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

backgroundSystemic 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.

methods14 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.

resultsFive 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

conclusionsWe 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.