SCZ risk genes.

Identifying risk genes associated with complex traits remains challenging. Integrating gene expression data with Genome-Wide Association Study (GWAS) through Transcriptome-Wide Association Study (TWAS) methods has discovered candidate risk genes for various complex traits. Splicing, which explains a comparable heritability of complex traits as gene expression, is under-explored due to its multidimensionality. To leverage multiple splicing events in a gene and shared splicing across tissues, we develop Multi-tissue Splicing Gene (MTSG), which employs tensor decomposition and sparse Canonical Correlation Analysis (sCCA) to extract meaningful information from high-dimensional multiple splicing events across multiple tissues. We build MTSG models using GTEx data and apply them to GWAS summary statistics of Alzheimer’s disease (AD) (111,326 cases and 677,663 controls) and schizophrenia (SCZ) (36,989 cases and 113,075 controls). We identify 174 and 497 significant splicing-mediated risk genes for AD and SCZ, respectively, at Bonferroni correction. For AD, our results demonstrate significant enrichment of AD related pathways and identify additional AD risk genes not detected in the single-tissue analysis, while preserving most top genes identified in the brain frontal cortex. Consistently, for SCZ, genes identified by our brain-wide MTSG model, built from a cluster of 13 brain tissues, exhibit stronger enrichment in SCZ-relevant genes and MTSG identifies unique SCZ risk genes compared to single-tissue models. These results showcase that our MTSG models capture distinctive splicing events across tissues, which might be overlooked when using single tissue alone. Our MTSG models can be applied to other complex traits to help identify splicing-mediated disease risk genes.

识别与复杂性状相关的风险基因仍具挑战性。通过转录组全关联研究（Transcriptome-Wide Association Study, TWAS）方法整合基因表达数据与全基因组关联研究（Genome-Wide Association Study, GWAS），已为多种复杂性状发掘出候选风险基因。可变剪接对复杂性状的遗传力解释程度与基因表达相当，但其具有多维特性，相关研究仍有待深入。为利用基因内的多种剪接事件及跨组织共享的剪接信息，我们开发了多组织剪接基因（Multi-tissue Splicing Gene, MTSG）模型，该模型采用张量分解（Tensor decomposition）与稀疏典型相关分析（sparse Canonical Correlation Analysis, sCCA），从多组织的高维多重剪接事件中提取有效信息。我们利用基因型-组织表达（Genotype-Tissue Expression, GTEx）数据集构建MTSG模型，并将其应用于阿尔茨海默病（Alzheimer’s disease, AD，含111326例病例与677663例对照）及精神分裂症（schizophrenia, SCZ，含36989例病例与113075例对照）的GWAS汇总统计数据。经邦费罗尼校正（Bonferroni correction）后，我们分别鉴定出174个与AD相关、497个与SCZ相关的显著剪接介导风险基因。针对AD，我们的研究结果显示其显著富集于AD相关通路，且鉴定出了单组织分析未检测到的额外AD风险基因，同时保留了大脑额叶皮层中鉴定出的多数顶级风险基因。同样，针对SCZ，我们基于13个脑组织集群构建的全脑MTSG模型所鉴定的基因，在SCZ相关基因中富集度更高，且相较于单组织模型，MTSG可识别出独特的SCZ风险基因。上述结果表明，我们的MTSG模型能够捕捉到跨组织的独特剪接事件，而仅使用单组织分析时可能会忽略这些事件。我们的MTSG模型可应用于其他复杂性状，助力鉴定剪接介导的疾病风险基因。