Epistasis Blog

From the Computational Genetics Laboratory at the University of Pennsylvania (www.epistasis.org)

Wednesday, November 21, 2018

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

I love seeing new extensions and modifications to our MDR method. Here is a new from Dr. Lou.

Hou TT, Lin F, Bai S, Cleves MA, Xu HM, Lou XY. Generalized multifactor dimensionality reduction approaches to identification of genetic interactions underlying ordinal traits. Genet Epidemiol, in press (2018)

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 method has a higher power and more accurate predictive ability than the GMDR methods based on a logit model and a multinomial logit model. We applied this new method to the genetic analysis of low‐density lipoprotein (LDL) cholesterol, a causal risk factor for coronary artery disease, in the Multi‐Ethnic Study of Atherosclerosis, and identified a significant joint action of the CELSR2, SERPINA12, HPGD, and APOB genes. This finding provides new information to advance the limited knowledge about genetic regulation and gene interactions in metabolic pathways of LDL cholesterol. In conclusion, the proportional odds model‐based GMDR is a useful tool that can boost statistical power and prediction accuracy in studying multifactor interactions underlying ordinal traits.

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