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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\u2014including transcriptomic, methylation, metabolomic\u2014data 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.<\/jats:p>","DOI":"10.1089\/omi.2021.0201","type":"journal-article","created":{"date-parts":[[2022,3,8]],"date-time":"2022-03-08T17:00:59Z","timestamp":1646758859000},"page":"130-141","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":22,"title":["Multi-Omics Integration in a Twin Cohort and Predictive Modeling of Blood Pressure Values"],"prefix":"10.1177","volume":"26","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3227-8483","authenticated-orcid":false,"given":"Gabin","family":"Drouard","sequence":"first","affiliation":[{"name":"Institute for Molecular Medicine Finland (FIMM), HiLIFE, University of Helsinki, Helsinki, Finland."}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Miina","family":"Ollikainen","sequence":"additional","affiliation":[{"name":"Institute for Molecular Medicine Finland (FIMM), HiLIFE, University of Helsinki, Helsinki, Finland."}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Juha","family":"Mykk\u00e4nen","sequence":"additional","affiliation":[{"name":"Centre for Population Health Research, University of Turku and Turku University Hospital, Turku, Finland."},{"name":"Research Centre of Applied and Preventive Cardiovascular Medicine, University of Turku, Turku, Finland."}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Olli","family":"Raitakari","sequence":"additional","affiliation":[{"name":"Centre for Population Health Research, University of Turku and Turku University Hospital, Turku, Finland."},{"name":"Research Centre of Applied and Preventive Cardiovascular Medicine, University of Turku, Turku, Finland."},{"name":"Department of Clinical Physiology and Nuclear Medicine, Turku University Hospital, Turku, Finland."}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Terho","family":"Lehtim\u00e4ki","sequence":"additional","affiliation":[{"name":"Department of Clinical Chemistry, Fimlab Laboratories and Finnish Cardiovascular Research Center-Tampere, Faculty of Medicine and Health Technology, Tampere University, Tampere, Finland."}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mika","family":"K\u00e4h\u00f6nen","sequence":"additional","affiliation":[{"name":"Department of Clinical Physiology, Tampere University Hospital, and Finnish Cardiovascular Research Center-Tampere, Faculty of Medicine and Health Technology, Tampere University, Tampere, Finland."}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Pashupati P.","family":"Mishra","sequence":"additional","affiliation":[{"name":"Department of Clinical Chemistry, Fimlab Laboratories and Finnish Cardiovascular Research Center-Tampere, Faculty of Medicine and Health Technology, Tampere University, Tampere, Finland."}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiaoling","family":"Wang","sequence":"additional","affiliation":[{"name":"Georgia Prevention Institute (GPI), Medical College of Georgia, Augusta University, Augusta, Georgia, USA."}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jaakko","family":"Kaprio","sequence":"additional","affiliation":[{"name":"Institute for Molecular Medicine Finland (FIMM), HiLIFE, University of Helsinki, Helsinki, Finland."}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"179","published-online":{"date-parts":[[2022,3,8]]},"reference":[{"key":"e_1_3_6_2_1","doi-asserted-by":"publisher","DOI":"10.1111\/j.1467-9876.2007.00613.x"},{"key":"e_1_3_6_3_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-1-62703-059-5_23"},{"key":"e_1_3_6_4_1","doi-asserted-by":"publisher","DOI":"10.1007\/s00125-019-05001-w"},{"key":"e_1_3_6_5_1","doi-asserted-by":"publisher","DOI":"10.1161\/CIRCRESAHA.118.311342"},{"key":"e_1_3_6_6_1","doi-asserted-by":"publisher","DOI":"10.1161\/CIRCULATIONAHA.120.049819"},{"key":"e_1_3_6_7_1","doi-asserted-by":"publisher","DOI":"10.1093\/bioinformatics\/btu049"},{"key":"e_1_3_6_8_1","first-page":"188","article-title":"Blood pressure prediction by a smartphone sensor using fully convolutional networks","volume":"2020","author":"Baek S","year":"2020","unstructured":"Baek S, , Jang J, , Cho SH, , Choi JM, , and Yoon S. 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