The elusive but ubiquitous multi-factor interactions represent a stumbling block that urgently needs to be removed in searching for determinants involved in human complex diseases. The dimensionality reduction approaches are a promising tool for this task. Many complex diseases exhibit composite syndromes required to be measured in a cluster of clinical traits with varying correlations and/or are inherently longitudinal in nature (changing over time and measured dynamically at multiple time points). A multivariate approach for detecting interactions is thus greatly needed on the purposes of handling a multifaceted phenotype and longitudinal data, as well as improving statistical power for multiple significance testing via a two-stage testing procedure that involves a multivariate analysis for grouped phenotypes followed by univariate analysis for the phenotypes in the significant group(s).
In this study, Dr. Xiang-Yang Lou, associate professor in the department of biostatistics, section on statistical genetics, at the University of Alabama at Birmingham—in collaboration with department colleague Dr. Nianjun Liu—proposes a multivariate extension of generalized multifactor dimensionality reduction (GMDR) based on multivariate generalized linear, multivariate quasi-likelihood, and generalized estimating equations models.
Simulations and real data analysis for the cohort from the Study of Addiction: Genetics and Environment were performed to investigate the properties and performance of the proposed method, as compared with the univariate method. Dr. Lou and his fellow researchers concluded the results suggest that the proposed multivariate GMDR substantially boosts statistical power.
“Multivariate Dimensionality Reduction Approaches to Identify Gene-Gene and Gene-Environment Interaction Underlying Multiple Complex Traits” was published in September in PLOS One.