| Application: | 9659, A genetic investigation of pleiotropy using the UK Biobank Data. |
| Title: | An Integrated Polygenic Tool Substantially Enhances Coronary Artery Disease Prediction |
| Size: | 2.2 MB |
| Archived: | 17 Mar 2021 |
| Stability: | Complete |
| Personal: | Contains individual-level data |
Return 3241
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Notes
Our published paper investigates strategies for reducing the risk of coronary artery and cardiovascular disease, and asks whether the addition of genetic information improves the performance of established tools for calculating disease risk. Our study finds that it does, using data from UK Biobank both to build a better disease risk model and (in a separate subset of UK Biobank participants) evaluate its effectiveness.What is new about this study?
- We combined the largest available data with established and novel methods to construct the most predictive polygenic risk score (PRS) to date for coronary artery disease.
- Our integrated risk tool, which combines our PRS and the established Pooled Cohort Equations (PCE) risk tool, has a significantly improved predictive performance (NRI = 5 7%, 95% CI 4 4 7 0) against PCE alone.
- This superior performance is enhanced when individuals are stratified into age-by-sex subgroups: all the subgroup NRIs are larger than the overall NRI, ranging from 7 7% 17 3%, with the largest improvement in younger middle-aged men (40-55yo, NRI 17 3% against PCE).
What are the clinical implications?
- Future iterations of cardiovascular risk prediction tools would benefit from the addition of PRS.
- The improved accuracy of risk estimation in 40-55yo men could motivate early prevention strategies.
Application 9659
A genetic investigation of pleiotropy using the UK Biobank Data.
The aim of the proposed research is to understand pleiotropy: that is the nature, extent, and effect of genetic variation on multiple phenotypes. The extent of pleiotropy in humans is an important open question. Apart from inherent interest, it is directly relevant to the use of human genetics for improving drug development pipelines (see below). By their nature, studies of pleiotropy require data on multiple phenotypes. We aim to investigate pleiotropy using genetic data together with baseline measurements and the biomarker data (as it becomes available). Our analysis ultimately aims to inform decisions about which genes and pathways are the best targets for drug development. The efficacy and safety of therapeutics depends on the consequences of perturbations, by the drug, of particular gene products. Genetic variants also perturb the nature or amount of gene products, and is informative for drug efficacy, with effects on other phenotypes informative for on-target safety effects. The proposed work, mainly on non-clinical phenotypes, will involve proof-of-principle studies and development of statistical methods. We will make a further application when more clinical phenotypes are available in UK Biobank The research will use computers to build statistical models of the correlation between the genetic variation in an individual?s genome and biological measurements collected by UK Biobank. We can use the research to ask: if the genetic difference in a gene mimics or is related to the effects of a treatment what is likely to be the (positive and negative) effects of giving it to patients? To do this effectively we will look at the relationship between genetic variation and multiple phenotypes at the same time. Full cohort.
| Lead investigator: | Professor Peter Donnelly |
| Lead institution: | Genomics PLC |
2 related Returns
| Return ID | App ID | Description | Archive Date |
|---|---|---|---|
| 2340 | 9659 | PRS initial release (Version 1) | 27 Mar 2024 |
| 3242 | 9659 | Validation of an Integrated Risk Tool, Including Polygenic Risk Score, for Atherosclerotic Cardiovascular Disease in Multiple Ethnicities and Ancestries | 17 Mar 2021 |
1 Publication
| Pub ID | Title | Author(s) | Year | Journal |
|---|---|---|---|---|
| 3242 | Integrated Polygenic Tool Substantially Enhances Coronary Artery Disease Prediction | Riveros-Mckay F et al. | 2021 | Circ Genom Precis Med |
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