A forest-based approach to identifying gene and gene gene interactions.
Multiple genes, gene-by-gene interactions, and gene-by-environment interactions are believed to underlie most complex diseases. However, such interactions are difficult to identify. Although there have been recent successes in identifying genetic variants for complex diseases, it still remains difficult to identify gene-gene and gene-environment interactions. To overcome this difficulty, we propose a forest-based approach and a concept of variable importance. The proposed approach is demonstrated by simulation study for its validity and illustrated by a real data analysis for its use. Analyses of both real data and simulated data based on published genetic models show the effectiveness of our approach. For example, our analysis of a published data set on age-related macular degeneration (AMD) not only confirmed a known genetic variant (P value = 2E-6) for AMD, but also revealed an unreported haplotype surrounding single-nucleotide polymorphism (SNP) rs10272438 on chromosome 7 that was significantly associated with AMD (P value = 0.0024). These significance levels are obtained after the consideration for a large number of SNPs. Thus, the importance of this work is twofold: it proposes a powerful and flexible method to identify high-risk haplotypes and their interactions and reveals a potentially protective variant for AMD.
Chen
Xiang
X
Department of Epidemiology and Public Health, Yale University School of Medicine, New Haven, CT 06520-8034, USA.
Liu
Ching-Ti
CT
Zhang
Meizhuo
M
Zhang
Heping
H
D003198:Computer Simulation; D003627:Data Interpretation, Statistical; D004777:Environment; D004843:Epistasis, Genetic; D008040:Genetic Linkage; D020022:Genetic Predisposition to Disease / Q000235:genetics*; D006239:Haplotypes; D006801:Humans; D008268:Macular Degeneration / Q000235:genetics; D008957:Models, Genetic; D020641:Polymorphism, Single Nucleotide