TY - JOUR
T1 - The GA and the GWAS
T2 - Using genetic algorithms to search for multilocus associations
AU - Mooney, Michael
AU - Wilmot, Beth
AU - McWeeney, Shannon
N1 - Funding Information:
The authors wish to thank Dr. Kemal Sonmez and Dr. Thomas Barrett for helpful comments and suggestions about the work, Ping-Hsun Hsieh for data preprocessing, and Dr. John Kelsoe for allowing access to the data. They also wish to extend their appreciation to the editor and reviewers for their thoughtful comments. This project was supported by grants from the National Library of Medicine (5T15LM007088), the National Institutes of Health, National Center for Research Resources (5UL1RR024140), the National Institutes of Health, National Cancer Institute (5 P30 CA069533), and by the Oregon Tax Check-Off Alzheimer’s Research Fund administered by the Layton Aging & Alzheimer’s Disease Center in collaboration with the Oregon Partnership for Alzheimer’s Research.
PY - 2012
Y1 - 2012
N2 - 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.
AB - 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.
KW - Biology and genetics
KW - evolutionary computing and genetic algorithms
KW - graphs and networks.
UR - http://www.scopus.com/inward/record.url?scp=84859186164&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84859186164&partnerID=8YFLogxK
U2 - 10.1109/TCBB.2011.145
DO - 10.1109/TCBB.2011.145
M3 - Article
C2 - 22025762
AN - SCOPUS:84859186164
SN - 1545-5963
VL - 9
SP - 899
EP - 910
JO - IEEE/ACM Transactions on Computational Biology and Bioinformatics
JF - IEEE/ACM Transactions on Computational Biology and Bioinformatics
IS - 3
M1 - 6060796
ER -