| Title: | Assessing the influence of genetic- and non-genetic risk factors on subclinical and clinical cardiovascular disease. |
| Lead Institution: | Geneva University Hospitals |
| Principal investigator: | Professor Georg Ehret |
Application 68746
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About
Cardiovascular disease (myocardial infarction, stroke, etc.) is more likely in individuals with a combination of risk factors, such as cholesterol or high blood pressure. In most cases, the genetic contribution to the risk factors originates in the cumulative effects of several altered genes ("many genes - small impact - frequent"). But for a significant proportion of individuals, the genetic predisposition is due to a single change in one gene with large impact, greatly increasing cardiovascular risk ("one gene - large impact - rare"). One prime example for a disease due to a single gene-change is familial hypercholesterolemia (FH). The same logic applies for familial blood pressure elevation (much more rare). The diagnosis of single rare gene-changes typically requires expensive DNA sequencing (reading the sequence of the DNA letter by letter) to find the abnormal gene. This study's primary objective is to design a statistical model that would predict the probability of finding an FH variant by sequencing, based on genetic and non-genetic evidence. Our objective is to prioritize individuals for sequencing. With the help of data from the UK biobank, we will compute statistical models, including non-genetic data (e.g. cholesterol and blood pressure levels) and genetic data (low-cost genotyping results). We call these models "risk scores" and all participants of UK Biobank will have their individual risk score for monogenic cholesterol and blood pressure elevation. We will then validate these risk scores established in the UK biobank in a smaller sample of the UK Biobank who have sequencing data available and in individuals from Switzerland. We will also analyze the impact of genetic- and non-genetic characteristics on cardiovascular disease, to improve our model and as secondary analyses. We expect the first results by year 2021 and the project is projected to end in 2024. Our objective is to contribute to much more efficient, cost-effective diagnosis of familial hypercholesterolemia and hypertension by checking patient risk scores before considering sequencing its DNA . This will enable more effective screening of a larger number of cases.1 Publication
| Pub ID | Title | Author(s) | Year | Journal |
|---|---|---|---|---|
| 14155 | Efficient Search Algorithms for Identifying Synergistic Associations in High-Dimensional Datasets | Cillian Hourican (+10) | 2024 | Entropy |
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