Generalized multifactor dimensionality reduction approaches to identification of genetic interactions underlying ordinal traits.

ABSTRACT: The manifestation of complex traits is influenced by gene-gene and gene-environment interactions, and the identification of multifactor interactions is an important but challenging undertaking for genetic studies. Many complex phenotypes such as disease severity are measured on an ordinal scale with more than two categories. A proportional odds model can improve statistical power for these outcomes, when compared to a logit model either collapsing the categories into two mutually exclusive groups or limiting the analysis to pairs of categories. In this study, we propose a proportional odds model-based generalized multifactor dimensionality reduction (GMDR) method for detection of interactions underlying polytomous ordinal phenotypes. Computer simulations demonstrated that this new GMDR met

SUBMITTER: Hou TT 

PROVIDER: S-EPMC8495755 | biostudies-literature | 2019 Feb

REPOSITORIES: biostudies-literature