Highlights
- •A new panel to assess ancestry via MPS technology was created.
- •The capability of panel to infer ancestry was evaluated via PLS-DA method.
- •Outstanding classification was observed for all populations except for Middle East.
- •The application of variable selection techniques to the panel was investigated.
- •Genetic Algorithm selection technique resulted in the best approach to select the variables.
Abstract
As evidenced by the large number of articles recently published in the literature,
forensic scientists are making great efforts to infer externally visible features
and biogeographical ancestry (BGA) from DNA analysis. Just as phenotypic, ancestry
information obtained from DNA can provide investigative leads to identify the victims
(missing/unidentified persons, crime/armed conflict/mass disaster victims) or trace
their perpetrators when no matches were found with the reference profile or in the
database. Recently, the advent of Massively Parallel Sequencing technologies associated
with the possibility of harnessing high-throughput genetic data allowed us to investigate
the associations between phenotypic and genomic variations in worldwide human populations
and develop new BGA forensic tools capable of simultaneously analyzing up to millions
of markers if for example the ancient DNA approach of hybridization capture was adopted
to target SNPs of interest. In the present study, a selection of more than 3000 SNPs
was performed to create a new BGA panel and the accuracy of the new panel to infer
ancestry from unknown samples was evaluated by the PLS-DA method. Subsequently, the
panel created was assessed using three variable selection techniques (Backward variable
elimination, Genetic Algorithm and Regularized elimination procedure), and the best
SNPs in terms of inferring bio-geographical ancestry at inter- and intra-continental
level were selected to obtain panels to predict BGA with a reduced number of selected
markers to be applied in routine forensic cases where PCR amplification is the best
choice to target SNPs.
Keywords
To read this article in full you will need to make a payment
Purchase one-time access:
Academic & Personal: 24 hour online accessCorporate R&D Professionals: 24 hour online accessOne-time access price info
- For academic or personal research use, select 'Academic and Personal'
- For corporate R&D use, select 'Corporate R&D Professionals'
Subscribe:
Subscribe to Forensic Science International: GeneticsAlready a print subscriber? Claim online access
Already an online subscriber? Sign in
Register: Create an account
Institutional Access: Sign in to ScienceDirect
References
- A multiplex assay with 52 single nucleotide polymorphisms for human identification.Electrophoresis. 2006; 27: 1713-1724https://doi.org/10.1002/elps.200500671
- Candidate SNPs for a universal individual identification panel.Hum. Genet. 2007; 121: 305-317https://doi.org/10.1007/S00439-007-0342-2
- Evaluation of the ion AmpliSeq™ PhenoTrivium panel: MPS-based assay for ancestry and phenotype predictions challenged by casework samples.Genes. 2020; 11: 1-24https://doi.org/10.3390/GENES11121398
- Development and evaluation of the ancestry informative marker panel of the VISAGE basic tool.Genes. 2021; 12https://doi.org/10.3390/GENES12081284
- Evaluation of the precision of ancestry inferences in South American admixed populations.Front. Genet. 2020; 11https://doi.org/10.3389/FGENE.2020.00966
- Building a forensic ancestry panel from the ground up: the EUROFORGEN global AIM-SNP set.Forensic Sci. Int. Genet. 2014; 11: 13-25https://doi.org/10.1016/j.fsigen.2014.02.012
- Developing a set of ancestry-sensitive DNA markers reflecting continental origins of humans.BMC Genet. 2009; 10: 69https://doi.org/10.1186/1471-2156-10-69
- Global analysis of population stratification using a smart panel of 27 continental ancestry-informative SNPs.Forensic Sci. Int. Genet. 2018; 35: e10-e12https://doi.org/10.1016/J.FSIGEN.2018.05.006
- Simple and cost-effective 14-loci SNP assay designed for differentiation of European, East Asian and African samples.Forensic Sci. Int. Genet. 2015; 14: 42-49https://doi.org/10.1016/J.FSIGEN.2014.09.009
- A small NGS-SNP panel of ancestry inference designed to distinguish African, European, East, and South Asian populations.Electrophoresis. 2020; 41: 649-656https://doi.org/10.1002/ELPS.201900231
- Developing a novel panel of genome-wide ancestry informative markers for bio-geographical ancestry estimates.Forensic Sci. Int. Genet. 2014; 8: 187-194https://doi.org/10.1016/J.FSIGEN.2013.09.004
- Differentiation of Hispanic biogeographic ancestry with 80 ancestry informative markers.Sci. Rep. 2020; 10https://doi.org/10.1038/S41598-020-64245-4
- Improving ancestry distinctions among Southwest Asian populations.Forensic Sci. Int. Genet. 2018; 35: 14-20https://doi.org/10.1016/J.FSIGEN.2018.03.010
- The MASTiFF panel-a versatile multiple-allele SNP test for forensics.Int. J. Leg. Med. 2020; 134https://doi.org/10.1007/S00414-019-02233-8
- Evaluation of a custom GeneRead™ massively parallel sequencing assay with 210 ancestry informative SNPs using the Ion S5™ and MiSeq platforms.Forensic Sci. Int. Genet. 2021; 50https://doi.org/10.1016/J.FSIGEN.2020.102411
- Multivariate statistical approach and machine learning for the evaluation of biogeographical ancestry inference in the forensic field.Sci. Rep. 2022; 121: 1-17https://doi.org/10.1038/s41598-022-12903-0
- Progress in forensic bone DNA analysis: lessons learned from ancient DNA.Forensic Sci. Int. Genet. 2021; 54https://doi.org/10.1016/J.FSIGEN.2021.102538
- GenoGeographer – a tool for genogeographic inference.Forensic Sci. Int. Genet. Suppl. Ser. 2017; 6: e463-e465https://doi.org/10.1016/J.FSIGSS.2017.09.196
- Application of machine learning for ancestry inference using multi-InDel markers.Forensic Sci. Int. Genet. 2022; 59https://doi.org/10.1016/J.FSIGEN.2022.102702
- Evaluation of the VISAGE basic tool for appearance and ancestry inference using ForenSeq® chemistry on the MiSeq FGx® system.Forensic Sci. Int. Genet. 2022; 58102675https://doi.org/10.1016/J.FSIGEN.2022.102675/ATTACHMENT/66F76B61-E2CD-45BB-850E-E4D2BAB1E1A2/MMC4.XLSX
- An overview of STRUCTURE: applications, parameter settings, and supporting software.Front. Genet. 2013; 4: 98https://doi.org/10.3389/FGENE.2013.00098/ABSTRACT
- Fast model-based estimation of ancestry in unrelated individuals.Genome Res. 2009; 19: 1655-1664https://doi.org/10.1101/GR.094052.109
- Inference of ancestry in forensic analysis II: analysis of genetic data.Methods Mol. Biol. 2016; 1420: 255-285https://doi.org/10.1007/978-1-4939-3597-0_19/COVER/
- Probabilistic ancestry maps: a method to assess and visualize population substructures in genetics.BMC Bioinform. 2019; 20: 1-11https://doi.org/10.1186/S12859-019-2680-1/TABLES/4
- Deep learning approach to biogeographical ancestry inference.Procedia Comput. Sci. 2019; 159: 552-561https://doi.org/10.1016/J.PROCS.2019.09.210
- Review of variable selection methods for discriminant-type problems in chemometrics.Front. Anal. Sci. 2022; 0: 10https://doi.org/10.3389/FRANS.2022.867938
- Chemometric methods for classification and feature selection.Compr. Anal. Chem. 2018; 82: 265-299https://doi.org/10.1016/BS.COAC.2018.08.006
- Assessing feature selection method performance with class imbalance data.Mach. Learn. Appl. 2021; 6100170https://doi.org/10.1016/J.MLWA.2021.100170
- Iterative Predictor Weighting (IPW) PLS: a technique for the elimination of useless predictors in regression problems.J. Chemom. 1999; 184: 165-184
- A review of variable selection methods in Partial Least Squares Regression.Chemom. Intell. Lab. Syst. 2012; 118: 62-69
- Comparison of variable selection methods in partial least squares regression.J. Chemom. 2020; 34e3226https://doi.org/10.1002/CEM.3226
- Intermediate least squares regression method.Chemom. Intell. Lab. Syst. 1987; 1: 233-242https://doi.org/10.1016/0169-7439(87)80067-9
- Elimination of uninformative variables for multivariate calibration.Anal. Chem. 1996; 68: 3851-3858https://doi.org/10.1021/AC960321M
- A Partial Least Squares based algorithm for parsimonious variable selection.Algorithms Mol. Biol. 2011; 6: 27https://doi.org/10.1186/1748-7188-6-27
- GA strategy for variable selection in QSAR studies: GA-based PLS analysis of calcium channel antagonists.J. Chem. Inf. Comput. Sci. 1997; 37: 306-310https://doi.org/10.1021/CI960047X
- Genetic algorithms as a strategy for feature selection.J. Chemom. 1992; 6: 267-281https://doi.org/10.1002/CEM.1180060506
- Eurasiaplex: a forensic SNP assay for differentiating European and South Asian ancestries.Forensic Sci. Int. Genet. 2013; 7: 359-366https://doi.org/10.1016/j.fsigen.2013.02.010
- AIM-SNPtag: a computationally efficient approach for developing ancestry-informative SNP panels.Forensic Sci. Int. Genet. 2019; 38: 245-253https://doi.org/10.1016/J.FSIGEN.2018.10.015
- A 50-SNP assay for biogeographic ancestry and phenotype prediction in the U.S. population.Forensic Sci. Int. Genet. 2014; 8: 101-108https://doi.org/10.1016/j.fsigen.2013.07.010
- Population relationships based on 170 ancestry SNPs from the combined Kidd and Seldin panels.Sci. Rep. 2019; 9: 18874https://doi.org/10.1038/s41598-019-55175-x
- Proportioning whole-genome single-nucleotide-polymorphism diversity for the identification of geographic population structure and genetic ancestry.Am. J. Hum. Genet. 2006; 78: 680-690https://doi.org/10.1086/501531
Verogen, ForenSeqTM Universal Analysis Software Guide, 2018.
- Development and validation of the EUROFORGEN NAME (North African and Middle Eastern) ancestry panel.Forensic Sci. Int. Genet. 2019; 42: 260-267https://doi.org/10.1016/J.FSIGEN.2019.06.010
- Forensic evaluation of the Asia Pacific ancestry-informative MAPlex assay.Forensic Sci. Int. Genet. 2020; 48102344https://doi.org/10.1016/J.FSIGEN.2020.102344
- Development of a panel of genome-wide ancestry informative markers to study admixture throughout the Americas.PLoS Genet. 2012; 8e1002554https://doi.org/10.1371/JOURNAL.PGEN.1002554
- Pacifiplex: an ancestry-informative SNP panel centred on Australia and the Pacific region.Forensic Sci. Int. Genet. 2016; 20: 71-80https://doi.org/10.1016/j.fsigen.2015.10.003
- Forensic ancestry analysis with two capillary electrophoresis ancestry informative marker (AIM) panels: results of a collaborative EDNAP exercise.Forensic Sci. Int. Genet. 2015; 19: 56-67https://doi.org/10.1016/j.fsigen.2015.06.004
- Establishing a second-tier panel of 18 ancestry informative markers to improve ancestry distinctions among Asian populations.Forensic Sci. Int. Genet. 2019; 41: 159-167https://doi.org/10.1016/j.fsigen.2019.05.001
- A panel of ancestry informative markers for estimating individual biogeographical ancestry and admixture from four continents: utility and applications.Hum. Mutat. 2008; 29: 648-658https://doi.org/10.1002/humu.20695
- Prediction of people’s origin from degraded DNA–presentation of SNP assays and calculation of probability.Int. J. Leg. Med. 2013; 127: 347-357https://doi.org/10.1007/s00414-012-0728-0
- SNP variation with latitude: analysis of the SNPforID 52-plex markers in north, mid-region and south Chilean populations.Forensic Sci. Int. Genet. 2014; 10: 12-16https://doi.org/10.1016/J.FSIGEN.2013.12.009
- Biogeographic origin prediction of three continental populations through 42 ancestry informative SNPs.Electrophoresis. 2020; 41: 235-245https://doi.org/10.1002/ELPS.201900241
- Development and validation of the VISAGE AmpliSeq basic tool to predict appearance and ancestry from DNA.Forensic Sci. Int. Genet. 2020; 48102336https://doi.org/10.1016/J.FSIGEN.2020.102336
- How to choose sets of ancestry informative markers: a supervised feature selection approach.Forensic Sci. Int. Genet. 2020; 46https://doi.org/10.1016/J.FSIGEN.2020.102259
- Evaluating self-declared ancestry of U.S. Americans with autosomal, Y-chromosomal and mitochondrial DNA.Hum. Mutat. 2010; 31: E1875-E1893https://doi.org/10.1002/humu.21366
- PIMA: a population informative multiplex for the Americas.Forensic Sci. Int. Genet. 2020; 44102200https://doi.org/10.1016/j.fsigen.2019.102200
- Forensic genetic informativeness of an SNP panel consisting of 19 multi-allelic SNPs.Forensic Sci. Int. Genet. 2018; 34: 49-56https://doi.org/10.1016/j.fsigen.2018.01.006
I. Yuasa, A. Akane, T. Yamamoto, A. Matsusue, M. Endoh, M. Nakagawa, K. Umetsu, T. Ishikawa, M. Iino, Japaneseplex: a forensic SNP assay for identification of Japanese people using Japanese-specific alleles, 2018. 〈https://doi.org/10.1016/j.legalmed.2018.04.008〉.
- A panel of 130 autosomal single-nucleotide polymorphisms for ancestry assignment in five Asian populations and in Caucasians.Forensic Sci. Med. Pathol. 2017; 13: 177-187https://doi.org/10.1007/s12024-017-9863-8
- Inference of human continental origin and admixture proportions using a highly discriminative ancestry informative 41-SNP panel.Investig. Genet. 2013; 4: 13https://doi.org/10.1186/2041-2223-4-13
- Evaluation of the classification method using ancestry SNP markers for ethnic group.Commun. Stat. Appl. Methods. 2019; 26: 1-9https://doi.org/10.29220/CSAM.2019.26.1.001
- A global reference for human genetic variation.Nature. 2015; 526: 68-74https://doi.org/10.1038/NATURE15393
A. Bergström, S.A. McCarthy, R. Hui, M.A. Almarri, Q. Ayub, P. Danecek, Y. Chen, S. Felkel, P. Hallast, J. Kamm, H. Blanché, J.F. Deleuze, H. Cann, S. Mallick, D. Reich, M.S. Sandhu, P. Skoglund, A. Scally, Y. Xue, R. Durbin, C. Tyler-Smith, Insights into human genetic variation and population history from 929 diverse genomes, vol. 367, 2020. 〈https://pubmed.ncbi.nlm.nih.gov/32193295/〉, (Accessed 27 April 2021).
- Classification tools in chemistry. Part 1: linear models. PLS-DA.Anal. Methods. 2013; 5: 3790-3798https://doi.org/10.1039/c3ay40582f
- Partial least squares discriminant analysis for chemometrics and metabolomics: how scores, loadings, and weights differ according to two common algorithms.J. Chemom. 2018; 32e3028https://doi.org/10.1002/CEM.3028
- Chemometric methods for classification and feature selection.in: Compr. Anal. Chem. Elsevier B.V., 2018: 265-299https://doi.org/10.1016/bs.coac.2018.08.006
- Genetic algorithms and adaptation.Adapt. Control Ill-Defin. Syst. 1984; : 317-333https://doi.org/10.1007/978-1-4684-8941-5_21
- A Partial Least Squares based algorithm for parsimonious variable selection.Algorithms Mol. Biol. 2011; 6https://doi.org/10.1186/1748-7188-6-27
- ggplot2: Elegant Graphics for Data Analysis.Springer-Verlag, New York2016
- mixOmics: an R package for ‘omics feature selection and multiple data integration.PLoS Comput. Biol. 2017; 13e1005752https://doi.org/10.1371/JOURNAL.PCBI.1005752
- Interactive Web-based Data Visualization with R, Plotly, and Shiny. 1st editio. CRC Press, 2020
- Sparse logistic principal components analysis for binary data.Ann. Appl. Stat. 2010; 4: 1579-1601https://doi.org/10.1214/10-AOAS327
- Characterization of greater middle eastern genetic variation for enhanced disease gene discovery.Nat. Genet. 2016; 48: 1071-1079https://doi.org/10.1038/ng.3592
- Genetic diversity and low stratification of the population of the United Arab Emirates.Front. Genet. 2020; 11: 608https://doi.org/10.3389/fgene.2020.00608
- Statistical inference on genetic data reveals the complex demographic history of human populations in Central Asia.Mol. Biol. Evol. 2015; 32: 1411-1424https://doi.org/10.1093/molbev/msv030
Article info
Publication history
Published online: November 11, 2022
Accepted:
November 10,
2022
Received in revised form:
November 9,
2022
Received:
July 18,
2022
Identification
Copyright
© 2022 Elsevier B.V. All rights reserved.
