Xue Chao, Zhou Miao
Medical College, Jiaying University, Meizhou 514031, China.
Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou 510080, China.
Biology (Basel). 2025 May 16;14(5):554. doi: 10.3390/biology14050554.
The tissues of origin and molecular mechanisms underlying human complex diseases remain incompletely understood. Previous studies have leveraged transcriptomic data to interpret genome-wide association studies (GWASs) for identifying disease-relevant tissues and fine-mapping causal genes. However, according to the central dogma, proteins more directly reflect cellular molecular activities than RNA. Therefore, in this study, we integrated proteomic data with GWAS to identify disease-associated tissues and genes.
We compiled proteomic and paired transcriptomic data for 12,229 genes across 32 human tissues from the GTEx project. Using three tissue inference approaches-S-LDSC, MAGMA, and DESE-we analyzed GWAS data for six representative complex diseases (bipolar disorder, schizophrenia, coronary artery disease, Crohn's disease, rheumatoid arthritis, and type 2 diabetes), with an average sample size of 260 K. We systematically compared disease-associated tissues and genes identified using proteomic versus transcriptomic data.
Tissue-specific protein abundance showed a moderate correlation with RNA expression (mean correlation coefficient = 0.46, 95% CI: 0.42-0.49). Proteomic data accurately identified disease-relevant tissues, such as the association between brain regions and schizophrenia and between coronary arteries and coronary artery disease. Compared to GWAS-based gene association estimates alone, incorporating proteomic data significantly improved gene association detection (AUC difference test, = 0.0028). Furthermore, proteomic data revealed unique disease-associated genes that were not identified using transcriptomic data, such as the association between bipolar disorder and .
Integrating proteomic data enables accurate identification of disease-associated tissues and provides irreplaceable advantages in fine-mapping genes for complex diseases.
人类复杂疾病的起源组织和分子机制仍未完全明确。先前的研究利用转录组数据来解读全基因组关联研究（GWAS），以识别与疾病相关的组织并精细定位因果基因。然而，根据中心法则，蛋白质比RNA更直接地反映细胞分子活动。因此，在本研究中，我们将蛋白质组数据与GWAS整合，以识别与疾病相关的组织和基因。
我们整理了来自基因型-组织表达（GTEx）项目的32个人类组织中12229个基因的蛋白质组和配对转录组数据。使用三种组织推断方法——S-LDSC、MAGMA和DESE——我们分析了六种代表性复杂疾病（双相情感障碍、精神分裂症、冠状动脉疾病、克罗恩病、类风湿性关节炎和2型糖尿病）的GWAS数据，平均样本量为26万。我们系统地比较了使用蛋白质组数据与转录组数据识别出的与疾病相关的组织和基因。
组织特异性蛋白质丰度与RNA表达呈中等程度的相关性（平均相关系数 = 0.46，95%置信区间：0.42 - 0.49）。蛋白质组数据准确地识别出了与疾病相关的组织，如脑区与精神分裂症之间以及冠状动脉与冠状动脉疾病之间的关联。与仅基于GWAS的基因关联估计相比，纳入蛋白质组数据显著提高了基因关联检测能力（AUC差异检验， = 0.0028）。此外，蛋白质组数据揭示了一些使用转录组数据未识别出的与疾病相关的独特基因，如双相情感障碍与 之间的关联。
整合蛋白质组数据能够准确识别与疾病相关组织，并在复杂疾病基因精细定位中提供不可替代的优势。
