Genetic variation within populations encodes the heritable component of differences among individuals, including in disease susceptibility, behavior and fitness.  While the broad strokes of these relationships have been appreciated for a century, we still understand little about how heritable variation arises and how it is shaped by natural selection.  Data gathered over the last two decades, however, have revolutionized what can be learned from genetic variation. 

We combine modeling and large-scale statistical inference to understand the mechanisms that generate and shape genetic variation and translate this mechanistic understanding into better genotype-to-phenotype maps.

Work in the group involves genomic data analysis, mathematical and statistical modeling, and some data collection (predominantly through collaborative efforts). Below you will find areas of current interest in the lab. However, we always aim to evolve our scope according to the diverse interests and expertise of lab members.

Complex traits

Complex (or ‘polygenic’) traits are traits affected by thousands of genetic variants along the genome, each with a small contribution. Most human traits of interest are complex, including many diseases, physical and behavioral traits. 

Due to their complexity, biological mechanisms can be hard to pin down, but complex traits still lend themselves to trait prediction using polygenic scores—functions that aggregate input from many genetic variants. Polygenic scores can, for example, predict a person’s risk for breast cancer or coronary artery disease even in the absence of other warning signs. This approach can lead to earlier intervention for at-risk individuals. 

We aim to elucidate the windfalls and pitfalls of polygenic trait prediction. More broadly, our group is geared towards understanding the maps between genotype and complex human phenotypes, with a focus on the role of the environment in mediating these maps.  

Population genetics

Heritable differences among individuals, populations and species are the result of mutations that arise randomly, get shuffled into new combinations by recombination, and go through the sieves of genetic drift and natural selection.

We are interested in modeling these dynamics and how they shape genetic and phenotypic variation. We are also interested in the flip side of this coin: Learning from patterns of genetic variation about the underlying processes involved.