Epistasis pp 327-346 | Cite as

Epistasis Analysis Using Artificial Intelligence

Part of the Methods in Molecular Biology book series (MIMB, volume 1253)


Here we introduce artificial intelligence (AI) methodology for detecting and characterizing epistasis in genetic association studies. The ultimate goal of our AI strategy is to analyze genome-wide genetics data as a human would using sources of expert knowledge as a guide. The methodology presented here is based on computational evolution, which is a type of genetic programming. The ability to generate interesting solutions while at the same time learning how to solve the problem at hand distinguishes computational evolution from other genetic programming approaches. We provide a general overview of this approach and then present a few examples of its application to real data.

Key words

Epistasis Machine learning Artificial intelligence Association studies Genetic analysis Gene–gene interaction Genetic programming 



This work was supported by National Institutes of Health (NIH) grants AI59694, EY022300, GM103534, GM103506, LM009012, LM010098, and LM011360.


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Copyright information

© Springer Science+Business Media New York 2015

Authors and Affiliations

  1. 1.Department of GeneticsGeisel School of Medicine, DHMCLebanonUSA
  2. 2.Department of Community and Family MedicineGeisel School of Medicine, DHMCLebanonUSA

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