美国宾夕法尼亚大学Michael J. Gandal和美国德克萨斯大学MD安德森癌症中心Arjun Bhattacharya共同合作，近期取得重要工作进展。他们通过同种异构体水平的转录组全关联揭示了人脑神经精神疾病的遗传风险机制。相关研究成果2023年11月30日在线发表于《自然—遗传学》杂志上。

据介绍，将遗传学与转录组相结合的方法仅在一小部分性状相关遗传基因座上优先考虑风险基因和机制，部分原因是过度依赖总基因表达作为分子结果测量。这一挑战与大脑特别相关，在大脑中，广泛的剪接为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，顺式窗口变体的异构体水平建模需要方法创新。

研究人员介绍了isoTWAS工具，这是一个整合遗传学、同种型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改进了异构体和基因表达预测，产生了更多可测试的基因，并提高了在15个神经精神特征的全基因组关联研究基因座中发现特征关联的能力。研究人员阐明了在基因水平上无法检测到的多种异TWAS关联，在患有多种疾病的精神分裂症和PCLO中优先考虑AKT3、CUL3和HSPD1的亚型。

总之，这一研究结果强调了在综合方法中结合异构体水平分辨率的重要性，以增加对特征关联的发现，特别是对大脑相关特征的发现。

附：英文原文

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

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

Issue&Volume: 2023-11-30

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.

DOI: 10.1038/s41588-023-01560-2

Source: https://www.nature.com/articles/s41588-023-01560-2