TY - JOUR
T1 - Genetic Correlations Between Diabetes and Glaucoma
T2 - An Analysis of Continuous and Dichotomous Phenotypes
AU - NEIGHBORHOOD Consortium
AU - UK Biobank
AU - International Glaucoma Genetics Consortium
AU - Laville, Vincent
AU - Kang, J. H.
AU - Cousins, Clara C.
AU - Iglesias, Adriana I.
AU - Nagy, Réka
AU - Cooke Bailey, Jessica N.
AU - Igo, Robert P.
AU - Song, Yeunjoo E.
AU - Chasman, Daniel I.
AU - Christen, William G.
AU - Kraft, P.
AU - Rosner, Bernard A.
AU - Hu, F.
AU - Wilson, James F.
AU - Gharahkhani, Puya
AU - Hewitt, Alex W.
AU - Mackey, David A.
AU - Hysi, Pirro G.
AU - Hammond, Christopher J.
AU - vanDuijn, Cornelia M.
AU - Haines, Jonathan L.
AU - Vitart, Veronique
AU - Fingert, John H.
AU - Hauser, Michael A.
AU - Aschard, Hugues
AU - Wiggs, Janey L.
AU - Khawaja, Anthony P.
AU - MacGregor, Stuart
AU - Pasquale, Louis R.
N1 - Publisher Copyright:
© 2019 Elsevier Inc.
PY - 2019/10
Y1 - 2019/10
N2 - 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 (rg) 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 rg between diabetes- and glaucoma-related traits. Specifically, we found a nonsignificant negative correlation between T2D and POAG (rg = −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 (rg ≥ 0.45; P ≤ 3.0 × 10−4) and between CDR and POAG were high (rg = 0.57; P = 2.8 × 10−10). However, genetic correlations between corneal properties (CCT, CRF, and CH) and POAG were low (rg 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.
AB - 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 (rg) 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 rg between diabetes- and glaucoma-related traits. Specifically, we found a nonsignificant negative correlation between T2D and POAG (rg = −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 (rg ≥ 0.45; P ≤ 3.0 × 10−4) and between CDR and POAG were high (rg = 0.57; P = 2.8 × 10−10). However, genetic correlations between corneal properties (CCT, CRF, and CH) and POAG were low (rg 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.
UR - http://www.scopus.com/inward/record.url?scp=85070893270&partnerID=8YFLogxK
U2 - 10.1016/j.ajo.2019.05.015
DO - 10.1016/j.ajo.2019.05.015
M3 - Article
C2 - 31121135
AN - SCOPUS:85070893270
SN - 0002-9394
VL - 206
SP - 245
EP - 255
JO - American Journal of Ophthalmology
JF - American Journal of Ophthalmology
ER -