WARNING: the interactive features of this website use CSS3, which your browser does not support. To use the full features of this website, please update your browser.
We utilized the unprecedentedly large genotype and phenotype dataset in the UK Biobank to perform a genome-wide association study (GWAS) which accounts for the interplay between genotype and dietary intake. We examined the interaction effects of fish oil supplementation on levels of blood lipids (LDL-C, HDL-C, TAGs, and total cholesterol). Our findings were replicated in the Atherosclerosis Risk in Communities (ARIC) Study. We found that at the genetic variant rs112803755 (A>G), the minor allele (G) is associated with a decrease in TAGs among individuals with fish oil supplementation, but is associated with an increase in TAGs among those without supplementation. In other words, only individuals carrying the minor allele benefit from fish oil supplementation in reducing TAG levels. We further analyzed rs112803755 with functional genomics data from the Genotype-Tissue Expression (GTEx) project to identify potential target genes, and found a connexin coding gene which has been previously reported to respond to cellular omega-3 levels. This research suggests that inter-personal variation in TAG response to fish oil supplementation is in part explained by genotype, and that fish oil dose adjustment based on genotype should be investigated as a means to protect against cardiovascular disease risk.
Evaluate the causal effects of diet-modifiable biomarkers on clinical outcomes using Mendelian randomization
Taking dietary supplements could be an effective means of managing health and preventing diseases. The specific health benefits of a dietary supplement have to be evaluated by a clinical trial, which is usually resource-intensive and complicated. An innovative statistical method, called Mendelian randomization, provides a cost-effective alternative to evaluate the causal effect of a specific environmental exposure on a clinical outcome. Our research will develop computational tools implementing this kind of methods and apply them to available data in the UK Biobank. We will un-biasedly evaluate the relationship between all diet-modifiable biomarkers and clinical outcomes. We expect the proposed project will take three years in implementation and then another year in the publication of findings and software. Our research will demonstrate the presence or absence of causal benefits of a biomarker on a clinical condition. These discoveries will guide our future practice of dietary recommendation.