METHODS article
Front. Genet.
Sec. Computational Genomics
Volume 16 - 2025 | doi: 10.3389/fgene.2025.1635734
A Likelihood Ratio Framework for Inferring Close Kinship from Dynamically Selected SNPs
Provisionally accepted
- 1Othram Inc, The Woodlands, United States
- 2Othram Inc., Houston, United States
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Forensic genetic genealogy (FGG) is a force-multiplier for human identification, leveraging dense single nucleotide polymorphism (SNP) data to infer relationships through identity-by-descent (IBD) segment analysis. Although powerful for investigative lead generation, broad adoption of SNP-based identification methods by the forensic community, especially medical examiners and crime laboratories, necessitates likelihood ratio (LR)-based relationship testing, to align with traditional kinship testing standards. To address this gap, a novel method was developed that incorporates LR calculations into FGG and SNP testing workflows. This approach is unique in that it dynamically selects unlinked, highly informative SNPs based on configurable thresholds for minor allele frequency (MAF) and minimum genetic distance for a robust and reliable analysis. Employing a curated panel of 222,366 SNPs from gnomAD v4 and data from the 1000 genomes project, high accuracy in resolving relationships up to second-degree relatives can be achieved. For example, a subset of 126 SNPs (MAF > 0.4, minimum genetic distance of 30cM) yielded 96.8% accuracy and a weighted F1 score of 0.975 across 2,244 tested pairs. This LR-based methodology enables forensic laboratories to select informative SNPs and integrate modern genomic data with existing accredited relationship testing frameworks, providing critical statistical support for close-relationship comparisons and enhances the rigor of FGG-and SNP-based human identification applications.
Keywords: forensic genetic genealogy, Single nucleotide polymorphism, likelihood ratio, Kinship analysis, Identity by descent, Relationship testing, whole genome sequencing
Received: 27 May 2025; Accepted: 24 Jun 2025.
Copyright: © 2025 Ge, Budowle, Cariaso, Mittelman and Mittelman. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: David Mittelman, Othram Inc., Houston, United States
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