Efficient Two-Step Testing of Gene-Gene Interactions in Genome-Wide Association Studies

2013 ◽  
Vol 37 (5) ◽  
pp. 440-451 ◽  
Author(s):  
Juan Pablo Lewinger ◽  
John L. Morrison ◽  
Duncan C. Thomas ◽  
Cassandra E. Murcray ◽  
David V. Conti ◽  
...  
Keyword(s):  
Association Studies ◽  
Genome Wide Association ◽  
Gene Interactions ◽  
Genome Wide Association Studies ◽  
Genome Wide

scholarly journals Gene-Based Testing of Interactions Using XGBoost in Genome-Wide Association Studies

2021 ◽  
Vol 9 ◽  
Author(s):  
Yingjie Guo ◽  
Chenxi Wu ◽  
Zhian Yuan ◽  
Yansu Wang ◽  
Zhen Liang ◽  
...  
Keyword(s):  
Association Studies ◽  
Real Data ◽  
Gene Interaction ◽  
Genome Wide Association ◽  
Superior Performance ◽  
Gene Interactions ◽  
Genome Wide Association Studies ◽  
Nucleotide Polymorphisms ◽  
Genome Wide ◽  
The Difference

Among the myriad of statistical methods that identify gene–gene interactions in the realm of qualitative genome-wide association studies, gene-based interactions are not only powerful statistically, but also they are interpretable biologically. However, they have limited statistical detection by making assumptions on the association between traits and single nucleotide polymorphisms. Thus, a gene-based method (GGInt-XGBoost) originated from XGBoost is proposed in this article. Assuming that log odds ratio of disease traits satisfies the additive relationship if the pair of genes had no interactions, the difference in error between the XGBoost model with and without additive constraint could indicate gene–gene interaction; we then used a permutation-based statistical test to assess this difference and to provide a statistical p-value to represent the significance of the interaction. Experimental results on both simulation and real data showed that our approach had superior performance than previous experiments to detect gene–gene interactions.

Sign in / Sign up

Export Citation Format

Share Document