Analytical validation of the IBD segment-based tool KinSNP<sup>®</sup> for human identification applications
Creators: name: Budowle, Bruce
name: Ge, Jianye
name: Baker, Lee
name: Mittelman, Kristen
name: Mittelman, David
Publisher: name: Taylor & Francis
Year: 2025
Subjects: subject: Space Science
subject: Genetics
subject: FOS: Biological sciences
subject: Evolutionary Biology
subject: Chemical Sciences not elsewhere classified
subject: Sociology
subject: FOS: Sociology
subject: Biological Sciences not elsewhere classified
subject: Information Systems not elsewhere classified

description: KinSNP<sup>®</sup> v1.0, a software tool for human identification, has been widely used to measure IBD segment sharing between individuals using dense SNP data. Herein, the tool was validated using simulated pedigree data (up to 9<sup>th</sup> degree relationships) from five diverse populations from the 1000 Genomes Project. Performance was further tested under conditions of simulated genotyping errors and allele or locus dropout. KinSNP data were benchmarked with IBIS, Ped-sim, and known ranges of centimorgan sharing. The calculated values from KinSNP aligned closely with IBIS and Ped-sim benchmarks, and accuracy was maintained with up to 75% simulated missing data. However, even slight increases in simulated sequence error rates negatively impacted performance. This study supports that KinSNP is a reliable solution for IBD-based analyses in forensic contexts. This validation study demonstrates KinSNP<sup>®</sup> v1.0 can reliably calculate shared centimorgan values between individuals. The software tool was benchmarked against ground truth simulated pedigree data from five populations (with and without missing data and introduced error), another IBD segment-based software tool, established ranges of shared centimorgans for a wide range of relationships, and empirical data form the 1000 Genomes Project. KinSNP calculated centimorgan values across all degrees of relationships were comparable with expected values and values generated by benchmarking. IBD segment-based data are robust with up to 75% missing data, but sensitive to error rates even as low as 5%. Forensic genetic genealogy relies heavily on dense SNP data to detect identity-by-descent segments shared between individuals to determine degrees of relationships.The degree of shared segments is directly correlated with the genetic distance between related individuals; more sharing with close relationships and less sharing with more distant relationshipsThe software tool KinSNP<sup>®</sup>, which has been widely used in forensic genetic genealogy cases, was validated to determine and support its reliability for detecting and calculating shared centimorgans between individuals.Validation was performed by simulating pedigrees (up to 9<sup>th</sup> degree relationships) from five populations groups with high quality and induced missing and error data, as well as assessing empirical data from the 1000 Genomes Project.The results demonstrate that KinSNP generates reliable shared centimorgan results across a broad range of near and distant relationships.KinSNP provides a reliable, scalable solution for Identity-by-descent-based analyses in forensic contexts, offering accurate and consistent kinship associations across a wide range of data qualities. Forensic genetic genealogy relies heavily on dense SNP data to detect identity-by-descent segments shared between individuals to determine degrees of relationships. The degree of shared segments is directly correlated with the genetic distance between related individuals; more sharing with close relationships and less sharing with more distant relationships The software tool KinSNP<sup>®</sup>, which has been widely used in forensic genetic genealogy cases, was validated to determine and support its reliability for detecting and calculating shared centimorgans between individuals. Validation was performed by simulating pedigrees (up to 9<sup>th</sup> degree relationships) from five populations groups with high quality and induced missing and error data, as well as assessing empirical data from the 1000 Genomes Project. The results demonstrate that KinSNP generates reliable shared centimorgan results across a broad range of near and distant relationships. KinSNP provides a reliable, scalable solution for Identity-by-descent-based analyses in forensic contexts, offering accurate and consistent kinship associations across a wide range of data qualities.