Seminar by Yun Ju Sung, Washington University

Abstract 

Alzheimer’s disease (AD) is the most common form of dementia and neurodegeneration without any effective treatment. Several Mendelian mutations and risk variants associated with disease risk have been identified. However, the functional mechanisms and downstream effects of those genes and variants that lead to disease have not yet been fully characterized. Our group has pioneered the generation and use of multi-omic data (genetics, epigenomics, transcriptomics, proteomics, metabolomics, and lipidomics). We have generated these data in tissues relevant to AD and neurodegeneration (brain, cerebrospinal fluid (CSF), and plasma) from clinically well-characterized AD cases and controls to 1) identify novel genes implicated in disease, 2) map additional GWAS loci, 3) understand the biology of the disease, 4) identify novel pathways and networks leading to disease, 5) identify new potential biomarkers, and 6) nominate potential drugs that could be repurposed for AD. In my talk, I will provide a brief introduction to the AD genetics and our past and on-going work, which ultimately help creating individualized disease risk evaluation and treatment. 

Bio

I am a Professor of Psychiatry and Biostatistics at Washington University School of Medicine in St. Louis. I obtained PhD in Statistics from University of Minnesota (with PhD work published in Sung and Geyer, Annals of Statistics, 2007) and post-doc in Medical Genetics from Univ of Washington in Seattle (with several publications including Sung et al., American Journal of Human Genetics, 2005). My earlier work focused on developing and evaluating numerous statistical methods for gene mapping of human diseases. I have over 100 publications including 25 first or last-author publications in statistical genetics. I worked on GWAS, imputation of genotypes in various multi-ancestry family studies, analysis of sequence data, and analysis of rare variants using various statistical methods. To decipher the genetic and environmental architecture of cardiovascular disease traits, I was critically involved in establishing the gene-lifestyle interactions working group within the CHARGE consortium (Psaty et al, Circ Cardiovasc Genet, 2009) and created robust infrastructure and analysis pipelines. Through gene-environment (GxE) interactions with six lifestyle factors (including smoking and physical activity), we identified several genetic loci showing biologic plausibility and clinical relevance for blood pressure and lipid homeostasis (Sung et al., American Journal of Human Genetics, 2018; Bentley and Sung et al., Nature Genetics 2019). I joined the NeuroGenomics and Informatics Center in 2020 and obtained an R01 grant to study sex-specific molecular profiling to understand pathology and identify causal genes and drug targets for Alzheimer’s disease (AD). I am involved in multiple multi-omic projects to identify multi-tissue molecular signatures for AD (Yang et al., Nature Neuroscience 2021; Sung et al., Science Translational Medicine 2023). I also play a critical role in teaching and mentoring at Washington University.