Skip to main content
PMC home page
Wiley Open Access Collection logoLink to Wiley Open Access Collection
. 2014 Jun 10;35(8):1021–1032. doi: 10.1002/humu.22599

Toward Male Individualization with Rapidly Mutating Y-Chromosomal Short Tandem Repeats

Kaye N Ballantyne 1,2,‡, Arwin Ralf 1,‡, Rachid Aboukhalid 3, Niaz M Achakzai 4, Maria J Anjos 5, Qasim Ayub 6, Jože Balažic 7, Jack Ballantyne 8,9, David J Ballard 10, Burkhard Berger 11, Cecilia Bobillo 12, Mehdi Bouabdellah 3, Helen Burri 13, Tomas Capal 14, Stefano Caratti 15, Jorge Cárdenas 16, François Cartault 17, Elizeu F Carvalho 18, Monica Carvalho 5, Baowen Cheng 19, Michael D Coble 20, David Comas 21, Daniel Corach 12, Maria E D'Amato 22, Sean Davison 22, Peter de Knijff 23, Maria Corazon A De Ungria 24, Ronny Decorte 25, Tadeusz Dobosz 26, Berit M Dupuy 27, Samir Elmrghni 28, Mateusz Gliwiński 29, Sara C Gomes 30, Laurens Grol 31, Cordula Haas 13, Erin Hanson 9, Jürgen Henke 32, Lotte Henke 32, Fabiola Herrera-Rodríguez 33, Carolyn R Hill 20, Gunilla Holmlund 34,35, Katsuya Honda 36, Uta-Dorothee Immel 37, Shota Inokuchi 38, Mark A Jobling 39, Mahmoud Kaddura 28, Jong S Kim 40, Soon H Kim 41, Wook Kim 42, Turi E King 39, Eva Klausriegler 43, Daniel Kling 27, Lejla Kovačević 44, Leda Kovatsi 45, Paweł Krajewski 46, Sergey Kravchenko 47, Maarten H D Larmuseau 25, Eun Young Lee 48, Ruediger Lessig 37, Ludmila A Livshits 47, Damir Marjanović 44, Marek Minarik 49, Natsuko Mizuno 38, Helena Moreira 50, Niels Morling 51, Meeta Mukherjee 52, Patrick Munier 17, Javaregowda Nagaraju 53,†, Franz Neuhuber 43, Shengjie Nie 54, Premlaphat Nilasitsataporn 55, Takeki Nishi 36, Hye H Oh 40, Jill Olofsson 51, Valerio Onofri 56, Jukka U Palo 57, Horolma Pamjav 58, Walther Parson 11,59, Michal Petlach 49, Christopher Phillips 16, Rafal Ploski 60, Samayamantri P R Prasad 53, Dragan Primorac 59,61,62, Gludhug A Purnomo 63, Josephine Purps 64, Hector Rangel-Villalobos 65, Krzysztof Rębała 29, Budsaba Rerkamnuaychoke 66, Danel Rey Gonzalez 16, Carlo Robino 15, Lutz Roewer 64, Alexandra Rosa 30,67, Antti Sajantila 57,68, Andrea Sala 12, Jazelyn M Salvador 24, Paula Sanz 21, Cornelia Schmitt 69, Anil K Sharma 52, Dayse A Silva 18, Kyoung-Jin Shin 48, Titia Sijen 31, Miriam Sirker 69, Daniela Siváková 70, Vedrana Škaro 71, Carlos Solano-Matamoros 72, Luis Souto 50, Vlastimil Stenzl 14, Herawati Sudoyo 63, Denise Syndercombe-Court 10, Adriano Tagliabracci 56, Duncan Taylor 73, Andreas Tillmar 34,35, Iosif S Tsybovsky 74, Chris Tyler-Smith 6, Kristiaan J van der Gaag 23, Daniel Vanek 75,76, Antónia Völgyi 58, Denise Ward 77, Patricia Willemse 23, Eric PH Yap 78, Rita YY Yong 78, Irena Zupanič Pajnič 7, Manfred Kayser 1,*
PMCID: PMC4145662  PMID: 24917567

Abstract

Relevant for various areas of human genetics, Y-chromosomal short tandem repeats (Y-STRs) are commonly used for testing close paternal relationships among individuals and populations, and for male lineage identification. However, even the widely used 17-loci Yfiler set cannot resolve individuals and populations completely. Here, 52 centers generated quality-controlled data of 13 rapidly mutating (RM) Y-STRs in 14,644 related and unrelated males from 111 worldwide populations. Strikingly, >99% of the 12,272 unrelated males were completely individualized. Haplotype diversity was extremely high (global: 0.9999985, regional: 0.99836–0.9999988). Haplotype sharing between populations was almost absent except for six (0.05%) of the 12,156 haplotypes. Haplotype sharing within populations was generally rare (0.8% nonunique haplotypes), significantly lower in urban (0.9%) than rural (2.1%) and highest in endogamous groups (14.3%). Analysis of molecular variance revealed 99.98% of variation within populations, 0.018% among populations within groups, and 0.002% among groups. Of the 2,372 newly and 156 previously typed male relative pairs, 29% were differentiated including 27% of the 2,378 father–son pairs. Relative to Yfiler, haplotype diversity was increased in 86% of the populations tested and overall male relative differentiation was raised by 23.5%. Our study demonstrates the value of RM Y-STRs in identifying and separating unrelated and related males and provides a reference database.

Keywords: Y-chromosome, Y-STRs, haplotypes, RM Y-STRs, paternal lineage, forensic

Introduction

Genetic characterization of male individuals and populations by means of Y-chromosome DNA polymorphisms is relevant in various fundamental and applied areas of human genetics such as in evolutionary genetics and population history, for example, for modeling global and regional human evolution, mapping migration patterns across the globe, and tracking cultural and demographic factors such as patrilocality, extrapair paternity, endogamy, and polygyny. Further, Y-chromosome DNA analysis is important in genetic genealogy and for community genetic purposes such as personal ancestry identification, as well as for the identification of male lineages and inferring paternal genetic ancestry for judicial and investigative purposes [Kayser et al., 1997; Underhill et al., 2000; Hammer et al., 2001; Oota et al., 2001; Jobling and Tyler-Smith, 2003; Roewer et al., 2005; Shi et al., 2010]. Similarities at Y-chromosome DNA markers are usually interpreted as indicating shared paternal ancestry of individuals and populations, whereas differences are used to conclude the absence of close paternal relationships. Such interpretations, however, depend in part on the underlying mutation rates of the Y-DNA markers used. Slowly evolving Y-chromosomal single-nucleotide polymorphisms (Y-SNPs), with an average mutation rate of about 3 × 10−8 per nucleotide per generation [Xue et al., 2009; Poznik et al., 2013], are especially suitable for studying distant relationships between male individuals and populations [Underhill et al., 2000; van Oven et al., 2014]. More quickly evolving Y-chromosomal short tandem repeat (Y-STRs) polymorphisms, also referred to as Y-microsatellites, with an average mutation rate of about 10−3 per locus per generation [Goedbloed et al., 2009; Ballantyne et al., 2010], have proven useful for testing short to medium timescale paternal relationships [Kayser et al., 2003; Kayser et al., 2005; Roewer et al., 2005; Coble et al., 2009; van Oven et al., 2011] such as enabling male lineages to be connected through common paternal ancestry [Coble et al., 2009], for the same lineages be separated and individualized within that shared ancestral lineage [King and Jobling, 2009], and for the origins of entire population groups be elucidated [Parkin et al., 2007; Rębała et al., 2007]. Forensic usage of Y-STRs has largely focused on identifying paternal lineages using a core set of markers, linking suspects, and crime samples for investigation purposes [Kayser et al., 1997; Roewer, 2009].

Haplotypes generated from conventional Y-STRs, such as the widely used 17 markers included in the commercially available AmpFlSTR® Yfiler® PCR Amplification Kit (Life Technologies, San Francisco, CA) (subsequently referred to as Yfiler), suffer from two main limitations: (1) their inability to conclusively resolve some male lineages due to identical haplotypes arising in individuals that are not of common descent because of recurrent mutation, and (2) their inability to differentiate between paternally related males due to the moderately low mutation rate of the loci tested. In general, Y-STRs with much higher mutation rates than those of the conventionally used loci are expected to overcome or at least reduce both limitations. In a previous comprehensive Y-STR mutation rate study where nearly 200 Y-STRs were investigated in almost 2,000 father–son pairs confirmed by autosomal DNA analysis [Ballantyne et al., 2010], 13 Y-STRs with exceptionally high (>10−2 per locus per generation) mutation rates were identified and termed rapidly mutating (RM) Y-STRs. Furthermore, in this previous study and a subsequent study [Ballantyne et al., 2012], theoretical and the first empirical evidence were provided to show that this set of 13 RM Y-STRs is able to achieve an order of magnitude higher male relative differentiation than is available with the commonly used Yfiler set, as well as to drastically improve male lineage differentiation over Yfiler.

However, to fully explore the potential for the RM Y-STR set in differentiating unrelated as well as related males for various purposes, much more data are needed. Therefore, the International RM Y-STR Study Group, a worldwide collaboration between 52 laboratories, was formed. Group members, chosen based on pre-existing practical experience in Y-STR analysis, genotyped under quality-controlled conditions the 13 RM Y-STRs in 14,644 males including 12,272 unrelated males from 111 worldwide populations and 4,744 closely related males. To compare the RM Y-STR set with conventional Y-STRs, Yfiler data were gathered in a subset of 7,784 unrelated men from 65 worldwide populations as well as in most of the male relatives analyzed for RM Y-STRs. The large collection of RM Y-STR haplotypes presented and explored in the present study enables a better understanding of their value to differentiate related and unrelated male individuals and populations, and additionally provides a suitable reference database for future use of RM Y-STRs in forensic, genealogical, anthropological, and population genetic studies.

Materials and Methods

DNA Samples

With a worldwide coverage in mind, contributing laboratories were identified and recruited based on their previous submission of Y-STR population data to the Y-chromosome Haplotype Reference Database (YHRD; http://www.yhrd.org), hence having proven experience in Y-STR analysis. Each of the 52 contributing laboratories within the International RM Y-STR Study Group genotyped a selection of their own in-house population sample sets, each consisting of seven to 634 individuals (median 100 individuals per population sample) across 111 defined population samples per laboratory. Population sets were to include only unrelated males. Three population samples were notable exceptions—the Biaka Pygmy group and the Bhutanese Lhokpu and Mönpa language groups. These populations, and particular sample sets used here, are known to contain high numbers of male relatives (Biaka), or were specifically selected as having extremely low resolution with Yfiler (Bhutan). Because these samples were ascertained differently to the other 108 populations within the study, they were excluded from the continental groups during analysis to avoid bias. The Aboriginal Australian samples used here were also not ascertained completely randomly, as they were selected based on Y-SNP haplogroups known to be authentic for Aboriginal Australians to avoid admixture effects as described elsewhere [Taylor et al. 2012].

A subset of 7,784 individuals from 65 populations were additionally genotyped with the AmpFlSTR Yfiler PCR Amplification Kit (Life Technologies) for the most commonly used panel of 17 Y-STRs. Relative pairs were also analyzed, with 2,339 newly genotyped father–son pairs, 30 brother pairs, and three uncle–nephew pairs, for which relationship had previously been confirmed by autosomal DNA analysis; this dataset of male relatives was supplemented by 156 previously published relative pairs [Ballantyne et al., 2012].

Y-STR genotyping

The organizing laboratory (Department of Forensic Molecular Biology, Erasmus MC University Medical Centre Rotterdam) provided genotyping protocols, allelic ladders, and tools for allele calling to all participating laboratories, and organized a quality control exercise prior to population data generation (for details see below). The 13 single- or multicopy RM Y-STR markers (DYF387S1, DYF399S1, DYF403S1, DYF404S1, DYS449, DYS518, DYS526, DYS547, DYS570, DYS576, DYS612, DYS626, and DYS627) were amplified in three multiplex PCRs, as described in Supp. Tables S1 and S2. PCR-amplified products were separated and detected using participating laboratories’ standard protocols for analyzing STRs—in either ABI310, ABI3100, ABI3130, ABI3500, or ABI3730 Genetic Analyzers with POP-4, POP-6, or POP-7, and size standards ILS-600, ILS-500, or Genescan 500-LIZ. Allele calling was performed with GeneMapper (Life Technologies) or GeneMarker (SoftGenetics, State College, PA) utilizing custom panel and bin sets. The participating laboratories used their own primers and other consumables (except allelic ladders as provided by the host).

The commercially available AmpFlSTR® Yfiler® PCR Amplification Kit (Life Technologies) targeting 17 conventional Y-STRs was used and allele calling was performed according to the manufacturer's instructions.

Genotyping Quality Control

To ensure genotyping consistency between the laboratories, all participants received allelic ladders prepared by the organizing laboratory, and six blind control DNA samples previously genotyped by the organizing laboratory. Genotyping of population samples was only allowed after a participating laboratory demonstrated the correct genotyping of these six blind control DNA samples at all 13 RM Y-STRs. If a participant reported erroneous genotypes, screenshots were requested and submitted to the organizing laboratory for an evaluation of the possible cause of error(s). Once the cause of error was identified, three additional blind control DNA samples were provided and genotyped by the participant. If these samples were typed correctly, the laboratory was allowed to type their population samples. In cases of unexpected results such as previously unknown alleles or microvariants, screenshots were requested and submitted to the organizing laboratory for inspection. Independent genotyping, and in some case DNA sequencing, was performed to resolve differences in genotyping of certain samples. Samples with missing data from more than one marker were excluded from data analysis to prevent low quality samples affecting genotype and haplotype distributions. Further, any differences observed between relative pairs were confirmed through duplicate, independent PCR amplifications, and genotyping, and in some cases, DNA sequencing.

Additional quality control was performed for the multicopy markers DYF403S1a+b and DYF399S1, whereby a subset of population sample electropherograms for these markers were sent to the organizing laboratory by all participants for blind confirmatory genotype scoring.

RM Y-STR Nomenclature

The nomenclature of the RM Y-STRs was updated to comply with the guidelines of the International Society of Forensic Genetics—ISFG [Gusmão et al., 2006], and to incorporate new variation observed in repeat structures. As such, all data collated were translated to comply with the updated nomenclature. Supp. Table S3 shows the repeat structure and allele designations used during the project, determined in collaboration with Life Technologies and L. Gusmao (IPATIMUP, Porto, Portugal). The RM Y-STR nomenclature used in the present study is in agreement with that used in the Powerplex® Y23 kit (Promega, Madison, WI) and in the Yfiler Plus Y-STR kit (Life Technologies) for the RM Y-STR markers included, respectively.

Data Analysis

Haplotype diversities, average number of differing loci between pairs of individuals, pairwise FST genetic distances, and analysis of molecular variance (AMOVA) were calculated with Arlecore v3.5.1.3 [Excoffier and Lischer, 2010]. Theta (θ) values were calculated as per the reference Weir and Hill (2002). Molecular relationships between samples were analyzed using Network v4.6.1.1., applying the median-joining method. Weighting was applied as described elsewhere [Qamar et al., 2002], although based on updated mutation rates from Ballantyne et al. (2010) and http://www.yhrd.org. Only single-copy markers were utilized in network construction, due to the inability to assign multicopy genotypes to individual loci. Testing of statistical significance (t-tests) was performed in SPSS v17.0. Theoretical estimates for the rates of relative differentiation for each Y-STR panel were calculated using the formula P(k < 0) = 1 − P(k = 0) = 1 − e−Nm [Ballantyne et al., 2010], where N represents the number of markers and m represents the average mutation rate of the set of markers obtained from the sampling from the posterior distribution.

Results and Discussion

Global Diversity of RM Y-STR Haplotypes and Male Lineage Differentiation

The RM Y-STR set applied here generated exceptionally high haplotype diversity in the worldwide sample set analyzed with 12,156 unique (i.e., not matching any other individual in this dataset) haplotypes observed in 12,272 unrelated samples from 111 worldwide populations (Table 1). The global haplotype diversity estimate was 0.9999985 (sampling variance = 2.85 × 10−8). Of the 102 haplotypes that were nonunique within this dataset (i.e., matched other individuals in this dataset), 90 were shared between two males, 10 between three males, and two between four males. All nonunique haplotypes were shared between males of the same geographic region, and all but six were from the same sampling population. The six nonunique haplotypes shared between different populations involved males from Argentina (Chubut and Rio Negro regions), Greenland (from two separate samples of Inuit), Czech Republic (from two sample sets), Cologne and Warsaw, and Bhutan (the Lhokpu and Mönpa tribes), respectively. As a result, the proportion of haplotypes shared between populations was exceptionally low at 0.05%, and the proportion of haplotypes shared within populations was slightly higher at 0. 8%.

Table 1.

RM Y-STR Haplotype Characteristics in a Global Set of 12,272 Individuals from 111 Populations Summarized for Regional Groups

Group Individuals Populations Haplotypes Haplotype diversity θ Average number of differing loci
Sub-Saharan Africa 303 5 300 0.99993 0.00006 16.5
North Africa 452 4 445 0.99992 0.00010 16.2
Middle East 100 1 100 1.0 0 15.26
Central Asia 86 1 80 0.99836 0.0012 16.18
South Asia 661 8 644 0.99992 0.00009 17.5
East Asia 967 7 964 0.999994 0.000006 17.3
South East Asia 634 6 630 0.99998 0.00002 16.6
Aboriginal Australian 100 1 96 0.99919 0.00069 16.8
Native American 365 10 357 0.99986 0.00015 17.4
Admixed Native American 764 12 758 0.99998 0.00002 17.5
European 5,618 38 5599 0.9999988 0.000001 17.4
Migrant Sub-Saharan Africa 663 5 659 0.99998 0.000004 16.3
Migrant European 731 3 730 0.999996 0.000004 17.0
Migrant Asian 649 7 645 0.99998 0.00002 16.7
Bhutana 78 2 56 0.99434 0.00794 13.9
Biaka Pygmya 101 1 94 0.99822 0.00226 17.5
Global 12,272 111 12,156 0.9999985 0.00000238 16.5
Open in a new tab
a

Bhutan and Biaka Pygmies were not assigned to a larger geographic group as the individuals sampled were ascertained differently (Bhutan), or are known to have different population characteristics to all other study populations (Biaka, see Materials and Methods and DNA Samples).

Within each regional geographic group (see Table 1), similar high levels of haplotype diversity were obtained ranging from 0.9999988 and 0.999996 in Europeans and Migrant Europeans (i.e., European populations sampled in North America and Australia), respectively, down to 0.99836 in Central Asians (although the sample size of the latter group was much lower than that of all other regional groups studied) (Table 1). The average number of differing loci was highest with 17.5 loci in Admixed Native Americans and South Asians, respectively, as well as with 17.4 loci in Native Americans and Europeans, respectively, and was lowest with 15.26 loci in Middle Easterners (Table 1). As the maximal possible number is 21, these results illustrate how strikingly different the haplotypes were within each of the regional groups.

At the population level (Supp. Table S4), similar high levels of haplotype diversity were observed across all populations tested. Of the 111 populations, 67 (60%) displayed haplotype diversities of 1.0, meaning that every individual tested per population (sample size ranging from seven to 467 in the various populations) had a different haplotype. The remaining populations had haplotype diversities ranging from 0.972 (Wichi, Salta Province, Argentina) to 0.999994 (Cologne, population 2).

Due to its highly multicopy nature, DYF403S1a+b caused concerns with some study group members regarding genotyping accuracy, so that additional quality control was performed for this marker (as well as for DYF399S1), as described in Materials and Methods section. There are four separate loci at the tetranucleotide DYF403S1a+b, three of which overlap in size. Differences in repeat sequences between the copies lead to the presence of partial (0.1, 0.2, and 0.3) alleles, which can challenge interpretation even in single source samples due to a lack of single base resolution with POP-4 during capillary electrophoresis. To determine the effect of removing this potentially problematic marker from the RM Y-STR panel, the entire dataset was additionally analyzed without DYF403S1a+b. The global haplotype diversity slightly decreased from 0.9999985 with the full 13-loci RM Y-STR set to 0.9999981 with the 12-loci set. This translated to an increase in the number of nonunique haplotypes from 116 to 152 in the 12,272 samples—a decrease of zero to five haplotypes per regional population, with an average of 0.32 fewer haplotypes within each of the 111 populations. Populations most affected by the omission of DYF403S1a+b were Aboriginal Australians (from 96 to 92 haplotypes among the 100 men tested), and Bhutan (from 56 to 51 haplotypes among the 78 men tested). However, the vast majority of the populations tested (90 of 111, 81.1%) were not affected by the removal of DYF403S1a+b, with the haplotype diversities remaining the same. Therefore, and because of the extra care in quality control employed for this marker, all following data analyses were performed based on the complete 13-loci RM Y-STR set.

Genetic–Geographic Population Substructure with RM Y-STRs

The θ values for all regional groups and for almost all populations were exceptionally low (Table 1; Supp. Table S4), indicating relatively little population substructure detected with the RM Y-STR set. Indeed, AMOVA based on FST values (the inability to accurately assign the alleles of the multicopy markers to specific loci prevented RST values being calculated) demonstrated that 99.98% of haplotype variation was within populations, extremely high for Y-chromosome markers [Willuweit and Roewer, 2007], whereas 0.02% was among populations within the same regional group, and only 0.002% among regional groups. The global FST was as low at 0.00017. Between the regional groups, the average pairwise FST value was only 0.000127, with a maximum value of 0.00058 observed between Aboriginal Australians and Middle Eastern populations. Even when considering individual populations, the highest pairwise FST value observed was only 0.02815 between the Bhutan Lhokpu and Argentinian Wichi from Salta Province (data not shown). Across all population comparisons, an average pairwise FST value of 0.000826 was obtained.

To illustrate the magnitude of haplotypic differences between populations and geographic areas, multidimensional scaling (MDS) analysis was performed on Slatkin's linearized FST values (Fig. 1). The majority of the populations formed a loose cluster, with moderate dispersion across both dimensions. While there were several outlier populations (for example Pakistani Punjabis, Japanese Gunma, and Angola Kimbundos), overall there was no geographic pattern emerging, in line with the AMOVA results. It is notable that populations from the same geographic group did not necessarily cluster together (Fig. 1). The differences in population sample size (Supp. Table S4) did not greatly affect the dispersion of populations—similar distributions were observed when the population sizes were restricted to 100/population (inset, Fig. 1), or 25/population (data not shown). This analysis, together with the AMOVA results, illustrates that no genetic–geographic structuring is detected with this set of RM Y-STR haplotypes, since their high mutation rates have likely removed signals of shared population history, and has driven the high number of unique haplotypes. The low between-population differentiation and lack of substructure could be expected as a result of the high within-population diversities, and the effect of mutation rates on the estimation of θ [Meirmans and Hedrick, 2011]. As a practical consequence, no population substructure correction needs to be applied when using this RM Y-STR set in forensic or other applications, as is usually needed for other DNA marker systems. However, the lack of substructure correction required does not necessary infer a lack of structure in the distribution of haplotypes across geographic regions. Nonrandom distributions, caused by shared population histories and common origins, would infer the need to generate and utilize regional or meta-population-specific databases for frequency estimation. As such, the ability for RM Y-STRs to differentiate between male lineage within and between different populations and the relative distribution of haplotypes between regional groups was investigated.

Figure 1.

Figure 1

Open in a new tab

Two-dimensional plot of MDS analysis of Slatkin's linearized FST values for RM Y-STR haplotypes in a global sample of 12,272 individuals from 111 populations (stress 0.07462). Smaller inset MDS shows the effect of equalized sample size (N = 100 individuals or fewer per population, stress = 0.02416).

Value of RM Y-STRs in Improving Male Lineage Differentiation

When considering the utility of the RM Y-STR set for future applications in forensic, genealogical or anthropological genetic studies, it is informative to compare its properties to those of conventional Y-STRs, such as the 17 loci included in the Yfiler kit. Hence, RM YSTR and Yfiler haplotypes were compiled for 7,784 individuals across 65 populations as a subset of the global dataset presented here. In every diversity measure applied, the 13 RM YSTR set provided enhanced estimates relative to the 17-loci Yfiler set. Global haplotype diversity was increased from 0.99995 with Yfiler to 0.999997 with RM Y-STRs; the total number of haplotypes detected was increased from 6,975 to 7,714, and the number of singletons from 6,469 to 7,647. All regional groups showed more haplotypes and higher diversity estimates with RM Y-STRs relative to Yfiler, that is, haplotype diversity increased on average by 0.00226 and the number of singletons on average by 77 (largest increase of 1.56-fold in Aboriginal Australians) (Table 2). At the population level, there were increases in the number of haplotypes and in the haplotype diversity estimates for 56 (86.2%) of the 65 populations with the RM Y-STR set compared with Yfiler, whereas for six (9.2%) populations the same number of haplotypes were obtained because all males were already fully individualized with Yfiler (often in small sample-sized populations), and for three populations (4.6%), one haplotype fewer was detected with the RM Y-STRs than with Yfiler (Supp. Table S5).

Table 2.

Comparison of RM Y-STR and Yfiler Haplotype Characteristics in a Global Set of 7,784 Individuals from 65 Populations Summarized for Regional Groups

Group Individuals Populations Yfiler haplotypes RM Y-STR haplotypes Yfiler haplotype diversity RM Y-STR haplotype diversity Yfiler average percentage of differing loci RM Y-STR average percentage of differing loci
North Africa 193 2 173 189 0.99792 0.99973 59% 73%
Central Asia 83 1 67 77 0.99060 0.99824 63% 77%
South Asia 497 6 450 483 0.99951 0.99988 68% 83%
East Asia 633 5 580 630 0.99952 0.99999 64% 82%
South East Asia 200 2 175 198 0.99759 0.99990 66% 81%
Aboriginal Australian 100 1 74 96 0.99152 0.99919 68% 80%
Native American 279 8 233 275 0.99761 0.99988 61% 82%
Admixed Native American 458 6 444 454 0.99986 0.99996 67% 81%
European 4,041 25 3,696 4,025 0.99991 0.999998 66% 82%
Migrant Sub-Saharan Africa 442 3 407 439 0.99909 0.99997 63% 81%
Migrant European 552 3 541 551 0.99993 0.99999 63% 81%
Migrant Asian 205 2 176 203 0.99737 0.99990 66% 83%
Biaka Pygmies 101 1 83 94 0.99505 0.99822 65% 83%
Global 7,784 65 6,975 7,714 0.99995 0.999997 65% 81%
Open in a new tab

There was a significant difference between the RM Y-STR and the Yfiler sets in the level of haplotype sharing between individuals within regional groups, between populations, and within populations (Fig. 2). At the global level, 506 Yfiler haplotypes were shared between 1,318 individuals, compared with only 66 RM Y-STR haplotypes shared between 70 individuals. For Yfiler at the population level, only three (4.6%) of the 65 populations did not display any matching individuals either within or outside populations; 58 (89.2%) and 52 (80.0%), respectively, showed within and outside population haplotype matches. Within- and outside-population haplotype matches for Yfiler were as high as 62%, for the Wichis—a Native American group from Argentina (although sample size was N = 13). Notably, the level of outside-population matches compared with within-population matches was markedly higher for most populations for Yfiler haplotypes, whereas strikingly reduced for RM Y-STRs (Fig. 2). Of the 65 populations, 35 (53.8%) did not show any haplotype matches within and outside populations. Only 29 (44.6%) of the 65 populations displayed haplotype matches within populations, with the highest proportion (11%) observed in Argentinian Wichi from the Salta province. For 49 of the 58 populations that showed within-population Yfiler haplotype matches, no RM Y-STR haplotype matches were observed. The reduction in haplotype matches for RM Y-STRs relative to Yfiler is even more striking when considering outside-population matches. Only four (6.2%) populations displayed outside-population RM Y-STR matches, namely, two Czech population samples (one haplotype), and Cologne and Warsaw (one haplotype). The finding that 61 populations (93.8%) showed no outside population haplotype matches for RM Y-STRs contrasts strongly with those found with Yfiler, for which the corresponding number is 13 (20%).

Figure 2.

Figure 2

Open in a new tab

Proportion of individuals with haplotypes shared within populations (right) and between populations (left) for Yfiler (light blue bars) and the RM Y-STR set (dark red bars).

Figure 3 provides an overview of the population pairs that showed between population haplotype matches for Yfiler (Fig. 3A, blue lines) and RM Y-STRs (Fig. 3B, blue lines). The significant reduction in between-population haplotype sharing (t84 = 8.091, P = 2.23 × 10−11) demonstrates the power of the RM Y-STR panel in male lineage differentiation. For RM Y-STRs, no haplotype sharing was observed between different populations sampled from the same countries, such as the five Indian, two Japanese, three Korean, three Austrian, two Belgian, three German, two Italian, two Polish, three US American, and two Hungarian populations, except for the two Czech populations. Further, almost no RM Y-STR haplotype sharing was observed between populations from the same geographic region such as the densely sampled continent of Europe except for Cologne and Warsaw. In contrast, many Yfiler haplotypes were shared between populations within countries, and between populations within regions such as Europe (see Fig. 3, insets).

Figure 3.

Figure 3

Open in a new tab

Geographic representation of pairwise between-population haplotype sharing. Blue lines connect population pairs showing shared haplotypes for A: Yfiler, and B: the RM Y-STRs set. Smaller insets show Europe enlarged.

The lack of RM Y-STR haplotype sharing between populations is not limited to the full 13-loci haplotype. Network analysis of 1,000 individuals, selected at random in the same population proportions as the full set of 7,784 samples and based on single-copy Y-STRs, displayed little geographic clustering with RM Y-STR haplotypes (Fig. 4B, perhaps with the exception of North Africans), whereas with Yfiler haplotypes for the same individuals (Fig. 4A), geographic clustering is seen with several groups. For RM Y-STRs, the network was constructed based on nine single-copy Y-STRs (excluding DYF387S1, DYF399S1, DYF403S1, and DYF404S1), and for Yfiler, based on 15 Y-STRs (excluding DYS385a/b). Unlike the Yfiler network, the RM Y-STR network provides almost no clustering of haplotypes according to geographic regions of sampling (except some of the North African haplotypes), which underlines the lack of population substructure as also seen in the MDS and AMOVA of the complete data. Hence, for RM Y-STRs, the need for regional (metapopulation) reference databases for haplotype frequency estimation in forensic and other applications is strongly reduced compared with conventional Y-STRs such as those in the Yfiler kit. Combining the RM Y-STR set and the Yfiler set to generate 30-marker Y-STR haplotypes resulted in the individualization of 25 additional men of the 137 not already individualized with the RM Y-STR set alone. While this does represent a slight improvement on the global scale, from 7,714 to 7,737 haplotypes, it is clear that the vast majority of differentiation was achieved by the RM Y-STR set alone.

Figure 4.

Figure 4

Open in a new tab

Weighted median-joining networks from single-copy Y-STRs for 1,000 individuals randomly selected from the total dataset, with regional geographic groups indicated by colors (see inset legend). A: Based on 15-loci Yfiler haplotypes (excluding DYS385a/b). B: Based on 10-loci RM Y-STR haplotypes (excluding DYF399S1, DYF403S1, and DYF404S1).

Ability of RM Y-STRs to Detect Hidden Paternal Relationships

The observation of RM Y-STR haplotype sharing within, but (almost) not between, populations might indicate that RM Y-STRs allow the detection of unknown (i.e., hidden) paternal relationships in populations. If true, more shared RM Y-STR haplotypes would be expected between putatively unrelated individuals sampled from rural areas, where male relatives often stay in the region (and thus can be sampled), and especially from endogamous populations, than among putatively unrelated individuals sampled from urban areas, where male relatives tend to not to stay (and thus cannot be sampled). To test this hypothesis, all populations were designated according to their urbanization status—each set of males was classified as either urban (genetically moderately outbred), rural (more likely to be genetically inbred), mixed (males from both rural and urban populations, in unspecified proportions), or endogamous (populations subscribing to the cultural practice of endogamy—restricting marriage outside of an individual's social class, ethnic group, or tribe). In support of the RM Y-STR set's ability to detect hidden paternal relationships, we indeed see significantly lower haplotype diversity and lower proportions of unique haplotypes in the endogamous group than in the rural group than in the urban group (diversity t2 = 7,960, P = 1.6 × 10−8, shared haplotypes t2 = 1.347, P = 0.310) (Table 3). Although the sample sizes differ considerably between the three categories, this does not affect the conclusions as simulations performed with equalized sample numbers (n = 558 per category) showed that the RM Y-STRs still result in decreased diversity in endogamous and rural populations compared with urban populations (data not shown; diversity t2 = 8936, P = 1.3 × 10−8, shared haplotypes t = 1.117, P = 0.380).

Table 3.

RM Y-STR Haplotype Characteristics in a Global Set of 12,272 Individuals from 111 Populations Summarized for Groups Based on Urbanization Status

Population classification Populations Individuals Haplotypes Singletons Haplotype diversity Proportion of nonunique haplotypes
Endogamous 7 558 512 478 0.999614 0.143
Rural 20 1,369 1,354 1,340 0.999982 0.021
Urban 51 7,198 7,164 7,131 0.999999 0.009
Mixed 26 2,379 2,361 2,342 0.999994 0.015
Unknown 7 770 767 764 0.999990 0.008
Open in a new tab

On the other hand, these data also demonstrate that even in populations with considerable hidden paternal relationships, such as in endogamous populations, the RM Y-STR set still allows the differentiation of a large number of men. A comparison between Yfiler and the RM Y-STR set for the available populations (Table 4) illustrates the improved value of RM Y-STRs relative to Yfiler in differentiating males when grouping populations into urban, rural, and endogamous. Most tellingly, even in the endogamous group, the haplotype diversity increased and the proportion of shared (i.e., nonunique) haplotypes decreased from 0.99947 and 0.101 with Yfiler to 0.99978 and 0.045 with the RM Y-STR set, respectively, in the rural group from 0.99916 and 0.157 to 0.99997 and 0.008, and even in the urban from 0.99994 and 0.08 to 0.999998 and 0.005.

Table 4.

Comparison of RM Y-STRs and Yfiler Haplotype Characteristics in a Global Set of 7,784 Individuals from 65 Populations Summarized for Groups Based on Urbanization Status

Population classification N Yfiler ht RM Y-STR ht Yfiler singletons RM Y-STR singletons Yfiler haplotype diversity RM Y-STR haplotype diversity Yfiler proportion of nonunique haplotypes RM Y-STR proportion of nonunique haplotypes Yfiler percentage average allelic difference RM Y-STR percentage average allelic difference
Endogamous 466 419 445 380 427 0.99947 0.99978 0.101 0.045 68% 83%
Rural 714 602 708 537 703 0.99916 0.99997 0.157 0.008 70% 85%
Urban 4,974 4,577 4,951 4,318 4,929 0.99994 0.999998 0.080 0.005 68% 83%
Mixed 1,422 1,332 1,407 1,267 1,392 0.99988 0.99999 0.063 0.011 67% 84%
Unknown 208 181 205 169 202 0.99684 0.99986 0.130 0.014 64% 83%
Open in a new tab

Value of RM Y-STRs for Male Relative Differentiation

In addition, we tested the value of RM Y-STRs for differentiating male relatives. On a theoretical level, the rate of relative differentiation per meiosis can be calculated using the mutation rates of each locus within a genotyping panel (see Materials and Methods section). For the Yfiler set, it is estimated that a mutation at one or more of the 17 Y-STR loci will occur with a probability of 0.047 (95% confidence interval 0.038–0.057) per meiosis, which for the RM Y-STR set is more than fourfold higher at 0.195 (95% CI 0.177–0.21). Notably, for the recently released PowerPlex Y-23 kit (Promega), which targets all 17 Yfiler markers together with two of the 13 RM Y-STR markers and four additional Y-STRs, this value is 0.092 (95% CI 0.077–0.107), nearly twofold higher than for Yfiler, but more than twofold lower than for the RM Y-STR set studied here.

To compare the theoretical expectation for the RM Y-STR and the Yfiler sets with empirical data, we genotyped 2,372 pairs of male relatives previously confirmed by autosomal DNA analysis (2,339 father–son pairs, 30 brother pairs, and three uncle–nephew pairs), confirmed the observed allelic differences by independent genotyping (and some by sequencing), and combined these new data with those from the 156 relative pairs separated by one to 20 paternal genetic transfers (or meioses) described previously [Ballantyne et al., 2012]. In this combined dataset, the RM Y-STR set allows differentiation by at least one allelic difference (i.e., mutation) in at least one locus in 742 (29%) of the total 2,528 pairs related by one to 20 generations, whereas Yfiler only allowed the separation of 118 (5.5%) of a subset of 2,161 of these relative pairs (Fig. 5). In particular, the RM Y-STR set differentiated fathers from their sons in 26.9% of the cases versus 4.5% with Yfiler, and brothers from each other in 56.3% of the cases versus 10.0% with Yfiler.

Figure 5.

Figure 5

Open in a new tab

Empirical male relative differentiation using 2,528 paternal relative pairs separated by one to 20 meioses for Yfiler (light blue bars) and the RM Y-STR set (dark red bars). The data combine the new 2,372 relative pairs from the current study with the previously used 156 relative pairs [Ballantyne et al., 2012]. Values above the bars indicate the absolute number of relative pairs the estimated differentiation rate is based upon. Error bars represent binomial confidence intervals.

It should be noted, however, that only for the 2,378 father–son pairs investigated here, was the sample size large enough to allow reliable conclusions, whereas for more distantly related males, future studies need to deliver more data. However, as the chance of mutations increases with each meiosis, it is expected that the more distantly related men are, the higher the chance that they will have different RM Y-STR haplotype; a trend that can be seen in our data (Fig. 5). The importance of sufficient sample size is illustrated in the following for father–son pairs. Our initial estimate of father–son differentiation was 70% based on only 20 pairs [Ballantyne et al., 2010], which was subsequently revised to 49% based on 39 pairs, including the initial 20 pairs [Ballantyne et al., 2012]. In the present study, we managed to increase the number of father–son pairs drastically to 2,378 pairs, now achieving a differentiation rate of 26.9% (95% confidence interval 25.1%–28.7%). Notably, the empirical rate achieved, now based on a large sample size, corresponds more closely to the theoretical estimate of 19.5%.

Removing DYF403S1a+b from the RM Y-STR panel resulted in the nondifferentiation of 84 relative pairs that could be differentiated when DYF403S1a+b was included; the overall relative differentiation rate dropped to 26%. However, the difference only occurred in pairs with fewer than five meioses separating the individuals—in all other pairs, there were sufficient mutations at other RM Y-STR loci to allow differentiation (data not shown).

As pointed out previously [Ballantyne et al. 2012], RM Y-STRs are not particularly useful for paternity testing or familial searching because of their high mutation rates and the resultant power of differentiation of male relatives. If, however, they are used for such purposes, correction factors would be needed to compensate for the likely observed mutations in the probability calculations. It has been suggested before that the presence of at least three mutations at any Y-STR loci (most commonly with the 17 Yfiler Y-STRs) is sufficient to exclude paternity [Kayser and Sajantila 2001; Gjertson et al., 2007]. In the current dataset however, 3.8% of the DNA-confirmed father–son pairs displayed three or four RM Y-STR mutations; as such, the number of mutations constituting exclusion should be increased when using the RM Y-STRs. As outlined elsewhere (Ballantyne et al. 2012), instead of an ad hoc cut-off, a dynamic threshold shall be used, estimated from the number of Y-STR loci genotyped and their locus-specific mutation rates. Because three mutations have also been observed occasionally among 17 Yfiler loci in DNA-confirmed father–son pairs [Goedbloed et al. 2009], this notion also applies to conventional Y-STR sets such as Yfiler, which due to their moderate mutation rates in principle are more appropriate for paternity and kinship testing than RM Y-STRs.

Conclusions

The large worldwide dataset compiled, presented, and analyzed here demonstrates the exceptional value of the RM Y-STR panel for differentiating male lineages on a global and regional scale. Many of the current limitations of Y-chromosome applications are reduced with this RM Y-STR set, providing increased utility and effectiveness to the genetic analysis of male populations and lineages. In particular, this 13 RM Y-STR set provides near-complete paternal lineage differentiation in general populations as well as in populations with otherwise reduced Y-chromosome diversity, due to peculiarities in population history or cultural practices. The effects of this near-complete male individualization will be of great benefit to numerous fields using the Y-chromosome genetics to investigate male lineages, such as in genealogical studies (e.g., to detect extrapair paternity or adventitious haplotype matches within different surnames/lineages), population genetic and genetic history studies (e.g., to assist in differentiating between lineages shared between populations with common history), and in forensic applications (e.g., to reduce the inclusion of innocent individuals in investigations due to adventitious haplotype matches). Moreover, the immense value of this RM Y-STR set to differentiate between both close and distant male relatives will have beneficial effects on these same fields, especially in forensic genetics, providing increased confidence that haplotype matches between unknown samples such as those from crime scenes and reference samples such as those from suspects are due to individual identity, rather than relatedness.

On the other hand, however, the extreme degree of RM Y-STR haplotype diversity highlights the limitations of the current approach for placing a statistical weight on Y-STR haplotype matches by using empirically derived haplotype frequency estimates obtained from Y-STR haplotype reference databases. Although already noted with the currently used Y-STR sets such as Yfiler, the problem that an observed haplotype is not present in the large and growing reference database, and how to deal with rare haplotypes in obtaining frequency estimates and estimating match probabilities for which no consensus has been reached yet [Krawczak, 2001; Brenner, 2010; Buckleton et al., 2011; Willuweit et al., 2011], will become drastically increased when using RM Y-STRs, as can be seen from the data presented here. Therefore, new statistical solutions shall be developed to estimate the weight of a RM Y-STR haplotype match with particular relevance for forensic applications.

To assist future studies utilizing RM Y-STRs, complete dataset of RM Y-STR and Yfiler haplotypes obtained from the 12,272 individuals analyzed here is made available via Supp. Table S6, with allele frequencies for individual populations, regional groups, and the complete dataset summarized in Supp. Table S7. Relative pair haplotypes for RM Y-STRs and Yfiler are available in Supp. Table S8.

Acknowledgments

The authors thank the following colleagues for their expert technical assistance: Linda S. H. Gan (Singapore) for the Singapore samples, Minttu Hedman (Helsinki) for the Finnish samples, Helena Nilsson (Linköping) for the Swedish samples, Kevin Cloete (Cape Town) for the South African samples, Nancy Vanderheyden (Leuven) for the Belgium samples, Baowen Cheng (Kunming) and Yanan Sun (Kunming) for the Kunming Chinese samples, Juliana Januzzi (Rio de Janeiro) for the Rio Brazil samples, Maria Lourdes D. Honrado (Quezon City) for the Filipino samples, and Harald Niederstätter (Innsbruck) for the Innsbruck Austrian samples as well as for his comments on the manuscript. Oscar Lao (Rotterdam) is acknowledged for statistical advice and help in some calculations. Certain commercial equipment, instruments, and materials are identified to specify experimental procedures as completely as possible. In no case does such identification imply a recommendation or endorsement by the National Institute of Standards and Technology (USA), nor does it imply that any of the materials, instruments, or equipment identified are necessarily the best available for the purpose.

Disclosure statement: KNB and MKay, together with two colleagues from Life Technologies, are coinventors on a registered patent about the RM Y-STRs used here and their forensic utility; however, no personal or institutional benefits were received. All other authors declare no conflict of interest.

Supporting Information

Disclaimer: Supplementary materials have been peer-reviewed but not copyedited.

Table S1. RM Y-STR Primer sequences and concentrations used in three multiplex PCRs

Table S2. Reaction composition and conditions for the three RM Y-STR multiplexes used

Table S3. RM Y-STR repeat structure, allelic designations and control 007 genotype

Table S4. RM Y-STR haplotype characteristics in a global set of 12,272 individuals from 111 populations summarized for population samples ordered according to regional groups

Table S5. Comparison of Yfiler and RM Y-STR haplotype characteristics in a global set of 7,784 individuals from 65 populations summarized for population samples ordered according to regional groups

humu0035-1021-SD1.pdf (414.7KB, pdf)

Table S6. RM Y-STR and Yfiler haplotypes obtained from the 12,272 individuals in 111 global populations

humu0035-1021-SD2.xlsx (2.1MB, xlsx)

Table S7. Allele frequencies and ranges for individual populations, regional groups, and the complete dataset for each RM Y-STR marker

humu0035-1021-SD3.xlsx (364.6KB, xlsx)

Table S8. RM Y-STR and Yfiler haplotypes for male relative pairs

humu0035-1021-SD4.xlsx (1.5MB, xlsx)

References

  1. Ballantyne KN, Goedbloed M, Fang R, Schaap O, Lao O, Wollstein A, Choi Y, van Duijn K, Vermeulen M, Brauer S, Decorte R, Poetsch M, et al. Mutability of Y-chromosomal microsatellites: rates, characteristics, molecular bases, and forensic implications. Am J Hum Genet. 2010;87:341–353. doi: 10.1016/j.ajhg.2010.08.006. [DOI] [PMC free article] [PubMed] [Google Scholar]
  2. Ballantyne KN, Keerl V, Wollstein A, Choi Y, Zuniga SB, Ralf A, Vermeulen M, de Knijff P, Kayser M. A new future of forensic Y-chromosome analysis: rapidly mutating YSTRs for differentiating male relatives and paternal lineages. Forensic Sci Int Genet. 2012;6:208–218. doi: 10.1016/j.fsigen.2011.04.017. [DOI] [PubMed] [Google Scholar]
  3. Brenner CH. Fundamental problem of forensic mathematics—the evidential value of a rare haplotype. Forensic Sci Int Genet. 2010;4:281–291. doi: 10.1016/j.fsigen.2009.10.013. [DOI] [PubMed] [Google Scholar]
  4. Buckleton JS, Krawczak M, Weir BS. The interpretation of lineage markers in forensic DNA testing. Forensic Sci Int Genet. 2011;5:78–83. doi: 10.1016/j.fsigen.2011.01.010. [DOI] [PMC free article] [PubMed] [Google Scholar]
  5. Coble MD, Loreille OM, Wadhams MJ, Edson SM, Maynard K, Meyer CE, Niederstatter H, Berger C, Berger B, Falsetti AB, Gill P, Parson W, et al. Mystery solved: the identification of the two missing Romanov children using DNA analysis. PLoS One. 2009;4:e4838. doi: 10.1371/journal.pone.0004838. [DOI] [PMC free article] [PubMed] [Google Scholar]
  6. Excoffier L, Lischer HEL. Arlequin suite ver 3.5: a new series of programs to perform population genetics analyses under Linux and Windows. Mol Ecol Resour. 2010;10:564–567. doi: 10.1111/j.1755-0998.2010.02847.x. [DOI] [PubMed] [Google Scholar]
  7. Gjertson DW, Brenner CH, Baur MP, Carracedo A, Guidet F, Luque JA, Lessig R, Mayr WR, Pascali VR, Prinz M, Schneider PM, Morling N. ISFG: recommendations on biostatistics in paternity testing. Forensic Sci Int Genet. 2007;1:223–231. doi: 10.1016/j.fsigen.2007.06.006. [DOI] [PubMed] [Google Scholar]
  8. Goedbloed M, Vermeulen M, Fang RN, Lembring M, Wollstein A, Ballantyne K, Lao O, Brauer S, Kruger C, Roewer L, Lessig R, Ploski R, et al. Comprehensive mutation analysis of 17 Y-chromosomal short tandem repeat polymorphisms included in the AmpFlSTR Yfiler PCR amplification kit. Int J Legal Med. 2009;123:471–482. doi: 10.1007/s00414-009-0342-y. [DOI] [PMC free article] [PubMed] [Google Scholar]
  9. Gusmão L, Butler JM, Carracedo A, Gill P, Kayser M, Mayr WR, Morling N, Prinz M, Roewer L, Tyler-Smith C, Schneider PM. DNA Commission of the International Society of Forensic Genetics (ISFG): an update of the recommendations on the use of YSTRs in forensic analysis. Int J Legal Med. 2006;120:191–200. doi: 10.1007/s00414-005-0026-1. [DOI] [PubMed] [Google Scholar]
  10. Hammer MF, Karafet TM, Redd AJ, Jarjanazi H, Santachiara-Benerecetti S, Soodyall H, Zegura SL. Hierarchical patterns of global human Y-chromosome diversity. Mol Biol Evol. 2001;18:1189–1203. doi: 10.1093/oxfordjournals.molbev.a003906. [DOI] [PubMed] [Google Scholar]
  11. Jobling MA, Tyler-Smith C. The human Y chromosome: an evolutionary marker comes of age. Nat Rev Genet. 2003;4:598–612. doi: 10.1038/nrg1124. [DOI] [PubMed] [Google Scholar]
  12. Kayser M, Caglia A, Corach D, Fretwell N, Gehrig C, Graziosi G, Heidorn F, Herrmann S, Herzog B, Hidding M, Honda K, Jobling M, et al. Evaluation of Ychromosomal STRs: a multicenter study. Int J Legal Med. 1997;110:125–133. doi: 10.1007/s004140050051. 141–129. [DOI] [PubMed] [Google Scholar]
  13. Kayser M, Sajantila A. Mutations at YSTR loci: implications for paternity testing and forensic analysis. Forensic Sci Int. 2001;118:116–121. doi: 10.1016/s0379-0738(00)00480-1. [DOI] [PubMed] [Google Scholar]
  14. Kayser M, Brauer S, Schadlich H, Prinz M, Batzer MA, Zimmerman PA, Boatin BA, Stoneking M. Y chromosome STR haplotypes and the genetic structure of US. populations of African, European, and Hispanic ancestry. Genome Res. 2003;13:624–634. doi: 10.1101/gr.463003. [DOI] [PMC free article] [PubMed] [Google Scholar]
  15. Kayser M, Lao O, Anslinger K, Augustin C, Bargel G, Edelmann J, Elias S, Heinrich M, Henke J, Henke L, Hohoff C, Illing A, et al. Significant genetic differentiation between Poland and Germany follows present-day political borders, as revealed by Y chromosome analysis. Human Genet. 2005;117:428–443. doi: 10.1007/s00439-005-1333-9. [DOI] [PubMed] [Google Scholar]
  16. King TE, Jobling M. What's in a name? Y chromosomes, surnames and the genetic genealogy revolution. Trends Genet. 2009;25:351–360. doi: 10.1016/j.tig.2009.06.003. [DOI] [PubMed] [Google Scholar]
  17. Krawczak M. Forensic evaluation of YSTR haplotype matches: a comment. Forensic Sci Int. 2001;118:114–115. doi: 10.1016/s0379-0738(00)00479-5. [DOI] [PubMed] [Google Scholar]
  18. Meirmans PG, Hedrick PW. Assessing population structure: FST and related measures. Mol Ecol Resour. 2011;11:5–18. doi: 10.1111/j.1755-0998.2010.02927.x. [DOI] [PubMed] [Google Scholar]
  19. Oota H, Settheetham-Ishida W, Tiwawech D, Ishida T, Stoneking M. Human mtDNA and Y-chromosome variation is correlated with matrilocal versus patrilocal residence. Nat Genet. 2001;29:20–21. doi: 10.1038/ng711. [DOI] [PubMed] [Google Scholar]
  20. Parkin EJ, Kraayenbrink T, Opgenort JRML, van Driem GL, Tuladhar NM, de Knijff P, Jobling MA. Diversity of 26-locus YSTR haplotypes in a Nepalese population sample: isolation and drift in the Himalayas. Forensic Sci Int. 2007;166:176–181. doi: 10.1016/j.forsciint.2006.05.007. [DOI] [PMC free article] [PubMed] [Google Scholar]
  21. Poznik GD, Henn BM, Yee MC, Sliwerska E, Euskirchen GM, Lin AA, Snyder M, Quintana-Murci L, Kidd JM, Underhill PA, Bustamante CD. Sequencing Y chromosomes resolves discrepancy in time to common ancestor of males versus females. Science. 2013;341:562–565. doi: 10.1126/science.1237619. [DOI] [PMC free article] [PubMed] [Google Scholar]
  22. Qamar R, Ayub Q, Mohyuddin A, Helgason A, Mazhar K, Mansoor A, Zerjal T, Tyler-Smith C, Mehdi SQ. Y-chromosomal DNA variation in Pakistan. Am J Hum Genet. 2002;70:1107–1124. doi: 10.1086/339929. [DOI] [PMC free article] [PubMed] [Google Scholar]
  23. Rębała K, Mikulich AI, Tsybovsky IS, Siváková D, Džupinková Z, Szczerkowska-Dobosz A, Szczerkowska Z. YSTR variation among Slavs: evidence for the Slavic homeland in the middle Dnieper basin. J Human Genet. 2007;52:406–414. doi: 10.1007/s10038-007-0125-6. [DOI] [PubMed] [Google Scholar]
  24. Roewer L, Croucher PJP, Willuweit S, Lu TT, Kayser M, Lessig R, De Knijff P, Jobling M, Tyler-Smith C, Krawczak M. Signature of recent historical events in the European Y-chromosomal STR haplotype distribution. Human Genet. 2005;116:279–291. doi: 10.1007/s00439-004-1201-z. [DOI] [PubMed] [Google Scholar]
  25. Roewer L. Y chromosome STR typing in crime casework. Forensic Sci Med Pathol. 2009;5:77–84. doi: 10.1007/s12024-009-9089-5. [DOI] [PubMed] [Google Scholar]
  26. Shi W, Ayub Q, Vermeulen M, Shao RG, Zuniga SB, Van der Gaag K, De Knijff P, Kayser M, Xue Y, Tyler-Smith C. A worldwide survey of human male demographic history based on YSNP and YSTR data from the HGDPCEPH populations. Mol Biol Evol. 2010;27:385–393. doi: 10.1093/molbev/msp243. [DOI] [PMC free article] [PubMed] [Google Scholar]
  27. Taylor D, Nagle N, Ballantyne KN, van Oorschot RA, Wilcox S, Henry J, Turakulov R, Mitchell RJ. An investigation of admixture in an Australian Aboriginal Y-chromosome STR database. Forensic Sci Int Genet. 2012;6:532–538. doi: 10.1016/j.fsigen.2012.01.001. [DOI] [PubMed] [Google Scholar]
  28. Underhill PA, Shen P, Lin AA, Jin L, Passarino G, Yang WH, Kauffman E, Bonne-Tamir B, Bertranpetit J, Francalacci P, Ibrahim M, Jenkins T, et al. Y chromosome sequence variation and the history of human populations. Nat Genet. 2000;26:358–361. doi: 10.1038/81685. [DOI] [PubMed] [Google Scholar]
  29. van Oven M, Hammerle JM, van Schoor M, Kushnick G, Pennekamp P, Zega I, Lao O, Brown L, Kennerknecht I, Kayser M. Unexpected island effects at an extreme: reduced Y chromosome and mitochondrial DNA diversity in Nias. Mol Biol Evol. 2011;28:1349–1361. doi: 10.1093/molbev/msq300. [DOI] [PubMed] [Google Scholar]
  30. van Oven M, van Geystelen A, Kayser M, Decorte R, Larmuseau MHD. Seeing the wood for the trees: a minimal reference phylogeny for the human Y chromosome. Hum Mutat. 2014;35:187–191. doi: 10.1002/humu.22468. [DOI] [PubMed] [Google Scholar]
  31. Weir BS, Hill WG. Estimating F-statistics. Annu Rev Genet. 2002;36:721–750. doi: 10.1146/annurev.genet.36.050802.093940. [DOI] [PubMed] [Google Scholar]
  32. Willuweit S, Caliebe A, Andersen MM, Roewer L. YSTR frequency surveying method: a critical reappraisal. Forensic Sci Int Genet. 2011;5:84–90. doi: 10.1016/j.fsigen.2010.10.014. [DOI] [PubMed] [Google Scholar]
  33. Willuweit S, Roewer L. Y chromosome haplotype reference database (YHRD): update. Forensic Sci Int Genet. 2007;1:83–87. doi: 10.1016/j.fsigen.2007.01.017. [DOI] [PubMed] [Google Scholar]
  34. Xue Y, Wang Q, Long Q, Ng BL, Swerdlow H, Burton J, Skuce C, Taylor R, Abdellah Z, Zhao Y, Asan, MacArthur DG, et al. Human Y chromosome base-substitution mutation rate measured by direct sequencing in a deep-rooting pedigree. Curr Biol. 2009;19:1453–1457. doi: 10.1016/j.cub.2009.07.032. [DOI] [PMC free article] [PubMed] [Google Scholar]

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

Table S1. RM Y-STR Primer sequences and concentrations used in three multiplex PCRs

Table S2. Reaction composition and conditions for the three RM Y-STR multiplexes used

Table S3. RM Y-STR repeat structure, allelic designations and control 007 genotype

Table S4. RM Y-STR haplotype characteristics in a global set of 12,272 individuals from 111 populations summarized for population samples ordered according to regional groups

Table S5. Comparison of Yfiler and RM Y-STR haplotype characteristics in a global set of 7,784 individuals from 65 populations summarized for population samples ordered according to regional groups

humu0035-1021-SD1.pdf (414.7KB, pdf)

Table S6. RM Y-STR and Yfiler haplotypes obtained from the 12,272 individuals in 111 global populations

humu0035-1021-SD2.xlsx (2.1MB, xlsx)

Table S7. Allele frequencies and ranges for individual populations, regional groups, and the complete dataset for each RM Y-STR marker

humu0035-1021-SD3.xlsx (364.6KB, xlsx)

Table S8. RM Y-STR and Yfiler haplotypes for male relative pairs

humu0035-1021-SD4.xlsx (1.5MB, xlsx)

Articles from Human Mutation are provided here courtesy of Wiley

RESOURCES