Steven Reilly, PhD
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Assistant Professor of Genetics
Biography
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:
- Developing new, large-scale experimental screens to perturb CREs, and new computational tools to model their function
- Identifying evolutionary adaptive alleles likely impacting modern human phenotypes
- Applying these functional genomic tools to phenotypically interesting loci important for human disease and evolution.
Appointments
Genetics
Assistant ProfessorPrimary
Other Departments & Organizations
Education & Training
- Postdoctoral Fellow
- Broad Institute of Harvard and MIT (2021)
- PhD
- Yale, Genetics (2015)
- BS
- Carnegie Mellon, Biology (2009)
Research
Overview
The Reilly Lab has multiple broad research areas:
Tools to interpret regulatory variation
We build and apply high-throughput tools to study the functions of cis-regulatory elements (CREs) in the genome. We aim to answer three main questions: 1) which CREs are actually functional in a cell? 2) which gene(s) do they regulate? and 3) how does variation in CREs impact their function? We’ve built tools like HCR-FlowFISH, a CRISPR perturbation strategy to directly characterize CREs, along with CASA, a Bayesian CRISPR analysis strategy. We’ve also applied the Massively Parallel Reporter Assay (MPRA) to understand how variants impact the function of CREs, and worked to expand this approach to other types of non-coding elements, including variation in 3’ UTRs.
Functional characterization Center
We work with the Sabeti and Tewhey labs in their efforts as an ENCODE Functional Characterization Center to directly characterize, rather than map, CREs in the genome. Steve co-leads the CRISPR working group, which is focused on developing best practices and meta-analyses of CRISPR non-coding screens. More broadly, we’ve used our CRE characterization tools to identify functional, causal variants in a wide variety of traits such as lipid metabolism and infectious disease.
Investigating positive human selection
We’re interested advancing a deep molecular understanding of how positive evolutionary selection has impacted our genomes, specifically how ancient selection impacts health and traits in modern global populations. We’ve built novel computational tools to better model human demographic histories from the 1000 Genomes dataset. We’ve also introduced DeepSweep, a machine learning algorithm to identify and detect positive selection in the human genome with vastly improved accuracy. As most of these selected variants are in non-coding regions of the genome, we applied our CRE characterization tools to link variants to function.
Learning Regulatory Grammar
Beyond new functional characterization tools for regulatory variants, more accurate models of regulatory grammar - the code and rulesets underlying the complex logic of cis-regulatory elements - would greatly accelerate our interpretation of the genome. Our lab intends to build new datasets of comprehensively characterized trait-associated regulatory elements, and then use these high-dimensional data to improve our models of CREs.
Characterizing species-specific variation
Our lab is also interested human-specific sequence changes that may be important for human evolution. As the vast majority of these changes are non-coding, our tools are well positioned to understand the functions of genomic changes between species. We’ve applied the MPRA to characterize all small human Conserved Deletions (hCONDELs) - likely functional sequences conserved in mammals that are deleted only in the human lineage. Understanding the phenotypic consequences of these changes is a major goal of the lab.