Genotyping of 75 SNPs using arrays for individual identification in five population groups

Hwa, Hsiao‐Lin; Wu, Lawrence Shih Hsin; Lin, Chun‐Yen; Huang, Tsun Ying; Yin, Hsiang I.; Tseng, Li‐Hong; Lee, James Chun-I

doi:10.1007/s00414-015-1250-y

Cited by 12 publications

(5 citation statements)

References 29 publications

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“…2b ). We removed the least heterozygous SNP of any two SNPs, with LD and close genetic distance based on criteria of Kidd et al [ 21 ] and Hwa et al [ 23 ]. In addition, SNPs located in the HLA region were removed.…”

Section: Resultsmentioning

confidence: 99%

“…There is no overlap between Dutch selected IISNPs and the established IISNP panels [ 13 , 20 , 23 , 35 ]. One of our selected SNPs (rs1866141) is located in an intronic region of the highly expressed GNLY gene (MIM # 188855).…”

Section: Discussionmentioning

confidence: 99%

“…The selected SNPs were compared with previously published panels such as SNPforID 52-plex, 75 Chinese SNPs, 30 Korean SNPs and 92 IISNPs [ 20 , 23 , 34 , 35 ]. Furthermore, the AAF of the 50 SNPs were compared to 1000 Genomes, Exome Aggregation Consortium (ExAC), NHLBI GO Exome Sequencing Project (Go.ESP) and the 1000 Genomes phase-3 populations.…”

Section: Methodsmentioning

confidence: 99%

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A SNP panel for identification of DNA and RNA specimens

et al. 2018

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BackgroundSNP panels that uniquely identify an individual are useful for genetic and forensic research. Previously recommended SNP panels are based on DNA profiles and mostly contain intragenic SNPs. With the increasing interest in RNA expression profiles, we aimed for establishing a SNP panel for both DNA and RNA-based genotyping.ResultsTo determine a small set of SNPs with maximally discriminative power, genotype calls were obtained from DNA and blood-derived RNA sequencing data belonging to healthy, geographically dispersed, Dutch individuals. SNPs were selected based on different criteria like genotype call rate, minor allele frequency, Hardy–Weinberg equilibrium and linkage disequilibrium. A panel of 50 SNPs was sufficient to identify an individual uniquely: the probability of identity was 6.9 × 10− 20 when assuming no family relations and 1.2 × 10− 10 when accounting for the presence of full sibs. The ability of the SNP panel to uniquely identify individuals on DNA and RNA level was validated in an independent population dataset. The panel is applicable to individuals from European descent, with slightly lower power in non-Europeans. Whereas most of the genes containing the 50 SNPs are expressed in various tissues, our SNP panel needs optimization for other tissues than blood.ConclusionsThis first DNA/RNA SNP panel will be useful to identify sample mix-ups in biomedical research and for assigning DNA and RNA stains in crime scenes to unique individuals.Electronic supplementary materialThe online version of this article (10.1186/s12864-018-4482-7) contains supplementary material, which is available to authorized users.

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Section: Resultsmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

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A SNP panel for identification of DNA and RNA specimens

et al. 2018

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“…These STR regions are amplified using the DNA samples from biological evidence, such as semen, blood, sperm, or skin cells, and analyzed for individual identification. 3–6…”

Section: Introductionmentioning

confidence: 99%

High efficiency sperm enrichment from forensic mock samples in bubble-based acoustic filtration devices for short tandem repeat (STR) analysis

Wan,

Hwa,

Lee

et al. 2024

Lab Chip

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“…Previously, a universal panel of 92 SNPs was developed for individual identification[ 9 ]. Another panel used 75 SNPs for Eastern Asian populations[ 10 ]. A recent simulation study showed that only 60 optimized SNPS are required to differentiate individuals in the global population[ 7 ].…”

Section: Introductionmentioning

confidence: 99%

A SNP panel and online tool for checking genotype concordance through comparing QR codes

Martin

McGee

et al. 2017

PLoS ONE

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In the current precision medicine era, more and more samples get genotyped and sequenced. Both researchers and commercial companies expend significant time and resources to reduce the error rate. However, it has been reported that there is a sample mix-up rate of between 0.1% and 1%, not to mention the possibly higher mix-up rate during the down-stream genetic reporting processes. Even on the low end of this estimate, this translates to a significant number of mislabeled samples, especially over the projected one billion people that will be sequenced within the next decade. Here, we first describe a method to identify a small set of Single nucleotide polymorphisms (SNPs) that can uniquely identify a personal genome, which utilizes allele frequencies of five major continental populations reported in the 1000 genomes project and the ExAC Consortium. To make this panel more informative, we added four SNPs that are commonly used to predict ABO blood type, and another two SNPs that are capable of predicting sex. We then implement a web interface (http://qrcme.tech), nicknamed QRC (for QR code based Concordance check), which is capable of extracting the relevant ID SNPs from a raw genetic data, coding its genotype as a quick response (QR) code, and comparing QR codes to report the concordance of underlying genetic datasets. The resulting 80 fingerprinting SNPs represent a significant decrease in complexity and the number of markers used for genetic data labelling and tracking. Our method and web tool is easily accessible to both researchers and the general public who consider the accuracy of complex genetic data as a prerequisite towards precision medicine.

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Genotyping of 75 SNPs using arrays for individual identification in five population groups

Cited by 12 publications

References 29 publications

A SNP panel for identification of DNA and RNA specimens

A SNP panel for identification of DNA and RNA specimens

High efficiency sperm enrichment from forensic mock samples in bubble-based acoustic filtration devices for short tandem repeat (STR) analysis

A SNP panel and online tool for checking genotype concordance through comparing QR codes

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