Data from: Using the generalized index of dissimilarity to detect gene-gene interactions in multi-class phenotypes

To find genetic association between complex diseases and phenotypic
traits, one important procedure is conducting a joint analysis.
Multifactor dimensionality reduction (MDR) is an efficient method of
examining the interactions between genes in genetic association studies.
It commonly assumes a dichotomous classification of the binary phenotypes.
Its usual approach to determining the genomic association is to construct
a confusion matrix to estimate a classification error, where a binary risk
status is determined and assigned to each genotypic multifactor class.
While multi-class phenotypes are commonly observed, the current MDR
approach does not handle these phenotypes appropriately because the
thresholds for the risk statuses may not be clear. In this study, we
suggest a new method for estimating gene-gene interactions for multi-class
phenotypes. Our approach adopts the index of dissimilarity (IDS) as an
evaluation measure. This is analytically equivalent to the common
association measure of balanced accuracy (BA) for the binary traits, while
it is not required to determine the risk status for the estimation.
Moreover, it is easily expandable to the generalized index of
dissimilarity (GIDS), which has an explicit form that can handle any
number of categories. The performance of the proposed method was compared
with those of other approaches via simulation studies in which fifteen
genetic models were generated with three class outcomes. A consistently
better performance was observed using the proposed method. The effect of a
varying number of categories was examined. The proposed method was also
illustrated using real genome-wide association studies (GWAS) data from
the Korean Association Resource (KARE) project.