Statistical interpretation of cases involving mixtures: A Spanish and Portuguese-Speaking Working Group (GHEP-ISFG) collaborative exercise
- Autores
- Costa, Camila; Álvarez, Juan Carlos; Angeletti, Sofia Claudia; Biagini, Sebastian; Caputo, Mariela; Carnevali, Eugenia; Castillo, María Ignacia; Cordoba, Sandra; Sierra, Simon Cortes; de la Puente, María; Dorigón, Agustina; García, Oscar; Ginart, Santiago; Guinudinik, Alejandra; Bedmar, Carina Merino; Minervino, Aline; Miozzo, Cecilia; Mosquera Miguel, Ana; Nicolotti, Maria Eugenia; Onofri, Martina; Palencia Madrid, Leire; Ramella, María Isabel; Rena, Viviana; Sala, Adriana Andrea; Trindade, Bruno; Vinueza Espinosa, Diana C.; Wirz, Leandro Nicolas; Prieto, Lourdes; Pinto, Nádia
- Año de publicación
- 2026
- Idioma
- inglés
- Tipo de recurso
- artículo
- Estado
- versión publicada
- Descripción
- The increasing complexity of forensic genetic samples has driven the development and implementation of probabilistic genotyping software (PGS) to assist experts in quantifying the weight of evidence. A common challenge lies in quantifying the likelihood that a person of interest (PoI) is a contributor to a DNA mixture, especially in samples with low quantity and quality, stochastic effects (drop-in allele, dropout, and/or heterozygotic peaks imbalance), shared alleles, and stutter peaks. PGS can be based on either qualitative models (allele presence/absence) or quantitative (including allele peak heights) to assess the likelihood ratio, which consists of comparing the probability of observing the evidence given two hypotheses: “H1 = The PoI is a contributor to the evidence” and “H2 = The PoI is not a contributor nor genetically related to any contributor”. Key parameters included in these calculations regarding population – allele frequency distributions, and co-ancestry coefficient –, analytical factors – drop-in, dropout, analytical threshold –, and stutter presence, can influence the outcomes depending on how they are set. This collaborative exercise, organized by the Spanish and Portuguese-speaking Working Group of the International Society for Forensic Genetics, aimed to assess the current state of knowledge, use, and implementation of PGS among forensic laboratories. The goal was to evaluate how non-binary informatics tools are applied in practice and to understand the methodologies used to statistically interpret complex DNA mixtures.Participants were asked to analyze 30 pairs of samples composed of DNA mixtures (with varying the number of contributors, mixture ratios, and degradation levels) and corresponding reference profiles, selected from the PROVEDIt database. Laboratories employed different tools and approaches to quantify the evidence’s weight, including decisions regarding the number of contributors (NoC), population, laboratory, and case-specific parameters, such as coancestry coefficient, minimum allele frequency, dropout frequency, drop-in (frequency and modeling), analytical threshold, and the modeling of stutter peaks and degradation. Even though all laboratories received the same genotypic and frequency data, methodological differences led to different LR results, particularly for more complex samples with low-template and degraded DNA. The greatest differences were observed in the interplay between analytical thresholds and NoC estimation, with discrepancies amplified when alleles from minor contributors overlapped with expected stutter positions. This exercise highlights the importance of expert training and underscores the need for a comprehensive understanding of the statistical models underlying PGS. Ensuring accurate and consistent interpretation of complex DNA evidence requires not only technical proficiency but also an integrated approach to parameter selection and genotypic data evaluation.
Fil: Costa, Camila. Universidad de Porto. Facultad de Ciências.; Portugal
Fil: Álvarez, Juan Carlos. Universidad de Granada; España
Fil: Angeletti, Sofia Claudia. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Córdoba; Argentina. Poder Judicial de la Provincia de Córdoba; Argentina
Fil: Biagini, Sebastian. Ministerio de Ciencia, Tec. E Innovación Productiva. Banco Nacional de Datos Geneticos; Argentina
Fil: Caputo, Mariela. Universidad de Buenos Aires. Facultad de Farmacia y Bioquímica. Servicio de Huellas Digitales Genéticas; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
Fil: Carnevali, Eugenia. Università di Perugia; Italia
Fil: Castillo, María Ignacia. Instituto Nacional de Medicina Legal y Ciencias Forenses; Colombia
Fil: Cordoba, Sandra. Instituto Nacional de Medicina Legal y Ciencias Forenses; Colombia
Fil: Sierra, Simon Cortes. Instituto Nacional de Medicina Legal y Ciencias Forenses; Colombia
Fil: de la Puente, María. Universidad de Santiago de Compostela; España
Fil: Dorigón, Agustina. Laboratorio de Genética Forense del Poder Judicial de Santiago del Estero; Argentina
Fil: García, Oscar. Laboratorio de Genética Forense; España
Fil: Ginart, Santiago. Ministerio de Ciencia, Tec. E Innovación Productiva. Banco Nacional de Datos Geneticos; Argentina
Fil: Guinudinik, Alejandra. Servicio de Biología Molecular Forense; Argentina
Fil: Bedmar, Carina Merino. Laboratorio de Genètica Forense – Mossos d′Esquadra; España
Fil: Minervino, Aline. Brazilian National DNA Database; Brasil
Fil: Miozzo, Cecilia. Laboratorio Regional de Genética Forense del Noa Poder; Argentina
Fil: Mosquera Miguel, Ana. Universidad de Santiago de Compostela; España
Fil: Nicolotti, Maria Eugenia. Instituto Universitario de Gendarmeria Nacional Argentina; Argentina
Fil: Onofri, Martina. Università di Perugia; Italia
Fil: Palencia Madrid, Leire. Laboratorio de Genética Forense; España
Fil: Ramella, María Isabel. Laboratorio Regional de Genética Forense del Noa Poder; Argentina
Fil: Rena, Viviana. Gobierno de la Provincia de Cordoba. Tribunal Superior de Justicia. Instituto de Genetica Forense.; Argentina
Fil: Sala, Adriana Andrea. Universidad de Buenos Aires. Facultad de Farmacia y Bioquímica. Servicio de Huellas Digitales Genéticas; Argentina
Fil: Trindade, Bruno. Forensic Genetics Service; Brasil
Fil: Vinueza Espinosa, Diana C.. Laboratorio de Identificación Genética; España
Fil: Wirz, Leandro Nicolas. Departamento Genética Forense; Argentina
Fil: Prieto, Lourdes. Universidad de Santiago de Compostela; España
Fil: Pinto, Nádia. Universidad de Porto; Portugal - Materia
-
DNA ANALYSIS
FORENSIC GENETICS REPORTING
PROVEDIT
PROBABILISTIC GENOTYPING SOFTWARE
SOFTWARE PARAMETERS
WEIGHT-OF-EVIDENCE - Nivel de accesibilidad
- acceso abierto
- Condiciones de uso
- https://creativecommons.org/licenses/by/2.5/ar/
- Repositorio
.jpg)
- Institución
- Consejo Nacional de Investigaciones Científicas y Técnicas
- OAI Identificador
- oai:ri.conicet.gov.ar:11336/286753
Ver los metadatos del registro completo
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Statistical interpretation of cases involving mixtures: A Spanish and Portuguese-Speaking Working Group (GHEP-ISFG) collaborative exerciseCosta, CamilaÁlvarez, Juan CarlosAngeletti, Sofia ClaudiaBiagini, SebastianCaputo, MarielaCarnevali, EugeniaCastillo, María IgnaciaCordoba, SandraSierra, Simon Cortesde la Puente, MaríaDorigón, AgustinaGarcía, OscarGinart, SantiagoGuinudinik, AlejandraBedmar, Carina MerinoMinervino, AlineMiozzo, CeciliaMosquera Miguel, AnaNicolotti, Maria EugeniaOnofri, MartinaPalencia Madrid, LeireRamella, María IsabelRena, VivianaSala, Adriana AndreaTrindade, BrunoVinueza Espinosa, Diana C.Wirz, Leandro NicolasPrieto, LourdesPinto, NádiaDNA ANALYSISFORENSIC GENETICS REPORTINGPROVEDITPROBABILISTIC GENOTYPING SOFTWARESOFTWARE PARAMETERSWEIGHT-OF-EVIDENCEhttps://purl.org/becyt/ford/1.6https://purl.org/becyt/ford/1The increasing complexity of forensic genetic samples has driven the development and implementation of probabilistic genotyping software (PGS) to assist experts in quantifying the weight of evidence. A common challenge lies in quantifying the likelihood that a person of interest (PoI) is a contributor to a DNA mixture, especially in samples with low quantity and quality, stochastic effects (drop-in allele, dropout, and/or heterozygotic peaks imbalance), shared alleles, and stutter peaks. PGS can be based on either qualitative models (allele presence/absence) or quantitative (including allele peak heights) to assess the likelihood ratio, which consists of comparing the probability of observing the evidence given two hypotheses: “H1 = The PoI is a contributor to the evidence” and “H2 = The PoI is not a contributor nor genetically related to any contributor”. Key parameters included in these calculations regarding population – allele frequency distributions, and co-ancestry coefficient –, analytical factors – drop-in, dropout, analytical threshold –, and stutter presence, can influence the outcomes depending on how they are set. This collaborative exercise, organized by the Spanish and Portuguese-speaking Working Group of the International Society for Forensic Genetics, aimed to assess the current state of knowledge, use, and implementation of PGS among forensic laboratories. The goal was to evaluate how non-binary informatics tools are applied in practice and to understand the methodologies used to statistically interpret complex DNA mixtures.Participants were asked to analyze 30 pairs of samples composed of DNA mixtures (with varying the number of contributors, mixture ratios, and degradation levels) and corresponding reference profiles, selected from the PROVEDIt database. Laboratories employed different tools and approaches to quantify the evidence’s weight, including decisions regarding the number of contributors (NoC), population, laboratory, and case-specific parameters, such as coancestry coefficient, minimum allele frequency, dropout frequency, drop-in (frequency and modeling), analytical threshold, and the modeling of stutter peaks and degradation. Even though all laboratories received the same genotypic and frequency data, methodological differences led to different LR results, particularly for more complex samples with low-template and degraded DNA. The greatest differences were observed in the interplay between analytical thresholds and NoC estimation, with discrepancies amplified when alleles from minor contributors overlapped with expected stutter positions. This exercise highlights the importance of expert training and underscores the need for a comprehensive understanding of the statistical models underlying PGS. Ensuring accurate and consistent interpretation of complex DNA evidence requires not only technical proficiency but also an integrated approach to parameter selection and genotypic data evaluation.Fil: Costa, Camila. Universidad de Porto. Facultad de Ciências.; PortugalFil: Álvarez, Juan Carlos. Universidad de Granada; EspañaFil: Angeletti, Sofia Claudia. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Córdoba; Argentina. Poder Judicial de la Provincia de Córdoba; ArgentinaFil: Biagini, Sebastian. Ministerio de Ciencia, Tec. E Innovación Productiva. Banco Nacional de Datos Geneticos; ArgentinaFil: Caputo, Mariela. Universidad de Buenos Aires. Facultad de Farmacia y Bioquímica. Servicio de Huellas Digitales Genéticas; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Carnevali, Eugenia. Università di Perugia; ItaliaFil: Castillo, María Ignacia. Instituto Nacional de Medicina Legal y Ciencias Forenses; ColombiaFil: Cordoba, Sandra. Instituto Nacional de Medicina Legal y Ciencias Forenses; ColombiaFil: Sierra, Simon Cortes. Instituto Nacional de Medicina Legal y Ciencias Forenses; ColombiaFil: de la Puente, María. Universidad de Santiago de Compostela; EspañaFil: Dorigón, Agustina. Laboratorio de Genética Forense del Poder Judicial de Santiago del Estero; ArgentinaFil: García, Oscar. Laboratorio de Genética Forense; EspañaFil: Ginart, Santiago. Ministerio de Ciencia, Tec. E Innovación Productiva. Banco Nacional de Datos Geneticos; ArgentinaFil: Guinudinik, Alejandra. Servicio de Biología Molecular Forense; ArgentinaFil: Bedmar, Carina Merino. Laboratorio de Genètica Forense – Mossos d′Esquadra; EspañaFil: Minervino, Aline. Brazilian National DNA Database; BrasilFil: Miozzo, Cecilia. Laboratorio Regional de Genética Forense del Noa Poder; ArgentinaFil: Mosquera Miguel, Ana. Universidad de Santiago de Compostela; EspañaFil: Nicolotti, Maria Eugenia. Instituto Universitario de Gendarmeria Nacional Argentina; ArgentinaFil: Onofri, Martina. Università di Perugia; ItaliaFil: Palencia Madrid, Leire. Laboratorio de Genética Forense; EspañaFil: Ramella, María Isabel. Laboratorio Regional de Genética Forense del Noa Poder; ArgentinaFil: Rena, Viviana. Gobierno de la Provincia de Cordoba. Tribunal Superior de Justicia. Instituto de Genetica Forense.; ArgentinaFil: Sala, Adriana Andrea. Universidad de Buenos Aires. Facultad de Farmacia y Bioquímica. Servicio de Huellas Digitales Genéticas; ArgentinaFil: Trindade, Bruno. Forensic Genetics Service; BrasilFil: Vinueza Espinosa, Diana C.. Laboratorio de Identificación Genética; EspañaFil: Wirz, Leandro Nicolas. Departamento Genética Forense; ArgentinaFil: Prieto, Lourdes. Universidad de Santiago de Compostela; EspañaFil: Pinto, Nádia. Universidad de Porto; PortugalElsevier Ireland2026-02info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/286753Costa, Camila; Álvarez, Juan Carlos; Angeletti, Sofia Claudia; Biagini, Sebastian; Caputo, Mariela; et al.; Statistical interpretation of cases involving mixtures: A Spanish and Portuguese-Speaking Working Group (GHEP-ISFG) collaborative exercise; Elsevier Ireland; Forensic Science International: Genetics; 81; 103383; 2-2026; 1-151872-4973CONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/url/https://www.sciencedirect.com/science/article/pii/S1872497325001632info:eu-repo/semantics/altIdentifier/doi/10.1016/j.fsigen.2025.103383info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2026-08-25T14:32:02Zoai:ri.conicet.gov.ar:11336/286753instacron:CONICETInstitucionalhttp://ri.conicet.gov.ar/Organismo científico-tecnológicoNo correspondehttp://ri.conicet.gov.ar/oai/requestdasensio@conicet.gov.ar; lcarlino@conicet.gov.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:34982026-08-25 14:32:02.792CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse |
| dc.title.none.fl_str_mv |
Statistical interpretation of cases involving mixtures: A Spanish and Portuguese-Speaking Working Group (GHEP-ISFG) collaborative exercise |
| title |
Statistical interpretation of cases involving mixtures: A Spanish and Portuguese-Speaking Working Group (GHEP-ISFG) collaborative exercise |
| spellingShingle |
Statistical interpretation of cases involving mixtures: A Spanish and Portuguese-Speaking Working Group (GHEP-ISFG) collaborative exercise Costa, Camila DNA ANALYSIS FORENSIC GENETICS REPORTING PROVEDIT PROBABILISTIC GENOTYPING SOFTWARE SOFTWARE PARAMETERS WEIGHT-OF-EVIDENCE |
| title_short |
Statistical interpretation of cases involving mixtures: A Spanish and Portuguese-Speaking Working Group (GHEP-ISFG) collaborative exercise |
| title_full |
Statistical interpretation of cases involving mixtures: A Spanish and Portuguese-Speaking Working Group (GHEP-ISFG) collaborative exercise |
| title_fullStr |
Statistical interpretation of cases involving mixtures: A Spanish and Portuguese-Speaking Working Group (GHEP-ISFG) collaborative exercise |
| title_full_unstemmed |
Statistical interpretation of cases involving mixtures: A Spanish and Portuguese-Speaking Working Group (GHEP-ISFG) collaborative exercise |
| title_sort |
Statistical interpretation of cases involving mixtures: A Spanish and Portuguese-Speaking Working Group (GHEP-ISFG) collaborative exercise |
| dc.creator.none.fl_str_mv |
Costa, Camila Álvarez, Juan Carlos Angeletti, Sofia Claudia Biagini, Sebastian Caputo, Mariela Carnevali, Eugenia Castillo, María Ignacia Cordoba, Sandra Sierra, Simon Cortes de la Puente, María Dorigón, Agustina García, Oscar Ginart, Santiago Guinudinik, Alejandra Bedmar, Carina Merino Minervino, Aline Miozzo, Cecilia Mosquera Miguel, Ana Nicolotti, Maria Eugenia Onofri, Martina Palencia Madrid, Leire Ramella, María Isabel Rena, Viviana Sala, Adriana Andrea Trindade, Bruno Vinueza Espinosa, Diana C. Wirz, Leandro Nicolas Prieto, Lourdes Pinto, Nádia |
| author |
Costa, Camila |
| author_facet |
Costa, Camila Álvarez, Juan Carlos Angeletti, Sofia Claudia Biagini, Sebastian Caputo, Mariela Carnevali, Eugenia Castillo, María Ignacia Cordoba, Sandra Sierra, Simon Cortes de la Puente, María Dorigón, Agustina García, Oscar Ginart, Santiago Guinudinik, Alejandra Bedmar, Carina Merino Minervino, Aline Miozzo, Cecilia Mosquera Miguel, Ana Nicolotti, Maria Eugenia Onofri, Martina Palencia Madrid, Leire Ramella, María Isabel Rena, Viviana Sala, Adriana Andrea Trindade, Bruno Vinueza Espinosa, Diana C. Wirz, Leandro Nicolas Prieto, Lourdes Pinto, Nádia |
| author_role |
author |
| author2 |
Álvarez, Juan Carlos Angeletti, Sofia Claudia Biagini, Sebastian Caputo, Mariela Carnevali, Eugenia Castillo, María Ignacia Cordoba, Sandra Sierra, Simon Cortes de la Puente, María Dorigón, Agustina García, Oscar Ginart, Santiago Guinudinik, Alejandra Bedmar, Carina Merino Minervino, Aline Miozzo, Cecilia Mosquera Miguel, Ana Nicolotti, Maria Eugenia Onofri, Martina Palencia Madrid, Leire Ramella, María Isabel Rena, Viviana Sala, Adriana Andrea Trindade, Bruno Vinueza Espinosa, Diana C. Wirz, Leandro Nicolas Prieto, Lourdes Pinto, Nádia |
| author2_role |
author author author author author author author author author author author author author author author author author author author author author author author author author author author author |
| dc.subject.none.fl_str_mv |
DNA ANALYSIS FORENSIC GENETICS REPORTING PROVEDIT PROBABILISTIC GENOTYPING SOFTWARE SOFTWARE PARAMETERS WEIGHT-OF-EVIDENCE |
| topic |
DNA ANALYSIS FORENSIC GENETICS REPORTING PROVEDIT PROBABILISTIC GENOTYPING SOFTWARE SOFTWARE PARAMETERS WEIGHT-OF-EVIDENCE |
| purl_subject.fl_str_mv |
https://purl.org/becyt/ford/1.6 https://purl.org/becyt/ford/1 |
| dc.description.none.fl_txt_mv |
The increasing complexity of forensic genetic samples has driven the development and implementation of probabilistic genotyping software (PGS) to assist experts in quantifying the weight of evidence. A common challenge lies in quantifying the likelihood that a person of interest (PoI) is a contributor to a DNA mixture, especially in samples with low quantity and quality, stochastic effects (drop-in allele, dropout, and/or heterozygotic peaks imbalance), shared alleles, and stutter peaks. PGS can be based on either qualitative models (allele presence/absence) or quantitative (including allele peak heights) to assess the likelihood ratio, which consists of comparing the probability of observing the evidence given two hypotheses: “H1 = The PoI is a contributor to the evidence” and “H2 = The PoI is not a contributor nor genetically related to any contributor”. Key parameters included in these calculations regarding population – allele frequency distributions, and co-ancestry coefficient –, analytical factors – drop-in, dropout, analytical threshold –, and stutter presence, can influence the outcomes depending on how they are set. This collaborative exercise, organized by the Spanish and Portuguese-speaking Working Group of the International Society for Forensic Genetics, aimed to assess the current state of knowledge, use, and implementation of PGS among forensic laboratories. The goal was to evaluate how non-binary informatics tools are applied in practice and to understand the methodologies used to statistically interpret complex DNA mixtures.Participants were asked to analyze 30 pairs of samples composed of DNA mixtures (with varying the number of contributors, mixture ratios, and degradation levels) and corresponding reference profiles, selected from the PROVEDIt database. Laboratories employed different tools and approaches to quantify the evidence’s weight, including decisions regarding the number of contributors (NoC), population, laboratory, and case-specific parameters, such as coancestry coefficient, minimum allele frequency, dropout frequency, drop-in (frequency and modeling), analytical threshold, and the modeling of stutter peaks and degradation. Even though all laboratories received the same genotypic and frequency data, methodological differences led to different LR results, particularly for more complex samples with low-template and degraded DNA. The greatest differences were observed in the interplay between analytical thresholds and NoC estimation, with discrepancies amplified when alleles from minor contributors overlapped with expected stutter positions. This exercise highlights the importance of expert training and underscores the need for a comprehensive understanding of the statistical models underlying PGS. Ensuring accurate and consistent interpretation of complex DNA evidence requires not only technical proficiency but also an integrated approach to parameter selection and genotypic data evaluation. Fil: Costa, Camila. Universidad de Porto. Facultad de Ciências.; Portugal Fil: Álvarez, Juan Carlos. Universidad de Granada; España Fil: Angeletti, Sofia Claudia. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Córdoba; Argentina. Poder Judicial de la Provincia de Córdoba; Argentina Fil: Biagini, Sebastian. Ministerio de Ciencia, Tec. E Innovación Productiva. Banco Nacional de Datos Geneticos; Argentina Fil: Caputo, Mariela. Universidad de Buenos Aires. Facultad de Farmacia y Bioquímica. Servicio de Huellas Digitales Genéticas; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina Fil: Carnevali, Eugenia. Università di Perugia; Italia Fil: Castillo, María Ignacia. Instituto Nacional de Medicina Legal y Ciencias Forenses; Colombia Fil: Cordoba, Sandra. Instituto Nacional de Medicina Legal y Ciencias Forenses; Colombia Fil: Sierra, Simon Cortes. Instituto Nacional de Medicina Legal y Ciencias Forenses; Colombia Fil: de la Puente, María. Universidad de Santiago de Compostela; España Fil: Dorigón, Agustina. Laboratorio de Genética Forense del Poder Judicial de Santiago del Estero; Argentina Fil: García, Oscar. Laboratorio de Genética Forense; España Fil: Ginart, Santiago. Ministerio de Ciencia, Tec. E Innovación Productiva. Banco Nacional de Datos Geneticos; Argentina Fil: Guinudinik, Alejandra. Servicio de Biología Molecular Forense; Argentina Fil: Bedmar, Carina Merino. Laboratorio de Genètica Forense – Mossos d′Esquadra; España Fil: Minervino, Aline. Brazilian National DNA Database; Brasil Fil: Miozzo, Cecilia. Laboratorio Regional de Genética Forense del Noa Poder; Argentina Fil: Mosquera Miguel, Ana. Universidad de Santiago de Compostela; España Fil: Nicolotti, Maria Eugenia. Instituto Universitario de Gendarmeria Nacional Argentina; Argentina Fil: Onofri, Martina. Università di Perugia; Italia Fil: Palencia Madrid, Leire. Laboratorio de Genética Forense; España Fil: Ramella, María Isabel. Laboratorio Regional de Genética Forense del Noa Poder; Argentina Fil: Rena, Viviana. Gobierno de la Provincia de Cordoba. Tribunal Superior de Justicia. Instituto de Genetica Forense.; Argentina Fil: Sala, Adriana Andrea. Universidad de Buenos Aires. Facultad de Farmacia y Bioquímica. Servicio de Huellas Digitales Genéticas; Argentina Fil: Trindade, Bruno. Forensic Genetics Service; Brasil Fil: Vinueza Espinosa, Diana C.. Laboratorio de Identificación Genética; España Fil: Wirz, Leandro Nicolas. Departamento Genética Forense; Argentina Fil: Prieto, Lourdes. Universidad de Santiago de Compostela; España Fil: Pinto, Nádia. Universidad de Porto; Portugal |
| description |
The increasing complexity of forensic genetic samples has driven the development and implementation of probabilistic genotyping software (PGS) to assist experts in quantifying the weight of evidence. A common challenge lies in quantifying the likelihood that a person of interest (PoI) is a contributor to a DNA mixture, especially in samples with low quantity and quality, stochastic effects (drop-in allele, dropout, and/or heterozygotic peaks imbalance), shared alleles, and stutter peaks. PGS can be based on either qualitative models (allele presence/absence) or quantitative (including allele peak heights) to assess the likelihood ratio, which consists of comparing the probability of observing the evidence given two hypotheses: “H1 = The PoI is a contributor to the evidence” and “H2 = The PoI is not a contributor nor genetically related to any contributor”. Key parameters included in these calculations regarding population – allele frequency distributions, and co-ancestry coefficient –, analytical factors – drop-in, dropout, analytical threshold –, and stutter presence, can influence the outcomes depending on how they are set. This collaborative exercise, organized by the Spanish and Portuguese-speaking Working Group of the International Society for Forensic Genetics, aimed to assess the current state of knowledge, use, and implementation of PGS among forensic laboratories. The goal was to evaluate how non-binary informatics tools are applied in practice and to understand the methodologies used to statistically interpret complex DNA mixtures.Participants were asked to analyze 30 pairs of samples composed of DNA mixtures (with varying the number of contributors, mixture ratios, and degradation levels) and corresponding reference profiles, selected from the PROVEDIt database. Laboratories employed different tools and approaches to quantify the evidence’s weight, including decisions regarding the number of contributors (NoC), population, laboratory, and case-specific parameters, such as coancestry coefficient, minimum allele frequency, dropout frequency, drop-in (frequency and modeling), analytical threshold, and the modeling of stutter peaks and degradation. Even though all laboratories received the same genotypic and frequency data, methodological differences led to different LR results, particularly for more complex samples with low-template and degraded DNA. The greatest differences were observed in the interplay between analytical thresholds and NoC estimation, with discrepancies amplified when alleles from minor contributors overlapped with expected stutter positions. This exercise highlights the importance of expert training and underscores the need for a comprehensive understanding of the statistical models underlying PGS. Ensuring accurate and consistent interpretation of complex DNA evidence requires not only technical proficiency but also an integrated approach to parameter selection and genotypic data evaluation. |
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2026 |
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2026-02 |
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http://hdl.handle.net/11336/286753 Costa, Camila; Álvarez, Juan Carlos; Angeletti, Sofia Claudia; Biagini, Sebastian; Caputo, Mariela; et al.; Statistical interpretation of cases involving mixtures: A Spanish and Portuguese-Speaking Working Group (GHEP-ISFG) collaborative exercise; Elsevier Ireland; Forensic Science International: Genetics; 81; 103383; 2-2026; 1-15 1872-4973 CONICET Digital CONICET |
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http://hdl.handle.net/11336/286753 |
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Costa, Camila; Álvarez, Juan Carlos; Angeletti, Sofia Claudia; Biagini, Sebastian; Caputo, Mariela; et al.; Statistical interpretation of cases involving mixtures: A Spanish and Portuguese-Speaking Working Group (GHEP-ISFG) collaborative exercise; Elsevier Ireland; Forensic Science International: Genetics; 81; 103383; 2-2026; 1-15 1872-4973 CONICET Digital CONICET |
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eng |
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eng |
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Elsevier Ireland |
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