| Title: | A deep learning based approach for phenome-wide association studies |
| Lead Institution: | University of Cambridge |
| Principal investigator: |
Application 100316
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About
The goal of this research is to use deep learning techniques to identify connections between a person's genetic makeup (genotypes) and their physical characteristics or diseases (phenotypes) using data from the UK Biobank. To do this, our aim is to create a new methodology and to identify specific non-coding genotypes (parts of DNA that do not contain instructions for making proteins) that are related to multiple diseases or physical characteristics. After testing this system with UKBB data, we hope to use this system in a manner that would allow us to analyze data from multiple institutions without exchanging information. The rationale is that traditional methods require data from large number of patients; however having large number of samples in single institutions is not possible, however data is sensitive and therefore cannot be shared across institutions. We plan to test this system using a large number of samples from the UK Biobank. The developed tool will perform phenome-wide association study (PheWAS), which is a powerful way for identifying the relationship between the genetic and a wide range of human diseases and traits. By studying large populations, PheWAS can identify genetic risk factors that may be missed by traditional disease-specific studies. This project will require approximately 3 years. The public health implications of PheWAS studies include a better understanding of the underlying causes of many diseases and the identification of new targets for prevention and treatment. Additionally, PheWAS studies can help to improve the accuracy of diagnosis and the development of more effective treatments. Furthermore, PheWAS results can also help to identify population subgroups that are at a higher risk of certain diseases, which can inform public health interventions and improve health outcomes for those populations. In summary, PheWAS studies have the potential to improve our understanding of the genetic basis of disease, and can inform the development of new treatments and public health interventions.2 Publications
| Pub ID | Title | Author(s) | Year | Journal |
|---|---|---|---|---|
| 16040 | Multi-domain rule-based phenotyping algorithms enable improved GWAS signal | Abigail Newbury (+2) | 2025 | npj Digital Medicine |
| 13976 | SQUiD: ultra-secure storage and analysis of genetic data for the advancement of precision medicine | Jacob Blindenbach (+5) | 2024 | Genome Biology |
Enabling scientific discoveries that improve human health