Incorporating non-genetic evidence in large scale missing person searches: A general approach beyond filtering.

The search for missing persons implies several steps, from the preliminary investigation that involves collecting background data related to the case to the genetic kinship testing. Despite its crucial importance in identifications, only some approaches mathematically formalize the possibility of using preliminary investigation data. In some cases, a filtering strategy is applied, which implies selecting a subset of possible victims where some non-genetic variables perfectly match those of the missing. Through a Bayesian approach, we propose a mathematical model for computing the prior odds based on non-genetic variables usually collected during the preliminary investigation, such as biological sex, hair colour, and age. We use computational simulations to show how to incorporate these prior odds in DNA-database searches. Importantly, our results suggest that applying the proposed model leads to better search performance in underpowered cases from the genetic point of view, where few or distant relatives of the missing person are available for genotyping. Furthermore, the results are also helpful when using non-genetic data for prior odds in well-powered cases, where genetic data are enough to reach a reliable conclusion. It performs better than other approaches, such as using non-genetic data for filtering. The software mispitools, freely available on CRAN, implements all described methods (https://CRAN.R-project.org/package=mispitools).

Making decisions in missing person identification cases with low statistical power.

The present work proposes a general strategy for dealing with missing person identification cases through DNA-database search. Our main example is the identification of abducted children in the last civic-dictatorship of Argentina, known as the "Missing Grandchildren of Argentina". Particularly we focus on those pedigrees where few, or only distant relatives of the missing person are available, resulting in low statistical power. For such complex cases we provide a statistical method for selecting a likelihood ratio (LR) threshold for each pedigree based on error rates. Furthermore, we provide an open-source user friendly software for computing LR thresholds and error rates. The strategy described in the paper could be applied to other large-scale cases of DNA-based identification hampered by low statistical power.

Prioritising family members for genotyping in missing person cases: A general approach combining the statistical power of exclusion and inclusion.

Missing person identification typically involves genetic matching of a person of interest against relatives of the missing person. In cases with few available relatives, exhumations or other substantial efforts may be necessary in order to secure adequate statistical power. We propose a simulation approach for solving prioritisation problems arising in such cases. Conditioning on the already typed individuals we estimate the power of each alternative, both to detect the true person, and to exclude false candidates. Graphical summaries of the simulations are given in complementary power plots, facilitating interpretation and decision making. Through a series of examples originating from the well-known Missing grandchildren of Argentina we demonstrate that our method may untangle complex prioritisation problems and other power-related questions. In particular we offer novel insights in recent cases where only children of the potential match are available for testing. We also show that X-chromosomal markers may give high statistical power in missing person identification, but that this requires careful selection of relatives for genotyping. All simulations, power calculations and plots are done with the R package forrel.

RNA Structure Duplication in the Dengue Virus 3' UTR: Redundancy or Host Specificity?

Flaviviruses include a diverse group of medically important viruses that cycle between mosquitoes and humans. During this natural process of switching hosts, each species imposes different selective forces on the viral population. Using dengue virus (DENV) as model, we found that paralogous RNA structures originating from duplications in the viral 3' untranslated region (UTR) are under different selective pressures in the two hosts. These RNA structures, known as dumbbells (DB1 and DB2), were originally proposed to be enhancers of viral replication. Analysis of viruses obtained from infected mosquitoes showed selection of mutations that mapped in DB2. Recombinant viruses carrying the identified variations confirmed that these mutations greatly increase viral replication in mosquito cells, with low or no impact in human cells. Use of viruses lacking each of the DB structures revealed opposite viral phenotypes. While deletion of DB1 reduced viral replication about 10-fold, viruses lacking DB2 displayed a great increase of fitness in mosquitoes, confirming a functional diversification of these similar RNA elements. Mechanistic analysis indicated that DB1 and DB2 differentially modulate viral genome cyclization and RNA replication. We found that a pseudoknot formed within DB2 competes with long-range RNA-RNA interactions that are necessary for minus-strand RNA synthesis. Our results support a model in which a functional diversification of duplicated RNA elements in the viral 3' UTR is driven by host-specific requirements. This study provides new ideas for understanding molecular aspects of the evolution of RNA viruses that naturally jump between different species.IMPORTANCE Flaviviruses constitute the most relevant group of arthropod-transmitted viruses, including important human pathogens such as the dengue, Zika, yellow fever, and West Nile viruses. The natural alternation of these viruses between vertebrate and invertebrate hosts shapes the viral genome population, which leads to selection of different viral variants with potential implications for epidemiological fitness and pathogenesis. However, the selective forces and mechanisms acting on the viral RNA during host adaptation are still largely unknown. Here, we found that two almost identical tandem RNA structures present at the viral 3' untranslated region are under different selective pressures in the two hosts. Mechanistic studies indicated that the two RNA elements, known as dumbbells, contain sequences that overlap essential RNA cyclization elements involved in viral RNA synthesis. The data support a model in which the duplicated RNA structures differentially evolved to accommodate distinct functions for viral replication in the two hosts.