Tensor decomposition of multi-dimensional splicing events across multiple tissues to identify splicing-mediated risk genes associated with complex traits.

IF 3.6 2区 生物学 Q1 BIOCHEMICAL RESEARCH METHODS
PLoS Computational Biology Pub Date : 2025-07-21 eCollection Date: 2025-07-01 DOI:10.1371/journal.pcbi.1013303
Yan Yan, Rui Chen, Hakmook Kang, Yuting Tan, Anshul Tiwari, Siyuan Ma, Zhexing Wen, Xue Zhong, Bingshan Li
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引用次数: 0

Abstract

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.

张量分解跨多个组织的多维剪接事件,以确定剪接介导的与复杂性状相关的风险基因。
识别与复杂性状相关的风险基因仍然具有挑战性。通过转录组全关联研究(TWAS)方法将基因表达数据与全基因组关联研究(GWAS)相结合,发现了多种复杂性状的候选风险基因。剪接解释了与基因表达相当的复杂性状的遗传能力,由于其多维性而未得到充分探索。为了利用基因中的多个剪接事件和跨组织的共享剪接,我们开发了多组织剪接基因(MTSG),该基因利用张量分解和稀疏典型相关分析(sCCA)从多个组织的高维多个剪接事件中提取有意义的信息。我们使用GTEx数据建立MTSG模型,并将其应用于阿尔茨海默病(AD)(111,326例,677,663例对照)和精神分裂症(SCZ)(36,989例,113,075例对照)的GWAS汇总统计。我们在Bonferroni校正中分别鉴定出174个和497个重要的AD和SCZ剪接介导的风险基因。对于阿尔茨海默病,我们的研究结果显示了阿尔茨海默病相关通路的显著富集,并确定了在单组织分析中未检测到的额外的阿尔茨海默病风险基因,同时保留了在大脑额叶皮层中发现的大多数顶级基因。对于SCZ,我们的全脑MTSG模型(由13个脑组织组成的集群构建)所鉴定的基因在SCZ相关基因中表现出更强的富集,与单一组织模型相比,MTSG识别出独特的SCZ风险基因。这些结果表明,我们的MTSG模型捕获了不同组织之间独特的剪接事件,这在单独使用单个组织时可能会被忽略。我们的MTSG模型可以应用于其他复杂性状,以帮助识别剪接介导的疾病风险基因。
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来源期刊
PLoS Computational Biology
PLoS Computational Biology BIOCHEMICAL RESEARCH METHODS-MATHEMATICAL & COMPUTATIONAL BIOLOGY
CiteScore
7.10
自引率
4.70%
发文量
820
审稿时长
2.5 months
期刊介绍: PLOS Computational Biology features works of exceptional significance that further our understanding of living systems at all scales—from molecules and cells, to patient populations and ecosystems—through the application of computational methods. Readers include life and computational scientists, who can take the important findings presented here to the next level of discovery. Research articles must be declared as belonging to a relevant section. More information about the sections can be found in the submission guidelines. Research articles should model aspects of biological systems, demonstrate both methodological and scientific novelty, and provide profound new biological insights. Generally, reliability and significance of biological discovery through computation should be validated and enriched by experimental studies. Inclusion of experimental validation is not required for publication, but should be referenced where possible. Inclusion of experimental validation of a modest biological discovery through computation does not render a manuscript suitable for PLOS Computational Biology. Research articles specifically designated as Methods papers should describe outstanding methods of exceptional importance that have been shown, or have the promise to provide new biological insights. The method must already be widely adopted, or have the promise of wide adoption by a broad community of users. Enhancements to existing published methods will only be considered if those enhancements bring exceptional new capabilities.
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