Analysis of Gene‐Gene Interactions

Diane Gilbert‐Diamond1, Jason H. Moore2

1 Computational Genetics Laboratory, Departments of Genetics and Community and Family Medicine, Dartmouth Medical School, Lebanon, New Hampshire, 2 Institute for Quantitative Biomedical Sciences, Dartmouth College, Hanover, New Hampshire
Publication Name:  Current Protocols in Human Genetics
Unit Number:  Unit 1.14
DOI:  10.1002/0471142905.hg0114s70
Online Posting Date:  July, 2011
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Abstract

The goal of this unit is to introduce gene‐gene interactions (epistasis) as a significant complicating factor in the search for disease susceptibility genes. This unit begins with an overview of gene‐gene interactions and why they are likely to be common. Then, it reviews several statistical and computational methods for detecting and characterizing genes with effects that are dependent on other genes. The focus of this unit is genetic association studies of discrete and quantitative traits because most of the methods for detecting gene‐gene interactions have been developed specifically for these study designs. Curr. Protoc. Hum. Genet. 70:1.14.1‐1.14.12 © 2011 by John Wiley & Sons, Inc.

Keywords: epistasis; genetics; statistics; bioinformatics

     
 
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Table of Contents

  • Introduction
  • What are Gene‐Gene Interactions?
  • Why are Gene‐Gene Interactions Likely to be Common?
  • Why are Gene‐Gene Interactions Difficult to Detect?
  • Methods for Detecting Gene‐Gene Interactions in Association Studies of Discrete Traits
  • Methods for Detecting Gene‐Gene Interactions in Association Studies of Quantitative Traits
  • Detecting Gene‐Gene Interactions on a Genome‐Wide Scale
  • Summary
  • Literature Cited
  • Figures
  • Tables
     
 
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Materials

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Internet Resources
  http://dceg.cancer.gov/tools/design/POWER
  The Power program for estimation of sample size and power for two‐locus interactions in both cohort and case‐control studies.
  http://hydra.usc.edu/fitf
  The focused interaction testing framework (FITF) for detecting gene‐gene interactions using logistic regression.
  http://hydra.usc.edu/gxe
  The Quanto program for estimation of sample size and power in matched case‐control, case‐sibling, case‐parent, and case‐only designs.
  http://www.epistasis.org
  Computational Genetics Laboratory at Dartmouth Medical School, where open‐source software for MDR can be obtained.
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