Recent Work

I have been mentored by Dr. Marylyn Ritchie and Dr. Benjamin Voight while pursuing my PhD. My graduate work has focused on using multi-trait GWAS and colocalization analysis between cardiometabolic traits and complex diseases to identify novel therapeutic targets.

Multi-trait rare variant gene burden tests for cardiometabolic traits

• Goal: Perform multi-trait rare variant gene burden tests for cardiometabolic traits to identify novel associated genes with potential to be therapeutic targets.
• Made a pipeline to perform multi-trait rare variant gene burden tests across the genome for biobank electronic health record data and applied it to Penn Medicine Biobank blood lipid levels and liver enzyme levels.
• I am currently working to expand our analyses to include binary disease phenotypes in these analyses, such as type 2 diabetes and coronary artery disease, and to replicate our analyses in UK Biobank

Multi-trait rare variant gene burden tests GitHub Repo

Developing a pipeline to identify candidate causal genes for GWAS data using eQTL and sQTL data

• Goal: Develop a pipeline, ColocQuiaL, to rapidly perform and visualize the results from a large number of colocalization analyses between GWAS signals for complex traits and available eQTL and sQTL data.
• I conceived of the idea and have done much of the software development for the ColocQuiaL pipeline. Over the past two years, I have primarily been mentoring an undergraduate student as he does further software development on this project. I have also run ColocQuiaL on GWAS results for the multi-trait GWAS projects I have worked on and collaborative projects.
• ColocQuiaL can perform colocalization analyses between loci from any GWAS summary statistics file and GTEx v8 single-tissue sQTL and eQTL data. We will soon release a version that can use other QTL datasets as well. I used different iterations of this pipeline to identify candidate causal genes for GWAS signals in three published manuscripts.

ColocQuiaL GitHub Repo

ColocQuiaL Paper

Investigating pleiotropy between cardiometabolic traits and complex diseases

• Goal: Detect pleiotropic loci associated with cardiometabolic traits and complex diseases to identify potential novel therapeutic targets.
• Performed the multi-trait GWAS and colocalization analyses between traits and GTEx eQTL data for a project centered around Alzheimer’s disease. I worked with my co-first author to perform these same experiments in a project centered around atherosclerosis. I also worked on the literature review to assess which genes were potential therapeutic targets.
• We detected potential therapeutic targets including DOCK4 for Alzheimer’s disease and PCSK6 for atherosclerosis as well as several other novel pleiotropic loci in both the Alzheimer’s-disease-centric and the atherosclerosis-centric multi-trait GWAS projects.

Multi-trait association studies discover pleiotropic loci between Alzheimer’s disease and cardiometabolic traits

Multi-Trait Genome-Wide Association Study of Atherosclerosis Detects Novel Pleiotropic Loci