Quantitative Methodologies in Computational Genetics: A Path to Personalized Health
A massive number of population-based databases have become available recently, providing novel research opportunities for the interrogation of DNA genotype-phenotype associations on unexplored clinical landscapes. The extensive phenotypic information encoded in large-scale EHR (electronic health records) databases, ranging from diagnosis code, physician reports, brain neuroimaging data to DNA genotype data, are valuable resources for clinical researchers to characterize the pleiotropic architecture of human complex traits. My research mission is to establish novel and scalable data-driven frameworks to efficiently utilize patient genetic & clinical data for developing various risk prediction models for the goal of precision medicine, in combination with recent advanced computational methodologies, including natural language processing(NLP), supervised/unsupervised machine learning(ML), and deep learning techniques.
In this seminar, I will provide a comprehensive overview of the application of several methodologies in risk prediction and causal modeling of health outcomes from a genomic perspective. To be specific, I will present the following scientific questions addressed by large-scale DNA biobank data with several pivotal data-driven methodologies: (1) identification of individuals at high risk for polycystic ovary syndrome(PCOS) / suicidal behaviors with their genetic and diagnosis data: a polygenic and phenotype risk score (PPRS) prediction model approach in multi-ancestry participant; (2) utilization of genetic data for predicting rate of cognitive decline in later ages with phenome-wide association studies; (3) investigation of causal effects of late/early menopause ages on various health outcomes with Mendelian Randomization (MR), a method of using measured variation in genes of known function to examine the causal effect of a modifiable exposure on disease in observational studies, etc. This seminar is particularly relevant for researchers interested in the transformative potential of genomic data science in advancing our understanding of the social sciences.
Biography: Yoonjung Yoonie Joo obtained B.S. (Major in Life Sciences, Minor Business Administration) at Korea University, and M.S. (Biotechnology) at Northwestern University McCormick School of Engineering, USA. She completed her Ph.D in Health and Biomedical Informatics at Northwestern University Feinberg School of Medicine, Chicago, USA. During her doctoral training at Northwestern Medicine, Yoonjung led and conducted several multi-institutional GWAS-PheWAS research with the nationwide EHR-linked biobanks within the eMERGE (electronic MEdical Records for GEnomic discovery) network. She got accepted as a postdoctoral fellow in the Department of Biomedical Informatics at Columbia University Irving Medical Center and was supposed to join the Tattonneti lab, but due to the unexpected circumstance of COVID-19, she turned back to Korea and worked at the Department of Psychology/Brain and Cognitive Sciences at Seoul National University as visiting postdoctoral fellow. In 2021, she joined the Institute of Data Science at Korea University where she started teaching ‘Data Science’ and ‘Introduction to Artificial Intelligences’ to 600+ undergraduate/graduate students while continuing her EHR-linked biobank research. She serves as the principal investigator for two projects funded by the National Research Foundation of Korea (NRF), titled “prediction of mental health outcomes in aging population with large-scale genomic and neuroimaging biobank data” and “Developing a multimodal deep learning approach for precision psychiatry: Integrating brain imaging-genomic data for mood disorder diagnosis in middle-aged and elderly adults”. She joined the Department of Digital Health at SAIHST as an Assistant Professor in September 2023.