I develop methods to analyze genome sequencing data in the context of other ‘omics and clinical health data to prioritize and functionally interpret genetic variants with roles in human disease. See my CV for details.

integration of patient phenotype in variant prioritization Image from our Nat Comm, 2023 paper.

Functional genomics for “N of 1” analyses

The genome is a big space, and accurately pinpointing variants that underlie specific human health conditions is a formidable challenge. Traditionally, genes have been treated as black box functional units, but we now know that individual variants within and between genes can have wildly different impacts. Because comprehensive, in vivo (in a living system) functional assessment of all possible genetic variants is (currently) infeasible, we instead turn to in silico (computational) variant functionality predictions. We develop integrative tools for assessing the functionality of specific genomic positions and are interested in leveraging multimodal biological and biomedical data to derive new insights on the function of genetic variants. [30535108, 33580225]

Integration of clinical phenotyping

Patient clinical phenotyping data is an essential component in interpreting the impact of genetic variants on human health. Phenotyping data can be noisy, unstructured, and difficult to obtain, and utilizing this information often requires deep clinical intuition. We are interested in developing computational approaches for streamlining the process of utilizing (standardized) phenotype data for automating diagnostic gene prioritization and interpretation. [37828001, medRxiv, bioRxiv]

Deriving insights from population-level analyses

Even though the genome is a big space, it is also a finite space. This means that as the number of sequenced genomes continues to grow, we will begin to observe all possible variants (and recurrence of functional variants in phenotypically-matched cohorts). Indeed, the number of sequenced tumor genomes has surpassed 10s of thousands, collective cohorts of sequenced Mendelian patients is exceeding 100s of thousands, and sequenced diverse, healthy populations is set to pass a million or more. By integrating variant functionality information, evolutionary constraint and mutational models, we will have the power to detect extremely rare variants that play roles in human cancers and other diseases. [32711844, bioRxiv]


:star: = project lead, :love_letter: = corresponding author, :busts_in_silhouette: = team science