The GA and the GWAS: Using Genetic Algorithms to Search for Multilocus Associations

Enormous data collection efforts and improvements in technology have made large genome-wide association studies a promising approach for better understanding the genetics of common diseases. Still, the knowledge gained from these studies may be extended even further by testing the hypothesis that ge...

Full description

Saved in:
Bibliographic Details
Published in:IEEE/ACM transactions on computational biology and bioinformatics 2012-05, Vol.9 (3), p.899-910
Main Authors: Mooney, M. A., Wilmot, B., McWeeney, S. K.
Format: Article
Language:English
Subjects:
Algorithms
Bioinformatics
Biology and genetics
Bipolar Disorder - genetics
Computer Simulation
Diseases
Educational institutions
evolutionary computing and genetic algorithms
Genetic algorithms
Genetic Predisposition to Disease
Genome-Wide Association Study - methods
Genomics
Genomics - methods
graphs and networks
Humans
Materials
Models, Genetic
Oligonucleotide Array Sequence Analysis
Polymorphism, Single Nucleotide
Reproducibility of Results
Translational Medical Research
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Enormous data collection efforts and improvements in technology have made large genome-wide association studies a promising approach for better understanding the genetics of common diseases. Still, the knowledge gained from these studies may be extended even further by testing the hypothesis that genetic susceptibility is due to the combined effect of multiple variants or interactions between variants. Here, we explore and evaluate the use of a genetic algorithm to discover groups of SNPs (of size 2, 3, or 4) that are jointly associated with bipolar disorder. The algorithm is guided by the structure of a gene interaction network, and is able to find groups of SNPs that are strongly associated with the disease, while performing far fewer statistical tests than other methods.
ISSN:1545-5963
1557-9964
DOI:10.1109/TCBB.2011.145