AbstractInflammatory Bowel Disease (IBD) is characterized by complex etiology and a disrupted colonic ecosystem. We provide a framework for the analysis of multi-omic data, which we apply to study the gut ecosystem in IBD. Specifically, we train and validate models using data on the metagenome metatranscriptome, virome, and metabolome from the Human Microbiome Project 2 IBD Multi-omics Database, with 1,785 repeated samples from 131 individuals (103 cases, 27 controls). After splitting the participants into training and testing groups, we used mixed effects least absolute shrinkage and selection operator (LASSO) regression to select features for each -omic. These features, with demographic covariates, were used to generate separate single-omic prediction scores. All four single-omic scores were then combined into a final regression to assess the relative importance of the individual -omics and the predictive benefits when considered together. We identified several species, pathways, and metabolites known to be associated with IBD risk, and we explored the connections between datasets. Individually, metabolomics and viromics scores were more predictive than metagenomics or metatranscriptomics, and when all four scores were combined, we predicted disease diagnosis with a Nagelkerke’s R2of 0.46 and an AUC of 0.80 [95% CI: 0.63, 0.98]. Our work suggests that some single-omic models for complex traits are more predictive than others, that incorporating multiple -omics datasets may improve prediction, and that each -omic data type provides a combination of unique and redundant information. This modeling framework can be extended to other complex traits and multi-omic datasets.ImportanceComplex traits are characterized by many biological and environmental factors, such that multi-omics datasets are well-positioned to help us understand their underlying etiologies. We applied a prediction framework across multiple -omics (metagenomics, metatranscriptomics, metabolomics, and viromics) from the gut ecosystem to predict inflammatory bowel disease (IBD) diagnosis. The predicted scores from our models highlighted key features and allowed us to compare the relative utility of each -omic dataset in single-omic versus multi-omics models. Our results emphasized the importance of metabolomics and viromics over metagenomics and metatranscriptomics for predicting IBD status. The greater predictive capability of metabolomics and viromics is likely because these -omics serve as markers of lifestyle factors such as diet. This study provides a modeling framework for multi-omic data, and our results show the utility of combining multiple -omic data types to disentangle complex disease etiologies and biological signatures.
