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Ambers A, Bus MM, King JL, Jones B, Durst J, Bruseth JE, Gill-King H, Budowle B. Forensic genetic investigation of human skeletal remains recovered from the La Belle shipwreck. Forensic Sci Int 2020; 306:110050. [DOI: 10.1016/j.forsciint.2019.110050] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/12/2019] [Revised: 10/10/2019] [Accepted: 11/08/2019] [Indexed: 10/25/2022]
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52
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West FL, Algee-Hewitt BF. Cadaveric blood cards: Assessing DNA quality and quantity and the utility of STRs for the individual estimation of trihybrid ancestry and admixture proportions. Forensic Sci Int Synerg 2020; 2:114-122. [PMID: 32412010 PMCID: PMC7219121 DOI: 10.1016/j.fsisyn.2020.03.002] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/23/2020] [Revised: 03/04/2020] [Accepted: 03/06/2020] [Indexed: 02/04/2023]
Abstract
As part a body donation program, blood samples were collected and stored on untreated (non-FTA) blood cards. The blood cards were evaluated in terms of DNA preservation and STR typing success with resulting profiles assessed with special consideration given to profile matching for positive identification and biogeographic ancestry estimation. While STR profiles were successfully generated for all samples, results indicate that the time interval between date of death and sample collection have an impact on DNA quantity and quality. There is a statistically significant decrease in relative fluorescent unit (RFU) values with increasing time interval between date of death and sample collection, indicating degradation in the blood card samples related to the post-mortem interval prior to sample collection. The STR profiles were used to estimate ancestry and admixture using the program STRUCTURE, demonstrating utility of these markers beyond individual identification purposes, with caveats for application based on population history.
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Affiliation(s)
- Frankie L. West
- Forensic Science Program, Western Carolina University, USA
- Corresponding author.
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53
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Veltre V, De Angelis F, Biondi G, Rickards O. Evaluation of skin-related variants in African ancestry populations and their role in personal identification. FORENSIC SCIENCE INTERNATIONAL GENETICS SUPPLEMENT SERIES 2019. [DOI: 10.1016/j.fsigss.2019.09.067] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/25/2022]
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54
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Meyer OS, Andersen JD, Børsting C. Presentation of the Human Pigmentation (HuPi) AmpliSeq™ custom panel. FORENSIC SCIENCE INTERNATIONAL GENETICS SUPPLEMENT SERIES 2019. [DOI: 10.1016/j.fsigss.2019.10.058] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
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55
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HIrisPlex-S system for eye, hair, and skin color prediction from DNA: Massively parallel sequencing solutions for two common forensically used platforms. Forensic Sci Int Genet 2019; 43:102152. [DOI: 10.1016/j.fsigen.2019.102152] [Citation(s) in RCA: 28] [Impact Index Per Article: 5.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/06/2019] [Revised: 07/26/2019] [Accepted: 08/22/2019] [Indexed: 11/22/2022]
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56
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Kukla-Bartoszek M, Pośpiech E, Woźniak A, Boroń M, Karłowska-Pik J, Teisseyre P, Zubańska M, Bronikowska A, Grzybowski T, Płoski R, Spólnicka M, Branicki W. DNA-based predictive models for the presence of freckles. Forensic Sci Int Genet 2019; 42:252-259. [DOI: 10.1016/j.fsigen.2019.07.012] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/18/2019] [Revised: 06/12/2019] [Accepted: 07/21/2019] [Indexed: 01/05/2023]
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57
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The Evolutionary History of Human Skin Pigmentation. J Mol Evol 2019; 88:77-87. [PMID: 31363820 DOI: 10.1007/s00239-019-09902-7] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/28/2019] [Accepted: 07/16/2019] [Indexed: 02/07/2023]
Abstract
Skin pigmentation is a complex, conspicuous, highly variable human trait that exhibits a remarkable correlation with latitude. The evolutionary history and genetic basis of skin color variation has been the subject of intense research in the last years. This article reviews the major hypotheses explaining skin color diversity and explores the implications of recent findings about the genes associated with skin pigmentation for understanding the evolutionary forces that have shaped the current patterns of skin color variation. A major aspect of these findings is that the genetic basis of skin color is less simple than previously thought and that geographic variation in skin pigmentation was influenced by the concerted action of different types of natural selection, rather than just by selective sweeps in a few key genes.
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58
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Liu F, Zhong K, Jing X, Uitterlinden AG, Hendriks AEJ, Drop SLS, Kayser M. Update on the predictability of tall stature from DNA markers in Europeans. Forensic Sci Int Genet 2019; 42:8-13. [PMID: 31207428 DOI: 10.1016/j.fsigen.2019.05.006] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2018] [Revised: 05/08/2019] [Accepted: 05/27/2019] [Indexed: 10/26/2022]
Abstract
Predicting adult height from DNA has important implications in forensic DNA phenotyping. In 2014, we introduced a prediction model consisting of 180 height-associated SNPs based on data from 10,361 Northwestern Europeans enriched with tall individuals (770 > 1.88 standard deviation), which yielded a mid-ranged accuracy (AUC = 0.75 for binary prediction of tall stature and R2 = 0.12 for quantitative prediction of adult height). Here, we provide an update on DNA-based height predictability considering an enlarged list of subsequently-published height-associated SNPs using data from the same set of 10,361 Europeans. A prediction model based on the full set of 689 SNPs showed an improved accuracy relative to previous models for both tall stature (AUC = 0.79) and quantitative height (R2 = 0.21). A feature selection analysis revealed a subset of 412 most informative SNPs while the corresponding prediction model retained most of the accuracy (AUC = 0.76 and R2 = 0.19) achieved with the full model. Over all, our study empirically exemplifies that the accuracy for predicting human appearance phenotypes with very complex underlying genetic architectures, such as adult height, can be improved by increasing the number of phenotype-associated DNA variants. Our work also demonstrates that a careful sub-selection allows for a considerable reduction of the number of DNA predictors that achieve similar prediction accuracy as provided by the full set. This is forensically relevant due to restrictions in the number of SNPs simultaneously analyzable with forensically suitable DNA technologies in the current days of targeted massively parallel sequencing in forensic genetics.
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Affiliation(s)
- Fan Liu
- CAS Key Laboratory of Genomic and Precision Medicine, Beijing Institute of Genomics, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Beijing, China; Department of Genetic Identification, Erasmus MC University Medical Center Rotterdam, Rotterdam, the Netherlands.
| | - Kaiyin Zhong
- Department of Genetic Identification, Erasmus MC University Medical Center Rotterdam, Rotterdam, the Netherlands.
| | - Xiaoxi Jing
- CAS Key Laboratory of Genomic and Precision Medicine, Beijing Institute of Genomics, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Beijing, China.
| | - André G Uitterlinden
- Department of Internal Medicine, Erasmus MC University Medical Center Rotterdam, Rotterdam, the Netherlands; Department of Epidemiology, Erasmus MC University Medical Center Rotterdam, Rotterdam, the Netherlands.
| | - A Emile J Hendriks
- Department of Pediatrics, Pediatric Endocrinology and Diabetes, University of Cambridge, United Kingdom.
| | - Stenvert L S Drop
- Department of Pediatrics, Division of Endocrinology, Sophia Children's Hospital, Erasmus MC University Medical Center Rotterdam, Rotterdam, the Netherlands.
| | - Manfred Kayser
- Department of Genetic Identification, Erasmus MC University Medical Center Rotterdam, Rotterdam, the Netherlands.
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59
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Saag L, Laneman M, Varul L, Malve M, Valk H, Razzak MA, Shirobokov IG, Khartanovich VI, Mikhaylova ER, Kushniarevich A, Scheib CL, Solnik A, Reisberg T, Parik J, Saag L, Metspalu E, Rootsi S, Montinaro F, Remm M, Mägi R, D'Atanasio E, Crema ER, Díez-Del-Molino D, Thomas MG, Kriiska A, Kivisild T, Villems R, Lang V, Metspalu M, Tambets K. The Arrival of Siberian Ancestry Connecting the Eastern Baltic to Uralic Speakers further East. Curr Biol 2019; 29:1701-1711.e16. [PMID: 31080083 PMCID: PMC6544527 DOI: 10.1016/j.cub.2019.04.026] [Citation(s) in RCA: 43] [Impact Index Per Article: 8.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/05/2019] [Revised: 03/18/2019] [Accepted: 04/09/2019] [Indexed: 01/08/2023]
Abstract
In this study, we compare the genetic ancestry of individuals from two as yet genetically unstudied cultural traditions in Estonia in the context of available modern and ancient datasets: 15 from the Late Bronze Age stone-cist graves (1200-400 BC) (EstBA) and 6 from the Pre-Roman Iron Age tarand cemeteries (800/500 BC-50 AD) (EstIA). We also included 5 Pre-Roman to Roman Iron Age Ingrian (500 BC-450 AD) (IngIA) and 7 Middle Age Estonian (1200-1600 AD) (EstMA) individuals to build a dataset for studying the demographic history of the northern parts of the Eastern Baltic from the earliest layer of Mesolithic to modern times. Our findings are consistent with EstBA receiving gene flow from regions with strong Western hunter-gatherer (WHG) affinities and EstIA from populations related to modern Siberians. The latter inference is in accordance with Y chromosome (chrY) distributions in present day populations of the Eastern Baltic, as well as patterns of autosomal variation in the majority of the westernmost Uralic speakers [1-5]. This ancestry reached the coasts of the Baltic Sea no later than the mid-first millennium BC; i.e., in the same time window as the diversification of west Uralic (Finnic) languages [6]. Furthermore, phenotypic traits often associated with modern Northern Europeans, like light eyes, hair, and skin, as well as lactose tolerance, can be traced back to the Bronze Age in the Eastern Baltic. VIDEO ABSTRACT.
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Affiliation(s)
- Lehti Saag
- Estonian Biocentre, Institute of Genomics, University of Tartu, Tartu 51010, Estonia; Department of Evolutionary Biology, Institute of Cell and Molecular Biology, University of Tartu, Tartu 51010, Estonia.
| | - Margot Laneman
- Department of Archaeology, Institute of History and Archaeology, University of Tartu, Tartu 51014, Estonia
| | - Liivi Varul
- School of Humanities, Tallinn University, Tallinn 10120, Estonia
| | - Martin Malve
- Department of Archaeology, Institute of History and Archaeology, University of Tartu, Tartu 51014, Estonia
| | - Heiki Valk
- Department of Archaeology, Institute of History and Archaeology, University of Tartu, Tartu 51014, Estonia
| | - Maria A Razzak
- Department of Slavic and Finnic Archaeology, Institute for the History of Material Culture, Russian Academy of Sciences, St. Petersburg 191186, Russia
| | - Ivan G Shirobokov
- Museum of Anthropology and Ethnography (Kunstkamera), Russian Academy of Sciences, St. Petersburg 199034, Russia
| | - Valeri I Khartanovich
- Museum of Anthropology and Ethnography (Kunstkamera), Russian Academy of Sciences, St. Petersburg 199034, Russia
| | | | - Alena Kushniarevich
- Estonian Biocentre, Institute of Genomics, University of Tartu, Tartu 51010, Estonia
| | - Christiana Lyn Scheib
- Estonian Biocentre, Institute of Genomics, University of Tartu, Tartu 51010, Estonia
| | - Anu Solnik
- Estonian Biocentre, Institute of Genomics, University of Tartu, Tartu 51010, Estonia
| | - Tuuli Reisberg
- Estonian Biocentre, Institute of Genomics, University of Tartu, Tartu 51010, Estonia
| | - Jüri Parik
- Estonian Biocentre, Institute of Genomics, University of Tartu, Tartu 51010, Estonia; Department of Evolutionary Biology, Institute of Cell and Molecular Biology, University of Tartu, Tartu 51010, Estonia
| | - Lauri Saag
- Estonian Biocentre, Institute of Genomics, University of Tartu, Tartu 51010, Estonia
| | - Ene Metspalu
- Estonian Biocentre, Institute of Genomics, University of Tartu, Tartu 51010, Estonia
| | - Siiri Rootsi
- Estonian Biocentre, Institute of Genomics, University of Tartu, Tartu 51010, Estonia
| | - Francesco Montinaro
- Estonian Biocentre, Institute of Genomics, University of Tartu, Tartu 51010, Estonia
| | - Maido Remm
- Department of Bioinformatics, Institute of Cell and Molecular Biology, University of Tartu, Tartu 51010, Estonia
| | - Reedik Mägi
- Estonian Genome Center, Institute of Genomics, University of Tartu, Tartu 51010, Estonia
| | | | | | - David Díez-Del-Molino
- Department of Bioinformatics and Genetics, Swedish Museum of Natural History, Stockholm 104 05, Sweden; Department of Archaeology and Classical Studies, Stockholm University, Stockholm 106 91, Sweden
| | - Mark G Thomas
- Research Department of Genetics, Evolution and Environment, University College London, London WC1E 6BT, UK; UCL Genetics Institute, University College London, London WC1E 6BT, UK
| | - Aivar Kriiska
- Department of Archaeology, Institute of History and Archaeology, University of Tartu, Tartu 51014, Estonia
| | - Toomas Kivisild
- Estonian Biocentre, Institute of Genomics, University of Tartu, Tartu 51010, Estonia; Department of Evolutionary Biology, Institute of Cell and Molecular Biology, University of Tartu, Tartu 51010, Estonia; Department of Human Genetics, KU Leuven, Leuven 3000, Belgium
| | - Richard Villems
- Estonian Biocentre, Institute of Genomics, University of Tartu, Tartu 51010, Estonia; Department of Evolutionary Biology, Institute of Cell and Molecular Biology, University of Tartu, Tartu 51010, Estonia
| | - Valter Lang
- Department of Archaeology, Institute of History and Archaeology, University of Tartu, Tartu 51014, Estonia
| | - Mait Metspalu
- Estonian Biocentre, Institute of Genomics, University of Tartu, Tartu 51010, Estonia
| | - Kristiina Tambets
- Estonian Biocentre, Institute of Genomics, University of Tartu, Tartu 51010, Estonia.
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60
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Abstract
Human skin and hair color are visible traits that can vary dramatically within and across ethnic populations. The genetic makeup of these traits-including polymorphisms in the enzymes and signaling proteins involved in melanogenesis, and the vital role of ion transport mechanisms operating during the maturation and distribution of the melanosome-has provided new insights into the regulation of pigmentation. A large number of novel loci involved in the process have been recently discovered through four large-scale genome-wide association studies in Europeans, two large genetic studies of skin color in Africans, one study in Latin Americans, and functional testing in animal models. The responsible polymorphisms within these pigmentation genes appear at different population frequencies, can be used as ancestry-informative markers, and provide insight into the evolutionary selective forces that have acted to create this human diversity.
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Affiliation(s)
- William J Pavan
- Genetic Disease Research Branch, National Human Genome Research Institute, National Institutes of Health, Bethesda, Maryland 20892, USA;
| | - Richard A Sturm
- Dermatology Research Centre, The University of Queensland Diamantina Institute, The University of Queensland, Brisbane, Queensland 4102, Australia;
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61
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Zaorska K, Zawierucha P, Nowicki M. Prediction of skin color, tanning and freckling from DNA in Polish population: linear regression, random forest and neural network approaches. Hum Genet 2019; 138:635-647. [PMID: 30980179 PMCID: PMC6554257 DOI: 10.1007/s00439-019-02012-w] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2018] [Accepted: 04/08/2019] [Indexed: 12/16/2022]
Abstract
Predicting phenotypes from DNA has recently become extensively studied field in forensic research and is referred to as Forensic DNA Phenotyping. Systems based on single nucleotide polymorphisms for accurate prediction of iris, hair and skin color in global population, independent of bio-geographical ancestry, have recently been introduced. Here, we analyzed 14 SNPs for distinct skin pigmentation traits in a homogeneous cohort of 222 Polish subjects. We compared three different algorithms: General Linear Model based on logistic regression, Random Forest and Neural Network in 18 developed prediction models. We demonstrate Random Forest to be the most accurate algorithm for 3- and 4-category estimations (total of 58.3% correct calls for skin color prediction, 47.2% for tanning prediction, 50% for freckling prediction). Binomial Logistic Regression was the best approach in 2-category estimations (total of 69.4% correct calls, AUC = 0.673 for tanning prediction; total of 52.8% correct calls, AUC = 0.537 for freckling prediction). Our study confirms the association of rs12913832 (HERC2) with all three skin pigmentation traits, but also variants associated solely with certain pigmentation traits, namely rs6058017 and rs4911414 (ASIP) with skin sensitivity to sun and tanning abilities, rs12203592 (IRF4) with freckling and rs4778241 and rs4778138 (OCA2) with skin color and tanning. Finally, we assessed significant differences in allele frequencies in comparison with CEU data and our study provides a starting point for the development of prediction models for homogeneous populations with less internal differentiation than in the global predictive testing.
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Affiliation(s)
- Katarzyna Zaorska
- Department of Histology and Embryology, University of Medical Sciences, 60-781, Poznan, Poland.
| | - Piotr Zawierucha
- Department of Anatomy, University of Medical Sciences, 60-781, Poznan, Poland
| | - Michał Nowicki
- Department of Histology and Embryology, University of Medical Sciences, 60-781, Poznan, Poland
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62
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Salvoro C, Faccinetto C, Zucchelli L, Porto M, Marino A, Occhi G, de Los Campos G, Vazza G. Performance of four models for eye color prediction in an Italian population sample. Forensic Sci Int Genet 2019; 40:192-200. [PMID: 30884346 DOI: 10.1016/j.fsigen.2019.03.008] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/07/2018] [Revised: 01/27/2019] [Accepted: 03/10/2019] [Indexed: 11/29/2022]
Abstract
Forensic DNA phenotyping (FDP) has recently provided important advancements in forensic investigations, by predicting the physical appearance of a subject from a biological sample, using SNP markers. The majority of operable prediction models have been developed for iris color; however, replication studies to understand their applicability on a worldwide scale are still limited for many of them. In this work, 4 models for eye color prediction (IrisPlex, Ruiz, Allwood and Hart models) were systematically evaluated in a sample of 296 subjects of Italian origin. Genotypes were determined by a custom NGS-based panel targeting all the predictive SNPs included in the 4 tested models. Overall, 60-69% of the Italian sample could be correctly predicted with the IrisPlex, Ruiz and Allwood models, applying the recommended threshold. The IrisPlex model showed the lowest frequency of errors (17%), but also the highest number of inconclusive results (18%). In the absence of the threshold, the highest proportion of correct predictions was again obtained with the IrisPlex model (76%), followed by the Allwood (73%) and the Ruiz (65%) models. Lastly, the Hart predictive algorithm had the lowest error rate (2%), but the majority of predictions (87%) were restricted to the less informative categories of "not-blue" and "not-brown", and correct color predictions were obtained only for 11% of the sample. As observed in previous studies, the majority of incorrect and undefined predictions were ascribable to the intermediate category, which represented 25% of the Italian sample. An adjustment of the IrisPlex (multinomial logistic regression) and Ruiz models (Snipper Bayesian classifier) with Italian allele frequencies gave only minor improvements in predicting intermediate eye color and no remarkable overall changes in performance. This suggests an incomplete knowledge underlying the intermediate colors. Considering the impact of this phenotype in the Italian sample as well as in other admixed populations, future improvements of eye color prediction methods should include a better genetic and phenotypic characterization of this category.
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Affiliation(s)
| | - Christian Faccinetto
- Reparto Carabinieri Investigazioni Scientifiche di Parma, Sezione Biologia, Parma, Italy.
| | - Luca Zucchelli
- Department of Biology, University of Padova, Padova, Italy
| | - Marika Porto
- Department of Biology, University of Padova, Padova, Italy
| | - Alberto Marino
- Reparto Carabinieri Investigazioni Scientifiche di Parma, Sezione Biologia, Parma, Italy
| | - Gianluca Occhi
- Department of Biology, University of Padova, Padova, Italy
| | - Gustavo de Los Campos
- Departments of Epidemiology & Biostatistics and Statistics & Probability, Institute for Quantitative Health Science and Engineering, Michigan State University, East Lansing, Michigan United States
| | - Giovanni Vazza
- Department of Biology, University of Padova, Padova, Italy.
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63
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Katsara MA, Nothnagel M. True colors: A literature review on the spatial distribution of eye and hair pigmentation. Forensic Sci Int Genet 2019; 39:109-118. [DOI: 10.1016/j.fsigen.2019.01.001] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/13/2018] [Revised: 12/11/2018] [Accepted: 01/01/2019] [Indexed: 10/27/2022]
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64
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Quillen EE, Norton HL, Parra EJ, Lona-Durazo F, Ang KC, Illiescu FM, Pearson LN, Shriver MD, Lasisi T, Gokcumen O, Starr I, Lin YL, Martin AR, Jablonski NG. Shades of complexity: New perspectives on the evolution and genetic architecture of human skin. AMERICAN JOURNAL OF PHYSICAL ANTHROPOLOGY 2018; 168 Suppl 67:4-26. [PMID: 30408154 DOI: 10.1002/ajpa.23737] [Citation(s) in RCA: 32] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/02/2018] [Revised: 09/17/2018] [Accepted: 09/20/2018] [Indexed: 02/06/2023]
Abstract
Like many highly variable human traits, more than a dozen genes are known to contribute to the full range of skin color. However, the historical bias in favor of genetic studies in European and European-derived populations has blinded us to the magnitude of pigmentation's complexity. As deliberate efforts are being made to better characterize diverse global populations and new sequencing technologies, better measurement tools, functional assessments, predictive modeling, and ancient DNA analyses become more widely accessible, we are beginning to appreciate how limited our understanding of the genetic bases of human skin color have been. Novel variants in genes not previously linked to pigmentation have been identified and evidence is mounting that there are hundreds more variants yet to be found. Even for genes that have been exhaustively characterized in European populations like MC1R, OCA2, and SLC24A5, research in previously understudied groups is leading to a new appreciation of the degree to which genetic diversity, epistatic interactions, pleiotropy, admixture, global and local adaptation, and cultural practices operate in population-specific ways to shape the genetic architecture of skin color. Furthermore, we are coming to terms with how factors like tanning response and barrier function may also have influenced selection on skin throughout human history. By examining how our knowledge of pigmentation genetics has shifted in the last decade, we can better appreciate how far we have come in understanding human diversity and the still long road ahead for understanding many complex human traits.
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Affiliation(s)
- Ellen E Quillen
- Department of Internal Medicine, Section of Molecular Medicine, Wake Forest School of Medicine, Winston-Salem, North Carolina.,Center for Precision Medicine, Wake Forest School of Medicine, Winston-Salem, North Carolina
| | - Heather L Norton
- Department of Anthropology, University of Cincinnati, Cincinnati, Ohio
| | - Esteban J Parra
- Department of Anthropology, University of Toronto - Mississauga, Mississauga, Ontario, Canada
| | - Frida Lona-Durazo
- Department of Anthropology, University of Toronto - Mississauga, Mississauga, Ontario, Canada
| | - Khai C Ang
- Department of Pathology and Jake Gittlen Laboratories for Cancer Research, Penn State College of Medicine, Hershey, Pennsylvania
| | - Florin Mircea Illiescu
- Department of Zoology, University of Cambridge, Cambridge, United Kingdom.,Centro de Estudios Interculturales e Indígenas - CIIR, P. Universidad Católica de Chile, Santiago, Chile
| | - Laurel N Pearson
- Department of Anthropology, Pennsylvania State University, University Park, Pennsylvania
| | - Mark D Shriver
- Department of Anthropology, Pennsylvania State University, University Park, Pennsylvania
| | - Tina Lasisi
- Department of Anthropology, Pennsylvania State University, University Park, Pennsylvania
| | - Omer Gokcumen
- Department of Biological Sciences, State University of New York at Buffalo, Buffalo, New York
| | - Izzy Starr
- Department of Biological Sciences, State University of New York at Buffalo, Buffalo, New York
| | - Yen-Lung Lin
- Department of Biological Sciences, State University of New York at Buffalo, Buffalo, New York
| | - Alicia R Martin
- Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, Massachusetts.,Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, Massachusetts.,Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, Massachusetts
| | - Nina G Jablonski
- Department of Anthropology, Pennsylvania State University, University Park, Pennsylvania
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65
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Current and emerging tools for the recovery of genetic information from post mortem samples: New directions for disaster victim identification. Forensic Sci Int Genet 2018; 37:270-282. [DOI: 10.1016/j.fsigen.2018.08.016] [Citation(s) in RCA: 22] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2018] [Revised: 08/27/2018] [Accepted: 08/27/2018] [Indexed: 01/14/2023]
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66
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Bradbury C, Köttgen A, Staubach F. Off-target phenotypes in forensic DNA phenotyping and biogeographic ancestry inference: A resource. Forensic Sci Int Genet 2018; 38:93-104. [PMID: 30391626 DOI: 10.1016/j.fsigen.2018.10.010] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2018] [Revised: 08/27/2018] [Accepted: 10/13/2018] [Indexed: 01/04/2023]
Abstract
With recent advances in DNA sequencing technologies it has become feasible and cost effective to genotype larger marker sets for forensic purposes. Two technologies that make use of the larger marker sets have come into focus in forensic research and applications; inference of biogeographic ancestry (BGA) and forensic DNA phenotyping (FDP). These methods hold the promise to reveal information about a yet unknown perpetrator from a DNA sample. In contrast, DNA-profiling, that is a standard practice in case work, relies on matching DNA-profiles between crime scene material and suspects on a database of DNA-profiles. Markers for DNA-profiling were developed under the premise to reveal as little additional information about the human source of the profile as possible, the rationale being that personal privacy rights have to be balanced against the public interest in solving a crime. The same argument holds for markers used in BGA and FDP; these markers might also reveal information on off-target phenotypes (OTPs), that go beyond BGA and the phenotypes targeted in FDP. In particular, health related OTPs might shift the balance between privacy protection and public interest. However, to our knowledge, there is currently no convenient resource available to incorporate knowledge on OTPs in BGA and FDP assay design and application. In order to provide such a resource, we performed a systematic search for OTPs associated with a comprehensive set of markers (1766 SNPs) used or suggested to be used for BGA inference and FDP. In this set, we identified a relatively small number of 27 SNPs (1.53%) that convey information on diverse health related OTPs such as cancer risk, induced asthma, or risk of alcoholism. Some of these SNPs are commonly used for FDP and BGA across different marker sets. We conclude that the effects of SNP markers used in FDP and BGA on OTPs are currently limited, with few exceptions that should be considered in a balanced decision on assay design and application.
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Affiliation(s)
- Cedric Bradbury
- University College Freiburg, Albert-Ludwigs-University Freiburg, Freiburg, Germany
| | - Anna Köttgen
- Institute of Genetic Epidemiology, Dept. of Biometry, Epidemiology and Medical Bioinformatics, Faculty of Medicine and Medical Center, University of Freiburg, Freiburg, Germany
| | - Fabian Staubach
- Institute of Biology I, Dept. of Evolutionary Biology and Ecology, Albert-Ludwigs-University Freiburg, Freiburg, Germany.
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Pośpiech E, Chen Y, Kukla-Bartoszek M, Breslin K, Aliferi A, Andersen JD, Ballard D, Chaitanya L, Freire-Aradas A, van der Gaag KJ, Girón-Santamaría L, Gross TE, Gysi M, Huber G, Mosquera-Miguel A, Muralidharan C, Skowron M, Carracedo Á, Haas C, Morling N, Parson W, Phillips C, Schneider PM, Sijen T, Syndercombe-Court D, Vennemann M, Wu S, Xu S, Jin L, Wang S, Zhu G, Martin NG, Medland SE, Branicki W, Walsh S, Liu F, Kayser M. Towards broadening Forensic DNA Phenotyping beyond pigmentation: Improving the prediction of head hair shape from DNA. Forensic Sci Int Genet 2018; 37:241-251. [PMID: 30268682 DOI: 10.1016/j.fsigen.2018.08.017] [Citation(s) in RCA: 21] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/26/2018] [Revised: 07/18/2018] [Accepted: 08/27/2018] [Indexed: 10/28/2022]
Abstract
Human head hair shape, commonly classified as straight, wavy, curly or frizzy, is an attractive target for Forensic DNA Phenotyping and other applications of human appearance prediction from DNA such as in paleogenetics. The genetic knowledge underlying head hair shape variation was recently improved by the outcome of a series of genome-wide association and replication studies in a total of 26,964 subjects, highlighting 12 loci of which 8 were novel and introducing a prediction model for Europeans based on 14 SNPs. In the present study, we evaluated the capacity of DNA-based head hair shape prediction by investigating an extended set of candidate SNP predictors and by using an independent set of samples for model validation. Prediction model building was carried out in 9674 subjects (6068 from Europe, 2899 from Asia and 707 of admixed European and Asian ancestries), used previously, by considering a novel list of 90 candidate SNPs. For model validation, genotype and phenotype data were newly collected in 2415 independent subjects (2138 Europeans and 277 non-Europeans) by applying two targeted massively parallel sequencing platforms, Ion Torrent PGM and MiSeq, or the MassARRAY platform. A binomial model was developed to predict straight vs. non-straight hair based on 32 SNPs from 26 genetic loci we identified as significantly contributing to the model. This model achieved prediction accuracies, expressed as AUC, of 0.664 in Europeans and 0.789 in non-Europeans; the statistically significant difference was explained mostly by the effect of one EDAR SNP in non-Europeans. Considering sex and age, in addition to the SNPs, slightly and insignificantly increased the prediction accuracies (AUC of 0.680 and 0.800, respectively). Based on the sample size and candidate DNA markers investigated, this study provides the most robust, validated, and accurate statistical prediction models and SNP predictor marker sets currently available for predicting head hair shape from DNA, providing the next step towards broadening Forensic DNA Phenotyping beyond pigmentation traits.
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Affiliation(s)
- Ewelina Pośpiech
- Institute of Zoology and Biomedical Research, Faculty of Biology, Jagiellonian University, Gronostajowa st. 9, 30-387, Kraków, Poland; Malopolska Centre of Biotechnology, Jagiellonian University, Gronostajowa st. 7A, 30-387, Kraków, Poland
| | - Yan Chen
- Key Laboratory of Genomic and Precision Medicine, Beijing Institute of Genomics, Chinese Academy of Sciences, Beichen West Road 1-104, Chaoyang, Beijing, 100101, PR China; University of Chinese Academy of Sciences, 19 Yuquan Road, Shijingshan, Beijing, 100049, PR China
| | - Magdalena Kukla-Bartoszek
- Faculty of Biochemistry, Biophysics and Biotechnology, Jagiellonian University, Gronostajowa st. 7, 30-387, Kraków, Poland
| | - Krystal Breslin
- Department of Biology, Indiana University Purdue University Indianapolis (IUPUI), IN, USA
| | - Anastasia Aliferi
- King's Forensics, Faculty of Life Sciences and Medicine, King's College London, 150 Stamford Street, London, United Kingdom
| | - Jeppe D Andersen
- Section of Forensic Genetics, Department of Forensic Medicine, Faculty of Health and Medical Sciences, University of Copenhagen, Frederik V's Vej 11, DK-2100, Copenhagen, Denmark
| | - David Ballard
- King's Forensics, Faculty of Life Sciences and Medicine, King's College London, 150 Stamford Street, London, United Kingdom
| | - Lakshmi Chaitanya
- Department of Genetic Identification, Erasmus MC University Medical Center Rotterdam, P.O. Box 2040, 3000 CA, Rotterdam, Netherlands
| | - Ana Freire-Aradas
- Institute of Legal Medicine, Medical Faculty, University of Cologne, Melatengürtel 60/62, D-50823, Cologne, Germany; Forensic Genetics Unit, Institute of Forensic Sciences, R/ San Francisco s/n, Faculty of Medicine, 15782, University of Santiago de Compostela, Santiago de Compostela, Spain
| | - Kristiaan J van der Gaag
- Division of Biological Traces, Netherlands Forensic Institute, P.O. Box 24044, 2490 AA, The Hague, The Netherlands
| | - Lorena Girón-Santamaría
- Forensic Genetics Unit, Institute of Forensic Sciences, R/ San Francisco s/n, Faculty of Medicine, 15782, University of Santiago de Compostela, Santiago de Compostela, Spain
| | - Theresa E Gross
- Institute of Legal Medicine, Medical Faculty, University of Cologne, Melatengürtel 60/62, D-50823, Cologne, Germany
| | - Mario Gysi
- Zurich Institute of Forensic Medicine, University of Zurich, Winterthurerstrasse 190, 8057, Zurich, Switzerland
| | - Gabriela Huber
- Institute of Legal Medicine, Medical University of Innsbruck, Müllerstrasse 44, 6020, Innsbruck, Austria
| | - Ana Mosquera-Miguel
- Forensic Genetics Unit, Institute of Forensic Sciences, R/ San Francisco s/n, Faculty of Medicine, 15782, University of Santiago de Compostela, Santiago de Compostela, Spain
| | - Charanya Muralidharan
- Department of Biology, Indiana University Purdue University Indianapolis (IUPUI), IN, USA
| | - Małgorzata Skowron
- Department of Dermatology, Collegium Medicum of the Jagiellonian University, Skawińska st. 8, 31-066, Kraków, Poland
| | - Ángel Carracedo
- Forensic Genetics Unit, Institute of Forensic Sciences, R/ San Francisco s/n, Faculty of Medicine, 15782, University of Santiago de Compostela, Santiago de Compostela, Spain; Center of Excellence in Genomic Medicine Research, King Abdulaziz University, Jeddah, KSA, Saudi Arabia
| | - Cordula Haas
- Zurich Institute of Forensic Medicine, University of Zurich, Winterthurerstrasse 190, 8057, Zurich, Switzerland
| | - Niels Morling
- Section of Forensic Genetics, Department of Forensic Medicine, Faculty of Health and Medical Sciences, University of Copenhagen, Frederik V's Vej 11, DK-2100, Copenhagen, Denmark
| | - Walther Parson
- Institute of Legal Medicine, Medical University of Innsbruck, Müllerstrasse 44, 6020, Innsbruck, Austria; Forensic Science Program, The Pennsylvania State University, 13 Thomas Building, University Park, PA, 16802, USA
| | - Christopher Phillips
- Forensic Genetics Unit, Institute of Forensic Sciences, R/ San Francisco s/n, Faculty of Medicine, 15782, University of Santiago de Compostela, Santiago de Compostela, Spain
| | - Peter M Schneider
- Institute of Legal Medicine, Medical Faculty, University of Cologne, Melatengürtel 60/62, D-50823, Cologne, Germany
| | - Titia Sijen
- Division of Biological Traces, Netherlands Forensic Institute, P.O. Box 24044, 2490 AA, The Hague, The Netherlands
| | - Denise Syndercombe-Court
- King's Forensics, Faculty of Life Sciences and Medicine, King's College London, 150 Stamford Street, London, United Kingdom
| | - Marielle Vennemann
- Institute of Legal Medicine, University of Münster, Röntgenstr. 23, 48149, Münster, Germany
| | - Sijie Wu
- University of Chinese Academy of Sciences, 19 Yuquan Road, Shijingshan, Beijing, 100049, PR China; Chinese Academy of Sciences Key Laboratory of Computational Biology, CAS-MPG Partner Institute for Computational Biology, Shanghai Institute for Biological Sciences, Chinese Academy of Sciences, 320 Yue Yang Road Shanghai, 200031, PR China
| | - Shuhua Xu
- University of Chinese Academy of Sciences, 19 Yuquan Road, Shijingshan, Beijing, 100049, PR China; Chinese Academy of Sciences Key Laboratory of Computational Biology, CAS-MPG Partner Institute for Computational Biology, Shanghai Institute for Biological Sciences, Chinese Academy of Sciences, 320 Yue Yang Road Shanghai, 200031, PR China; State Key Laboratory of Genetic Engineering and Ministry of Education Key Laboratory of Contemporary Anthropology, Collaborative Innovation Center for Genetics and Development, School of Life Sciences, Fudan University, 2005 Song Hu Road Shanghai, 200438, PR China; School of Life Science and Technology, Shanghai-Tech University, 393 Middle Huaxia Road, Pudong, Shanghai, 201210, PR China
| | - Li Jin
- Chinese Academy of Sciences Key Laboratory of Computational Biology, CAS-MPG Partner Institute for Computational Biology, Shanghai Institute for Biological Sciences, Chinese Academy of Sciences, 320 Yue Yang Road Shanghai, 200031, PR China; State Key Laboratory of Genetic Engineering and Ministry of Education Key Laboratory of Contemporary Anthropology, Collaborative Innovation Center for Genetics and Development, School of Life Sciences, Fudan University, 2005 Song Hu Road Shanghai, 200438, PR China
| | - Sijia Wang
- University of Chinese Academy of Sciences, 19 Yuquan Road, Shijingshan, Beijing, 100049, PR China; Chinese Academy of Sciences Key Laboratory of Computational Biology, CAS-MPG Partner Institute for Computational Biology, Shanghai Institute for Biological Sciences, Chinese Academy of Sciences, 320 Yue Yang Road Shanghai, 200031, PR China; State Key Laboratory of Genetic Engineering and Ministry of Education Key Laboratory of Contemporary Anthropology, Collaborative Innovation Center for Genetics and Development, School of Life Sciences, Fudan University, 2005 Song Hu Road Shanghai, 200438, PR China
| | - Ghu Zhu
- Queensland Institute of Medical Research, Royal Brisbane Hospital, QLD 4029, Brisbane, Australia
| | - Nick G Martin
- Queensland Institute of Medical Research, Royal Brisbane Hospital, QLD 4029, Brisbane, Australia
| | - Sarah E Medland
- Queensland Institute of Medical Research, Royal Brisbane Hospital, QLD 4029, Brisbane, Australia
| | - Wojciech Branicki
- Malopolska Centre of Biotechnology, Jagiellonian University, Gronostajowa st. 7A, 30-387, Kraków, Poland
| | - Susan Walsh
- Department of Biology, Indiana University Purdue University Indianapolis (IUPUI), IN, USA
| | - Fan Liu
- Key Laboratory of Genomic and Precision Medicine, Beijing Institute of Genomics, Chinese Academy of Sciences, Beichen West Road 1-104, Chaoyang, Beijing, 100101, PR China; University of Chinese Academy of Sciences, 19 Yuquan Road, Shijingshan, Beijing, 100049, PR China; Department of Genetic Identification, Erasmus MC University Medical Center Rotterdam, P.O. Box 2040, 3000 CA, Rotterdam, Netherlands
| | - Manfred Kayser
- Department of Genetic Identification, Erasmus MC University Medical Center Rotterdam, P.O. Box 2040, 3000 CA, Rotterdam, Netherlands.
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68
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Bulbul O, Filoglu G. Development of a SNP panel for predicting biogeographical ancestry and phenotype using massively parallel sequencing. Electrophoresis 2018; 39:2743-2751. [DOI: 10.1002/elps.201800243] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2018] [Revised: 07/18/2018] [Accepted: 07/23/2018] [Indexed: 11/08/2022]
Affiliation(s)
- Ozlem Bulbul
- Institute of Forensic Science; Istanbul University; Istanbul Turkey
| | - Gonul Filoglu
- Institute of Forensic Science; Istanbul University; Istanbul Turkey
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69
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Kidd KK, Pakstis AJ, Speed WC, Lagace R, Wootton S, Chang J. Selecting microhaplotypes optimized for different purposes. Electrophoresis 2018; 39:2815-2823. [PMID: 29931757 DOI: 10.1002/elps.201800092] [Citation(s) in RCA: 29] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2018] [Revised: 06/13/2018] [Accepted: 06/14/2018] [Indexed: 12/22/2022]
Abstract
Massively parallel sequencing is transforming forensic work by allowing various useful forensic markers, such as STRPs and SNPs, to be multiplexed providing information on ancestry, individual and familial identification, phenotypes for eye/hair/skin pigmentation, and the deconvolution of mixtures. Microhaplotypes also become feasible with massively parallel sequencing, these are DNA segments (smaller than 300 nucleotides) that are selected to contain multiple SNPs unambiguously defining three or more haplotype alleles occurring at common frequencies. The physical extent of a microhaplotype can thus be covered by a single sequence read making these loci phase-known codominant genetic systems. Such microhaplotypes supply significantly more information than a single SNP can. Our efforts to develop useful sets of microhaplotypes have already identified 182 such loci that we have studied on a large number of human populations from around the world. We present various analyses on 83 populations in our ongoing study for a subset of the best microhaplotypes currently available illustrating their characteristics and potential utility for ancestry, identification, and mixture deconvolution.
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Affiliation(s)
- Kenneth K Kidd
- Department of Genetics, Yale University School of Medicine, New Haven, CT, USA
| | - Andrew J Pakstis
- Department of Genetics, Yale University School of Medicine, New Haven, CT, USA
| | - William C Speed
- Department of Genetics, Yale University School of Medicine, New Haven, CT, USA
| | - Robert Lagace
- Human Identification Group, ThermoFisher Scientific, South San Francisco, CA, USA
| | - Sharon Wootton
- Human Identification Group, ThermoFisher Scientific, South San Francisco, CA, USA
| | - Joseph Chang
- Human Identification Group, ThermoFisher Scientific, South San Francisco, CA, USA
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70
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Bulbul O, Zorlu T, Filoglu G. Prediction of human eye colour using highly informative phenotype SNPs (PISNPs). AUST J FORENSIC SCI 2018. [DOI: 10.1080/00450618.2018.1484161] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/28/2022]
Affiliation(s)
- Ozlem Bulbul
- Institute of Forensic Science, Istanbul University, Istanbul, Turkey
| | - Tolga Zorlu
- Institute of Forensic Science, Istanbul University, Istanbul, Turkey
| | - Gonul Filoglu
- Institute of Forensic Science, Istanbul University, Istanbul, Turkey
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71
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Chaitanya L, Breslin K, Zuñiga S, Wirken L, Pośpiech E, Kukla-Bartoszek M, Sijen T, Knijff PD, Liu F, Branicki W, Kayser M, Walsh S. The HIrisPlex-S system for eye, hair and skin colour prediction from DNA: Introduction and forensic developmental validation. Forensic Sci Int Genet 2018; 35:123-135. [DOI: 10.1016/j.fsigen.2018.04.004] [Citation(s) in RCA: 138] [Impact Index Per Article: 23.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/16/2017] [Revised: 03/05/2018] [Accepted: 04/06/2018] [Indexed: 11/29/2022]
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Hunter P. Uncharted waters: Next-generation sequencing and machine learning software allow forensic science to expand into phenotype prediction from DNA samples. EMBO Rep 2018; 19:embr.201845810. [PMID: 29437774 DOI: 10.15252/embr.201845810] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022] Open
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