• Presentations and Publications
  • Software Packages
  • Resources
  • Ontology

Presentations and Publications

Quality of Prenatal and Childhood Diet Predicts Neurodevelopmental Outcomes among Children in Mexico City.

Malin AJ, Busgang SA, Cantoral AJ, Svensson K, Orjuela MA, Pantic I, Schnaas L, Oken E, Baccarelli AA, Téllez-Rojo MM, Wright RO, Gennings C. Nutrients. 2018 Aug 15;10(8).

Lagged kernel machine regression for identifying time windows of susceptibility to exposures of complex mixtures.

Liu SH, Bobb JF, Lee KH, Gennings C, Claus Henn B, Bellinger D Austin C, Schnaas L, Tellez-Rojo MM, Hu H, Wright RO, Arora M, Coull BA. Biostatistics, 2018 Jul 1;19(3):325-341.

Prenatal exposure to PM2.5 and birth weight: A pooled analysis from three North American longitudinal pregnancy cohort studies

Rosa, MJ, Pajak, A, Just, AC, Sheffield, PE, Kloog, I, Schwartz, J , Coull, B, Enlow, MB , Baccarelli, AA, Huddleston, K, Niederhuber, JE, Téllez-Rojo, MM, Wright, RO, Gennings, C, Wright, RJ Environmental International. (2017) 107:173-180.

Recurrence quantification analysis to characterize cyclical components of environmental elemental exposures during fetal and postnatal development

Curtin, P, Curtin, A, Austin, C., Gennings, C, Arora, M PLOS ONE. (2017)

Extending the distributed lag model framework to handle chemical mixtures

Bello GA, Arora M, Austin C, Horton MK, Wright RO, Gennings C. Environmental Research; 2017 156:253-264.

Toward Greater Implementation of the Exposome Research Paradigm within Environmental Epidemiology.

Stingone JA, Buck Louis GM, Nakayama SF, Vermeulen RC, Kwok RK, Cui Y, Balshaw DM, Teitelbaum SL. Annu Rev Public Health. 2017 Mar 20;38:315-327. Review.

Big and disparate data: considerations for pediatric consortia.

Stingone JA, Mervish N, Kovatch P, McGuinness DL, Gennings C, Teitelbaum SL.
Curr Opin Pediatr. 2017 Apr;29(2):231-239. Review.

Software Packages

R Package ‘gWQS’

Description: Fits Weighted Quantile Sum (WQS) regressions for continuous or binomial outcomes.
Usage: gwqs(formula, mix_name, data, q = 4, validation = 0.6, valid_var = NULL, b = 100, b1_pos = TRUE, family = "gaussian", seed = NULL, wqs2 = FALSE, plots = FALSE, tables = FALSE)



Metabolomics Tutorial

Metabolomics involves the identification and measurement of small-molecule metabolites of endogenous and exogenous origin in a biospecimen. These metabolites represent a diverse group of low-molecular-weight structures, such as lipids, amino acids, peptides, nucleic acids, organic acids, vitamins, thiols, carbohydrates, environmental chemicals, and dietary compounds. Different approaches and analytical platforms are used to detect, characterize, and quantify metabolites and related metabolic pathways, including untargeted and targeted liquid chromatography-mass spectrometry (LC-MS), gas chromatography-MS (GC-MS), and nuclear magnetic resonance (NMR). In CHEAR, most metabolomics studies use a LC-MS platform to perform untargeted metabolomics. Therefore, the purpose of the tutorial is to provide a basic overview for non-experts of how LC-MS-based untargeted metabolomics datasets are generated, which should aid in data analysis and interpretation.

CHEAR Data Submission and Review Portal - User Manual for CHEAR PI

This User Manual outlines the Major Functions and Processes supported by the CHEAR Data Submission and Review Portal, and how to use them. This manual is intended for use by Primary Investigators (i.e., “PI”s) and their CoInvestigators. These users will be accessing the portal to upload their study results data, generate CHEAR Participant IDs (PIDs) and Specimen IDs (SIDs), map CHEAR SIDs to PIDs, retrieving lab result data, and other related activities.

CHEAR Data Submission and Review Portal - User Manual for CHEAR LH

CHEAR Targeted Data Template for CHEAR LHs Wide Form

CHEAR Targeted Data Template for CHEAR LHs Long Form

File Hash Checker

If you are not sure how to verify the SHA-512 hash of the file that you downloaded or uploaded, you can use this in-browser file hasher. This will calculate the hash without uploading.


The Data Center is responsible for creating and maintaining the CHEAR Ontology—a common vocabulary for use in the CHEAR program. The Ontology is evolving with the program and will connect to best-in-class existing vocabularies, thus facilitating the integration of data from multiple studies. The Data Center assists PIs in applying the Ontology to their studies. Services include:

  • Facilitating the mapping of variables from data dictionaries into terms consistent with the CHEAR Ontology

  • Incorporating the study's data into the CHEAR Ontology to support collaborative research across the CHEAR consortium, including pooled analyses from cohort studies participating in CHEAR

  • Developing methods and services for comparing similar variables from different data dictionaries, starting with very basic mappings of equivalent terms and moving into more sophisticated analyses of relationships among variables

  • Providing tools and services to manage the CHEAR Ontology evolution