In our previous study (project #34303), we successfully identified a contribution of ADIPOQ variant for multiple diseases.
We also identified from our COVID research using UKBiobank data that germline variants lead alteration of protein structure can be identified a major contributor for disease associated phenotype (https://doi.org/10.1371/journal.pcbi.1009834).
Based on our acquired knowledge (i.e. how to prioritize a germline variant for diverse disease phenotype via functional evidence), we hypothesize that germline variants associated ligand and receptor genes would affect individual’s drug responses resulting diverse health related outcomes, such as distinct disease comorbidity pattern and debilitation process.
For example, genetic polymorphism of GLP1, a known target of Ozempic (anti-diabetic drug), showed minor impact for the response of Ozempic. However, these of germline variants of GLP1 affect the efficacy of vaccination.
Thus, identification of the relationship between genetic polymorphism of known drug targets and associated drug information is essential to understand personalized drug responses including vaccines, and antibiotics.
The expected milestones of our study would follow these:
1) Year #1: Build of diagnosis trajectory of all participants.
-In our previous study, we already established a general framework to generate sequential trajectory of disease diagnosis. (Paik et ak, Translational Psychiatry 12 (1), 389; Paik et al, Plos one 16 (10), e0257894; Paik et al, Scientific data 6 (1), 201)
-Re-applying this method we will systematically generate population-wide disease diagnosis trajectory including death outcomes.
2) Year #2~3: Identification of germline variants of stratified patients which may affect distinct drug responses.
– Using our specialty in big-data analytics, we will prioritized disease comorbids for in-depth analysis including personalized difference in drug response.