TY - JOUR
T1 - Multi-Omics Integration in a Twin Cohort and Predictive Modeling of Blood Pressure Values
AU - Drouard, Gabin
AU - Ollikainen, Miina
AU - Mykkänen, Juha
AU - Raitakari, Olli
AU - Lehtimäki, Terho
AU - Kähönen, Mika
AU - Mishra, Pashupati P.
AU - Wang, Xiaoling
AU - Kaprio, Jaakko
N1 - Funding Information:
The Young Finns Study has been financially supported by the Academy of Finland: grants 322098, grants 338395, 330809, and 104821, 286284, 134309 (Eye), 126925, 121584, 124282, 129378 (Salve), 117787 (Gendi), and 41071 (Skidi); the Social Insurance Institution of Finland; Competitive State Research Financing of the Expert Responsibility area of Kuopio, Tampere and Turku University Hospitals (Grant X51001); Juho Vainio Foundation; Paavo Nurmi Foundation; Finnish Foundation for Cardiovascular Research; Finnish Cultural Foundation; the Sigrid Juselius Foundation; Tampere Tuberculosis Foundation; Emil Aaltonen Foundation; Yrjö Jahnsson Foundation; Signe and Ane Gyllenberg Foundation; and Diabetes Research Foundation of Finnish Diabetes Association.
Funding Information:
This project has received funding from the European Union’s Horizon 2020 research and innovation program under grant agreements No. 848146 for To Aition and grant agreement 755320 for TAXINOMISIS; European Research Council (Grant 742927 for MULTIEPIGEN project); Tampere University Hospital Supporting Foundation, Finnish Society of Clinical Chemistry and the Cancer Foundation Finland (for Terho Lehtimäki Grant No.) (decision day November 16, 2016).
Funding Information:
The FTC has been supported by the Academy of Finland (Grants 265240, 263278, 308248, 312073, 336832 to Jaakko Kaprio and 297908 to Miina Ollikainen) and the Sigrid Juselius Foundation (to Miina Ollikainen). The DNA methylation study in FTC was supported by NIH/NHLBI grant HL104125.
Publisher Copyright:
© Gabin Drouard, et al., 2022. Published by Mary Ann Lierbert, Inc. 2022.
PY - 2022/3
Y1 - 2022/3
N2 - 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.
AB - 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.
KW - blood pressure
KW - hypertension
KW - multi-omics
KW - phenomics
KW - predictive modeling
KW - sparse multi-block partial least square
KW - twins
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U2 - 10.1089/omi.2021.0201
DO - 10.1089/omi.2021.0201
M3 - Article
C2 - 35259029
AN - SCOPUS:85126079261
SN - 1536-2310
VL - 26
SP - 130
EP - 141
JO - OMICS A Journal of Integrative Biology
JF - OMICS A Journal of Integrative Biology
IS - 3
ER -