Dna Fragments Used In Forensics Project Assignment


ACTIVITY 2. SEPARATING THE DNA FRAGMENTS

Agarose gel electrophoresis

Any separation of DNA fragments containing allelic regions to be compared among individuals is done by electrophoresis. The form of electrophoresis that is used in most research laboratories to separate pieces of DNA is called slab or agarose-gel electrophoresis.In this method, the DNA fragments are separated according to size. Thus, the smallest DNA fragments move the fastest and are found at the bottom of the gel when the run is finished. Since DNA is negatively charged, the fragments move down the gel toward the positive pole. View the following videos which show the preparation of the agarose gel and assembly of the electrophoretic apparatus. 

video -- Preparing the agarose gel

video -- Assembling the electrophoretic apparatus

Most introductory laboratories that demonstrate DNA electrophoresis utilize very short DNA segments of different lengths that can be separated in an hour or so. Below is a photograph of a simulated crime scene based on analysis of such fragments. The 6 lanes of DNA fragments exhibit banding patterns that can be used comparatively, in this case to match evidence with a suspect.

Separation of DNA fragments on an agarose gel

Lane 1 contains the victim's DNA fragments, lane 2 the DNA evidence, and lanes 3-6 DNA from four suspects. 


In the Genetic Technology & Bioinformatics topic, you have seen a real DNA fingerprint that was used several years ago as evidence in a trial. This older method of DNA fingerprinting was based on the banding patterns of DNA fragments produced by agarose gel electrophoresis. Note that several different fingerprints representing comparisons of several loci were always used to exclude (or include) a potential suspect when this method was used. View the following animation to summarize the agarose gel method.

animation - DNA electrophoresis

When you understand how agarose gel electrophoresis works and how it has been used in DNA fingerprinting, answer questions 2 and 3.

Capillary electrophoresis

Separation of DNA fragments by agarose gel electrophoresis is still a very powerful tool in the research laboratory (which is why it is featured in this laboratory assignment). It is still used to help diagnose and follow the inheritance of some diseases. However, in forensic science it has been replaced by capillary gel electrophoresis. This type of electrophoresis uses narrow silica capillaries (tubes) containing a polymer solution through which the negatively charged DNA molecules migrate under the influence of a high voltage electric field. It is fast, easily automated, and allows analysis of smaller amounts of DNA than required for agarose gel electrophoresis.

This diagram shows the basic setup of a capillary electrophoresis system. Note that a computer is attached to a detector at the bottom of the capillary tube. For more information on the system, view these photographs.

Whereas agarose gels produce a banding pattern of DNA fragments, the capillary method is computerized and produces a pattern of peaks on a graph.

In the capillary method, the DNA fragments are labeled to make them fluorescent. A detector measures fluorescence as the fragments pass through the capillary tube. Each peak on the graph represents a fragment of different molecular weight (size). The pattern of peaks is the DNA fingerprint of one individual. Thus, it corresponds to one lane of bands in an agarose gel.

When your understand how the capillary method differs from gel electrophoresis, answer question 4.

ACTIVITY 3. OBTAINING ALLELIC DNA FRAGMENTS

Be sure to review the material on restriction enzymes before beginning this activity.

The RFLP method of DNA fingerprinting

There are two ways to obtain the DNA fragments needed for fingerprinting, and sometimes they are combined in specific applications. Jeffreys subjected nuclear DNA to restriction enzyme digestion. The fragments obtained are known as RFLPs  because they represent restriction fragments with length polymorphisms. The DNA sequences targeted were arrays of moderately repetitive and short (10-60 bases) DNA sequences which are dispersed throughout the genome. They are known as "variable number tandem repeats" (VNTRs), and their degree of repetition is two to several hundred at each locus. There are thousands of loci, but each locus shows a distinctive repeat unit. For any one locus, most of the polymorphism observed among individuals is caused by variation in the number of repeats, although individuals can also vary in the number of sites to which a particular restrictive enzyme can attach while digesting the DNA into fragments. View the following animation which shows the digestion of DNA into fragments that vary in the number of repeats they contain.

animation - digestion of DNA into fragments

The resulting fragments are separated by size using agarose gel electrophoresis. The desired result is a banding pattern that reflects the differences in repeats that vary among individuals. However, the gel must be processed further to obtain a distinct banding pattern of allelic sites. This is because there are so many different restriction fragments on the gel, it usually appears as a smear rather than discrete bands. To obtain a discrete banding pattern, the DNA is denatured into single strands by incubation with NaOH, then transferred to a stable surface which is a sheet of nitrocellulose or nylon. This procedure is called "blotting"; the DNA fragments retain the same pattern of separation that they had on the gel. The blot is then incubated with probes that recognize specific allelic fragments.

Each probe is a single-stranded DNA that is complementary to the sequence of nucleotides in one or more of the restriction fragments. The probes are either radioactive or fluorescent to allow them to be detected by exposure to X-ray film.

This diagram illustrates the steps in DNA fingerprinting by the RFLP method. You should also view the animation below, since it includes some important details.

animation- the RFLP method of DNA fingerprinting

Criteria for identifying suspects are based on the probabilities derived from analyzing several gels, using probes for at least 5-10 different loci. Study the following example in which two loci are examined:

Here are repetitive sites at two loci (A and B) which vary among individuals. This example shows the pattern for A and B in two individuals (sample 1 and sample 2).

In this gel, the banding pattern of the suspect is the same as found in the evidence.

The frequency of each allele type in the population is known (left column).

The probability that any individual would share the same banding pattern with the evidence is 1 in 7,452,000 (product of frequencies for the 4 alleles). Thus, it appears that the suspect is guilty. Note that if more than 4 loci were examined, the probability of 2 individuals sharing the same alleles would be in the billions.

When you understand the RFLP method and how probabilities are calculated, answer questions 5 -8. For question 8, use this DNA fingerprint.

Abstract

The field of forensic science is increasingly based on biomolecular data and many European countries are establishing forensic databases to store DNA profiles of crime scenes of known offenders and apply DNA testing. The field is boosted by statistical and technological advances such as DNA microarray sequencing, TFT biosensors, machine learning algorithms, in particular Bayesian networks, which provide an effective way of evidence organization and inference. The aim of this article is to discuss the state of art potentialities of bioinformatics in forensic DNA science. We also discuss how bioinformatics will address issues related to privacy rights such as those raised from large scale integration of crime, public health and population genetic susceptibility-to-diseases databases.

forensic science, DNA testing, CODIS, Bayesian networks, DNA microarray

INTRODUCTION

Bioinformatics and forensic DNA are inherently interdisciplinary and draw their techniques from statistics and computer science bringing them to bear on problems in biology and law. Personal identification and relatedness to other individuals are the two major subjects of forensic DNA analysis. Typical contexts for forensic analysis are disputes on kinship; for example paternity disputes, suspected incest case, corpse identification, alimentary frauds (e.g. OGM, poisonous food, etc), semen detection on underwear for suspected infidelity, insurance company fraud investigations when the actual driver in a vehicle accident is in question, criminal matters, autopsies for human identification following accident investigations. Genetic tests have been widely used for forensic evidences and mass-fatality identification (terrorist attacks, airplane crash, tsunami disaster) [1,2]. Genetic testing results are integrated with information collected by multidisciplinary teams composed of medical examiners, forensic pathologists, anthropologists, forensic dentists, fingerprint specialists, radiologists and experts in search and recovery of physical evidence. Large scale tissue sampling and long-term DNA preservation under desiccation conditions with potential applications in mass fatalities has been recently described [2–4].

In several countries new rules could allow fingerprints and DNA samples to be taken from anyone they arrest, whether they are charged or not. This will be certainly facilitated by the introduction of three different key innovations, in data acquisition, such as thin film transistors (TFT) [5], in DNA sample identification, such as microarray re-sequencing and in statistical methodologies, such as Bayesian networks (BNs), which provide an effective way of evidence organization and inference.

The TFT, which can be seen as a combination of an intelligent version of liquid crystal display and wafer thin technology, will allow the DNA to be identified on the crime scene or in the police station [5,6]. Current genome sequencing projects employ high-throughput shotgun sequencing at large centers. Rapid DNA sequencing technology is nowadays based on microarrays; for example, the entire sequence of the mitochondrial genome (16 500 bases) can be re-sequenced in a single (three PCR) 48 h experiment allowing to detect variants over all the sequence and not just restricted to the hypervariable regions [7].

Taking into account this scenario, we discuss links between bioinformatics and forensic statistics which is a discipline focusing on the experimental design of forensic examinations and evidences [8–10]. Bioinformatics will affect how forensic statistics will address hypothesis formulation on DNA samples, deciding on minimal population sample sizes when studying populations of similar units of evidence and determining the statistical significance of the outcome of tests. Particularly we aim at discussing the role of BNs which is becoming very useful in the study of the implications of forensic examinations on defendant and prosecution positions during crime investigation and criminal court proceedings.

THE BASICS OF FORENSIC DNA PROCEDURES

Personal identification relies on identifiable characteristics such as biological (DNA, blood, saliva, etc.), physiological (fingerprints, eye irises and retinas, hand palms and geometry and facial geometry), behavioral (dynamic signature, gait, keystroke dynamics, and lip motion) and on mixture of physiological and dynamical characteristics such as the voice.

DNA has become the most important personal identification characteristic because all genetic differences whether being expressed regions of DNA (genes) or some segments of DNA with no known coding function but whose pattern of inheritance can be monitored can be used as markers.

Any two humans are >99% identical in their DNA sequences, still have millions of genetic differences, making them different in their risk of getting certain diseases and response to environmental factors.

The most important sources of genetic variations are copy number variation (CNV) [10], large genomic regions that are absent from, or duplicated in different individuals, and SNP, single nucleotide polymorphisms i.e. single base difference among two different individuals of the same species. SNPs in humans occur in average every 1/2000 bases.

The human genome is also highly repetitious. Repetitions occur at most of sequence length scales, number and dispersion [11]. Examples of such repetitions are homo- and di-nucleotide repeats (microsatellites), and families of interspersed, mobile elements hundreds of base pairs long such as the ALU sequences. There are more than one million ALU sequences in the human genome, each 300 bases long, which are able to copy themselves in other parts of the genome, generating mutations.

Forensic DNA typing often requires the use of techniques that allow the detection of genetic variations among humans, usually short, repetitive loci. variable number of tandem repeats (VNTRs) polymorphism were used till few years ago. Such loci are composed of core units three, four or five nucleotides long and the number of repeated segments at a locus varies between individuals. One VNTR in humans is a 17 bp sequence of DNA repeated between 70 and 450 times in the genome. The total number of base pairs at this locus could vary from 1190 to 7650. VNTRs are identified by cutting genomic DNA with restriction enzymes such as HaeIII, HinfI or HindIII, separating the DNA fragments electrophoretically in a gel, and then detecting the variable fragments by the use of short DNA stretches that bind specifically to variable loci (probes). Nowadays the use of VNTR has been replaced by STR (short tandem repeats). CNV are supposed to be major determinants of human traits, and they may become useful in forensic science, particularly in the determination of the population substructures.

STR system and CODIS

The use of PCR allows to analyze DNA from samples as small as a single cell and, therefore, DNA typing analysis using STRs can be performed on a large variety of materials, such as cigarette ends, skeletal remains, urine, tissues on a gun muzzle and on bullets, dismembered and decayed body parts, paraffin embedded tumor tissue, dirt under fingernails, epithelia of an offender from the victim's neck after strangling, mummified newborns, blowflies preserved in ethanol, burned corpses, dentin, dried chewing gum, body parts after mass disasters, human feces and skeletonized flood victims.

At present, the most discriminative power in DNA identification is obtained by matching 13–17 of nuclear STR markers of a victim's profile (personal items, like toothbrushes and used shavers) to a direct antemortem sample of the victim or to family references: either or both biological parents of the victim. A system of 13 STRs constitutes the Combined DNA Index System (CODIS) which is used in USA and Canada [12], while most of European countries have their own systems and databases (see subsequently).

Although CODIS strictly represents the USA and Canadian felons and forensic samples database, sometimes it is used to match probability in mass disasters outside USA. For example in the Madrid terrorist attack case, the CODIS database was used to match probabilities of 220 body remains against 98 reference samples, including 67 samples from relatives, representing 40 family groups and 27 antemortem direct references.

In Table 1 we describe the Codis system of STR. For each marker chromosomal location and the GenBank accession are reported; for example, the table shows that the D3S1358 marker is on chromosome 3, has GenBank accession 11449919 from which we find that it has 18 repeats, of the form TCTA(TCTG)2(TCTA)15. The website http://www.cstl.nist.gov/div831/strbase/fbicore.htm contains information (frequencies) of the marker's alleles, i.e. the common variants.

Table 1:

Distribution of Codis STR on human chromosomes

Combined DNA Index System (CODIS) 
D3S1358 3p21 (11449919) 
vWA 12p12-pter (M25858) 
FGA 4q28; located in the 3rd intron of human alpha fibrinogen gene (M64982) 
D8S1179 8q24.1-24.2; (GO8710) 
D21S11 21q21.1 (M84567) 
D18S51 18q21.3 (X91254) 
D5S818 5q21-q31 (G08446) 
D13S317 13q22-q31 (G09017) 
D7S820 7q (G08616) 
D16S539 16q22-24 (G07925) 
THO1 11p15-15.5; intron 1 of tyrosine hydroxylase gene (D00269) 
TPOX 2p23-2pter; intron 10 of human thyroid peroxidase gene (M68651) 
CSF1PO 5q33.3-34; c-fms proto-oncogene for CSF-1 receptor gene (X14720) 
AMEL X Xp22.3-p22 (M86932) 
AMEL Y Yp11 (M86933) 
Combined DNA Index System (CODIS) 
D3S1358 3p21 (11449919) 
vWA 12p12-pter (M25858) 
FGA 4q28; located in the 3rd intron of human alpha fibrinogen gene (M64982) 
D8S1179 8q24.1-24.2; (GO8710) 
D21S11 21q21.1 (M84567) 
D18S51 18q21.3 (X91254) 
D5S818 5q21-q31 (G08446) 
D13S317 13q22-q31 (G09017) 
D7S820 7q (G08616) 
D16S539 16q22-24 (G07925) 
THO1 11p15-15.5; intron 1 of tyrosine hydroxylase gene (D00269) 
TPOX 2p23-2pter; intron 10 of human thyroid peroxidase gene (M68651) 
CSF1PO 5q33.3-34; c-fms proto-oncogene for CSF-1 receptor gene (X14720) 
AMEL X Xp22.3-p22 (M86932) 
AMEL Y Yp11 (M86933) 

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When the genomic DNA is too scarce or too degraded for standard forensic analysis, the sequence of two hypervariable regions of the mitochondrial DNA is considered informative.

Multiple STR systems

The knowledge of the frequency of a certain STR allele in a population enables computing how often an allele combination appears in a given population. Because of their high variability, i.e. high numbers of rare alleles, classical VNTR loci alone often lead to much higher exclusion (or inclusion) probabilities than single modern STR systems alone, which often have quite common and widespread alleles. Thus the detection of an allele combination in only a single STR system in a biological stain seldom constitutes conclusive proof of identity. If, however, alleles in stain are observed not to be identical to those of a person's reference body fluid, in extreme cases even one STR profile can exclude the person from the suspicion of having left the stain.

Severely degraded DNA samples could contain only very short DNA template molecules (under 150 bp) making conventional STR typing (150–400 bp) unsuccessful. Damaged DNA templates (very old bones, hair shafts) and minute amounts of cells occasionally lead to the elimination of single or, in the worst case, all alleles, and occasionally one even obtains nonreproducible results. Multiplex PCR involves using several sets of PCR primers to the reaction and allows to target multiple locations throughout the genome. This is an ideal technique for DNA typing because the probability of identical alleles in two individuals decreases with the increase in the number of polymorphic loci examined. Currently, STR multiplex systems have a discrimination power (i.e. matching probability) greater than a combination of five classical single locus DNA fingerprints. For example, a third-generation multiplex PCR developed at the Forensic Science Service (FSS) in Birmingham (http://www.forensic.gov.uk) matches persons to a stain with a probability of 1:10 [13]. Figure 1 shows the key time developments of the forensic technology and the increased resolution of the DNA analyses.

Figure 1:

Time scales of major events in forensic DNA typing (left) and examples of resolution power of the different techniques (right).

Figure 1:

Time scales of major events in forensic DNA typing (left) and examples of resolution power of the different techniques (right).

The multiplexing technology can save time and money, but difficulties may arise when coamplifying several loci. Primers for one locus can complex with those of other loci and completely inhibit the amplification. This effect may be exhibited by dropout of a specific STR locus under certain conditions (e.g. sample mixtures). Finding the optimum PCR conditions, particularly the annealing temperature and the primer concentrations, can be challenging and time-consuming.

The procedure used when a match is found, consists in typing again the DNA by a scientist or technician who does not know which sample he/she is processing (bar code, no information about former typing result). If a new PCR analysis of the stored biological material confirms the match, fresh material is taken from the alleged suspect and analyzed in another laboratory. Only after a third confirmation of the PCR results in 13 of 14 STR systems, the match is communicated to the responsible authority. Noteworthy, German courts generally consider five or six STRs to be sufficiently strong evidence of identity [14–18].

Gender determination

Forensic investigations may take full advantage of bioinformatics resources. For examples, given a tissues specimen, the length of the chromosomal ends and mitochondrial numbers may tell us about the age process while testing the amelogenin marker tells us information of the gender. Amelogenin is a matrix protein which comprises 90% of all the proteins in the tooth enamel. It regulates the initiation and growth of hydroxyapatite crystals during the mineralization of enamel and is involved in the development of cementum by directing cells that form cementum to the root surface of teeth. Using primers specific for intron 1 of the amelogenin gene, the X chromosome gives a 106 base pairs amplification product and the Y chromosome a 112 base pairs amplicon [19]. Therefore, samples from male sources (XY) will show two bands on an agarose gel, while females (XX) will show only one band. A region in the exon 6 is a hot spot of mutations, particularly amino acid insertions or deletions, in all mammals. In this region, numerous triplet repeats (PXQ) have been inserted recently and independently in five mammal lineages, while most of the hydrophobic exon 6 region probably had its origin in several rounds of triplet insertions, early in vertebrate evolution [14]. These differences may allow to use amelogenin in animal identification.

Using plant, bacteria, pollen and other bioinformatics data

Whenever crime scene investigation needs identification of bacteria, insects, and plants, genomic sequences can be resequenced using microarray [15–18, 20, 21] and analyzed using bioinformatics standard techniques. For example, practice in forensic entomology allows to determine postmortem intervals by analyzing the developmental status of certain hexapod species on corpses and has a role in toxicological analysis. The use of phylogenetic inference may lead to more precise taxonomic identification of the species, providing geographical information. Similarly, pollen and spores analysis, i.e. palynology, may provide information of a particular place and a certain time frame. Feline, canine and white-tailed deer DNA evidence has been presented in court, and follows the procedures for human DNA forensics.

Linkage disequilibrium and haplotyping

In order to avoid pitfalls in the inference process of the forensic evidences, it is important to discuss the patterns of occurrences of mutations in the human genome. There are strong statistical associations between polymorphisms in the human genome, such that the presence of a particular variant at one site on a chromosome can predict or ‘tag’ the presence of a particular variant at another site. Linkage disequilibrium (LD), is the nonrandom pattern of association between alleles at different loci within a population. An association in inheritance between characters means that the parental character combinations appear among the progeny more often than the nonparental. The closer two or more markers are on a chromosome the greater the probability that they will be inherited together [22–24].

LD is generally low near telomeres, elevated near centromeres and correlated with chromosome length, particularly high in few regions, termed recombination hotspots which are enriched of retrotransposon-like elements. LD is low in regions containing genes involved in immune responses and neurophysiological processes, and high in regions containing genes involved in DNA and RNA metabolism, response to DNA damage and the cell cycle [13].

Variants that associate together are known as a ‘haplotypes’. Therefore, a haplotype is a set of closely linked genetic markers present on one chromosome which tend to be inherited together. Intuitively, haplotypes (which can be regarded as a collection of ordered markers) may be more powerful than individual, unorganized markers [23].

Haplotype patterns reflect the fact that all modern humans originated in Africa more than 150 000 years ago. Some of the descendents of this group remained in Africa, whereas others migrated, eventually reaching all parts of the world. DNA events such as mutations and recombinations, natural selection and random drift which have caused population expansions and bottlenecks, founder effects, have influenced (generated or eliminated) the haplotype patterns in populations in different parts of the world.

While the reference sequence constructed by the Human Genome Project is informative of the vast majority of bases that are invariant across individuals, the HapMap project (http://www.hapmap.org) [13] focuses on DNA sequence differences among individuals. The Hapmap project consisted of compiling data on groups of individuals representative of four populations for more than a million single nucleotide polymorphisms, or SNPs. If a similar project will be carried out for CNV, identification of risk factors for common human diseases will be helpful in treatment or prevention and forensic information on human population will be complete.

Software for haplotype scoring, selection, visualization

There is a large variety of software useful for haplotype analysis. Most of this software comes with example data sets and manuals so it is easy to try different programs and make comparison on the basis of the specific needs and data sets. We describe a list of software relevant to haplotyping and linkage disequilibrium analysis that we have found particularly useful in forensic bioinformatics [25–33].

Haploview (www.broad.mit.edu/mpg/haploview) is designed to simplify and expedite the process of haplotype analysis by providing a common interface to several tasks relating to such analyses. Haploview currently supports the following functionalities. LD and haplotype block analysis, haplotype population frequency estimation, single SNP and haplotype association tests, permutation testing for association significance, implementation of Tagger (see subsequently), tag SNP selection algorithm. Haploview computes single locus and multimarker haplotype association tests, outputting the chi square and P-value for the allele frequencies in cases versus control. For family trios, all probands (affected individual with genotyped parents) are used to compute transmission disequilibrium test (TDT) values. Haploview can only interpret biallelic markers—markers with greater than two alleles (e.g. microsatellites) will not work correctly.

Haplofreq (http://www.cs.princeton.edu/haplofreq/) estimates the haplotype frequencies over a short genomic region given the genotypes with missing data. Haplofreq's approach incorporates a maximum likelihood model based on a simple random generative model which assumes that the genotypes are independently sampled from the population.

Haplofreq accepts as an input a set of gentoypes of the same length, and produces the haplotype distribution in the population, estimated from these haplotypes. The input format contains a genotype in each line. A genotype is described by a string of A,G,C,T,H and ?, where A,G,C,T correspond to homozygous sites, H corresponds to heterozygous site and ? corresponds to missing data. Example:

AAGACCTT

GGAAHHHH

HHH???TT

GGGGTACC

Tagger (www.broad.mit.edu/mpg/tagger/) is a tool for the selection and evaluation of tag SNPs from genotype data such as that from the International HapMap Project. It combines the simplicity of pair-wise tagging methods with the efficiency benefits of multimarker haplotype approaches. As input, users can upload genotype data in raw HapMap format or standard ‘pedigree’ linkage format. Alternatively, users can specify chromosomal landmarks to indicate genomic regions of interest within which tag SNPs are to be picked. This feature will be particularly useful for multiplex tag SNP design of candidate genes. Tagger has been implemented in the stand-alone program Haploview [25] (see above).

QTDT (http://bioinformatics.well.ox.ac.uk/project-ld.shtml) Linkage and association variance components analysis of quantitative traits.

GOLD (http://bioinformatics.well.ox.ac.uk/project-ld.shtml) Graphical Display of Linkage Disequilibrium: color-coding of LD-coefficient matrices. Distribution also includes some handy programs for calculation of LD coefficients [31].

GRR (http://bioinformatics.well.ox.ac.uk/GRR) Graphical Representation of Relationship errors. Simple representations of observed allele sharing in families to highlight erroneous coding of relationships amongst members [32].

SNPtagger (http://www.well.ox.ac.uk/~xiayi/haplotype) selective definition of haplotype tag SNPs: web-tool for picking minimal sets of nonredundant markers to capture information in input haplotypes.

PLINK (http://pngu.mgh.harvard.edu/~purcell/plink/haplo.shtml) Whole genome association analysis toolset.

PHASE (http://www.stat.washington.edu/stephens/software.html) software for haplotype reconstruction, and recombination rate estimation from population data.

Snphap (http://www-gene.cimr.cam.ac.uk/clayton/software/snphap.txt) program for estimating frequencies of haplotypes of large numbers of diallelic markers from unphased genotype data from unrelated subjects.

Arlequin (http://anthro.unige.ch/arlequin/software/) Multipurpose population genetics software implementing a large set of methods such as AMOVA using microsatellite data and dominant markers (RAPDs, AFLPs) [33].

Several ‘R’ functions (http://lib.stat.cmu.edu/R/CRAN/) are used for likelihood inference of trait associations with haplotypes and other covariates in generalized linear models. The functions accommodate uncertain haplotype phase and can handle missing genotypes at some SNPs. They need R release ≥2.0.1, and the following libraries: stats and survival.

Hapassoc: Likelihood inference of trait associations with SNP

Haplo.ccs: Estimates haplotype and covariate relative risks in case-control data by weighted logistic regression. Diplotype probabilities, which are estimated by the Expectation Maximization algorithm with progressive insertion of loci, are utilized as weights.

Haplo.stats: Statistical Analysis of Haplotypes with Traits and Covariates. It is a suite of routines for the analysis of indirectly measured haplotypes. The genetic markers are assumed to be codominant and it is possible to refer to the measurements of genetic markers as genotypes. The main functions in Haplo Stats are: haplo.em, haplo.glm and haplo.score.

Hapsim: Package for haplotype data simulation. Haplotypes are generated such that their allele frequencies and linkage disequilibrium coefficients match those estimated from an input data set.

Other reposititories with interesting software and data sets are http://www.nslij-genetics.org/soft/,

http://linkage.rockefeller.edu/soft/,

http://www.animalgenome.org/soft,

www.hapmap.org/download/encode1.html,

http://www.broad.mit.edu/tools/data.html.

DATABASES

One of the most contentious issues in forensic use of DNA evidence is how to estimate the probability that two DNA profiles match by chance. In order to determine the probability that a particular genotype might occur at random in a population, extensive population data must be gathered to make an estimate of the frequency of each possible allele and genotype. A sample size much larger than 100 samples is needed to make reliable projections about a genotype's frequency in a large population [34]. Given allele frequencies, DNA profiles are screened automatically for matches between profiles of person to person(s), person to scene(s) and scene to scene(s). Although the most used set of STR loci are spread on all the chromosomes (some chromosomes have just one), they have different mutation rate.

It is noteworthy to describe the information contained in the STR database (see for instance http://www.cstl.nist.gov/biotech/strbase/index.htm and http://www.str-base.org/index.php): the use of STRs; facts and sequence information on each STR system, descriptions of annotated sequence, population data, commonly used multiplex STR systems, their chromosomal locations, mutation rates for common loci, PCR primers and conditions, description of various technologies for analysis of STR alleles, addresses of scientists and organizations working in the field and a comprehensive reference listing of material on STRs used for DNA typing purposes.

A range of organizations are currently involved in developing and promoting DNA databases across the European Union (EU). For example: the European DNA Profiling Group (EDNAP) has existed since 1988 with the aim of establishing systematic procedures for data-sharing across the European community; the Standardization of DNA Profiling in the EU (STADNAP) exists to promote co-operation across the EU in order to use DNA profiling to detect ‘mobile serial offenders’. The European Network of Forensic Science Institutes [35] (ENFSI, http://www.enfsi.org/) has similar ambitions to standardize forensic practices in support of policing across the whole of the EU. The EU itself provides funds (for example, to STADNAP) to ascertain best practices of data-sharing across criminal jurisdictions.

Differences exist amongst EU member and candidate states regarding the existence of a database (e.g. the Republic of Ireland and Portugal do not currently operate national databases); the conditions for including profiles (e.g. Belgium limits the inclusion of profiles to cases of serious offences against persons after a conviction is secured); profile removal (virtually all states, unlike the UK, remove profiles of the acquitted); the taking of samples with or without consent (e.g. France has strict rules for allowing DNA only to be taken with consent); and sample destruction (e.g. Germany specifies that all samples must be destroyed after profiling is completed, regardless of investigative and prosecutorial outcome).

In Italy collective efforts by large number of public (hospitals/universities/national criminal justice service) and private laboratories scattered around Italy brought to nine loci typed in about 2800 individuals and six loci typed in more than 1500 individuals [21] (see http://www.gefi-forensicdna.it.).

The ENFSI has undertaken an extensive study collecting STR-data from 24 European populations using the AMPFLSTR SGM Plus system, which has become one of the standard STR multiplexes to be used within Europe for the purpose of constructing national DNA criminal intelligence databases. This allele proportion (frequency) database—further referred to as the ‘ENFSI DNA WG STR Population Database’—can be used to calculate match probabilities of DNA profiles from cosmopolitan Caucasian populations across all Europe, regardless of their specific country of origin.

Other examples are the ENFSI DNA WG STR Population Database (http://www.str-base.org/index.php) which uses 5699 samples from 24 European populations with SGM Plus kit loci. Noteworthy the Canadian Random Match Calculator for Profiler Plus and COfiler kits (http://www.csfs.ca/pplus/profiler.htm).

MASS DISASTER IDENTIFICATION

Pair-wise comparisons of DNA profiles in mass fatality incidents will require the bioinformatics capability to search (all-against-all) large STR and SNP data sets [36–38]. This task is performed using at least two different searching algorithms for autosomal markers: (i) looking for a perfect match: number of loci at which both alleles were found to match, a number which is expected to be equal to the number of loci analyzed among fragments of the same body or between a victim and a direct reference; (ii) allele sharing by kinship: number of loci at which at least one allele was found to match, a number which is expected to be equal to the number of loci analyzed for parent–child relationships.

The software should also have the capability to rank the significance of the DNA match. Specific software are Mass Disaster Kinship Analysis Program (MDKAP) and Mass Fatality Identification System (M-FYSis) which were widely used in the WTC disaster. They can align profiles derived from the remains to a reduced number of consensus profiles, assemble the overlapping partial profiles and compute the likelihood ratios for each pair-wise comparison at various relationships, such as parent–child, sibling or half-sibling; apart from STR data; they also manage mtDNA and SNP data.

STATISTICAL INFERENCE: ‘BEYOND THE REASONABLE DOUBT’

A key concept in forensic statistics is the Hardy–Weinberg equilibrium (HWE) [39,40] which assesses that in a large, randomly mating population, in which the evolutionary forces such as selection, migration and mutation are not acting, allele and genotypic frequencies do not change. Given a locus with two alleles, A, B, with frequencies pA, pB = 1 − pA, and genotypes AA, AB, BB with genotypic frequencies PAA, PAB, PBB, the relationship is PAA = p2, pAB = 2pA(1 − pA), and PBB = (1 − pA)2, holds and the locus is said to be in HWE. One generation of random mating is sufficient to produce HWE. Classical statistical tests such as goodness-of-fit, exact Fisher, likelihood ratio and z-tests can be applied to HWE analysis [41–44]. The deviation from the HW is generally tested with the Pearson's chi-squared test, using the observed genotype frequencies obtained from the data and the expected genotype frequencies obtained using the HWE. Simulations show that one of the most powerful tests for HWE is the exact test, particularly when the number of alleles is large.

When there is a large number of alleles, this may result in data with many empty possible genotypes and low genotype counts, because there are often not enough individuals present in the sample to adequately represent all genotype classes. If this is the case, the asymptotic assumption of the chi-square distribution, will no longer hold, and it may be necessary to use Fisher's exact test.

In the common practice, if a bin contains very few bands, the FBI merges that bin into an adjacent bin of higher frequency. This merging of bins is believed to yield a more conservative estimate of the probability of a random match.

For each CODIS marker we can determine the genotype and the frequency of the different alleles; the probability (P) for a DNA profile is usually computed as the product of the probability for each individual locus, i.e. the profile probability = (P1) (P2) … (Pn). This is called the Product rule technique. This probability estimate is based on the assumption that the individual alleles are independent of each other, which is usually not the case, as discussed in the previous sections. If the probabilities of the individual alleles are not independent (i.e. if certain alleles are often associated), multiplying the individual allele frequencies may underestimate or overestimate the true probability of matching alleles in the chosen population and thereby mis-state the incriminating value of the evidence. Critics of the product rule technique contend that in some ethnic subpopulations the alleles identified by commonly used genetic probes are so extreme that the use of a broad-based comparison of populations is inappropriate. There is therefore a strong need to use large databases with information on ethnic haplotype frequencies. Current practice for estimating the probability of a genotype given the defendant's genotype is to use equations accounting for population substructure. See recommendation 4.2 from the National Research Council's 1996 report ‘The Evaluation of Forensic DNA Evidence’ (NRC Press, 1996).

Bayesian networks: a revolution?

All sort of heterogeneous information representing evidences in forensic science can be incorporated into BNs [45–49

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