Identification of forensically relevant body fluids using a panel of differentially expressed microRNAs

The serology-based methods routinely used in forensic casework for the identification of biological fluids are costly in terms of time and sample and have varying degrees of sensitivity and specificity. Recently, the use of a molecular genetics-based approach using messenger RNA (mRNA) profiling has been proposed to supplant conventional methods for body fluid identification. However, the size of the amplification products used in these mRNA assays (∼ 200-300 nt) might not be ideal for use with degraded or compromised samples frequently encountered in forensic casework. Recently, there has been an explosion of interest in a novel class of small noncoding RNAs, microRNAs (miRNAs, ∼20-25 bases in length), with numerous published studies reporting that some miRNAs are expressed in a tissue-specific manner. In this article, we provide the first comprehensive evaluation of miRNA expression in dried, forensically relevant biological fluids—blood, semen, saliva, vaginal secretions, and menstrual blood—in an attempt to identify putative body fluid-specific miRNAs. Most of the 452 human miRNAs tested (∼67% of the known miRNAome) were either expressed in multiple body fluids or not expressed at all. Nevertheless, we have identified a panel of nine miRNAs—miR451, miR16, miR135b, miR10b, miR658, miR205, miR124a, miR372, and miR412—that are differentially expressed to such a degree as to permit the identification of the body fluid origin of forensic biological stains using as little as 50 pg of total RNA. The miRNA-based body fluid identification assays were highly specific because the miRNA expression profile for each body fluid was different from that obtained from 21 human tissues. The results of this study provide an initial indication that miRNA profiling may provide a promising alternative approach to body fluid identification for forensic casework.     

DNA methylation: the future of crime scene investigation?

Proper detection and subsequent analysis of biological evidence is crucial for crime scene reconstruction. The number of different criminal acts is increasing rapidly. Therefore, forensic geneticists are constantly on the battlefield, trying hard to find solutions how to solve them. One of the essential defensive lines in the fight against the invasion of crime is relying on DNA methylation. In this review, the role of DNA methylation in body fluid identification and other DNA methylation applications are discussed. Among other applications of DNA methylation, age determination of the donor of biological evidence, analysis of the parent-of-origin specific DNA methylation markers at imprinted loci for parentage testing and personal identification, differentiation between monozygotic twins due to their different DNA methylation patterns, artificial DNA detection and analyses of DNA methylation patterns in the promoter regions of circadian clock genes are the most important ones. Nevertheless, there are still a lot of open chapters in DNA methylation research that need to be closed before its final implementation in routine forensic casework.     

Investigation of altering single-nucleotide polymorphism density on the power to detect trait loci and frequency of false positive in nonparametric linkage analyses of qualitative traits

Genome-wide linkage analysis using microsatellite markers has been successful in the identification of numerous Mendelian and complex disease loci. The recent availability of high-density single-nucleotide polymorphism (SNP) maps provides a potentially more powerful option. Using the simulated and Collaborative Study on the Genetics of Alcoholism (COGA) datasets from the Genetics Analysis Workshop 14 (GAW14), we examined how altering the density of SNP marker sets impacted the overall information content, the power to detect trait loci, and the number of false positive results. For the simulated data we used SNP maps with density of 0.3 cM, 1 cM, 2 cM, and 3 cM. For the COGA data we combined the marker sets from Illumina and Affymetrix to create a map with average density of 0.25 cM and then, using a sub-sample of these markers, created maps with density of 0.3 cM, 0.6 cM, 1 cM, 2 cM, and 3 cM. For each marker set, multipoint linkage analysis using MERLIN was performed for both dominant and recessive traits derived from marker loci. Our results showed that information content increased with increased map density. For the homogeneous, completely penetrant traits we created, there was only a modest difference in ability to detect trait loci. Additionally, as map density increased there was only a slight increase in the number of false positive results when there was linkage disequilibrium (LD) between markers. The presence of LD between markers may have led to an increased number of false positive regions but no clear relationship between regions of high LD and locations of false positive linkage signals was observed.     

A suite of DNA methylation markers that can detect most common human cancers

Cancer-specific DNA methylation from the tumor derived fraction of cell free DNA found in blood samples could be used for minimally invasive detection and monitoring of cancer. The knowledge of marker regions with cancer-specific DNA methylation is necessary to the success of such a process. We analyzed the largest cancer DNA methylation dataset available—TCGA Illumina HumanMethylation450 data with over 8,500 tumors—in order to find cancer-specific DNA methylation markers for most common human cancers. First, we identified differentially methylated regions for individual cancer types and those were further filtered against data from normal tissues to obtain marker regions with cancer-specific methylation, resulting in a total of 1,250 hypermethylated and 584 hypomethylated marker CpGs. From hypermethylated markers, optimal sets of six markers for each TCGA cancer type were chosen that could identify most tumors with high specificity and sensitivity [area under the curve (AUC): 0.969-1.000] and a universal 12 marker set that can detect tumors of all 33 TCGA cancer types (AUC >0.84). In addition to hundreds of new DNA methylation markers, our approach also identified markers that are in current clinical use, SEPT9 and GSTP1, indicating the validity of our approach and a significant potential utility for the newly discovered markers. The hypermethylated markers are linked to polycomb associated loci and a significant fraction of the discovered markers is within noncoding RNA genes; one of the best markers is MIR129-2. Future clinical testing of herein discovered markers will confirm new markers that will improve minimally invasive diagnosis and monitoring for multiple cancers.     

Paternity testing and forensic DNA typing by multiplex STR analysis using ABI PRISM 310 Genetic Analyzer

Short tandem repeats (STRs) are widespread throughout the human genome and are a rich source of highly polymorphic markers which can be detected by PCR. To gain a better appreciation for how the polymorphism at a particular locus impacts the individual identity, the present study was undertaken to explore the use of 15 STR loci in forensic investigation and paternity testing. Multiplex STR typing was used to study the 15 STR loci (D8S1179, D21S11, D7S820, CSF1PO, D3S1358, TH01, D13S317, D16S539, D2S1338, D19S433, vWA, TPOX, D18S51, D5S818 and FGA) in addition to a gender identification marker, amelogenin, by capillary electrophoresis on 310 Genetic Analyzer. Samples from 85 trio and duo cases of disputed paternity were investigated. The data were analyzed to give information on paternity index, probability of paternity, frequency of number of exclusions and rate of mismatch at each STR locus. The method was also successfully applied to forensic personal identification in theft and murder cases. The results demonstrated that the STR typing is a reliable and robust tool for analyzing the forensic practice as well as for paternity testing. The advantages of using multiplex STR analysis over other conventional methods are discussed.     

Development of diagnostic microsatellite markers from whole‐genome sequences of Ammodramus sparrows for assessing admixture in a hybrid zone

Studies of hybridization and introgression and, in particular, the identification of admixed individuals in natural populations benefit from the use of diagnostic genetic markers that reliably differentiate pure species from each other and their hybrid forms. Such diagnostic markers are often infrequent in the genomes of closely related species, and genomewide data facilitate their discovery. We used whole‐genome data from Illumina HiSeqS2000 sequencing of two recently diverged (600,000 years) and hybridizing, avian, sister species, the Saltmarsh (Ammodramus caudacutus) and Nelson's (A. nelsoni) Sparrow, to develop a suite of diagnostic markers for high‐resolution identification of pure and admixed individuals. We compared the microsatellite repeat regions identified in the genomes of the two species and selected a subset of 37 loci that differed between the species in repeat number. We screened these loci on 12 pure individuals of each species and report on the 34 that successfully amplified. From these, we developed a panel of the 12 most diagnostic loci, which we evaluated on 96 individuals, including individuals from both allopatric populations and sympatric individuals from the hybrid zone. Using simulations, we evaluated the power of the marker panel for accurate assignments of individuals to their appropriate pure species and hybrid genotypic classes (F1, F2, and backcrosses). The markers proved highly informative for species discrimination and had high accuracy for classifying admixed individuals into their genotypic classes. These markers will aid future investigations of introgressive hybridization in this system and aid conservation efforts aimed at monitoring and preserving pure species. Our approach is transferable to other study systems consisting of closely related and incipient species.     

Genome-Scale Discovery of DNA-Methylation Biomarkers for Blood-Based Detection of Colorectal Cancer

Background

There is an increasing demand for accurate biomarkers for early non-invasive colorectal cancer detection. We employed a genome-scale marker discovery method to identify and verify candidate DNA methylation biomarkers for blood-based detection of colorectal cancer.

Methodology/Principal Findings

We used DNA methylation data from 711 colorectal tumors, 53 matched adjacent-normal colonic tissue samples, 286 healthy blood samples and 4,201 tumor samples of 15 different cancer types. DNA methylation data were generated by the Illumina Infinium HumanMethylation27 and the HumanMethylation450 platforms, which determine the methylation status of 27,578 and 482,421 CpG sites respectively. We first performed a multistep marker selection to identify candidate markers with high methylation across all colorectal tumors while harboring low methylation in healthy samples and other cancer types. We then used pre-therapeutic plasma and serum samples from 107 colorectal cancer patients and 98 controls without colorectal cancer, confirmed by colonoscopy, to verify candidate markers. We selected two markers for further evaluation: methylated THBD (THBD-M) and methylated C9orf50 (C9orf50-M). When tested on clinical plasma and serum samples these markers outperformed carcinoembryonic antigen (CEA) serum measurement and resulted in a high sensitive and specific test performance for early colorectal cancer detection.

Conclusions/Significance

Our systematic marker discovery and verification study for blood-based DNA methylation markers resulted in two novel colorectal cancer biomarkers, THBD-M and C9orf50-M. THBD-M in particular showed promising performance in clinical samples, justifying its further optimization and clinical testing.     

Genome-scale screen for DNA methylation-based detection markers for ovarian cancer

Background

The identification of sensitive biomarkers for the detection of ovarian cancer is of high clinical relevance for early detection and/or monitoring of disease recurrence. We developed a systematic multi-step biomarker discovery and verification strategy to identify candidate DNA methylation markers for the blood-based detection of ovarian cancer.

Methodology/Principal Findings

We used the Illumina Infinium platform to analyze the DNA methylation status of 27,578 CpG sites in 41 ovarian tumors. We employed a marker selection strategy that emphasized sensitivity by requiring consistency of methylation across tumors, while achieving specificity by excluding markers with methylation in control leukocyte or serum DNA. Our verification strategy involved testing the ability of identified markers to monitor disease burden in serially collected serum samples from ovarian cancer patients who had undergone surgical tumor resection compared to CA-125 levels.We identified one marker, IFFO1 promoter methylation (IFFO1-M), that is frequently methylated in ovarian tumors and that is rarely detected in the blood of normal controls. When tested in 127 serially collected sera from ovarian cancer patients, IFFO1-M showed post-resection kinetics significantly correlated with serum CA-125 measurements in six out of 16 patients.

Conclusions/Significance

We implemented an effective marker screening and verification strategy, leading to the identification of IFFO1-M as a blood-based candidate marker for sensitive detection of ovarian cancer. Serum levels of IFFO1-M displayed post-resection kinetics consistent with a reflection of disease burden. We anticipate that IFFO1-M and other candidate markers emerging from this marker development pipeline may provide disease detection capabilities that complement existing biomarkers.     

Insertion site‐based polymorphism markers open new perspectives for genome saturation and marker‐assisted selection in wheat

In wheat, the deployment of marker‐assisted selection has long been hampered by the lack of markers compatible with high‐throughput cost‐effective genotyping techniques. Recently, insertion site‐based polymorphism (ISBP) markers have appeared as very powerful new tools for genomics and genetic studies in hexaploid wheat. To demonstrate their possible use in wheat breeding programmes, we assessed their potential to meet the five main requirements for utilization in MAS: flexible and high‐throughput detection methods, low quantity and quality of DNA required, low cost per assay, tight link to target loci and high level of polymorphism in breeding material. Toward this aim, we developed a programme, IsbpFinder, for the automated design of ISBP markers and adapted three detection methods (melting curve analysis, SNaPshot^® Multiplex System and Illumina BeadArray technology) for high throughput and flexible detection of ISBP or ISBP‐derived SNP markers. We demonstrate that the high level of polymorphism of the ISBPs combined with cost‐effective genotyping methods can be used to efficiently saturate genetic maps, discriminate between elite cultivars, and design tightly linked diagnostic markers for virtually all target loci in the wheat genome. All together, our results suggest that ISBP markers have the potential to lead to a breakthrough in wheat marker‐assisted selection.     

Elucidating the Landscape of Aberrant DNA Methylation in Hepatocellular Carcinoma

Background

Hepatocellular carcinoma (HCC) is one of the most common cancers and frequently presents with an advanced disease at diagnosis. There is only limited knowledge of genome-scale methylation changes in HCC.

Methods and Findings

We performed genome-wide methylation profiling in a total of 47 samples including 27 HCC and 20 adjacent normal liver tissues using the Illumina HumanMethylation450 BeadChip. We focused on differential methylation patterns in the promoter CpG islands as well as in various less studied genomic regions such as those surrounding the CpG islands, i.e. shores and shelves. Of the 485,577 loci studied, significant differential methylation (DM) was observed between HCC and adjacent normal tissues at 62,692 loci or 13% (p<1.03e-07). Of them, 61,058 loci (97%) were hypomethylated and most of these loci were located in the intergenic regions (43%) or gene bodies (33%). Our analysis also identified 10,775 differentially methylated (DM) loci (17% out of 62,692 loci) located in or surrounding the gene promoters, 4% of which reside in known Differentially Methylated Regions (DMRs) including reprogramming specific DMRs and cancer specific DMRs, while the rest (10,315) involving 4,106 genes could be potential new HCC DMR loci. Interestingly, the promoter-related DM loci occurred twice as frequently in the shores than in the actual CpG islands. We further characterized 982 DM loci in the promoter CpG islands to evaluate their potential biological function and found that the methylation changes could have effect on the signaling networks of Cellular development, Gene expression and Cell death (p = 1.0e-38), with BMP4, CDKN2A, GSTP1, and NFATC1 on the top of the gene list.

Conclusion

Substantial changes of DNA methylation at a genome-wide level were observed in HCC. Understanding epigenetic changes in HCC will help to elucidate the pathogenesis and may eventually lead to identification of molecular markers for liver cancer diagnosis, treatment and prognosis.     

Sequence overlap between autosomal and sex-linked probes on the Illumina HumanMethylation27 microarray

The Illumina Infinium HumanMethylation27 BeadChip (Illumina 27k) microarray is a high-throughput platform capable of interrogating the human DNA methylome. In a search for autosomal sex-specific DNA methylation using this microarray, we discovered autosomal CpG loci showing significant methylation differences between the sexes. However, we found that the majority of these probes cross-reacted with sequences from sex chromosomes. Moreover, we determined that 6-10% of the microarray probes are non-specific and map to highly homologous genomic sequences. Using probes targeting different CpGs that are exact duplicates of each other, we investigated the precision of these repeat measurements and concluded that the overall precision of this microarray is excellent. In addition, we identified a small number of probes targeting CpGs that include single-nucleotide polymorphisms. Overall, our findings address several technical issues associated with the Illumina 27k microarray that, once considered, will enhance the analysis and interpretation of data generated from this platform.     

Evaluation of linkage disequilibrium and its effect on non-parametric multipoint linkage analysis using two high density single-nucleotide polymorphism mapping panels

Genotype data from the Illumina Linkage III SNP panel (n = 4,720 SNPs) and the Affymetrix 10 k mapping array (n = 11,120 SNPs) were used to test the effects of linkage disequilibrium (LD) between SNPs in a linkage analysis in the Collaborative Study on the Genetics of Alcoholism pedigree collection (143 pedigrees; 1,614 individuals). The average r2 between adjacent markers across the genetic map was 0.099 +/- 0.003 in the Illumina III panel and 0.17 +/- 0.003 in the Affymetrix 10 k array. In order to determine the effect of LD between marker loci in a nonparametric multipoint linkage analysis, markers in strong LD with another marker (r2 > 0.40) were removed (n = 471 loci in the Illumina panel; n = 1,804 loci in the Affymetrix panel) and the linkage analysis results were compared to the results using the entire marker sets. In all analyses using the ALDX1 phenotype, 8 linkage regions on 5 chromosomes (2, 7, 10, 11, X) were detected (peak markers p < 0.01), and the Illumina panel detected an additional region on chromosome 6. Analysis of the same pedigree set and ALDX1 phenotype using short tandem repeat markers (STRs) resulted in 3 linkage regions on 3 chromosomes (peak markers p < 0.01). These results suggest that in this pedigree set, LD between loci with spacing similar to the SNP panels tested may not significantly affect the overall detection of linkage regions in a genome scan. Moreover, since the data quality and information content are greatly improved in the SNP panels over STR genotyping methods, new linkage regions may be identified due to higher information content and data quality in a dense SNP linkage panel.     

Direct selection on allozymes is not required to explain heterogeneity among marker loci across a Mytilus hybrid zone

Unequal differentiation between two types of loci (allozyme and DNA markers) across a Mytilus hybrid zone has recently been claimed as evidence for direct selection on some allozyme loci. We provide here a counter-example: a noncoding DNA locus that exhibits as much differentiation as the incriminated allozymes do. The levels of genetic differentiation varied widely among both allozymes and noncoding DNA markers and no clear difference emerged between the two types of markers. This suggests that the strong interlocus variance in genetic differentiation has been confounded with a discrepancy between marker types as a result of an insufficient and unbalanced locus sampling. Heterogeneity in differentiation among neutral loci can be created by stochastic variance during the allopatric divergence preceding a secondary contact. In hybrid zones, a further source of variance is differential introgression among chromosomal regions after the secondary contact owing to the local influence of selected genes on more or less distant markers. However, the degree of differentiation alone gives no way to distinguish indirect pseudo-selection (a regular and ubiquitous feature of hybrid zones) from direct selection. More generally, we suggest that comparative neutrality tests based on discrepancies among marker types have to be applied with caution when the presence of semi-permeable genetic barriers to gene exchange is suspected.     

An mRNA and DNA co-isolation method for forensic casework samples

RNA analysis is expected to play an increasingly important role in the area of biomolecular forensic analysis. For example, mRNA expression analysis performed on a total RNA sample isolated from a biological stain may be used to identify the nature of the tissue(s) comprising the stain. Many of the physiological stains encountered at crime scenes involve heterogeneous mixtures of different body fluids (e.g., semen and saliva, semen and vaginal secretions). Separate sampling of these mixed stains from different "geographical" locations of the stains to isolate DNA and RNA could result in a misleading estimate of the ratio of the body fluids present and, in extreme cases, even fail to detect one of the contributors. Thus, a prerequisite for the use of mRNA expression profiling in routine forensic analysis is the ability to co-extract DNA and RNA from the same stain. This article describes an optimized method that was specifically developed to co-extract mRNA and DNA from the same physiological stain and that appears to be sufficiently sensitive and robust for routine forensic use.     

基于MSAP和SSR标记的高粱甲基化连锁群LGC、LGD的构建

以高粱(Sorghum bicolor(L.)Moench)品种‘B_2V_4’和‘1383-2’杂交获得的F_2群体为材料,通过SSR和MSAP标记检测高粱基因组差异,构建其甲基化遗传连锁群。结果显示,高粱甲基化连锁群LGC含有3个SSR标记和23个甲基化标记,覆盖高粱基因组44.3 cM;甲基化连锁群LGD含有4个SSR标记和8个甲基化标记,覆盖高粱基因组46.2 cM。LGC上甲基化位点仅来源于EcoRⅠ/MspⅠ酶切组合,而LGD上有来源于EcoRⅠ/MspⅠ和EcoRⅠ/HpaⅡ两种酶切组合的甲基化位点。在LGC连锁群Xtxp 69附近检测到一个密集的甲基化位点区域。研究结果表明MSAP标记可以快速检测植物基因组甲基化差异,适用于构建甲基化连锁群。     

Evaluating outlier loci and their effect on the identification of pedigree errors

Homozygosity outlier loci, which show patterns of variation that are extremely divergent from the rest of the genome, can be evaluated by comparison of the homozygosity under Hardy-Weinberg proportions (the sum of the squares of allele frequencies) with the expected homozygosity under neutrality. Such outlier loci are potentially under selection (balancing selection or directional selection) when genome-wide effects (such as bottleneck and rapid population growth) are excluded. Outlier loci show skewed allele frequencies with respect to neutrality and may therefore affect the identification of pedigree errors. However, choosing neutral markers (excluding outlier loci) for the identification of pedigree errors has been neglected thus far. Our results showed that 4.1%, 5.5%, and 1.5% of the microsatellite markers, Illumina single-nucleotide polymorphisms (SNPs), and Affymetrix SNPs, respectively, on the autosomes appear to be under balancing selection (p or=40%) appear to be under balancing selection. Pedigree structure errors in 15 of 143 pedigrees were detected using microsatellite markers from the autosomes and/or selected SNPs from chromosomes 1 to 18 of the Illumina and/or selected SNPs from chromosomes 1 to 16 of the Affymetrix. Outlier loci did not make a major difference to the identification of pedigree errors. The Collaborative Study on the Genetics of Alcoholism data has pedigree errors and some of them may be due to sample mix up.     

Log-linear models for gene mapping with affected sib pair data

In genome-wide screens of genetic marker loci, non-mendelian inheritance of a marker is taken to indicate its vicinity to a disease locus. Heritable complex traits are thought to be under the influence of multiple possibly interacting susceptibility loci yet the most frequently used methods of linkage and association analysis focus on one susceptibility locus at a time. Here we introduce log-linear models for the joint analysis of multiple marker loci and interaction effects between them. Our approach focuses on affected sib pair data and identical by descent (IBD) allele sharing values observed on them. For each heterozygous parent, the IBD values at linked markers represent a sequence of dependent binary variables. We develop log-linear models for the joint distribution of these IBD values. An independence log-linear model is proposed to model the marginal means and the neighboring interaction model is advocated to account for associations between adjacent markers. Under the assumption of conditional independence, likelihood methods are applied to simulated data containing one or two susceptibility loci. It is shown that the neighboring interaction log-linear model is more efficient than the independence model, and incorporating interaction in the two-locus analysis provides increased power and accuracy for mapping of the trait loci.