Multi-locus genetic risk profiles, or genetic risk scores (GRS) aggregate data from multiple SNPs to provide a robust genetic instrument for traits of interest. They are more powerful than single SNP GWAS scores as the use of multiple sites reduces the effects of pleitropy and minor allele frequencies. GRS have been developed for many conditions including CAD and obesity. They provide a useful genetic tool for probing the causal pathways of multifactorial diseases.

In this study we intend to use multiple GRSs developed from CARDioGRAMplusC4D SNPs as a genetic instrument of CAD to investigate associations between predictors of CAD and differences in metabolite profile in ALSPAC mothers and children. GRSs are the exposure variable in this case, and altered metabolite profile is the exposure. We will use a range of GRSs developed using varied SNP significance threshlds to demnstrate the robustness of GRSs a genetic instrument, while investigating the genetic influence on the metabolism at a pathway specific and global level.

Three GRSs for CAD will be produced based on highly strict, moderate, and lenient SNP significance thresholds using CARDioGRAMplusC4D data. A selection of the most significant SNPs will also be analysed seperately. The individual SNPs and strict GRS should reveal how specific SNPs or groups of SNPs influence isolated metabolic pathways related to CAD, while the more lenient GRSs demostrate the wider influence of genetic variants on global metabolism. These GRSs will be regressed against the top ten principle components of NMR metabolite data from mothers and children. Principle components that are significantly associated can then be deconstructed to reveal specific changes in metabolite levels. By using principle components instead of individual metabolite concentrations, the number of analyses is significantly reduced, and the components themselves may represent biologically meaningful metabolic pathways.