Increasing genetic variants associated with sepsis have been identified by candidate-gene and genome-wide association studies, but single variant conferred minimal alterations in risk prediction. Our aimed to evaluate whether a weighted genetic risk score (wGRS) that aggregate information from multiple variants could improve risk discrimination of traumatic sepsis . Methods: 64 genetic variants potential relating to sepsis were genotyped in Chinese trauma cohort. Genetic variants with mean

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... ase accuracy (MDA)>1.0 by random forest algorithms were selected to construct the multilocus wGRS. Area under curve (AUC) and Net reclassification improvement (NRI) were used to evaluate the discriminatory and reclassification ability of wGRS. Results: Seventeen variants were extracted to construct the wGRS in 883 trauma patients. The wGRS was significantly associated with traumatic sepsis (OR=2.19, 95%CI=1.53-3.15, P=2.01×10 -5 ) after adjusted by age, sex, and ISS. Patients with higher wGRS have an increasing incidence of traumatic sepsis (P trend =6.81×10 -8 ), higher SOFA (P trend =5.00×10 -3 ) and APACHEII score (P trend =1.00×10 -3 ). The AUC of risk prediction model incorporating wGRS into the clinical variables was 0.768 (95%CI=0.739-0.796), with an increase of 3.40% (P=8.00×10 -4 ) versus clinical factors-only model. Furthermore, the NRI increased 25.18% (95%CI=17.84-32.51%) (P=6.00×10 -5 ). Conclusions: Our finding indicated that genetic predictors could improve the predictive ability of risk model for sepsis and highlighted the application among trauma populations, indicating that the sepsis risk assessment model will be a promising tool for high risk population screening and prediction.