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Investigation of 74 microhaplotypes for kinship testing in US populations

NCJ Number
307450
Journal
Forensic Science International Genetics Supplement Series Volume: 8 Dated: December 2022 Pages: 40-41
Date Published
December 2022
Length
2 pages
Annotation

 In this study, authors examined the potential of a recently developed 74plex-MH panel for kinship testing using the Familias software.

Abstract

This study investigated the potential of a recently developed 74plex-MH panel for kinship testing using the Familias software. Microhaplotypes are markers that consist of haplotype blocks of SNPs, which can be analyzed by massively parallel sequencing technologies. These allow determining the haplotype phase at every locus by clonal sequencing each DNA strand. MHs are polymorphic loci with same size alleles, no stutter, and lower mutation rate than STRs. They can provide the same power of discrimination of STR-kits, thus useful for mixture deconvolution, but more accurate ancestry prediction than STRs. Samples from families of four major US population groups were collected and genotyped using the 74plex-MH panel. MH allele frequency data from 347 individuals were imported into Familias software along with STR allele frequency data of 29 loci (NIST dataset) from 1036 individuals. Different family scenarios were tested and these included unrelated vs parent-child, unrelated vs full siblings, unrelated vs half siblings, unrelated vs cousin pairs. The prediction of the kinship relation for the four populations of interest was reported as Log10 of the likelihood ratio (LR). Overall, the panel of 74MHs and 29STRs showed similar performance in predicting the correct kinship scenarios tested. Correct prediction was reported for parent-child, full siblings, and half sibling scenarios, but not for the cousin pairs scenario. The panel of 74 MHs showed larger Log10LR values than the 29 STR-assay, thus demonstrating the effectiveness of this biomarker as a tool for kinship testing in addition to mixture deconvolution and ancestry prediction. (Published Abstract Provided)

Date Published: December 1, 2022