| Title: | Scalable and accurate methods for understanding human complex traits |
| Lead Institution: | Cornell University |
| Principal investigator: |
Application 97908
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About
The UK Biobank recently released a whole genome sequence data with information from more than 150,000 people and over 585 million single nucleotide polymorphisms (SNPs). This information has the potential to help us better understand the genetic causes of human diseases. However, it also brings new challenges, such as needing more powerful computers and more advanced statistics because of the huge amount of data. Our project aims to make it easier to find out about human complex traits by using the UK Biobank data. We want to create new methods that can handle the large amount of information in a more efficient and accurate way. We will use the family tree information in the data to make the calculations faster and more accurate. By using these new methods and the UK Biobank data, we hope to learn about the genetics behind different traits in different populations, how genes interact with each other, and how natural selection affects human diseases, physiology, and aging. Our research will give us a better understanding of the evolution of human complex traits, and the project will take 36 months to complete.1 Publication
| Pub ID | Title | Author(s) | Year | Journal |
|---|---|---|---|---|
| 14046 | Enabling efficient analysis of biobank-scale data with genotype representation graphs | Drew DeHaas (+2) | 2024 | Nature Computational Science |
Enabling scientific discoveries that improve human health