A database of human exposomes and phenomes from the US National Health and Nutrition Examination Survey

Patel, Chirag; Pho, Nam; McDuffie, Michael; Easton-Marks, Jeremy R.; Kothari, Cartik R.; Kohane, Isaac S.; Avillach, Paul

doi:10.1038/sdata.2016.96

Cited by 111 publications

(80 citation statements)

References 27 publications

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“…The survey consists of dietary, socioeconomic, and general health measurements obtained through both questionnaire responses and clinical laboratory measurements. Curated data for 41,474 individuals and 1,191 variables across four survey periods (1999-2000, 2001-2002, 2003-2004, 2005-2006), are freely available on GitHub (https://github.com/chiragjp/nhanes_scidata) and through the NHANES Dataset Explorer (Patel et al, 2016;Kohane and Avilach, 2018).…”

Section: Methods Nhanes Datasetmentioning

confidence: 99%

“…To demonstrate this, we applied CLARITE to a novel EWAS of body mass index (BMI) using data from the National Health and Nutrition Examination Survey (NHANES), obtained from https://github. com/chiragjp/nhanes_scidata (Patel et al, 2016;Kohane and Avilach, 2018), which contains over 1,100 measures on a total of 22,624 adult samples across four surveys from 1999-2006. Here, we present our QC pipeline and EWAS on categorical, binary, and continuous exposures (including questionnaire, nutrient, and pharmaceutical measures, among others) on BMI in Discovery and Replication datasets.…”

Section: Introductionmentioning

confidence: 99%

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CLARITE Facilitates the Quality Control and Analysis Process for EWAS of Metabolic-Related Traits

et al. 2019

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While genome-wide association studies are an established method of identifying genetic variants associated with disease, environment-wide association studies (EWAS) highlight the contribution of nongenetic components to complex phenotypes. However, the lack of high-throughput quality control (QC) pipelines for EWAS data lends itself to analysis plans where the data are cleaned after a first-pass analysis, which can lead to bias, or are cleaned manually, which is arduous and susceptible to user error. We offer a novel software, CLeaning to Analysis: Reproducibility-based Interface for Traits and Exposures (CLARITE), as a tool to efficiently clean environmental data, perform regression analysis, and visualize results on a single platform through user-guided automation. It exists as both an R package and a Python package. Though CLARITE focuses on EWAS, it is intended to also improve the QC process for phenotypes and clinical lab measures for a variety of downstream analyses, including phenome-wide association studies and geneenvironment interaction studies. With the goal of demonstrating the utility of CLARITE, we performed a novel EWAS in the National Health and Nutrition Examination Survey (NHANES) (N overall Discovery=9063, N overall Replication=9874) for body mass index (BMI) and over 300 environment variables post-QC, adjusting for sex, age, race, socioeconomic status, and survey year. The analysis used survey weights along with cluster and strata information in order to account for the complex survey design. Sixteen BMI results replicated at a Bonferroni corrected p < 0.05. The top replicating results were serum levels of g-tocopherol (vitamin E) (Discovery Bonferroni p: 8.67x10-12 , Replication Bonferroni p: 2.70x10-9) and iron (Discovery Bonferroni p: 1.09x10-8 , Replication Bonferroni p: 1.73x10-10). Results of this EWAS are important to consider for metabolic trait analysis, as BMI is tightly associated with these phenotypes. As such, exposures predictive of BMI may be useful for covariate and/or interaction assessment of metabolic-related traits. CLARITE allows improved data quality for EWAS, gene-environment interactions, and phenome-wide association studies by establishing a high-throughput quality control infrastructure. Thus, CLARITE is recommended for studying the environmental factors underlying complex disease.

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Section: Methods Nhanes Datasetmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

CLARITE Facilitates the Quality Control and Analysis Process for EWAS of Metabolic-Related Traits

et al. 2019

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“…PMS_DN leverages the capabilities of the i2b2/tranSMART knowledge management platform (Patel et al, 2016; Perakslis, van Dam, & Szalma, 2010; Scheufele et al, 2014; Szalma, Koka, Khasanova, & Perakslis, 2010) to integrate heterogeneous datasets—including phenome, exposome, and genome data—and to facilitate browsing and comparative analysis of these datasets. The i2b2/tranSMART platform is layered upon the Informatics for Integrating Biology with Bedside (i2b2) clinical and biomedical data integration platform (Kohane, Churchill, & Murphy, 2012; Murphy et al, 2010).…”

Section: Methodsmentioning

confidence: 99%

Phelan‐McDermid syndrome data network: Integrating patient reported outcomes with clinical notes and curated genetic reports

Kothari

Wack

Hassen‐Khodja

et al. 2017

American J of Med Genetics Pt B

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The heterogeneity of patient phenotype data are an impediment to the research into the origins and progression of neuropsychiatric disorders. This difficulty is compounded in the case of rare disorders such as Phelan‐McDermid Syndrome (PMS) by the paucity of patient clinical data. PMS is a rare syndromic genetic cause of autism and intellectual deficiency. In this paper, we describe the Phelan‐McDermid Syndrome Data Network (PMS_DN), a platform that facilitates research into phenotype–genotype correlation and progression of PMS by: a) integrating knowledge of patient phenotypes extracted from Patient Reported Outcomes (PRO) data and clinical notes—two heterogeneous, underutilized sources of knowledge about patient phenotypes—with curated genetic information from the same patient cohort and b) making this integrated knowledge, along with a suite of statistical tools, available free of charge to authorized investigators on a Web portal https://pmsdn.hms.harvard.edu. PMS_DN is a Patient Centric Outcomes Research Initiative (PCORI) where patients and their families are involved in all aspects of the management of patient data in driving research into PMS. To foster collaborative research, PMS_DN also makes patient aggregates from this knowledge available to authorized investigators using distributed research networks such as the PCORnet PopMedNet. PMS_DN is hosted on a scalable cloud based environment and complies with all patient data privacy regulations. As of October 31, 2016, PMS_DN integrates high‐quality knowledge extracted from the clinical notes of 112 patients and curated genetic reports of 176 patients with preprocessed PRO data from 415 patients.

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“…However, individual-level exposome data sets must also be archived and shared for their continued use and integration for data-driven discovery (e.g., 44). Progress in archiving and sharing these types of data is dependent on the continued development, adoption, and dissemination of standardized data dictionaries and ontologies that enable exposure nomenclature (analogous to gene names) and that catalog how exposures are measured (e.g., Figure 2).…”

Section: Data Standards and Infrastructure Requirements For Human Expmentioning

confidence: 99%

Informatics and Data Analytics to Support Exposome-Based Discovery for Public Health

Manrai

Cui

Bushel

et al. 2017

Annu. Rev. Public Health

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111

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The complexity of the human exposome—the totality of environmental exposures encountered from birth to death—motivates systematic, high-throughput approaches to discover new environmental determinants of disease. In this review, we describe the state of science in analyzing the human exposome and provide recommendations for the public health community to consider in dealing with analytic challenges of exposome-based biomedical research. We describe extant and novel analytic methods needed to associate the exposome with critical health outcomes and contextualize the data-centered challenges by drawing parallels to other research endeavors such as human genomics research. We discuss efforts for training scientists who can bridge public health, genomics, and biomedicine in informatics and statistics. If an exposome data ecosystem is brought to fruition, it will likely play a role as central as genomic science has had in molding the current and new generations of biomedical researchers, computational scientists, and public health research programs.

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A database of human exposomes and phenomes from the US National Health and Nutrition Examination Survey

Cited by 111 publications

References 27 publications

CLARITE Facilitates the Quality Control and Analysis Process for EWAS of Metabolic-Related Traits

CLARITE Facilitates the Quality Control and Analysis Process for EWAS of Metabolic-Related Traits

Phelan‐McDermid syndrome data network: Integrating patient reported outcomes with clinical notes and curated genetic reports

Informatics and Data Analytics to Support Exposome-Based Discovery for Public Health

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