Research

Statistical genetics of lipid traits

My work uses large-scale genetic studies to understand why blood lipid levels vary across people and populations. I combine GWAS meta-analysis, whole-genome sequencing, rare-variant association testing, and Bayesian modeling to move from loci to mechanisms: which variants matter, which genes they implicate, and how robust those signals are across studies and ancestries.

GWAS meta-analysis Whole-genome sequencing Rare variants Functional genomics Bayesian methods
5 Papers
2 Themes
4 Journals
Theme A

Lipid GWAS meta-analysis

The first systematic maps of the lipid genome came from genome-wide association studies: experiments that genotype hundreds of thousands of people and test millions of common DNA variants for association with cholesterol and triglyceride levels. My work in the Global Lipids Genetics Consortium pushes this from cataloguing loci toward interpreting which genes, biological pathways, and regulatory mechanisms those loci implicate.
Theme B

Lipid whole-genome sequencing

Array-based GWAS captures mostly common variants. Whole-genome sequencing reads every base pair, opening the genome to rarer and potentially more impactful variation. In TOPMed and related lipid sequencing studies, I help connect rare noncoding and coding variation to lipid biology at biobank scale.