Publication Date
12-1-2023
Journal
Nature Genetics
Abstract
Methods integrating genetics with transcriptomic reference panels prioritize risk genes and mechanisms at only a fraction of trait-associated genetic loci, due in part to an overreliance on total gene expression as a molecular outcome measure. This challenge is particularly relevant for the brain, in which extensive splicing generates multiple distinct transcript-isoforms per gene. Due to complex correlation structures, isoform-level modeling from cis-window variants requires methodological innovation. Here we introduce isoTWAS, a multivariate, stepwise framework integrating genetics, isoform-level expression and phenotypic associations. Compared to gene-level methods, isoTWAS improves both isoform and gene expression prediction, yielding more testable genes, and increased power for discovery of trait associations within genome-wide association study loci across 15 neuropsychiatric traits. We illustrate multiple isoTWAS associations undetectable at the gene-level, prioritizing isoforms of AKT3, CUL3 and HSPD1 in schizophrenia and PCLO with multiple disorders. Results highlight the importance of incorporating isoform-level resolution within integrative approaches to increase discovery of trait associations, especially for brain-relevant traits.
Keywords
Humans, Transcriptome, Genome-Wide Association Study, Quantitative Trait Loci, Genetic Predisposition to Disease, Brain, Protein Isoforms, Polymorphism, Single Nucleotide
Included in
Bioinformatics Commons, Biomedical Informatics Commons, Internal Medicine Commons, Neurology Commons, Neurosciences Commons, Psychiatric and Mental Health Commons
Comments
Supplementary Materials
PMID: 38036788