Objective

To evaluate the detection power of three half-sibling testing combinations with different STRs detection systems, and to provide scientific evidence for optimizing half-sibling testing strategies. 

Methods

The three half-sibling testing combinations represent the three common scenarios encountered in practice: the dyad combination (pairwise testing of two alleged half-siblings), the triad combination (testing involving two known full siblings and one alleged half-sibling), and the tetrad combination (testing involving two alleged half-siblings and the respective other parent of each). For each combinations, 10,000 true families and false families were randomly generated. Following SF/T 0131-2023 Technical specification for biological half-sibling testing issued by the Ministry of Justice of the People's Republic of China, cumulative half-sibling index for STR detection systems containing 19~100 loci were calculated using the likelihood ratio method. Detection power parameters were computed with 0.0001 and 10,000 as decision thresholds. 

Results

As the number of STR loci increased, sensitivity, specificity, positive predictive value, and negative predictive value progressively improved, while false positive rates, false negative rates, and inconclusive rates decreased, indicating enhanced system effectiveness. Within the same detection system, the system effectiveness decreases from high to low as follows: tetrad > triad > dyad combinations, and the three combinations achieved system effectiveness exceeding 0.75 at 35, 39 and 73 STR loci, respectively. 

Conclusion

The detection power table covering 19~100 STR loci for half-sibling testing established in this study provides scientific guidance for kinship testing practices and ensures the interpretability and reliability of detection conclusions.

Authors

• Shiyun Meng (Faculty of Forensic Medicine, Zhongshan School of Medicine, Sun Yat-sen University, China)
• Tingjun Li (Faculty of Forensic Medicine, Zhongshan School of Medicine, Sun Yat-sen University, China)
• Ran Li (Faculty of Forensic Medicine, Zhongshan School of Medicine, Sun Yat-sen University, China)
• Hongyu Sun (Faculty of Forensic Medicine, Zhongshan School of Medicine, Sun Yat-sen University, China)