We seek to understand the genetic basis of common diseases and to translate genetic association data into biological insights. Most of our research involves the development of new statistical methods and the application of these methods to large-scale genetic datasets.
Genetic architecture of common and rare variation
Both common and rare genetic variants influence common diseases and complex traits. We have developed methods to characterize the genetic architecture of common and rare variants, with a particular focus on how and why they differ. A major focus of our ongoing research is the development of methods to analyze rare variant association data.
Linkage disequilibrium graphical models
Statistical methods to analyze common variants must account for linkage disequilibrium (LD), especially for studies that are ancestrally diverse. We developed an approach to model LD using extremely sparse LD graphical models (LDGMs), which we infer from genome-wide genealogies. We are developing applications of LDGMs to polygenic risk prediction and heritability partitioning.
Pleiotropy and causality
Most disease-associated SNPs are pleiotropically associated with other traits as well, pointing to shared mechanisms or even causal relationships. We have developed methods to characterize genetic and biological relationships among genetically correlated traits.