A multi-modal framework improves prediction of tissue-specific gene expression from a surrogate tissue

作者：Yue Xu, Chunfeng He, Jiayao Fan, Yuan Zhou, Chunxiao Cheng, Ran Meng, Ya Cui, Wěi Li, Eric R. Gamazon, Dan Zhou · 发表于：EBioMedicine · 年份：2024 · DOI：10.1016/j.ebiom.2024.105305 · 被引用次数：3 · 研究领域：Genetic Associations and Epidemiology、Cardiovascular Disease and Adiposity、Single-cell and spatial transcriptomics

BACKGROUND: Tissue-specific analysis of the transcriptome is critical to elucidating the molecular basis of complex traits, but central tissues are often not accessible. We propose a methodology, Multi-mOdal-based framework to bridge the Transcriptome between PEripheral and Central tissues (MOTPEC). METHODS: Multi-modal regulatory elements in peripheral blood are incorporated as features for gene expression prediction in 48 central tissues. To demonstrate the utility, we apply it to the identification of BMI-associated genes and compare the tissue-specific results with those derived directly from surrogate blood. FINDINGS: MOTPEC models demonstrate superior performance compared with both baseline models in blood and existing models across the 48 central tissues. We identify a set of BMI-associated genes using the central tissue MOTPEC-predicted transcriptome data. The MOTPEC-based differential gene expression (DGE) analysis of BMI in the central tissues (including brain caudate basal ganglia and visceral omentum adipose tissue) identifies 378 genes overlapping the results from a TWAS of BMI, while only 162 overlapping genes are identified using gene expression in blood. Cellular perturbation analysis further supports the utility of MOTPEC for identifying trait-associated gene sets and narrowing the effect size divergence between peripheral blood and central tissues. INTERPRETATION: The MOTPEC framework improves the gene expression prediction accuracy for central tissues and e...