Refreshments are available starting at 10:15 a.m. The seminar will begin at 10:30 a.m.
Abstract
Many human diseases have a substantial genetic component, which genetic association studies are increasingly capable of characterizing as they are empowered by ever-growing sample sizes. These associations have the potential to elucidate complex disease biology and prioritize therapeutic interventions; however, it is challenging to resolve the impacted genes, pathways, and cellular states, since most risk variants are noncoding. David Knowles will describe strategies his team has explored to address this challenge, particularly in the context of neurodegenerative disease. First, they mapped genetic effects on expression, splicing and RNA editing in over 10k postmortem brain samples, enhancing interpretation of common variant associations. Second, they developed a Mendelian randomization-based causal network inference approach to estimate how genetic effects propagate through the gene network to converge on disease risk. Third, they trained and assessed deep learning models of pre- and post-transcriptional regulation, finding that they they can refine functional fine-mapping, improve the portability of polygenic risk scores across ancestries, and increase power in novel annotation-aware noncoding rare variant association studies. Finally, his team designed a CRISPR/Cas13-based strategy to perform isoform-specific knockdown, opening the door for functional characterization of putative disease-causal transcriptomic changes in appropriate cellular model systems.
Speaker Biography
David Knowles is an associate professor of computer science, an interdisciplinary appointee in Systems Biology, and an affiliate member of the Data Science Institute at Columbia University. He is also a core faculty member at the New York Genome Center. His group develops methods to better understand the genetic basis of human disease. He studied natural sciences and information engineering at Cambridge University before obtaining an MSc in bioinformatics and systems biology at the Imperial College of Science, Technology and Medicine in London. During his PhD in the Cambridge Machine Learning Group under Zoubin Ghahramani, Knowles worked on variational inference and Bayesian nonparametric models. He was also a postdoctoral researcher at Stanford University, where he developed methods for functional genomics with Daphne Koller, Sylvia Plevritis, and Jonathan Pritchard.