Epistasis is central in many domains of biology, but it has not yet proven useful for complex traits. This is partly because complex trait epistasis involves polygenic interactions that are poorly captured in current models. I will introduce a new model called Epistasis Factor Analysis (EFA). EFA assumes that polygenic epistasis can be factorized into interactions between a few latent pathways, or Epistasis Factors (EFs). I will describe how EFA builds on our recent "Coordinated" model of polygenic epistasis and contrast it with traditional polygenic epistasis models. I will show that EFA can improve predictions in complex yeast traits. In complex human traits, I will show that EFA improves power to detect epistasis and that the EFs can partly recover known biological pathways without prior knowledge. Overall, realistic statistical models can identify meaningful epistasis in complex traits, which indicates that epistatic models have promise for precision medicine and characterizing the biology underlying GWAS results.
About Dr. Dahl:
Dahl completed his math undergrad at the University of Chicago and returned as a member of the faculty in 2020. His research has focused on developing statistical methods to analyze the genetic basis of common diseases, starting with an MS in statistics at UChicago (with Jonathan Prtichard), then a DPhil at Oxford (with David Steinsaltz and others), and a postdoc at UCSF/UCLA (with Noah Zaitlin). His current research focuses on genetic heterogeneity, i.e., genetic effects that vary between people due to gene-gene interaction, gene-environment interaction, or disease subtypes. The long-term goal of this work is to improve equity in genetic medicine. Outside of work, Prof. Dahl enjoyed climbing, skiing, and playing piano. Link to the Dahl Lab website here
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