Exploration of gene-gene interaction effects using entropy-based methods.
Gene-gene interaction may play important roles in complex disease studies, in which interaction effects coupled with single-gene effects are active. Many interaction models have been proposed since the beginning of the last century. However, the existing approaches including statistical and data mining methods rarely consider genetic interaction models, which make the interaction results lack biological or genetic meaning. In this study, we developed an entropy-based method integrating two-locus genetic models to explore such interaction effects. We performed our method to simulated and real data for evaluation. Simulation results show that this method is effective to detect gene-gene interaction and, furthermore, it is able to identify the best-fit model from various interaction models. Moreover, our method, when applied to malaria data, successfully revealed negative epistatic effect between sickle cell anemia and alpha(+)-thalassemia against malaria.
Dong
Changzheng
C
Bioinformatics Center, Key Lab of Systems Biology, Shanghai Institutes for Biological Sciences, the Chinese Academy of Sciences, Shanghai, China.
Chu
Xun
X
Wang
Ying
Y
Wang
Yi
Y
Jin
Li
L
Shi
Tieliu
T
Huang
Wei
W
Li
Yixue
Y
D000755:Anemia, Sickle Cell / Q000235:genetics; D016022:Case-Control Studies; D002648:Child; D016000:Cluster Analysis; D003198:Computer Simulation; D019277:Entropy; D004843:Epistasis, Genetic; D020869:Gene Expression Profiling; D006801:Humans; D008288:Malaria / Q000235:genetics; D008957:Models, Genetic; D020641:Polymorphism, Single Nucleotide; D013789:Thalassemia / Q000235:genetics