Many clinically related diseases and traits share overlapping clinical findings but differ significantly in genetics. The extensive clinical correlations across such diseases and traits in terms of clinical manifestation, medical imaging findings and environmental risk factors greatly confound the differential diagnosis, hindering the advance of personalized treatment for clinically related diseases. Dissecting the key relative differences in genetic architecture among clinically related traits may improve classification, differential diagnosis and therapeutic targeting.
To this end, we aims to investigate the shared and distinct genetic architectures and distinguish clinically related traits by leveraging systematic genetic approaches for both binary and quantitative traits, focusing on two phenotype groups that are highly clinically related: (1) upper gastrointestinal (GI) diseases (e.g., peptic ulcer and gastric cancer) and (2) brain imaging-derived phenotypes (IDPs, e.g., cerebral hemodynamics ). We will systematically evaluate the shared and distinct genetic architectures of major upper GI diseases and brain IDPs under a unified framework through multi-trait and case-case (for binary traits) genome-wide analyses across ancestries, based on which we will develop prediction models for polygenic score-informed differential diagnosis.
By identifying shared and distinct genetic factors and developing genetic prediction models across clinically related phenotypes, this study is expected to advance the understanding of clinically related diseases including GI diseases and brain IDPs, and contribute to genetics-informed differential diagnosis and prediction of clinically related phenotypes.