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Multimodal Integration of Protein Interactomes With Genomic and Molecular Data Discovers Distinct Rheumatoid

Javad Rahimikollu1,2,3, Priyamvada Guha Roy1,2,4, Akash Kishore1,2,3

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Genetic differences between anti-cyclic citrullinated peptide antibody-positive (CCP+) and CCP-negative (CCP-) rheumatoid arthritis (RA) were identified using network-based genome-wide association studies. These findings reveal distinct genetic risk factors influencing RA subtypes and inform precision medicine strategies.

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Area of Science:

  • Genetics
  • Immunology
  • Rheumatology

Background:

  • Rheumatoid arthritis (RA) is a heterogeneous autoimmune disease with distinct clinical and molecular subtypes, particularly anti-cyclic citrullinated peptide antibody-positive (CCP+) and CCP-negative (CCP-) RA.
  • CCP+ RA patients often experience more severe disease and different treatment responses compared to CCP- RA patients.
  • Genetic differences between these RA subtypes remain understudied despite known cellular and molecular variations.

Purpose of the Study:

  • To investigate the understudied genetic differences between CCP+ and CCP- rheumatoid arthritis (RA) subtypes.
  • To identify novel genetic risk factors and molecular pathways that differentiate these RA patient groups.
  • To leverage network-based approaches integrating multi-omic data for comprehensive genetic analysis.

Main Methods:

  • Utilized the Rheumatoid Arthritis Comparative Effectiveness Research (RACER) cohort, including CCP+/RF+ and CCP-/RF+ RA patients.
  • Employed a novel framework integrating network-based genome-wide association study (GWAS) with multi-omic data.
  • Performed heritability partitioning and multivariate expression analyses to validate identified gene modules.

Main Results:

  • A significant difference in heritability was observed between CCP+ and CCP- RA groups.
  • Network-based GWAS identified 14 putative gene modules, many outside HLA loci, explaining genetic disparities between RA subtypes.
  • Four gene modules were validated, highlighting novel genetic loci associated with phenotypic differences, with functional significance confirmed across orthogonal cohorts.

Conclusions:

  • Network-based approaches effectively reveal differential genetic risk factors for CCP+ and CCP- RA.
  • Disease-associated gene modules in synovial tissue were also detected in peripheral blood, suggesting systemic reflection of joint-specific molecular programs.
  • Findings support the potential for blood-based assays in stratifying RA patients and developing precision-guided treatment strategies based on distinct genetic underpinnings.