Case Study
Integrating Multiomics to Understand Disease Pathophysiology
These results highlight the power of integrating multiple sets of molecular traits and GWAS results to deepen understanding of disease pathophysiology.
Here, researchers combined the power of two types of omics data (metabolomics and transcriptomics) with GWAS results to shed light on disease molecular mechanisms. Integrating these data helped pinpoint metabolic pathways from genes to diseases. They discovered that genetic variants that control metabolite levels were more likely to regulate gene expression and disease risk compared to the ones that do not.
Here, researchers combined the power of two types of omics data (metabolomics and transcriptomics) with GWAS results to shed light on disease molecular mechanisms. Integrating these data helped pinpoint metabolic pathways from genes to diseases. They discovered that genetic variants that control metabolite levels were more likely to regulate gene expression and disease risk compared to the ones that do not.
The Challenge: Better Interpretation of GWAS
Genome-wide association studies (GWAS) seek to identify genes associated with a particular disease. Despite often successfully identifying disease-associated genes, the underlying biological mechanisms for most associations remain unclear. Interpreting GWAS findings remains challenging and requires other biological sources.1 Integrative analysis of GWAS and omics data will expand the interpretation of GWAS results and provide insight into the pathogenesis of complex diseases and their causative factors.
Metabolon Insight: Metabolomic Pathway Identification
The Metabolon Discovery: Global Panel profiled plasma samples from 6,136 Finnish men from the Metabolic Syndrome in Men (METSIM) study. The goal was to elucidate molecular mechanisms that underlie disease processes by integrating metabolomics data with GWAS results.
The Solution: Using Multiomics Approaches to Interpret GWAS Results
This research group integrated transcriptomics results for 49 tissues in 706 individuals, metabolomics results for 1,391 plasma metabolites in 6,136 individuals, and GWAS results for 2,861 disease traits in 260,405 individuals. Their analysis suggested the significant impact of the genome on regulating metabolite levels. First, they integrated GWAS results with metabolomics data using probabilistic transcriptome-wide association studies (PTWASs). PTWASs were performed for the 1,391 metabolites assayed to identify the impact of gene expression on plasma metabolite levels. PTWASs identified 3,914 genes associated with 1,274 metabolites. PTWASs also identified 1 to 83 genes per metabolite, for a total of 12,575 gene-metabolite pairs. They also used PTWASs and colocalization analysis to integrate transcriptomics data with metabolomics results, prioritizing 397 genes for 521 metabolites. Integrating transcriptomics and metabolomics with GWAS results identified 1,597 genes for 790 disease traits.
The Outcome: Integrating Multiomics and GWAS Results Capture the Whole Picture
Here, the researchers combined the power of two types of omics data (metabolomics and transcriptomics) with GWAS results to shed insights into disease molecular mechanisms. Integrating all these data helped pinpoint metabolic pathways from genes to diseases. They discovered that genetic variants that control metabolite levels were more likely to regulate gene expression and disease risk compared to the ones that do not. These results highlight the power of integrating multiple sets of molecular traits and GWAS results to deepen understanding of disease pathophysiology.
References
1. Yin X, Bose D, Kwon A, et al. Integrating transcriptomics, metabolomics, and GWAS helps reveal molecular mechanisms for metabolite levels and disease risk. Am J Hum Genet. Oct 06 2022;109(10):1727-1741. doi:10.1016/j.ajhg.2022.08.007
