Approved Research

Development of multi-omic methods to examine the role of genetic variation in complex human traits and disease

Lay summary

Every person has a unique genetic makeup and the scientific community has made immense progress in identifying differences between people's genetic makeup that result in differences between our traits. This has led to better understanding of diverse diseases and our natural biology. While we have been able to identify these differences, we have a lot to learn about the mechanism by which these genetic differences affect us. The root cause of this paradox lies in the fact that the mechanisms by which genetic differences act, the tissues they affect, and identifying which differences have the greatest impact remain technically challenging. We propose the development of statistical and machine learning approaches that would combine multiple types of molecular and cellular information from the UK BioBank and other large datasets to uncover these mechanisms.

Our study lies at the core of the BioBank's stated purpose to improve the "prevention, diagnosis and treatment of a wide range of serious and life-threatening illnesses" as we will pursue unknown mechanisms and pathways related to a variety of human diseases with potential medical implications. For instance, these insights could subsequently be pharmacologically leveraged for improved patient care and be used for earlier, more accurate diagnosis. We will make our methods publicly available for other researchers to reproduce and expand our analyses. The full cohort currently available through UK BioBank will be used in this analysis.