ML-enabled Genetic Analysis of High-Content Phenotypes

Abstract: In my talk, I will discuss new machine learning (ML) approaches for human genetics. First, I will present ML-enhanced genetic analysis of histological traits, where we leverage a novel semantic autoencoder to compress histological images into trait embeddings for GWAS. In an application to multiple tissues from the GTEx dataset, we discover 4 genome-wide significant loci associated with histological changes, which we can visualise and interpret for each of the discovered variants thanks to our decoder.

Second, I will introduce a new method combining machine learning and genetic causal inference for risk predictions. A key advantage of this method is that it doesn't require longitudinal data. This allows for risk prediction of late-onset diseases in large biobanks, where follow-up cases are often limited.

Overall, these contributions demonstrate the transformative power of ML in human genetics. Our approaches enable more nuanced analyses of high-dimensional traits and facilitate biomarker discovery.

Bio: Francesco Paolo Casale studied physics at the University of Naples Federico II, Italy. He received his PhD in statistical genetics at the University of Cambridge and the European Bioinformatics Institute in 2016, where he developed new computational methods for genetic association studies and contributed to landmark international projects such as the last phase of the 1000 Genomes Project and the Blueprint initiative. He conducted his postdoctoral studies at the Microsoft Research New England lab in Boston, working on deep generative models for imaging genetics and automated machine learning. In 2019, he joined insitro, a drug discovery and development company located in the bay area. There, he led the statistical genetics team, working at the intersection of human genetics, machine learning and functional genomics to enable target identification and characterization. Since January 2022, he is a Principal Investigator in Machine Learning in Biomedicine at the Helmholtz Munich Institute of AI for Health.