Genome-wide association studies (GWAS) for nonsyndromic cleft lip with or without cleft palate (CL/P) have identified multiple genes as important in the etiology of this common birth defect. We performed a candidate gene/pathway analysis explicitly considering gene-gene (G × G) interaction to further explore the etiology of CL/P. Animal models have shown the WNT signaling pathway plays an important role in mid-facial development, and various genes in this pathway have been associated with nonsyndromic CL/P in previous studies. We propose a combined approach to search for possible G × G interactions using machine learning and regression-based methods to test for interactions between genes in the WNT family, and between these genes and other genes identified by GWAS in case-parent trios. Using this combined approach of regression-based and machine learning methods in CL/P case-parent trios, we found robust evidence of G × G interaction between markers in WNT5B and MAFB (empiric P-values = 0.0076 among Asian trios and P-values = 0.018 among European trios). Additional evidence for epistatic interaction between markers in WNT5A, IRF6, and C1orf107 was seen among Asian trios, and markers in the 8q24 region and WNT5B among European trios.
Keywords: complex disease; family-based study; machine learning.
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