# Improving Prediction of Asthma-related Outcomes with Genetic Ancestry-informed Lung Function Equations

> **NIH NIH K23** · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · 2024 · $174,016

## Abstract

PROJECT SUMMARY
Lung function measurements are routinely compared to racial/ethnic norms, biasing interpretation and
perpetuating asthma disparities. The race/ethnicity-based lung function reference equations used to calculate
these norms do not account for genetic ancestry—the genetic origin of one’s population, which can explain over
15% of lung function variation within a racial/ethnic group. Consequently, race/ethnicity-based equations
misestimate lung function, often resulting in delayed disease detection and inadequate treatment, especially
among populations disproportionately affected by asthma. Dr. Witonsky (candidate) derived equations that use
genetic ancestry instead of race/ethnicity to more accurately predict lung function. While genetic ancestry-
informed equations appear to remove racial/ethnic bias from lung function measurement, establishing their
clinical utility and equity requires evidence that they better predict asthma-related outcomes. In addition, further
research is needed to disentangle the social and genetic determinants of genetic ancestry differences in lung
function. The proposed mentored research will address these knowledge gaps using data from existing and new
cohorts of Black and Hispanic/Latino individuals with and without asthma via three specific aims: (1) to evaluate
genetic ancestry-informed, race/ethnicity-based, and “one size fits all” lung function equations for predicting
asthma-related outcomes, (2) to quantify the proportion of genetic ancestry differences in lung function that is
explained by social exposures, and (3) to quantify the proportion of genetic ancestry differences in lung function
that is explained by known lung function-associated genetic loci. In support of this research and Dr. Witonsky’s
goal of becoming an independent clinical investigator, this K23 proposal includes formal training with experts in
the areas of asthma translational and clinical research (Dr. Prescott Woodruff, primary mentor); advanced
statistical and predictive analytic methods (Dr. Stephen Shiboski, co-mentor); social epidemiology and health
disparities research (Dr. Luísa Borrell, co-mentor); genetic epidemiology (Dr. Elad Ziv, co-mentor); and statistical
genetics (Dr. Noah Zaitlen, advisor). In addition, professional development planning will involve structured
meetings with Dr. Woodruff and a leader within Dr. Witonsky’s Division of Pediatric Allergy, Immunology, and
Bone Marrow Transplant (Dr. Morna Dorsey, advisor). As a faculty member in the Department of Pediatrics at
the University of California, San Francisco, Dr. Witonsky will have access to world-class biomedical and research
facilities, workshops and seminars, and an NCATS-funded K Scholars Program. Completion of the proposed
research and career development activities in this application will inform the development of an R01 proposal
and enable Dr. Witonsky to develop an innovative research program applying computational precision health
methods that int...

## Key facts

- **NIH application ID:** 10893574
- **Project number:** 5K23HL169911-02
- **Recipient organization:** UNIVERSITY OF CALIFORNIA, SAN FRANCISCO
- **Principal Investigator:** Jonathan Witonsky
- **Activity code:** K23 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $174,016
- **Award type:** 5
- **Project period:** 2023-08-01 → 2028-07-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10893574

## Citation

> US National Institutes of Health, RePORTER application 10893574, Improving Prediction of Asthma-related Outcomes with Genetic Ancestry-informed Lung Function Equations (5K23HL169911-02). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10893574. Licensed CC0.

---

*[NIH grants dataset](/datasets/nih-grants) · CC0 1.0*