A1 Refereed original research article in a scientific journal
Multi-Omics Integration in a Twin Cohort and Predictive Modeling of Blood Pressure Values
Authors: Drouard Gabin, Ollikainen Miina, Mykkänen Juha, Raitakari Olli, Lehtimaki Terho, Kähönen Mika, Mishra Pashupati P, Wang Xiaoling L, Kaprio Jaakko
Publisher: MARY ANN LIEBERT, INC
Publication year: 2022
Journal: OMICS
Journal name in source: OMICS-A JOURNAL OF INTEGRATIVE BIOLOGY
Journal acronym: OMICS
Volume: 26
Issue: 3
First page : 130
Last page: 141
Number of pages: 12
ISSN: 1536-2310
DOI: https://doi.org/10.1089/omi.2021.0201
Web address : https://doi.org/10.1089/omi.2021.0201
Self-archived copy’s web address: https://research.utu.fi/converis/portal/detail/Publication/174873052
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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