Structural equation modeling analysis for causal inference from multiple omics datasets
Seminar Room 1, Newton Institute
AbstractRecent developments in technology allow us to collect multiple highly-dimensional 'omics' datasets from thousands of individuals in a highly standardized and unbiased manner. Open questions remain how best to integrate the multiple omics datasets to un- derstand underlying biological mechanisms and infer causal pathways. We have begun exploring causal relationships between genetic variants, clinically-relevant quantitative phenotypes and metabolomics datasets using Structural Equation Modeling (SEM), ap- plied to a subset of the common disease loci identified from genome-wide association studies. We provide proof-of-principle evidence that SEM analysis is able to identify reproducible path models supporting association of SNPs to intermediate phenotypes through metabolomics intermediates. We address further challenges arising from the analysis of multiple omics datasets and suggest future directions including nonlinear model based approaches and the simultaneous dimension reduction (or variable selection) methods.
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