| Title: | Efficient whole-genome scans for positive selection in large population genomic datasets |
| Lead Institution: | Johannes Gutenberg University |
| Principal investigator: | Dr Yoan Diekmann |
Application 63023
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About
The struggle for survival has challenged the human body to adapt to its environment. Natural selection is the main force responsible for adaptation, and therefore fundamental to address questions like "why was our body left vulnerable to disease?". Today, the genome sequences of many individuals are known, and present the unprecedented opportunity to look for signatures left by natural selection in the human genome. However, currently computational approaches to look for these signatures are usually slow, too slow for datasets with hundreds of thousands of individuals and more. Here, our aim is to test a computational method that we have developed to detect natural selection in the human genome. It is fast but accurate, and we intend to show that it works well on a data set even of the size managed by the UK Biobank. Our goal is to provide the biomedical research community with a tool that will continue to work efficiently in the future, when even more genomes are going to be available. To successfully accomplish our project, we will require access to the UK Biobank data for 24 months. The tool we designed gives researchers the ability to detect natural selection, and therefore parts of the genome that influence human health and survival. This is important as these regions could harbour targets for new drugs, help elucidate disease mechanisms and ultimately contribute to answering the question why we get sick.1 Publication
| Pub ID | Title | Author(s) | Year | Journal |
|---|---|---|---|---|
| 11092 | HaploBlocks: Efficient Detection of Positive Selection in Large Population Genomic Datasets | Benedikt Kirsch-Gerweck (+9) | 2023 | Molecular Biology and Evolution |
Enabling scientific discoveries that improve human health