University of Oxford

Academic Institute

OFHS240180

This study aims to discover new associations between genes and disease and between genes and risk factors for disease, and aims to validate already known associations. In addition, we will use the higher representation of younger adults, and underrepresented minorities to assess the consistency of these associations. Preliminary aims: 1. Analyse risk factors for high blood pressure and high blood cholesterol, including age, BMI, sex, SES, and regional differences. 2. Perform a genome-wide association study (GWAS) stratified by ancestry for height, BMI, blood pressure, lipids, type 2 diabetes, self-reported depression, and self-reported and cancer registry recorded breast cancer, and compare results to existing GWAS summary statistics for the same traits and ancestries. 3. Use the results from GWAS analyses to estimate SNP effects using Bayesian regression methods that incorporate functional annotation. This step achieves a better estimate of the effects on individual DNA variants on disease and other traits than the standard approach. It will rely on imputed data being available by mid-2026. Aims 2 and 3 will be a direct validation of the quality of the data because we know from prior research that associations between phenotypes and genetic loci replicate well in new samples from the same ancestry. Differences between people in most characteristics, including diseases, are caused by a combination of inherited genetic factors and environmental factors. Individual-level genetic data can be captured using modern genome technologies. Understanding the role variants in our DNA play in influencing variation in characteristics such as weight and blood pressure can contribute to preventing diseases. It can also help to discover new drugs. Genome-wide association studies (GWAS) of common diseases and their risk factors have shown that these complex characteristics are due to many different DNA variants, that contribute to differences between people in their risk of disease. The influence of each specific DNA variant is small on average so that large sample sizes are needed to discover them with sufficient statistical evidence. OFH is the largest cohort study in the world with information on these DNA variants and, given its sample size, will lead to new discoveries using GWAS for many characteristics. We also expect to be able to confirm known disease-associated DNA variants in OFH. Similarly, we hope to discover new relationships between risk factors and disease and confirm established relationships.

The proposed research will contribute to a better understanding of the causal factors underlying differences between people in their risk of disease, contribute to a more precise prediction of individual risk using a genetic score and may lead to the discovery of new potential drug targets for disease. A better understanding of causal risk factors is important because it can identify risk factors that are modifiable, for example by early intervention or therapeutically. The discovery of new associations between DNA variants and complex traits will lead to a more precise prediction of a person’s future risk of disease and can thereby identify individuals who are at extremely high risk if such predictions were to be rolled out nationally. Past studies have shown that new drugs that are developed with genetic evidence are more likely to make it to market than those that do not, so that new discoveries about gene variants and disease may be useful in drug development pipelines. The higher representation of younger adults and underrepresented minorities will also allow us to assess the consistency and generalizability of these associations.

Public Health Research

09/07/2025

04/06/2025

TRE