# A comprehensive imaging genetics framework for osteoarthritis research

> **NIH NIH R01** · UNIV OF NORTH CAROLINA CHAPEL HILL · 2024 · $705,837

## Abstract

Osteoarthritis (OA) is the most common form of arthritis and affects millions of people in the United States. No
disease-modifying drugs exist. Hence, treatment is currently limited to pain management, exercise and weight loss, or
ultimately total joint replacement. Approaches which can identify early signs of OA and OA progression are critically
needed for drug development. To better understand OA and to help identify genes associated with OA which might be
targetable for drug development, genome-wide association studies (GWAS) have been conducted. As GWAS typically
require large sample sizes, meta-analyses have commonly been used to combine data from OA-specific and large-scale
population studies reaching close to a million samples.
However, endpoints for such studies are generally defined by radiographic diagnosis of OA, total joint replacement, ICD
codes, or self-reported OA. Hence, complicating the differentiation of genetic associations with early signs of OA
progression from genetic associations with later OA disease stages. Several studies indicate the utility of image
biomarkers for GWAS; but these imaging genetics (IG) studies only use relatively simple image biomarkers (such as bone
shape, bone area, alpha angle, or minimum joint space width). On the other hand, deep learning approaches (DL), which
can use image information in a more comprehensive way, have been successfully applied to segment cartilage and bone
from images at scale and have shown promise for disease prediction. Hence, this project will develop advanced image
biomarkers and related statistical approaches to improve OA GWAS results and to identify druggable genes associated
with early OA disease stages. By learning image biomarkers from longitudinal data we will directly link biomarkers to
progression.
Unfortunately, while OA image datasets amenable to IG exist, those are typically either relatively small, not OA-specific,
or provide heterogenous imaging modalities and acquisitions. This project will therefore develop computational
approaches which can take advantage of such heterogeneous datasets. We will base our analyses on the Osteoarthritis
Initiative, Johnston County, and UK Biobank datasets resulting in over 100,000 patients. We will focus on knee OA for
which no IG studies exist and will extract image biomarkers from DXAs, radiographs, and 3D MRIs, including from
longitudinal data to develop progression-sensitive biomarkers. Our resulting image analysis and statistical approaches will
be general and therefore applicable beyond knee OA.
To assure reproducible results we will provide all our analysis approaches in open-source form. They will be easy to use
and will simultaneously be targeted at users wishing to build upon our approaches as well as users who want to use them
for their own analyses. We will provide zero-install web-based data exploration capabilities to simplify analyses and
quality control. Our analysis approaches will be developed based on industr...

## Key facts

- **NIH application ID:** 10822142
- **Project number:** 1R01AR082684-01A1
- **Recipient organization:** UNIV OF NORTH CAROLINA CHAPEL HILL
- **Principal Investigator:** Marc Niethammer
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $705,837
- **Award type:** 1
- **Project period:** 2024-02-21 → 2029-01-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10822142

## Citation

> US National Institutes of Health, RePORTER application 10822142, A comprehensive imaging genetics framework for osteoarthritis research (1R01AR082684-01A1). Retrieved via AI Analytics 2026-05-28 from https://api.ai-analytics.org/grant/nih/10822142. Licensed CC0.

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