TIGP (BIO)—Harnessing polygenic architectures for disease risk and health outcome prediction

Abstract

The genomics era has revolutionized our understanding of how genetic variation impacts human health, revealing a great extent of polygenicity and pleiotropy underlying common, complex traits and diseases. In tandem with epidemiological prediction modeling, innovative methods and approaches have been developed that leverage the polygenic architecture to estimate individual disease risk based on their unique genetic profiles. In this presentation, I will outline our recent collaborative efforts in developing polygenic prediction models using single and cross-ancestry information, the applications of these models to inform risk stratification of the onset and treatment response of neuropsychiatric disorders, and how genetic predictability transfers from the Eurocentric studies to the Taiwanese population. Lastly, I will address the potential and challenges of polygenic risk prediction in the context of population health going forward.

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