# Contextualizing and Addressing Population-Level Bias in Social Epigenomics Study of Asthma in Childhood

> **NIH NIH R01** · CHILDREN'S MERCY HOSP (KANSAS CITY, MO) · 2022 · $301,930

## Abstract

SUMMARY
6.1 million children in the US currently suffer from asthma, making it the most common chronic disease
experienced during childhood. Significant racial and ethnic disparities exist with African American (AA) children
being 8 times more likely to die of asthma relative to non-Hispanic white children. Genetic, environmental, and
psychosocial factors are believed to jointly cause the disease by affecting biological pathways related to asthma
pathophysiology. Within our parent R01 award (5R01MD015409) – abbreviated as the “Stress, Epigenome and
Asthma” (SEA) study, we hypothesize that exposure to psychosocial stress in childhood may act at a mechanistic
(biological) level impacting the function of our genome by epigenetic modifications. To test our hypothesis, we
are collecting large amounts of data in a prospective social epigenomics study of asthmatic AA children/families
including high-resolution epigenetic profiles, comprehensive social determinants of health (SDOH), and chronic
stress information. While we propose within the parent award to make the ‘omics’ dataset ready for downstream
AI/ML approaches we recognize the need to also prepare our SDOH and chronic stress data for similar
applications which is however outside of the scope of the parent award. Specifically, we argue the SEA study
data will greatly benefit from use of AI/ML techniques such as ensemble models that are capable of naively
capturing differential outcomes across combinations of features. However, given that exposure to chronic
stressors is tied to a child’s social environment, to develop reliable models will require significant efforts to
prepare and contextualize the collected data. We hypothesize this can be accomplished through the linking of
collected social and clinical data with disparate population level datasets. Our supplement will address two aims:
1) We will develop novel quantitative measures to define the representativeness of study participant data. By
utilizing publicly available population-level data (e.g., Census data) we will develop a framework to compare the
sociodemographic profile of study participations against an expected distribution of individuals in a geographic
reference area. And, by doing so, identify subgroups that may misaligned to the community on which results are
expected to generalize. By further linking this alignment to data quality measures (e.g., missingness), we can
create a standardized tool to convey the dataset’s intrinsic biases on population subsets to aid in designing
analyses and interpreting AI/ML model results; and 2) We will extend traditional AI/ML imputation preprocessing
methods to account for socioeconomic factors. Understanding that chronic stress is deeply interconnected with
children’s social environment and that sampling is not balanced by geographic region, current imputation
estimates for data in subgroups with a high degree of missingness, would be primarily driven by relationships
found in cohorts with m...

## Key facts

- **NIH application ID:** 10593797
- **Project number:** 3R01MD015409-03S1
- **Recipient organization:** CHILDREN'S MERCY HOSP (KANSAS CITY, MO)
- **Principal Investigator:** Elin Grundberg
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $301,930
- **Award type:** 3
- **Project period:** 2020-08-26 → 2025-04-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10593797

## Citation

> US National Institutes of Health, RePORTER application 10593797, Contextualizing and Addressing Population-Level Bias in Social Epigenomics Study of Asthma in Childhood (3R01MD015409-03S1). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10593797. Licensed CC0.

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