Genome-wide association study of medication-use and associated disease in the UK Biobank

Genome-wide association studies (GWASs) of medication use may contribute to understanding of disease etiology, could generate new leads relevant for drug discovery and can be used to quantify future risk of medication taking. Here, we conduct GWASs of self-reported medication use from 23 medication categories in approximately 320,000 individuals from the UK Biobank. A total of 505 independent genetic loci that meet stringent criteria (P < 10−8/23) for statistical significance are identified. We investigate the implications of these GWAS findings in relation to biological mechanism, potential drug target identification and genetic risk stratification of disease. Amongst the medication-associated genes are 16 known therapeutic-effect target genes for medications from 9 categories. Two of the medication classes studied are for disorders that have not previously been subject to large GWAS (hypothyroidism and gastro-oesophageal reflux disease).


Genome-wide association study (GWAS) on 23 medication-taking traits
Case-control GWAS of medication use GWAS results and biological mechanism Linking genes associated with medication-taking to drug targets Putative causal relationship of diseases for using medication Shared genetic architecture between medication-taking traits and relevant complex traits Identification of putative causal relationship for 23 medication-taking traits using Mendelian randomization (Fig.

and Supplementary Data 8)
Estimation of genetic correlation with complex traits or diseases using bivariate LD score regression ( Fig. 4

and Supplementary Data 7)
SNP-heritability were estimated using univariate linkage disequilibrium (LD) score regression (Supplementary Fig. 6 and Supplementary Odds ratio for taking medication as function of the exposure "genetic risk" (Fig. 3   The number outside the outermost circle is chromosome number. The code (corresponding to the nearest endocentric circle) between each circle is the medication trait ATC code.
Each circle represents a medication trait (except the outermost circle).
The number (corresponding to the nearest endocentric circle) between each circle is the -log 10 (P) for the most significant SNP.
Red dots represent SNP with P value < 1e-8. Figure 6. The SNP-based heritability of medication-taking trait. Texts on the right side represent each medication-taking trait. Error bar represent the upper 95% confidence interval for the observed SNP-based heritability of each medication-taking trait. The significant level were labelled as an asterisk after 23 tests (P < 0.05/23). The SNP-based heritability were estimated from linkage disequilibrium (LD) score regression 1 .

Supplementary Table 5. The SNP-based heritability (h 2 ) of the 23 medication-taking traits, representing the proportion of variance of the case/control individuals in the UK Biobank attributable to genome-wide SNPs.
a The h 2 on the sample scale has phenotypic variance approximately P * (1 -P), where P is the proportion of the sample that takes the medication. We note that h 2 increases with P. Transformation to the liability scale assumes that the population risk of taking the medication, K is the same as the sample risk P, K = P.
The transformation to the liability scale assumes an underlying normally distributed phenotype of liability to medication-taking. IDE (Entrez ID: 3416) was associated with taking A10 medications (diabetes drugs) in our analyses and the inhibitor of insulin-degrading enzyme (encoded by IDE) has shown anti-diabetic activity (Majanti et al, 2014). AGT (Entrez ID: 183) was associated with taking C07 (beta-blockers) and C09 (reninangiotensin agents) and a liver-selective angiotensinogen inhibitor from a recent study shows improvements in efficacy and tolerability on hypertension (Mullick et al, 2017). Since inhibition of insulin-degrading enzyme (encoded by IDE) and angiotensinogen (encoded by AGT) showed medication category relevant therapeutic effects, we also used the CLUE Touchstone tool (https://clue.io/touchstone) (Subramanian et al, 2017) to check the correlation between signatures of medication-taking from their corresponding medication category and knocking down the IDE and AGT gene in different cell lines. IDE and AGT showed a high correlation (using the criterion of Connectivity score > 90 as recommended by CLUE Connectopedia) with corresponding medication categories (above), suggesting that these genes may be involved in how these medications exert effect or in the etiology of medication-related diseases.