For our next talk, in the BDI/CHG (gen)omics Seminar series, we will be hearing from Professor Nathan Palpant, Group Leader at The University of Queensland’s Institute for Molecular Bioscience (IMB). We’re delighted to host Nathan in what promises to be a great talk!
Date: Tuesday 8 July
Time: 9:30 am – 10:30 am
Talk title: Unsupervised Approaches to Decode the Functional Impact of Genetic Variation
Location: Big Data Institute, Seminar Room 0
Bio:
Professor Nathan Palpant is a Group Leader at The University of Queensland’s Institute for Molecular Bioscience (IMB). After PhD training at the University of Michigan and a postdoctoral fellowship at the University of Washington’s Institute for Stem Cell and Regenerative Medicine, he established his independent research group at the IMB in 2015. His research program focuses on studying mechanisms of cardiovascular development and disease, drawing on interdisciplinary approaches in stem cell biology, genetics and genomics, and drug discovery. He has particular interest in developing cell type agnostic models of genome regulation to gain insights into molecular regulation of cell identity in health and disease. Dr. Palpant is a Heart Foundation Fellow and has received numerous awards including the International Society for Heart Research Young Investigator Award, the Lorne Genome Millennium Science Award, and the Australian Cardiovascular Alliance Excellence in Cardiovascular Research Translation Award. Drawing on seminal studies in his lab on novel stress pathways in heart disease, Dr Palpant co-founded Infensa Bioscience to develop ASIC1a inhibitors as first-in-class therapeutics for ischemic heart disease and stroke.
Abstract:
The increasing availability of large-scale data is transforming our ability to study genetic regulation of cell states. However, understanding how genetic variation governs cellular function and complex diseases remains a challenge, requiring new analytical frameworks capable of integrating diverse genomic datasets to infer functional relationships. This seminar will present new unsupervised computational approaches for dissecting genetic regulation of cellular phenotypes. Our analysis of evolutionary and epigenetic conservation across human cell types has identified domains under cellular constraint that encode functional determinants of cell identity. By calculating genome-wide, single base resolution cellular constraint scores, I will demonstrate their utility in fine-mapping causal variants from genome-wide association studies, improving polygenic risk models, and predicting clinical outcomes in machine learning-based cancer survival models. These findings form the basis for development of multi-omic genome-wide unsupervised machine learning frameworks and variant-to-trait models that provide powerful approaches for functional annotation of non-coding variants and partitioning disease-associated genetic variants governing complex trait and disease sub-phenotypes. I will illustrate the versatility of these methods across various experimental applications including the study of multi-lineage differentiation from pluripotent stem cells and ongoing efforts to study population-scale data to parse the genetic basis of complex diseases. These studies illustrate new strategies to bridge the gap between genomic variation and cellular function for guiding scalable and interpretable solutions to advance our understanding of human development, disease, and therapeutic discovery.
All members of the University are welcome to join, please let reception at BDI know you’re here for the seminar and sign-in. We hope you can join us!
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As a reminder, the (gen)omics seminar series runs every other Tuesday morning and is intended to increase interaction between individuals working in genomics across Oxford. We encourage in-person attendance where possible. There is time for discussion over, tea, coffee and pastries after the talks.
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Hybrid Option:
Please note that these meetings are closed meetings and only open to members of the University of Oxford to encourage sharing of new and unpublished data. Please respect our speakers and do not share the link with anyone outside of the university.
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Passcode: sr3mi9iL
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