Identifying high-dimensional genomic factors associated with biological networks

Music City Center 

Gaussian graphical models are widely used to construct networks for analyzing associations among biological features e.g. gene expression, microbial taxa, and metabolites. However, there is no general statistical framework for investigating how genomic factors influence these networks, particularly when the number of candidate regulators is large. In this work, we propose an efficient algorithm to identify high-dimensional genomic factors associated with biological networks. Our two-step procedure first constructs a base network without incorporating genomic factors and then identifies genomic factors that modify edges of the inferred network. Also, we develop a permutation-based approach for accurate false discovery rate control. We illustrate the utility of our method through three applications: (i) identifying host genetic variants that regulate the oral microbiome network in the Prostate, Lung, Colorectal, and Ovarian Cancer Screening Trial, (ii) detecting metagenomic features that influence the gut metabolite network in colorectal cancer, and (iii) mapping somatic mutations that regulate gene expression networks in lung adenocarcinoma using data from The Cancer Genome Atlas.

Genomics

Gaussian graphical models

Statistical Genetics

Microbiome

Networks

High-dimensional data analysis 

Section on Statistics in Genomics and Genetics