An in silico evaluation of tri-allelic SNPs from 1kGP Phase IV data for distant kinship identification and mixture...

Multi-allelic Single Nucleotide Polymorphisms (SNPs) are a class of genetic markers in forensic genetics, potentially offering higher discriminatory power than their bi-allelic counterparts. However, well-characterized panels specifically designed and evaluated for distant kinship identification and complex mixture analysis remain limited. Here, we developed a marker set of tri-allelic SNPs by applying a rigorous, multi-stage filtering process to the 1000 Genomes Project Phase IV high-coverage data. The process included stringent quality and polymorphism criteria followed by Hardy-Weinberg equilibrium (HWE) testing and linkage disequilibrium (LD) pruning, yielding a final set of 2495 markers. Extensive in silico simulations under idealized conditions showed high discriminatory power of the set for distinguishing up to 3rd-degree relatives from unrelated individuals, with potential for investigating 4th-degree relationships. For mixture analysis, the set estimated the number of contributors (NOC) in 2- to 5-person mixtures with high accuracy under the simulation framework. Furthermore, we established a quantitative framework to estimate the Minimum Necessary Number (MNN) of markers required for these analyses, providing guidance for future panel design. Overall, this study presented a rigorously vetted set of 2495 tri-allelic SNPs and established performance benchmarks within an in silico framework, highlighting the potential utility of multi-allelic markers for forensic applications.