Statistical approaches for understanding the aetiology of psoriatic arthritis: genetics, environment and comorbidities

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Abstract

Background: Psoriatic arthritis (PsA) is a seronegative inflammatory arthritis affecting patients with psoriasis. Early identification of PsA could result in less joint damage and better outcomes and highlight potential clinical targets. Several studies have tried to elucidate the aetiology of PsA by investigating its genetic basis using genome-wide association studies, the contribution of environmental and lifestyle factors to its development and the prevalence of comorbidities in patients with psoriasis and/or PsA. However, the small sample sizes used in these studies along with the unclear phenotypic characterisation have led to the identification of only a handful PsA-specific risk factors. Aims: The broad aim of this study was to improve the understanding of the pathogenesis of PsA by investigating the genetic and the environmental contribution, along with the prevalence of multi-morbidity that has an impact on clinical outcomes. Firstly, the study aimed to explore the association and causality of environmental factors with PsA and the prevalence of comorbidities using the wealth of data UK Biobank offers. Secondly, the study aimed to identify novel genetic variants underpinning PsA using state-of-the-art techniques that leverage power from genetic studies performed in other correlated musculoskeletal diseases. Methods: The association of PsA with known environmental factors and comorbidities was investigated using logistic regression in the UK Biobank. To further define the genetic variants underpinning PsA, GWAS data from other musculoskeletal diseases were tested for correlation with PsA using LD score regression and cross-trait analysis was subsequently performed. Conditional False Discovery Rate analysis and two alternative meta-analysis methods (Multi-Trait analysis of GWAS and subset-based analysis) were used because of their ability to exploit the pleiotropy among correlated traits and increase the power of polymorphism detection. Finally, the causal role of the statistically significant environmental factors was then determined using Mendelian Randomisation. Results: Body mass index was confirmed to play a causal role in the development of PsA in patients with psoriasis. In addition, using LD score regression rheumatoid arthritis, systemic lupus erythematosus, ankylosing spondylitis and juvenile idiopathic arthritis were found to be genetically correlated with PsA. Twenty one novel SNPs were found by all three methods to be associated with PsA, the majority of which are mapped to genes that have not previously been associated with PsA. Summary: This work has carried forward the research of detecting PsA risk factors. It includes the first cross-trait study investigating PsA along with other musculoskeletal diseases, the first study to explore UK Biobank data for associations of the disease with lifestyle risk factors and known comorbidities and finally the first study to assess the causal role of obesity, smoking status and alcohol frequency consumption in the onset of PsA. All this evidence can be taken forward for further functional and clinical applications.

Keyword(s)

Comorbidities; Environmental factors; Genetics; Mendelian Randomization; Pleiotropy; Psoriasis; Psoriatic arthritis

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