Speaker:  Cory McLean (Google Health)

Abstract:  Genome-wide association studies have shed light on the genetic architecture of many diseases and complex traits. As genotyping becomes increasingly commoditized, the major challenge for discovering genotype/phenotype interactions is accurate phenotyping at scale. High-dimensional clinical data provide a unique opportunity to perform accurate phenotyping with deep learning models. This talk will overview multiple techniques for coupling machine-learning-based phenotyping with biobank-scale genetic data to improve genomic discovery and risk prediction for respiratory, circulatory, and eye morphology diseases and traits.

Bio: Cory McLean is the engineering lead for the Genomics team in Google Research. He completed his PhD at Stanford as a Bio-X fellow, a postdoc at UCSF as a Damon Runyon Cancer Research Foundation fellow, and spent 3 years in the Research team at 23andMe before joining Google in 2015. His research interests broadly include applying machine learning to the analysis and interpretation of genomic data and publishing tools and methods as open-source software.

In person or on Zoom at https://mit.zoom.us/j/93513735220