Genetic Correlations Between Diabetes and Glaucoma: An Analysis of Continuous and Dichotomous Phenotypes

Vincent Laville; Jae H Kang; Clara C Cousins; Adriana I Iglesias; Réka Nagy; Jessica N Cooke Bailey; Robert P Igo Jr; Yeunjoo E Song; Daniel I Chasman; William G Christen; Peter Kraft; Bernard A Rosner; Frank Hu; James F Wilson; Puya Gharahkhani; Alex W Hewitt; David A Mackey; Pirro G Hysi; Christopher J Hammond; Cornelia M vanDuijn; Jonathan L Haines; Veronique Vitart; John H Fingert; Michael A Hauser; Hugues Aschard; Janey L Wiggs; Anthony P Khawaja; Stuart MacGregor; Louis R Pasquale; UK Biobank; International Glaucoma Genetics Consortium; NEIGHBORHOOD Consortium

doi:10.1016/j.ajo.2019.05.015

Genetic Correlations Between Diabetes and Glaucoma: An Analysis of Continuous and Dichotomous Phenotypes

Am J Ophthalmol. 2019 Oct:206:245-255. doi: 10.1016/j.ajo.2019.05.015. Epub 2019 May 20.

Authors

Vincent Laville¹, Jae H Kang², Clara C Cousins³, Adriana I Iglesias⁴, Réka Nagy⁵, Jessica N Cooke Bailey⁶, Robert P Igo Jr⁷, Yeunjoo E Song⁶, Daniel I Chasman⁸, William G Christen⁸, Peter Kraft⁹, Bernard A Rosner¹⁰, Frank Hu¹¹, James F Wilson¹², Puya Gharahkhani¹³, Alex W Hewitt¹⁴, David A Mackey¹⁵, Pirro G Hysi¹⁶, Christopher J Hammond¹⁶, Cornelia M vanDuijn¹⁷, Jonathan L Haines⁶, Veronique Vitart⁵, John H Fingert¹⁸, Michael A Hauser¹⁹, Hugues Aschard²⁰, Janey L Wiggs³, Anthony P Khawaja²¹, Stuart MacGregor¹³, Louis R Pasquale²²; UK Biobank; International Glaucoma Genetics Consortium; NEIGHBORHOOD Consortium

Affiliations

¹ Department of Computational Biology, Institut Pasteur, Paris, France.
² Channing Division of Network Medicine, Harvard Medical School, Brigham and Women's Hospital, Boston, Massachusetts, USA.
³ Department of Ophthalmology, Massachusetts Eye and Ear Infirmary, Harvard Medical School, Boston, Massachusetts, USA.
⁴ Departments of Ophthalmology and Clinical Genetics, Erasmus Medical Center, Rotterdam, The Netherlands.
⁵ MRC Human Genetics Unit, Institute of Genetics and Molecular Medicine, University of Edinburgh, Edinburgh, United Kingdom.
⁶ Department of Population and Quantitative Health Sciences, Case Western Reserve University School of Medicine, Cleveland, Ohio, USA; Institute for Computational Biology, Case Western Reserve University School of Medicine, Cleveland, Ohio, USA.
⁷ Department of Population and Quantitative Health Sciences, Case Western Reserve University School of Medicine, Cleveland, Ohio, USA.
⁸ Division of Preventive Medicine, Harvard Medical School, Brigham and Women's Hospital, Boston, Massachusetts, USA.
⁹ Department of Epidemiology, Harvard T. H. Chan School of Public Health, Harvard Medical School, Boston, Massachusetts, USA; Department of Biostatistics, Harvard T. H. Chan School of Public Health, Harvard Medical School, Boston, Massachusetts, USA.
¹⁰ Channing Division of Network Medicine, Harvard Medical School, Brigham and Women's Hospital, Boston, Massachusetts, USA; Department of Biostatistics, Harvard T. H. Chan School of Public Health, Harvard Medical School, Boston, Massachusetts, USA.
¹¹ Department of Epidemiology, Harvard T. H. Chan School of Public Health, Harvard Medical School, Boston, Massachusetts, USA; Department of Nutrition, Harvard T. H. Chan School of Public Health, Harvard Medical School, Boston, Massachusetts, USA.
¹² MRC Human Genetics Unit, Institute of Genetics and Molecular Medicine, University of Edinburgh, Edinburgh, United Kingdom; Centre for Global Health Research, Usher Institute for Population Health Sciences and Informatics, University of Edinburgh, Edinburgh, United Kingdom.
¹³ Statistical Genetics, QIMR Berghofer Medical Research Institute, Brisbane, Australia.
¹⁴ Centre for Eye Research Australia, University of Melbourne, Royal Victorian Eye and Ear Hospital, East Melbourne, Australia; School of Medicine, Menzies Institute for Medical Research, University of Tasmania, Hobart, Tasmania, Australia.
¹⁵ Lions Eye Institute, Centre for Ophthalmology and Visual Science, University of Western Australia, Perth, Western Australia, Australia.
¹⁶ Department of Twin Research and Genetic Epidemiology, King's College London, United Kingdom.
¹⁷ Departments of Ophthalmology and Clinical Genetics, Erasmus Medical Center, Rotterdam, The Netherlands; Nuffield Department of Population Health, University of Oxford, Oxford, United Kingdom.
¹⁸ Department of Ophthalmology and Visual Science, University of Iowa, Iowa City, Iowa, USA.
¹⁹ Departments of Ophthalmology and Medicine, Duke University, Durham, North Carolina, USA.
²⁰ Department of Computational Biology, Institut Pasteur, Paris, France; Department of Epidemiology, Harvard T. H. Chan School of Public Health, Harvard Medical School, Boston, Massachusetts, USA.
²¹ Department of Public Health and Primary Care, Institute of Public Health, University of Cambridge School of Clinical Medicine, Cambridge, United Kingdom.
²² Channing Division of Network Medicine, Harvard Medical School, Brigham and Women's Hospital, Boston, Massachusetts, USA; Department of Ophthalmology, Icahn School of Medicine at Mount Sinai, New York, New York, USA. Electronic address: [email protected].

Abstract

Purpose: A genetic correlation is the proportion of phenotypic variance between traits that is shared on a genetic basis. Here we explore genetic correlations between diabetes- and glaucoma-related traits.

Design: Cross-sectional study.

Methods: We assembled genome-wide association study summary statistics from European-derived participants regarding diabetes-related traits like fasting blood sugar (FBS) and type 2 diabetes (T2D) and glaucoma-related traits (intraocular pressure [IOP], central corneal thickness [CCT], corneal hysteresis [CH], corneal resistance factor [CRF], cup-to-disc ratio [CDR], and primary open-angle glaucoma [POAG]). We included data from the National Eye Institute Glaucoma Human Genetics Collaboration Heritable Overall Operational Database, the UK Biobank, and the International Glaucoma Genetics Consortium. We calculated genetic correlation (r_g) between traits using linkage disequilibrium score regression. We also calculated genetic correlations between IOP, CCT, and select diabetes-related traits based on individual level phenotype data in 2 Northern European population-based samples using pedigree information and Sequential Oligogenic Linkage Analysis Routines.

Results: Overall, there was little r_g between diabetes- and glaucoma-related traits. Specifically, we found a nonsignificant negative correlation between T2D and POAG (r_g = -0.14; P = .16). Using Sequential Oligogenic Linkage Analysis Routines, the genetic correlations between measured IOP, CCT, FBS, fasting insulin, and hemoglobin A1c were null. In contrast, genetic correlations between IOP and POAG (r_g ≥ 0.45; P ≤ 3.0 × 10^-4) and between CDR and POAG were high (r_g = 0.57; P = 2.8 × 10^-10). However, genetic correlations between corneal properties (CCT, CRF, and CH) and POAG were low (r_g range -0.18 to 0.11) and nonsignificant (P ≥ .07).

Conclusion: These analyses suggest that there is limited genetic correlation between diabetes- and glaucoma-related traits.

Publication types

Multicenter Study
Research Support, N.I.H., Extramural
Research Support, Non-U.S. Gov't

MeSH terms

Adolescent
Adult
Aged
Aged, 80 and over
Cross-Sectional Studies
Diabetes Mellitus, Type 2 / epidemiology
Diabetes Mellitus, Type 2 / genetics*
Europe / epidemiology
Female
Genome-Wide Association Study / methods*
Glaucoma, Open-Angle / epidemiology
Glaucoma, Open-Angle / genetics*
Glaucoma, Open-Angle / physiopathology
Humans
Incidence
Intraocular Pressure / physiology*
Male
Middle Aged
Pedigree
Phenotype
Tonometry, Ocular
United States / epidemiology
Young Adult

Abstract

Publication types

MeSH terms

Grants and funding