| Title: | Evaluating the impact of computational and experimental measures of rare variant deleteriousness on burden-based association testing |
| Lead Institution: | Sinai Health Systems |
| Principal investigator: | Mr Da (Kevin) Kuang |
Application 51135
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About
Coronary artery disease (CAD) and other forms of atherosclerosis are common and serious health concerns. Elevated LDL cholesterol levels increase the risk of these conditions, and measurement and control of LDL levels is a major component of modern preventative health care. Our knowledge of the genetic causes of these diseases remains incomplete. For example, ~70% of the genetic sources ("heritability") of CAD remain unexplained. One under-explored source of CAD is rare variation. Because rare variants may appear only once or twice in a large cohort study, statistical tests that examine only one variant at a time will fail to detect associations with the disease, even where they truly exist. By aggregating all of the rare variants within a gene, we measure whether there is an elevated rate of variation in participants with and without particular phenotypes. These tests can be made even more sensitive if we can filter out or down-weight variants that have no functional impact on their genes. However, there are multiple strategies that we might use to derive these filters or weights, so we would like to test which are most effective, using atherosclerosis-related phenotypes of participants in the UK Biobank study as a practical and medically-relevant starting point.1 Publication
| Pub ID | Title | Author(s) | Year | Journal |
|---|---|---|---|---|
| 11744 | Benchmarking computational variant effect predictors by their ability to infer human traits | Daniel R. Tabet (+10) | 2024 | Genome Biology |
Enabling scientific discoveries that improve human health