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  • Title: Candidate SNPs for a universal individual identification panel.
    Author: Pakstis AJ, Speed WC, Kidd JR, Kidd KK.
    Journal: Hum Genet; 2007 May; 121(3-4):305-17. PubMed ID: 17333283.
    Abstract:
    Single nucleotide polymorphisms (SNPs) are likely in the near future to have a fundamental role both in human identification and description. However, because allele frequencies can vary greatly among populations, a critical issue is the population genetics underlying calculation of the probabilities of unrelated individuals having identical multi-locus genotypes. Here we report on progress in identifying SNPs that show little allele frequency variation among a worldwide sample of 40 populations, i.e., have a low F(st), while remaining highly informative. Such markers have match probabilities that are nearly uniform irrespective of population and become candidates for a universally applicable individual identification panel applicable in forensics and paternity testing. They are also immediately useful for efficient sample identification/tagging in large biomedical, association, and epidemiologic studies. Using our previously described strategy for both identifying and characterizing such SNPs (Kidd et al. in Forensic Sci Int 164:20-32, 2006), we have now screened a total of 432 SNPs likely a priori to have high heterozygosity and low allele frequency variation and from these have selected the markers with the lowest F(st) in our set of 40 populations to produce a panel of 40 low F(st), high heterozygosity SNPs. Collectively these SNPs give average match probabilities of less than 10(-16) in most of the 40 populations and less than 10(-14) in all but one small isolated population; the range is 2.02 x 10(-17) to 1.29 x 10(-13). These 40 SNPs constitute excellent candidates for the global forensic community to consider for a universally applicable SNP panel for human identification. The relative ease with which these markers could be identified also provides a cautionary lesson for investigations of possible balancing selection.
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