Current methods integrating genetics with transcriptomic reference panels prioritize risk genes and mechanisms at only a fraction of trait-associated genetic loci, due to an overreliance on total gene expression. This is particularly relevant for the brain, with extensive splicing generating multiple transcript isoforms per gene. This paper introduces isoTWAS, a multivariate framework integrating genetics, isoform-level expression, and phenotypic associations. Compared to gene-level methods, isoTWAS improves isoform and gene expression prediction, yielding more testable genes and increased power for discovering trait associations across 15 neuropsychiatric traits. isoTWAS reveals associations undetectable at the gene level, prioritizing isoforms of AKT3, CUL3, HSPD1 in schizophrenia and PCLO across multiple disorders. Results highlight the importance of isoform-level resolution in integrative approaches.

Arjun Bhattacharya, Daniel D. Vo, Connor Jops, Minsoo Kim, Cindy Wen, Jonatan L. Hervoso, Bogdan Pasaniuc, Michael J. Gandal