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      Forensic use of Y-chromosome DNA: a general overview

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      Human Genetics
      Springer Berlin Heidelberg

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          Abstract

          The male-specific part of the human Y chromosome is widely used in forensic DNA analysis, particularly in cases where standard autosomal DNA profiling is not informative. A Y-chromosomal gene fragment is applied for inferring the biological sex of a crime scene trace donor. Haplotypes composed of Y-chromosomal short tandem repeat polymorphisms (Y-STRs) are used to characterise paternal lineages of unknown male trace donors, especially suitable when males and females have contributed to the same trace, such as in sexual assault cases. Y-STR haplotyping applied in crime scene investigation can (i) exclude male suspects from involvement in crime, (ii) identify the paternal lineage of male perpetrators, (iii) highlight multiple male contributors to a trace, and (iv) provide investigative leads for finding unknown male perpetrators. Y-STR haplotype analysis is employed in paternity disputes of male offspring and other types of paternal kinship testing, including historical cases, as well as in special cases of missing person and disaster victim identification involving men. Y-chromosome polymorphisms are applied for inferring the paternal bio-geographic ancestry of unknown trace donors or missing persons, in cases where autosomal DNA profiling is uninformative. In this overview, all different forensic applications of Y-chromosome DNA are described. To illustrate the necessity of forensic Y-chromosome analysis, the investigation of a prominent murder case is described, which initiated two changes in national forensic DNA legislation both covering Y-chromosome use, and was finally solved via an innovative Y-STR dragnet involving thousands of volunteers after 14 years. Finally, expectations for the future of forensic Y-chromosome DNA analysis are discussed.

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          Human Y Chromosome Base-Substitution Mutation Rate Measured by Direct Sequencing in a Deep-Rooting Pedigree

          Summary Understanding the key process of human mutation is important for many aspects of medical genetics and human evolution. In the past, estimates of mutation rates have generally been inferred from phenotypic observations or comparisons of homologous sequences among closely related species [1–3]. Here, we apply new sequencing technology to measure directly one mutation rate, that of base substitutions on the human Y chromosome. The Y chromosomes of two individuals separated by 13 generations were flow sorted and sequenced by Illumina (Solexa) paired-end sequencing to an average depth of 11× or 20×, respectively [4]. Candidate mutations were further examined by capillary sequencing in cell-line and blood DNA from the donors and additional family members. Twelve mutations were confirmed in ∼10.15 Mb; eight of these had occurred in vitro and four in vivo. The latter could be placed in different positions on the pedigree and led to a mutation-rate measurement of 3.0 × 10−8 mutations/nucleotide/generation (95% CI: 8.9 × 10−9–7.0 × 10−8), consistent with estimates of 2.3 × 10−8–6.3 × 10−8 mutations/nucleotide/generation for the same Y-chromosomal region from published human-chimpanzee comparisons [5] depending on the generation and split times assumed.
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            A Predominantly Neolithic Origin for European Paternal Lineages

            Introduction Events underlying the distribution of genetic diversity among modern European populations have been the subject of intense debate since the first genetic data became available [1]. Anatomically modern humans, originating in East Africa, colonized Europe from the Near East ∼40 thousand years ago (KYA), then during the last glacial maximum populations retreated into the peninsulas of Iberia, Italy, and the Balkans, followed by northward recolonization from these refugia ∼14 KYA. The most important cultural transition was the adoption of agriculture originating in the Fertile Crescent in the Near East at the start of the Neolithic, ∼10 KYA [2]. It spread rapidly westwards via Anatolia [3] (Figure 1A), reaching Ireland by 6 KYA, accompanied by the development of sedentary populations and demographic expansion. Debate has focused on whether this spread was due to the movement and expansion of Near-Eastern farmers (demic diffusion), or to the transmission of cultural innovation to existing populations (acculturation), who then themselves expanded. 10.1371/journal.pbio.1000285.g001 Figure 1 Maps showing dates of the spread of early farming in Europe, and the frequency and microsatellite variance of haplogroup R1b1b2. (A) Isochron map representing dates of early Neolithic sites in Europe, based on data of Pinhasi et al. (2005) [3]. KYBP, thousand years before present. (B) Geographical distribution of haplogroup frequency of hgR1b1b2, shown as an interpolated spatial frequency surface. Filled circles indicate populations for which microsatellite data and TMRCA estimates are available. Unfilled circles indicate populations included to illustrate R1b1b2 frequency only. Population codes are defined in Table 1. (C) Geographical distribution of mean microsatellite variance within hgR1b1b2, shown as an interpolated spatial frequency surface. Samples shown are those used for the calculation of variance only. The observation of southeast–northwest frequency clines for “classical” genetic markers [1],[4], autosomal DNA markers [5],[6], and Y-chromosomal markers [7],[8] (though not for mitochondrial DNA [mtDNA] [9]) has been used to support the demic diffusion model. No dates can be automatically attached to these clines, however, and some [1], detected by principal component analysis, may simply reflect isolation by distance [10]. The direction of movement underlying a cline can also be ambiguous: the high-frequency pole could indicate the area of preexisting substrate least affected by a migration originating far away, or the final destination of a wave of migration into thinly populated territory, where expansion and drift have had their greatest effects [11]. The origins of a frequency cline of a lineage can be illuminated by analysing the diversity within it. For Y-chromosomal lineages defined by binary markers (haplogroups), this can be done using multiple microsatellites. This approach has been applied to haplogroups E, J [12], and I [13] within Europe, but the major western European lineage has not yet been focused upon. The frequency of the major western European lineage, haplogroup (hg) R1b1b2, follows a cline from 12% in Eastern Turkey to 85% in Ireland (Figure 1B), and is currently carried by some 110 million European men. Previous studies of lineages approximately equivalent to hgR1b1b2 [7],[8] suggested that it has a Paleolithic origin, based simply on its high frequency in the west. Here, in contrast, we show that the geographical distribution of diversity within the haplogroup is best explained by its spread from a single source from the Near East via Anatolia during the Neolithic. Taken together with the evidence on the origins of many other European haplogroups, this indicates that the great majority of the Y chromosomes of Europeans have their origins in the Neolithic expansion. Results To investigate the origins of hgR1b1b2, we assembled a dataset of 840 chromosomes from this haplogroup with associated nine-locus microsatellite haplotypes (Table 1; Table S1). The diversity of the lineage within each population (measured by mean microsatellite variance) should reflect its age: under a hypothesis of recolonization from southern refugia, we expect a gradient of diversity correlating with latitude, whereas Neolithic expansion from Anatolia predicts a correlation primarily with longitude. Figure 1C shows the geographical distribution of mean microsatellite variance, and Figure 2 shows that although there is no evidence for correlation with latitude (R 2 = 0.06; p = 0.268), the correlation with longitude is significant (R 2 = 0.358; p = 0.004), with greatest diversity in the east (strongly influenced by highly diverse samples within Turkey), thus providing support for the Neolithic colonization hypothesis. 10.1371/journal.pbio.1000285.g002 Figure 2 Relationship of diversity among 840 R1b1b2 chromosomes with (A) longitude and (B) latitude. Population codes are defined in Table 1. 10.1371/journal.pbio.1000285.t001 Table 1 Frequency of haplogroup R1b1b2 in European populations, with geographical coordinates for sampled populations. Country Area of Sampling (Population) Abbreviation Included in All Analyses? Longitude West Latitude North N a % R1b1b2b Source Bosnia-Herzegovina National BO 17.650 43.850 256 3.9 [40] Denmark National DK Y 9.654 54.513 56 42.9 Present study England Cornwall EN1 Y −4.955 50.442 64 78.1 Present study England Leicestershire EN2 Y −1.130 52.637 43 62.0 Present study France Basques FR1 Y −1.305 43.384 61 75.4 Present study France Baie de Somme FR2 Y 1.603 50.237 43 62.8 Present study France Finistère FR3 Y −4.264 48.233 75 76.0 Present study France Haute-Garonne FR4 Y 1.443 43.604 57 78.9 Present study France Ile et Vilaine FR5 −1.605 48.170 82 80.5 Present study France Loire-Atlantique FR6 −1.741 47.348 48 77.1 Present study France Vendée FR7 Y −1.469 46.676 50 68.0 Present study Germany Bavaria GE1 Y 11.319 48.985 80 32.3 Present study Germany National GE Y 10.451 51.165 1215 38.9 [41] c Greece National GR 21.824 39.074 171 13.5 [42] Italy North-East (Ladin) IT1 Y 11.552 46.528 79 60.8 Present study Italy North-West IT2 Y 7.912 44.875 99 45.0 Present study Ireland National IR Y −8.244 53.413 796 85.4 [43] Italy Sardinia IT3 8.948 39.991 930 17.0 [44] Netherlands National NL Y 5.417 52.246 84 42.0 Present study Poland National PL 19.145 51.919 913 11.6 [41] c Portugal South PO −8.176 37.750 78 46.2 Present study Russia Belgorod RU1 36.480 50.780 143 2.8 [45] Russia Ostrov RU2 28.320 57.350 75 2.7 [45] Russia Pristen RU3 36.710 51.230 45 2.2 [45] Russia Repievka RU4 38.650 51.080 96 5.2 [45] Russia Roslavl RU5 32.870 53.950 107 11.2 [45] Spain Andalucia East SP1 −3.209 37.513 95 72.0 Present study Spain Andalucia West SP2 Y −5.17 36.34 72 55.0 Present study Spain Basques SP3 −2.430 42.580 116 87.1 Present study Spain Catalonia SP4 Y 2.460 41.560 80 81.3 Present study Spain Castilla La Mancha SP5 Y −3.15 39.41 63 72.0 Present study Serbia National SB 20.759 44.178 100 10.0 Present study Spain Galicia SP6 Y −8.150 42.510 88 58.0 Present study Slovenia National SL 15.366 45.609 70 20.6 Present study Turkey Central TK1 Y 34.036 38.942 152 19.1 [14] Turkey East TK2 Y 40.110 38.921 208 12.0 [14] Turkey West TK3 Y 28.570 39.243 163 13.5 [14] Wales National WA −3.793 52.170 65 92.3 Present study a Figures in bold indicate samples typed for M269 in this study. b The number of men currently carrying hgR1b1b2 chromosomes (see Introduction) was approximated from these proportions and population census sizes given at http://www.populationdata.net/europe.php. c Chromosomes considered to belong to hgR1*(xR1a1). Y, yes. The two hypotheses also make different predictions for the number of sources of diversity within hgR1b1b2: under the postglacial recolonization model, we expect multiple sources, whereas under the Neolithic expansion model, we expect only one. We can test this by examining the phylogenetic relationships among microsatellite haplotypes. A reduced median network of 859 haplotypes (Figure 3) shows a simple star-like structure indicative of expansion from one source: 74 haplotypes (8.6%) lie in its central node, and this node plus its single-step mutational neighbours together comprise 214 haplotypes (24.9%). Haplotypes belonging to populations from all three refugia are present in the core of the network. This pattern seems incompatible with recolonization from differentiated refugial populations, and in terms of the history of hgR1b1b2, the refugia possess no special status. The core of the network also contains haplotypes from Turkey (Anatolia), which is compatible with a subpopulation from this region acting as a source for the westwards-expanding lineage. 10.1371/journal.pbio.1000285.g003 Figure 3 Reduced median network of microsatellite haplotypes within haplogroup R1b1b2. Molecular relationships between the nine-locus microsatellite haplotypes of 849 hgR1b1b2 chromosomes, including seven Serbian and two Greek haplotypes not included in the other analyses because population sample sizes were too small. Circles represent haplotypes, with area proportional to frequency and coloured according to population. Lines between circles represent microsatellite mutational steps. Does the time to the most recent common ancestor (TMRCA) of the hgR1b1b2 chromosomes support a Paleolithic origin? Mean estimates for individual populations vary (Table 2), but the oldest value is in Central Turkey (7,989 y [95% confidence interval (CI): 5,661–11,014]), and the youngest in Cornwall (5,460 y [3,764–7,777]). The mean estimate for the entire dataset is 6,512 y (95% CI: 4,577–9,063 years), with a growth rate of 1.95% (1.02%–3.30%). Thus, we see clear evidence of rapid expansion, which cannot have begun before the Neolithic period. 10.1371/journal.pbio.1000285.t002 Table 2 Estimates of TMRCA for individual populations, arranged from west to east. Population TMRCA/y (mean [95% CI]) Ireland 5,533 (4,094–7,391) Spain–Galicia 6,584 (4,923–8,684) Spain–Andalucia West 6,208 (4,476–8,463) England–Cornwall 5,460 (3,764–7,777) France–Finistère 6,432 (4,786–8,571) Spain–Castilla La Mancha 6,706 (4,772–9,261) France–Vendée 6,787 (4,575–9,853) France–Basques 5,797 (4,133–8,065) England–Leicestershire 5,981 (4,051–8,439) France–Haute–Garonne 5,925 (4,296–8,114) France–Baie de Somme 7,384 (5,259–10,131) Spain–Catalonia 5,800 (4,410–7,544) Netherlands 6,952 (5,051–9,410) Italy–North-West 5,944 (3,718–8,842) Denmark 6,555 (4,391–9,386) Germany–National 6,138 (4,627–7,997) Germany–Bavaria 7,282 (5,059–10,139) Italy–North-East (Ladin) 6,995 (4,635–10,396) Turkey–West 7,304 (5,022–10,359) Turkey–Central 7,989 (5,661–11,014) Turkey–East 7,000 (4,423–10,490) The similarity between the isochron map of Neolithic sites (Figure 1A; [3]) and those of hgR1b1b2 frequency (Figure 1B) and diversity (Figure 1C) is striking. Further support for the association of the expansion of hgR1b1b2 with that of farming comes from a statistical comparison of the variables. The frequency of hgR1b1b2 at different points in Europe is significantly negatively correlated (R 2 = 0.390; p = 0.0005) with the dates of local Neolithic sites (Figure 4A). For the local variance of the microsatellite haplotypes within hgR1b1b2, the correlation with Neolithic dates is significantly positive (R 2 = 0.331; p = 0.0124; Figure 4B). 10.1371/journal.pbio.1000285.g004 Figure 4 Correlation of dates of Neolithic sites with hgR1b1b2 (A) frequency and (B) variance. Population codes are defined in Table 1. YBP, years before present. Discussion Previous observations of the east–west clinal distribution of the common Western European hgR1b1b2 (or its equivalent) [7],[8] considered it to be part of a Paleolithic substrate into which farmers from the Near East had diffused. Later analyses have also considered variance, and have conformed to the Paleolithic explanation [14],[15]. Here, we concur that the cline results from demic diffusion, but our evidence supports a different interpretation: that R1b1b2 was carried as a rapidly expanding lineage from the Near East via Anatolia to the western fringe of Europe during the Neolithic. Such mutations arising at the front of a wave of expansion have a high probability of surviving and being propagated, and can reach high frequencies far from their source [11]. Successive founder effects at the edge of the expansion wave can lead to a reduction in microsatellite diversity, even as the lineage increases in frequency. The innovations in the Near East also spread along the southern shore of the Mediterranean, reflected in the expansion of hgE1b1b1b (E-M81) [16], which increases in frequency and reduces in diversity from east to west. In sub-Saharan Africa, hgE1b1a (E-M2) underwent a massive expansion associated with the Bantu expansion [17],[18]. In India, the spread of agriculture has been associated with the introduction of several Y lineages [19], and in Japan, lineages within hgO spread with the Yayoi migration [20], which brought wet rice agriculture to the archipelago. On a more recent timescale, the expansion of the Han culture in China has been linked to demic diffusion [21]. In this context, the apparently low contribution of incoming Y chromosomes to the European Neolithic, despite its antiquity and impact, has appeared anomalous. Our interpretation of the history of hgR1b1b2 now makes Europe a prime example of how expansion of a Y-chromosomal lineage tends to accompany technological and cultural change. Other lineages also show evidence of European Neolithic expansion, hgE1b1b1 (E-M35) and hgJ, in particular [12]. Indeed, hgI is the only major lineage for which a Paleolithic origin is generally accepted, but it comprises only 18% of European Y chromosomes [13]. The Basques contain only 8%–20% of this lineage, but 75%–87% hgR1b1b2 (Table S1); our findings therefore challenge their traditional “Mesolithic relict” status, and in particular, their use as a proxy for a Paleolithic parental population in admixture modelling of European Y-chromosomal prehistory [22]. Is the predominance of Neolithic-expansion lineages among Y chromosomes reflected in other parts of the genome? Mitochondrial DNA diversity certainly presents a different picture: no east–west cline is discernible, most lineages have a Paleolithic TMRCA [23], and hgH [24] and hgV [25] show signatures of postglacial expansion from the Iberian peninsula. Demic diffusion involves both females and males, but the disparity between mtDNA and Y-chromosomal patterns could arise from an increased and transmitted reproductive success for male farmers compared to indigenous hunter-gatherers, without a corresponding difference between females from the two groups. This would lead to the expansion of incoming Y lineages—as suggested by the high growth rate observed for hgR1b1b2. Similar conclusions have been reached for the Bantu expansion (in which the current Bantu-speaking populations carry many mtDNA lineages originating from hunter-gatherers [26]), the introduction of agriculture to India [19] and the Han expansion [21]. Some studies have found evidence of east–west clines for autosomal loci [6],[27]. By contrast, recent genome-wide SNP typing surveys [28]–[30] find a basic south–north division or gradient, including greater diversity in the south, but they provide no indication of the time-depth of the underlying events, which could in principle involve contributions from the original colonization, postglacial Paleolithic recolonization, Neolithic expansion, and later contact between Africa and southern Europe [31]. The distinction between the geographical patterns of variation of the Y chromosome and those of mtDNA suggest sex-specific factors in patterning European diversity, but the rest of the genome has yet to reveal definitive information. Detailed studies of X-chromosomal and autosomal haplotypes promise to further illuminate the roles of males and females in prehistory. Materials and Methods Ethics Statement Males were recruited with informed consent, following ethical approval by the Leicestershire Research Ethics Committee and the ethics committees of the Universities of Ferrara, Pavia, and Exeter and Plymouth. DNA Samples and Haplotyping A total of 2,574 DNA samples from European males, assigned to populations based on two generations of residence, were typed for the SNP M269 [17], defining hgR1b1b2. Following PCR amplification using the primers 5′-CTAAAGATCAGAGTATCTCCCTTTG-3′ and 5′-ATTTCTAGGAGTTCACTGTATTAC-3′, the T to C transition was analysed by digestion with BstNI, which cleaves M269-C-allele chromosomes only. Samples from the Iberian peninsula were typed using the SNaPshot (ABI) procedure [31]. Haplotype data were obtained for up to 20 Y-specific microsatellites [32],[33]. Data from the Ysearch database (http://www.ysearch.org) for Germany (GE) and Ireland (IR) were added, together with published data for Turkey, subdivided into East, West, and Central subpopulations based on published sampling information [14]. To avoid a bias from very large samples of hgR1b1b2 (GE and IR), these were randomly subsampled to give sample sizes of 75. This allowed a comparison of nine-locus haplotypes (DYS19, DYS388, DYS389I, DYS389II, DYS390, DYS391, DYS392, DYS393, and DYS439) for 849 hgR1b1b2 chromosomes, subdivided into 23 populations. Greek and Serbian samples were too small for population-based analyses, but were included in Network analysis. Analysis Neolithic dates, frequencies of hgR1b1b2, and local microsatellite variances were displayed using Surfer 8.02 (Golden Software) by the gridding method. Latitudes and longitudes were based on sampling centres. Intrahaplogroup diversity was assessed for populations with hgR1b1b2 sample size ≥15 as the mean of the individual microsatellite variances [34], as has been done elsewhere (e.g., [35]); this measure is highly correlated (R 2 = 0.871; p = 6.72×10−10) with a more conventional measure, average squared distance (ASD) [36]. Regression analyses were carried out in the R statistical package [37] to compare these two measures, and also to compare mean of variance with latitude and longitude. A reduced median network [38] of microsatellite haplotypes was constructed using Network 4.5 and Network Publisher, using weighting based on the inverse of the microsatellite variances. TMRCA and population growth rates were estimated using BATWING [39], under a model of exponential population growth and splitting. Whereas standard use of BATWING assumes a random sample from a population, we validated its use to analyse single haplogroups. Justification of this, together with other details, is given in Text S1. To assess the correlation between the dates of Neolithic sites and the local hgR1b1b2 frequency and variance, we considered 765 sites and their associated calibrated radiocarbon dates [3]. We identified sites lying within a buffer-zone of 150-km radius around each location for which we had frequency or variance data (Figure 1B and 1C). When more than one site was identified in a given buffer-zone, we considered the mean of the dates. Regression analyses were carried out as described above. Supporting Information Table S1 Haplotype data. Population abbreviations are as in Table 1; for each microsatellite (DYS19–DYS439), repeat unit numbers are given. (0.14 MB PDF) Click here for additional data file. Text S1 Details of application of BATWING. (0.14 MB DOC) Click here for additional data file.
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              The Y-Chromosome Tree Bursts into Leaf: 13,000 High-Confidence SNPs Covering the Majority of Known Clades

              Many studies of human populations have used the male-specific region of the Y chromosome (MSY) as a marker, but MSY sequence variants have traditionally been subject to ascertainment bias. Also, dating of haplogroups has relied on Y-specific short tandem repeats (STRs), involving problems of mutation rate choice, and possible long-term mutation saturation. Next-generation sequencing can ascertain single nucleotide polymorphisms (SNPs) in an unbiased way, leading to phylogenies in which branch-lengths are proportional to time, and allowing the times-to-most-recent-common-ancestor (TMRCAs) of nodes to be estimated directly. Here we describe the sequencing of 3.7 Mb of MSY in each of 448 human males at a mean coverage of 51×, yielding 13,261 high-confidence SNPs, 65.9% of which are previously unreported. The resulting phylogeny covers the majority of the known clades, provides date estimates of nodes, and constitutes a robust evolutionary framework for analyzing the history of other classes of mutation. Different clades within the tree show subtle but significant differences in branch lengths to the root. We also apply a set of 23 Y-STRs to the same samples, allowing SNP- and STR-based diversity and TMRCA estimates to be systematically compared. Ongoing purifying selection is suggested by our analysis of the phylogenetic distribution of nonsynonymous variants in 15 MSY single-copy genes.
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                Author and article information

                Contributors
                +31 10 7038073 , m.kayser@erasmusmc.nl
                Journal
                Hum Genet
                Hum. Genet
                Human Genetics
                Springer Berlin Heidelberg (Berlin/Heidelberg )
                0340-6717
                1432-1203
                17 March 2017
                17 March 2017
                2017
                : 136
                : 5
                : 621-635
                Affiliations
                ISNI 000000040459992X, GRID grid.5645.2, Department of Genetic Identification, , Erasmus MC University Medical Center Rotterdam, ; PO Box 2040, 3000 CA Rotterdam, The Netherlands
                Article
                1776
                10.1007/s00439-017-1776-9
                5418305
                28315050
                26459f9c-c268-4cfe-acb1-ec5bbd10d993
                © The Author(s) 2017

                Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License ( http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.

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                : 5 February 2017
                : 8 March 2017
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                Genetics
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