Harnessing publicly available genetic data to prioritize lipid modifying therapeutic targets for prevention of coronary heart disease based on dysglycemic risk

Tragante, Vinicius and Asselbergs, Folkert W. and Swerdlow, Daniel I. and Palmer, Tom M. and Moore, Jason H. and Bakker, Paul I. W. and Keating, Brendan J. and Holmes, Michael V. (2016) Harnessing publicly available genetic data to prioritize lipid modifying therapeutic targets for prevention of coronary heart disease based on dysglycemic risk. Human Genetics, 135 (5). pp. 453-467. ISSN 1432-1203

[img]
Preview
PDF (art%3A10.1007%2Fs00439-016-1647-9)
art_3A10.1007_2Fs00439_016_1647_9.pdf - Published Version
Available under License Creative Commons Attribution.

Download (1MB)

Abstract

Therapeutic interventions that lower LDL-cholesterol effectively reduce the risk of coronary artery disease (CAD). However, statins, the most widely prescribed LDL-cholesterol lowering drugs, increase diabetes risk. We used genome-wide association study (GWAS) data in the public domain to investigate the relationship of LDL-C and diabetes and identify loci encoding potential drug targets for LDL-cholesterol modification without causing dysglycemia. We obtained summary-level GWAS data for LDL-C from GLGC, glycemic traits from MAGIC, diabetes from DIAGRAM and CAD from CARDIoGRAMplusC4D consortia. Mendelian randomization analyses identified a one standard deviation (SD) increase in LDL-C caused an increased risk of CAD (odds ratio [OR] 1.63 (95 % confidence interval [CI] 1.55, 1.71), which was not influenced by removing SNPs associated with diabetes. LDL-C/CAD-associated SNPs showed consistent effect directions (binomial P = 6.85 × 10−5). Conversely, a 1-SD increase in LDL-C was causally protective of diabetes (OR 0.86; 95 % CI 0.81, 0.91), however LDL-cholesterol/diabetes-associated SNPs did not show consistent effect directions (binomial P = 0.15). HMGCR, our positive control, associated with LDL-C, CAD and a glycemic composite (derived from GWAS meta-analysis of four glycemic traits and diabetes). In contrast, PCSK9, APOB, LPA, CETP, PLG, NPC1L1 and ALDH2 were identified as “druggable” loci that alter LDL-C and risk of CAD without displaying associations with dysglycemia. In conclusion, LDL-C increases the risk of CAD and the relationship is independent of any association of LDL-C with diabetes. Loci that encode targets of emerging LDL-C lowering drugs do not associate with dysglycemia, and this provides provisional evidence that new LDL-C lowering drugs (such as PCSK9 inhibitors) may not influence risk of diabetes.

Item Type:
Journal Article
Journal or Publication Title:
Human Genetics
Additional Information:
© The Author(s) 2016. This article is published with open access at Springerlink.com
Uncontrolled Keywords:
/dk/atira/pure/subjectarea/asjc/2700/2716
Subjects:
ID Code:
78545
Deposited By:
Deposited On:
07 Mar 2016 10:32
Refereed?:
Yes
Published?:
Published
Last Modified:
03 Apr 2020 03:17