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Association study (especially the genome-wide association study) now has a key function in identification and characterization of disease-predisposing genetic variant(s), which customarily involve multiple single nucleotide polymorphisms (SNPs) in a candidate region or across the genome. Case-control association design remains the most popular and a challenging issue in the statistical analysis is the optimal use of all information contained in these SNPs. Previous approaches often treated gene-gene interaction as deviation from additive genetic effects or replaced it with SNP-SNP interaction. However, these approaches are limited for their failure of consideration of gene-gene interaction or gene-gene co-association at gene level. Although the co-association of the SNPs within a candidate gene can be detected by principal component analysis-based logistic regression model, the detection of co-association between genes in genome remains uncertain. Here, we proposed a canonical correlation-based U statistic (CCU) for detecting gene-based gene-gene co-association in the case-control design. We explored its type I error rates and power through simulation and analyzed two real data sets. By treating gene as a functional unit in analysis, we found that CCU was a strong alternative to previous approaches. We discussed the performance of CCU as a gene-based gene-gene co-association statistic and the prospect of further improvement.

A gene-based method for detecting gene–gene co-association in a case–control association study