Kinship classification from a machine learning perspective: a pilot study based on genotyping data

Aim: Kinship analysis in trace amounts and degraded biological samples has consistently posed a challenge in forensic practice. With shorter amplicons and no stutter peak, Insertion/Deletion polymorphisms (InDels) significantly improve kinship analyses of deceased individuals and their potential living relatives. However, room for improvement remains in identifying 2nd-degree and more distant kinships. To address this issue, a kinship analysis workflow based on machine learning (ML) models was proposed. Methods: Based on multiple kinship parameters including identity-by-state (IBS) scores, k coefficients, proportion identity-by-descent (IBD), and likelihood ratio (LR) values, this pilot study applied a recently validated InDel locus to preliminarily develop an ML workflow for forensic kinship multi-classification. Results: In the binary classification of 2nd-degree relatives and unrelated pairs, the LR cutoff threshold workflow and the ML workflow achieved a similar accuracy of 0.9194. However, the ML method had a conclusiveness rate (CR) of 1.0, compared to 0.7066 for the LR workflow. In the multiclass task, the LR-based workflow had a macro F1 score of 0.6955/0.5212 and a CR of 0.7375/0.7046 for single and dual thresholds methods, respectively. However, the ML-based workflow showed that the optimal model - feature combination (XGBoost-IBD+LR) could classify all samples conclusively, with a macro F1 score of 0.9020. Conclusion: In summary, the ML workflow enhanced the kinship analysis efficiency based on the InDel genotyping system by combining multiple parameters, aiming to provide a more flexible and efficient solution for large-scale database screening.