skip to content

Structural equation modeling analysis for causal inference from multiple omics datasets

Monday 26th September 2011 - 14:50 to 15:00
INI Seminar Room 1
Recent 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.
The video for this talk should appear here if JavaScript is enabled.
If it doesn't, something may have gone wrong with our embedded player.
We'll get it fixed as soon as possible.
University of Cambridge Research Councils UK
    Clay Mathematics Institute London Mathematical Society NM Rothschild and Sons