DeepMind Talk: Genomic Prediction of Complex Traits and Disease Risks via Machine Learning

I'll be at DeepMind in London next week to give the talk below. Quite a thrill for me given how much I've admired their AI breakthroughs in recent years. Perhaps AlphaGo can lead to AlphaGenome :-)

Hope the weather holds up!

Title: Genomic Prediction of Complex Traits and Disease Risks via Machine Learning

Abstract: After a brief review (suitable for non-specialists) of computational genomics and complex traits, I describe recent progress in this area. Using methods from Compressed Sensing (L1-penalized regression; Donoho-Tanner phase transition with noise) and the UK BioBank dataset of 500k SNP genotypes, we construct genomic predictors for several complex traits. Our height predictor captures nearly all of the predicted SNP heritability for this trait -- actual heights of most individuals in validation tests are within a few cm of predicted heights. I also discuss application of these methods to cognitive ability and polygenic disease risk: sparsity estimates (of the number of causal loci), combined with phase transition scaling analysis, allow estimates of the amount of data required to construct good predictors. We can now identify risk outliers for conditions such as heart disease, diabetes, breast cancer, hypothyroidism, etc. using inexpensive genotyping. Finally, I discuss how these advances will affect human reproduction (embryo selection for In Vitro Fertilization (IVF); gene editing) in the coming decade.

Bio: Stephen Hsu is VP for Research and Professor of Theoretical Physics at Michigan State University. He is also a researcher in computational genomics and founder of several Silicon Valley startups, ranging from information security to biotech. Educated at Caltech and Berkeley, he was a Harvard Junior Fellow and held faculty positions at Yale and the University of Oregon before joining MSU.

Action Photos!