science and Learning, Taronga Conservation Society Australia, Mosman, New South Wales, Australia
| | - J DeYoung
- Center for Neurobehavioral Genetics, Semel Institute for Neuroscience and Human Behavior, Department of Psychiatry and Biobehavioral Sciences, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA
| | - S DiRocco
- SeaWorld of Florida, Orlando, FL, USA
| | - C Dold
- Zoological Operations, SeaWorld Parks and Entertainment, Orlando, FL, USA
| | | | - C K Emmons
- Conservation Biology Division, Northwest Fisheries Science Center, National Marine Fisheries Service, National Oceanic and Atmospheric Administration, Seattle, WA, USA
| | - S Emmrich
- Departments of Biology and Medicine, University of Rochester, Rochester, NY, USA
| | - E Erbay
- Altos Labs, San Francisco, CA, USA
| | - C Erlacher-Reid
- SeaWorld of Florida, Orlando, FL, USA
- SeaWorld Orlando, Orlando, FL, USA
| | - C G Faulkes
- School of Biological and Behavioural Sciences, Queen Mary University of London, London, UK
| | - S H Ferguson
- Fisheries and Oceans Canada, Freshwater Institute, Winnipeg, Manitoba, Canada
- Department of Biological Sciences, University of Manitoba, Winnipeg, Manitoba, Canada
| | - C J Finno
- Department of Population Health and Reproduction, University of California, Davis School of Veterinary Medicine, Davis, CA, USA
| | | | - J M Gaillard
- Universite de Lyon, Universite Lyon 1, CNRS, Laboratoire de Biometrie et Biologie Evolutive, Villeurbanne, France
| | - E Garde
- Greenland Institute of Natural Resources, Nuuk, Greenland
| | - L Gerber
- Evolution and Ecology Research Centre, School of Biological, Earth and Environmental Sciences, UNSW Sydney, Sydney, New South Wales, Australia
| | - V N Gladyshev
- Division of Genetics, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - V Gorbunova
- Departments of Biology and Medicine, University of Rochester, Rochester, NY, USA
| | - R G Goya
- Biochemistry Research Institute of La Plata, Histology and Pathology, School of Medicine, University of La Plata, La Plata, Argentina
| | - M J Grant
- Applied Translational Genetics Group, School of Biological Sciences, Centre for Brain Research, the University of Auckland, Auckland, New Zealand
| | - C B Green
- Department of Neuroscience, Peter O'Donnell Jr. Brain Institute, University of Texas Southwestern Medical Center, Dallas, TX, USA
| | - E N Hales
- Department of Population Health and Reproduction, University of California, Davis School of Veterinary Medicine, Davis, CA, USA
| | - M B Hanson
- Conservation Biology Division, Northwest Fisheries Science Center, National Marine Fisheries Service, National Oceanic and Atmospheric Administration, Seattle, WA, USA
| | - D W Hart
- Department of Zoology and Entomology, University of Pretoria, Hatfield, South Africa
| | - M Haulena
- Vancouver Aquarium, Vancouver, British Columbia, Canada
| | - K Herrick
- SeaWorld of California, San Diego, CA, USA
| | - A N Hogan
- Cancer Genetics and Comparative Genomics Branch, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD, USA
| | - C J Hogg
- School of Life and Environmental Sciences, the University of Sydney, Sydney, New South Wales, Australia
| | - T A Hore
- Department of Anatomy, University of Otago, Dunedin, New Zealand
| | - T Huang
- Division of Human Genetics, Department of Pediatrics, University at Buffalo, Buffalo, NY, USA
- Division of Genetics and Metabolism, Oishei Children's Hospital, Buffalo, NY, USA
| | | | - A J Jasinska
- Center for Neurobehavioral Genetics, Semel Institute for Neuroscience and Human Behavior, Department of Psychiatry and Biobehavioral Sciences, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA
| | - G Jones
- School of Biological Sciences, University of Bristol, Bristol, UK
| | | | - O Kashpur
- Mother Infant Research Institute, Tufts Medical Center, Boston, MA, USA
| | - H Katcher
- Yuvan Research, Mountain View, CA, USA
| | | | - V Kaza
- Peromyscus Genetic Stock Center, University of South Carolina, Columbia, SC, USA
| | - H Kiaris
- Peromyscus Genetic Stock Center, University of South Carolina, Columbia, SC, USA
- Department of Drug Discovery and Biomedical Sciences, College of Pharmacy, University of South Carolina, Columbia, SC, USA
| | - M S Kobor
- Edwin S.H. Leong Healthy Aging Program, Centre for Molecular Medicine and Therapeutics, University of British Columbia, Vancouver, British Columbia, Canada
| | - P Kordowitzki
- Institute of Animal Reproduction and Food Research of the Polish Academy of Sciences, Olsztyn, Poland
- Institute for Veterinary Medicine, Nicolaus Copernicus University, Torun, Poland
| | - W R Koski
- LGL Limited, King City, Ontario, Canada
| | - M Krützen
- Evolutionary Genetics Group, Department of Evolutionary Anthropology, University of Zurich, Zurich, Switzerland
| | - S B Kwon
- Bioinformatics Interdepartmental Program, University of California, Los Angeles, CA, USA
- Department of Biological Chemistry, University of California, Los Angeles, Los Angeles, CA, USA
| | - B Larison
- Department of Ecology and Evolutionary Biology, UCLA, Los Angeles, CA, USA
- Center for Tropical Research, Institute for the Environment and Sustainability, UCLA, Los Angeles, CA, USA
| | - S G Lee
- Division of Genetics, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - M Lehmann
- Biochemistry Research Institute of La Plata, Histology and Pathology, School of Medicine, University of La Plata, La Plata, Argentina
| | - J F Lemaitre
- Universite de Lyon, Universite Lyon 1, CNRS, Laboratoire de Biometrie et Biologie Evolutive, Villeurbanne, France
| | - A J Levine
- Department of Neurology, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA
| | - C Li
- Texas Pregnancy and Life-course Health Center, Southwest National Primate Research Center, San Antonio, TX, USA
- Department of Animal Science, College of Agriculture and Natural Resources, Laramie, WY, USA
| | - X Li
- Technology Center for Genomics and Bioinformatics, Department of Pathology and Laboratory Medicine, University of California, Los Angeles, Los Angeles, CA, USA
| | - A R Lim
- Department of Human Genetics, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA
| | - D T S Lin
- Centre for Molecular Medicine and Therapeutics, BC Children's Hospital Research Institute, University of British Columbia, Vancouver, British Columbia, Canada
| | | | - T J Little
- Institute of Ecology and Evolution, School of Biological Sciences, University of Edinburgh, Edinburgh, UK
| | - N Macoretta
- Departments of Biology and Medicine, University of Rochester, Rochester, NY, USA
| | - D Maddox
- White Oak Conservation, Yulee, FL, USA
| | - C O Matkin
- North Gulf Oceanic Society, Homer, AK, USA
| | - J A Mattison
- Translational Gerontology Branch, National Institute on Aging Intramural Research Program, National Institutes of Health, Baltimore, MD, USA
| | | | - J Mergl
- Marineland of Canada, Niagara Falls, Ontario, Canada
| | - J J Meudt
- Biomedical and Genomic Research Group, Department of Animal and Dairy Sciences, University of Wisconsin-Madison, Madison, WI, USA
| | - G A Montano
- Zoological Operations, SeaWorld Parks and Entertainment, Orlando, FL, USA
| | - K Mozhui
- Department of Preventive Medicine, University of Tennessee Health Science Center, College of Medicine, Memphis, TN, USA
- Department of Genetics, Genomics and Informatics, University of Tennessee Health Science Center, College of Medicine, Memphis, TN, USA
| | - J Munshi-South
- Louis Calder Center-Biological Field Station, Department of Biological Sciences, Fordham University, Armonk, NY, USA
| | - A Naderi
- Department of Drug Discovery and Biomedical Sciences, College of Pharmacy, University of South Carolina, Columbia, SC, USA
| | - M Nagy
- Museum fur Naturkunde, Leibniz Institute for Evolution and Biodiversity Science, Berlin, Germany
| | - P Narayan
- Applied Translational Genetics Group, School of Biological Sciences, Centre for Brain Research, the University of Auckland, Auckland, New Zealand
| | - P W Nathanielsz
- Texas Pregnancy and Life-course Health Center, Southwest National Primate Research Center, San Antonio, TX, USA
- Department of Animal Science, College of Agriculture and Natural Resources, Laramie, WY, USA
| | - N B Nguyen
- Division of Cardiology, Department of Internal Medicine, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA
| | - C Niehrs
- Institute of Molecular Biology, Mainz, Germany
- Division of Molecular Embryology, DKFZ-ZMBH Alliance, Heidelberg, Germany
| | - J K O'Brien
- Taronga Institute of Science and Learning, Taronga Conservation Society Australia, Mosman, New South Wales, Australia
| | - P O'Tierney Ginn
- Mother Infant Research Institute, Tufts Medical Center, Boston, MA, USA
- Department of Obstetrics and Gynecology, Tufts University School of Medicine, Boston, MA, USA
| | - D T Odom
- Cancer Research UK Cambridge Institute, University of Cambridge, Cambridge, UK
- Division of Regulatory Genomics and Cancer Evolution, Deutsches Krebsforschungszentrum, Heidelberg, Germany
| | - A G Ophir
- Department of Psychology, Cornell University, Ithaca, NY, USA
| | - S Osborn
- SeaWorld of Texas, San Antonio, TX, USA
| | - E A Ostrander
- Cancer Genetics and Comparative Genomics Branch, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD, USA
| | - K M Parsons
- Conservation Biology Division, Northwest Fisheries Science Center, National Marine Fisheries Service, National Oceanic and Atmospheric Administration, Seattle, WA, USA
| | - K C Paul
- Department of Neurology, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA
| | - M Pellegrini
- Department of Molecular Cell and Developmental Biology, University of California, Los Angeles, Los Angeles, CA, USA
| | - K J Peters
- Evolutionary Genetics Group, Department of Evolutionary Anthropology, University of Zurich, Zurich, Switzerland
- School of Earth, Atmospheric and Life Sciences, University of Wollongong, Wollongong, Australia
| | - A B Pedersen
- Institute of Evolutionary Biology, School of Biological Sciences, University of Edinburgh, Edinburgh, UK
| | - J L Petersen
- Department of Animal Science, University of Nebraska, Lincoln, NE, USA
| | - D W Pietersen
- Mammal Research Institute, Department of Zoology and Entomology, University of Pretoria, Hatfield, South Africa
| | - G M Pinho
- Department of Ecology and Evolutionary Biology, UCLA, Los Angeles, CA, USA
| | - J Plassais
- Cancer Genetics and Comparative Genomics Branch, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD, USA
| | - J R Poganik
- Division of Genetics, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - N A Prado
- Department of Biology, College of Arts and Science, Adelphi University, Garden City, NY, USA
| | - P Reddy
- Altos Labs, San Diego Institute of Science, San Diego, CA, USA
- Salk Institute for Biological Studies, La Jolla, CA, USA
| | - B Rey
- Universite de Lyon, Universite Lyon 1, CNRS, Laboratoire de Biometrie et Biologie Evolutive, Villeurbanne, France
| | - B R Ritz
- Department of Epidemiology, UCLA Fielding School of Public Health, Los Angeles, CA, USA
- Department of Environmental Health Sciences, UCLA Fielding School of Public Health, Los Angeles, CA, USA
- Department of Neurology, UCLA David Geffen School of Medicine, Los Angeles, CA, USA
| | - J Robbins
- Center for Coastal Studies, Provincetown, MA, USA
| | | | - J Russell
- SeaWorld of California, San Diego, CA, USA
| | - E Rydkina
- Departments of Biology and Medicine, University of Rochester, Rochester, NY, USA
| | - L L Sailer
- Department of Psychology, Cornell University, Ithaca, NY, USA
| | - A B Salmon
- The Sam and Ann Barshop Institute for Longevity and Aging Studies and Department of Molecular Medicine, UT Health San Antonio and the Geriatric Research Education and Clinical Center, South Texas Veterans Healthcare System, San Antonio, TX, USA
| | | | - K M Schachtschneider
- Department of Radiology, University of Illinois at Chicago, Chicago, IL, USA
- Department of Biochemistry and Molecular Genetics, University of Illinois at Chicago, Chicago, IL, USA
- National Center for Supercomputing Applications, University of Illinois at Urbana-Champaign, Urbana, IL, USA
| | - D Schmitt
- College of Agriculture, Missouri State University, Springfield, MO, USA
| | - T Schmitt
- SeaWorld of California, San Diego, CA, USA
| | | | - L B Schook
- Department of Radiology, University of Illinois at Chicago, Chicago, IL, USA
- Department of Animal Sciences, University of Illinois at Urbana-Champaign, Champaign, IL, USA
| | - K E Sears
- Department of Ecology and Evolutionary Biology, UCLA, Los Angeles, CA, USA
- Department of Molecular Cell and Developmental Biology, University of California, Los Angeles, Los Angeles, CA, USA
| | - A W Seifert
- Department of Biology, University of Kentucky, Lexington, KY, USA
| | - A Seluanov
- Departments of Biology and Medicine, University of Rochester, Rochester, NY, USA
| | - A B A Shafer
- Department of Forensic Science, Environmental and Life Sciences, Trent University, Peterborough, Ontario, Canada
| | - D Shanmuganayagam
- Biomedical and Genomic Research Group, Department of Animal and Dairy Sciences, University of Wisconsin-Madison, Madison, WI, USA
- Department of Surgery, University of Wisconsin School of Medicine and Public Health, Madison, WI, USA
| | - A V Shindyapina
- Division of Genetics, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | | | - K Singh
- Shobhaben Pratapbhai Patel School of Pharmacy and Technology Management, SVKM'S NMIMS University, Mumbai, India
| | - I Sinha
- Department of Ecology and Evolutionary Biology, UCLA, Los Angeles, CA, USA
| | - J Slone
- Division of Human Genetics, Department of Pediatrics, University at Buffalo, Buffalo, NY, USA
| | - R G Snell
- Applied Translational Genetics Group, School of Biological Sciences, Centre for Brain Research, the University of Auckland, Auckland, New Zealand
| | - E Soltanmaohammadi
- Department of Drug Discovery and Biomedical Sciences, College of Pharmacy, University of South Carolina, Columbia, SC, USA
| | - M L Spangler
- Department of Animal Science, University of Nebraska, Lincoln, NE, USA
| | | | - L Staggs
- SeaWorld of Florida, Orlando, FL, USA
| | | | - K J Steinman
- Species Preservation Laboratory, SeaWorld San Diego, San Diego, CA, USA
| | - D T Stewart
- Biology Department, Acadia University, Wolfville, Nova Scotia, Canada
| | - V J Sugrue
- Department of Anatomy, University of Otago, Dunedin, New Zealand
| | - B Szladovits
- Department of Pathobiology and Population Sciences, Royal Veterinary College, Hatfield, UK
| | - J S Takahashi
- Department of Neuroscience, Peter O'Donnell Jr. Brain Institute, University of Texas Southwestern Medical Center, Dallas, TX, USA
- Howard Hughes Medical Institute, Department of Neuroscience, University of Texas Southwestern Medical Center, Dallas, TX, USA
| | - M Takasugi
- Departments of Biology and Medicine, University of Rochester, Rochester, NY, USA
| | - E C Teeling
- School of Biology and Environmental Science, University College Dublin, Dublin, Ireland
| | - M J Thompson
- Department of Molecular Cell and Developmental Biology, University of California, Los Angeles, Los Angeles, CA, USA
| | - B Van Bonn
- John G. Shedd Aquarium, Chicago, IL, USA
| | - S C Vernes
- School of Biology, the University of St Andrews, Fife, UK
- Neurogenetics of Vocal Communication Group, Max Planck Institute for Psycholinguistics, Nijmegen, the Netherlands
| | - D Villar
- Blizard Institute, Faculty of Medicine and Dentistry, Queen Mary University of London, London, UK
| | - H V Vinters
- Department of Pathology and Laboratory Medicine, David Geffen School of Medicine at UCLA, Los Angeles, CA, USA
| | - M C Wallingford
- Mother Infant Research Institute, Tufts Medical Center, Boston, MA, USA
- Division of Obstetrics and Gynecology, Tufts University School of Medicine, Boston, MA, USA
| | - N Wang
- Center for Neurobehavioral Genetics, Jane and Terry Semel Institute for Neuroscience and Human Behavior, University of California, Los Angeles, Los Angeles, CA, USA
- Department of Psychiatry and Biobehavioral Sciences, David Geffen School of Medicine at UCLA, Los Angeles, CA, USA
| | - R K Wayne
- Department of Ecology and Evolutionary Biology, UCLA, Los Angeles, CA, USA
| | - G S Wilkinson
- Department of Biology, University of Maryland, College Park, MD, USA
| | - C K Williams
- Department of Neurology, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA
| | - R W Williams
- Department of Genetics, Genomics and Informatics, University of Tennessee Health Science Center, College of Medicine, Memphis, TN, USA
| | - X W Yang
- Center for Neurobehavioral Genetics, Jane and Terry Semel Institute for Neuroscience and Human Behavior, University of California, Los Angeles, Los Angeles, CA, USA
- Department of Psychiatry and Biobehavioral Sciences, David Geffen School of Medicine at UCLA, Los Angeles, CA, USA
| | - M Yao
- Department of Biostatistics, Fielding School of Public Health, University of California, Los Angeles, Los Angeles, CA, USA
| | - B G Young
- Fisheries and Oceans Canada, Winnipeg, Manitoba, Canada
| | - B Zhang
- Division of Genetics, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Z Zhang
- Departments of Biology and Medicine, University of Rochester, Rochester, NY, USA
| | - P Zhao
- Division of Cardiology, Department of Internal Medicine, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA
- Eli and Edythe Broad Center of Regenerative Medicine and Stem Cell Research, University of California, Los Angeles, CA, USA
| | - Y Zhao
- Departments of Biology and Medicine, University of Rochester, Rochester, NY, USA
| | - W Zhou
- Center for Computational and Genomic Medicine, Children's Hospital of Philadelphia, Philadelphia, PA, USA
- Department of Pathology and Laboratory Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - J Zimmermann
- Department of Mathematics and Technology, University of Applied Sciences Koblenz, Koblenz, Germany
| | - J Ernst
- Bioinformatics Interdepartmental Program, University of California, Los Angeles, CA, USA
- Department of Biological Chemistry, University of California, Los Angeles, Los Angeles, CA, USA
| | - K Raj
- Altos Labs, Cambridge Institute of Science, Cambridge, UK
| | - S Horvath
- Department of Human Genetics, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA.
- Altos Labs, San Diego Institute of Science, San Diego, CA, USA.
- Department of Biostatistics, Fielding School of Public Health, University of California, Los Angeles, Los Angeles, CA, USA.
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Lu AT, Fei Z, Haghani A, Robeck TR, Zoller JA, Li CZ, Lowe R, Yan Q, Zhang J, Vu H, Ablaeva J, Acosta-Rodriguez VA, Adams DM, Almunia J, Aloysius A, Ardehali R, Arneson A, Baker CS, Banks G, Belov K, Bennett NC, Black P, Blumstein DT, Bors EK, Breeze CE, Brooke RT, Brown JL, Carter GG, Caulton A, Cavin JM, Chakrabarti L, Chatzistamou I, Chen H, Cheng K, Chiavellini P, Choi OW, Clarke SM, Cooper LN, Cossette ML, Day J, DeYoung J, DiRocco S, Dold C, Ehmke EE, Emmons CK, Emmrich S, Erbay E, Erlacher-Reid C, Faulkes CG, Ferguson SH, Finno CJ, Flower JE, Gaillard JM, Garde E, Gerber L, Gladyshev VN, Gorbunova V, Goya RG, Grant MJ, Green CB, Hales EN, Hanson MB, Hart DW, Haulena M, Herrick K, Hogan AN, Hogg CJ, Hore TA, Huang T, Izpisua Belmonte JC, Jasinska AJ, Jones G, Jourdain E, Kashpur O, Katcher H, Katsumata E, Kaza V, Kiaris H, Kobor MS, Kordowitzki P, Koski WR, Krützen M, Kwon SB, Larison B, Lee SG, Lehmann M, Lemaitre JF, Levine AJ, Li C, Li X, Lim AR, Lin DTS, Lindemann DM, Little TJ, Macoretta N, Maddox D, Matkin CO, Mattison JA, McClure M, Mergl J, Meudt JJ, Montano GA, Mozhui K, Munshi-South J, Naderi A, Nagy M, Narayan P, Nathanielsz PW, Nguyen NB, Niehrs C, O'Brien JK, O'Tierney Ginn P, Odom DT, Ophir AG, Osborn S, Ostrander EA, Parsons KM, Paul KC, Pellegrini M, Peters KJ, Pedersen AB, Petersen JL, Pietersen DW, Pinho GM, Plassais J, Poganik JR, Prado NA, Reddy P, Rey B, Ritz BR, Robbins J, Rodriguez M, Russell J, Rydkina E, Sailer LL, Salmon AB, Sanghavi A, Schachtschneider KM, Schmitt D, Schmitt T, Schomacher L, Schook LB, Sears KE, Seifert AW, Seluanov A, Shafer ABA, Shanmuganayagam D, Shindyapina AV, Simmons M, Singh K, Sinha I, Slone J, Snell RG, Soltanmaohammadi E, Spangler ML, Spriggs MC, Staggs L, Stedman N, Steinman KJ, Stewart DT, Sugrue VJ, Szladovits B, Takahashi JS, Takasugi M, Teeling EC, Thompson MJ, Van Bonn B, Vernes SC, Villar D, Vinters HV, Wallingford MC, Wang N, Wayne RK, Wilkinson GS, Williams CK, Williams RW, Yang XW, Yao M, Young BG, Zhang B, Zhang Z, Zhao P, Zhao Y, Zhou W, Zimmermann J, Ernst J, Raj K, Horvath S. Universal DNA methylation age across mammalian tissues. Nat Aging 2023; 3:1144-1166. [PMID: 37563227 PMCID: PMC10501909 DOI: 10.1038/s43587-023-00462-6] [Citation(s) in RCA: 28] [Impact Index Per Article: 28.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/29/2022] [Accepted: 06/21/2023] [Indexed: 08/12/2023]
Abstract
Aging, often considered a result of random cellular damage, can be accurately estimated using DNA methylation profiles, the foundation of pan-tissue epigenetic clocks. Here, we demonstrate the development of universal pan-mammalian clocks, using 11,754 methylation arrays from our Mammalian Methylation Consortium, which encompass 59 tissue types across 185 mammalian species. These predictive models estimate mammalian tissue age with high accuracy (r > 0.96). Age deviations correlate with human mortality risk, mouse somatotropic axis mutations and caloric restriction. We identified specific cytosines with methylation levels that change with age across numerous species. These sites, highly enriched in polycomb repressive complex 2-binding locations, are near genes implicated in mammalian development, cancer, obesity and longevity. Our findings offer new evidence suggesting that aging is evolutionarily conserved and intertwined with developmental processes across all mammals.
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Affiliation(s)
- A T Lu
- Department of Human Genetics, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA
- Altos Labs, San Diego Institute of Science, San Diego, CA, USA
| | - Z Fei
- Department of Biostatistics, Fielding School of Public Health, University of California, Los Angeles, Los Angeles, CA, USA
- Department of Statistics, University of California, Riverside, Riverside, CA, USA
| | - A Haghani
- Department of Human Genetics, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA
- Altos Labs, San Diego Institute of Science, San Diego, CA, USA
| | - T R Robeck
- Zoological SeaWorld Parks and Entertainment, Orlando, FL, USA
| | - J A Zoller
- Department of Biostatistics, Fielding School of Public Health, University of California, Los Angeles, Los Angeles, CA, USA
| | - C Z Li
- Department of Biostatistics, Fielding School of Public Health, University of California, Los Angeles, Los Angeles, CA, USA
| | - R Lowe
- Altos Labs, Cambridge Institute of Science, Cambridge, UK
| | - Q Yan
- Altos Labs, San Diego Institute of Science, San Diego, CA, USA
| | - J Zhang
- Department of Human Genetics, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA
| | - H Vu
- Bioinformatics Interdepartmental Program, University of California, Los Angeles, CA, USA
- Department of Biological Chemistry, University of California, Los Angeles, Los Angeles, CA, USA
| | - J Ablaeva
- Department of Biology, University of Rochester, Rochester, NY, USA
| | - V A Acosta-Rodriguez
- Department of Neuroscience, Peter O'Donnell Jr. Brain Institute, University of Texas Southwestern Medical Center, Dallas, TX, USA
| | - D M Adams
- Department of Biology, University of Maryland, College Park, MD, USA
| | - J Almunia
- Loro Parque Fundacion, Puerto de la Cruz, Spain
| | - A Aloysius
- Department of Biology, University of Kentucky, Lexington, KY, USA
| | - R Ardehali
- Division of Cardiology, Department of Internal Medicine, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA
| | - A Arneson
- Bioinformatics Interdepartmental Program, University of California, Los Angeles, CA, USA
- Department of Biological Chemistry, University of California, Los Angeles, Los Angeles, CA, USA
| | - C S Baker
- Marine Mammal Institute, Oregon State University, Newport, OR, USA
| | - G Banks
- School of Science and Technology, Clifton Campus, Nottingham Trent University, Nottingham, UK
| | - K Belov
- School of Life and Environmental Sciences, the University of Sydney, Sydney, New South Wales, Australia
| | - N C Bennett
- Department of Zoology and Entomology, University of Pretoria, Hatfield, South Africa
| | - P Black
- Busch Gardens Tampa, Tampa, FL, USA
| | - D T Blumstein
- Department of Ecology and Evolutionary Biology, University of California, Los Angeles, Los Angeles, CA, USA
- Rocky Mountain Biological Laboratory, Crested Butte, CO, USA
| | - E K Bors
- Marine Mammal Institute, Oregon State University, Newport, OR, USA
| | - C E Breeze
- Altius Institute for Biomedical Sciences, Seattle, WA, USA
| | - R T Brooke
- Epigenetic Clock Development Foundation, Los Angeles, CA, USA
| | - J L Brown
- Center for Species Survival, Smithsonian Conservation Biology Institute, Front Royal, VA, USA
| | - G G Carter
- Department of Evolution, Ecology and Organismal Biology, The Ohio State University, Columbus, OH, USA
| | - A Caulton
- AgResearch, Invermay Agricultural Centre, Mosgiel, New Zealand
- Department of Biochemistry, University of Otago, Dunedin, New Zealand
| | - J M Cavin
- Gulf World, Dolphin Company, Panama City Beach, FL, USA
| | - L Chakrabarti
- School of Veterinary Medicine and Science, University of Nottingham, Nottingham, UK
| | - I Chatzistamou
- Department of Pathology, Microbiology and Immunology, School of Medicine, University of South Carolina, Columbia, SC, USA
| | - H Chen
- Department of Pharmacology, Addiction Science and Toxicology, the University of Tennessee Health Science Center, Memphis, TN, USA
| | - K Cheng
- Medical Informatics, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA
| | - P Chiavellini
- Biochemistry Research Institute of La Plata, Histology and Pathology, School of Medicine, University of La Plata, La Plata, Argentina
| | - O W Choi
- Center for Neurobehavioral Genetics, Semel Institute for Neuroscience and Human Behavior, Department of Psychiatry and Biobehavioral Sciences, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA
| | - S M Clarke
- AgResearch, Invermay Agricultural Centre, Mosgiel, New Zealand
| | - L N Cooper
- Department of Anatomy and Neurobiology, Northeast Ohio Medical University, Rootstown, OH, USA
| | - M L Cossette
- Department of Environmental and Life Sciences, Trent University, Peterborough, Ontario, Canada
| | - J Day
- Taronga Institute of Science and Learning, Taronga Conservation Society Australia, Mosman, New South Wales, Australia
| | - J DeYoung
- Center for Neurobehavioral Genetics, Semel Institute for Neuroscience and Human Behavior, Department of Psychiatry and Biobehavioral Sciences, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA
| | - S DiRocco
- SeaWorld of Florida, Orlando, FL, USA
| | - C Dold
- Zoological Operations, SeaWorld Parks and Entertainment, Orlando, FL, USA
| | | | - C K Emmons
- Conservation Biology Division, Northwest Fisheries Science Center, National Marine Fisheries Service, National Oceanic and Atmospheric Administration, Seattle, WA, USA
| | - S Emmrich
- Departments of Biology and Medicine, University of Rochester, Rochester, NY, USA
| | - E Erbay
- Altos Labs, San Francisco, CA, USA
| | - C Erlacher-Reid
- SeaWorld of Florida, Orlando, FL, USA
- SeaWorld Orlando, Orlando, FL, USA
| | - C G Faulkes
- School of Biological and Behavioural Sciences, Queen Mary University of London, London, UK
| | - S H Ferguson
- Fisheries and Oceans Canada, Freshwater Institute, Winnipeg, Manitoba, Canada
- Department of Biological Sciences, University of Manitoba, Winnipeg, Manitoba, Canada
| | - C J Finno
- Department of Population Health and Reproduction, University of California, Davis School of Veterinary Medicine, Davis, CA, USA
| | | | - J M Gaillard
- Universite de Lyon, Universite Lyon 1, CNRS, Laboratoire de Biometrie et Biologie Evolutive, Villeurbanne, France
| | - E Garde
- Greenland Institute of Natural Resources, Nuuk, Greenland
| | - L Gerber
- Evolution and Ecology Research Centre, School of Biological, Earth and Environmental Sciences, UNSW Sydney, Sydney, New South Wales, Australia
| | - V N Gladyshev
- Division of Genetics, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - V Gorbunova
- Departments of Biology and Medicine, University of Rochester, Rochester, NY, USA
| | - R G Goya
- Biochemistry Research Institute of La Plata, Histology and Pathology, School of Medicine, University of La Plata, La Plata, Argentina
| | - M J Grant
- Applied Translational Genetics Group, School of Biological Sciences, Centre for Brain Research, the University of Auckland, Auckland, New Zealand
| | - C B Green
- Department of Neuroscience, Peter O'Donnell Jr. Brain Institute, University of Texas Southwestern Medical Center, Dallas, TX, USA
| | - E N Hales
- Department of Population Health and Reproduction, University of California, Davis School of Veterinary Medicine, Davis, CA, USA
| | - M B Hanson
- Conservation Biology Division, Northwest Fisheries Science Center, National Marine Fisheries Service, National Oceanic and Atmospheric Administration, Seattle, WA, USA
| | - D W Hart
- Department of Zoology and Entomology, University of Pretoria, Hatfield, South Africa
| | - M Haulena
- Vancouver Aquarium, Vancouver, British Columbia, Canada
| | - K Herrick
- SeaWorld of California, San Diego, CA, USA
| | - A N Hogan
- Cancer Genetics and Comparative Genomics Branch, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD, USA
| | - C J Hogg
- School of Life and Environmental Sciences, the University of Sydney, Sydney, New South Wales, Australia
| | - T A Hore
- Department of Anatomy, University of Otago, Dunedin, New Zealand
| | - T Huang
- Division of Human Genetics, Department of Pediatrics, University at Buffalo, Buffalo, NY, USA
- Division of Genetics and Metabolism, Oishei Children's Hospital, Buffalo, NY, USA
| | | | - A J Jasinska
- Center for Neurobehavioral Genetics, Semel Institute for Neuroscience and Human Behavior, Department of Psychiatry and Biobehavioral Sciences, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA
| | - G Jones
- School of Biological Sciences, University of Bristol, Bristol, UK
| | | | - O Kashpur
- Mother Infant Research Institute, Tufts Medical Center, Boston, MA, USA
| | - H Katcher
- Yuvan Research, Mountain View, CA, USA
| | | | - V Kaza
- Peromyscus Genetic Stock Center, University of South Carolina, Columbia, SC, USA
| | - H Kiaris
- Peromyscus Genetic Stock Center, University of South Carolina, Columbia, SC, USA
- Department of Drug Discovery and Biomedical Sciences, College of Pharmacy, University of South Carolina, Columbia, SC, USA
| | - M S Kobor
- Edwin S.H. Leong Healthy Aging Program, Centre for Molecular Medicine and Therapeutics, University of British Columbia, Vancouver, British Columbia, Canada
| | - P Kordowitzki
- Institute of Animal Reproduction and Food Research of the Polish Academy of Sciences, Olsztyn, Poland
- Institute for Veterinary Medicine, Nicolaus Copernicus University, Torun, Poland
| | - W R Koski
- LGL Limited, King City, Ontario, Canada
| | - M Krützen
- Evolutionary Genetics Group, Department of Evolutionary Anthropology, University of Zurich, Zurich, Switzerland
| | - S B Kwon
- Bioinformatics Interdepartmental Program, University of California, Los Angeles, CA, USA
- Department of Biological Chemistry, University of California, Los Angeles, Los Angeles, CA, USA
| | - B Larison
- Department of Ecology and Evolutionary Biology, UCLA, Los Angeles, CA, USA
- Center for Tropical Research, Institute for the Environment and Sustainability, UCLA, Los Angeles, CA, USA
| | - S G Lee
- Division of Genetics, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - M Lehmann
- Biochemistry Research Institute of La Plata, Histology and Pathology, School of Medicine, University of La Plata, La Plata, Argentina
| | - J F Lemaitre
- Universite de Lyon, Universite Lyon 1, CNRS, Laboratoire de Biometrie et Biologie Evolutive, Villeurbanne, France
| | - A J Levine
- Department of Neurology, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA
| | - C Li
- Texas Pregnancy and Life-course Health Center, Southwest National Primate Research Center, San Antonio, TX, USA
- Department of Animal Science, College of Agriculture and Natural Resources, Laramie, WY, USA
| | - X Li
- Technology Center for Genomics and Bioinformatics, Department of Pathology and Laboratory Medicine, University of California, Los Angeles, Los Angeles, CA, USA
| | - A R Lim
- Department of Human Genetics, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA
| | - D T S Lin
- Centre for Molecular Medicine and Therapeutics, BC Children's Hospital Research Institute, University of British Columbia, Vancouver, British Columbia, Canada
| | | | - T J Little
- Institute of Ecology and Evolution, School of Biological Sciences, University of Edinburgh, Edinburgh, UK
| | - N Macoretta
- Departments of Biology and Medicine, University of Rochester, Rochester, NY, USA
| | - D Maddox
- White Oak Conservation, Yulee, FL, USA
| | - C O Matkin
- North Gulf Oceanic Society, Homer, AK, USA
| | - J A Mattison
- Translational Gerontology Branch, National Institute on Aging Intramural Research Program, National Institutes of Health, Baltimore, MD, USA
| | | | - J Mergl
- Marineland of Canada, Niagara Falls, Ontario, Canada
| | - J J Meudt
- Biomedical and Genomic Research Group, Department of Animal and Dairy Sciences, University of Wisconsin-Madison, Madison, WI, USA
| | - G A Montano
- Zoological Operations, SeaWorld Parks and Entertainment, Orlando, FL, USA
| | - K Mozhui
- Department of Preventive Medicine, University of Tennessee Health Science Center, College of Medicine, Memphis, TN, USA
- Department of Genetics, Genomics and Informatics, University of Tennessee Health Science Center, College of Medicine, Memphis, TN, USA
| | - J Munshi-South
- Louis Calder Center-Biological Field Station, Department of Biological Sciences, Fordham University, Armonk, NY, USA
| | - A Naderi
- Department of Drug Discovery and Biomedical Sciences, College of Pharmacy, University of South Carolina, Columbia, SC, USA
| | - M Nagy
- Museum fur Naturkunde, Leibniz Institute for Evolution and Biodiversity Science, Berlin, Germany
| | - P Narayan
- Applied Translational Genetics Group, School of Biological Sciences, Centre for Brain Research, the University of Auckland, Auckland, New Zealand
| | - P W Nathanielsz
- Texas Pregnancy and Life-course Health Center, Southwest National Primate Research Center, San Antonio, TX, USA
- Department of Animal Science, College of Agriculture and Natural Resources, Laramie, WY, USA
| | - N B Nguyen
- Division of Cardiology, Department of Internal Medicine, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA
| | - C Niehrs
- Institute of Molecular Biology, Mainz, Germany
- Division of Molecular Embryology, DKFZ-ZMBH Alliance, Heidelberg, Germany
| | - J K O'Brien
- Taronga Institute of Science and Learning, Taronga Conservation Society Australia, Mosman, New South Wales, Australia
| | - P O'Tierney Ginn
- Mother Infant Research Institute, Tufts Medical Center, Boston, MA, USA
- Department of Obstetrics and Gynecology, Tufts University School of Medicine, Boston, MA, USA
| | - D T Odom
- Cancer Research UK Cambridge Institute, University of Cambridge, Cambridge, UK
- Division of Regulatory Genomics and Cancer Evolution, Deutsches Krebsforschungszentrum, Heidelberg, Germany
| | - A G Ophir
- Department of Psychology, Cornell University, Ithaca, NY, USA
| | - S Osborn
- SeaWorld of Texas, San Antonio, TX, USA
| | - E A Ostrander
- Cancer Genetics and Comparative Genomics Branch, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD, USA
| | - K M Parsons
- Conservation Biology Division, Northwest Fisheries Science Center, National Marine Fisheries Service, National Oceanic and Atmospheric Administration, Seattle, WA, USA
| | - K C Paul
- Department of Neurology, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA
| | - M Pellegrini
- Department of Molecular Cell and Developmental Biology, University of California, Los Angeles, Los Angeles, CA, USA
| | - K J Peters
- Evolutionary Genetics Group, Department of Evolutionary Anthropology, University of Zurich, Zurich, Switzerland
- School of Earth, Atmospheric and Life Sciences, University of Wollongong, Wollongong, Australia
| | - A B Pedersen
- Institute of Evolutionary Biology, School of Biological Sciences, University of Edinburgh, Edinburgh, UK
| | - J L Petersen
- Department of Animal Science, University of Nebraska, Lincoln, NE, USA
| | - D W Pietersen
- Mammal Research Institute, Department of Zoology and Entomology, University of Pretoria, Hatfield, South Africa
| | - G M Pinho
- Department of Ecology and Evolutionary Biology, UCLA, Los Angeles, CA, USA
| | - J Plassais
- Cancer Genetics and Comparative Genomics Branch, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD, USA
| | - J R Poganik
- Division of Genetics, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - N A Prado
- Department of Biology, College of Arts and Science, Adelphi University, Garden City, NY, USA
| | - P Reddy
- Altos Labs, San Diego Institute of Science, San Diego, CA, USA
- Salk Institute for Biological Studies, La Jolla, CA, USA
| | - B Rey
- Universite de Lyon, Universite Lyon 1, CNRS, Laboratoire de Biometrie et Biologie Evolutive, Villeurbanne, France
| | - B R Ritz
- Department of Epidemiology, UCLA Fielding School of Public Health, Los Angeles, CA, USA
- Department of Environmental Health Sciences, UCLA Fielding School of Public Health, Los Angeles, CA, USA
- Department of Neurology, UCLA David Geffen School of Medicine, Los Angeles, CA, USA
| | - J Robbins
- Center for Coastal Studies, Provincetown, MA, USA
| | | | - J Russell
- SeaWorld of California, San Diego, CA, USA
| | - E Rydkina
- Departments of Biology and Medicine, University of Rochester, Rochester, NY, USA
| | - L L Sailer
- Department of Psychology, Cornell University, Ithaca, NY, USA
| | - A B Salmon
- The Sam and Ann Barshop Institute for Longevity and Aging Studies and Department of Molecular Medicine, UT Health San Antonio and the Geriatric Research Education and Clinical Center, South Texas Veterans Healthcare System, San Antonio, TX, USA
| | | | - K M Schachtschneider
- Department of Radiology, University of Illinois at Chicago, Chicago, IL, USA
- Department of Biochemistry and Molecular Genetics, University of Illinois at Chicago, Chicago, IL, USA
- National Center for Supercomputing Applications, University of Illinois at Urbana-Champaign, Urbana, IL, USA
| | - D Schmitt
- College of Agriculture, Missouri State University, Springfield, MO, USA
| | - T Schmitt
- SeaWorld of California, San Diego, CA, USA
| | | | - L B Schook
- Department of Radiology, University of Illinois at Chicago, Chicago, IL, USA
- Department of Animal Sciences, University of Illinois at Urbana-Champaign, Champaign, IL, USA
| | - K E Sears
- Department of Ecology and Evolutionary Biology, UCLA, Los Angeles, CA, USA
- Department of Molecular Cell and Developmental Biology, University of California, Los Angeles, Los Angeles, CA, USA
| | - A W Seifert
- Department of Biology, University of Kentucky, Lexington, KY, USA
| | - A Seluanov
- Departments of Biology and Medicine, University of Rochester, Rochester, NY, USA
| | - A B A Shafer
- Department of Forensic Science, Environmental and Life Sciences, Trent University, Peterborough, Ontario, Canada
| | - D Shanmuganayagam
- Biomedical and Genomic Research Group, Department of Animal and Dairy Sciences, University of Wisconsin-Madison, Madison, WI, USA
- Department of Surgery, University of Wisconsin School of Medicine and Public Health, Madison, WI, USA
| | - A V Shindyapina
- Division of Genetics, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | | | - K Singh
- Shobhaben Pratapbhai Patel School of Pharmacy and Technology Management, SVKM'S NMIMS University, Mumbai, India
| | - I Sinha
- Department of Ecology and Evolutionary Biology, UCLA, Los Angeles, CA, USA
| | - J Slone
- Division of Human Genetics, Department of Pediatrics, University at Buffalo, Buffalo, NY, USA
| | - R G Snell
- Applied Translational Genetics Group, School of Biological Sciences, Centre for Brain Research, the University of Auckland, Auckland, New Zealand
| | - E Soltanmaohammadi
- Department of Drug Discovery and Biomedical Sciences, College of Pharmacy, University of South Carolina, Columbia, SC, USA
| | - M L Spangler
- Department of Animal Science, University of Nebraska, Lincoln, NE, USA
| | | | - L Staggs
- SeaWorld of Florida, Orlando, FL, USA
| | | | - K J Steinman
- Species Preservation Laboratory, SeaWorld San Diego, San Diego, CA, USA
| | - D T Stewart
- Biology Department, Acadia University, Wolfville, Nova Scotia, Canada
| | - V J Sugrue
- Department of Anatomy, University of Otago, Dunedin, New Zealand
| | - B Szladovits
- Department of Pathobiology and Population Sciences, Royal Veterinary College, Hatfield, UK
| | - J S Takahashi
- Department of Neuroscience, Peter O'Donnell Jr. Brain Institute, University of Texas Southwestern Medical Center, Dallas, TX, USA
- Howard Hughes Medical Institute, Department of Neuroscience, University of Texas Southwestern Medical Center, Dallas, TX, USA
| | - M Takasugi
- Departments of Biology and Medicine, University of Rochester, Rochester, NY, USA
| | - E C Teeling
- School of Biology and Environmental Science, University College Dublin, Dublin, Ireland
| | - M J Thompson
- Department of Molecular Cell and Developmental Biology, University of California, Los Angeles, Los Angeles, CA, USA
| | - B Van Bonn
- John G. Shedd Aquarium, Chicago, IL, USA
| | - S C Vernes
- School of Biology, the University of St Andrews, Fife, UK
- Neurogenetics of Vocal Communication Group, Max Planck Institute for Psycholinguistics, Nijmegen, the Netherlands
| | - D Villar
- Blizard Institute, Faculty of Medicine and Dentistry, Queen Mary University of London, London, UK
| | - H V Vinters
- Department of Pathology and Laboratory Medicine, David Geffen School of Medicine at UCLA, Los Angeles, CA, USA
| | - M C Wallingford
- Mother Infant Research Institute, Tufts Medical Center, Boston, MA, USA
- Division of Obstetrics and Gynecology, Tufts University School of Medicine, Boston, MA, USA
| | - N Wang
- Center for Neurobehavioral Genetics, Jane and Terry Semel Institute for Neuroscience and Human Behavior, University of California, Los Angeles, Los Angeles, CA, USA
- Department of Psychiatry and Biobehavioral Sciences, David Geffen School of Medicine at UCLA, Los Angeles, CA, USA
| | - R K Wayne
- Department of Ecology and Evolutionary Biology, UCLA, Los Angeles, CA, USA
| | - G S Wilkinson
- Department of Biology, University of Maryland, College Park, MD, USA
| | - C K Williams
- Department of Neurology, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA
| | - R W Williams
- Department of Genetics, Genomics and Informatics, University of Tennessee Health Science Center, College of Medicine, Memphis, TN, USA
| | - X W Yang
- Center for Neurobehavioral Genetics, Jane and Terry Semel Institute for Neuroscience and Human Behavior, University of California, Los Angeles, Los Angeles, CA, USA
- Department of Psychiatry and Biobehavioral Sciences, David Geffen School of Medicine at UCLA, Los Angeles, CA, USA
| | - M Yao
- Department of Biostatistics, Fielding School of Public Health, University of California, Los Angeles, Los Angeles, CA, USA
| | - B G Young
- Fisheries and Oceans Canada, Winnipeg, Manitoba, Canada
| | - B Zhang
- Division of Genetics, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Z Zhang
- Departments of Biology and Medicine, University of Rochester, Rochester, NY, USA
| | - P Zhao
- Division of Cardiology, Department of Internal Medicine, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA
- Eli and Edythe Broad Center of Regenerative Medicine and Stem Cell Research, University of California, Los Angeles, CA, USA
| | - Y Zhao
- Departments of Biology and Medicine, University of Rochester, Rochester, NY, USA
| | - W Zhou
- Center for Computational and Genomic Medicine, Children's Hospital of Philadelphia, Philadelphia, PA, USA
- Department of Pathology and Laboratory Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - J Zimmermann
- Department of Mathematics and Technology, University of Applied Sciences Koblenz, Koblenz, Germany
| | - J Ernst
- Bioinformatics Interdepartmental Program, University of California, Los Angeles, CA, USA
- Department of Biological Chemistry, University of California, Los Angeles, Los Angeles, CA, USA
| | - K Raj
- Altos Labs, Cambridge Institute of Science, Cambridge, UK
| | - S Horvath
- Department of Human Genetics, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA.
- Altos Labs, San Diego Institute of Science, San Diego, CA, USA.
- Department of Biostatistics, Fielding School of Public Health, University of California, Los Angeles, Los Angeles, CA, USA.
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See GM, Trenhaile-Grannemann MD, Spangler ML, Ciobanu DC, Mote BE. A genome-wide association study for gestation length in swine. Anim Genet 2019; 50:539-542. [PMID: 31297858 DOI: 10.1111/age.12822] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 05/08/2019] [Indexed: 11/26/2022]
Abstract
Selection for increased litter size in swine has potentially resulted in a correlated increase in preweaning mortality. Additional selection criteria should be considered when selecting for increased litter size to account for associated decreases in piglet quality, specifically piglet survival, initial weight and growth. Traits such as gestation length (GL), which have been associated with piglet performance, could be utilized to improve piglet development and survivability. The objective of this study was to conduct a genome-wide association study to identify genomic regions associated with GL in differing parities in swine (n = 831) from the University of Nebraska-Lincoln reproductive longevity project. Gestation length was calculated as the number of days between last insemination administered and farrowing. Sows were genotyped with the Illumina SNP60 BeadArray, and the data were analyzed using Bayesian mixture models for GL at parity 1, 2, 3 and 4 (GL1, GL2, GL3 and GL4 respectively). Means (SD) for GL1-GL4 were 113 (1.4), 114 (1.2), 114 (1.3) and 115 (1.2) respectively. Posterior mean heritability estimates (PSD) for GL1, GL2, GL3 and GL4 were 0.33 (0.06), 0.34 (0.07), 0.32 (0.08) and 0.20 (0.08) respectively. Rank correlations between genomic estimated breeding values between GL1 and GL2, GL3 and GL4 respectively were moderate: 0.67, 0.65 and 0.60. The top SNP (ASGA0017859, SSC4, 7.8 Mb), located in the top common genomic region associated with GL1, GL2 and GL3, was associated with a difference of 1.1 days in GL1 between homozygote genotypes (P < 0.0001). The results of this study suggest that GL is a largely polygenic trait with relatively minor contributions from multiple genomic regions.
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Affiliation(s)
- G M See
- University of Nebraska-Lincoln Department of Animal Science, Lincoln, 68588, Nebraska
| | | | - M L Spangler
- University of Nebraska-Lincoln Department of Animal Science, Lincoln, 68588, Nebraska
| | - D C Ciobanu
- University of Nebraska-Lincoln Department of Animal Science, Lincoln, 68588, Nebraska
| | - B E Mote
- University of Nebraska-Lincoln Department of Animal Science, Lincoln, 68588, Nebraska
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Rovadoscki GA, Pertile SFN, Alvarenga AB, Cesar ASM, Pértille F, Petrini J, Franzo V, Soares WVB, Morota G, Spangler ML, Pinto LFB, Carvalho GGP, Lanna DPD, Coutinho LL, Mourão GB. Estimates of genomic heritability and genome-wide association study for fatty acids profile in Santa Inês sheep. BMC Genomics 2018; 19:375. [PMID: 29783944 PMCID: PMC5963081 DOI: 10.1186/s12864-018-4777-8] [Citation(s) in RCA: 27] [Impact Index Per Article: 4.5] [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: 06/06/2017] [Accepted: 05/10/2018] [Indexed: 12/22/2022] Open
Abstract
BACKGROUND Despite the health concerns and nutritional importance of fatty acids, there is a relative paucity of studies in the literature that report genetic or genomic parameters, especially in the case of sheep populations. To investigate the genetic architecture of fatty acid composition of sheep, we conducted genome-wide association studies (GWAS) and estimated genomic heritabilities for fatty acid profile in Longissimus dorsi muscle of 216 male sheep. RESULTS Genomic heritability estimates for fatty acid content ranged from 0.25 to 0.46, indicating that substantial genetic variation exists for the evaluated traits. Therefore, it is possible to alter fatty acid profiles through selection. Twenty-seven genomic regions of 10 adjacent SNPs associated with fatty acids composition were identified on chromosomes 1, 2, 3, 5, 8, 12, 14, 15, 16, 17, and 18, each explaining ≥0.30% of the additive genetic variance. Twenty-three genes supporting the understanding of genetic mechanisms of fat composition in sheep were identified in these regions, such as DGAT2, TRHDE, TPH2, ME1, C6, C7, UBE3D, PARP14, and MRPS30. CONCLUSIONS Estimates of genomic heritabilities and elucidating important genomic regions can contribute to a better understanding of the genetic control of fatty acid deposition and improve the selection strategies to enhance meat quality and health attributes.
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Affiliation(s)
- G A Rovadoscki
- Department of Animal Science, University of São Paulo (USP) / Luiz de Queiroz College of Agriculture (ESALQ), Av. Pádua Dias, 11, ESALQ/USP, Piracicaba, São Paulo, 13418-900, Brazil
| | - S F N Pertile
- Department of Animal Science, University of São Paulo (USP) / Luiz de Queiroz College of Agriculture (ESALQ), Av. Pádua Dias, 11, ESALQ/USP, Piracicaba, São Paulo, 13418-900, Brazil
| | - A B Alvarenga
- Department of Animal Science, University of São Paulo (USP) / Luiz de Queiroz College of Agriculture (ESALQ), Av. Pádua Dias, 11, ESALQ/USP, Piracicaba, São Paulo, 13418-900, Brazil
| | - A S M Cesar
- Department of Animal Science, University of São Paulo (USP) / Luiz de Queiroz College of Agriculture (ESALQ), Av. Pádua Dias, 11, ESALQ/USP, Piracicaba, São Paulo, 13418-900, Brazil
| | - F Pértille
- Department of Animal Science, University of São Paulo (USP) / Luiz de Queiroz College of Agriculture (ESALQ), Av. Pádua Dias, 11, ESALQ/USP, Piracicaba, São Paulo, 13418-900, Brazil
| | - J Petrini
- Department of Animal Science, University of São Paulo (USP) / Luiz de Queiroz College of Agriculture (ESALQ), Av. Pádua Dias, 11, ESALQ/USP, Piracicaba, São Paulo, 13418-900, Brazil
| | - V Franzo
- Department of Animal Science, University of São Paulo (USP) / Luiz de Queiroz College of Agriculture (ESALQ), Av. Pádua Dias, 11, ESALQ/USP, Piracicaba, São Paulo, 13418-900, Brazil
| | - W V B Soares
- Institute of Zootechny (IZ), Nova Odessa, SP, Brazil
| | - G Morota
- Department of Animal Science, University of Nebraska, Lincoln, NE, USA
| | - M L Spangler
- Department of Animal Science, University of Nebraska, Lincoln, NE, USA
| | - L F B Pinto
- Department of Animal Science, Federal University of Bahia (UFBA), Salvador, BA, Brazil
| | - G G P Carvalho
- Department of Animal Science, Federal University of Bahia (UFBA), Salvador, BA, Brazil
| | - D P D Lanna
- Department of Animal Science, University of São Paulo (USP) / Luiz de Queiroz College of Agriculture (ESALQ), Av. Pádua Dias, 11, ESALQ/USP, Piracicaba, São Paulo, 13418-900, Brazil
| | - L L Coutinho
- Department of Animal Science, University of São Paulo (USP) / Luiz de Queiroz College of Agriculture (ESALQ), Av. Pádua Dias, 11, ESALQ/USP, Piracicaba, São Paulo, 13418-900, Brazil
| | - G B Mourão
- Department of Animal Science, University of São Paulo (USP) / Luiz de Queiroz College of Agriculture (ESALQ), Av. Pádua Dias, 11, ESALQ/USP, Piracicaba, São Paulo, 13418-900, Brazil.
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Wijesena HR, Lents CA, Riethoven JJ, Trenhaile-Grannemann MD, Thorson JF, Keel BN, Miller PS, Spangler ML, Kachman SD, Ciobanu DC. GENOMICS SYMPOSIUM: Using genomic approaches to uncover sources of variation in age at puberty and reproductive longevity in sows. J Anim Sci 2018; 95:4196-4205. [PMID: 28992028 DOI: 10.2527/jas2016.1334] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
Abstract
Genetic variants associated with traits such as age at puberty and litter size could provide insight into the underlying genetic sources of variation impacting sow reproductive longevity and productivity. Genomewide characterization and gene expression profiling were used using gilts from the University of Nebraska-Lincoln swine resource population ( = 1,644) to identify genetic variants associated with age at puberty and litter size traits. From all reproductive traits studied, the largest fraction of phenotypic variation explained by the Porcine SNP60 BeadArray was for age at puberty (27.3%). In an evaluation data set, the predictive ability of all SNP from high-ranked 1-Mb windows (1 to 50%), based on genetic variance explained in training, was greater (12.3 to 36.8%) compared with the most informative SNP from these windows (6.5 to 23.7%). In the integrated data set ( = 1,644), the top 1% of the 1-Mb windows explained 6.7% of the genetic variation of age at puberty. One of the high-ranked windows detected (SSC2, 12-12.9 Mb) showed pleiotropic features, affecting both age at puberty and litter size traits. The RNA sequencing of the hypothalami arcuate nucleus uncovered 17 differentially expressed genes (adjusted < 0.05) between gilts that became pubertal early (<155 d of age) and late (>180 d of age). Twelve of the differentially expressed genes are upregulated in the late pubertal gilts. One of these genes is involved in energy homeostasis (), a function in which the arcuate nucleus plays an important contribution, linking nutrition with reproductive development. Energy restriction during the gilt development period delayed age at puberty by 7 d but increased the probability of a sow to produce up to 3 parities ( < 0.05). Identification of pleotropic functional polymorphisms may improve accuracy of genomic prediction while facilitating a reduction in sow replacement rates and addressing welfare concerns.
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Spangler ML. 201 Current Trends in Beef Cattle Genetic Evaluation. J Anim Sci 2018. [DOI: 10.1093/jas/sky073.198] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/14/2022] Open
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Howard JT, Rathje TA, Bruns CE, Wilson-Wells DF, Kachman SD, Spangler ML. 34 The Impact of Truncating Data on the Predictive Ability of Selection Candidate EBV in Swine Using Ssgblup. J Anim Sci 2018. [DOI: 10.1093/jas/sky073.032] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/14/2022] Open
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Yu H, Spangler ML, Lewis RM, Morota G. 37 An Assessment of Genomic Relatedness across Management Units. J Anim Sci 2018. [DOI: 10.1093/jas/sky073.035] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022] Open
Affiliation(s)
- H Yu
- University of Nebraska-Lincoln, Lincoln, NE
| | | | - R M Lewis
- University of Nebraska-Lincoln, Lincoln, NE
| | - G Morota
- University of Nebraska-Lincoln, Lincoln, NE
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Speidel SE, Buckley BA, Boldt RJ, Enns RM, Lee J, Spangler ML, Thomas MG. Genome-wide association study of Stayability and Heifer Pregnancy in Red Angus cattle. J Anim Sci 2018; 96:846-853. [PMID: 29471369 PMCID: PMC6093520 DOI: 10.1093/jas/sky041] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.3] [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: 11/07/2017] [Accepted: 02/15/2018] [Indexed: 11/12/2022] Open
Abstract
Reproductive performance is the most important component of cattle production from the standpoint of economic sustainability of commercial beef enterprises. Heifer Pregnancy (HPG) and Stayability (STAY) genetic predictions are 2 selection tools published by the Red Angus Association of America (RAAA) to assist with improvements in reproductive performance. Given the importance of HPG and STAY to the profitability of commercial beef enterprises, the objective of this study was to identify QTL associated with both HPG and STAY in Red Angus cattle. A genome-wide association study (GWAS) was performed using deregressed HPG and STAY EBV, calculated using a single-trait animal model and a 3-generation pedigree with data from the Spring 2015 RAAA National Cattle Evaluation. Each individual animal possessed 74,659 SNP genotypes. Individual animals with a deregressed EBV reliability > 0.05 were merged with the genotype file and marker quality control was performed. Criteria for sifting genotypes consisted of removing those markers where any of the following were found: average call rate less than 0.85, minor allele frequency < 0.01, lack of Hardy-Weinberg equilibrium (P < 0.0001), or extreme linkage disequilibrium (r2 > 0.99). These criteria resulted in 2,664 animals with 62,807 SNP available for GWAS. Association studies were performed using a Bayes Cπ model in the BOLT software package. Marker significance was calculated as the posterior probability of inclusion (PPI), or the number of instances a specific marker was sampled divided by the total number of samples retained from the Markov chain Monte Carlo chains. Nine markers, with a PPI ≥ 3% were identified as QTL associated with HPG on BTA 1, 11, 13, 23, and 29. Twelve markers, with a PPI ≥ 75% were identified as QTL associated with STAY on BTA 6, 8, 9, 12, 15, 18, 22, and 23.
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Affiliation(s)
- S E Speidel
- Department of Animal Sciences, Colorado State University, Fort Collins, CO
| | - B A Buckley
- Department of Human Nutrition, Food and Animal Sciences, University of Hawaii at Manoa, Honolulu, HI
| | - R J Boldt
- Department of Animal Sciences, Colorado State University, Fort Collins, CO
| | - R M Enns
- Department of Animal Sciences, Colorado State University, Fort Collins, CO
| | - J Lee
- Department of Animal Science, University of Nebraska-Lincoln, Lincoln, NE
| | - M L Spangler
- Department of Animal Science, University of Nebraska-Lincoln, Lincoln, NE
| | - M G Thomas
- Department of Animal Sciences, Colorado State University, Fort Collins, CO
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11
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Ochsner KP, MacNeil MD, Lewis RM, Spangler ML. Economic selection index development for Beefmaster cattle II: General-purpose breeding objective. J Anim Sci 2018; 95:1913-1920. [PMID: 28726989 DOI: 10.2527/jas.2016.1232] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
Abstract
An economic selection index was developed for Beefmaster cattle in a general-purpose production system in which bulls are mated to a combination of heifers and mature cows, with resulting progeny retained as replacements or sold at weaning. National average prices from 2010 to 2014 were used to establish income and expenses for the system. Genetic parameters were obtained from the literature. Economic values were estimated by simulating 100,000 animals and approximating the partial derivatives of the profit function by perturbing traits 1 at a time, by 1 unit, while holding the other traits constant at their respective means. Relative economic values for the objective traits calving difficultly direct (CDd), calving difficulty maternal (CDm), weaning weight direct (WWd), weaning weight maternal (WWm), mature cow weight (MW), and heifer pregnancy (HP) were -2.11, -1.53, 18.49, 11.28, -33.46, and 1.19, respectively. Consequently, under the scenario assumed herein, the greatest improvements in profitability could be made by decreasing maintenance energy costs associated with MW followed by improvements in weaning weight. The accuracy of the index lies between 0.218 (phenotypic-based index selection) and 0.428 (breeding values known without error). Implementation of this index would facilitate genetic improvement and increase profitability of Beefmaster cattle operations with a general-purpose breeding objective when replacement females are retained and with weaned calves as the sale end point.
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12
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Wijesena HR, Lents CA, Riethoven JJ, Trenhaile-Grannemann MD, Thorson JF, Keel BN, Miller PS, Spangler ML, Kachman SD, Ciobanu DC. GENOMICS SYMPOSIUM: Using genomic approaches to uncover sources of variation in age at puberty and reproductive longevity in sows1,2. J Anim Sci 2017. [DOI: 10.2527/jas.2016.1334] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/30/2022] Open
Affiliation(s)
- H. R. Wijesena
- Department of Animal Science, University of Nebraska, Lincoln 68583
| | - C. A. Lents
- USDA, ARS, U.S. Meat Animal Research Center, Clay Center, NE, 68933
| | - J.-J. Riethoven
- Center for Biotechnology, University of Nebraska, Lincoln 68583
| | | | - J. F. Thorson
- USDA, ARS, U.S. Meat Animal Research Center, Clay Center, NE, 68933
| | - B. N. Keel
- USDA, ARS, U.S. Meat Animal Research Center, Clay Center, NE, 68933
| | - P. S. Miller
- Department of Animal Science, University of Nebraska, Lincoln 68583
| | - M. L. Spangler
- Department of Animal Science, University of Nebraska, Lincoln 68583
| | - S. D. Kachman
- Department of Statistics, University of Nebraska, Lincoln 68583
| | - D. C. Ciobanu
- Department of Animal Science, University of Nebraska, Lincoln 68583
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Lee J, Kachman SD, Spangler ML. The impact of training strategies on the accuracy of genomic predictors in United States Red Angus cattle1. J Anim Sci 2017; 95:3406-3414. [DOI: 10.2527/jas.2017.1604] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/09/2023] Open
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14
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Yu H, Spangler ML, Lewis RM, Morota G. 189 Genomic relatedness strengthens genetic connectedness across management units. J Anim Sci 2017. [DOI: 10.2527/asasann.2017.189] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
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15
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Snelling WM, Kachman SD, Bennett GL, Spangler ML, Kuehn LA, Thallman RM. 197 Functional SNP associated with birth weight in independent populations identified with a permutation step added to GBLUP-GWAS. J Anim Sci 2017. [DOI: 10.2527/asasann.2017.197] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
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16
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Buckley BA, Speidel SE, Boldt RJ, Enns RM, Zeng X, Spangler ML, Lee J, Thomas MG. 195 Genome-wide association study of heifer pregnancy in Red Angus cattle. J Anim Sci 2017. [DOI: 10.2527/asasann.2017.195] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
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17
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Ochsner KP, MacNeil MD, Lewis RM, Spangler ML. Economic selection index development for Beefmaster cattle I: Terminal breeding objective. J Anim Sci 2017; 95:1063-1070. [PMID: 28380518 DOI: 10.2527/jas.2016.1231] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
Abstract
The objective of this study was to develop an economic selection index for Beefmaster cattle in a terminal production system where bulls are mated to mature cows with all resulting progeny harvested. National average prices from 2010 to 2014 were used to establish income and expenses for the system. Phenotypic and genetic parameter values among the selection criteria and goal traits were obtained from literature. Economic values were estimated by simulating 100,000 animals and approximating the partial derivatives of the profit function by perturbing traits one at a time, by 1 unit, while holding the other traits constant at their respective means. Relative economic values (REV) for the terminal objective traits HCW, marbling score (MS), ribeye area (REA), 12th-rib fat (FAT), and feed intake (FI) were 91.29, 17.01, 8.38, -7.07, and -29.66, respectively. Consequently, improving the efficiency of beef production is expected to impact profitability greater than improving carcass merit alone. The accuracy of the index lies between 0.338 (phenotypic selection) and 0.503 (breeding values known without error). The application of this index would aid Beefmaster breeders in their sire selection decisions, facilitating genetic improvement for a terminal breeding objective.
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Ahlberg CM, Kuehn LA, Thallman RM, Kachman SD, Snelling WM, Spangler ML. Breed effects and genetic parameter estimates for calving difficulty and birth weight in a multibreed population. J Anim Sci 2017; 94:1857-64. [PMID: 27285683 DOI: 10.2527/jas.2015-0161] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
Abstract
Birth weight (BWT) and calving difficulty (CD) were recorded on 4,579 first-parity females from the Germplasm Evaluation Program at the U.S. Meat Animal Research Center (USMARC). Both traits were analyzed using a bivariate animal model with direct and maternal effects. Calving difficulty was transformed from the USMARC scores to corresponding -scores from the standard normal distribution based on the incidence rate of the USMARC scores. Breed fraction covariates were included to estimate breed differences. Heritability estimates (SE) for BWT direct, CD direct, BWT maternal, and CD maternal were 0.34 (0.10), 0.29 (0.10), 0.15 (0.08), and 0.13 (0.08), respectively. Calving difficulty direct breed effects deviated from Angus ranged from -0.13 to 0.77 and maternal breed effects deviated from Angus ranged from -0.27 to 0.36. Hereford-, Angus-, Gelbvieh-, and Brangus-sired calves would be the least likely to require assistance at birth, whereas Chiangus-, Charolais-, and Limousin-sired calves would be the most likely to require assistance at birth. Maternal breed effects for CD were least for Simmental and Charolais and greatest for Red Angus and Chiangus. Results showed that the diverse biological types of cattle have different effects on both BWT and CD. Furthermore, results provide a mechanism whereby beef cattle producers can compare EBV for CD direct and maternal arising from disjoined and breed-specific genetic evaluations.
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Taylor JF, Beever JE, Decker JE, Freetly HC, Garrick DJ, Hansen SL, Johnson KA, Kerley MS, Loy DD, Neibergs HL, Saatchi M, Schnabel RD, Seabury CM, Shike DW, Spangler ML, Weaber RL. 331 The genetic improvement of feed efficiency in beef cattle. J Anim Sci 2017. [DOI: 10.2527/asasmw.2017.331] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
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20
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Lee J, Kachman SD, Spangler ML. The impact of training strategies on the accuracy of genomic predictors in United States Red Angus cattle. J Anim Sci 2017. [DOI: 10.2527/jas2017.1604] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
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21
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Ochsner KP, MacNeil MD, Lewis RM, Spangler ML. Economic selection index development for Beefmaster cattle II: General-purpose breeding objective. J Anim Sci 2017. [DOI: 10.2527/jas2016.1232] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
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22
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Ochsner KP, MacNeil MD, Lewis RM, Spangler ML. Economic selection index development for Beefmaster cattle I: Terminal breeding objective. J Anim Sci 2017. [DOI: 10.2527/jas2016.1231] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
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23
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Ochsner KP, Lewis RM, MacNeil MD, Spangler ML. 0390 Economic selection index coefficients for terminal traits in Beefmaster cattle. J Anim Sci 2016. [DOI: 10.2527/jam2016-0390] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
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24
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Ghebrewold R, Spangler ML. 0319 A genome-wide association study for changes in dry matter intake due to temperature variation in an admixed beef cattle population. J Anim Sci 2016. [DOI: 10.2527/jam2016-0319] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
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25
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Ciobanu DC, Kachman SD, Olson S, Spangler ML, Trenhaile MD, Wijesena H, Miller PS, Riethoven JJ, Lents CA, Thorson JF, Massey R, Safranski TJ. 0691 Translational genomics for improving sow reproductive longevity. J Anim Sci 2016. [DOI: 10.2527/jam2016-0691] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
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26
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Schweer KR, Kachman SD, Kuehn LA, Spangler ML. P5065 Comparison of SNP and haplotype models for genome-wide association studies for feed efficiency traits in crossbred beef cattle. J Anim Sci 2016. [DOI: 10.2527/jas2016.94supplement4147x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
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He J, Wu XL, Bauck S, Xu JQ, Lee J, Morota G, Kachman SD, Spangler ML. P1028 Comparing 2 strategies for selecting low density SNPs for imputation-mediated, multiple-trait genomic prediction in a U.S. Holstein population. J Anim Sci 2016. [DOI: 10.2527/jas2016.94supplement428a] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
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28
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Torres-Vázquez JA, Spangler ML. Genetic parameters for docility, weaning weight, yearling weight, and intramuscular fat percentage in Hereford cattle. J Anim Sci 2016; 94:21-7. [PMID: 26812308 DOI: 10.2527/jas.2015-9566] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/17/2023] Open
Abstract
Cattle behavior, including measures of docility, is important to beef cattle producers not only from a human safety perspective but also due to potential correlations to economically relevant traits. Field data from the American Hereford Association was used to estimate genetic parameters for chute score (CS; = 25,037), weaning weight (WW; = 24,908), yearling weight (YW; = 23,978), and intramuscular fat percentage (IMF; = 12,566). Single-trait and bivariate animal models were used to estimate heritabilities and genetic correlations. All models included fixed effects of sex and contemporary group, defined as herd-year-season, and direct genetic and residual components were included as random effects. For CS and WW, additional random effects of maternal genetic and maternal permanent environment were also fitted. For CS, WW, YW, and IMF, heritability estimates were 0.27 ± 0.02, 0.35 ± 0.03, 0.36 ± 0.02, and 0.27 ± 0.02, respectively. Genetic correlations between CS and WW, CS and YW, CS and IMF, WW and YW, WW and IMF, and YW and IMF were -0.12 ± 0.06, -0.10 ± 0.05, -0.08 ± 0.06, 0.47 ± 0.05, -0.19 ± 0.09, and -0.41 ± 0.05, respectively. Heritability estimates for all traits suggest that they would respond favorably to selection and that selection for increased WW or YW could decrease marbling. Genetic correlations between CS and WW, YW, and IMF were all favorable but weak, suggesting that selection for improved docility will not have negative consequences on growth or carcass quality. Furthermore, maternal additive and maternal permanent environmental variances for CS were near 0, suggesting that their inclusion in National Cattle Evaluations is not warranted.
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Schiermiester LN, Thallman RM, Kuehn LA, Kachman SD, Spangler ML. Estimation of breed-specific heterosis effects for birth, weaning, and yearling weight in cattle. J Anim Sci 2015; 93:46-52. [PMID: 25568356 DOI: 10.2527/jas.2014-8493] [Citation(s) in RCA: 38] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
Abstract
Heterosis, assumed proportional to expected breed heterozygosity, was calculated for 6834 individuals with birth, weaning and yearling weight records from Cycle VII and advanced generations of the U.S. Meat Animal Research Center (USMARC) Germplasm Evaluation (GPE) project. Breeds represented in these data included: Angus, Hereford, Red Angus, Charolais, Gelbvieh, Simmental, Limousin and Composite MARC III. Heterosis was further estimated by proportions of British × British (B × B), British × Continental (B × C) and Continental × Continental (C × C) crosses and by breed-specific combinations. Model 1 fitted fixed covariates for heterosis within biological types while Model 2 fitted random breed-specific combinations nested within the fixed biological type covariates. Direct heritability estimates (SE) for birth, weaning ,and yearling weight for Model 1 were 0.42 (0.04), 0.22 (0.03), and 0.39 (0.05), respectively. The direct heritability estimates (SE) of birth, weaning, and yearling weight for Model 2 were the same as Model 1, except yearling weight heritability was 0.38 (0.05). The B × B, B × C, and C × C heterosis estimates for birth weight were 0.47 (0.37), 0.75 (0.32), and 0.73 (0.54) kg, respectively. The B × B, B × C, and C × C heterosis estimates for weaning weight were 6.43 (1.80), 8.65 (1.54), and 5.86 (2.57) kg, respectively. Yearling weight estimates for B × B, B × C, and C × C heterosis were 17.59(3.06), 13.88 (2.63), and 9.12 (4.34) kg, respectively. Differences did exist among estimates of breed-specific heterosis for weaning and yearling weight, although the variance component associated with breed-specific heterosis was not significant. These results illustrate that there are differences in breed-specific heterosis and exploiting these differences can lead to varying levels of heterosis among mating plans.
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Affiliation(s)
| | - R M Thallman
- United States Department of Agriculture, Agricultural Research Service (USDA, ARS), Roman L. Hruska U.S. Meat Animal Research Center (USMARC), Clay Center, NE 68933
| | - L A Kuehn
- United States Department of Agriculture, Agricultural Research Service (USDA, ARS), Roman L. Hruska U.S. Meat Animal Research Center (USMARC), Clay Center, NE 68933
| | - S D Kachman
- Statistics Department, University of Nebraska, Lincoln 68583
| | - M L Spangler
- Animal Science Department, University of Nebraska, Lincoln 68583
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Boyd BM, Shackelford SD, Hales KE, Brown-Brandl TM, Bremer ML, Spangler ML, Wheeler TL, King DA, Erickson GE. Effects of shade and feeding zilpaterol hydrochloride to finishing steers on performance, carcass quality, heat stress, mobility, and body temperature1. J Anim Sci 2015; 93:5801-11. [DOI: 10.2527/jas.2015-9613] [Citation(s) in RCA: 27] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
Affiliation(s)
- B. M. Boyd
- Department of Animal Science, University of Nebraska-Lincoln, Lincoln 68583
| | | | - K. E. Hales
- USDA, ARS, U.S. Meat Animal Research Center, Clay Center, NE 68933
| | | | - M. L. Bremer
- Department of Animal Science, University of Nebraska-Lincoln, Lincoln 68583
| | - M. L. Spangler
- Department of Animal Science, University of Nebraska-Lincoln, Lincoln 68583
| | - T. L. Wheeler
- USDA, ARS, U.S. Meat Animal Research Center, Clay Center, NE 68933
| | - D. A. King
- USDA, ARS, U.S. Meat Animal Research Center, Clay Center, NE 68933
| | - G. E. Erickson
- Department of Animal Science, University of Nebraska-Lincoln, Lincoln 68583
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Lucot KL, Spangler ML, Trenhaile MD, Kachman SD, Ciobanu DC. Evaluation of reduced subsets of single nucleotide polymorphisms for the prediction of age at puberty in sows. Anim Genet 2015; 46:403-9. [DOI: 10.1111/age.12310] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 04/15/2015] [Indexed: 11/30/2022]
Affiliation(s)
- K. L. Lucot
- Animal Science Department; University of Nebraska; Lincoln 68583 NE USA
| | - M. L. Spangler
- Animal Science Department; University of Nebraska; Lincoln 68583 NE USA
| | - M. D. Trenhaile
- Animal Science Department; University of Nebraska; Lincoln 68583 NE USA
| | - S. D. Kachman
- Department of Statistics; University of Nebraska; Lincoln 68583 NE USA
| | - D. C. Ciobanu
- Animal Science Department; University of Nebraska; Lincoln 68583 NE USA
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Howard JT, Kachman SD, Nielsen MK, Mader TL, Spangler ML. The effect of myostatin genotype on body temperature during extreme temperature events1. J Anim Sci 2013; 91:3051-8. [DOI: 10.2527/jas.2012-6180] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
Affiliation(s)
- J. T. Howard
- Animal Science Department, University of Nebraska, Lincoln 68583
| | - S. D. Kachman
- Department of Statistics, University of Nebraska, Lincoln 68583
| | - M. K. Nielsen
- Animal Science Department, University of Nebraska, Lincoln 68583
| | - T. L. Mader
- Haskell Agricultural Laboratory, Northeast Research and Extension Center, Concord, NE 68728
| | - M. L. Spangler
- Animal Science Department, University of Nebraska, Lincoln 68583
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Tart JK, Johnson RK, Bundy JW, Ferdinand NN, McKnite AM, Wood JR, Miller PS, Rothschild MF, Spangler ML, Garrick DJ, Kachman SD, Ciobanu DC. Genome-wide prediction of age at puberty and reproductive longevity in sows. Anim Genet 2013; 44:387-97. [PMID: 23437861 DOI: 10.1111/age.12028] [Citation(s) in RCA: 33] [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] [Accepted: 01/06/2013] [Indexed: 11/27/2022]
Abstract
Traditional selection for sow reproductive longevity is ineffective due to low heritability and late expression of the trait. Incorporation of DNA markers into selection programs is potentially a more practical approach for improving sow lifetime productivity. Using a resource population of crossbred gilts, we explored pleiotropic sources of variation that influence age at puberty and reproductive longevity. Of the traits recorded before breeding, only age at puberty significantly affected the probability that females would produce a first parity litter. The genetic variance explained by 1-Mb windows of the sow genome, compared across traits, uncovered regions that influence both age at puberty and lifetime number of parities. Allelic variants of SNPs located on SSC5 (27-28 Mb), SSC8 (36-37 Mb) and SSC12 (1.2-2 Mb) exhibited additive effects and were associated with both early expression of puberty and a greater than average number of lifetime parities. Combined analysis of these SNPs showed that an increase in the number of favorable alleles had positive impact on reproductive longevity, increasing number of parities by up to 1.36. The region located on SSC5 harbors non-synonymous alleles in the arginine vasopressin receptor 1A (AVPR1A) gene, a G-protein-coupled receptor associated with social and reproductive behaviors in voles and humans and a candidate for the observed effects. This region is characterized by high levels of linkage disequilibrium in different lines and could be exploited in marker-assisted selection programs across populations to increase sow reproductive longevity.
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Affiliation(s)
- J K Tart
- Animal Science Department, University of Nebraska, Lincoln, NE, 68583, USA
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Abstract
A simulation study was carried out to develop an alternative method of selecting animals to be genotyped. Simulated pedigrees included 5000 animals, each assigned genotypes for a bi-allelic single nucleotide polymorphism (SNP) based on assumed allelic frequencies of 0.7/0.3 and 0.5/0.5. In addition to simulated pedigrees, two beef cattle pedigrees, one from field data and the other from a research population, were used to test selected methods using simulated genotypes. The proposed method of ant colony optimization (ACO) was evaluated based on the number of alleles correctly assigned to ungenotyped animals (AK(P)), the probability of assigning true alleles (AK(G)) and the probability of correctly assigning genotypes (APTG). The proposed animal selection method of ant colony optimization was compared to selection using the diagonal elements of the inverse of the relationship matrix (A(-1)). Comparisons of these two methods showed that ACO yielded an increase in AK(P) ranging from 4.98% to 5.16% and an increase in APTG from 1.6% to 1.8% using simulated pedigrees. Gains in field data and research pedigrees were slightly lower. These results suggest that ACO can provide a better genotyping strategy, when compared to A(-1), with different pedigree sizes and structures.
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Affiliation(s)
- M L Spangler
- Animal and Dairy Science Department, University of Georgia, Athens, GA 30602-2771, USA
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Spangler ML, Sapp RL, Bertrand JK, MacNeil MD, Rekaya R. Different methods of selecting animals for genotyping to maximize the amount of genetic information known in the population. J Anim Sci 2008; 86:2471-9. [DOI: 10.2527/jas.2007-0492] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
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Abstract
The use of marker assisted selection in the beef cattle industry to date has involved using traditional EPD in tandem with molecular test information. In the current study, a multiple-trait simulation was carried out to create a beef cattle data set using genetic parameter estimates from the literature to identify the best procedure for combining both sources of information and to assess the added benefit of the procedure. To reach these objectives, the following simulation/ analysis steps were implemented: (1) varying percentages (100, 5, or 0) of available records for the trait of interest, (2) varying percentages (100, 50, 25, or 0) of animals with molecular information, (3) scenarios where the favorable (F) or the unfavorable (U) allele was more frequent, and (4) analysis of the response due to selection over 5 generations. The data sets included 3 correlated traits in which 2 of them, birth weight and postweaning gain, had complete recording and the availability of records for the third trait (marbling score) varied. It was further assumed that molecular information was available for the third trait for a causative gene that explained 10% of the genetic variation. Estimates of Pearson correlations between true and predicted breeding values for marbling score declined as the amount of information declined, and instances in which the molecular information was recorded were always closer to the true values than in the case in which the molecular information was absent. When the U allele was more frequent, rank correlation estimates were increased among top sires, low accuracy sires, and high accuracy sires by approximately 24.9, 12.1, and 4.7% with limited marbling score records and complete genotyping compared with limited marbling score records and no genotyping. Similar results were seen when the F allele was more frequent. When there was a complete absence of recording for the trait of interest, the same trends in correlations were observed and were lower than when the trait of interest was recorded. Jointly considering molecular and phenotypic information showed a greater long-term response compared with tandem selection, showing that discrimination of candidates for selection based solely on molecular information is not optimal.
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Affiliation(s)
- M L Spangler
- Animal and Dairy Science Department, University of Georgia, Athens, GA 30602-2771, USA.
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Spangler ML, Sapp RL, Rekaya R, Bertrand JK. Success at first insemination in Australian Angus cattle: Analysis of uncertain binary responses1. J Anim Sci 2006; 84:20-4. [PMID: 16361487 DOI: 10.2527/2006.84120x] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
Abstract
Field data from Australian Angus herds were used to investigate 2 methods of analyzing uncertain binary responses for success or failure at first insemination. A linear mixed model that included herd, year, and month of mating as fixed effects; unrelated service sire, additive animal, and residual as random effects; and linear and quadratic effects of age at mating as covariates was used to analyze binary data. An average gestation length (GL) derived from artificial insemination data was used to assign an insemination date to females mated to natural service sires. Females that deviated from this average GL led to uncertain binary responses. Two analyses were carried out: 1) a threshold model fitted to uncertain binary data, ignoring uncertainty (M1); and 2) a threshold model fitted to uncertain binary data, accounting for uncertainty via fuzzy logic classification (M2). There was practically no difference between point estimates obtained from M1 and M2 for service sire and herd variance; however, when uncertain binary data were analyzed ignoring uncertainty (M1), additive variance and heritability estimates were greater than with M2. Pearson correlations indicated that no major reranking would be expected for service sire effects and animal breeding values using M1 and M2. Given the results of the current study, a threshold model contemplating uncertainty is suggested for noisy binary data to avoid bias when estimating genetic parameters.
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Affiliation(s)
- M L Spangler
- Animal and Dairy Science Department, University of Georgia, Athens, 30602-2771, USA.
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Wilder JA, Dyreson EG, O'Neill RJ, Spangler ML, Gupta R, Wilder AS, Hollocher H. Contrasting modes of natural selection acting on pigmentation genes in the Drosophila dunni subgroup. J Exp Zool B Mol Dev Evol 2005; 302:469-82. [PMID: 15384167 DOI: 10.1002/jez.b.21012] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
Abstract
Genes that encode for divergent adaptive traits may have genealogies that contrast with those from loci that are not functionally involved in differentiation. Here, we examine DNA sequence variation among the species of the eastern Caribbean Drosophila dunni subgroup at two loci, yellow and dopa decaboxylase (Ddc), which both play integral roles in pigmentation patterning of adult Drosophila. Phylogenetic analyses of these loci produce gene genealogies with topologies that mirror those described for other nuclear genes: the six morphologically distinct species within the subgroup are divided into only three lineages, with one lineage containing four species that share extensive ancestral polymorphism. At the Ddc locus these major lineages are delineated only by silent site variation. We observe a significantly higher rate of synonymous site divergence than non-synonymous divergence, consistent with strong purifying selection acting on the locus. In contrast, the yellow locus exhibits patterns of amino acid divergence and nucleotide diversity that are consistent with recent diversifying selection acting in two different lineages. This selection appears to be targeting amino acid variants in the signal sequence of the Yellow protein, a region which is tightly constrained among members of the larger D. cardini radiation. This result highlights not only the potential importance of yellow in the evolution of divergent pigmentation patterns among members of the D. dunni subgroup, but also hints that variation in signal peptide sequences may play a role in phenotypic diversification.
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Affiliation(s)
- J A Wilder
- Department of Ecology and Evolutionary Biology, Princeton University, Princeton, NJ 08544, USA
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