Research in the Harpak lab combines modelling and large-scale statistical inference to understand the evolutionary mechanisms that generate and shape genetic variation and translate this mechanistic understanding into better genotype-to-phenotype maps.

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. Research in the Harpak lab combines modelling 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.

The lab has a special interest in complex (or ‘polygenic’) human traits — traits affected by thousands of genetic variants along the genome, each with a small contribution. 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. One of the foci of interest in the lab is elucidating the windfalls and pitfalls of complex trait prediction.

Visit www.harpaklab.com/publications for a list of publications.

- Research electives for Statistics and Data Science (SDS) students.

- Laboratory rotations for first-year Cell and Molecular Biology (CMB) graduate students.

- Accepting new PhD students through the Ecology, Evolution and Behavior (EEB) program.