| Title: | Delineating the relationship between human evolutionary history on the architectural complexity and predictability of traits |
| Lead Institution: | Princeton University |
| Principal investigator: |
Application 104628
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About
Differences in DNA sequences among individuals contribute to inherited variation in traits and diseases. In addition, patterns of DNA sequence variation that exist among individuals today were shaped by our past. Therefore, a better understanding of human history will facilitate a better understanding of how and why differences in DNA sequences among individuals leads to differences in traits and disease susceptibility. The UK Biobank provides a unique opportunity to ask fundamentally important questions about the relationship between human history and our current distribution and burden of disease. Our research will develop new tools for leveraging the massively large set of DNA sequences in the UK Biobank to infer aspects of human evolutionary history and how our past shapes patterns of heritable variation in traits and disease among contemporary individuals. We will also use this deeper understanding of how human history affects patterns of DNA sequence variation across the genome to develop new tools for decoding the genetic basis of traits and diseases. For instance, some mutations might influence multiple traits or diseases and therefore methods that simultaneously consider two or more traits or diseases would potentially make it easier to identify such mutations. We anticipate the project will take three years to complete because it involves analyzing very large amounts of data and developing new methods. The impact of the proposed research is a deeper understanding of the relationship between differences in DNA sequence and variation in traits and diseases among individuals, as well as new methodological tools for analyzing massively large genetics data sets.1 Publication
| Pub ID | Title | Author(s) | Year | Journal |
|---|---|---|---|---|
| 20273 | A generalized test of genotype-phenotype causality in population-sampled nuclear families. | Yushi Tang (+1) | 2026 | PLOS Genetics |
Enabling scientific discoveries that improve human health