Investigating genetic risk factors of coronary artery disease using genome editing

Genome-wide association studies (GWAS) have identified the genetic loci associated with many complex diseases including coronary artery disease (CAD). The challenge now is to elucidate the biological and cellular pathways affected by disease-associated loci. In order to fully understand the functional mechanisms, the causal genetic variants need to be identified. The majority of GWAS loci lack candidate genes, and may instead be located in regulatory regions, making the functional effects of specific variants difficult to appreciate. Recently, genome editing techniques have become available that allow targeted alteration of the genome, producing isogenic cell lines that differ only at the site of interest. In this study, recombinant adeno-associated virus (rAAV) genome editing was established and used to investigate potentially functional disease-associated variants in the 1p13 and 9p21 CAD loci. Evidence from previous work suggests that 1p13 (rs12740374) and 9p21 (rs10811656 and rs10757278) single nucleotide polymorphisms (SNPs) affect transcription factor binding, leading to dysregulation of local genes. Specific alteration of these SNPs using this technique enabled the examination of these hypotheses directly. The 1p13 study has provided evidence to support the hypothesis that rs12740374 is the causal SNP at this locus. We observed genotype-dependent effects upon C/EBPα binding and the expression of four 1p13 genes (SORT1, CELSR2, PSRC1 and MYBPHL). Our examination of the 9p21 SNPs showed that these variants are capable of influencing STAT1 binding, but local gene expression was not affected. This suggests that variation of just rs10811656 and rs10757278 is insufficient to affect gene expression, and that other pathways may be involved. The first study to utilise the rAAV technique to examine non-coding, regulatory SNPs, this work demonstrates that isogenic cell lines produced by rAAV genome editing allow for the quantification of subtle, genotype-specific effects. This work suggests that this adaptable technology may be beneficial for other studies examining the genetics of complex diseases.