Approved research

Transcriptome-wide Association Study of Cardiometabolic Traits in the UK Biobank Cohort

Lay summary

The proposed research will leverage novel transcriptome-wide association study (TWAS) approaches to impute predicted gene expression in the UK Biobank cohort. We will then assess association of predicted gene expression with a number of quantitative cardiometabolic traits relevant to diseases like diabetes, heart disease, and stroke. This analysis will allow us to compare the results from novel and published TWAS methods in a large, well-phenotyped cohort. Along with this methods development goal, we will hopefully validate known genes related to cardiometabolic traits and may discover new genes which have been missed by single variant genetic association analyses. This research will help test a new genetic analysis method, which can be applied to understanding the genetic risk factors for a diverse range of disease-related traits. We will also apply these methods to attempt to discover novel genetic risk factors for important cardiometabolic risk factors relevant to common, complex diseases. Genetic risk factor discovery can lead both to improved risk prediction and novel understanding of disease mechanisms, potentially guiding development of new therapies. There are a number of publically available datasets (for example, the Genotype-Tissue Expression (GTEx) project) which relate genotypes at millions of sites across the genome to measured gene expression in a number of different tissues, such as adipose, brain, or muscle. These datasets can be used to estimate gene expression in large numbers of individuals where gene expression has not been assessed. We will test both a novel and existing method for estimating gene expression. We will then test to see if this predicted gene expression is associated with changes in a number of cardiometabolic traits. We plan to include all UK biobank participants with available genome-wide association study data and data for any of the requested cardiometabolic phenotypes.