Statistical approaches for assessing the role of genetics in predicting disease outcomes: an application to rheumatic musculoskeletal diseases.

Student thesis: Phd

Abstract

Aim: This study aims to further investigate (1) the increased occurrence of coronary artery disease (CAD) in patients with rheumatoid arthritis (RA) and (2) Psoriatic Arthritis (PsA) in patients with cutaneous-only psoriasis (PsC). The main objective is to apply existing methodologies that have been widely used for developing polygenic risk scores and utilizing genetic information, in order to better understand potential causal factors. These methods have not yet been specifically applied to assess the relationship between RA and CAD or PsA and psoriasis. This doctoral project aims to further investigate the intricate associations between multiple health conditions in Musculoskeletal disorders. Methods: First, a systematic literature review was conducted. Second, a genome-wide meta-analyses was conducted on a large number of participants, including patients with PsA, healthy controls, and patients with PsC from the UK biobank. Biological pathways that distinguish between PsA and PsC were identified using Priority Index software. To assess the generalizability of previously published risk prediction models for predicting the development of PsA, external validation techniques were employed. Third, utilizing longitudinal data from the Norfolk Arthritis Register (NOAR), a predictive model for development of CAD was developed using conventional risk factors and multiple CAD Polygenic risk scores by leveraging effect sizes obtained from three extensive genome-wide association studies within a subset of NOAR patients with available genetic information. Cox proportional hazards models were utilized to derive risk equations for evaluating an individual's 10-year likelihood of developing CAD. Finally,a causal relationship was estimated using two-sample MR using large-scale summary-level genetic data. Results: The study identified a novel genome-wide significant susceptibility locus for the development of PsA on chromosome 22q11 (rs5754467; P = 1.61 × 10−9) and key pathways that differentiate PsA from PsC, including NF-κB signaling (adjusted P = 1.4 × 10−45) and Wnt signaling (adjusted P = 9.5 × 10−58). Using NOAR, the inclusion of a CAD meta-GRS improved Harrell’s C-statistics to 0.79 (95% CI 0.78, 0.80), explaining more of the variance at 81% (95% CI 79, 82%) with a calibration slope of 0.93. A likelihood ratio test indicates that the integrated model is a better fit (p = 0.04). When estimating causal association, the combined odds ratios showed an increase in CAD risk by 1.06 per unit rise in log odds of RA seropositive individuals (95% Confidence interval [1.05-1.07, P = 0.04). Conclusion: The inclusion of genetics in risk assessment has been shown to significantly improve prediction accuracy for patients with PsC and RA. This highlights the importance of further exploring the underlying biological pathways involved in these disorders to gain a more profound understanding and develop robust predictive models that are applicable across diverse populations.
Date of Award31 Dec 2023
Original languageEnglish
Awarding Institution
  • The University of Manchester
SupervisorAnne Barton (Supervisor), John Bowes (Supervisor) & Suzanne Verstappen (Supervisor)

Keywords

  • Rheumatoid Arthritis

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