GHEP-ISFG collaborative simulated exercise for DVI/MPI: Lessons learned about large-scale profile database comparisons
Highlights • The Spanish and Portuguese-speaking working group of ISFG has designed a simulated exercise on database comparisons for human identification. • The aim of the exercise was to introduce participants to the complexity of the identification of victims in disasters (DVI) or missing persons...
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| Published in | Forensic science international : genetics Vol. 21; pp. 45 - 53 |
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| Main Authors | , , , , , , , , , , , , , , , , , , , |
| Format | Journal Article |
| Language | English |
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Elsevier Ireland Ltd
01.03.2016
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| Abstract | Highlights • The Spanish and Portuguese-speaking working group of ISFG has designed a simulated exercise on database comparisons for human identification. • The aim of the exercise was to introduce participants to the complexity of the identification of victims in disasters (DVI) or missing persons identification (MPI) scenarios. • The exercise focused on biostatistics for direct matching and kinship analysis in a Bayesian framework. • This study demonstrates that although a DNA forensic laboratory is experienced in kinship or criminalistics analysis, additional training in DVI or MPI is necessary in order to properly investigate these scenarios. |
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| AbstractList | Highlights • The Spanish and Portuguese-speaking working group of ISFG has designed a simulated exercise on database comparisons for human identification. • The aim of the exercise was to introduce participants to the complexity of the identification of victims in disasters (DVI) or missing persons identification (MPI) scenarios. • The exercise focused on biostatistics for direct matching and kinship analysis in a Bayesian framework. • This study demonstrates that although a DNA forensic laboratory is experienced in kinship or criminalistics analysis, additional training in DVI or MPI is necessary in order to properly investigate these scenarios. The GHEP-ISFG Working Group has recognized the importance of assisting DNA laboratories to gain expertise in handling DVI or missing persons identification (MPI) projects which involve the need for large-scale genetic profile comparisons. Eleven laboratories participated in a DNA matching exercise to identify victims from a hypothetical conflict with 193 missing persons. The post mortem database was comprised of 87 skeletal remain profiles from a secondary mass grave displaying a minimal number of 58 individuals with evidence of commingling. The reference database was represented by 286 family reference profiles with diverse pedigrees. The goal of the exercise was to correctly discover re-associations and family matches. The results of direct matching for commingled remains re-associations were correct and fully concordant among all laboratories. However, the kinship analysis for missing persons identifications showed variable results among the participants. There was a group of laboratories with correct, concordant results but nearly half of the others showed discrepant results exhibiting likelihood ratio differences of several degrees of magnitude in some cases. Three main errors were detected: (a) some laboratories did not use the complete reference family genetic data to report the match with the remains, (b) the identity and/or non-identity hypotheses were sometimes wrongly expressed in the likelihood ratio calculations, and (c) many laboratories did not properly evaluate the prior odds for the event. The results suggest that large-scale profile comparisons for DVI or MPI is a challenge for forensic genetics laboratories and the statistical treatment of DNA matching and the Bayesian framework should be better standardized among laboratories. •The Spanish and Portuguese-speaking working group of ISFG has designed a simulated exercise on database comparisons for human identification.•The aim of the exercise was to introduce participants to the complexity of the identification of victims in disasters (DVI) or missing persons identification (MPI) scenarios.•The exercise focused on biostatistics for direct matching and kinship analysis in a Bayesian framework.•This study demonstrates that although a DNA forensic laboratory is experienced in kinship or criminalistics analysis, additional training in DVI or MPI is necessary in order to properly investigate these scenarios. The GHEP-ISFG Working Group has recognized the importance of assisting DNA laboratories to gain expertise in handling DVI or missing persons identification (MPI) projects which involve the need for large-scale genetic profile comparisons. Eleven laboratories participated in a DNA matching exercise to identify victims from a hypothetical conflict with 193 missing persons. The post mortem database was comprised of 87 skeletal remain profiles from a secondary mass grave displaying a minimal number of 58 individuals with evidence of commingling. The reference database was represented by 286 family reference profiles with diverse pedigrees. The goal of the exercise was to correctly discover re-associations and family matches. The results of direct matching for commingled remains re-associations were correct and fully concordant among all laboratories. However, the kinship analysis for missing persons identifications showed variable results among the participants. There was a group of laboratories with correct, concordant results but nearly half of the others showed discrepant results exhibiting likelihood ratio differences of several degrees of magnitude in some cases. Three main errors were detected: (a) some laboratories did not use the complete reference family genetic data to report the match with the remains, (b) the identity and/or non-identity hypotheses were sometimes wrongly expressed in the likelihood ratio calculations, and (c) many laboratories did not properly evaluate the prior odds for the event. The results suggest that large-scale profile comparisons for DVI or MPI is a challenge for forensic genetics laboratories and the statistical treatment of DNA matching and the Bayesian framework should be better standardized among laboratories. |
| Author | Farfán, María José Šakić, Mustafa Romanini, Carola Penacino, Gustavo Zabalza, Santiago Catelli, Laura Hernandez, Alexis Parsons, Thomas Jimenez Pleguezuelos, María Jose Anjos Porto, Maria João Prieto, Victoria Puente Prieto, Jorge Prieto, Lourdes Alvarez, David Saragoni, Victor G Vullo, Carlos M Miguel Manterola, Irati Bofarull Castro, Alicia Romero, Magdalena Hernández Bolaños, Alejandro |
| Author_xml | – sequence: 1 fullname: Vullo, Carlos M – sequence: 2 fullname: Romero, Magdalena – sequence: 3 fullname: Catelli, Laura – sequence: 4 fullname: Šakić, Mustafa – sequence: 5 fullname: Saragoni, Victor G – sequence: 6 fullname: Jimenez Pleguezuelos, María Jose – sequence: 7 fullname: Romanini, Carola – sequence: 8 fullname: Anjos Porto, Maria João – sequence: 9 fullname: Puente Prieto, Jorge – sequence: 10 fullname: Bofarull Castro, Alicia – sequence: 11 fullname: Hernandez, Alexis – sequence: 12 fullname: Farfán, María José – sequence: 13 fullname: Prieto, Victoria – sequence: 14 fullname: Alvarez, David – sequence: 15 fullname: Penacino, Gustavo – sequence: 16 fullname: Zabalza, Santiago – sequence: 17 fullname: Hernández Bolaños, Alejandro – sequence: 18 fullname: Miguel Manterola, Irati – sequence: 19 fullname: Prieto, Lourdes – sequence: 20 fullname: Parsons, Thomas |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/26716885$$D View this record in MEDLINE/PubMed |
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| CitedBy_id | crossref_primary_10_1016_j_fsigen_2017_08_006 crossref_primary_10_1016_j_fsigen_2021_102527 crossref_primary_10_1016_j_fsigen_2017_03_003 crossref_primary_10_1016_j_remle_2023_08_003 crossref_primary_10_1016_j_fsigen_2020_102376 crossref_primary_10_1016_j_remle_2021_08_002 crossref_primary_10_1016_j_reml_2021_08_003 crossref_primary_10_1016_j_fsigen_2018_08_016 crossref_primary_10_1016_j_reml_2023_04_005 crossref_primary_10_1016_j_reml_2019_09_003 crossref_primary_10_1111_1556_4029_15553 crossref_primary_10_1016_j_fsigss_2019_09_044 crossref_primary_10_1016_j_fsisyn_2021_100154 crossref_primary_10_1016_j_jflm_2021_102254 crossref_primary_10_1016_j_forsciint_2017_07_002 crossref_primary_10_1007_s12024_020_00248_x crossref_primary_10_1016_j_fsigen_2019_102156 crossref_primary_10_1038_s41598_021_93071_5 |
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| Keywords | Simulated identification exercise Database comparisons DVI Missing person identification Disaster victim identification MPI |
| Language | English |
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| Snippet | Highlights • The Spanish and Portuguese-speaking working group of ISFG has designed a simulated exercise on database comparisons for human identification. •... •The Spanish and Portuguese-speaking working group of ISFG has designed a simulated exercise on database comparisons for human identification.•The aim of the... The GHEP-ISFG Working Group has recognized the importance of assisting DNA laboratories to gain expertise in handling DVI or missing persons identification... |
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| SubjectTerms | Bayes Theorem Biometric Identification - methods Cooperative Behavior Database comparisons Databases, Genetic Disaster victim identification Disasters DNA - analysis DNA - genetics DNA Fingerprinting - methods DVI Forensic Genetics - methods Humans Microsatellite Repeats Missing person identification MPI Pathology Pedigree Portugal Simulated identification exercise Spain |
| Title | GHEP-ISFG collaborative simulated exercise for DVI/MPI: Lessons learned about large-scale profile database comparisons |
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