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Cited by 298

The DOI matches its Crossref record; 15 of the 15 references it cites were found in a registry or an index.

Forensically Relevant SNP Classes

Open access
Budowle, Bruce, van Daal, Angela·2008·BioTechniques

Abstract

Forensic samples that contain too little template DNA or are too degraded require alternate genetic marker analyses or approaches to what is currently used for routine casework. Single nucleotide polymorphisms (SNPs) offer promise to support forensic DNA analyses because of an abundance of potential markers, amenability to automation, and potential reduction in required fragment length to only 60-80 bp. The SNP markers will serve an important role in analyzing challenging forensic samples, such as those that are very degraded, for augmenting the power of kinship analyses and family reconstructions for missing persons and unidentified human remains, as well as for providing investigative lead value in some cases without a suspect (and no genetic profile match in CODIS). The SNPs for forensic analyses can be divided into four categories: identity-testing SNPs; lineage informative SNPs; ancestry informative SNPs; and phenotype informative SNPs. In addition to discussing the applications of these different types of SNPs, this article provides some discussion on privacy issues so that society and policymakers can be more informed.

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Metadata

Authors
Budowle, B., & van Daal, A.
Venue
BioTechniques
Published
2008-04
Volume
44
Issue
5
Pages
603-610
ISSN
  • 0736-6205 (ISSN-L)
  • 1940-9818
OpenAlex
W2023178384
Cited by
298
References
15
FWCI
7.03
Type
journal article
Language
en
Open access
gold

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CoChat. (2026). Citation check: Forensically Relevant SNP Classes. https://app.cochat.ai/papers/10.2144/000112806
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Budowle, B., & van Daal, A. (2008). Forensically Relevant SNP Classes. BioTechniques. https://doi.org/10.2144/000112806

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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.