Steven Reilly, PhD

Steven Reilly received his B.S. in Biology from Carnegie Mellon University in 2009. Motivated by the rapid emergence of new technologies to map the full epigenomes, he joined Jim Noonan's Lab in the Genetics Department of Yale School of Medicine. There he built gene regulatory maps of the developing human, rhesus, and mouse cortex to identify changes underlying unique aspects of human brain morphology and cognitive abilities. Steve received his Ph.D. in 2015 and then joined the laboratory of Pardis Sabeti at the Broad Institute of Harvard and MIT to interrogate the function of genetic variants at the intersection of natural selection and human disease. As evolutionary adaptive genetic variants have been shown to underlie diversity in disease risk and morphology across human populations, the lens of evolution remains a powerful, yet underutilized method for understanding human biology He is specifically interested in furthering our understanding of non-coding variation, the main cache of human genetic diversity. The has created novel machine-learning methods to predict the subset of human variants under selection that are functional, and experimental methods to characterize variants in a massively parallel fashion. Steve has developed endogenous CRISPR perturbation methods and synthetic DNA technologies coupled with genomic readouts to directly assess the cellular phenotypes of non-coding alleles. Steve joined the Yale Department of Genetics as an Assistant Professor in September, 2021.

The Reilly lab develops and applies new high-throughput experimental approaches to interrogate the genome, such as non-coding CRISPR screens and the Massively Parallel Reporter Assay. Computationally, we also develop machine-learning approaches to predict the functions of these CRE perturbations. Together with these new tools, we use evolution as a powerful lens for characterizing genomic signals of positive selection that impact modern human phenotypes and diseases.

The lab has three main foci:

1. Developing new, large-scale experimental screens to perturb CREs, and new computational tools to model their function
2. Identifying evolutionary adaptive alleles likely impacting modern human phenotypes
3. Applying these functional genomic tools to phenotypically interesting loci important for human disease and evolution.