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Senior Vice-President for Research and Innovation, Professor of Theoretical Physics, Michigan State University

Saturday, August 31, 2019

Genomic Prediction of Complex Traits and Disease Risks (video of talk at IGI and OpenAI)

Seminar at the Innovative Genomics Institute (IGI, Berkeley and UCSF) July 17 2019. I gave a similar talk the following day at OpenAI. Jennifer Doudna, one of the co-discoverers of CRISPR-Cas9 gene editing, is the Executive Director of IGI. You might recognize her voice if you can hear the audience questions.
IGI began in 2014 through the Li Ka Shing Center for Genetic Engineering, which was created thanks to a generous donation from the Li Ka Shing Foundation. The Innovative Genomics Initiative formed as a partnership between the University of California, Berkeley and the University of California, San Francisco. Combining the fundamental research expertise and the biomedical talent at UCB and UCSF, the Innovative Genomics Initiative focused on unraveling the mechanisms underlying CRISPR-based genome editing and applying this technology to improve human health.
Slides -- slightly updated from the ones I used in the talk.
Title: Genomic Prediction of Complex Traits and Disease Risks via AI/ML and Large Genomic Datasets

Abstract: The talk is divided into two parts. The first gives an overview of the rapidly advancing area of genomic prediction of disease risks using polygenic scores. We can now identify risk outliers (e.g., with 5 or 10 times normal risk) for about 20 common disease conditions, ranging from diabetes to heart diseases to breast cancer, using inexpensive SNP genotypes (i.e., as offered by 23andMe). We can also predict some complex quantitative traits (e.g., adult height with accuracy of few cm, using ~20k SNPs). I discuss application of these results in precision medicine as well as embryo selection in IVF, and give some details about genetic architectures. The second part covers the AI/ML used to build these predictors, with an emphasis on "sparse learning" and phase transitions in high dimensional statistics.

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