A multivariate statistical approach for the estimation of the ethnic origin of unknown genetic profiles in forensic genetics

Abstract

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Multivariate statistical approach and machine learning for the evaluation of biogeographical ancestry inference in the forensic field

“…One of the most known unsupervised methodologies (also known as exploratory analyses) is Principal Components Analysis (PCA) 44 . On the other hand, supervised/classification modeling techniques can present an important family of strategies known as discrimination models, such as Partial Least Squares-Discriminant Analysis (PLS-DA) 45 , that aim to calculate specific boundaries in the multidimensional space that allow separating the different objects within their corresponding classes 46 . Therefore, for the first time, we decided to adopt a multivariate methodology in the present study, such as Partial Least Squares-Discriminant Analysis (PLS-DA) on several SNPs datasets involving instances of different populations showing different BGA.…”

Review of the Forensic Applicability of Biostatistical Methods for Inferring Ancestry from Autosomal Genetic Markers

“…In forensic genetics, STRs are presently the standard markers used for identification and relationship testing. Furthermore, variation in allele frequencies may be used to infer an individual’s ancestry [ 15 , 16 ]. Other types of AIMs are microhaplotypes [ 17 ], which are groups of closely located SNPs (often positioned within 500 bp), and insertion–deletion polymorphisms (indels) [ 18 , 19 ].…”

Establishing a missing person DNA Biobank as a form of human rights protection

“…The advantages of STR markers is that they are cost-effective, and the personnel usually has extensive expertise and training in their analysis, resulting in their wider acceptability in courtrooms. For the application in BGA predictions, in fact, STRs are evaluated using Bayesian methods that assess the strength of the hypothesis, expressed as a Likelihood Ratio, that an individual belongs to a specific geographic area rather than another [48]. However, STRs marker are less informative for BGA inference due to their mutational instability which produces a high variability within a population but a relatively low variability among different populations, resulting in error percentages higher than SNPs [48,49].…”

“…For the application in BGA predictions, in fact, STRs are evaluated using Bayesian methods that assess the strength of the hypothesis, expressed as a Likelihood Ratio, that an individual belongs to a specific geographic area rather than another [48]. However, STRs marker are less informative for BGA inference due to their mutational instability which produces a high variability within a population but a relatively low variability among different populations, resulting in error percentages higher than SNPs [48,49]. In addition, other STRs' limitations reside in the DNA mixtures interpretation, in the definition of population clusters [48,50], and in the absence of software with userfriendly graphic interfaces, resulting in the development and employment of multivariate statistical approaches (Multivariate Data Analysis) for STRs analysis for the estimation of geographic origin [48].…”