Longitudinal study of a 9p21.3 SNP using a national electronic healthcare database

Genome-wide association studies have identified a number of SNPs associated with complex disease. The longitudinal significance of these variants is uncertain and clinical genomic studies are required to elucidate what clinical value these variants have. Linking DNA to clinical health information databases is a powerful and potentially low-cost means of performing such research. Here, we describe a proof-of-principle study demonstrating the potential of this method.

A total of 376 individuals presenting to a hospital with severe coronary artery disease were enrolled into a prospective cohort study. DNA, demographic data, ethnicity and other clinical information was collected in an electronic database. Genotyping for SNPs rs2383207 and rs10757278 was performed using Sequenom^® (CA, USA) matrix-assisted laser desorption/ionisation-time of flight mass spectrometry. Health outcomes were tracked from when patients were discharged from the hospital using the New Zealand Health Information Service (Wellington, New Zealand).

A total of 253 (67%) patients were of New Zealand European descent, 47 (13%) patients were of Maori descent and 21 (6%) were of Pacific Island ancestry. The Maori and Pacific Island group were younger at presentation (63 ± 11 vs 70 ± 9 years of age; p < 0.0001) and had a higher prevalence of cardiovascular risk factors. The frequency of the at-risk rs2383207 G allele in the Maori and Pacific group was 70%, compared with 54% in Europeans (p = 0.002). Similarly, the rs10757278 G allele was also present at a higher frequency (68 vs 52%; p = 0.003). No association was seen between the rs10757278 SNP and cardiovascular risk factors or markers of disease severity. GA and GG individuals had a higher rate of cardiovascular (p = 0.04) and all-cause death (p = 0.02).

The linking of genetic data to electronic medical databases is an effective tool to assess the longitudinal effect of gene variants on health outcomes and will aid in the implementation of personalized medicine. Larger sample sizes with longer study duration may yield clinically useful information that aids preventative healthcare.