Isoform-level transcriptome-wide association uncovers genetic risk mechanisms for neuropsychiatric disorders in the human brain.

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. A multivariate framework for isoform-resolution transcriptome-wide association studies enables modeling of a greater number of genes, with the benefit of identifying isoform-specific associations with psychiatric traits not observed at the gene level.

将遗传学与转录组参考面板相结合的方法仅在一小部分与性状相关的基因位点上确定风险基因和机制的优先级，部分原因是过度依赖总基因表达作为一种分子结果衡量指标。这一挑战对于大脑尤其相关，因为大脑中广泛的剪接作用使每个基因产生多种不同的转录异构体。由于复杂的相关性结构，从顺式窗口变异进行异构体水平的建模需要方法上的创新。在此我们介绍isoTWAS，这是一个整合遗传学、异构体水平表达和表型关联的多变量、逐步框架。与基因水平的方法相比，isoTWAS提高了异构体和基因表达的预测能力，产生了更多可检验的基因，并提高了在15种神经精神性状的全基因组关联研究位点中发现性状关联的能力。我们展示了在基因水平无法检测到的多种isoTWAS关联，确定了精神分裂症中AKT3、CUL3和HSPD1的异构体以及与多种疾病相关的PCLO的异构体的优先级。研究结果强调了在综合方法中纳入异构体水平分辨率以增加性状关联发现的重要性，特别是对于与大脑相关的性状。 一个用于异构体分辨率的转录组全关联研究的多变量框架能够对更多的基因进行建模，其优势在于能够识别与精神性状相关的异构体特异性关联，而这些关联在基因水平上是无法观察到的。