| Title: | Phenome-wide association studies to inform drug development |
| Lead Institution: | University of Leicester |
| Principal investigator: | Professor Martin Tobin |
Application 43027
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About
Understanding of the genetic causes of diseases is developing rapidly, but we still don't know how the knowledge gained in recent years can be best translated into better patient care. In particular, there remains poor understanding of how the genetic causes of disease we identify act on the wider body, and why so few people respond well to commonly-used drugs without unwanted side-effects. The research will study available de-identified data to investigate how genetic variation influences health-related traits, such as disease susceptibility and disease progression, and drug response. It will utilise the available measures and questionnaire data in UK Biobank and linked longitudinal health care records, which enable disease progression and drug response to be better characterised. We will use methods that we and others have developed and applied in UK Biobank to date, as well as methods that we are developing in linked health care data from the Leicester-based EXCEED study. Application of these methods will develop some new data fields that we will deposit with UK Biobank to enrich the resource, enabling future scientific projects by other approved researchers into multi-morbidity and drug response. We will search across the phenome (range of available health-related measures or phenotypes) to highlight new knowledge about how we can use new drugs and better target existing drugs. The research will improve understanding of how disease is caused and how it progresses, inform the development of new drugs and how to target the prescribing of the right drug to the right patient at the right dose to maximise benefit and avoid adverse effects (precision medicine).2 Publications
| Pub ID | Title | Author(s) | Year | Journal |
|---|---|---|---|---|
| 7959 | DeepPheWAS: an R package for phenotype generation and association analysis for phenome-wide association studies | Richard J Packer (+15) | 2023 | Bioinformatics |
| 9342 | Genome-wide association study of thyroid-stimulating hormone highlights new genes, pathways and associations with thyroid disease | Alexander T. Williams (+31) | 2023 | Nature Communications |
Enabling scientific discoveries that improve human health