Multi-Omics Integration in a Twin Cohort and Predictive Modeling of Blood Pressure Values.
Multi-Omics Integration in a Twin Cohort and Predictive Modeling of Blood Pressure Values.
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DOI：
10.1089/omi.2021.0201
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发表时间：
2022-03
期刊：
影响因子：
--
通讯作者：
Kaprio J
中科院分区：
文献类型：
--
作者：
Drouard G;Ollikainen M;Mykkänen J;Raitakari O;Lehtimäki T;Kähönen M;Mishra PP;Wang X;Kaprio J
血压异常与高患病率疾病的风险密切相关，使血压研究成为一项重大的公共卫生挑战。虽然高血压的生物学机制在单组学水平上已经被发现，多组学综合分析使用连续变化的血压值仍然有限。我们使用了一种基于多组学回归的方法，称为稀疏多块偏最小二乘法，用于收缩压和舒张压值研究中的综合，解释和预测兴趣。从芬兰双胞胎队列中获得了多达444对双胞胎的各种数据集。包括转录组学、甲基化、代谢组学在内的组学数据块以及多基因风险评分和临床数据被整合到建模中，并得到交叉验证的支持。使用来自Young Finns研究的外部参与者研究了每个组学块在预测血压值时的预测贡献。除了揭示有趣的组学之间的关联外，我们还发现，一旦多组学数据被整合，每个组学块都会不均匀地改善血压值的预测。该模型揭示了多种与文献一致的临床、转录组学和代谢组学因素，这些因素在解释血压单位变化中起主导作用。这些发现证明了（1）我们的综合方法的鲁棒性，以利用单组学判别分析获得的结果，以及（2）多组学方法在复杂表型（如血压）研究中的预测和探索收益的附加值。
Abnormal blood pressure is strongly associated with risk of high-prevalence diseases, making the study of blood pressure a major public health challenge. Although biological mechanisms underlying hypertension at the single omic level have been discovered, multi-omics integrative analyses using continuous variations in blood pressure values remain limited. We used a multi-omics regression-based method, called sparse multi-block partial least square, for integrative, explanatory, and predictive interests in study of systolic and diastolic blood pressure values. Various datasets were obtained from the Finnish Twin Cohort for up to 444 twins. Blocks of omics—including transcriptomic, methylation, metabolomic—data as well as polygenic risk scores and clinical data were integrated into the modeling and supported by cross-validation. The predictive contribution of each omics block when predicting blood pressure values was investigated using external participants from the Young Finns Study. In addition to revealing interesting inter-omics associations, we found that each block of omics heterogeneously improved the predictions of blood pressure values once the multi-omics data were integrated. The modeling revealed a plurality of clinical, transcriptomic, and metabolomic factors consistent with the literature and that play a leading role in explaining unit variations in blood pressure. These findings demonstrate (1) the robustness of our integrative method to harness results obtained by single omics discriminant analyses, and (2) the added value of predictive and exploratory gains of a multi-omics approach in studies of complex phenotypes such as blood pressure.
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影响因子：
3.7
作者：
Boks MP;Derks EM;Weisenberger DJ;Strengman E;Janson E;Sommer IE;Kahn RS;Ophoff RA
通讯作者：
Ophoff RA
DOI：
10.1016/j.gpb.2017.07.003
发表时间：
2018-03
期刊：
Genomics, proteomics & bioinformatics
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影响因子：
3.3
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影响因子：
5.7
作者：
Domingo-Relloso A;Huan T;Haack K;Riffo-Campos AL;Levy D;Fallin MD;Terry MB;Zhang Y;Rhoades DA;Herreros-Martinez M;Garcia-Esquinas E;Cole SA;Tellez-Plaza M;Navas-Acien A
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