Cart (Loading....) | Create Account
Close category search window
 

An Evolutionary Algorithm to Find Associations in Dense Genetic Maps

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

3 Author(s)
Clark, T.G. ; Wellcome Trust Centre for Human Genetics, Oxford Univ., Oxford ; De Iorio, M. ; Griffths, R.C.

Discovering the genetic basis of common human diseases will be assisted by large-scale association studies with a large number of individuals and genetic markers, such as single-nucleotide polymorphisms (SNPs). The potential size of the data and the resulting model space require the development of efficient methodology to unravel associations between epidemiological outcomes and SNPs in dense genetic maps. We apply an evolutionary algorithm (EA) to construct models consisting of logic trees. These trees are Boolean expressions involving nodes that contain strings of SNPs in high linkage disequilibrium (LD), that is, SNPs that are highly correlated with each other. At each generation of the algorithm, a population of logic tree models is modified using selection, crossover, and mutation moves. Logic trees are selected for the next generation using a fitness function based on the marginal likelihood in a Bayesian regression framework. Mutation and crossover moves use LD measures to propose changes to the trees, and facilitate the movement through the model space. We demonstrate our method on data from a candidate gene study of quantitative genetic variation.

Published in:

Evolutionary Computation, IEEE Transactions on  (Volume:12 ,  Issue: 3 )

Date of Publication:

June 2008

Need Help?


IEEE Advancing Technology for Humanity About IEEE Xplore | Contact | Help | Terms of Use | Nondiscrimination Policy | Site Map | Privacy & Opting Out of Cookies

A not-for-profit organization, IEEE is the world's largest professional association for the advancement of technology.
© Copyright 2014 IEEE - All rights reserved. Use of this web site signifies your agreement to the terms and conditions.