Epistasisis is the interaction between two or more genes to affect phenotype. It is now widely accepted that epistasis plays an important role in susceptibility to many common and complex diseases. The crucial challenge to analyzing epistasis is finding a way to efficiently handle high-dimensional genomic data. This career award is to develop a succinct Bayesian network model representing epistasis and efficient algorithms, which are tailored to investigating such models, integrate the algorithms into methods for learning epistasis, and use simulated datasets to test the effectiveness of the methods and compare their performance to other methods. This K99/R00 Project (grant # LM010822) is funded by NLM/NIH. It is a 5-year project with total funding in the amount of $927,000.
Research Projects and Collaborations
Detecting Genome-Wide Epistasis with Efficient Bayesian Network Learning