Our Research

Our primary goal is to understand the genetic basis of human disease to uncover better therapeutics. We are a hybrid wet/dry lab, using large datasets to identify genetic causes of disease and functional biology to better understand these associations' mechanisms.

Uncovering disease biology with human genetics and multi-omics

We use large-scale whole-exome and whole-genome sequencing studies to find rare genetic variants associated with human disease. Although some of our work focuses on specific diseases, we also perform "phenome-wide" association studies in datasets like the UK Biobank. We are now leading an initiative to build a large, ancestrally diverse biobank at Texas Children's Hospital, the largest pediatric hospital in the world.

Our work has led to the discovery of novel genetic associations for several diseases, including epilepsy, idiopathic pulmonary fibrosis, and diabetes. However, realizing the full potential of precision medicine requires the translation of these genetic discoveries into an understanding of disease mechanisms. To achieve this, we have begun integrating other omic modalities into our genomics studies, including transcriptomics, metabolomics, and proteomics, to gain insight into the pathophysiology of disease-associated variants.

Idetifying convergent mechanisms in neurodevelopmental disease

Up to 40% of genes implicated in neurodevelopmental disease encode transcriptional regulators, including chromatin modifiers, transcription factors, and RNA-binding proteins. It is likely that many of these genes converge on shared downstream pathways, the identification of which could dramatically accelerate drug discovery.

We use CRISPR-based functional genomics platform in human iPSC-derived neurons to identify convergent mechanisms in intellectual disability, autism spectrum disorder, and epilepsy. We employ a variety of functional approaches, including single-cell RNA-sequencing, chromatin profiling, and electrophysiology assays.

Developing computational methods to inform genetic and therapeutic discoveries

We use population genetics and machine learning to improve the interpretation of genetic variation. We are particularly interested in identifying regions of the human genome that are most likely to be associated with disease. We have developed methods that quantify each gene's intolerance to mutational changes and have extended this approach to the non-coding genome. We have also introduced machine learning methods that predict novel risk genes for neurodevelopmental disorders.

We also use computational approaches for drug discovery. Most recently, we used transcriptomic profiling to identify a drug repositioning opportunity for a devastating pain syndrome called trigeminal neuralgia.