. 2023 Aug 11;381(6658):eabq5693.

doi: 10.1126/science.abq5693. Epub 2023 Aug 11.

DNA methylation networks underlying mammalian traits

Amin Haghani^#^{1

2}, Caesar Z Li^#^{3

4}, Todd R Robeck⁵, Joshua Zhang¹, Ake T Lu^{1

2}, Julia Ablaeva⁶, Victoria A Acosta-Rodríguez⁷, Danielle M Adams⁸, Abdulaziz N Alagaili⁹, Javier Almunia¹⁰, Ajoy Aloysius¹¹, Nabil M S Amor¹², Reza Ardehali¹³, Adriana Arneson^{14

15}, C Scott Baker¹⁶, Gareth Banks¹⁷, Katherine Belov¹⁸, Nigel C Bennett¹⁹, Peter Black²⁰, Daniel T Blumstein^{21

22}, Eleanor K Bors¹⁶, Charles E Breeze²³, Robert T Brooke²⁴, Janine L Brown²⁵, Gerald Carter²⁶, Alex Caulton^{27

28}, Julie M Cavin²⁹, Lisa Chakrabarti³⁰, Ioulia Chatzistamou³¹, Andreas S Chavez^{26

32}, Hao Chen³³, Kaiyang Cheng³⁴, Priscila Chiavellini³⁵, Oi-Wa Choi^{36

37}, Shannon Clarke²⁷, Joseph A Cook³⁸, Lisa N Cooper³⁹, Marie-Laurence Cossette⁴⁰, Joanna Day⁴¹, Joseph DeYoung^{36

37}, Stacy Dirocco⁴², Christopher Dold⁵, Jonathan L Dunnum³⁸, Erin E Ehmke⁴³, Candice K Emmons⁴⁴, Stephan Emmrich⁶, Ebru Erbay^{45

46

47}, Claire Erlacher-Reid⁴², Chris G Faulkes^{48

49}, Zhe Fei^{3

50}, Steven H Ferguson^{51

52}, Carrie J Finno⁵³, Jennifer E Flower⁵⁴, Jean-Michel Gaillard⁵⁵, Eva Garde⁵⁶, Livia Gerber^{57

58}, Vadim N Gladyshev⁵⁹, Rodolfo G Goya³⁵, Matthew J Grant⁶⁰, Carla B Green⁷, M Bradley Hanson⁴⁴, Daniel W Hart¹⁹, Martin Haulena⁶¹, Kelsey Herrick⁶², Andrew N Hogan⁶³, Carolyn J Hogg¹⁸, Timothy A Hore⁶⁴, Taosheng Huang⁶⁵, Juan Carlos Izpisua Belmonte², Anna J Jasinska^{36

66

67}, Gareth Jones⁶⁸, Eve Jourdain⁶⁹, Olga Kashpur⁷⁰, Harold Katcher⁷¹, Etsuko Katsumata⁷², Vimala Kaza⁷³, Hippokratis Kiaris⁷⁴, Michael S Kobor⁷⁵, Pawel Kordowitzki⁷⁶, William R Koski⁷⁷, Michael Krützen⁷⁸, Soo Bin Kwon^{14

15}, Brenda Larison^{21

79}, Sang-Goo Lee⁵⁹, Marianne Lehmann³⁵, Jean-François Lemaître⁵⁵, Andrew J Levine⁸⁰, Xinmin Li⁸¹, Cun Li^{82

83}, Andrea R Lim¹, David T S Lin⁸⁴, Dana M Lindemann⁴², Schuyler W Liphardt⁸⁵, Thomas J Little⁸⁶, Nicholas Macoretta⁶, Dewey Maddox⁸⁷, Craig O Matkin⁸⁸, Julie A Mattison⁸⁹, Matthew McClure⁹⁰, June Mergl⁹¹, Jennifer J Meudt⁹², Gisele A Montano⁵, Khyobeni Mozhui⁹³, Jason Munshi-South⁹⁴, William J Murphy^{95

96}, Asieh Naderi⁷⁴, Martina Nagy⁹⁷, Pritika Narayan⁶⁰, Peter W Nathanielsz^{82

83}, Ngoc B Nguyen¹³, Christof Niehrs^{98

99}, Batsaikhan Nyamsuren¹⁰⁰, Justine K O'Brien⁴¹, Perrie O'Tierney Ginn⁷⁰, Duncan T Odom^{101

102}, Alexander G Ophir¹⁰³, Steve Osborn¹⁰⁴, Elaine A Ostrander⁶³, Kim M Parsons⁴⁴, Kimberly C Paul⁸⁰, Amy B Pedersen⁸⁶, Matteo Pellegrini¹⁰⁵, Katharina J Peters^{78

106}, Jessica L Petersen¹⁰⁷, Darren W Pietersen¹⁰⁸, Gabriela M Pinho²¹, Jocelyn Plassais⁶³, Jesse R Poganik⁵⁹, Natalia A Prado^{109

110}, Pradeep Reddy^{2

111}, Benjamin Rey⁵⁵, Beate R Ritz^{80

112

113}, Jooke Robbins¹¹⁴, Magdalena Rodriguez¹¹⁵, Jennifer Russell¹⁰⁴, Elena Rydkina⁶, Lindsay L Sailer¹⁰³, Adam B Salmon¹¹⁶, Akshay Sanghavi⁷¹, Kyle M Schachtschneider^{117

118

119}, Dennis Schmitt¹²⁰, Todd Schmitt⁶², Lars Schomacher⁹⁸, Lawrence B Schook^{117

121}, Karen E Sears²¹, Ashley W Seifert¹¹, Aaron B A Shafer¹²², Anastasia V Shindyapina⁵⁹, Melanie Simmons⁴³, Kavita Singh¹²³, Ishani Sinha²¹, Jesse Slone⁶⁵, Russel G Snell⁶⁰, Elham Soltanmohammadi⁷⁴, Matthew L Spangler¹⁰⁷, Maria Spriggs²⁰, Lydia Staggs⁴², Nancy Stedman²⁰, Karen J Steinman¹²⁴, Donald T Stewart¹²⁵, Victoria J Sugrue⁶⁴, Balazs Szladovits¹²⁶, Joseph S Takahashi^{7

127}, Masaki Takasugi⁶, Emma C Teeling¹²⁸, Michael J Thompson¹⁰⁵, Bill Van Bonn¹²⁹, Sonja C Vernes^{130

131}, Diego Villar¹³², Harry V Vinters¹³³, Ha Vu^{14

15}, Mary C Wallingford⁷⁰, Nan Wang^{36

37}, Gerald S Wilkinson⁸, Robert W Williams¹³⁴, Qi Yan^{2

3}, Mingjia Yao³, Brent G Young⁵², Bohan Zhang⁵⁹, Zhihui Zhang⁶, Yang Zhao⁶, Peng Zhao^{13

135}, Wanding Zhou^{136

137}, Joseph A Zoller³, Jason Ernst^{14

15}, Andrei Seluanov¹³⁸, Vera Gorbunova¹³⁸, X William Yang^{36

37}, Ken Raj¹³⁹, Steve Horvath^{1

2

139}

Affiliations

¹ Department of Human Genetics, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA.
² Altos Labs, San Diego, CA, USA.
³ Department of Biostatistics, Fielding School of Public Health, University of California Los Angeles, Los Angeles, CA, USA.
⁴ Janssen Research & Development, Spring House, PA, USA.
⁵ Zoological Operations, SeaWorld Parks and Entertainment, Orlando, FL, USA.
⁶ Department of Biology, University of Rochester, Rochester, NY, USA.
⁷ Department of Neuroscience, Peter O'Donnell Jr. Brain Institute, University of Texas Southwestern Medical Center, Dallas, TX, USA.
⁸ Department of Biology, University of Maryland, College Park, MD, USA.
⁹ Department of Zoology, College of Science, King Saud University, Riyadh, Saudi Arabia.
¹⁰ Loro Parque Fundacion, Avenida Loro Parque, Puerto de la Cruz, Tenerife, Spain.
¹¹ Department of Biology, University of Kentucky, Lexington, KY, USA.
¹² Laboratory of Biodiversity, Parasitology, and Ecology, University of Tunis El Manar, Tunis, Tunisia.
¹³ Division of Cardiology, Department of Internal Medicine, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA.
¹⁴ Bioinformatics Interdepartmental Program, University of California, Los Angeles, CA, USA.
¹⁵ Department of Biological Chemistry, University of California, Los Angeles, Los Angeles, CA, USA.
¹⁶ Marine Mammal Institute, Oregon State University, Newport, OR, USA.
¹⁷ Mammalian Genetics Unit, MRC Harwell Institute, Harwell Science and Innovation Campus, Oxfordshire, UK.
¹⁸ School of Life and Environmental Sciences, The University of Sydney, Sydney, New South Wales, Australia.
¹⁹ Department of Zoology and Entomology, University of Pretoria, Hatfield, South Africa.
²⁰ Busch Gardens Tampa, Tampa, FL, USA.
²¹ Department of Ecology and Evolutionary Biology, University of California Los Angeles, Los Angeles, CA, USA.
²² The Rocky Mountain Biological Laboratory, Crested Butte, CO, USA.
²³ Altius Institute for Biomedical Sciences, Seattle, WA, USA.
²⁴ Epigenetic Clock Development Foundation, Los Angeles, CA, USA.
²⁵ Center for Species Survival, Smithsonian National Zoo and Conservation Biology, Front Royal, VA, USA.
²⁶ Department of Evolution, Ecology and Organismal Biology, The Ohio State University, Columbus, OH, USA.
²⁷ AgResearch, Invermay Agricultural Centre, Mosgiel, Otago, New Zealand.
²⁸ Department of Biochemistry, University of Otago, Dunedin, Otago, New Zealand.
²⁹ Gulf World Marine Park - Dolphin Company, Panama City Beach, FL, USA.
³⁰ School of Veterinary Medicine and Science, University of Nottingham, Nottingham, UK.
³¹ Department of Pathology, Microbiology & Immunology, School of Medicine, University of South Carolina, Columbia, SC, USA.
³² Translational Data Analytics Institute, The Ohio State University, Columbus, OH, USA.
³³ Department of Pharmacology, Addiction Science and Toxicology, The University of Tennessee Health Science Center, Memphis, TN, USA.
³⁴ Medical Informatics, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA.
³⁵ Biochemistry Research Institute of La Plata, Histology and Pathology, School of Medicine, University of La Plata, La Plata, Argentina.
³⁶ Center for Neurobehavioral Genetics, Semel Institute for Neuroscience and Human Behavior, University of California, Los Angeles, Los Angeles, CA, USA.
³⁷ Department of Psychiatry and Biobehavioral Sciences, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA.
³⁸ University of New Mexico, Department of Biology and Museum of Southwestern Biology, Albuquerque, NM, USA.
³⁹ Department of Anatomy and Neurobiology, Northeast Ohio Medical University, Rootstown, OH, USA.
⁴⁰ Department of Environmental & Life Sciences, Trent University, Peterborough, Ontario, Canada.
⁴¹ Taronga Institute of Science and Learning, Taronga Conservation Society Australia, Mosman, New South Wales, Australia.
⁴² SeaWorld Orlando, Orlando, FL, USA.
⁴³ Duke Lemur Center, Durham, NC, USA.
⁴⁴ Conservation Biology Division, Northwest Fisheries Science Center, National Marine Fisheries Service, National Oceanic and Atmospheric Administration, Seattle, WA, USA.
⁴⁵ Altos Labs, Bay Area Institute of Science, Redwood City, CA, USA.
⁴⁶ Department of Cardiology, Smidt Heart Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA.
⁴⁷ David Geffen School of Medicine, University of California, Los Angeles, CA, USA.
⁴⁸ School of Biological and Chemical Sciences, Queen Mary University of London, London, UK.
⁴⁹ School of Biological and Behavioural Sciences, Queen Mary University of London, London, UK.
⁵⁰ Department of Statistics, University of California, Riverside, CA, USA.
⁵¹ Department of Biological Sciences, University of Manitoba, Winnipeg, Manitoba, Canada.
⁵² Fisheries and Oceans Canada, Winnipeg, Manitoba, Canada.
⁵³ Department of Population Health and Reproduction, University of California, Davis School of Veterinary Medicine, Davis, CA, USA.
⁵⁴ Mystic Aquarium, Mystic, CT, USA.
⁵⁵ University of Lyon, CNRS, Laboratoire de Biométrie et Biologie Evolutive, Villeurbanne, France.
⁵⁶ Greenland Institute of Natural Resources, Nuuk, Greenland.
⁵⁷ School of Biological, Earth and Environmental Sciences, University of New South Wales, Sydney, New South Wales, Australia.
⁵⁸ Australian National Wildlife Collection, CSIRO, Canberra, Australia.
⁵⁹ Division of Genetics, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA.
⁶⁰ Applied Translational Genetics Group, School of Biological Sciences, Centre for Brain Research, The University of Auckland, Auckland, New Zealand.
⁶¹ Vancouver Aquarium, Vancouver, British Columbia, Canada.
⁶² SeaWorld San Diego, San Diego, CA, USA.
⁶³ Cancer Genetics and Comparative Genomics Branch, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD, USA.
⁶⁴ Department of Anatomy, University of Otago, Dunedin, New Zealand.
⁶⁵ Division of Human Genetics, Department of Pediatrics, University at Buffalo, Buffalo, NY, USA.
⁶⁶ Division of Infectious Diseases, Department of Medicine, School of Medicine, University of Pittsburgh, Pittsburgh, PA, USA.
⁶⁷ Department of Molecular Genetics, Institute of Bioorganic Chemistry, Polish Academy of Sciences, Poznan, Poland.
⁶⁸ School of Biological Sciences, University of Bristol, Bristol, UK.
⁶⁹ Norwegian Orca Survey, Andenes, Norway.
⁷⁰ Mother Infant Research Institute, Tufts Medical Center, Boston, MA, USA.
⁷¹ Yuvan Research, Mountain View, CA, USA.
⁷² Kamogawa Sea World, Kamogawa, Chiba, Japan.
⁷³ Peromyscus Genetic Stock Center, University of South Carolina, Columbia, SC, USA.
⁷⁴ Department of Drug Discovery and Biomedical Sciences, College of Pharmacy, University of South Carolina, Columbia, SC, USA.
⁷⁵ Edwin S. H. Leong Healthy Aging Program, Centre for Molecular Medicine and Therapeutics, University of British Columbia, Vancouver, British Columbia, Canada.
⁷⁶ Institute of Veterinary Medicine, Nicolaus Copernicus University, Torun, Poland.
⁷⁷ LGL Limited, King City, Ontario, Canada.
⁷⁸ Evolutionary Genetics Group, Department of Anthropology, University of Zurich, Zurich, Switzerland.
⁷⁹ Center for Tropical Research, Institute of the Environment and Sustainability, University of California, Los Angeles, Los Angeles, CA, USA.
⁸⁰ Department of Neurology, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA.
⁸¹ Technology Center for Genomics and Bioinformatics, Department of Pathology and Laboratory Medicine, University of California, Los Angeles, Los Angeles, CA, USA.
⁸² Texas Pregnancy and Life-course Health Center, Southwest National Primate Research Center, San Antonio, TX, USA.
⁸³ Department of Animal Science, College of Agriculture and Natural Resources, Laramie, WY, USA.
⁸⁴ Centre for Molecular Medicine and Therapeutics, BC Children's Hospital Research Institute, University of British Columbia, Vancouver, British Columbia, Canada.
⁸⁵ Biology Department, University of New Mexico, Albuquerque, NM, USA.
⁸⁶ Institute of Ecology and Evolution, School of Biological Sciences, University of Edinburgh, Edinburgh, UK.
⁸⁷ White Oak Conservation Center, Yulee, FL, USA.
⁸⁸ North Gulf Oceanic Society, Homer, AK, USA.
⁸⁹ Translational Gerontology Branch, National Institute on Aging Intramural Research Program, National Institutes of Health, Baltimore, MD, USA.
⁹⁰ ABS Global, DeForest, WI, USA.
⁹¹ Marineland of Canada, Niagara Falls, Ontario, Canada.
⁹² Biomedical and Genomic Research Group, Department of Animal and Dairy Sciences, University of Wisconsin Madison, Madison, WI, USA.
⁹³ Department of Preventive Medicine, University of Tennessee Health Science Center, College of Medicine, Memphis, TN, USA.
⁹⁴ Louis Calder Center - Biological Field Station, Department of Biological Sciences, Fordham University, Armonk, NY, USA.
⁹⁵ Veterinary Integrative Biosciences, Texas A&M University, College Station, TX, USA.
⁹⁶ Interdisciplinary Program in Genetics and Genomics, Texas A&M University, College Station, TX, USA.
⁹⁷ Museum fur Naturkunde, Leibniz-Institute for Evolution and Biodiversity Science, Berlin, Germany.
⁹⁸ Institute of Molecular Biology (IMB), Mainz, Germany.
⁹⁹ Division of Molecular Embryology, DKFZ-ZMBH Alliance, Heidelberg, Germany.
¹⁰⁰ Mammalogical Society of Mongolia, Ulaanbaatar, Mongolia.
¹⁰¹ Cancer Research UK Cambridge Institute, University of Cambridge, Cambridge, UK.
¹⁰² Deutsches Krebsforschungszentrum, Division of Regulatory Genomics and Cancer Evolution, Heidelberg, Germany.
¹⁰³ Department of Psychology, Cornell University, Ithaca, NY, USA.
¹⁰⁴ SeaWorld San Antonio, San Antonio, TX, USA.
¹⁰⁵ Department Molecular Cell and Developmental Biology, University of California, Los Angeles, Los Angeles, CA, USA.
¹⁰⁶ School of Earth, Atmospheric and Life Sciences, University of Wollongong, Wollongong, New South Wales, Australia.
¹⁰⁷ Department of Animal Science, University of Nebraska, Lincoln, NE, USA.
¹⁰⁸ Mammal Research Institute, Department of Zoology and Entomology, University of Pretoria, Hatfield, South Africa.
¹⁰⁹ Department of Biology, College of Arts and Science, Adelphi University, Garden City, NY, USA.
¹¹⁰ Center for Species Survival, Smithsonian Conservation Biology Institute, Front Royal, VA, USA.
¹¹¹ Salk Institute for Biological Studies, La Jolla, CA, USA.
¹¹² Department of Epidemiology, UCLA Fielding School of Public Health, Los Angeles, CA, USA.
¹¹³ Department of Environmental Health Sciences, UCLA Fielding School of Public Health, Los Angeles, CA, USA.
¹¹⁴ Center for Coastal Studies, Provincetown, MA, USA.
¹¹⁵ Miami Seaquarium, Miami, FL, USA.
¹¹⁶ 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.
¹¹⁷ Department of Radiology, University of Illinois at Chicago, Chicago, IL, USA.
¹¹⁸ Department of Biochemistry and Molecular Genetics, University of Illinois at Chicago, Chicago, IL, USA.
¹¹⁹ National Center for Supercomputing Applications, University of Illinois at Urbana-Champaign, Urbana, IL, USA.
¹²⁰ College of Agriculture, Missouri State University, Springfield, MO, USA.
¹²¹ Department of Animal Sciences, University of Illinois at Urbana-Champaign, Urbana, IL, USA.
¹²² Department of Forensic Science, Environmental & Life Sciences, Trent University, Peterborough, Ontario, Canada.
¹²³ Shobhaben Pratapbhai Patel School of Pharmacy & Technology Management, SVKM'S NMIMS University, Mumbai, India.
¹²⁴ Species Preservation Laboratory, SeaWorld San Diego, San Diego, CA, USA.
¹²⁵ Biology Department, Acadia University, Wolfville, Nova Scotia, Canada.
¹²⁶ Department of Pathobiology and Population Sciences, Royal Veterinary College, Hatfield, UK.
¹²⁷ Howard Hughes Medical Institute, Department of Neuroscience, University of Texas Southwestern Medical Center, Dallas, TX, USA.
¹²⁸ School of Biology and Environmental Science, University College Dublin, Belfield, Dublin, Ireland.
¹²⁹ Animal Care and Science Division, John G. Shedd Aquarium, Chicago, IL, USA.
¹³⁰ School of Biology, The University of St. Andrews, Fife, UK.
¹³¹ Neurogenetics of Vocal Communication Group, Max Planck Institute for Psycholinguistics, Nijmegen, Netherlands.
¹³² Blizard Institute, Faculty of Medicine and Dentistry, Queen Mary University of London, London, UK.
¹³³ Department of Pathology and Laboratory Medicine, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA.
¹³⁴ Department of Genetics, Genomics and Informatics, University of Tennessee Health Science Center, College of Medicine, Memphis, TN, USA.
¹³⁵ Eli and Edythe Broad Center of Regenerative Medicine and Stem Cell Research, University of California, Los Angeles, Los Angeles, CA, USA.
¹³⁶ Center for Computational and Genomic Medicine, Children's Hospital of Philadelphia, Philadelphia, PA, USA.
¹³⁷ Department of Pathology and Laboratory Medicine, University of Pennsylvania, Philadelphia, PA, USA.
¹³⁸ Departments of Biology and Medicine, University of Rochester, Rochester, NY, USA.
¹³⁹ Altos Labs, Cambridge, UK.

^# Contributed equally.

PMID: 37561875
PMCID: PMC11180965
DOI: 10.1126/science.abq5693

DNA methylation networks underlying mammalian traits

Amin Haghani et al. Science. 2023.

. 2023 Aug 11;381(6658):eabq5693.

doi: 10.1126/science.abq5693. Epub 2023 Aug 11.

Authors

Affiliations

¹ Department of Human Genetics, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA.
² Altos Labs, San Diego, CA, USA.
³ Department of Biostatistics, Fielding School of Public Health, University of California Los Angeles, Los Angeles, CA, USA.
⁴ Janssen Research & Development, Spring House, PA, USA.
⁵ Zoological Operations, SeaWorld Parks and Entertainment, Orlando, FL, USA.
⁶ Department of Biology, University of Rochester, Rochester, NY, USA.
⁷ Department of Neuroscience, Peter O'Donnell Jr. Brain Institute, University of Texas Southwestern Medical Center, Dallas, TX, USA.
⁸ Department of Biology, University of Maryland, College Park, MD, USA.
⁹ Department of Zoology, College of Science, King Saud University, Riyadh, Saudi Arabia.
¹⁰ Loro Parque Fundacion, Avenida Loro Parque, Puerto de la Cruz, Tenerife, Spain.
¹¹ Department of Biology, University of Kentucky, Lexington, KY, USA.
¹² Laboratory of Biodiversity, Parasitology, and Ecology, University of Tunis El Manar, Tunis, Tunisia.
¹³ Division of Cardiology, Department of Internal Medicine, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA.
¹⁴ Bioinformatics Interdepartmental Program, University of California, Los Angeles, CA, USA.
¹⁵ Department of Biological Chemistry, University of California, Los Angeles, Los Angeles, CA, USA.
¹⁶ Marine Mammal Institute, Oregon State University, Newport, OR, USA.
¹⁷ Mammalian Genetics Unit, MRC Harwell Institute, Harwell Science and Innovation Campus, Oxfordshire, UK.
¹⁸ School of Life and Environmental Sciences, The University of Sydney, Sydney, New South Wales, Australia.
¹⁹ Department of Zoology and Entomology, University of Pretoria, Hatfield, South Africa.
²⁰ Busch Gardens Tampa, Tampa, FL, USA.
²¹ Department of Ecology and Evolutionary Biology, University of California Los Angeles, Los Angeles, CA, USA.
²² The Rocky Mountain Biological Laboratory, Crested Butte, CO, USA.
²³ Altius Institute for Biomedical Sciences, Seattle, WA, USA.
²⁴ Epigenetic Clock Development Foundation, Los Angeles, CA, USA.
²⁵ Center for Species Survival, Smithsonian National Zoo and Conservation Biology, Front Royal, VA, USA.
²⁶ Department of Evolution, Ecology and Organismal Biology, The Ohio State University, Columbus, OH, USA.
²⁷ AgResearch, Invermay Agricultural Centre, Mosgiel, Otago, New Zealand.
²⁸ Department of Biochemistry, University of Otago, Dunedin, Otago, New Zealand.
²⁹ Gulf World Marine Park - Dolphin Company, Panama City Beach, FL, USA.
³⁰ School of Veterinary Medicine and Science, University of Nottingham, Nottingham, UK.
³¹ Department of Pathology, Microbiology & Immunology, School of Medicine, University of South Carolina, Columbia, SC, USA.
³² Translational Data Analytics Institute, The Ohio State University, Columbus, OH, USA.
³³ Department of Pharmacology, Addiction Science and Toxicology, The University of Tennessee Health Science Center, Memphis, TN, USA.
³⁴ Medical Informatics, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA.
³⁵ Biochemistry Research Institute of La Plata, Histology and Pathology, School of Medicine, University of La Plata, La Plata, Argentina.
³⁶ Center for Neurobehavioral Genetics, Semel Institute for Neuroscience and Human Behavior, University of California, Los Angeles, Los Angeles, CA, USA.
³⁷ Department of Psychiatry and Biobehavioral Sciences, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA.
³⁸ University of New Mexico, Department of Biology and Museum of Southwestern Biology, Albuquerque, NM, USA.
³⁹ Department of Anatomy and Neurobiology, Northeast Ohio Medical University, Rootstown, OH, USA.
⁴⁰ Department of Environmental & Life Sciences, Trent University, Peterborough, Ontario, Canada.
⁴¹ Taronga Institute of Science and Learning, Taronga Conservation Society Australia, Mosman, New South Wales, Australia.
⁴² SeaWorld Orlando, Orlando, FL, USA.
⁴³ Duke Lemur Center, Durham, NC, USA.
⁴⁴ Conservation Biology Division, Northwest Fisheries Science Center, National Marine Fisheries Service, National Oceanic and Atmospheric Administration, Seattle, WA, USA.
⁴⁵ Altos Labs, Bay Area Institute of Science, Redwood City, CA, USA.
⁴⁶ Department of Cardiology, Smidt Heart Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA.
⁴⁷ David Geffen School of Medicine, University of California, Los Angeles, CA, USA.
⁴⁸ School of Biological and Chemical Sciences, Queen Mary University of London, London, UK.
⁴⁹ School of Biological and Behavioural Sciences, Queen Mary University of London, London, UK.
⁵⁰ Department of Statistics, University of California, Riverside, CA, USA.
⁵¹ Department of Biological Sciences, University of Manitoba, Winnipeg, Manitoba, Canada.
⁵² Fisheries and Oceans Canada, Winnipeg, Manitoba, Canada.
⁵³ Department of Population Health and Reproduction, University of California, Davis School of Veterinary Medicine, Davis, CA, USA.
⁵⁴ Mystic Aquarium, Mystic, CT, USA.
⁵⁵ University of Lyon, CNRS, Laboratoire de Biométrie et Biologie Evolutive, Villeurbanne, France.
⁵⁶ Greenland Institute of Natural Resources, Nuuk, Greenland.
⁵⁷ School of Biological, Earth and Environmental Sciences, University of New South Wales, Sydney, New South Wales, Australia.
⁵⁸ Australian National Wildlife Collection, CSIRO, Canberra, Australia.
⁵⁹ Division of Genetics, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA.
⁶⁰ Applied Translational Genetics Group, School of Biological Sciences, Centre for Brain Research, The University of Auckland, Auckland, New Zealand.
⁶¹ Vancouver Aquarium, Vancouver, British Columbia, Canada.
⁶² SeaWorld San Diego, San Diego, CA, USA.
⁶³ Cancer Genetics and Comparative Genomics Branch, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD, USA.
⁶⁴ Department of Anatomy, University of Otago, Dunedin, New Zealand.
⁶⁵ Division of Human Genetics, Department of Pediatrics, University at Buffalo, Buffalo, NY, USA.
⁶⁶ Division of Infectious Diseases, Department of Medicine, School of Medicine, University of Pittsburgh, Pittsburgh, PA, USA.
⁶⁷ Department of Molecular Genetics, Institute of Bioorganic Chemistry, Polish Academy of Sciences, Poznan, Poland.
⁶⁸ School of Biological Sciences, University of Bristol, Bristol, UK.
⁶⁹ Norwegian Orca Survey, Andenes, Norway.
⁷⁰ Mother Infant Research Institute, Tufts Medical Center, Boston, MA, USA.
⁷¹ Yuvan Research, Mountain View, CA, USA.
⁷² Kamogawa Sea World, Kamogawa, Chiba, Japan.
⁷³ Peromyscus Genetic Stock Center, University of South Carolina, Columbia, SC, USA.
⁷⁴ Department of Drug Discovery and Biomedical Sciences, College of Pharmacy, University of South Carolina, Columbia, SC, USA.
⁷⁵ Edwin S. H. Leong Healthy Aging Program, Centre for Molecular Medicine and Therapeutics, University of British Columbia, Vancouver, British Columbia, Canada.
⁷⁶ Institute of Veterinary Medicine, Nicolaus Copernicus University, Torun, Poland.
⁷⁷ LGL Limited, King City, Ontario, Canada.
⁷⁸ Evolutionary Genetics Group, Department of Anthropology, University of Zurich, Zurich, Switzerland.
⁷⁹ Center for Tropical Research, Institute of the Environment and Sustainability, University of California, Los Angeles, Los Angeles, CA, USA.
⁸⁰ Department of Neurology, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA.
⁸¹ Technology Center for Genomics and Bioinformatics, Department of Pathology and Laboratory Medicine, University of California, Los Angeles, Los Angeles, CA, USA.
⁸² Texas Pregnancy and Life-course Health Center, Southwest National Primate Research Center, San Antonio, TX, USA.
⁸³ Department of Animal Science, College of Agriculture and Natural Resources, Laramie, WY, USA.
⁸⁴ Centre for Molecular Medicine and Therapeutics, BC Children's Hospital Research Institute, University of British Columbia, Vancouver, British Columbia, Canada.
⁸⁵ Biology Department, University of New Mexico, Albuquerque, NM, USA.
⁸⁶ Institute of Ecology and Evolution, School of Biological Sciences, University of Edinburgh, Edinburgh, UK.
⁸⁷ White Oak Conservation Center, Yulee, FL, USA.
⁸⁸ North Gulf Oceanic Society, Homer, AK, USA.
⁸⁹ Translational Gerontology Branch, National Institute on Aging Intramural Research Program, National Institutes of Health, Baltimore, MD, USA.
⁹⁰ ABS Global, DeForest, WI, USA.
⁹¹ Marineland of Canada, Niagara Falls, Ontario, Canada.
⁹² Biomedical and Genomic Research Group, Department of Animal and Dairy Sciences, University of Wisconsin Madison, Madison, WI, USA.
⁹³ Department of Preventive Medicine, University of Tennessee Health Science Center, College of Medicine, Memphis, TN, USA.
⁹⁴ Louis Calder Center - Biological Field Station, Department of Biological Sciences, Fordham University, Armonk, NY, USA.
⁹⁵ Veterinary Integrative Biosciences, Texas A&M University, College Station, TX, USA.
⁹⁶ Interdisciplinary Program in Genetics and Genomics, Texas A&M University, College Station, TX, USA.
⁹⁷ Museum fur Naturkunde, Leibniz-Institute for Evolution and Biodiversity Science, Berlin, Germany.
⁹⁸ Institute of Molecular Biology (IMB), Mainz, Germany.
⁹⁹ Division of Molecular Embryology, DKFZ-ZMBH Alliance, Heidelberg, Germany.
¹⁰⁰ Mammalogical Society of Mongolia, Ulaanbaatar, Mongolia.
¹⁰¹ Cancer Research UK Cambridge Institute, University of Cambridge, Cambridge, UK.
¹⁰² Deutsches Krebsforschungszentrum, Division of Regulatory Genomics and Cancer Evolution, Heidelberg, Germany.
¹⁰³ Department of Psychology, Cornell University, Ithaca, NY, USA.
¹⁰⁴ SeaWorld San Antonio, San Antonio, TX, USA.
¹⁰⁵ Department Molecular Cell and Developmental Biology, University of California, Los Angeles, Los Angeles, CA, USA.
¹⁰⁶ School of Earth, Atmospheric and Life Sciences, University of Wollongong, Wollongong, New South Wales, Australia.
¹⁰⁷ Department of Animal Science, University of Nebraska, Lincoln, NE, USA.
¹⁰⁸ Mammal Research Institute, Department of Zoology and Entomology, University of Pretoria, Hatfield, South Africa.
¹⁰⁹ Department of Biology, College of Arts and Science, Adelphi University, Garden City, NY, USA.
¹¹⁰ Center for Species Survival, Smithsonian Conservation Biology Institute, Front Royal, VA, USA.
¹¹¹ Salk Institute for Biological Studies, La Jolla, CA, USA.
¹¹² Department of Epidemiology, UCLA Fielding School of Public Health, Los Angeles, CA, USA.
¹¹³ Department of Environmental Health Sciences, UCLA Fielding School of Public Health, Los Angeles, CA, USA.
¹¹⁴ Center for Coastal Studies, Provincetown, MA, USA.
¹¹⁵ Miami Seaquarium, Miami, FL, USA.
¹¹⁶ 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.
¹¹⁷ Department of Radiology, University of Illinois at Chicago, Chicago, IL, USA.
¹¹⁸ Department of Biochemistry and Molecular Genetics, University of Illinois at Chicago, Chicago, IL, USA.
¹¹⁹ National Center for Supercomputing Applications, University of Illinois at Urbana-Champaign, Urbana, IL, USA.
¹²⁰ College of Agriculture, Missouri State University, Springfield, MO, USA.
¹²¹ Department of Animal Sciences, University of Illinois at Urbana-Champaign, Urbana, IL, USA.
¹²² Department of Forensic Science, Environmental & Life Sciences, Trent University, Peterborough, Ontario, Canada.
¹²³ Shobhaben Pratapbhai Patel School of Pharmacy & Technology Management, SVKM'S NMIMS University, Mumbai, India.
¹²⁴ Species Preservation Laboratory, SeaWorld San Diego, San Diego, CA, USA.
¹²⁵ Biology Department, Acadia University, Wolfville, Nova Scotia, Canada.
¹²⁶ Department of Pathobiology and Population Sciences, Royal Veterinary College, Hatfield, UK.
¹²⁷ Howard Hughes Medical Institute, Department of Neuroscience, University of Texas Southwestern Medical Center, Dallas, TX, USA.
¹²⁸ School of Biology and Environmental Science, University College Dublin, Belfield, Dublin, Ireland.
¹²⁹ Animal Care and Science Division, John G. Shedd Aquarium, Chicago, IL, USA.
¹³⁰ School of Biology, The University of St. Andrews, Fife, UK.
¹³¹ Neurogenetics of Vocal Communication Group, Max Planck Institute for Psycholinguistics, Nijmegen, Netherlands.
¹³² Blizard Institute, Faculty of Medicine and Dentistry, Queen Mary University of London, London, UK.
¹³³ Department of Pathology and Laboratory Medicine, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA.
¹³⁴ Department of Genetics, Genomics and Informatics, University of Tennessee Health Science Center, College of Medicine, Memphis, TN, USA.
¹³⁵ Eli and Edythe Broad Center of Regenerative Medicine and Stem Cell Research, University of California, Los Angeles, Los Angeles, CA, USA.
¹³⁶ Center for Computational and Genomic Medicine, Children's Hospital of Philadelphia, Philadelphia, PA, USA.
¹³⁷ Department of Pathology and Laboratory Medicine, University of Pennsylvania, Philadelphia, PA, USA.
¹³⁸ Departments of Biology and Medicine, University of Rochester, Rochester, NY, USA.
¹³⁹ Altos Labs, Cambridge, UK.

^# Contributed equally.

PMID: 37561875
PMCID: PMC11180965
DOI: 10.1126/science.abq5693

Abstract

Using DNA methylation profiles (n = 15,456) from 348 mammalian species, we constructed phyloepigenetic trees that bear marked similarities to traditional phylogenetic ones. Using unsupervised clustering across all samples, we identified 55 distinct cytosine modules, of which 30 are related to traits such as maximum life span, adult weight, age, sex, and human mortality risk. Maximum life span is associated with methylation levels in HOXL subclass homeobox genes and developmental processes and is potentially regulated by pluripotency transcription factors. The methylation state of some modules responds to perturbations such as caloric restriction, ablation of growth hormone receptors, consumption of high-fat diets, and expression of Yamanaka factors. This study reveals an intertwined evolution of the genome and epigenome that mediates the biological characteristics and traits of different mammalian species.

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Figures

**Fig. 1.. Phyloepigenetic trees parallel the mammalian evolutionary tree.**
(A) The traditional phylogenetic tree from the TimeTree database (44) based on 321 (of 348) species in our study. A full description of the species in our study is reported in table S1. (B) Blood-based phyloepigenetic tree created from hierarchical clustering of DNAm data in this study (for additional analysis, see fig. S3, A and B). We formed the mean value per cytosine across samples for each species. The clustering used 1 minus the Pearson correlation (1-cor) as a pairwise dissimilarity measure and the average linkage method as intergroup dissimilarity. Phyloepigenetic trees for skin and liver can be found in fig. S2. Additional analyses, e.g., involving different choices of CpGs or intergroup dissimilarity measures, are reported in the supplementary materials (fig. S2). The colored bars reflect the branch height. (C) Scatter plot of the distances in blood phyloepigenetic (1-cor) versus the traditional evolutionary tree. (D) Scatter plots displaying the log-odds ratios of regions exhibiting phylogenetic signals relative to the TSS are presented. The phylogenetic signal is determined using Blomberg’s K statistic (32). In this analysis, CpGs were grouped into categories using sliding windows relative to the TSS. To assess enrichment, the Fisher’s exact overlap test was used, focusing on the top 500 CpGs displaying phylogenetic signals within each region. The red dots highlight the regions with the Fisher’s exact P value < 0.05. The results indicate notable enrichment (OR > 3) in certain intergenic and genic regions but not in promoters. For additional analysis, see fig. S4.

**Fig. 2.. DNAm network relates to species and individual characteristics in mammalian species.**
(A) the WGCNA network of 14,705 conserved CpGs in eutherian species (Net1). The identified modules related to species or individual sample characteristics. Net1 modules were compared with eight additional networks (fig. S5). The modules with strong associations with species and sample characteristics are labeled below the dendrogram. Gray color indicates CpGs that are outside of modules. (B) Summary of the modules showing strong associations with species and individual sample characteristics. The plus and minus labels are the direction of association with each trait. (C) Top defined functional biological processes related to Net1 modules (for details, see fig. S9 and table S4). (D) Mammalian comethylation modules form clusters of proteins in the STRING protein-protein interaction (PPI) network. For the sake of visualization, the analysis was limited to the top 50 CpGs with the highest module membership value per module. Colors indicate mammalian Net1. The lollipop plot shows the global cluster coefficient (36) of the proteins within a module (up to 500 top CpGs) in a PPI network. Our permutation analysis matched the distribution of the original module sizes. We evaluated 1100 random permutations, i.e., 20 for each of the 55 modules. The boxplot reports the global clustering coefficient per module (y axis) versus permutation status: module resulting from a random selection of proteins (left) versus original module resulting from WGCNA (right). The modules with cluster coefficients larger than the maximum permutation cluster coefficient were considered as significant at P = 0.001. The dashed vertical line corresponds to the maximum global clustering coefficient observed in the 1100 random permutations.

**Fig. 3.. Comethylation modules related to mammalian maximum life span, weight, human mortality, and age.**
(A and B) Modules associated with log maximum life span (P < 10⁻²⁰) (A) or log average species weight (P < 10⁻¹⁷) (B) in marginal association (correlation test with the mean module eigengene of the species). The module eigengene is defined as the first principal component of the scaled CpGs underlying a module. The species are randomly labeled by their animal number (table S1). (C) The top modules associated with median life expectancy, upper limit life expectancy, or average adult weight of 93 dog breeds, model (marginal correlation test of the mean module eigengene with target variables; for detailed breed characteristics, see table S8). R, Pearson correlation* coefficient; P, correlation test P value. (D) Forest plots of the top modules associated with mortality risk in the Framingham Heart Study Offspring Cohort (FHS), and Women’s Health Initiative (WHI) study totaling 4651 individuals (1095, 24% death). n denotes the number of deaths per total number of individuals in each study. We report the meta-analysis P value in the title of the forest plot. (E) Module that correlates significantly (P < 1 × 10⁻³⁰⁰) with relative age (defined as ratio of age/maximum life span) across mammalian species using a multivariate regression model. Covariates were tissue, sex, and species differences. Each dot corresponds to a eutherian tissue sample (n = 14,542). Dots are colored by taxonomic order as in Fig. 1. (F) Volcano plot of the rmCorr of all purple module genes in GTEx data (for additional analysis, see fig. S11).

**Fig. 4.. The effects of different pro-aging and anti-aging interventions on selected DNAm modules.**
Six DNAm modules respond to life span–related intervention experiments and are associated with the life expectancy of the mouse models. By contrast, the mammalian maximum life span modules do not correspond directly to the benefits or stress triggered by the intervention in the murine samples. (A) Changes in the intervention modules in the liver parallel smaller size and longer life expectancy of growth hormone receptor mouse models (GHRKO). Sample size: GHRKO, n = 11 (n = 5 female, n = 6 male); wild type, n = 18 (n = 9 male, n = 9 female). Age range was 6 to 8 months. (B) Caloric restriction (CR) DNAm module signature predicts longer life span in this treated group (age = 18 months; sex = male; CR, n = 59; control, n = 36). (C) High-fat diet accelerates aging in five modules including the purple (RelativeAge⁺) module. High-fat diet, n = 133 (n = 125 females, n = 8 males); control (ad libitum feeding), n = 212 (n = 10 male, n = 202 female). Age range was 3 to 32 months. (D and E) Examining the effects of in vivo partial reprogramming on intervention modules. (D) Schematic view of the partial programming experiment in 4F mice (39). A systemic Yamanaka factors expression (Oct4, Sox2, Klf4, and Myc) was periodically induced by adding doxycycline to the drinking water for 2 days per week. Partial programming was done at three different durations. Sample size: control (C57BL/6+dox), n = 7; 1 month (1m) 4F, n = 3; 7 months (7m) 4F, n = 5; 10 months (10m) 4F. All tissues except skin, n = 3; skin, n = 2. (E) scatter plots of the linear changes of the intervention modules in the skin and kidney of mice treated with different durations (dosages) of Yamanaka factors. Intervention modules indicate a dose-dependent rejuvenation of skin and kidney by this partial programming regimen.

**Fig. 5.. EWAS of mammalian log-transformed maximum life span.**
(A) CpG-specific association with maximum life span across n = 333 eutherian species. For EWAS, the mean methylation values of each CpG (per species) were regressed on log maximum life span. The right portion of the panel reports EWAS results after adjustment for average adult weight. Genome annotation indicates human hg19. Blue dotted line indicates Bonferroni-corrected two-sided P value < 1.8 × 10⁻⁶. The point colors indicate the corresponding modules. The bar plot indicates the top enriched (hypergeometric test, eutherian probes as background) modules for the top 1000 (500 negative CpGs, nominal P < 1.1 × 10⁻¹¹, FDR = 1 × 10⁻¹⁰; 500 negative and positive CpGs, nominal P < 1.5 × 10⁻²¹, FDR = 7.5 × 10⁻²⁰) significant CpGs for different EWASs. (B) Venn diagram of the overlaps between top hits from EWAS of maximum life span and meta-analysis of age [meta-analysis results are from (7); for additional analysis, see fig. S20]. (C) Venn diagram of the overlaps between the genes adjacent to the EWAS results and top age-related mRNA changes in human tissues (P < 1 × 10⁻⁵⁰). (D) Gene set enrichment analysis of the genes proximal to CpGs associated with mammalian maximum life span. We only report enrichment terms that are significant after adjustment for multiple comparisons (hypergeometric FDR < 0.01) and contain at least five significant genes. The top three significant terms per column (EWAS) and enrichment database are shown. (E) Ingenuity potential upstream regulator analysis (40) of the differentially methylated genes related to mammalian maximum life span. Only significant (FDR < 0.05) regulators are represented in the bar plot. (F) Venn diagram of three gene lists. Gene list 1 is the top 646 genes adjacent to 1000 life span–related CpGs (500 positive and 500 negative). Gene lists 2 and 3 are based on CpGs that are differentially methylated (nominal Wald test P < 0.005, up to 500 positive and 500 negatively related CpGs) after OSKM overexpression in murine kidney (583 genes) and skin (686 genes) (39). We observed significant overlap between the gene lists (nominal Fisher’s exact P = 9.9 × 10⁻³⁰ for skin and life span; P = 4.5 × 10⁻²⁵ for kidney and life span). (G) Transcriptional factor motif enrichment analysis of life span modules and life span–related CpGs. The enrichment results for LifespanAdjWeight. negative were not significant. The overlap is assessed by a hypergeometric test for the CpGs within the motifs based on the human hg19 genome.

**Fig. 6.. CpGs linked to life span in various taxonomic orders and tissues.**
Using the nonparametric rankPvalue method (33), we combined 25 EWAS of life span results from various taxonomic order or tissue type strata, calculating the significance of a CpG’s consistently high (or low) rank based on the 25 EWASs of log maximum life span (meta.lifespan and underlying EWAS results can be found in table S24 and data S19). (A) The overlap of top 1000 (500 per direction) meta.lifespan CpGs with EWAS of life span in all eutherians (nominal Fisher’s exact P = 1 × 10⁻¹⁷⁵). (B) Scatter plots illustrating the top meta.lifespan CpGs categorized into different tissue-phylogenetic order strata. Each panel displays only the strata that exhibit significant relationships. Each dot represents a species colored by taxonomic order. Each row corresponds to a different selection of tissue type. “bval” denotes the beta value, measuring DNAm at a CpG site, with 0 indicating no methylation and 1 indicating full methylation. (C) Gene set enrichment analysis of the genes proximal to CpGs associated with mammalian maximum life span. We only report enrichment terms that are significant after adjustment for multiple comparisons (hypergeometric FDR < 0.01) and contain at least five significant genes. The top three significant terms per column (EWAS) and enrichment database are shown. (D) Venn diagram of three gene lists. Gene list 1 (the bottom circle) is the top 407 genes adjacent to 1000 meta.lifespan CpGs (500 positive and 500 negative). Gene lists 2 and 3 (the top circles) are based on CpGs that are differentially methylated (nominal Wald test P < 0.005, up to 500 positive and 500 negatively related CpGs) after OSKM overexpression in murine kidney (583 genes) and skin (686 genes) (39).

**Fig. 7.. Chromatin state analysis and distance to the TSS for the life span–related CpGs.**
(A) Illustrated plot presenting mean methylation across species (displayed on the left y axis) and EWAS of maximum life span Z statistics (shown on the right y axis), all plotted against the distances to the closest TSS (represented on the x axis). (B) Mean methylation across species (y axis) plotted against EWAS Z statistics for log maximum life span in different genomic regions (intergenic, promoter, and gene body). Additional EWAS results after adjustment for phylogenetic relationships can be found in figs. S17 to S20, and corresponding enrichment results can be found in figs. S22 to S24. Pearson correlation coefficients and P values are reported in different panels. (C and D) Chromatin annotation enrichment analysis of the top 500 negatively life span–related CpGs (C) and the top 500 positively life span–related CpGs (D). The columns in each panel correspond to EWAS results for log-transformed maximum life span across (i) all tissues combined (Lifespan.All), (ii) blood samples only (Lifespan.Blood), and (iii) skin samples only (Lifespan.Skin), (iv) meta analysis of lifespan in different tissues (meta.lifespan), and the corresponding results after adjustment for average adult weight (LifespanAdjWeight). The last column reports enrichment with respect to the RelativeAge⁺ module (purple). We used the same significance thresholds as in Fig. 5. Cell shading corresponds to fold enrichment between comethylation modules and each chromatin state. Numeric values correspond to the P value of such enrichments based on the hypergeometric test, and only cell values with significant P < 0.001 (equivalent to FDR < 0.02) are shown. The chromatin states are learned based on epigenetic datasets profiling chromatin mark signals in different human cell and tissue types resulting in a genome annotation shared across cell types (41). The common partially methylated domains (commonPMD), solo CpGs (WCGW), and highly methylated domain (HMD) annotations are from (42). PRC1 and PRC2 binding site: are obtained from the ChIP-seq datasets of PCR1 and PCR2 from ENCODE (45).

**Fig. 8.. Mammalian methylation meta-modules based on chromatin states and external genome annotations.**
The heatmap shows the enrichments between (1) mammalian comethylation modules and significant life span–related EWAS CpG groups (x axis) and (2) chromatin states or other genomic annotation (y axis). Cell shading corresponds to log-transformed fold enrichment values (observed CpG count divided by expected count). Hypergeometric tests were used to evaluate the enrichment significance in each cell. *Nominal P < 0.001 (FDR < 0.10). Only chromatin states and external genome annotations with at least one significant enrichment (FDR < 0.10) are shown. The chromatin states are based on a human-based universal chromatin annotation of human cell and tissue types (41). Other genomic annotations include the commonPMD, solo CpGs (WCGW), HMD annotations, and neither (CpGs outside these annotations) which are from (42). In addition, PRC1 and PRC2 binding sites are defined from the ChIP-seq data of PRC1 and PRC2 from ENCODE (45). The row and column hierarchical clustering trees (average linkage) are based on a dissimilarity measure (1 minus the pairwise Pearson correlation between log-transformed fold enrichment values). The left barplot indicates the mean methylation levels of the CpGs in each state for all eutherian samples in our data. We used the 14,705 eutherian CpGs as the background for enrichment of the comethylation modules. By contrast, 28,318 CpGs (high-quality probes in humans and mice) were used as a background for enrichment of significant life span–related EWAS CpG groups with chromatin states and genome annotations. Each EWAS CpG group includes up to 500 most significant CpGs per direction (positively or negatively related with life span), as detailed in the caption of Fig. 5.

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References

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DNA methylation networks underlying mammalian traits

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DNA methylation networks underlying mammalian traits

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