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      Current genetic methodologies in the identification of disaster victims and in forensic analysis

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          Abstract

          This review presents the basic problems and currently available molecular techniques used for genetic profiling in disaster victim identification (DVI). The environmental conditions of a mass disaster often result in severe fragmentation, decomposition and intermixing of the remains of victims. In such cases, traditional identification based on the anthropological and physical characteristics of the victims is frequently inconclusive. This is the reason why DNA profiling became the gold standard for victim identification in mass-casualty incidents (MCIs) or any forensic cases where human remains are highly fragmented and/or degraded beyond recognition. The review provides general information about the sources of genetic material for DNA profiling, the genetic markers routinely used during genetic profiling (STR markers, mtDNA and single-nucleotide polymorphisms [SNP]) and the basic statistical approaches used in DNA-based disaster victim identification. Automated technological platforms that allow the simultaneous analysis of a multitude of genetic markers used in genetic identification (oligonucleotide microarray techniques and next-generation sequencing) are also presented. Forensic and population databases containing information on human variability, routinely used for statistical analyses, are discussed. The final part of this review is focused on recent developments, which offer particularly promising tools for forensic applications (mRNA analysis, transcriptome variation in individuals/populations and genetic profiling of specific cells separated from mixtures).

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          Most cited references120

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          Microarray analysis shows that some microRNAs downregulate large numbers of target mRNAs.

          MicroRNAs (miRNAs) are a class of noncoding RNAs that post-transcriptionally regulate gene expression in plants and animals. To investigate the influence of miRNAs on transcript levels, we transfected miRNAs into human cells and used microarrays to examine changes in the messenger RNA profile. Here we show that delivering miR-124 causes the expression profile to shift towards that of brain, the organ in which miR-124 is preferentially expressed, whereas delivering miR-1 shifts the profile towards that of muscle, where miR-1 is preferentially expressed. In each case, about 100 messages were downregulated after 12 h. The 3' untranslated regions of these messages had a significant propensity to pair to the 5' region of the miRNA, as expected if many of these messages are the direct targets of the miRNAs. Our results suggest that metazoan miRNAs can reduce the levels of many of their target transcripts, not just the amount of protein deriving from these transcripts. Moreover, miR-1 and miR-124, and presumably other tissue-specific miRNAs, seem to downregulate a far greater number of targets than previously appreciated, thereby helping to define tissue-specific gene expression in humans.
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            Genetic analysis of genome-wide variation in human gene expression.

            Natural variation in gene expression is extensive in humans and other organisms, and variation in the baseline expression level of many genes has a heritable component. To localize the genetic determinants of these quantitative traits (expression phenotypes) in humans, we used microarrays to measure gene expression levels and performed genome-wide linkage analysis for expression levels of 3,554 genes in 14 large families. For approximately 1,000 expression phenotypes, there was significant evidence of linkage to specific chromosomal regions. Both cis- and trans-acting loci regulate variation in the expression levels of genes, although most act in trans. Many gene expression phenotypes are influenced by several genetic determinants. Furthermore, we found hotspots of transcriptional regulation where significant evidence of linkage for several expression phenotypes (up to 31) coincides, and expression levels of many genes that share the same regulatory region are significantly correlated. The combination of microarray techniques for phenotyping and linkage analysis for quantitative traits allows the genetic mapping of determinants that contribute to variation in human gene expression.
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              Common regulatory variation impacts gene expression in a cell type-dependent manner.

              Studies correlating genetic variation to gene expression facilitate the interpretation of common human phenotypes and disease. As functional variants may be operating in a tissue-dependent manner, we performed gene expression profiling and association with genetic variants (single-nucleotide polymorphisms) on three cell types of 75 individuals. We detected cell type-specific genetic effects, with 69 to 80% of regulatory variants operating in a cell type-specific manner, and identified multiple expressive quantitative trait loci (eQTLs) per gene, unique or shared among cell types and positively correlated with the number of transcripts per gene. Cell type-specific eQTLs were found at larger distances from genes and at lower effect size, similar to known enhancers. These data suggest that the complete regulatory variant repertoire can only be uncovered in the context of cell-type specificity.
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                Author and article information

                Contributors
                zietkiee@man.poznan.pl
                Journal
                J Appl Genet
                Journal of Applied Genetics
                Springer-Verlag (Berlin/Heidelberg )
                1234-1983
                2190-3883
                15 October 2011
                15 October 2011
                February 2012
                : 53
                : 1
                : 41-60
                Affiliations
                [1 ]Institute of Human Genetics, Polish Academy of Sciences, Poznań, Poland
                [2 ]Department of Disaster Medicine, University of Medical Sciences, Poznań, Poland
                [3 ]Cancer Center and Institute of Oncology, Gliwice, Poland
                [4 ]Emergency Medicine Unit, Medical University, Lublin, Poland
                [5 ]International Institute of Molecular and Cell Biology, Warsaw, Poland
                Article
                68
                10.1007/s13353-011-0068-7
                3265735
                22002120
                13cca75e-aeef-4830-a52a-4c459e8ea141
                © The Author(s) 2011
                History
                : 30 July 2011
                : 22 September 2011
                : 23 September 2011
                Categories
                Human Genetics • Review
                Custom metadata
                © Institute of Plant Genetics, Polish Academy of Sciences, Poznan 2012

                Genetics
                automated technological platforms,transcriptome analysis,dna mixtures analysis,statistical analysis in dvi,genetic markers in dvi,polymorphisms databases,dna profiling

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