Multi-Omics Integration in a Twin Cohort and Predictive Modeling of Blood Pressure Values

Gabin Drouard, Miina Ollikainen, Juha Mykkänen, Olli Raitakari, Terho Lehtimäki, Mika Kähönen, Pashupati P. Mishra, Xiaoling Wang, Jaakko Kaprio

Research output: Contribution to journalArticlepeer-review

Abstract

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.

Original languageEnglish (US)
Pages (from-to)130-141
Number of pages12
JournalOMICS A Journal of Integrative Biology
Volume26
Issue number3
DOIs
StatePublished - Mar 2022

Keywords

  • blood pressure
  • hypertension
  • multi-omics
  • phenomics
  • predictive modeling
  • sparse multi-block partial least square
  • twins

ASJC Scopus subject areas

  • Biotechnology
  • Biochemistry
  • Molecular Medicine
  • Molecular Biology
  • Genetics

Fingerprint

Dive into the research topics of 'Multi-Omics Integration in a Twin Cohort and Predictive Modeling of Blood Pressure Values'. Together they form a unique fingerprint.

Cite this