A polygenic risk score model for psoriasis based on the protein interactions of psoriasis susceptibility loci

Charalabos Antonatos; Fotios Koskeridis; Christiana M Ralliou; Evangelos Evangelou; Katerina Grafanaki; Sophia Georgiou; Konstantinos K Tsilidis; Ioanna Tzoulaki; Yiannis Vasilopoulos

doi:10.3389/fgene.2024.1451679

A polygenic risk score model for psoriasis based on the protein interactions of psoriasis susceptibility loci

Front Genet. 2024 Nov 6:15:1451679. doi: 10.3389/fgene.2024.1451679. eCollection 2024.

Authors

Affiliations

¹ Laboratory of Genetics, Section of Genetics, Cell Biology and Development, Department of Biology, University of Patras, Patras, Greece.
² Department of Hygiene and Epidemiology, University of Ioannina Medical School, Ioannina, Greece.
³ Department of Biomedical Research, Institute of Molecular Biology and Biotechnology, Foundation for Research and Technology-Hellas, Ioannina, Greece.
⁴ Department of Epidemiology and Biostatistics, MRC Centre for Environment and Health, Imperial College London, London, United Kingdom.
⁵ Department of Dermatology-Venereology, School of Medicine, University of Patras, Patras, Greece.
⁶ Department of Biochemistry, School of Medicine, University of Patras, Patras, Greece.
⁷ Centre for Systems Biology, Biomedical Research Foundation, Academy of Athens, Athens, Greece.

Abstract

Introduction: Polygenic Risk Scores (PRS) are an emerging tool for predicting an individual's genetic risk to a complex trait. Several methods have been proposed to construct and calculate these scores. Here, we develop a biologically driven PRS using the UK BioBank cohort through validated protein interactions (PPI) and network construction for psoriasis, incorporating variants mapped to the interacting genes of 14 psoriasis susceptibility (PSORS) loci, as identified from previous genetic linkage studies.

Methods: We constructed the PPI network via the implementation of two major meta-databases of protein interactions, and identified variants mapped to the identified PSORS-interacting genes. We selected only European unrelated participants including individuals with psoriasis and randomly selected healthy controls using an at least 1:4 ratio to maximize statistical power. We next compared our PPI-PRS model to (i) clinical risk models and (ii) conventional PRS calculations through p-value thresholding.

Results: Our PPI-PRS model provides comparable results to both clinical risk models and conventional approaches, despite the incorporation of a limited number of variants which have not necessarily reached genome-wide significance (GWS). Exclusion of variants mapped to the HLA-C locus, an established risk locus for psoriasis resulted in highly similar associations compared to our primary model, indicating the contribution of the genetic variability mapped to non-GWS variants in PRS computations.

Discussion: Our findings support the implementation of biologically driven approaches in PRS calculations in psoriasis, highlighting their potential clinical utility in risk assessment and treatment management.

Keywords: genome-wide association study; polygenic risk score; protein-protein interaction; psoriasis; risk prediction.

Grants and funding

The author(s) declare that financial support was received for the research, authorship, and/or publication of this article. This research has been conducted using the United Kingdom Biobank Resource under application number 22102: (PI: EE). All authors would like to sincerely thank Drs Lam C. Tsoi and James T. Elder for their kindness in providing genome-wide summary statistics of the latest reported psoriasis GWAS. The publication of this article has been financed by the Research Committee of the University of Patras.