# Identify the heterogeneity and commonality of chronic overlapping pain conditions (COPCs) through phenotypic and genomic perspectives

> **NIH NIH R21** · MAYO CLINIC ROCHESTER · 2022 · $251,879

## Abstract

PROJECT SUMMARY/ABSTRACT
With an estimated prevalence of 20% in American adults and annual costs above $500 million, chronic pains
pose a high toll to public health. Many patients developed chronic overlapping pain conditions (COPCs), where
craniofacial pains like temporomandibular disorder (TMD) represent a unique component that co-occurs
frequently with other chronic pains including irritable bowel syndrome (IBS). Not only do the comorbidities
complicate pain management, but the etiology of COPCs remains unclear. Heterogeneity exists within and
across COPCs, so mechanism-based classification schemes are needed to identify safe and effective therapies
for distinct subgroups of patients. However, existing research surrounding COPC taxonomy has not fully
integrated phenotypic and genotypic data at the population level. Reliance on disease-specific study cohorts
seriously limited the sample size and diversity. On the other hand, informatics and data science have advanced
secondary use of biomedical data, presenting a strong alternative to hypothesis-driven, controlled studies. We
propose to elicit COPC subgroups by mining three distinct population-based clinical datasets and imputing the
biological underpinnings of co-occurring COPCs by using functional genomic knowledge bases. Our approach
consists of two aims: 1) Identify COPC subgroups and other commonly associated phenotypes from rich
longitudinal clinical data and notes, including over one million patients in the Rochester Epidemiology Project.
The clinical datasets will be computationally screened for COPCs and other co-occurring phenotypes based on
diagnosis codes and natural language processing. We will identify statistically significant COPC comorbidities
and progression trajectories using novel and tailored statistics. The discovered trajectories will be clustered into
subgroups using cutting-edge graph clustering algorithms. The patients will be assigned to the best matched
subgroups, for which additional phenotypic characteristics of each group will be determined by least absolute
shrinkage and selection operator. 2) Impute biological underpinnings for comorbid COPCs by integrating
phenotypic, genetic, and genomic data in biobanks and biorepositories. We will conduct genome-wide and
phenome-wide association studies based on the diagnoses and genotypes from the UK Biobank and All of Us
Research Program, leading to identification of additional genotypes that are associated with the COPCs and
beyond those in the NHGRI-EBI GWAS catalog. We will apply our information-theoretic framework to impute the
functional similarity and shared biological mechanisms across COPCs by using GTEx expression quantitative
trait loci data and gene ontology annotations. The findings of shared mechanisms among COPCs will provide
novel insight into the genetic factors, particularly in noncoding regions, and functional linkages that are pivotal to
developing applications such as drug repurposing for COPCs. The two...

## Key facts

- **NIH application ID:** 10525765
- **Project number:** 1R21DE031424-01A1
- **Recipient organization:** MAYO CLINIC ROCHESTER
- **Principal Investigator:** Jungwei Wilfred Fan
- **Activity code:** R21 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $251,879
- **Award type:** 1
- **Project period:** 2022-08-01 → 2024-07-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10525765

## Citation

> US National Institutes of Health, RePORTER application 10525765, Identify the heterogeneity and commonality of chronic overlapping pain conditions (COPCs) through phenotypic and genomic perspectives (1R21DE031424-01A1). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10525765. Licensed CC0.

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