Meta-Analysis

. 2024 May 24;15(1):4417.

doi: 10.1038/s41467-024-48394-y.

Validation of human telomere length multi-ancestry meta-analysis association signals identifies POP5 and KBTBD6 as human telomere length regulation genes

Rebecca Keener¹, Surya B Chhetri¹, Carla J Connelly², Margaret A Taub³, Matthew P Conomos⁴, Joshua Weinstock¹, Bohan Ni⁵, Benjamin Strober⁶, Stella Aslibekyan⁷, Paul L Auer⁸, Lucas Barwick⁹, Lewis C Becker¹⁰, John Blangero¹¹, Eugene R Bleecker^{12

13}, Jennifer A Brody¹⁴, Brian E Cade^{15

16}, Juan C Celedon¹⁷, Yi-Cheng Chang^{18

19

20}, L Adrienne Cupples^{21

22}, Brian Custer^{23

24}, Barry I Freedman²⁵, Mark T Gladwin²⁶, Susan R Heckbert²⁷, Lifang Hou²⁸, Marguerite R Irvin²⁹, Carmen R Isasi³⁰, Jill M Johnsen³¹, Eimear E Kenny^{32

33}, Charles Kooperberg³⁴, Ryan L Minster³⁵, Take Naseri^{36

37}, Satupa'itea Viali^{38

39

40}, Sergei Nekhai⁴¹, Nathan Pankratz⁴², Patricia A Peyser⁴³, Kent D Taylor⁴⁴, Marilyn J Telen⁴⁵, Baojun Wu⁴⁶, Lisa R Yanek⁴⁷, Ivana V Yang⁴⁸, Christine Albert^{49

50}, Donna K Arnett⁵¹, Allison E Ashley-Koch⁴⁵, Kathleen C Barnes⁵², Joshua C Bis¹⁴, Thomas W Blackwell^{53

54}, Eric Boerwinkle⁵⁵, Esteban G Burchard^{56

57}, April P Carson⁵⁸, Zhanghua Chen⁵⁹, Yii-Der Ida Chen⁴⁴, Dawood Darbar⁶⁰, Mariza de Andrade⁶¹, Patrick T Ellinor⁶², Myriam Fornage⁶³, Bruce D Gelb⁶⁴, Frank D Gilliland⁵⁹, Jiang He⁶⁵, Talat Islam⁵⁹, Stefan Kaab⁶⁶, Sharon L R Kardia⁶⁷, Shannon Kelly^{23

68}, Barbara A Konkle⁶⁹, Rajesh Kumar^{70

71}, Ruth J F Loos³², Fernando D Martinez⁷², Stephen T McGarvey⁷³, Deborah A Meyers^{12

13}, Braxton D Mitchell⁷⁴, Courtney G Montgomery⁷⁵, Kari E North⁷⁶, Nicholette D Palmer⁷⁷, Juan M Peralta¹¹, Benjamin A Raby^{78

79}, Susan Redline^{15

16}, Stephen S Rich⁸⁰, Dan Roden⁸¹, Jerome I Rotter⁴⁴, Ingo Ruczinski³, David Schwartz⁸², Frank Sciurba⁸³, M Benjamin Shoemaker⁸⁴, Edwin K Silverman¹⁵, Moritz F Sinner⁶⁶, Nicholas L Smith⁶⁵, Albert V Smith⁸⁵, Hemant K Tiwari⁸⁶, Ramachandran S Vasan⁸⁷, Scott T Weiss^{15

49}, L Keoki Williams⁴⁶, Yingze Zhang⁸⁸, Elad Ziv⁵⁶, Laura M Raffield⁸⁹, Alexander P Reiner³⁴; NHLBI Trans-Omics for Precision Medicine (TOPMed) Consortium; TOPMed Hematology and Hemostasis Working Group; TOPMed Structural Variation Working Group; Marios Arvanitis⁹⁰, Carol W Greider^{91

92}, Rasika A Mathias⁹³, Alexis Battle^{94

95

96

97

98}

Affiliations

¹ Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, USA.
² Department of Molecular Biology and Genetics, Johns Hopkins University, Baltimore, MD, USA.
³ Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA.
⁴ Department of Biostatistics, School of Public Health, University of Washington, Seattle, WA, USA.
⁵ Department of Computer Science, Johns Hopkins University, Baltimore, MD, USA.
⁶ Department of Epidemiology, Harvard School of Public Health, Boston, MA, USA.
⁷ University of Alabama at Birmingham, Birmingham, AL, USA.
⁸ Division of Biostatistics, Institute for Health & Equity, and Cancer Center, Medical College of Wisconsin, Milwaukee, WI, USA.
⁹ LTRC Data Coordinating Center, The Emmes Company, LLC, Rockville, MD, USA.
¹⁰ GeneSTAR Research Program, Department of Medicine, Johns Hopkins School of Medicine, Baltimore, MD, USA.
¹¹ Department of Human Genetics and South Texas Diabetes and Obesity Institute, University of Texas Rio Grande Valley School of Medicine, Brownsville, TX, USA.
¹² Department of Medicine, Division of Genetics, Genomics and Precision Medicine, University of Arizona, Tucson, AZ, USA.
¹³ Division of Pharmacogenomics, University of Arizona, Tucson, AZ, USA.
¹⁴ Cardiovascular Health Research Unit, Department of Medicine, University of Washington, Seattle, WA, USA.
¹⁵ Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital, Boston, MA, USA.
¹⁶ Division of Sleep Medicine, Harvard Medical School, Boston, MA, USA.
¹⁷ Division of Pediatric Pulmonary Medicine, University of Pittsburgh, Pittsburgh, PA, USA.
¹⁸ Department of Internal Medicine, National Taiwan University, Taipei, Taiwan.
¹⁹ Graduate Institute of Medical Genomics and Proteomics, National Taiwan University, Taipei, Taiwan.
²⁰ Institute of Biomedical Sciences, Academia Sinica, Taipei, Taiwan.
²¹ Department of Biostatistics, Boston University School of Public Health, Boston, MA, USA.
²² The National Heart, Lung, and Blood Institute, Boston University's Framingham Heart Study, Framingham, MA, USA.
²³ Vitalant Research Institute, San Francisco, CA, USA.
²⁴ Department of Laboratory Medicine, University of California San Francisco, San Francisco, CA, USA.
²⁵ Internal Medicine - Nephrology, Wake Forest University School of Medicine, Winston-Salem, NC, USA.
²⁶ School of Medicine, University of Maryland, Baltimore, MD, USA.
²⁷ Department of Epidemiology, University of Washington, Seattle, WA, USA.
²⁸ Department of Preventive Medicine, Feinberg School of Medicine, Northwestern University, Evanston, IL, USA.
²⁹ Department of Epidemiology, University of Alabama Birmingham, Birmingham, AL, USA.
³⁰ Department of Epidemiology and Population Health, Albert Einstein College of Medicine, Bronx, NY, USA.
³¹ Department of Medicine and Institute for Stem Cell & Regenerative Medicine, University of Washington, Seattle, WA, USA.
³² The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
³³ Center for Genomic Health, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
³⁴ Division of Public Health Sciences, Fred Hutchinson Cancer Center, Seattle, WA, USA.
³⁵ Department of Human Genetics, University of Pittsburgh Graduate School of Public Health, Pittsburgh, PA, USA.
³⁶ Naseri & Associates Public Health Consultancy Firm and Family Health Clinic, Apia, Samoa.
³⁷ International Health Institute, School of Public Health, Brown University, Providence, RI, USA.
³⁸ Oceania University of Medicine, Apia, Samoa.
³⁹ School of Medicine, National University of Samoa, Apia, Samoa.
⁴⁰ Department of Chronic Disease Epidemiology, Yale University School of Public Health, New Haven, CT, USA.
⁴¹ Center for Sickle Cell Disease and Department of Medicine, College of Medicine, Howard University, Washington DC, USA.
⁴² Department of Laboratory Medicine and Pathology, University of Minnesota, Minneapolis, MN, USA.
⁴³ Department of Epidemiology, School of Public Health, University of Michigan, Ann Arbor, MI, USA.
⁴⁴ The Institute for Translational Genomics and Population Sciences, Department of Pediatrics, The Lundquist Institute for Biomedical Innovation at Harbor-UCLA Medical Center, Torrance, CA, USA.
⁴⁵ Department of Medicine, Duke University Medical Center, Durham, NC, USA.
⁴⁶ Center for Individualized and Genomic Medicine Research (CIGMA), Department of Internal Medicine, Henry Ford Health System, Detroit, MI, USA.
⁴⁷ Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, USA.
⁴⁸ Departments of Biomedical Informatics, Medicine, and Epidemiology, University of Colorado, Boulder, CO, USA.
⁴⁹ Harvard Medical School, Boston, MA, USA.
⁵⁰ Division of Cardiovascular, Brigham and Women's Hospital, Boston, MA, USA.
⁵¹ Department of Epidemiology and Biostatistics, University of South Carolina, Columbia, SC, USA.
⁵² Department of Medicine, University of Colorado Denver, Anschutz Medical Campus, Aurora, CO, USA.
⁵³ Department of Biostatistics, University of Michigan School of Public Health, Ann Arbor, MI, USA.
⁵⁴ Center for Statistical Genetics, University of Michigan School of Public Health, Ann Arbor, MI, USA.
⁵⁵ Human Genetics Center, Department of Epidemiology, Human Genetics, and Environmental Sciences, School of Public Health, University of Texas Health Science Center at Houston, Houston, TX, USA.
⁵⁶ Department of Medicine, University of California San Francisco, San Francisco, CA, USA.
⁵⁷ Department of Bioengineering and Therapeutic Sciences, University of California San Francisco, San Francisco, CA, USA.
⁵⁸ Department of Medicine, University of Mississippi Medical Center, Jackson, MI, USA.
⁵⁹ Department of Population and Public Health Sciences, University of Southern California, Los Angeles, CA, USA.
⁶⁰ Division of Cardiology, University of Illinois at Chicago, Chicago, IL, USA.
⁶¹ Division of Biomedical Statistics and Informatics, Mayo Clinic, Rochester, MN, USA.
⁶² Cardiovascular Disease Initiative, The Broad Institute of MIT and Harvard, Cambridge, MA, USA.
⁶³ Institute of Molecular Medicine, McGovern Medical School, the University of Texas Health Science Center at Houston, Houston, TX, USA.
⁶⁴ Mindich Child Health and Development Institute and Departments of Pediatrics and Genetics and Genomic Sciences, Icahn School of Medicine, New York, NY, USA.
⁶⁵ Department of Medicine, Tulane University School of Medicine, New Orleans, LA, USA.
⁶⁶ Department of Cardiology, University Hospital, LMU Munich, Munich, Germany.
⁶⁷ Department of Epidemiology, University of Michigan School of Public Health, Ann Arbor, MI, USA.
⁶⁸ University of California San Francisco Benioff Children's Hospital, Oakland, CA, USA.
⁶⁹ Department of Medicine, University of Washington, Seattle, WA, USA.
⁷⁰ Northwestern University Feinberg School of Medicine, Chicago, IL, USA.
⁷¹ The Ann and Robert H. Lurie Children's Hospital of Chicago, Chicago, IL, USA.
⁷² Asthma & Airway Disease Research Center, University of Arizona, Tucson, AZ, USA.
⁷³ Department of Epidemiology & International Health Institute, Brown University School of Public Health, Providence, RI, USA.
⁷⁴ Department of Medicine, University of Maryland School of Medicine, Baltimore, MD, USA.
⁷⁵ Genes and Human Disease, Oklahoma Medical Research Foundation, Oklahoma City, OK, USA.
⁷⁶ Department of Epidemiology, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.
⁷⁷ Department of Biochemistry, Wake Forest University School of Medicine, Winston-Salem, NC, USA.
⁷⁸ Division of Pulmonary and Critical Care, Brigham and Women's Hospital, Boston, MA, USA.
⁷⁹ Division of Pulmonary Medicine, Boston Children's Hospital, Boston, MA, USA.
⁸⁰ Center for Public Health Genomics, Department of Public Health Sciences, University of Virginia, Charlottesville, VA, USA.
⁸¹ Department of Medicine, Vanderbilt University School of Medicine, Nashville, TN, USA.
⁸² Departments of Medicine and Immunology, University of Colorado, Boulder, CO, USA.
⁸³ Division of Pulmonary, Allergy and Critical Care Medicine, University of Pittsburgh, Pittsburgh, PA, USA.
⁸⁴ Departments of Medicine, Pharmacology, and Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN, USA.
⁸⁵ Department of Biostatistics, University of Michigan, Ann Arbor, MI, USA.
⁸⁶ Department of Biostatistics, University of Alabama Birmingham, Birmingham, AL, USA.
⁸⁷ Department of Medicine, Boston University School of Medicine, Boston, MA, USA.
⁸⁸ Division of Pulmonary Allergy and Critical Care Medicine, University of Pittsburgh, Pittsburgh, PA, USA.
⁸⁹ Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.
⁹⁰ Department of Medicine, Division of Cardiology, Johns Hopkins University, Baltimore, MD, USA.
⁹¹ Department of Molecular Cell and Developmental Biology, University of California Santa Cruz, Santa Cruz, CA, USA.
⁹² University Professor Johns Hopkins University, Baltimore, MD, USA.
⁹³ Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, USA. rmathias@jhmi.edu.
⁹⁴ Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, USA. ajbattle@jhu.edu.
⁹⁵ Department of Computer Science, Johns Hopkins University, Baltimore, MD, USA. ajbattle@jhu.edu.
⁹⁶ Department of Genetic Medicine, Johns Hopkins University, Baltimore, MD, USA. ajbattle@jhu.edu.
⁹⁷ Malone Center for Engineering in Healthcare, Johns Hopkins University, Baltimore, MD, USA. ajbattle@jhu.edu.
⁹⁸ Data Science and AI Institute, Johns Hopkins University, Baltimore, MD, USA. ajbattle@jhu.edu.

PMID: 38789417
PMCID: PMC11126610
DOI: 10.1038/s41467-024-48394-y

Meta-Analysis

Validation of human telomere length multi-ancestry meta-analysis association signals identifies POP5 and KBTBD6 as human telomere length regulation genes

Rebecca Keener et al. Nat Commun. 2024.

. 2024 May 24;15(1):4417.

doi: 10.1038/s41467-024-48394-y.

Authors

Affiliations

¹ Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, USA.
² Department of Molecular Biology and Genetics, Johns Hopkins University, Baltimore, MD, USA.
³ Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA.
⁴ Department of Biostatistics, School of Public Health, University of Washington, Seattle, WA, USA.
⁵ Department of Computer Science, Johns Hopkins University, Baltimore, MD, USA.
⁶ Department of Epidemiology, Harvard School of Public Health, Boston, MA, USA.
⁷ University of Alabama at Birmingham, Birmingham, AL, USA.
⁸ Division of Biostatistics, Institute for Health & Equity, and Cancer Center, Medical College of Wisconsin, Milwaukee, WI, USA.
⁹ LTRC Data Coordinating Center, The Emmes Company, LLC, Rockville, MD, USA.
¹⁰ GeneSTAR Research Program, Department of Medicine, Johns Hopkins School of Medicine, Baltimore, MD, USA.
¹¹ Department of Human Genetics and South Texas Diabetes and Obesity Institute, University of Texas Rio Grande Valley School of Medicine, Brownsville, TX, USA.
¹² Department of Medicine, Division of Genetics, Genomics and Precision Medicine, University of Arizona, Tucson, AZ, USA.
¹³ Division of Pharmacogenomics, University of Arizona, Tucson, AZ, USA.
¹⁴ Cardiovascular Health Research Unit, Department of Medicine, University of Washington, Seattle, WA, USA.
¹⁵ Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital, Boston, MA, USA.
¹⁶ Division of Sleep Medicine, Harvard Medical School, Boston, MA, USA.
¹⁷ Division of Pediatric Pulmonary Medicine, University of Pittsburgh, Pittsburgh, PA, USA.
¹⁸ Department of Internal Medicine, National Taiwan University, Taipei, Taiwan.
¹⁹ Graduate Institute of Medical Genomics and Proteomics, National Taiwan University, Taipei, Taiwan.
²⁰ Institute of Biomedical Sciences, Academia Sinica, Taipei, Taiwan.
²¹ Department of Biostatistics, Boston University School of Public Health, Boston, MA, USA.
²² The National Heart, Lung, and Blood Institute, Boston University's Framingham Heart Study, Framingham, MA, USA.
²³ Vitalant Research Institute, San Francisco, CA, USA.
²⁴ Department of Laboratory Medicine, University of California San Francisco, San Francisco, CA, USA.
²⁵ Internal Medicine - Nephrology, Wake Forest University School of Medicine, Winston-Salem, NC, USA.
²⁶ School of Medicine, University of Maryland, Baltimore, MD, USA.
²⁷ Department of Epidemiology, University of Washington, Seattle, WA, USA.
²⁸ Department of Preventive Medicine, Feinberg School of Medicine, Northwestern University, Evanston, IL, USA.
²⁹ Department of Epidemiology, University of Alabama Birmingham, Birmingham, AL, USA.
³⁰ Department of Epidemiology and Population Health, Albert Einstein College of Medicine, Bronx, NY, USA.
³¹ Department of Medicine and Institute for Stem Cell & Regenerative Medicine, University of Washington, Seattle, WA, USA.
³² The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
³³ Center for Genomic Health, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
³⁴ Division of Public Health Sciences, Fred Hutchinson Cancer Center, Seattle, WA, USA.
³⁵ Department of Human Genetics, University of Pittsburgh Graduate School of Public Health, Pittsburgh, PA, USA.
³⁶ Naseri & Associates Public Health Consultancy Firm and Family Health Clinic, Apia, Samoa.
³⁷ International Health Institute, School of Public Health, Brown University, Providence, RI, USA.
³⁸ Oceania University of Medicine, Apia, Samoa.
³⁹ School of Medicine, National University of Samoa, Apia, Samoa.
⁴⁰ Department of Chronic Disease Epidemiology, Yale University School of Public Health, New Haven, CT, USA.
⁴¹ Center for Sickle Cell Disease and Department of Medicine, College of Medicine, Howard University, Washington DC, USA.
⁴² Department of Laboratory Medicine and Pathology, University of Minnesota, Minneapolis, MN, USA.
⁴³ Department of Epidemiology, School of Public Health, University of Michigan, Ann Arbor, MI, USA.
⁴⁴ The Institute for Translational Genomics and Population Sciences, Department of Pediatrics, The Lundquist Institute for Biomedical Innovation at Harbor-UCLA Medical Center, Torrance, CA, USA.
⁴⁵ Department of Medicine, Duke University Medical Center, Durham, NC, USA.
⁴⁶ Center for Individualized and Genomic Medicine Research (CIGMA), Department of Internal Medicine, Henry Ford Health System, Detroit, MI, USA.
⁴⁷ Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, USA.
⁴⁸ Departments of Biomedical Informatics, Medicine, and Epidemiology, University of Colorado, Boulder, CO, USA.
⁴⁹ Harvard Medical School, Boston, MA, USA.
⁵⁰ Division of Cardiovascular, Brigham and Women's Hospital, Boston, MA, USA.
⁵¹ Department of Epidemiology and Biostatistics, University of South Carolina, Columbia, SC, USA.
⁵² Department of Medicine, University of Colorado Denver, Anschutz Medical Campus, Aurora, CO, USA.
⁵³ Department of Biostatistics, University of Michigan School of Public Health, Ann Arbor, MI, USA.
⁵⁴ Center for Statistical Genetics, University of Michigan School of Public Health, Ann Arbor, MI, USA.
⁵⁵ Human Genetics Center, Department of Epidemiology, Human Genetics, and Environmental Sciences, School of Public Health, University of Texas Health Science Center at Houston, Houston, TX, USA.
⁵⁶ Department of Medicine, University of California San Francisco, San Francisco, CA, USA.
⁵⁷ Department of Bioengineering and Therapeutic Sciences, University of California San Francisco, San Francisco, CA, USA.
⁵⁸ Department of Medicine, University of Mississippi Medical Center, Jackson, MI, USA.
⁵⁹ Department of Population and Public Health Sciences, University of Southern California, Los Angeles, CA, USA.
⁶⁰ Division of Cardiology, University of Illinois at Chicago, Chicago, IL, USA.
⁶¹ Division of Biomedical Statistics and Informatics, Mayo Clinic, Rochester, MN, USA.
⁶² Cardiovascular Disease Initiative, The Broad Institute of MIT and Harvard, Cambridge, MA, USA.
⁶³ Institute of Molecular Medicine, McGovern Medical School, the University of Texas Health Science Center at Houston, Houston, TX, USA.
⁶⁴ Mindich Child Health and Development Institute and Departments of Pediatrics and Genetics and Genomic Sciences, Icahn School of Medicine, New York, NY, USA.
⁶⁵ Department of Medicine, Tulane University School of Medicine, New Orleans, LA, USA.
⁶⁶ Department of Cardiology, University Hospital, LMU Munich, Munich, Germany.
⁶⁷ Department of Epidemiology, University of Michigan School of Public Health, Ann Arbor, MI, USA.
⁶⁸ University of California San Francisco Benioff Children's Hospital, Oakland, CA, USA.
⁶⁹ Department of Medicine, University of Washington, Seattle, WA, USA.
⁷⁰ Northwestern University Feinberg School of Medicine, Chicago, IL, USA.
⁷¹ The Ann and Robert H. Lurie Children's Hospital of Chicago, Chicago, IL, USA.
⁷² Asthma & Airway Disease Research Center, University of Arizona, Tucson, AZ, USA.
⁷³ Department of Epidemiology & International Health Institute, Brown University School of Public Health, Providence, RI, USA.
⁷⁴ Department of Medicine, University of Maryland School of Medicine, Baltimore, MD, USA.
⁷⁵ Genes and Human Disease, Oklahoma Medical Research Foundation, Oklahoma City, OK, USA.
⁷⁶ Department of Epidemiology, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.
⁷⁷ Department of Biochemistry, Wake Forest University School of Medicine, Winston-Salem, NC, USA.
⁷⁸ Division of Pulmonary and Critical Care, Brigham and Women's Hospital, Boston, MA, USA.
⁷⁹ Division of Pulmonary Medicine, Boston Children's Hospital, Boston, MA, USA.
⁸⁰ Center for Public Health Genomics, Department of Public Health Sciences, University of Virginia, Charlottesville, VA, USA.
⁸¹ Department of Medicine, Vanderbilt University School of Medicine, Nashville, TN, USA.
⁸² Departments of Medicine and Immunology, University of Colorado, Boulder, CO, USA.
⁸³ Division of Pulmonary, Allergy and Critical Care Medicine, University of Pittsburgh, Pittsburgh, PA, USA.
⁸⁴ Departments of Medicine, Pharmacology, and Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN, USA.
⁸⁵ Department of Biostatistics, University of Michigan, Ann Arbor, MI, USA.
⁸⁶ Department of Biostatistics, University of Alabama Birmingham, Birmingham, AL, USA.
⁸⁷ Department of Medicine, Boston University School of Medicine, Boston, MA, USA.
⁸⁸ Division of Pulmonary Allergy and Critical Care Medicine, University of Pittsburgh, Pittsburgh, PA, USA.
⁸⁹ Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.
⁹⁰ Department of Medicine, Division of Cardiology, Johns Hopkins University, Baltimore, MD, USA.
⁹¹ Department of Molecular Cell and Developmental Biology, University of California Santa Cruz, Santa Cruz, CA, USA.
⁹² University Professor Johns Hopkins University, Baltimore, MD, USA.
⁹³ Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, USA. rmathias@jhmi.edu.
⁹⁴ Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, USA. ajbattle@jhu.edu.
⁹⁵ Department of Computer Science, Johns Hopkins University, Baltimore, MD, USA. ajbattle@jhu.edu.
⁹⁶ Department of Genetic Medicine, Johns Hopkins University, Baltimore, MD, USA. ajbattle@jhu.edu.
⁹⁷ Malone Center for Engineering in Healthcare, Johns Hopkins University, Baltimore, MD, USA. ajbattle@jhu.edu.
⁹⁸ Data Science and AI Institute, Johns Hopkins University, Baltimore, MD, USA. ajbattle@jhu.edu.

PMID: 38789417
PMCID: PMC11126610
DOI: 10.1038/s41467-024-48394-y

Abstract

Genome-wide association studies (GWAS) have become well-powered to detect loci associated with telomere length. However, no prior work has validated genes nominated by GWAS to examine their role in telomere length regulation. We conducted a multi-ancestry meta-analysis of 211,369 individuals and identified five novel association signals. Enrichment analyses of chromatin state and cell-type heritability suggested that blood/immune cells are the most relevant cell type to examine telomere length association signals. We validated specific GWAS associations by overexpressing KBTBD6 or POP5 and demonstrated that both lengthened telomeres. CRISPR/Cas9 deletion of the predicted causal regions in K562 blood cells reduced expression of these genes, demonstrating that these loci are related to transcriptional regulation of KBTBD6 and POP5. Our results demonstrate the utility of telomere length GWAS in the identification of telomere length regulation mechanisms and validate KBTBD6 and POP5 as genes affecting telomere length regulation.

PubMed Disclaimer

Conflict of interest statement

The authors declare the following competing interests: Juan C. Celedon received inhaled steroids from Merck for an NIH_funded study, unrelated to this work. Ivana V. Yang is a consultant for Eleven P15, a company focused on the early diagnosis and treatment of lung fibrosis. Dr. Patrick T. Ellinor receives sponsored research support from Bayer AG, IBM Research, Bristol Myers Squibb and Pfizer; he has also served on advisory boards or consulted for Bayer AG, MyoKardia and Novartis. Dr. David Schwartz is a founder and chief scientific officer of Eleven P15, a company focused on the early diagnosis and treatment of lung fibrosis. Laura M. Raffield is a consultant for the TOPMed Admistrative Coordinating Center (through Westat). Alexis Battle is a shareholder in Alphabet, Inc.; consultant for Third Rock Ventures, LLC; and founder of CellCipher, Inc. The views expressed in this manuscript are those of the authors and do not necessarily represent the views of the National Heart, Lung, and Blood Institute; the National Institutes of Health; or the U.S. Department of Health and Human Services. The remaining authors declare no competing interests.

Figures

**Fig. 1. Multi-ancestry meta-analysis of leukocyte telomere length identifies 5 novel signals.**
Manhattan plot showing the results from the GWAS meta-analysis. SNPs with p-value < 0.1 are plotted. The novel signals are shown in blue. Red line indicates genome-wide significance after multiple testing correction (p < 5 × 10⁻⁸). The blue horizontal line indicates a suggestive threshold (p < 5 × 10⁻⁵). N = 211,369 individuals. The inset pie chart displays the proportion of broad ancestry groups used in the meta-analysis (Supplementary Data 1 and Source Data are provided as a Source Data file).

**Fig. 2. Fine-mapping analyses nominate putative causal variants and genes affecting telomere length.**
A Colocalization events between a meta-analysis signal and a QTL for any gene across QTL datasets. B Colocalization of meta-analysis signals with any gene QTL in any cell type. C Manhattan plots for the meta-analysis signal near rs10111287 colored by r² with the lead SNP (black diamond) and *VIRMA* eQTLs in three GTEx tissues. Colocalization results for each eQTL with the meta-analysis signal are indicated in the top right corner. Colocalization analysis between the eQTLs suggests there are shared causal SNPs: thyroid eQTL with stomach eQTL PPH3 = 0.090 PPH4 = 0.906, thyroid eQTL with whole blood eQTL PPH3 = 0.144 PPH4 = 0.745, stomach eQTL with whole blood eQTL PPH3 = 0.190 PPH4 = 0.655. D Manhattan plot for the meta-analysis signal near rs7193541 colored by r² with the lead SNP (black diamond) and *RFWD3* sQTL. Colocalization results for the QTL with the meta-analysis signal are in the top right corner. In the LeafCutter splicing cluster diagram gray boxes represent the *RFWD3* exons involved in the splicing cluster, the central exon is exon 14 (hg38: chr16:74630780-74630957). Curved lines represent the average number of reads spanning each exon-exon junction across individuals. Thinner, purple curves represent lower expressed exon-exon junctions and thicker, pink/red curves represent higher expressed exon-exon junctions. The vertical gray line indicates the location of the lead SNP. The line at the bottom shows the linear base pair position of each exon and intron depicted in the plots. TT N = 167, TC N = 236, and CC N = 80. E. Manhattan plot showing the SuSiE 95% credible sets for the signal led by rs12637184. Credible set 1 (black diamonds, 10 SNPs) and credible set 2 (black squares, 4 SNPs). r² is calculated with respect to the lead SNP. Source data are provided as a Source Data file.

Fig. 3. Enrichment analysis of transcription factor binding sites of transcription factors with roles in telomere length regulation highlights a PAX5 binding site near *PSMB4.*
A Enrichment of 95% credible set SNPs across all transcription factors with ChIP-seq data available from ENCODE using a one-sided binomial test (Methods). Red points represent transcription factors with known roles in regulating telomere length maintainence (TLM) genes and blue points represent transcription factors with known roles in the alternative telomere lengthening (ALT) pathway. There were 320 transcription factors plotted (28 red, 8 blue, 284 gray). There were 18 transcription factors that fall at the (0,0) coordinate that are not plotted for the sake of clarity; one (XRCC3) had known roles in ALT. A complete list of transcription factors is provided in Supplementary Data 9 and source data are provided as a Source Data file. B ChIP-seq data for the indicated DNA binding factor (red) or histone mark (blue) was generated by ENCODE and downloaded as bigwig files from the UCSC genome browser. The gene structure and genomic coordinates are depicted below the ChIP-seq data.

**Fig. 4. *TCL1A* 95% credible set SNPs are more strongly associated with telomere length in older individuals.**
A, B Manhattan plot for the region around rs2296312 (red diamond) using (A) summary statistics from TOPMed Pooled GWAS (B) summary statistics from age and genotype interaction GWAS. The log10(p-value) for the interaction covariate is plotted on the y-axis. C, D 95% confidence interval for the effect size estimate is shown and the size of the data point reflects the standard error. C Effect size estimate for rs2296312 (tested, minor allele = C) across age groups from the age-stratified GWAS. D Effect size estimate for rs2296312 across ancestry groups from ancestry-stratified GWAS. European MAC = 16,443; Black/ African MAC = 19,963; Asian MAC = 5,683; Hispanic/Latino MAC = 18,019. E Manhattan plots for the rs2296312 (black diamond) locus in age-stratified GWAS. Color indicates r² calculated with respect to rs2296312. Source data for Fig. 4 are provided as a Source Data file.

**Fig. 5. Blood and immune cells are a key cell type for telomere length.**
A Hierarchical clustering of the enrichment of meta-analysis lead SNPs in predicted states using the Roadmap Epigenomics 25 state chromHMM model (p-values from a one-sided binomial test). Dark red cells indicate the strongest enrichment, largely in predicted state 3: PromD1 (Promoter Downstream TSS 1) and largely for rows corresponding to Blood and T-cell samples. B, C Stratified LDSC was conducted on 130,246 meta-analyzed European individuals ^, using the 1000 Genomes European linkage disequilibrium reference panel. Each dot represents a cell type assigned to the broader tissue categories specified on the x-axis by Finucane et al. 2018. The gray dotted line represents the significance threshold of FDR < 0.05 at –log10(p-value) = 2.75. Source data for Fig. 5 are provided as a Source Data file.

**Fig. 6. Overexpression of *POP5* or *KBTBD6* increases telomere length in HeLa-FRT cells.**
*KBTBD6*, *POP5*, or *GFP* was constitutively overexpressed from the CMV promoter in HeLa-FRT cells using the FLP-in system. A, C Telomere Southern blots showing the bulk telomere length from a population of cells. Molecular weight standards were run alongside the samples and their size is indicated in kilobases (kb). Three time points are shown for each clone and the estimated number of population doublings (PD) for each timepoint are indicated below the Southern blot. Each clone has the opportunity to form a distinct starting telomere length distribution which is why the first timepoint for some clones appear to have distinct telomere length distributions, for example the starting timepoint for the *POP5* clones compared to the *GFP* clones. All transfection experiments began from the same population of HeLa-FRT cells. Three biological replicates/clones for each overexpression gene were tested and the trends shown here were consistent across all clones in all cases. B, D The Southern blot densitometry was analyzed using ImageQuant TL to generate line plots of the pixel density. The software estimated the median telomere length (orange bar) as the pixels with greatest density and estimated a molecular weight for that position taking into account the molecular weight standards on both sides of the gel. The ImageQuant TL line plots (Supplementary Fig. 7) were used to estimate the minimum (purple triangle) and maximum (red triangle) telomere lengths in the bulk telomere band. A simulated diagram in the bottom left of the plot representing the ImageQuant TL plots is provided as a guide for the source of these values. The y-axis is plotted on a log10 scale to better estimate how linear DNA moves through an agarose gel at a rate inversely proportional to its length. Source data for Fig. 6 are provided as a Source Data file.

**Fig. 7. CRISPR removal of *KBTBD6* and *POP5* regulatory regions reduced expression of each gene.**
A Knock-out N = 9, control N = 17. One-sided t test p-value = 0.047 (*). Boxplot center is the mean and box bounds represent the 25th and 75th percentiles. Source data are provided as a Source Data file. B A Manhattan plot of the 99% SuSiE credible set colored by r² with the lead SNP. Black diamonds: SNPs in credible set 1. Black boxes: SNPs in credible set 2. Source data are provided as a Source Data file. C ATAC-seq peak regions are represented as boxes. Points above the plot area represent SNPs 99% credible set (red = rs9525462). NK cell = natural killer cell. D Roadmap chromatin ChIP-seq for hg19 chr13:41768158-41769095 (yellow). Samples: E044, E039, and E047. E, F Boxplot center is the mean and box bounds represent the 25th and 75th percentiles. E Knock-out N = 20, control N = 11. One-sided t test p-value = 0.003037 (**). Source data are provided as a Source Data file. F Knock-out N = 18, control N = 9. One-sided t test p-value = 2.093 × 10⁻⁵ (***). Source data are provided as a Source Data file.

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References

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Validation of human telomere length multi-ancestry meta-analysis association signals identifies POP5 and KBTBD6 as human telomere length regulation genes

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Validation of human telomere length multi-ancestry meta-analysis association signals identifies POP5 and KBTBD6 as human telomere length regulation genes

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