Genome-Wide Association Analysis of High-Density Lipoprotein Cholesterol in the Population-Based KORA Study Sheds New Light on Intergenic Regions

Abstract

Background— High-density lipoprotein cholesterol (HDLC) is a strong risk factor for atherosclerosis and is assumed to be under considerable genetic control. We aimed to identify gene regions that influence HDLC levels by a genome-wide association analysis in the population-based KORA (Cooperative Health Research in the Region of Augsburg) study. Methods and Results— In KORA S3/F3 (n=1643), we analyzed 377 865 quality-checked single-nucleotide polymorphisms (SNPs; 500K, Affymetrix, Santa Clara, Calif), complemented by the publicly available genome-wide association results from the Diabetes Genetics Initiative (n=2631) and by replication data from KORA S4 (n=4037) and the Copenhagen City Heart Study (n=9205). Among the 13 SNPs selected from the KORA S3/F3 500K probability value list, 3 showed consistent associations in subsequent replications: 1 SNP 10 kb upstream of CETP (pooled probability value=8.5�10 −27 ), 1 SNP approximately 40 kb downstream of LIPG (probability value=4.67�10 −10 ), both independent of previously reported SNPs, and 1 from an already reported region of LPL (probability value=2.82�10 −11 ). Bioinformatical analyses indicate a potential functional relevance of the respective SNPs. Conclusions— The present genome-wide association study identified 2 interesting HDLC-relevant regions upstream of CETP and downstream of LIPG . This draws attention to the importance of long-range effects of intergenic regions, which have been underestimated so far, and may impact future candidate-gene–association studies toward extending the region analyzed. Furthermore, the present study reinforced CETP and LPL as HDLC genes and thereby underscores the power of this type of genome-wide association approach to pinpoint associations of common polymorphisms with effects explaining as little as 0.5% of the HDLC variance in the general population.