Development of a novel meta-polygenic risk score for risk assessment and clinical manifestation prediction in systemic lupus erythematosus

Background Systemic lupus erythematosus (SLE) is a clinically heterogeneous autoimmune disease with multifactorial pathogenesis. Although polygenic risk scores (PRSs) have been developed to enable early prediction, their accuracy remains limited. To address this limitation, we constructed a meta-polygenic risk score (metaPRS) integrating genetic markers associated with multiple SLE-associated traits.Methods 14 SLE-associated traits were identified through literature review, and trait-level PRSs were constructed based on the largest available East Asian genome-wide association studies datasets using SNP genotyping data of 2388 patients and 1132 controls from our own cohort. Significant trait-PRSs were integrated into a metaPRS using elastic net regression, which was further evaluated for disease prediction, risk stratification and clinical manifestation correlation, with internal validation via bootstrapping.Results Five trait-level PRSs were significantly associated with SLE in East Asian population: SLE development, smoking initiation, serum selenium levels, endometriosis and Graves’ disease. The metaPRS merged from these five traits exhibited robust predictive performance (OR=2.12, area under the receiver operating characteristic curve (AUC)=0.69) and risk stratification (high risk vs low risk: OR=4.93, p<2e−16). Compared with the conventional PRS based solely on SLE risk genetic variants, the metaPRS achieved a 4.43% increase in OR and exhibited a statistically significant improvement in diagnostic discrimination, as measured by the AUC (p=0.046). Furthermore, metaPRS was associated with positivity for multiple autoantibodies and demonstrated better performance in childhood-onset SLE compared with adult-onset cases. Decomposition of the metaPRS revealed that both PRS SLE and PRSriskfactor contributed to SLE susceptibility, while clinical manifestations were exclusively driven by PRSSLE.Conclusions We developed the first metaPRS of SLE by integrating genetic characteristics from multiple SLE-related risk factors, offering a new perspective for risk stratification and early diagnosis.