. 2025 Feb 7;387(6734):eadp4753.

doi: 10.1126/science.adp4753. Epub 2025 Feb 7.

Kidney multiome-based genetic scorecard reveals convergent coding and regulatory variants

Hongbo Liu^{1

2

3

4}, Amin Abedini^{1

2

3}, Eunji Ha^{1

2

3}, Ziyuan Ma^{1

2

3}, Xin Sheng^{1

2

3

5

6}, Bernhard Dumoulin^{1

2

3}, Chengxiang Qiu^{1

2

3}, Tamas Aranyi^{1

2

3

7

8}, Shen Li^{1

2

3}, Nicole Dittrich^{1

2

3

9}, Hua-Chang Chen¹⁰, Ran Tao^{10

11}, Der-Cherng Tarng^{12

13}, Feng-Jen Hsieh¹⁴, Shih-Ann Chen^{15

16

17

18}, Shun-Fa Yang^{19

20}, Mei-Yueh Lee^{21

22

23}, Pui-Yan Kwok^{14

24}, Jer-Yuarn Wu¹⁴, Chien-Hsiun Chen¹⁴, Atlas Khan²⁵, Nita A Limdi²⁶, Wei-Qi Wei²⁷, Theresa L Walunas²⁸, Elizabeth W Karlson²⁹, Eimear E Kenny^{30

31

32}, Yuan Luo³³, Leah Kottyan³⁴, John J Connolly³⁵, Gail P Jarvik³⁶, Chunhua Weng³⁷, Ning Shang²⁵, Joanne B Cole^{38

39

40

41

42}, Josep M Mercader^{38

39

40

43}, Ravi Mandla^{38

39

43

44

45}, Timothy D Majarian^{38

46}, Jose C Florez^{38

39

40

43}, Mary E Haas⁴⁷, Luca A Lotta⁴⁷; Regeneron Genetics Center‡; GHS-RGC DiscovEHR Collaboration§; Theodore G Drivas⁴⁸; Penn Medicine BioBank¶; Ha My T Vy^{49

50}, Girish N Nadkarni^{49

50

51

52}, Laura K Wiley^{53

54}, Melissa P Wilson⁵⁴, Christopher R Gignoux^{53

54}, Humaira Rasheed^{55

56}, Laurent F Thomas^{55

57

58}, Bjørn Olav Åsvold^{55

59}, Ben M Brumpton^{55

56

60}, Stein I Hallan^{57

61}, Kristian Hveem⁵⁵, Jie Zheng^{56

62

63}, Jacklyn N Hellwege^{11

64}, Matthew Zawistowski⁶⁵, Sebastian Zöllner^{65

66}, Nora Franceschini⁶⁷, Hailong Hu^{1

2

3}, Jianfu Zhou^{1

2

3}, Krzysztof Kiryluk²⁵, Marylyn D Ritchie^{3

68}, Matthew Palmer⁶⁹, Todd L Edwards⁷⁰, Benjamin F Voight^{3

71

72}, Adriana M Hung^{73

74}, Katalin Susztak^{1

2

3

4}; Regeneron Genetics Center; GHS-RGC DiscovEHR Collaboration; Penn Medicine BioBank

Collaborators, Affiliations

Collaborators

Aris Baras, Gonçalo Abecasis, Adolfo Ferrando, Giovanni Coppola, Andrew Deubler, Aris Economides, Luca A Lotta, John D Overton, Jeffrey G Reid, Alan Shuldiner, Katherine Siminovitch, Jason Portnoy, Marcus B Jones, Lyndon Mitnaul, Alison Fenney, Jonathan Marchini, Manuel Allen Revez Ferreira, Maya Ghoussaini, Mona Nafde, William Salerno, John D Overton, Christina Beechert, Erin Fuller, Laura M Cremona, Eugene Kalyuskin, Hang Du, Caitlin Forsythe, Zhenhua Gu, Kristy Guevara, Michael Lattari, Alexander Lopez, Kia Manoochehri, Prathyusha Challa, Manasi Pradhan, Raymond Reynoso, Ricardo Schiavo, Maria Sotiropoulos Padilla, Chenggu Wang, Sarah E Wolf, Hang Du, Kristy Guevara, Amelia Averitt, Nilanjana Banerjee, Dadong Li, Sameer Malhotra, Justin Mower, Mudasar Sarwar, Deepika Sharma, Sean Yu, Aaron Zhang, Muhammad Aqeel, Jeffrey G Reid, Mona Nafde, Manan Goyal, George Mitra, Sanjay Sreeram, Rouel Lanche, Vrushali Mahajan, Sai Lakshmi Vasireddy, Gisu Eom, Krishna Pawan Punuru, Sujit Gokhale, Benjamin Sultan, Pooja Mule, Eliot Austin, Xiaodong Bai, Lance Zhang, Sean O'Keeffe, Razvan Panea, Evan Edelstein, Ayesha Rasool, William Salerno, Evan K Maxwell, Boris Boutkov, Alexander Gorovits, Ju Guan, Lukas Habegger, Alicia Hawes, Olga Krasheninina, Samantha Zarate, Adam J Mansfield, Lukas Habegger, Gonçalo Abecasis, Joshua Backman, Kathy Burch, Adrian Campos, Liron Ganel, Sheila Gaynor, Benjamin Geraghty, Arkopravo Ghosh, Salvador Romero Martinez, Christopher Gillies, Lauren Gurski, Joseph Herman, Eric Jorgenson, Tyler Joseph, Michael Kessler, Jack Kosmicki, Adam Locke, Priyanka Nakka, Jonathan Marchini, Karl Landheer, Olivier Delaneau, Maya Ghoussaini, Anthony Marcketta, Joelle Mbatchou, Arden Moscati, Aditeya Pandey, Anita Pandit, Jonathan Ross, Carlo Sidore, Eli Stahl, Timothy Thornton, Sailaja Vedantam, Rujin Wang, Kuan-Han Wu, Bin Ye, Blair Zhang, Andrey Ziyatdinov, Yuxin Zou, Jingning Zhang, Kyoko Watanabe, Mira Tang, Frank Wendt, Suganthi Balasubramanian, Suying Bao, Kathie Sun, Chuanyi Zhang, Adolfo Ferrando, Giovanni Coppola, Luca A Lotta, Alan Shuldiner, Katherine Siminovitch, Brian Hobbs, Jon Silver, William Palmer, Rita Guerreiro, Amit Joshi, Antoine Baldassari, Cristen Willer, Sarah Graham, Ernst Mayerhofer, Erola Pairo Castineira, Mary Haas, Niek Verweij, George Hindy, Jonas Bovijn, Tanima De, Parsa Akbari, Luanluan Sun, Olukayode Sosina, Arthur Gilly, Peter Dornbos, Juan Rodriguez-Flores, Moeen Riaz, Manav Kapoor, Gannie Tzoneva, Momodou W Jallow, Anna Alkelai, Ariane Ayer, Veera Rajagopal, Sahar Gelfman, Vijay Kumar, Jacqueline Otto, Neelroop Parikshak, Aysegul Guvenek, Jose Bras, Silvia Alvarez, Jessie Brown, Jing He, Hossein Khiabanian, Joana Revez, Kimberly Skead, Valentina Zavala, Jae Soon Sul, Lei Chen, Sam Choi, Amy Damask, Nan Lin, Charles Paulding, Marcus B Jones, Esteban Chen, Michelle G LeBlanc, Jason Mighty, Jennifer Rico-Varela, Nirupama Nishtala, Nadia Rana, Jaimee Hernandez, Alison Fenney, Randi Schwartz, Jody Hankins, Anna Han, Samuel Hart, Ann Perez-Beals, Gina Solari, Johannie Rivera-Picart, Michelle Pagan, Sunilbe Siceron, Adam Buchanan, David J Carey, Christa L Martin, Michelle Meyer, Kyle Retterer, David Rolston, Daniel J Rader, Marylyn D Ritchie, JoEllen Weaver, Nawar Naseer, Giorgio Sirugo, Afiya Poindexter, Yi-An Ko, Kyle P Nerz, Meghan Livingstone, Fred Vadivieso, Stephanie DerOhannessian, Teo Tran, Julia Stephanowski, Salma Santos, Ned Haubein, Joseph Dunn, Anurag Verma, Colleen Morse Kripke, Marjorie Risman, Renae Judy, Colin Wollack, Shefali S Verma, Scott M Damrauer, Yuki Bradford, Scott M Dudek, Theodore G Drivas

Affiliations

¹ Department of Medicine, Renal Electrolyte and Hypertension Division, University of Pennsylvania, Philadelphia, PA, USA.
² Institute of Diabetes Obesity and Metabolism, University of Pennsylvania, Philadelphia, PA, USA.
³ Department of Genetics, University of Pennsylvania, Philadelphia, PA, USA.
⁴ Penn-CHOP Kidney Innovation Center, University of Pennsylvania, Philadelphia, PA, USA.
⁵ Liangzhu Laboratory, Zhejiang University, 1369 West Wenyi Road, Hangzhou, Zhejiang, China.
⁶ Department of Nephrology, Children' Hospital, Zhejiang University School of Medicine, National Clinical Research Center for Child Health, Hangzhou, Zhejiang, China.
⁷ Institute of Molecular Life Sciences, HUN-REN Research Center for Natural Sciences, Budapest, Hungary.
⁸ Department of Molecular Biology, Semmelweis University, Budapest, Hungary.
⁹ Department of Medicine, Federal University of São Paulo, São Paulo, Brazil.
¹⁰ Department of Biostatistics, Vanderbilt University Medical Center, Nashville, TN, USA.
¹¹ Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, TN, USA.
¹² Institute of Clinical Medicine, School of Medicine, National Yang Ming Chiao Tung University, Taipei, Taiwan, ROC.
¹³ Division of Nephrology, Department of Medicine, Taipei Veterans General Hospital, Taipei, Taiwan, ROC.
¹⁴ Institute of Biomedical Sciences, Academia Sinica, Taipei, Taiwan, ROC.
¹⁵ Cardiovascular Center, Taichung Veterans General Hospital, Taichung, Taiwan, ROC.
¹⁶ National Chung Hsing University, Taichung, Taiwan, ROC.
¹⁷ Heart Rhythm Center, Division of Cardiology, Department of Medicine, Taipei Veterans General Hospital, Taipei, Taiwan, ROC.
¹⁸ Department of Internal Medicine, College of Medicine, National Yang Ming Chiao Tung University, Taipei, Taiwan, ROC.
¹⁹ Institute of Medicine, Chung Shan Medical University, Taichung, Taiwan, ROC.
²⁰ Department of Medical Research, Chung Shan Medical University Hospital, Taichung, Taiwan, ROC.
²¹ Division of Endocrinology and Metabolism, Department of Internal Medicine, Kaohsiung Medical University Hospital, Kaohsiung, Taiwan, ROC.
²² School of Medicine, College of Medicine, Kaohsiung Medical University, Kaohsiung, Taiwan, ROC.
²³ Department of Internal Medicine, Kaohsiung Medical University Gangshan Hospital, Kaohsiung, Taiwan, ROC.
²⁴ Institute for Human Genetics, University of California, San Francisco, CA, USA.
²⁵ Division of Nephrology, Department of Medicine, Vagelos College of Physicians & Surgeons, Columbia University, New York, NY, USA.
²⁶ Department of Neurology, School of Medicine, University of Alabama at Birmingham, Birmingham, AL, USA.
²⁷ Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN, USA.
²⁸ Department of Medicine, Division of General Internal Medicine and Center for Health Information Partnerships, Institute for Public Health and Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL, USA.
²⁹ Department of Medicine, Brigham and Women's Hospital, Boston, MA, USA.
³⁰ Institute for Genomic Health, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
³¹ Division of Genomic Medicine, Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
³² Division of General Internal Medicine, Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
³³ Department of Preventive Medicine, Feinberg School of Medicine, Northwestern University, Chicago, IL, USA.
³⁴ The Center for Autoimmune Genomics and Etiology, Division of Human Genetics, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, USA.
³⁵ Center for Applied Genomics, Children's Hospital of Philadelphia, Philadelphia, PA, USA.
³⁶ Departments of Medicine (Medical Genetics) and Genome Sciences, University of Washington, Seattle, WA, USA.
³⁷ Department of Biomedical Informatics, Vagelos College of Physicians & Surgeons, Columbia University, New York, NY, USA.
³⁸ Programs in Metabolism and Medical & Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA.
³⁹ Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA.
⁴⁰ Department of Medicine, Harvard Medical School, Boston, MA, USA.
⁴¹ Division of Endocrinology, Boston Children's Hospital, Boston, MA, USA.
⁴² Department of Biomedical Informatics, University of Colorado School of Medicine, Aurora, CO, USA.
⁴³ Diabetes Unit, Endocrine Division, Department of Medicine, Massachusetts General Hospital, Boston, MA, USA.
⁴⁴ Department of Medicine and Cardiovascular Research Institute, Cardiology Division, University of California, San Francisco, CA, USA.
⁴⁵ Graduate Program in Genomics and Computational Biology, University of Pennsylvania, Philadelphia, PA, USA.
⁴⁶ Vertex Pharmaceuticals, Boston, MA, USA.
⁴⁷ Regeneron Genetics Center, Regeneron Pharmaceuticals, Tarrytown, NY, USA.
⁴⁸ Division of Translational Medicine and Human Genetics, Department of Medicine, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA, USA.
⁴⁹ Division of Nephrology, Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
⁵⁰ The Charles Bronfman Institute of Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
⁵¹ The Hasso Plattner Institute of Digital Health, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
⁵² The Mount Sinai Clinical Intelligence Center, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
⁵³ Colorado Center for Personalized Medicine, University of Colorado Anschutz Medical Campus, Aurora, CO, USA.
⁵⁴ Department of Biomedical Informatics, University of Colorado School of Medicine, University of Colorado Anschutz Medical Campus, Aurora, CO, USA.
⁵⁵ KGJebsen Center for Genetic Epidemiology, Department of Public Health and Nursing, Faculty of Medicine and Health Sciences, NTNU, Norwegian University of Science and Technology, Trondheim, Norway.
⁵⁶ MRC Integrative Epidemiology Unit, Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, United Kingdom.
⁵⁷ Department of Clinical and Molecular Medicine, NTNU, Norwegian University of Science and Technology, Trondheim, Norway.
⁵⁸ BioCore - Bioinformatics Core Facility, Norwegian University of Science and Technology, Trondheim, Norway.
⁵⁹ Department of Endocrinology, Clinic of Medicine, St. Olav's Hospital, Trondheim University Hospital, Trondheim, Norway.
⁶⁰ Clinic of Thoracic and Occupational Medicine, St. Olav's Hospital, Trondheim University Hospital, Trondheim, Norway.
⁶¹ Department of Nephrology, St. Olav's Hospital, Trondheim University Hospital, Trondheim, Norway.
⁶² Department of Endocrine and Metabolic Diseases, Shanghai Institute of Endocrine and Metabolic Diseases, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
⁶³ Shanghai National Clinical Research Center for Metabolic Diseases, Key Laboratory for Endocrine and Metabolic Diseases of the National Health Commission of the PR China, Shanghai Key Laboratory for Endocrine Tumor, Shanghai Digital Medicine Innovation Center, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
⁶⁴ Division of Genetic Medicine, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA.
⁶⁵ Department of Biostatistics and Center for Statistical Genetics, University of Michigan, Ann Arbor, MI, USA.
⁶⁶ Department of Psychiatry, University of Michigan, Ann Arbor, MI, USA.
⁶⁷ Department of Epidemiology, Gillings School of Global Public Health, University of North Carolina, Chapel Hill, NC, USA.
⁶⁸ Institute for Biomedical Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
⁶⁹ Pathology and Laboratory Medicine at the Hospital of the University of Pennsylvania, Philadelphia, PA, USA.
⁷⁰ Division of Epidemiology, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA.
⁷¹ Department of Systems Pharmacology and Translational Therapeutics, University of Pennsylvania, Philadelphia, PA, USA.
⁷² Institute for Translational Medicine and Therapeutics, University of Pennsylvania, Philadelphia, PA, USA.
⁷³ Division of Nephrology and Hypertension, Vanderbilt Center for Kidney Disease, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA.
⁷⁴ VA Tennessee Valley Healthcare System, Clinical Sciences Research and Development, Nashville, TN, USA.

PMID: 39913582
PMCID: PMC12013656
DOI: 10.1126/science.adp4753

Kidney multiome-based genetic scorecard reveals convergent coding and regulatory variants

Hongbo Liu et al. Science. 2025.

. 2025 Feb 7;387(6734):eadp4753.

doi: 10.1126/science.adp4753. Epub 2025 Feb 7.

Authors

Collaborators

Aris Baras, Gonçalo Abecasis, Adolfo Ferrando, Giovanni Coppola, Andrew Deubler, Aris Economides, Luca A Lotta, John D Overton, Jeffrey G Reid, Alan Shuldiner, Katherine Siminovitch, Jason Portnoy, Marcus B Jones, Lyndon Mitnaul, Alison Fenney, Jonathan Marchini, Manuel Allen Revez Ferreira, Maya Ghoussaini, Mona Nafde, William Salerno, John D Overton, Christina Beechert, Erin Fuller, Laura M Cremona, Eugene Kalyuskin, Hang Du, Caitlin Forsythe, Zhenhua Gu, Kristy Guevara, Michael Lattari, Alexander Lopez, Kia Manoochehri, Prathyusha Challa, Manasi Pradhan, Raymond Reynoso, Ricardo Schiavo, Maria Sotiropoulos Padilla, Chenggu Wang, Sarah E Wolf, Hang Du, Kristy Guevara, Amelia Averitt, Nilanjana Banerjee, Dadong Li, Sameer Malhotra, Justin Mower, Mudasar Sarwar, Deepika Sharma, Sean Yu, Aaron Zhang, Muhammad Aqeel, Jeffrey G Reid, Mona Nafde, Manan Goyal, George Mitra, Sanjay Sreeram, Rouel Lanche, Vrushali Mahajan, Sai Lakshmi Vasireddy, Gisu Eom, Krishna Pawan Punuru, Sujit Gokhale, Benjamin Sultan, Pooja Mule, Eliot Austin, Xiaodong Bai, Lance Zhang, Sean O'Keeffe, Razvan Panea, Evan Edelstein, Ayesha Rasool, William Salerno, Evan K Maxwell, Boris Boutkov, Alexander Gorovits, Ju Guan, Lukas Habegger, Alicia Hawes, Olga Krasheninina, Samantha Zarate, Adam J Mansfield, Lukas Habegger, Gonçalo Abecasis, Joshua Backman, Kathy Burch, Adrian Campos, Liron Ganel, Sheila Gaynor, Benjamin Geraghty, Arkopravo Ghosh, Salvador Romero Martinez, Christopher Gillies, Lauren Gurski, Joseph Herman, Eric Jorgenson, Tyler Joseph, Michael Kessler, Jack Kosmicki, Adam Locke, Priyanka Nakka, Jonathan Marchini, Karl Landheer, Olivier Delaneau, Maya Ghoussaini, Anthony Marcketta, Joelle Mbatchou, Arden Moscati, Aditeya Pandey, Anita Pandit, Jonathan Ross, Carlo Sidore, Eli Stahl, Timothy Thornton, Sailaja Vedantam, Rujin Wang, Kuan-Han Wu, Bin Ye, Blair Zhang, Andrey Ziyatdinov, Yuxin Zou, Jingning Zhang, Kyoko Watanabe, Mira Tang, Frank Wendt, Suganthi Balasubramanian, Suying Bao, Kathie Sun, Chuanyi Zhang, Adolfo Ferrando, Giovanni Coppola, Luca A Lotta, Alan Shuldiner, Katherine Siminovitch, Brian Hobbs, Jon Silver, William Palmer, Rita Guerreiro, Amit Joshi, Antoine Baldassari, Cristen Willer, Sarah Graham, Ernst Mayerhofer, Erola Pairo Castineira, Mary Haas, Niek Verweij, George Hindy, Jonas Bovijn, Tanima De, Parsa Akbari, Luanluan Sun, Olukayode Sosina, Arthur Gilly, Peter Dornbos, Juan Rodriguez-Flores, Moeen Riaz, Manav Kapoor, Gannie Tzoneva, Momodou W Jallow, Anna Alkelai, Ariane Ayer, Veera Rajagopal, Sahar Gelfman, Vijay Kumar, Jacqueline Otto, Neelroop Parikshak, Aysegul Guvenek, Jose Bras, Silvia Alvarez, Jessie Brown, Jing He, Hossein Khiabanian, Joana Revez, Kimberly Skead, Valentina Zavala, Jae Soon Sul, Lei Chen, Sam Choi, Amy Damask, Nan Lin, Charles Paulding, Marcus B Jones, Esteban Chen, Michelle G LeBlanc, Jason Mighty, Jennifer Rico-Varela, Nirupama Nishtala, Nadia Rana, Jaimee Hernandez, Alison Fenney, Randi Schwartz, Jody Hankins, Anna Han, Samuel Hart, Ann Perez-Beals, Gina Solari, Johannie Rivera-Picart, Michelle Pagan, Sunilbe Siceron, Adam Buchanan, David J Carey, Christa L Martin, Michelle Meyer, Kyle Retterer, David Rolston, Daniel J Rader, Marylyn D Ritchie, JoEllen Weaver, Nawar Naseer, Giorgio Sirugo, Afiya Poindexter, Yi-An Ko, Kyle P Nerz, Meghan Livingstone, Fred Vadivieso, Stephanie DerOhannessian, Teo Tran, Julia Stephanowski, Salma Santos, Ned Haubein, Joseph Dunn, Anurag Verma, Colleen Morse Kripke, Marjorie Risman, Renae Judy, Colin Wollack, Shefali S Verma, Scott M Damrauer, Yuki Bradford, Scott M Dudek, Theodore G Drivas

Affiliations

¹ Department of Medicine, Renal Electrolyte and Hypertension Division, University of Pennsylvania, Philadelphia, PA, USA.
² Institute of Diabetes Obesity and Metabolism, University of Pennsylvania, Philadelphia, PA, USA.
³ Department of Genetics, University of Pennsylvania, Philadelphia, PA, USA.
⁴ Penn-CHOP Kidney Innovation Center, University of Pennsylvania, Philadelphia, PA, USA.
⁵ Liangzhu Laboratory, Zhejiang University, 1369 West Wenyi Road, Hangzhou, Zhejiang, China.
⁶ Department of Nephrology, Children' Hospital, Zhejiang University School of Medicine, National Clinical Research Center for Child Health, Hangzhou, Zhejiang, China.
⁷ Institute of Molecular Life Sciences, HUN-REN Research Center for Natural Sciences, Budapest, Hungary.
⁸ Department of Molecular Biology, Semmelweis University, Budapest, Hungary.
⁹ Department of Medicine, Federal University of São Paulo, São Paulo, Brazil.
¹⁰ Department of Biostatistics, Vanderbilt University Medical Center, Nashville, TN, USA.
¹¹ Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, TN, USA.
¹² Institute of Clinical Medicine, School of Medicine, National Yang Ming Chiao Tung University, Taipei, Taiwan, ROC.
¹³ Division of Nephrology, Department of Medicine, Taipei Veterans General Hospital, Taipei, Taiwan, ROC.
¹⁴ Institute of Biomedical Sciences, Academia Sinica, Taipei, Taiwan, ROC.
¹⁵ Cardiovascular Center, Taichung Veterans General Hospital, Taichung, Taiwan, ROC.
¹⁶ National Chung Hsing University, Taichung, Taiwan, ROC.
¹⁷ Heart Rhythm Center, Division of Cardiology, Department of Medicine, Taipei Veterans General Hospital, Taipei, Taiwan, ROC.
¹⁸ Department of Internal Medicine, College of Medicine, National Yang Ming Chiao Tung University, Taipei, Taiwan, ROC.
¹⁹ Institute of Medicine, Chung Shan Medical University, Taichung, Taiwan, ROC.
²⁰ Department of Medical Research, Chung Shan Medical University Hospital, Taichung, Taiwan, ROC.
²¹ Division of Endocrinology and Metabolism, Department of Internal Medicine, Kaohsiung Medical University Hospital, Kaohsiung, Taiwan, ROC.
²² School of Medicine, College of Medicine, Kaohsiung Medical University, Kaohsiung, Taiwan, ROC.
²³ Department of Internal Medicine, Kaohsiung Medical University Gangshan Hospital, Kaohsiung, Taiwan, ROC.
²⁴ Institute for Human Genetics, University of California, San Francisco, CA, USA.
²⁵ Division of Nephrology, Department of Medicine, Vagelos College of Physicians & Surgeons, Columbia University, New York, NY, USA.
²⁶ Department of Neurology, School of Medicine, University of Alabama at Birmingham, Birmingham, AL, USA.
²⁷ Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN, USA.
²⁸ Department of Medicine, Division of General Internal Medicine and Center for Health Information Partnerships, Institute for Public Health and Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL, USA.
²⁹ Department of Medicine, Brigham and Women's Hospital, Boston, MA, USA.
³⁰ Institute for Genomic Health, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
³¹ Division of Genomic Medicine, Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
³² Division of General Internal Medicine, Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
³³ Department of Preventive Medicine, Feinberg School of Medicine, Northwestern University, Chicago, IL, USA.
³⁴ The Center for Autoimmune Genomics and Etiology, Division of Human Genetics, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, USA.
³⁵ Center for Applied Genomics, Children's Hospital of Philadelphia, Philadelphia, PA, USA.
³⁶ Departments of Medicine (Medical Genetics) and Genome Sciences, University of Washington, Seattle, WA, USA.
³⁷ Department of Biomedical Informatics, Vagelos College of Physicians & Surgeons, Columbia University, New York, NY, USA.
³⁸ Programs in Metabolism and Medical & Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA.
³⁹ Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA.
⁴⁰ Department of Medicine, Harvard Medical School, Boston, MA, USA.
⁴¹ Division of Endocrinology, Boston Children's Hospital, Boston, MA, USA.
⁴² Department of Biomedical Informatics, University of Colorado School of Medicine, Aurora, CO, USA.
⁴³ Diabetes Unit, Endocrine Division, Department of Medicine, Massachusetts General Hospital, Boston, MA, USA.
⁴⁴ Department of Medicine and Cardiovascular Research Institute, Cardiology Division, University of California, San Francisco, CA, USA.
⁴⁵ Graduate Program in Genomics and Computational Biology, University of Pennsylvania, Philadelphia, PA, USA.
⁴⁶ Vertex Pharmaceuticals, Boston, MA, USA.
⁴⁷ Regeneron Genetics Center, Regeneron Pharmaceuticals, Tarrytown, NY, USA.
⁴⁸ Division of Translational Medicine and Human Genetics, Department of Medicine, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA, USA.
⁴⁹ Division of Nephrology, Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
⁵⁰ The Charles Bronfman Institute of Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
⁵¹ The Hasso Plattner Institute of Digital Health, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
⁵² The Mount Sinai Clinical Intelligence Center, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
⁵³ Colorado Center for Personalized Medicine, University of Colorado Anschutz Medical Campus, Aurora, CO, USA.
⁵⁴ Department of Biomedical Informatics, University of Colorado School of Medicine, University of Colorado Anschutz Medical Campus, Aurora, CO, USA.
⁵⁵ KGJebsen Center for Genetic Epidemiology, Department of Public Health and Nursing, Faculty of Medicine and Health Sciences, NTNU, Norwegian University of Science and Technology, Trondheim, Norway.
⁵⁶ MRC Integrative Epidemiology Unit, Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, United Kingdom.
⁵⁷ Department of Clinical and Molecular Medicine, NTNU, Norwegian University of Science and Technology, Trondheim, Norway.
⁵⁸ BioCore - Bioinformatics Core Facility, Norwegian University of Science and Technology, Trondheim, Norway.
⁵⁹ Department of Endocrinology, Clinic of Medicine, St. Olav's Hospital, Trondheim University Hospital, Trondheim, Norway.
⁶⁰ Clinic of Thoracic and Occupational Medicine, St. Olav's Hospital, Trondheim University Hospital, Trondheim, Norway.
⁶¹ Department of Nephrology, St. Olav's Hospital, Trondheim University Hospital, Trondheim, Norway.
⁶² Department of Endocrine and Metabolic Diseases, Shanghai Institute of Endocrine and Metabolic Diseases, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
⁶³ Shanghai National Clinical Research Center for Metabolic Diseases, Key Laboratory for Endocrine and Metabolic Diseases of the National Health Commission of the PR China, Shanghai Key Laboratory for Endocrine Tumor, Shanghai Digital Medicine Innovation Center, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
⁶⁴ Division of Genetic Medicine, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA.
⁶⁵ Department of Biostatistics and Center for Statistical Genetics, University of Michigan, Ann Arbor, MI, USA.
⁶⁶ Department of Psychiatry, University of Michigan, Ann Arbor, MI, USA.
⁶⁷ Department of Epidemiology, Gillings School of Global Public Health, University of North Carolina, Chapel Hill, NC, USA.
⁶⁸ Institute for Biomedical Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
⁶⁹ Pathology and Laboratory Medicine at the Hospital of the University of Pennsylvania, Philadelphia, PA, USA.
⁷⁰ Division of Epidemiology, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA.
⁷¹ Department of Systems Pharmacology and Translational Therapeutics, University of Pennsylvania, Philadelphia, PA, USA.
⁷² Institute for Translational Medicine and Therapeutics, University of Pennsylvania, Philadelphia, PA, USA.
⁷³ Division of Nephrology and Hypertension, Vanderbilt Center for Kidney Disease, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA.
⁷⁴ VA Tennessee Valley Healthcare System, Clinical Sciences Research and Development, Nashville, TN, USA.

PMID: 39913582
PMCID: PMC12013656
DOI: 10.1126/science.adp4753

Abstract

Kidney dysfunction is a major cause of mortality, but its genetic architecture remains elusive. In this study, we conducted a multiancestry genome-wide association study in 2.2 million individuals and identified 1026 (97 previously unknown) independent loci. Ancestry-specific analysis indicated an attenuation of newly identified signals on common variants in European ancestry populations and the power of population diversity for further discoveries. We defined genotype effects on allele-specific gene expression and regulatory circuitries in more than 700 human kidneys and 237,000 cells. We found 1363 coding variants disrupting 782 genes, with 601 genes also targeted by regulatory variants and convergence in 161 genes. Integrating 32 types of genetic information, we present the "Kidney Disease Genetic Scorecard" for prioritizing potentially causal genes, cell types, and druggable targets for kidney disease.

PubMed Disclaimer

Conflict of interest statement

Competing interests: The laboratory of Dr. Susztak receives funding from GSK, Regeneron, Gilead, Merck, Boehringer Ingelheim, Bayer, Novartis Maze, Jnana, Ventus and Novo Nordisk. The funders had no influence on the data analysis. Dr. Susztak serves on the SAB of Jnana pharmaceuticals and receives equity. Dr. Ritchie serves on the SAB for Goldfinch Bio and Cipherome. The laboratory of Dr. Walunas receives funding from Gilead Sciences. The other authors declare no competing interests.

Figures

**Fig. 1.. eGFRcrea GWAS of 2.2 million individuals.**
**(A)** Manhattan plots of eGFRcrea GWAS of 2,287,877 individuals of multi-ancestry and 1,785,582 individuals of European-ancestry. The x-axis is the chromosomal location of SNP. The y-axis is the strength of association −log10(p value, GWAS meta-analysis) (the scale is capped at 300). Lead SNPs of independent loci are highlighted. Green dots represent loci overlapping or nearby (within 500 kb or LD R² > 0.001) previously reported sentinel variants, and purple represents loci not reported previously. Prioritized genes for new loci are highlighted. **(B)** Fraction of known and new independent loci identified in GWAS of multi-ancestry. **(C)** The number of independent loci with at least one variant validated by eGFRcys and BUN. **(D)** Number of GWAS loci identified in down-sampled GWAS of European-ancestry. The lines represent loci defined by different locus window sizes (35 kb, 50 kb, and 100 kb). (E) Overlap of credible sets by fine-mapping in eGFRcrea GWAS of 2,287,877 individuals of multi-ancestry and 1,785,582 individuals of European-ancestry. **(F)** Enrichment of credible set variants in non-synonymous variants, kidney enhancers, and active promoters. The y-axis is the odds ratio, and the chi-square test significance is presented in each bar. (G) Locus-zoom of credible sets by fine-mapping in the *IRF5* locus in European-ancestry GWAS. The x-axis is the chromosomal location of SNP. The panels from top to bottom show the original GWAS, posterior inclusion probability, credible set 1, credible set 2, and gene annotations. (H) Pleiotropy maps of eGFRcrea and other traits. The x-axis is the number of colocalization loci between eGFRcrea and related GWAS traits listed on the y-axis.

**Fig. 2.. Allele-specific expression (ASE) analysis in tubular and glomerular compartments.**
**(A)** Analytical scheme and Manhattan plots of the ASE analyses in tubule and glomeruli compartments. In the Manhattan plot, the x-axis is the chromosomal location of SNP. The y-axis represents the strength of association −log10(p value, ASE) (the scale is capped at 300). **(B)** Overlap of ASE genes identified in tubule and glomerular compartments using RASQUAL. **(C)** Number of ASE genes identified by RASQUAL and eQTL genes identified by MatrixeQTL in the same dataset (left panel) and fraction of genes validated by AlleleDB ASEs (right panel). **(D)** Locus-zoom of eGFRcrea GWAS, tubule ASE, glomeruli ASE, and kidney eQTL in *GATM* locus. The y-axis represents the strength of association −log10(p value). PP.H4 is the posterior probability of colocalization between ASE and eGFRcrea GWAS. **(E)** Number of eGFRcrea-associated variants with ASEs/eQTLs (left panel) and the fraction of ASE-only/eQTL-only variants in fine-mapping credible sets of eGFRcrea GWAS (right panel). **(F)** Number of genes whose ASEs/eQTLs are colocalized with eGFRcrea GWAS (left panel) and the fraction of ASE-only/eQTL-only genes reported as nephropathy genes (right panel). **(G)** Locus-zoom of eGFRcrea GWAS, tubule ASE, glomeruli ASE, and kidney eQTL in the *SLC22A2* locus. The y-axis represents the strength of association −log10(p value). PP.H4 is the posterior probability of colocalization between ASE/eQTL and eGFRcrea GWAS.

**Fig. 3.. Allele-specific accessibility analysis in human kidneys.**
**(A)** Analytical workflow and Manhattan plot of the bASA analyses in human kidneys. Manhattan plot: the x-axis is the chromosomal location of SNP, and the y-axis is the strength of association −log10(p value, bASA). **(B)** Enrichment of bASA peaks in kidney chromatin states which were estimated using histone modifications. The x-axis is the log2-transformed odds ratio. The y-axis is enrichment significance −log10(p value). **(C)** Association of effect sizes between kidney bASAs (x-axis) and meQTLs (y-axis) (Spearman correlation test). **(E)** Prioritization of GWAS variants and loci using bASAs in conjunction with ASEs and meQTLs. The upper panel represents the analytical workflow to identify bASA and GWAS variants associated with open chromatin and kidney function. The middle panel shows the fraction of bASA and GWAS variants overlapped with ASEs and/or meQTLs. The middle panel also shows the fraction of bASA and GWAS colocalization loci, which also colocalized with ASEs and/or meQTLs. **(F)** Regulatory elements and locus-zoom of eGFRcrea GWAS, kidney bASA, meQTL, and tubule ASE in *USP24* locus. Chromatin states are represented by different colors. In the locus-zoom plots, the y-axis is the strength of association −log10(p value). PP.H4 is the posterior probability of colocalization between eGFRcrea GWAS and molecular traits.

**Fig. 4.. Single cell-level allele-specific accessibility analysis in human kidneys.**
**(A)** Analytical workflow of snASA analyses in human kidney cell types. **(B)** Number of cells (x-axis) and snASA SNPs (y-axis) identified in each cell type. **(C)** Fraction of snASA SNPs within open chromatin peaks. **(D)** Cell type-specific and shared snASA SNPs (rows) across kidney cell types (columns). The color represents the effect size of ASA from low (blue) to high (red). GWAS variants were marked in the right column. **(E)** Fraction of cell type-specific and shared snASA SNPs validated by bASA. The significance of shared snASA SNPs with bASA was calculated using the chi-squared test. **(F)** Association of effect sizes between PT snASAs (x-axis) and kidney bASAs (y-axis). The correlation of effect size was calculated by the Spearman correlation test. **(G)** Enrichment of cell type-specific snASA SNPs on TFBSs. White block; non-significant. Yellow to red indicates the significance of TF enrichment (enrichment score > 3 and p value < 3.68×10⁻⁵, Bonferroni’s threshold accounting for 1,358 tests of 7 cell types and 194 TFs). **(H)** Fraction of snASA & GWAS variants overlapped with TF binding sites. **(I)** Examples of snASA & GWAS variants overlapped with cell type-specific open chromatin peaks and TFBS.

**Fig. 5.. Human kidney single cell multi-omics Open4Gene associations link GWAS variants to target genes.**
**(A)** Analytical workflow of single-nucleus multiome (RNA+ATAC) and Open4Gene analyses in human kidney cells. **(B)** Number of cells (x-axis) and Open4Gene links (y-axis) identified in each cell type. **(C)** Fraction of open chromatin peaks targeting unique genes and multiple genes. **(D)** Prioritization of target genes of GWAS variants using Open4Gene links. **(E)** eGFRcrea GWAS strength (y-axis) and distance from GWAS variant to Open4Gene peaks (x-axis). **(F)** The number of GWAS variants identified for each gene using Open4Gene links. The y-axis is the number of GWAS variants, and the x-axis shows the genes with a given number of GWAS variants. Top genes and known kidney disease genes were labeled. **(G)** Locus-zoom of eGFRcrea GWAS, Open4Gene links, and regulatory elements in the *LRP2* locus. In the locus-zoom plot, the y-axis is the strength of association −log10(p value). Chromatin states are represented by different colors. The tracks on the left panel show the open chromatin in each cell type, and the right panel shows the expression of *LRP2* in each cell type.

**Fig. 6.. Kidney Disease Genetic Scorecard indicates the convergence of coding and regulatory variants in GWAS loci.**
**(A)** Analytical workflow of prioritization of eGFRcrea GWAS variants by integrating kidney multi-omics. **(B)** Prioritization of regulatory variants by integrating kidney multi-omics. The left y-axis is the number (bar chart) of non-coding variants prioritized using each prioritization score cutoff on the x-axis. The right y-axis shows the fraction (red dot plot) of variants targeting unique genes and the fraction (blue dot plot) of validated nephropathy genes prioritized by each prioritization score cutoff on the x-axis. The gray dashed line shows the prioritization threshold that covers all validated nephropathy genes. **(C)** The number of eGFRcrea GWAS variants located in different genome features (left panel), and enrichment test with log-transformed odds ratio on the x-axis and significance labeled for each feature (right panel). **(D)** Fraction of coding GWAS variants annotated as pLOF, missense, and synonymous. **(E)** Number of eGFRcrea GWAS loci prioritized by regulatory variants (prioritization score ≥ 10) and coding variants. **(F)** Number of genes validated by eGFRcys/BUN, with the convergence of coding and regulatory variants, cell type-specific expression, and targeted by FDA-approved drugs. **(G)** Kidney Disease Genetic Scorecard of genes with convergence of coding and regulatory variants (prioritization score ≥ 12). This figure only shows the genes with the highest expression and open chromatin on their regulatory variants in the same cell type. The full list is available in table S25.

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References

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Kidney multiome-based genetic scorecard reveals convergent coding and regulatory variants

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Kidney multiome-based genetic scorecard reveals convergent coding and regulatory variants

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