Festus M. Nyasimi

MetaboXcan predicts plasma metabolites from genetic data and associates them with complex traits, identifying disease risk genes and metabolites in chronic kidney disease.

RatXcan extends transcriptome-wide association studies to rats, enabling cross-species integration of GWAS and gene expression data to identify shared genetic mechanisms of complex traits.

BrainXcan uses genetic and imaging data to identify brain features linked to psychiatric traits, finding reduced axonal density and hippocampal features associated with schizophrenia risk.

scPrediXcan improves transcriptome-wide association studies by integrating deep learning for cell-type-specific gene expression prediction, identifying more causal genes and explaining more GWAS loci in type 2 diabetes and systemic lupus erythematosus.

The paper shows that polygenicity inflates false positives in transcriptome-wide association studies and proposes a variance control method to correct for this.