Geolocation prediction from STR genotyping: a pilot study in five geographically distinct global populations
Background Traditional CE-based STR profiles are highly useful for the purpose of individualisation. However, they do not give any additional information without the presence of the reference sample for comparison. Aim To assess the usability of STR-based genotypes for the prediction of an individua...
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| Format: | Article |
| Language: | English |
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Taylor & Francis Group
2023-01-01
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| Series: | Annals of Human Biology |
| Subjects: | |
| Online Access: | http://dx.doi.org/10.1080/03014460.2023.2217382 |
| _version_ | 1797685123816095744 |
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| author | Mansi Arora Hirak Ranjan Dash |
| author_facet | Mansi Arora Hirak Ranjan Dash |
| author_sort | Mansi Arora |
| collection | DOAJ |
| description | Background Traditional CE-based STR profiles are highly useful for the purpose of individualisation. However, they do not give any additional information without the presence of the reference sample for comparison. Aim To assess the usability of STR-based genotypes for the prediction of an individual’s geolocation. Subjects and Methods Genotype data from five geographically distinct populations, i.e. Caucasian, Hispanic, Asian, Estonian, and Bahrainian, were collected from the published literature. Results A significant difference (p < 0.05) in the observed genotypes was found between these populations. D1S1656 and SE33 showed substantial differences in their genotype frequencies across the tested populations. SE33, D12S391, D21S11, D19S433, D18S51, and D1S1656 were found to have the highest occurrence of “unique genotype’s” in different populations. In addition, D12S391 and D13S317 exhibited distinct population-specific “most frequent genotypes.” Conclusions Three different prediction models have been proposed for genotype to geolocation prediction, i.e. (i) use of unique genotypes of a population, (ii) use of the most frequent genotype, and (iii) a combinatorial approach of unique and most frequent genotypes. These models could aid the investigating agencies in cases where no reference sample is available for comparison of the profile. |
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| format | Article |
| id | doaj.art-186f5ce19d514ee1ae87b9a906c82ea0 |
| institution | Directory Open Access Journal |
| issn | 0301-4460 1464-5033 |
| language | English |
| last_indexed | 2024-03-12T00:39:50Z |
| publishDate | 2023-01-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | Annals of Human Biology |
| spelling | doaj.art-186f5ce19d514ee1ae87b9a906c82ea02023-09-15T08:45:21ZengTaylor & Francis GroupAnnals of Human Biology0301-44601464-50332023-01-0150127428110.1080/03014460.2023.22173822217382Geolocation prediction from STR genotyping: a pilot study in five geographically distinct global populationsMansi Arora0Hirak Ranjan Dash1Department of Forensic Science, National Forensic Sciences UniversityDepartment of Forensic Science, National Forensic Sciences UniversityBackground Traditional CE-based STR profiles are highly useful for the purpose of individualisation. However, they do not give any additional information without the presence of the reference sample for comparison. Aim To assess the usability of STR-based genotypes for the prediction of an individual’s geolocation. Subjects and Methods Genotype data from five geographically distinct populations, i.e. Caucasian, Hispanic, Asian, Estonian, and Bahrainian, were collected from the published literature. Results A significant difference (p < 0.05) in the observed genotypes was found between these populations. D1S1656 and SE33 showed substantial differences in their genotype frequencies across the tested populations. SE33, D12S391, D21S11, D19S433, D18S51, and D1S1656 were found to have the highest occurrence of “unique genotype’s” in different populations. In addition, D12S391 and D13S317 exhibited distinct population-specific “most frequent genotypes.” Conclusions Three different prediction models have been proposed for genotype to geolocation prediction, i.e. (i) use of unique genotypes of a population, (ii) use of the most frequent genotype, and (iii) a combinatorial approach of unique and most frequent genotypes. These models could aid the investigating agencies in cases where no reference sample is available for comparison of the profile.http://dx.doi.org/10.1080/03014460.2023.2217382dna fingerprintingstrgenotypegeolocationprediction model |
| spellingShingle | Mansi Arora Hirak Ranjan Dash Geolocation prediction from STR genotyping: a pilot study in five geographically distinct global populations Annals of Human Biology dna fingerprinting str genotype geolocation prediction model |
| title | Geolocation prediction from STR genotyping: a pilot study in five geographically distinct global populations |
| title_full | Geolocation prediction from STR genotyping: a pilot study in five geographically distinct global populations |
| title_fullStr | Geolocation prediction from STR genotyping: a pilot study in five geographically distinct global populations |
| title_full_unstemmed | Geolocation prediction from STR genotyping: a pilot study in five geographically distinct global populations |
| title_short | Geolocation prediction from STR genotyping: a pilot study in five geographically distinct global populations |
| title_sort | geolocation prediction from str genotyping a pilot study in five geographically distinct global populations |
| topic | dna fingerprinting str genotype geolocation prediction model |
| url | http://dx.doi.org/10.1080/03014460.2023.2217382 |
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