Christopher H. Arehart

The landscape of polygenic score prediction accuracy and transportability across the phenome in ancestrally diverse populations

This study evaluates polygenic score prediction accuracy and cross-ancestry portability across 3,994 PGSs in diverse populations, identifying factors affecting their performance and equity.

Biological network identifies pathways linking mental health drugs to adverse weight-related side effects

The paper uses network pharmacology to identify biological pathways linking mental health drugs to weight-related side effects, highlighting key molecular mechanisms.

A simple approach for multiple observations improves power to detect genetic effects and genomic prediction accuracy.

This paper shows that using the mean or median of multiple observations improves GWAS power and polygenic prediction accuracy by reducing phenotypic noise.

Modeling the genomic architecture of adiposity and anthropometrics across the lifespan

This study models the genomic architecture of adiposity and anthropometrics across the lifespan, identifying shared and distinct genetic factors and their associations with health outcomes.

Prediction and Characterization of Genetically Regulated Expression of Target Tissues in Asthma

The paper develops gene expression prediction models for asthma-related tissues in African-ancestry populations and identifies 17 candidate causal asthma genes using transcriptome-wide association study.

Characterizing substructure via mixture modeling in large-scale genetic summary statistics

Summix2 uses mixture modeling to estimate and adjust for substructure in genetic summary data, improving the robustness and equity of research using diverse datasets.

Poly-omic risk scores predict inflammatory bowel disease diagnosis

The paper develops a multi-omic prediction framework to diagnose IBD using metagenomic, metatranscriptomic, metabolomic, and viromic data, finding that metabolomics and viromics provide stronger predictive power than other omics.

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

This study introduces a method to analyze gene-gene interactions across multiple traits and tissues, identifying novel associations and pathways through transcriptome-wide interaction studies.