Detecting gene-gene interactions for complex quantitative traits using generalized fuzzy classification

Quantitative traits or continuous outcomes related to complex diseases can provide more information and therefore more accurate analysis for identifying gene-gene and gene- environment interactions associated with complex diseases. Multifactor Dimensionality Reduction (MDR) is originally proposed to identify gene-gene and gene- environment interactions associated with binary status of complex diseases. Some efforts have been made to extend it to quantitative traits (QTs) and ordinal traits. However these and other methods are still not computationally efficient or effective.

Generalized Fuzzy Quantitative trait MDR (GFQMDR) is proposed in this paper to strengthen identification of gene-gene interactions associated with a quantitative trait by first transforming it to an ordinal trait and then selecting best sets of genetic markers, mainly single nucleotide polymorphisms (SNPs) or simple sequence length polymorphic markers (SSLPs), as having strong association with the trait through generalized fuzzy classification using extended member functions. Experimental results on simulated datasets and real datasets show that our algorithm has better success rate, classification accuracy and consistency in identifying gene-gene interactions associated with QTs.

The proposed algorithm provides a more effective way to identify gene-gene interactions associated with quantitative traits.