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Approved research

Phenome-wide association study of variants in developmental pathway genes

Principal Investigator: Dr Charles Hong
Approved Research ID: 49852
Approval date: October 17th 2019

Lay summary

The genes involved in the making of the embryo (embryonic development) often get turned on later in life to cause adult diseases, such as heart disease and cancer. Therefore, different versions (variants) of these genes may contribute to diseases in adults, not just children. We will search the UK Biobank to find links between variants in the genes important for embryonic development and human diseases. The genes to be examined will include those already known to be important for embryonic development based on the search of the scientific literature and based on ongoing studies into drug like chemicals that affect embryonic development. Next, we will use advanced mathematical technique to rank the strongest and most relevant gene-disease relationships. In addition, we will build a web-based search engine that will help us these gene-disease relationships rapidly and compared them to findings in other studies.

Scope extension: We plan to extend our current scope to include new omics data available (soon-to-be available) from the UK Biobank. Doing this will require improved statistical models and methods for this "big data" and we plan tap the talents of our current team of collaborators at the University of Maryland School of Medicine, specifically Dr. Jeffery O'Connell, to not only develop improved methods but also guide us in applying them to the goals outlined in our current scope.  Whole-genome sequencing (WGS) will potentially identify over 2 billion variants, including singletons, and genomics such as whole-genome bisulfite sequencing (WGBS) or methylation arrays will identify over a million methylation probe sites and the clinical data has over 10,000 phenotypes. Improved statistical models, algorithmic methods, data mining, and both local and cloud-based compute strategies will be needed to enable our team to extract full value of the rich UK Biobank data set as we explore developmental pathway genes.