Bioinformatics Core, Weill Cornell Medicine-Qatar, Education City, Doha, Qatar.
Department of Physiology and Biophysics, Weill Cornell Medicine, New York, NY, USA.
Nat Commun. 2024 Aug 19;15(1):7111. doi: 10.1038/s41467-024-51134-x.
In-depth multiomic phenotyping provides molecular insights into complex physiological processes and their pathologies. Here, we report on integrating 18 diverse deep molecular phenotyping (omics-) technologies applied to urine, blood, and saliva samples from 391 participants of the multiethnic diabetes Qatar Metabolomics Study of Diabetes (QMDiab). Using 6,304 quantitative molecular traits with 1,221,345 genetic variants, methylation at 470,837 DNA CpG sites, and gene expression of 57,000 transcripts, we determine (1) within-platform partial correlations, (2) between-platform mutual best correlations, and (3) genome-, epigenome-, transcriptome-, and phenome-wide associations. Combined into a molecular network of > 34,000 statistically significant trait-trait links in biofluids, our study portrays "The Molecular Human". We describe the variances explained by each omics in the phenotypes (age, sex, BMI, and diabetes state), platform complementarity, and the inherent correlation structures of multiomics data. Further, we construct multi-molecular network of diabetes subtypes. Finally, we generated an open-access web interface to "The Molecular Human" ( http://comics.metabolomix.com ), providing interactive data exploration and hypotheses generation possibilities.
深入的多组学表型分析为复杂的生理过程及其病理提供了分子见解。在这里,我们报告了将 18 种不同的深度分子表型(组学)技术整合到来自多民族糖尿病卡塔尔代谢组学糖尿病研究(QMDiab)的 391 名参与者的尿液、血液和唾液样本中的情况。使用 6304 个具有 1221345 个遗传变异的定量分子特征、470837 个 DNA CpG 位点的甲基化和 57000 个转录本的基因表达,我们确定了(1)在平台内的部分相关性,(2)在平台间的相互最佳相关性,以及(3)基因组、表观基因组、转录组和表型组的全基因组关联。将这些结果结合到生物流体中 >34000 个具有统计学意义的特征-特征联系的分子网络中,我们的研究描绘了“分子人类”。我们描述了每个组学在表型(年龄、性别、BMI 和糖尿病状态)中的可解释方差、平台互补性以及多组学数据的固有相关结构。此外,我们构建了糖尿病亚型的多分子网络。最后,我们生成了一个名为“分子人类”的开放访问网络界面(http://comics.metabolomix.com),提供了交互式数据探索和假设生成的可能性。