Integrative omics, the joint analysis of outcome and multiple types of omics data, such as genomics, epigenomics and transcriptomics data, constitutes a promising approach for powerful and biologically relevant association studies. These studies often employ a case-control design, and often include non-omics covariates, such as age and gender, that may modify the underlying omics risk factors. An open question is how to best integrate multiple omics and non-omics information to maximize statistical power in case-control studies that ascertain individuals based on the phenotype. Recent work on integrative omics have used prospective approaches, modeling case-control status conditional on omics and non-omics risk factors. Compared to univariate approaches, jointly analyzing multiple risk factors with a prospective approach increases power in non-ascertained cohorts. However, these prospective approaches often lose power in case-control studies. In this article, we propose a novel statistical method for integrating multiple omics and non-omics factors in case-control association studies. Our method is based on a retrospective likelihood function that models the joint distribution of omics and non-omics factors conditional on case-control status. This directory contains the R implementation of the method.
BrunildaBalliu/IntegrativeOmics
R implementation of http://onlinelibrary.wiley.com/doi/10.1002/gepi.21884/abstract
R