Abstract

BackgroundCoronary artery disease (CAD), and one of its intermediate risk factors, dyslipidemia, possess a demonstrable genetic component, although the genetic architecture is incompletely defined. We previously reported a linkage peak on chromosome 5q31-33 for early-onset CAD where the strength of evidence for linkage was increased in families with higher mean low density lipoprotein-cholesterol (LDL-C). Therefore, we sought to fine-map the peak using association mapping of LDL-C as an intermediate disease-related trait to further define the etiology of this linkage peak. The study populations consisted of 1908 individuals from the CATHGEN biorepository of patients undergoing cardiac catheterization; 254 families (N = 827 individuals) from the GENECARD familial study of early-onset CAD; and 162 aorta samples harvested from deceased donors. Linkage disequilibrium-tagged SNPs were selected with an average of one SNP per 20 kb for 126.6-160.2 MB (region of highest linkage) and less dense spacing (one SNP per 50 kb) for the flanking regions (117.7-126.6 and 160.2-167.5 MB) and genotyped on all samples using a custom Illumina array. Association analysis of each SNP with LDL-C was performed using multivariable linear regression (CATHGEN) and the quantitative trait transmission disequilibrium test (QTDT; GENECARD). SNPs associated with the intermediate quantitative trait, LDL-C, were then assessed for association with CAD (i.e., a qualitative phenotype) using linkage and association in the presence of linkage (APL; GENECARD) and logistic regression (CATHGEN and aortas).ResultsWe identified four genes with SNPs that showed the strongest and most consistent associations with LDL-C and CAD: EBF1, PPP2R2B, SPOCK1, and PRELID2. The most significant results for association of SNPs with LDL-C were: EBF1, rs6865969, p = 0.01; PPP2R2B, rs2125443, p = 0.005; SPOCK1, rs17600115, p = 0.003; and PRELID2, rs10074645, p = 0.0002). The most significant results for CAD were EBF1, rs6865969, p = 0.007; PPP2R2B, rs7736604, p = 0.0003; SPOCK1, rs17170899, p = 0.004; and PRELID2, rs7713855, p = 0.003.ConclusionUsing an intermediate disease-related quantitative trait of LDL-C we have identified four novel CAD genes, EBF1, PRELID2, SPOCK1, and PPP2R2B. These four genes should be further examined in future functional studies as candidate susceptibility loci for cardiovascular disease mediated through LDL-cholesterol pathways.

Highlights

  • Coronary artery disease (CAD), and one of its intermediate risk factors, dyslipidemia, possess a demonstrable genetic component, the genetic architecture is incompletely defined

  • Using ordered subset analysis (OSA) to dissect genetic heterogeneity and using lipid levels as quantitative traits, we found that the evidence for linkage on chromosome 5q was increased in families with higher mean total and low density lipoprotein-cholesterol (LDL-C) [10]

  • Family-based association resulted in 32 SNPs in 17 distinct genes that were significantly associated with LDL-C (Additional File 1 Table S1), with the four most significant SNPs residing in the gene PRELID2, (PRELI domain containing 2 isoform), which were all in linkage disequilibrium with each other (D’ ranging from 0.85-0.95)

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Summary

Introduction

Coronary artery disease (CAD), and one of its intermediate risk factors, dyslipidemia, possess a demonstrable genetic component, the genetic architecture is incompletely defined. SNPs associated with the intermediate quantitative trait, LDL-C, were assessed for association with CAD (i.e., a qualitative phenotype) using linkage and association in the presence of linkage (APL; GENECARD) and logistic regression (CATHGEN and aortas). There are many well-established risk factors for CAD that are partitioned between extrinsic (smoking, sedentary lifestyle, poor nutrition) and intrinsic (sex, age, lipid levels, hypertension) factors, each of which may have underlying genetic components, making it difficult to divide CAD risk into genetic and non-genetic factors. Using these intermediate disease-related intrinsic factors as genetic traits may help to identify novel CAD genetic loci

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