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. 2026 Jan 5;27(1):543.
doi: 10.3390/ijms27010543.

Identifying a Common Autoimmune Gene Core as a Tool for Verifying Biological Significance and Applicability of Polygenic Risk Scores

Victoria Sergeevna Shchekina  1   2 , Nikita Aleksandrovich Batashkov  1   2 , Anna Arkadievna Maznina  1 , Julia Aleksandrovna Krupinova  1   2   3 , Viktor Pavlovich Bogdanov  1   2 , Anna Vasilievna Korobeinikova  4 , Dmitry Igorevich Tychinin  4 , Olga Valentinovna Glushkova  4 , Ekaterina Sergeevna Petriaikina  4 , Dmitry Vladimirovich Svetlichnyy  4 , Mary Woroncow  2 , Vladimir Sergeevich Yudin  4 , Anton Arturovich Keskinov  4 , Sergey Mikhailovich Yudin  4 , Veronika Igorevna Skvortsova  5 , Dmitry Vyacheslavovich Tabakov  1   2 , Andrei Andreevich Deviatkin  4 , Pavel Yu Volchkov  1   2   3
Affiliations

Identifying a Common Autoimmune Gene Core as a Tool for Verifying Biological Significance and Applicability of Polygenic Risk Scores

Victoria Sergeevna Shchekina et al. Int J Mol Sci. .

Abstract

Polygenic autoimmune diseases (ADs) have several common features that are caused by a complex interplay of genetic and environmental factors. Common pathophysiological mechanisms include dysregulation of the immune system, chronic inflammation, and epigenetic changes influenced by external factors. For the prediction of the genetic predisposition of AD manifestation, polygenic risk scale (PRS), or polygenic scores (PGSs), are used. Use of PRSs faces several challenges such as applicability on a specific population, performance comparison, and estimation of biological relevance based on SNP number. We compared PRS with different numbers of SNPs and tried to find the common genetic core of ADs. Our analysis revealed a list of the most common altered genes, which we annotated and interpreted. Clustering of PRS based on used genes showed that clusters of ADs remained consistent across all chosen PRS sizes. We concluded that PRS size does not have an impact on biological relevance.

Keywords: PRS; SNP; autoimmune disease; polygenic risk score.

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Conflict of interest statement

The authors declare no conflicts of interest.

Figures

Figure 1
Figure 1
Clustering of autoimmune diseases based on common genes in polygenic risk scores. (A) For PRSs up to 1000 genes. (B) For PRSs up to 500 genes. (C) For PRSs up to 250 genes. (D) For PRSs with the best AUC per disease.
Figure 2
Figure 2
Pathway enrichment in identified clusters by common genes (A) across all PRS sizes (B) across PRS 1000.
Figure 3
Figure 3
For each AD, SNPs from PRSs were obtained from the PGS Catalog. Different SNP sets were then created: all SNPs from PRSs containing up to 250 variants were combined into one set, then the same was carried out for PRSs with up to 500, and up to 1000 variants. Additionally, SNPs for the five top-performing PRSs based on their AUC were selected for each AD. Each SNP was mapped to its corresponding gene, and a binary matrix was constructed, indicating the presence of SNPs in specific genes for each disease. Finally, tSNE clustering was applied to each SNP set to identify common patterns among the autoimmune diseases.
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