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

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.

FIGURE 1

The top 15 enriched pathways based on the Reactome database for the 1,575 autosomal PSORS-interacting genes implemented in our approach. The x-axis represents the -log10 of the False Discovery Rate (FDR) adjusted p-value.

FIGURE 2

Associations between adjusted polygenic risk scores and psoriasis. (A) Kernel density plot of the variants’ p-values incorporated into the PPI model. (B) Forest plot showing the log (odds ratios) (log (OR)) and 95% confidence intervals (95% CIs) comparing the protein-protein interactions (PPI) model to p-value ≤ 0.1 and p-value ≤ 5 × 10⁻⁸ thresholding models. (C) Discriminative performance of our major polygenic risk score (PRS) models summarized according to OR. Estimated ORs and 95% CIs within each decile were estimated from logistic regression.

FIGURE 3

Distribution and discriminative ability of the clinical risk model, adjusted polygenic risk scores (PRSs) and the combined approach for psoriasis. (A) Standardized PRS distributions across the three primary models between psoriasis cases and noncases. (B) Forest plot showing the c-statistic and corresponding 95% confidence intervals (95% CIs) comparing the protein-protein interactions (PPI) model to conventional clinical risk model, p-value ≤ 0.1 and p-value ≤ 5 × 10⁻⁸ thresholding models and the combined approach. The clinical risk model includes age, sex, body mass index (BMI) and smoking status. Discrete color scales were used to discriminate between models. Discrete shape scales were used to discriminate between baseline and combined approaches.