Charleen D Adams
Research Fellows

Charleen D Adams

Research Fellow

Environmental Health



Well-rounded epidemiologist with a decade-plus experience (7 post-PhD, 10 post-MPH, 16 years total), specializing in genetic signatures to understand environmental (aka, non-nucleotide-based) impacts on health. PhD in Public Health Genetics (University of Washington), MPH in Genetic Epidemiology (Johns Hopkins University). Proven expertise combining Mendelian randomization (MR) and machine learning for causal inference. Recent use of this approach with proteomic-based biological clocks (machine-learning predictors of age). R&D guidance for a biotech start-up (Teal Omics), including a 1-2-year research plan and spearheading the acquisition of a cloud-computing platform for big-data analytics. 27 published articles, most first or senior author, and 2 under review. Experience in aging research, population neuroscience, cancer biology, rare and common diseases, chronobiology, and molecular, genetic, psychiatric, environmental, reproductive, integrative, and chronic-disease epidemiology: open to all topics in epidemiology! And all methods, including learning new ones. While specializing in integrative approaches mentioned above, I choose methods depending on the question, the data, and my collaborators’ interests and expertise: I’m for team science. I collaborate with investigators in statistical genetics, biology, ethics, psychology, and more.


16 years designing & conducting etiological, biomarker, & real-world data (RWD) studies in humans, including integrative “omics” for drug-target identification & drug repurposing.

• Biomedical Research: 3 cancer-research centers, 3 governmental agencies, & 4 universities.
• Teaching Experience: 3 universities.
• Data Science & Translational Research: Applied statistical genetics & molecular epidemiology.
• Machine Learning: Prediction & novel biomarker discovery.
• Main Epidemiological Content Areas: cancer, aging, metabolic, neuroscience, chronic disease, reproductive, & integrative.
• Coding Skills: R, command line, & high-performance computing (HPC).
• Consulting: State of Washington's Newborn Screening Program & a biotech start-up, providing R&D guidance.

Principal Scientist / Consultant, 11/2023, Causal inference with proteomics
Teal Omics, Cambridge, MA

BA, 2000, Speech pathology & audiology
Northern Arizona University, Flagstaff, AZ

MA-TESL, 2002, Applied linguistics
Northern Arizona University, Flagstaff, AZ

MPH, 2012, Genetic epidemiology
Johns Hopkins University, Baltimore, MD

Predoc, 2013, Clinical genetics
National Cancer Institute, Division of Cancer Epidemiology & Genetics, Rockville, MD

PhD, 2016, Public-health genetics (epigenetics & bioethics)
University of Washington & Fred Hutchinson Cancer Research Center, Seattle, WA

Postdoc, 2018, Mendelian randomization
University of Bristol, Integrative Epidemiology Unit, Bristol, England

Postdoc, 2020, Cancer bioinformatics
City of Hope Cancer Center, Los Angeles, CA

Lead Scientist for SEED & Postdoc for MIPS, Current, Machine learning for reproductive epidemiology & aging
Harvard University, Boston, MA


Maternal anxiety during pregnancy and newborn epigenome-wide DNA methylation.

Sammallahti S, Cortes Hidalgo AP, Tuominen S, Malmberg A, Mulder RH, Brunst KJ, Alemany S, McBride NS, Yousefi P, Heiss JA, McRae N, Page CM, Jin J, Pesce G, Caramaschi D, Rifas-Shiman SL, Koen N, Adams CD, Magnus MC, Baïz N, Ratanatharathorn A, Czamara D, Håberg SE, Colicino E, Baccarelli AA, Cardenas A, DeMeo DL, Lawlor DA, Relton CL, Felix JF, van IJzendoorn MH, Bakermans-Kranenburg MJ, Kajantie E, Räikkönen K, Sunyer J, Sharp GC, Houtepen LC, Nohr EA, Sørensen TIA, Téllez-Rojo MM, Wright RO, Annesi-Maesano I, Wright J, Hivert MF, Wright RJ, Zar HJ, Stein DJ, London SJ, Cecil CAM, Tiemeier H, Lahti J.

Mol Psychiatry. 2021 06. 26(6):1832-1845. PMID: 33414500