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Christopher Arehart

Predoctoral Trainee

Ecology & Evolutionary Biology

Christopher.H.Arehart@Colorado.EDU

Research Interests:

I am interested in how the intersection of disciplines such as applied mathematics, biology, and computer science can help us better understand the biological mechanisms underlying complex traits and diseases. I am passionate about how team science improves forward-thinking approaches to personalized medicine for the diverse spectrum of humankind.

Mentors:

Luke Evans, Chris Gignoux

Highlighted publications

Poly-omic risk scores predict inflammatory bowel disease diagnosis

The health burden of inflammatory bowel disease (IBD) among affected individuals is large, and its complex etiology has been studied using high throughput -omics technology. We applied a prediction framework across multiple -omics from the gut microbiome (metagenomics, metatranscriptomics, metabolomics, and viromics) to predict diagnoses of Crohn’s disease and ulcerative colitis. The predicted scores from our models illustrated key features and allowed us to compare the relative utility of each -omic data type when used individually versus when combined in a multi-omics model.

(preprint)

Boulder plot of pAUDIT (top) and GAD (bottom) interaction association p-values using imputed transcription

Transcriptome-Wide Gene-Gene Interaction Association Study Elucidates Pathways and Functional Enrichment of Complex Traits

We introduce a new approach using predicted gene expression to perform exhaustive transcriptome-wide interaction studies (TWISs) for multiple traits across all pairs of genes expressed in several tissue types. Through using imputed transcriptomes, we simultaneously reduce the computational challenge, improve interpretability, and statistical power. In this analysis discover and replicate several interaction associations, and find several hub genes with numerous interactions.