Most human traits of interest are highly “polygenic” (or “complex”): the bulk of the heritable variation is due to a combination of numerous genetic variants of small marginal effects. A polygenic score is a predictor of a person’s complex trait value computed from his or her genotype. Polygenic scores sum over the genetic effects of the alleles carried by a person—as estimated in a genome-wide association study (GWAS) for the trait of interest. Fields as diverse as clinical risk classification, evolutionary genetics, social sciences and embryo selection are rapidly adopting polygenic scores.
I will show that the prediction accuracy of polygenic scores can be highly sensitive to tiny biases in GWAS effect estimates, and further that that the prediction accuracy of polygenic scores depends on characteristics such as the socio-economic status, age or sex of the people in which the GWAS and the prediction are conducted. These dependencies highlight the complexities of interpreting polygenic scores and the potential for serious inequities in their application in the clinic and beyond.
A key reason for these dependencies is in the fact that GWAS estimates are also influenced by factors other than direct genetic effects—including population structure confounding, mating patterns, indirect genetic effects of relatives and other gene-by-environment interactions. I will discuss the development of tools to tease apart the different factors contributing to GWAS associations, and ultimately improve the prediction ability and the interpretation of polygenic scores.
Arbel Harpak studies how genetic variation is shaped by evolutionary processes and how human genetic variation maps to phenotypic variation. Harpak is a fellow at the Simons Foundation's Society of Fellows and a postdoctoral researcher in Dr. Molly Przeworski’s lab at Columbia University, working on statistical and population genetics. He holds a B.S. in mathematics and physics and an M.S. in ecology/evolution from the Hebrew University of Jerusalem. He also holds an M.S. in statistics and a Ph.D. in biology from Stanford University, where he was advised by Dr. Jonathan Pritchard as a Stanford Center for Evolutionary and Human Genomics fellow.