A promising strategy for inferring function from a locus is to search for overlap with GWAS loci for other traits. We systematically screened the 58 loci in Table 1 for overlap with GWAS hits for classical risk factors of CHD or MI (lipids, blood pressure/hypertension, diabetes-related phenotypes) and added information from the CARDIoGRAMplusC4D consortium [18▪▪], which also tested for overlap with genetic variants for established risk factors (Table 1). As summarized in Fig. 1, we found that GWAS loci for CHD and MI overlap with 14 loci for lipids (24% of all risk loci), six loci for blood pressure/hypertension (10%), one locus for diabetes mellitus (2%), and two loci with at least two risk factors (4%). Thirty-five (60%) loci did not co-segregate with loci of classical risk factors but out of these, six overlapped with loci from seemingly unrelated GWAS (Table 1; Supplementary material).
Another approach to get insights into function is to investigate the effects of the genotype on mRNA expression of genes at GWAS loci and to map eQTLs. This might be particularly helpful to identify the culprit gene(s) at loci harboring many genes. Testing cis-regulation of a genetic variant at a genome-wide level requires large cohorts where transcriptome-wide mRNA expression has been assayed in each individual and where genome-wide SNP data are also available. Folkersen et al. have systematically tested lead SNPs from GWAS of CHD and MI and found evidence for cis-regulation at five loci in different vascular tissues and liver samples (Chr1p13.3: SORT1, PSRC1, CELSR2; Chr2q33.2: NBEAL1; Chr3q22.3: MRAS; Chr6q25.1: MTHFD1L; Chr21q22.11: SLC5A3). Wild et al. performed a comparable analysis using mRNA expression data from monocytes of 1494 individuals from a population-based study  and found three eQTLs (Chr1p13.3: PSRC1; Chr2q33.2: WDR12; Chr10q23.31: LIPA). Results at Chr2q33.2 are particularly interesting since expression analysis in different tissues apparently led to different findings. A similar approach was taken by the C4D consortium, which systematically tested for eQTLs at newly identified loci [9▪]. A current limitation of this very promising approach is the limited availability of large cohorts with tissue collections for transcriptome-wide expression analysis.
Chr6p24.1 is the second most often identified GWAS hit for CHD and MI. The locus was found in European, Asian, and Middle Eastern populations and therefore appears to be relevant across ethnicities [9▪,10▪▪,15,16,18▪▪,19]. Chr6p24.1 is also associated with coronary calcification . Until now, virtually nothing is known about the mechanism of Chr6p24.1 in atherogenesis. The region contains a single gene, protein phosphatase and actin regulator 1 (PHACTR1), spanning a very large genomic distance of ∼500 kb, and extending over three haplotype blocks (Fig. 2b). Lead SNPs for CHD and MI are in the proximal haplotype block and the same SNPs were independently identified in a GWAS for migraine . Intriguingly, alleles conferring migraine susceptibility were also associated with risk for CHD suggesting a common pathophysiology. The distal haplotype block of PHACTR1 also contains hits in the GWAS catalogue (www.genome.gov/gwastudies; accessed May 2013; ), originating from a 100k GWAS for femoral neck width in females of the Framingham Heart Study  and a linkage study for pulse pressure in 63 Chinese sib-pairs  (Fig. 2b). However, these findings have not been firmly replicated and their significance is still unclear. In addition, these SNPs are ∼300-kb apart and seemingly unrelated to the lead atherosclerosis SNPs, speaking against a causal relation.
The Chr1p13.3 locus has been discovered in the first surge of GWAS for CHD even before it was also identified as one of the top GWAS hits for plasma LDL cholesterol concentrations [84–86]. Genetic variation at the locus is associated with reduced plasma LDL-cholesterol and reduced risk of coronary artery disease [10▪▪,25,45] suggesting that Chr1p13.3 exerts its effect on atherosclerosis by modulating LDL-cholesterol levels. The lead SNPs of CHD and LDL-cholesterol are located in a haplotype block encoding three genes, cadherin EGF LAG seven-pass G-type receptor 2 (CELSR2), proline/serine-rich coiled-coil 1 (PSRC1), and myosin binding protein H-like (MYBPHL) (Fig. 2c). Wild et al. found differential expression of PSRC1 in monocytes at the locus . The majority of functional work, however, has focused on sortilin 1 (SORT1), which is located in a haplotype block distal of PSRC1, CELSR2, and MYBPHL (Fig. 2c) containing GWAS hits for major depressive disorder  and chronic kidney disease . Schadt et al. and Folkersen et al. found that mRNA expression of CELSR2, PSRC1, and SORT1 were all strongly associated with Chr1p13.3 in liver. Although SORT1 was highly expressed in many tissues, genotype-dependent differential regulation was only seen in liver . Musunuru et al. identified a SNP in linkage disequilibrium with the lead SNP, creating a C/EBP transcription factor binding site in the 3’ UTR of CELSR2 and altering expression of SORT1. These data suggested that SORT1 expression might be affected by cis-regulation through the neighboring haplotype block .
Until now, the majority of work on the molecular mechanism at Chr1p13.3 has clearly focused on SORT1. Very little is known about the functions of PSRC1, CELSR2, and MYBPHL, which are closer to the lead Chr1p13.3 SNPs. More work is clearly warranted to establish or firmly exclude a role of these genes in lipid metabolism and atherogenesis.
Current GWAS have added additional loci to the ‘genomic landscape’ of CHD and MI bringing the total count to 58 at a significance cutoff of P < 10−7. Recent advances in functional characterization of some loci promise the discovery of hitherto unknown pathways influencing atherosclerosis risk. One such example is the most replicated locus on Chr9p21.3, which might influence atherogenesis through epigenetic chromatin modification by the long ncRNA ANRIL. Nevertheless, our current understanding of potential causal variants and mechanisms at most GWAS loci of atherosclerotic cardiovascular disease is very limited. Although some of these loci co-segregate with known risk factors suggesting a potential causal relation, the majority is still ‘terra incognita’. This is exemplified by the second most frequently found locus on Chr6p24.1, where virtually nothing is known about its function in atherogenesis. Owing to their small effect size, the utility of genetic variants for diagnostic purposes is limited. The major promise of identified GWAS loci therefore lies in understanding their function in atherogenesis as a prerequisite for later therapeutic targeting.
Papers of particular interest, published within the annual period of review, have been highlighted as:
Additional references related to this topic can also be found in the Current World Literature section in this issue (p. 456).
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