Location: Arkansas Children's Nutrition CenterTitle: Generalized multifactor dimensionality reduction approaches to identification of genetic interactions underlying ordinal traits
|HOU, TING-TING - University Arkansas For Medical Sciences (UAMS)|
|LIN, FENG - Zhejiang University|
|BAI, SHASHA - Arkansas Children'S Nutrition Research Center (ACNC)|
|CLEVES, MARIO - University Arkansas For Medical Sciences (UAMS)|
|XU, HAI-MING - University Arkansas For Medical Sciences (UAMS)|
|LOU, XIANG-YANG - University Arkansas For Medical Sciences (UAMS)|
Submitted to: Genetic Epidemiology
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 9/21/2018
Publication Date: 11/2/2018
Citation: Hou, T., Lin, F., Bai, S., Cleves, M.A., Xu, H., Lou, X. 2018. Generalized multifactor dimensionality reduction approaches to identification of genetic interactions underlying ordinal traits. Genetic Epidemiology. https://doi.org/10.1002/gepi.22169.
Interpretive Summary: Most, if not all, biological traits including human development and disease susceptibility are controlled by complex interactions between genetic, environmental, lifestyle, and other factors. Identifying interactions between different genes and these other factors is the crucial first step in understanding of the mechanism of a complex trait. So-called "ordinal" phenotypic traits (traits that fit into one of several ordered categories) are an important part of complex outcomes in humans, and relate to such things as healthy development of the body's systems, disease avoidance or severity, and resistance to pathogens. In this study, we present a new statistical method for detecting the interactions underlying ordinal phenotype traits by integrating a dimensionality reduction algorithm with a proportional odds model. Computer simulation showed that our method is superior in statistical power and prediction accuracy compared to existing methods. To determine applicability of the method to health outcomes, we identified a noteworthy four-gene interaction, namely CELSR2, SERPINA12, HPGD, and APOB genes, using the low-density lipoprotein (LDL) cholesterol data (measured by five categories with ordering) in the Multi-Ethnic Study of Atherosclerosis. This provides new information to advance the limited knowledge about genetic regulation and gene interactions in metabolic pathways of LDL cholesterol. Since LDL cholesterol is considered as the most important risk factor for coronary heart disease, this finding may facilitate development of more effective prevention and intervention strategies for coronary heart disease in the future. In addition, identifying the gene interactions of cholesterol regulation could have implications in determining how one's lifelong blood cholesterol metabolism is controlled and programmed by maternal or early-life factors.
Technical 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.