Contributors

Chemicals effects on children’s health

B. Eskenazi (UC Berkeley)

Brenda Eskenazi is a professor of public health at the UC Berkeley School of Public Health. She works locally and globally on the effects of environmental exposures on the health of children. She is interested in environmental exposures ranging from chemical exposure, such as pesticides and dioxins, to air pollution to climate change, and studies how these environmental exposures may interact with social adversities to affect the development of children. Her work tends to focus on populations who are of lower income and who may be at higher risk of adverse effects. Much of her research questions are answered by the conduct of birth cohort studies and she has engaged in or advised birth cohorts around the world.

Study design of exposome studies

M. Vrijheid (ISGlobal Barcelona)

Martine Vrijheid is Research Professor at the Global Health Institute (ISGlobal) in Barcelona, Spain. She previously held affiliations at the International Agency for Research on Cancer, Lyon, and at the London School of Hygiene and Tropical Medicine.
Her research focuses on the effects of environmental exposures on child health and development. Most recently, she has spearheaded the push to collect better and more complete data on multiple exposures, molecular pathways, and health outcomes during early life critical periods as leader of the HELIX (“Building the Human Early Life Exposome”) project, which involves birth cohorts in 6 European countries and was funded by EC FP7. She has further been instrumental in the building of a network of birth cohorts in Europe (under the CHICOS and ENRIECO projects), resulting in a framework for data sharing and harmonization across more than 30 European birth cohorts.
She is PI of the newly funded EU (H2020) ATHLETE exposome project.

Issues related to exposure biomarkers measurement error

R. Slama (Inserm)

Rémy Slama (PhD) is Senior Investigator at Inserm (the French Institute of Health and Medical Research) where he leads the Inserm-Grenoble-Alpes University joint research team in Environmental Epidemiology applied to Reproduction and Respiratory Health.
His research aims at characterizing the influence of environmental contaminants on human reproduction and childhood health. A specific focus is the influence of early life (intra-uterine) environmental exposures on the health of the foetus and the child (Developmental Origins of Health and Diseases, or DOHaD, concept). In that context, his team is particularly interested in the effects of atmospheric pollutants, short half-lived endocrine disruptors (phenols, phthalates) and, more recently, the exposome as a whole. His methodological research track focuses on approaches to limit exposure misclassification (such as the within-subject biospecimens pooling approach) and on study design. He has led the statistical work package of HELIX (EU) early-life exposome project and is co-PI of ATHLETE (EU H2020) ATHLETE project. He published over 170 articles.
R. Slama is president of the scientific council of the French research program on endocrine disruptors (PNRPE); he belongs to several experts groups and scientific councils related to environmental health. He received the Tony McMichael award from the International Society of Environmental Epidemiology (ISEE).

Introduction to the Superlearner and Targeted Maximum Likelihood Estimation (TMLE)

C. Kennedy (UC Berkeley)

Chris Kennedy is a BIDS Data Science Fellow and a PhD student in biostatistics, where he works with Alan Hubbard. He is also a D-Lab instructor and consultant, and an NIH biomedical big data trainee. His methodological interests encompass targeted machine learning, randomized trials, causal inference, deep learning, text analysis, signal processing, and computer vision. His applications are primarily in precision medicine, public health, genomics, and election campaigns.
His software projects include the SuperLearner ensemble learning system and varImpact for variable importance estimation; he leverages high performance computing on Savio and XSEDE clusters to accelerate his work.

General strategy for the analysis of Exposome data

X. Basagaña (IS Global Barcelona)

Xavier Basagaña earned a BSc (1999) and a MSc in Statistics (2002) from Universitat Politècnica de Catalunya (UPC), and a PhD in Biostatistics from Harvard University (2007). He worked as Assistant Research Professor at the Centre for Research in Environmental Epidemiology (CREAL) and is currently Associate Research Professor at ISGlobal. He is coordinating the European project CitieS-Health, a citizen science project on urban environment and health, and leading the ATENC!Ó project, a project investigating if air pollution can affect attention in high-school students. Apart from that, he collaborates in other projects conducting research on air pollution, heat exposure, the exposome and respiratory health.

Challenges of epigenetics epidemiology

J. Lepeule (Inserm)

Dr Johanna Lepeule is an environmental epidemiologist. She holds a researcher position at INSERM (the National Institute of Health and Medical Research) U 1209, Institute for Advanced Biosciences, Grenoble. She is also affiliated with CNRS UMR 5309 and University Grenoble Alpes. She was formerly a postdoctoral fellow under Prof. Joel Schwartz supervision and visiting researcher at Harvard School of Public Health.
Her current research interests include the modelling of environmental exposures, the evaluation of the influence of exposures to air pollutants, tobacco smoking, and meteorological conditions on health, and the investigation of the role of epigenetic marks in such associations. A specific focus of her researcher is the influence of prenatal exposures on the health of the child in the context of the DOHaD -Developmental Origins of Health and Diseases- hypothesis.

Omic data analysis with R

J.R. Gonzalez (ISGlobal)

J.R. Gonzalez is an Associate Research Professor at ISGlobal where leads the Bioinformatic Group in Genetic Epidemiology (BRGE). He has co-authored more than 150 scientific papers published in peer-reviewed journals and has ample experience in large international research projects. As an Adjunct Professor at the Department of Mathematics at Autonomous University of Barcelona, his educational activities focus on providing graduate lectures on advanced statistical methods and post-graduate lectures in biostatistics and in 'Omic' data analyses based on his book “Omic association studies with R and Bioconductor”. He is highly skilled in providing R courses for analyzing genetic data.

Clustering methods for environmental health research

V. Siroux (Inserm)

V. Siroux is senior investigator at Inserm, affiliated to Institute for Advanced Biosciences, a joint research center from Inserm, CNRS and University Grenoble Alpes located in Grenoble (France). Her research project focuses on respiratory epidemiology and is aimed to clarify the phenotypic heterogeneity and the etiology of asthma. She has experiences in analysing large-scale data, including genome-wide/epigenome-wide association studies (GWAS/EWAS) and exposome studies. She is coordinating the Epidemiological Study on the Genetics and Environment of Asthma (EGEA) and the health axis in the couple-child SEPAGES cohort. At the European level, she was / is part of several programs (GABRIEL on the genetics of asthma, ESCAPE on the health effects of air pollution, MEDALL on allergy, HELIX on exposome, SYSCLAD on Chronic Lung Allograft Dysfunction and ATHLETE on exposome).

Bayesian Hierarchical Modeling

Patrick T Bradshaw (UC Berkeley)

Patrick Bradshaw is an Assistant Professor of Epidemiology at the School of Public Health, at UC Berkeley and the Martin Sisters Endowed Chair of Medical Research and Public Health. Dr. Bradshaw received bachelors and masters degree in economics from Florida State University, a masters degree in Statistics from the University of Florida and a doctorate in epidemiology from the University of North Carolina at Chapel Hill. Dr. Bradshaw’s research is focused on obesity-related chronic disease outcomes, including cardiometabolic disorders and cancer survival. . He also has a keen interest in addressing quantitative methodological issues in these areas, including methods for multiple exposure (mixture) modeling, bias analysis and missing data.

Acknowledgment

We wish to thank France-Berkeley Fund (University of California Berkeley)