Analytical validation of the IBD segment-based tool KinSNP^® for human identification applications.

Area of Science:

• Genetics and Bioinformatics
• Forensic Science
• Computational Biology

Background:

• KinSNP is a widely used software for measuring identity by descent (IBD) segment sharing using dense single nucleotide polymorphism (SNP) data.
• Accurate IBD segment measurement is critical for human identification and kinship analysis in forensic contexts.

Purpose of the Study:

• To validate the performance of KinSNP v1.0 for measuring IBD segment sharing.
• To assess KinSNP's accuracy under various simulated conditions, including genotyping errors and missing data, across diverse populations.

Main Methods:

• Validation using simulated pedigree data from five diverse populations (1000 Genomes Project) up to 9th-degree relationships.
• Performance testing with simulated genotyping errors, allele dropout, and locus dropout.
• Benchmarking KinSNP results against IBIS, Ped-sim, and known centimorgan sharing ranges.

Main Results:

• KinSNP calculated values closely aligned with IBIS and Ped-sim benchmarks.
• Accuracy was maintained with up to 75% simulated missing data (allele or locus dropout).
• Slight increases in simulated sequence error rates significantly impacted KinSNP performance.

Conclusions:

• KinSNP v1.0 is a reliable tool for identity by descent (IBD)-based analyses.
• The software demonstrates robustness in the presence of substantial missing genotype data.
• Sequence error rates represent a critical factor affecting the accuracy of KinSNP analyses in forensic applications.