Pharmacogenetic GWAS meta-analysis of LDL cholesterol response to statins (original) (raw)

Abstract

ABSTRACT Abstracts from the 2014 Annual Scientific Meeting of the BHS (doi:10.1038/jhh.2014.60) Pharmacogenetic GWAS meta-analysis of LDL cholesterol response to statins Purpose: Statins are widely prescribed for the prevention and treatment of cardiovascular disease with great proven effectiveness of 20-30%. However, it is also established that there is inter-individual variability in LDL-C response to statins, which may be partly due to pharmacogenetic variation. The only genetic variants consistently reported from previous studies are located within the APOE and LPA gene regions. To determine whether additional loci may influence LDL-C response to statins, we formed the Genomic Investigation of Statin Therapy (GIST) consortium and conducted a pharmacogenetic meta-analysis of genome-wide association studies of LDL-C response to statins, which is the largest, most comprehensive study of its kind conducted to date. Methods: The meta-analysis comprises GWAS datasets from both randomized controlled trials (RCTs) and observational studies, including approximately 40,000 statin-treated subjects overall, divided into a first discovery stage and a second validation stage. The response variable analysed is the difference between the natural log transformed LDL-C levels on- and off-treatment, and is adjusted for baseline LDL-C, in order to eliminate the confounded effect of association between the genetic variant and baseline LDL-C levels. Further adjustment was made for the type and dose of statins used, in order to combine several different types of statins across the contributing trials and within the observational studies. Results: Overall, at genome-wide significant level, we have identified two new loci: SORT1/CELSR2/PSRC1 (rs646776, β =-0.013, SE=0.002, P=1.05x10-9) and SLCO1B1 (rs2900478, β=0.016, SE=0.003, P=1.22x10-9), which have not been previously identified in GWAS. Furthermore we have successfully confirmed the known associations with APOE (rs445925, β=-0.043, SE=0.005, P=1.58 x 10-18) and LPA (rs10455872, β=-0.059, SE=0.006, P=1.95 x 10-11). Our results were further investigated and validated with additional functional analyses, such as conditional, eQTL and pathway analyses. For example, the genome-wide conditional analysis highlighted 14 independent SNPs explaining 5% of the variance, of which 6 SNPs reached genome-wide significance in our combined meta-analysis. Collectively our functional and pathway analysis confirmed a strong biological and functional role in statin response for several strongly associated gene loci, including APOE, and SORT1/CELSR2/PSRC1. Furthermore, a genetic risk score including our 4 lead SNPs was significantly associated with coronary disease risk in the CARDIoGRAM and C4D consortium. Conclusions: Our findings advance the understanding of the pharmacogenetic architecture of statin response, and illustrate that SNPs with modest effect on LDL response to these widely used drugs can influence coronary artery disease risk.

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