Performance of the ForenSeq^TM Imagen Kit for Forensic DNA Phenotyping Under Partial Genotyping Conditions

Forensic DNA phenotyping (FDP) enables the inference of externally visible characteristics (EVCs) and biogeographic ancestry when conventional STR profiling is inconclusive. The ForenSeq™ Imagen kit (107 SNPs) integrates phenotype-, ancestry-, and Y-SNPs markers; however, its performance under partial genotyping conditions has not been systematically evaluated.

Ninety-four samples from a Mexican mestizo population were analyzed using the ForenSeq™ Imagen kit on the MiSeq FGx™ platform. Due to incomplete genotype recovery, 41 samples with >60% locus detection were selected for downstream analyses. Phenotype prediction was performed using the HIrisPlex-S model, and ancestry inference was assessed through principal component analysis. In silico simulations were conducted to evaluate locus-specific dropout effects.

Eye color prediction showed both reduced feasibility (68.3%) and lower overall accuracy (56.1%), primarily driven by systematic prediction failure when rs12913832 (HERC2) was absent, although accuracy among successfully predicted samples remained high (82.1%). In contrast, hair and skin color inference remained feasible in >97% and 100% of evaluable samples, respectively; however, classification accuracy was moderate (70% for hair and 61% for skin), improving substantially when allowing adjacent-category concordance (90.2% for skin). Ancestry inference was robust when at least 27 aiSNPs were detected, and Y-SNPs reliably distinguished male and female samples. In silico analyses confirmed the critical contribution of rs12913832 to eye color model operability.

FDP performance under partial genotyping reflects a trade-off between prediction feasibility and accuracy and depends on locus-specific integrity rather than overall genotype completeness. The ForenSeq™ Imagen kit shows robustness for ancestry, sex, hair, and skin prediction, although with variable accuracy, whereas eye color inference remains structurally vulnerable to drop out of high-impact variants. Evaluating FDP systems under realistic non-ideal conditions is essential to define their true operational limits and ensure scientifically robust and responsible implementation.