BACKGROUND: Reduction in the cost of genomic assays has generated large amounts of biomedical-related data. As a result, current studies perform multiple experiments in the same subjects. While Bioconductor's methods and classes implemented in different packages manage individual experiments, there is not a standard class to properly manage different omic datasets from the same subjects. In addition, most R/Bioconductor packages that have been designed to integrate and visualize biological data often use basic data structures with no clear general methods, such as subsetting or selecting samples. RESULTS: To cover this need, we have developed MultiDataSet, a new R class based on Bioconductor standards, designed to encapsulate multiple data sets. MultiDataSet deals with the usual difficulties of managing multiple and non-complete data sets while offering a simple and general way of subsetting features and selecting samples. We illustrate the use of MultiDataSet in three common situations: 1) performing integration analysis with third party packages; 2) creating new methods and functions for omic data integration; 3) encapsulating new unimplemented data from any biological experiment.CONCLUSIONS: MultiDataSet is a suitable class for data integration under R and Bioconductor framework. ; This work has been partly funded by the Spanish Ministry of Economy and Competitiveness (MTM2015-68140-R). CH-F was supported by a grant from European Community's Seventh Framework Programme (FP7/2007-2013) under grant agreement no 308333 – the HELIX project. CR-A was supported by a FI fellowship from Catalan Government (#016FI_B 00272)
DNA methylation profiles of aggressive behavior may capture lifetime cumulative effects of genetic, stochastic, and environmental influences associated with aggression. Here, we report the first large meta-analysis of epigenome-wide association studies (EWAS) of aggressive behavior (N = 15,324 participants). In peripheral blood samples of 14,434 participants from 18 cohorts with mean ages ranging from 7 to 68 years, 13 methylation sites were significantly associated with aggression (alpha = 1.2 × 10-7; Bonferroni correction). In cord blood samples of 2425 children from five cohorts with aggression assessed at mean ages ranging from 4 to 7 years, 83% of these sites showed the same direction of association with childhood aggression (r = 0.74, p = 0.006) but no epigenome-wide significant sites were found. Top-sites (48 at a false discovery rate of 5% in the peripheral blood meta-analysis or in a combined meta-analysis of peripheral blood and cord blood) have been associated with chemical exposures, smoking, cognition, metabolic traits, and genetic variation (mQTLs). Three genes whose expression levels were associated with top-sites were previously linked to schizophrenia and general risk tolerance. At six CpGs, DNA methylation variation in blood mirrors variation in the brain. On average 44% (range = 3-82%) of the aggression-methylation association was explained by current and former smoking and BMI. These findings point at loci that are sensitive to chemical exposures with potential implications for neuronal functions. We hope these results to be a starting point for studies leading to applications as peripheral biomarkers and to reveal causal relationships with aggression and related traits. ; This work was supported by ACTION. ACTION receives funding from the European Union Seventh Framework Program (FP7/2007–2013) under grant agreement no 602768.
IMPORTANCE: The balance of mercury risk and nutritional benefit from fish intake during pregnancy for the metabolic health of offspring to date is unknown. OBJECTIVE: To assess the associations of fish intake and mercury exposure during pregnancy with metabolic syndrome in children and alterations in biomarkers of inflammation in children. DESIGN, SETTING, AND PARTICIPANTS: This population-based prospective birth cohort study used data from studies performed in 5 European countries (France, Greece, Norway, Spain, and the UK) between April 1, 2003, and February 26, 2016, as part of the Human Early Life Exposome (HELIX) project. Mothers and their singleton offspring were followed up until the children were aged 6 to 12 years. Data were analyzed between March 1 and August 2, 2019. EXPOSURES: Maternal fish intake during pregnancy (measured in times per week) was assessed using validated food frequency questionnaires, and maternal mercury concentration (measured in micrograms per liter) was assessed using maternal whole blood and cord blood samples. MAIN OUTCOMES AND MEASURES: An aggregate metabolic syndrome score for children was calculated using the z scores of waist circumference, systolic and diastolic blood pressures, and levels of triglyceride, high-density lipoprotein cholesterol, and insulin. A higher metabolic syndrome score (score range, -4.9 to 7.5) indicated a poorer metabolic profile. Three protein panels were used to measure several cytokines and adipokines in the plasma of children. RESULTS: The study included 805 mothers and their singleton children. Among mothers, the mean (SD) age at cohort inclusion or delivery of their infant was 31.3 (4.6) years. A total of 400 women (49.7%) had a high educational level, and 432 women (53.7%) were multiparous. Among children, the mean (SD) age was 8.4 (1.5) years (age range, 6-12 years). A total of 453 children (56.3%) were boys, and 734 children (91.2%) were of white race/ethnicity. Fish intake consistent with health recommendations (1 to 3 times per week) during pregnancy was associated with a 1-U decrease in metabolic syndrome score in children (β = -0.96; 95% CI, -1.49 to -0.42) compared with low fish consumption (<1 time per week) after adjusting for maternal mercury levels and other covariates. No further benefit was observed with fish intake of more than 3 times per week. A higher maternal mercury concentration was independently associated with an increase in the metabolic syndrome score of their offspring (β per 2-fold increase in mercury concentration = 0.18; 95% CI, 0.01-0.34). Compared with low fish intake, moderate and high fish intake during pregnancy were associated with reduced levels of proinflammatory cytokines and adipokines in children. An integrated analysis identified a cluster of children with increased susceptibility to metabolic disease, which was characterized by low fish consumption during pregnancy, high maternal mercury levels, decreased levels of adiponectin in children, and increased levels of leptin, tumor necrosis factor α, and the cytokines interleukin 6 and interleukin 1β in children. CONCLUSIONS AND RELEVANCE: Results of this study suggest that moderate fish intake consistent with current health recommendations during pregnancy was associated with improvements in the metabolic health of children, while high maternal mercury exposure was associated with an unfavorable metabolic profile in children. ; This study was supported by grant 308333 from the European Community Seventh Framework Programme; grant 874583 from the European Union Horizon 2020 Research and Innovation Programme; grant SEV-2012-0208 from the Centro de Excelencia Severo Ochoa 2013-2017, Spanish Ministry of Science, Innovation and Universities; grant 2017SGR595 from the Secretaria d'Universitats i Recerca del Departament d'Economia i Coneixement de la Generalitat de Catalunya; grant CB06/021/0041 from the Consorcio de Investigación Biomedica en Red de Epidemiologia y Salud Publica; grant 1999SGR00241 from the Comissió Interdepartamental de Recerca i Innovació Tecnologica, Generalitat de Catalunya; grant 31V-66 from the Lithuanian Agency for Science Innovation and Technology; grant PT17/0019 via the Plan Estatal de I+D+I 2013- 2016 project from the Instituto de Salud Carlos III and the European Regional Development Fund; grants R21 ES029681 and P30 ES007048-23 from the National Institute for Health Sciences (Dr Stratakis); grant P30 DK048522-24 from the National Institute of Diabetes and Digestive and Kidney Diseases (Dr Stratakis); grants P01CA196569, R01CA140561, and R01 ES016813 from the National Institute for Health Sciences (Dr Conti); grant MS16/00128 from the Ministry of Economy and Competitiveness at the Instituto de Salud Carlos III (Dr Casas); grants R21 ES029681, P30 ES007048-23, and P01 ES022845 from the National Institute for Health Sciences (Dr McConnell); grant RD-83544101 from the Environmental Protection Agency (Dr McConnell); and grants R01 ES029944, R21 ES029681, R21 ES028903, and P30 ES007048-23 from the National Institute for Health Sciences (Dr Chatzi)
Multivariate methods have the potential to better capture complex relationships that may exist between different biological levels. Multiple Factor Analysis (MFA) is one of the most popular methods to obtain factor scores and measures of discrepancy between data sets. However, singular value decomposition in MFA is based on PCA, which is adequate only if the data is normally distributed, linear or stationary. In addition, including strongly correlated variables can overemphasize the contribution of the estimated components. In this work, we introduced a novel method referred as Independent Multifactorial Analysis (ICA-MFA) to derive relevant features from multiscale data. This method is an extended implementation of MFA, where the component value decomposition is based on Independent Component Analysis. In addition, ICA-MFA incorporates a predictive step based on an Independent Component Regression. We evaluated and compared the performance of ICA-MFA with both, the MFA method and traditional univariate analyses, in a simulation study. We showed how ICA-MFA explained up to 10-fold more variance than MFA and univariate methods. We applied the proposed algorithm in a study of 4057 individuals belonging to the population-based Rotterdam Study with available genetic and neuroimaging data, as well as information about executive cognitive functioning. Specifically, we used ICA-MFA to detect relevant genetic features related to structural brain regions, which in turn were involved, in the mechanisms of executive cognitive function. The proposed strategy makes it possible to determine the degree to which the whole set of genetic and/or neuroimaging markers contribute to the variability of the symptomatology jointly, rather than individually. While univariate results and MFA combinations only explained a limited proportion of variance (less than 2%), our method increased the explained variance (10%) and allowed the identification of significant components that maximize the variance explained in the model. The potential application of the ICA-MFA algorithm constitutes an important aspect of integrating multivariate multiscale data, specifically in the field of Neurogenetics. ; Natalia Vilor-Tejedor is funded by a pre-doctoral grant from the Agència de Gestió d'Ajuts Universitaris i de Recerca (2017 FI_B 00636), Generalitat de Catalunya – Fons Social Europeu. This work has been partially supported by a STSM Grant from EU COST Action 15120 Open Multiscale Systems Medicine (OpenMultiMed) and Centro de Investigación Biomédica en Red de Epidemiología y Salud Pública (CIBERESP). Further support was obtained through the Ministerio de Economía e Innovación (Spain), grant MTM2015-68140-R. ISGlobal is a member of the CERCA Programme, Generalitat de Catalunya. Silvia Alemany thanks the Institute of Health Carlos III for her Sara Borrell postdoctoral grant (CD14/00214). The generation and management of GWAS genotype data for the Rotterdam Study are supported by the Netherlands Organization of Scientific Research NWO Investments (no. 175.010.2005.011, 911-03- 012). This study is funded by the Research Institute for Diseases in the Elderly (014-93-015; RIDE2), the Netherlands Genomics Initiative (NGI)/ Netherlands Organization for Scientific Research (NWO) project no. 050- 060-810. The Rotterdam Study is funded by Erasmus Medical Center and Erasmus University, Rotterdam, Netherlands Organization for the Health Research and Development (ZonMw), the Research Institute for Diseases in the Elderly (RIDE), the Ministry of Education, Culture and Science, the Ministry for Health, Welfare and Sports, the European Commission (DG XII), and the Municipality of Rotterdam. This research is supported by the Dutch Technology Foundation STW (12723), which is part of the NWO, and which is partly funded by the Ministry of Economic Affairs. This project has received funding from the European Research Council (ERC) under the European Union's Horizon 2020 research and innovation programme (project: ORACLE, grant agreement No: 678543)
