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Title: Micro-geographic distribution of Y-chromosomal variation in the central-western European region Brabant
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Forensic Science International: Genetics 5 (2011) 95–99

Contents lists available at ScienceDirect

Forensic Science International: Genetics
journal homepage: www.elsevier.com/locate/fsig

Micro-geographic distribution of Y-chromosomal variation in the central-western
European region Brabant
Maarten H.D. Larmuseau a,b,c,*, Nancy Vanderheyden a, Manon Jacobs a, Monique Coomans a,
Lucie Larno a, Ronny Decorte a,b
a
b
c

UZ Leuven, Department of Forensic Medicine, Laboratory of Forensic Genetics and Molecular Archaeology, Kapucijnenvoer 33, B-3000 Leuven, Belgium
Katholieke Universiteit Leuven, Department of Human Genetics, Campus Gasthuisberg, Herestraat 49, B-3000 Leuven, Belgium
Katholieke Universiteit Leuven, Laboratory of Animal Diversity and Systematics, Deberiotstraat 32, B-3000 Leuven, Belgium

A R T I C L E I N F O

A B S T R A C T

Article history:
Received 25 June 2010
Received in revised form 12 August 2010
Accepted 25 August 2010

One of the future issues in the forensic application of the haploid Y-chromosome (Y-chr) is surveying the
distribution of the Y-chr variation on a micro-geographical scale. Studies on such a scale require
observing Y-chr variation on a high resolution, high sampling efforts and reliable genealogical data of all
DNA-donors. In the current study we optimised this framework by surveying the micro-geographical
distribution of the Y-chr variation in the central-western European region named Brabant. The Duchy of
Brabant was a historical region in the Low Countries containing three contemporary Belgian provinces
and one Dutch province (Noord-Brabant). 477 males from five a priori defined regions within Brabant
were selected based on their genealogical ancestry (known pedigree at least before 1800). The Yhaplotypes were determined based on 37 Y-STR loci and the finest possible level of substructuring was
defined according to the latest published Y-chr phylogenetic tree. In total, eight Y-haplogroups and 32
different subhaplogroups were observed, whereby 70% of all participants belonged to only four
subhaplogroups: R1b1b2a1 (R-U106), R1b1b2a2* (R-P312*), R1b1b2a2g (R-U152) and I1* (I-M253*).
Significant micro-geographical differentiation within Brabant was detected between the Dutch (NoordBrabant) vs. the Flemish regions based on the differences in (sub)haplogroup frequencies but not based
on Y-STR variation within the main subhaplogroups. A clear gradient was found with higher frequencies
of R1b1b2 (R-M269) chromosomes in the northern vs. southern regions, mainly related to a trend in the
frequency of R1b1b2a1 (R-U106).
ß 2010 Elsevier Ireland Ltd. All rights reserved.

Keywords:
Genealogy
Low Countries
Population genetics
West-Europe
Y-chromosome

1. Introduction
Genetic diversity is geographically unequally distributed
among human populations. The ancestral origin and evolutionary
forces such as selection, drift and migration have played a crucial
role in genetic population differentiation [1]. It is required that the
geographical distribution of genetic variation is known when
genetic tools are being used in forensic science [2]. This is
especially the case for the application of the haploid Y-chromosome (Y-chr) due to the high effects of genetic drift and to the
strong susceptibility of founder events for this chromosome [3,4].
In addition, the occurrence of patrilocality in approximately 70% of
the modern human societies increases the micro-geographical
clustering of the Y-chr variation in comparison with mitochondrial

* Corresponding author at: Laboratory of Forensic Genetics and Molecular
Archaeology, Kapucijnenvoer 33, B-3000 Leuven, Belgium. Tel.: +32 16 33 66 63;
fax: +32 16 34 59 97.
E-mail address: maarten.larmuseau@bio.kuleuven.be (M.H.D. Larmuseau).
1872-4973/$ – see front matter ß 2010 Elsevier Ireland Ltd. All rights reserved.
doi:10.1016/j.fsigen.2010.08.020

variation [5]. Currently, the Y-chr distribution is well known on a
continental scale, nevertheless, one of the future issues will be to
study the regional distribution of the genetic variation [1,6].
Research on a regional or micro-geographical scale requires
attention to essential issues such as an intensive sampling
campaign and a fine resolution detection of Y-chr variation to
differentiate unrelated families [1]. During sampling, most
population studies of Y-chr diversity classify donors into local
subpopulations on the basis of at least two generations of
residence [7]. However, this is compromised by migration in
preceding generations, especially in Western Europe since the
beginning of the 19th century with the industrial revolution. It is
therefore essential to know the genealogical context of each donor
for many generations to study regional population structure.
The framework to study population stratification on a microgeographical scale for Y-chr was optimised for a selected centralwestern European region named Brabant. The Duchy of Brabant
was a historical region in the Low Countries between the 12th and
18th century and consisted of a present-day Dutch province and
three contemporary Belgian provinces together with the Brussels-

96

M.H.D. Larmuseau et al. / Forensic Science International: Genetics 5 (2011) 95–99

Capital Region. The total area is 14.425 km2 with approximately
150 km between the two most remote places in Brabant. The main
reason for selecting this region was the ability to obtain reliable
genealogical data of the patrilineal line for each of the numerous
donors living together on a small geographical scale. This provided
an optimal starting point to study micro-geographic distribution of
Y-chr variation in Western Europe.
2. Materials and methods
Buccal swab samples were collected from a total of 477 males
representing 423 different surnames. Only males that provided
genealogical data of the patrilineal line with at least one known
ancestor living in the 18th century were selected for this study.
According to the residence of the oldest known parental ancestor,
each donor was assigned to one of the five ‘genealogical regions’
within Brabant based on contemporary administrative borders
(Noord-Brabant, Antwerpen, Kempen, Mechelen and Vlaams- and
Waals-Brabant; Fig. S1). DNA was extracted by using the Maxwell1
16 System (Promega, Madison, USA) and quantified by real-time
PCR (QuantifilerTM Human DNA kit, Applied Biosystems).
In total 37 STR loci were genotyped for all samples as described in
a previous study [8] based on PowerPlex1 Y (Promega, Madison,
USA) (DYS391, DYS389-I, DYS439, DYS389-II, DYS438, DYS437,
DYS19, DYS392, DYS393, DYS390, DYS385) and three novel multiplexes (DYS426, DYS393, DYS390, DYS385, DYS460, GATA H4.1,
DYS447, DYS448, DYS459, DYS576, DYS464, YCAII, DYS456, DYS458,
DYS607, DYS455, DYS570, DYS724, DYS454, DYS388, DYS442). The
inclusion of DYS464 into two assays facilitated the interpretation of
the alleles and peak height ratios [9]. In addition, some STRs were
included in more than one multiplex to serve as an internal control.
The whole process was reproduced with new primer sets for all
individuals that showed non-amplified loci to exclude technical
errors or mutations in the standard primer positions.
All haplotypes were submitted to Whit Atheys’ Haplogroup
Predictor (Athey 2005; Athey 2006) to obtain probabilities for the
inferred haplogroups. This strategy was used to avoid redundant
SNP-typing, though, verification of the haplogroup with Y-SNPs
was required [10]. Based on these results, the samples were
assigned to a specific SNP assay to confirm the haplogroup and to
assign the subhaplogroup to the lowest possible level of the latest
Y-chr tree reported by Karafet et al. [11] and according to the
update on the Y Chromosome Consortium web page (http://
ycc.biosci.arizona.edu/nomenclature_system/index.html),
with
exception of the substructuring within subhaplogroups
R1b1b2a1 (R-U106) and R1b1b2a2g (R-U152). Fifteen multiplex
systems with Y-SNPs were developed using SNaPshot minisequencing assays (Applied Biosystems, Foster City, CA) and
analyzed on an ABI3130XL Genetic Analyzer (Applied Biosystems)
according to a previously published protocol [12]. Some Y-SNPs
were analysed by sequencing using the BigDye Terminator v. 3.1
(Applied Biosystems) or by allele-specific-amplification using
SYBR green with the 7500 real-time PCR system (Applied
Biosystems). All primer sequences and concentrations for the
analysis of the 103 Y-SNPs are available from the authors upon
request.
The genetic relationship between different populations was
assessed by means of FST, an analogue of Wright’s FST that takes the
evolutionary distance between individual haplotypes into account
[13]. Estimations of FST were calculated based on the Y-SNP
subhaplogroup frequencies and on the 25 single-copy Y-STRs
(including ‘DYS389-1’ instead of DYS389-I and DYS389-2, which is
DYS389-II–DYS389-I) between all regions, as well as between a
single region and all the other regions combined. To calculate the
genetic relationship between populations based on microsatellite
data also the RST, another analogue of the FST, was used which takes

the difference in repeat numbers between alleles into account [13].
RST-values, estimated as r [14], were calculated based on the Y-STR
data between all regions as well as between a single region and all
the other regions combined. FST and RST estimates were also
calculated based on Y-STR data within the two most frequent
observed subhaplogroups R1b1b2a1 (R-U106) and R1b1b2a2* (RR312*). All FST- and RST-values were obtained by taking only one
participant into account for pairs with the same family name, the
same ‘genealogical region’ and belonging to the same subhaplogroup, to exclude the possibility of family effect in the analysis.
All values were estimated using ARLEQUIN v.3.1 [15] and tested for
statistical significance by means of random permutation of
samples in 10,000 replicates. For the pairwise FST- and RST-values,
the sequential Bonferroni correction was applied to correct
significance levels for multiple testing [16].
Median joining networks for all haplogroups and the main
subhaplogroups were constructed based on all 25 single-copy YSTRs by NETWORK 4.5.1.0. [17] (http://www.fluxus-engineering.com) using the weighting scheme described by Qamar et al. [18]
due to different mutation rates among the markers. To estimate the
time to the most recent common ancestor (tMRCA) of the main
subhaplogroups, we used all 25 single-copy Y-STRs and applied the
average square distance (ASD) method [19], where the ancestral
haplotype was assumed to be the haplotype carrying the most
frequent allele at each microsatellite locus. We employed a
microsatellite evolutionary effective mutation rate based on the
observed father-to-son transmissions of all used microsatellites
according to Vermeulen et al. [2] and using the correction of
Zhivotovsky et al. [20]. The tMRCA estimates and confidence
intervals (CI) were calculated with the software Ytime v.2.08 [21].
3. Results
3.1. Y-chromosomal variation
All individuals were correctly assigned to the main haplogroups
using the Whit Atheys’ Haplogroup Predictor. In total, eight main
haplogroups were observed with almost 85% of the samples
belonging to haplogroup R (63%) and I (21%) (Table 1). On the
lowest observed level of the phylogenetic tree 32 subhaplogroups
were found in the data set, whereby nearly 70% of all samples
belonged to only four subhaplogroups: R1b1b2a1 (R-U106),
R1b1b2a2* (R-P312*), R1b1b2a2g (R-U152) and I1* (I-M253*)
(Table 1).
For the 477 males, a total of 286 different ‘minimal haplotypes’
(=DYS19, DYS389-1, DYS389-2, DYS390, DYS391, DYS392, DYS393
and DYS385a,b) were observed, of which 209 were unique. The
most frequent ‘minimal haplotype’ occurred 33 times (7%); the
frequencies of all 77 ‘minimal haplotypes’ that were observed
more than once in the dataset are given in Table S1. A total of 337
different ‘extended haplotypes’ (=‘minimal haplotype’ + DYS438
and DYS439) were observed, of which 271 were unique. The two
most frequent ‘extended haplotypes’ occurred both 18 times
(3.8%); the frequencies of all 66 ‘extended haplotypes’ that were
observed more than once in the dataset are given in Table S2. Many
similar ‘minimal and extended haplotypes’ belonged to individuals
that were assigned to a different subhaplogroep based on Y-SNPs.
Using all 37 Y-STRs, 473 haplotypes were observed in the study, of
which 469 were unique. The four duos with the same 37-STR
haplotype also had an identical surname. All ‘extended haplotypes’
together with the SNP-typing results have been submitted to the YSTR Haplotype Reference Database (www.yhrd.org; Accession
numbers YA003651-YA003652-YA003653).
Network analyses of all single-allele Y-STR haplotypes within
the main haplogroups was able to differentiate the Y-SNP defined
subhaplogroups from each other, except for the subhaplogroups

M.H.D. Larmuseau et al. / Forensic Science International: Genetics 5 (2011) 95–99

97

Table 1
Frequencies of the Y-chromosomal haplogroups and subhaplogroups in Brabant and in each genealogical region within Brabant separately.
Region

Noord-Brabant

Antwerpen

Kempen

Mechelen

Vlaams

Total (sub)Haplogroup
& Waals Brabant

E
E1b1b1a1 (E-V12)
E1b1b1a2 (E-V13)
E1b1b1a3 (E-V22)
E1b1b1c* (E-M123*)
E1b1b1c1 (E-M34)
G
G2a* (G-P15*)
I
I1* (I-M253*)
I1c (I-P109)
I2* (I-P215*)
I2a* (I-P37.2*)
I2b* (I-M223*)
I2b1 (I-M284)
I2b3 (I-P78)
I2b4 (I-P95)
J
J1* (J-M267*)
J1e* (J-P58*)
J2a* (J-M410*)
J2a2* (J-M67*)
J2a2a* (J-M92*)
J2a8 (J-M319)
J2b2* (J-M241*)
L
L1 (L-M27)
L2 (L-M317)
Q
Q1* (Q-P36.2*)
R
R1a1* (R-M17*)
R1b1b2* (R-M269*)
R1b1b2a* (R-M310*)
R1b1b2a1 (R-U106)
R1b1b2a2* (R-P312*)
R1b1b2a2d (R-SRY2627)
R1b1b2a2g (R-U152)
T
T* (T-M70*)

3.85
0.00
1.54
0.77
0.77
0.77
3.08
3.08
16.15
8.46
0.00
0.77
2.31
3.85
0.00
0.00
0.77
6.92
0.77
0.77
1.54
0.77
2.31
0.00
0.77
0.00
0.00
0.00
0.00
0.00
68.46
1.54
0.77
1.54
35.38
20.77
3.08
5.38
1.54
1.54

6.94
0.00
4.17
0.00
0.00
2.78
2.78
2.78
20.83
12.50
0.00
1.39
0.00
5.56
1.39
0.00
0.00
2.78
0.00
0.00
1.39
0.00
0.00
0.00
1.39
0.00
0.00
0.00
0.00
0.00
66.67
4.17
0.00
0.00
26.39
18.06
0.00
18.06
0.00
0.00

1.30
0.00
1.30
0.00
0.00
0.00
2.60
2.60
28.57
19.48
1.30
0.00
3.90
1.30
1.30
1.30
0.00
2.60
0.00
0.00
1.30
0.00
0.00
0.00
1.30
1.30
1.30
0.00
2.60
2.60
61.04
7.79
1.30
0.00
25.97
19.48
0.00
6.49
0.00
0.00

4.76
1.59
3.17
0.00
0.00
0.00
4.76
4.76
19.05
9.52
3.17
3.17
0.00
3.17
0.00
0.00
0.00
7.94
0.00
1.59
3.17
1.59
0.00
1.59
0.00
3.17
3.17
0.00
1.59
1.59
58.73
3.17
3.17
1.59
30.16
15.87
0.00
4.76
0.00
0.00

6.67
0.00
5.19
0.74
0.00
0.74
3.70
3.70
22.22
11.85
0.74
2.96
0.74
4.44
0.00
0.74
0.74
8.15
0.74
2.22
0.74
1.48
0.74
0.74
1.48
0.74
0.00
0.74
0.00
0.00
58.52
3.70
0.74
0.74
20.74
22.22
0.74
9.63
0.00
0.00

4.82
0.21
3.14
0.42
0.21
0.84
3.35
3.35
20.96
11.95
0.84
1.68
1.47
3.77
0.42
0.42
0.42
6.10
0.42
1.05
1.47
0.84
0.84
0.42
1.05
0.84
0.63
0.21
0.63
0.63
62.89
3.77
1.05
0.84
27.67
19.92
1.05
8.60
0.42
0.42

within R1b1b2 (R-M269) and I2b (I-M223) (Fig. S2, S3).
Nevertheless, the high number of STR-loci could separate two
clusters of individuals within J2a* (J-M410*) (Fig. S4). This
clustering was confirmed by the occurrence of a DYS464.01
micro-variant in all individuals of one cluster in contrast to all
other individuals of the second cluster within J2a*. No further
substructure was found in the network analyses of all main
subhaplogroups in the dataset, though, a huge star pattern was
always observed. The youngest tMRCA’s for the main subhaplogroups were observed for E1b1b1a2 (E-V13) and I1*(I-M253*),
respectively 4182–5855 and 4531–6344 years ago. The oldest
tMRCA was observed for G2a*, between 9326 and 13,056 years ago.
The tMRCA values of the other main subhaplogroups in haplogroups R and I were estimated between 6000 and 10,000 years
(Table S3).
3.2. Differentiation within Brabant
Based on the a priori defined regions in Brabant, a strong
downward trend in the frequency of haplogroup R was observed
from North to South (Table 1; Fig. S5). The difference in the
frequency of R haplogroups was circa 10% between the most
northern and southern part, mainly due to the downward
frequency of R1b1b2a1 (R-U106). There was no clear increasing
trend of another haplogroup from North to South that replaced
haplogroup R. Statistical significant differentiation between the

regions in Brabant was found between the Dutch region (NoordBrabant) in comparison with the combined data of the four defined
Belgian regions. This observation was based on the Y-SNPs as well
as on the Y-STRs (Table 2). No significant differentiation was found
between Y-STR haplotypes within the two main subhaplogroups
R1b1b2a1 (R-U106) and R1b1b2a2* (R-P312*). Moreover, no
clustering related to the geographical region was found in the
network analyses based on the STR-haplotypes.
4. Discussion
The study on Brabant used an optimised approach to survey
genetic variation and differentiation on micro-geographical scale
by combining high resolution detection of Y-chr variation with a
strong sampling effort and extensive and reliable genealogical data
from each participant. The observed Y-chr lineages in Brabant were
expected for Western Europe, with nearly 85% belonging to
haplogroup R and I [11,22]. In contrast to the small sampling area, a
remarkable high diversity was found with 32 different subhaplogroups on the lowest phylogenetic level. Nevertheless, almost
70% of all participants to this study belonged to only four Y-chr
subhaplogroups, R1b1b2a1 (R-U106), R1b1b2a2* (R-P312*),
R1b1b2a2g (R-U152) and I1* (I-M253*). A comparison with other
regions in (Western) Europe is difficult due to the limited number
of population studies with a comparable resolution of the Y-chr
variation.

M.H.D. Larmuseau et al. / Forensic Science International: Genetics 5 (2011) 95–99

98

Table 2
Tests for population differentiation based on the Y-haplogroup frequencies and Y-haplotypes.
Region

N

Ns

Nh

Noord-Brabant (NL)
Antwerpen (B)
Kempen (B)
Mechelen (B)
Vlaams and Waals Brabant (B)

130
72
77
63
135

128
68
72
61
131

24
13
17
19
26

FST value on Y-SNPs
(P-value)
0.00605 (0.03*)
0.00111 (0.29)
0.00150 (0.27)
0.00297 (0.72)
0.00234 (0.16)

FST value on Y-STRs
(P-value)
0.00398 (0.03*)
0.00031 (0.48)
0.00339 (0.09)
0.00121 (0.74)
0.00131 (0.17)

RST value on Y-STRs
(P-value)
0.00494
0.00059
0.00339
0.00117
0.00131

(0.06)
(0.35)
(0.09)
(0.48)
(0.17)

The Y-chromosomes from one region were compared with all chromosomes from the other regions in Brabant combined. N, number of individuals; Ns, number of individuals
when only one participant was taken into account for pairs with the same family name, the same genealogical region and belonging to the same subhaplogroup; Nh, number
of observed subhaplogroups; NL, The Netherlands; B, Belgium. Significant P-values (P-value < 0.05) are given in bold and with an asterisk.

Based on the 37 Y-STR loci, all non-related individuals were
distinct from each other. This is in contrast to the ‘minimal and
extended haplotypes’ based on nine and eleven STR loci,
respectively. Moreover, several individuals revealed the same
‘minimal and extended haplotypes’ but belonged to a different
subhaplogroup based on SNP-typing. This illustrates the necessity
to genotype more than the usual nine or eleven Y-STRs to declare
biological relatedness between individuals. The usefulness of a
high number of genotyped Y-STRs was also reflected in the ability
to find clusters within the network analyses associated with the
observed subhaplogroups in haplogroups E and J. Moreover, it was
even possible to detect further substructuring within subhaplogroup J2a* (J-M410*) based on the network analysis of all
single-allele Y-STR haplotypes. Nevertheless, it was remarkable
that the network analyses could not differentiate all observed
subhaplogroups within R1b1b2 (R-M269) and I2b (I-M223). This
might be due to the relatively young age of these specific
subhaplogroups making it impossible to differentiate these groups
based on the Y-STRs. Though, the estimated tMRCAs of these
subhaplogroups were not that much younger in comparison with
other subhaplogroups. A different reason might be the high
effective population size of these haplogroups with a high present
variation causing a high occurrence of back mutation and
homoplasy.
Population differentiation based on Y-chr variation was
observed across a transect of approximately 150 km. The genetic
differentiation was statistically significant between the Dutch and
the combined Belgian areas within Brabant based on the
frequencies of the subhaplogroups, rather than on the haplotypes
within the main subhaplogroups. Although the FST-values were
low, the differentiation was indeed detectable along the transect
based on the haplogroup frequencies. There is a clear downward
trend of the frequency of haplogroup R with a difference of 10%
across the most northern and southern part of Brabant. The main
reason for this observation was the downward trend in R1b1b2a1
(R-U106), which is the subhaplogroup with the highest average
frequency in Brabant (27.67%). It is likely that the trend on R-U106
is linked to the genetic barrier between The Netherlands and
France that was previous announced [23,24]. Based on limited data
of Y-chr subhaplogroup frequencies, there seems to be a higher
occurrence of subhaplogroup R1b1b2a1 (R-U106) in The Netherlands (37.2%) than in France (7.1%) [25]. Therefore it is likely that
the differentiation observed in this study, which is mainly based on
R-U106, is indeed related to the suggested barrier of Rosser et al.
[23]. The study on Brabant indicates that the ‘barrier’ between The
Netherlands and France is in fact a long gradient according to
isolation-by-distance instead of a steep clinal shift according to a
barrier to gene flow. Nevertheless, because of the strong
heterogeneous distribution of the paternally inherited surnames
in Belgium related to the different language communities, it was
likely that the suggested barrier was associated with the RomanceGermanic language border within Western Europe [26]. To

understand the present micro-geographical differentiation within
Brabant and its relationship to the language border, the inclusions
of other regions in Belgium into the analysis is required.
The results of this study on Brabant clearly show that significant
differences in Y-chr (sub)haplogroup frequencies on a microgeographical scale are important to be taken into account for
forensic applications. For this reason, future research needs to
focus on genetic differentiation on a regional scale using reliable
genealogical data. This study exemplifies the necessity of
collaboration between forensic and population genetic researchers
and the genetic genealogy community.
Acknowledgements
The authors thank all the volunteers who donated DNA samples
used in this study. They acknowledge the Flemish Society for
Genealogical Research (Antwerp) that was involved in the
collection of the samples and the genealogical data. They are also
grateful to Jeroen Van Houdt and Bram Bekaert for useful
discussions. This study was funded by the Flemish Society for
Genealogical Research (Antwerp) and by a grant from the Flanders
Ministry of Culture. MHDL received a postdoctoral position of the
K.U.Leuven (BOF PDM-Kort).

Appendix A. Supplementary data
Supplementary data associated with this article can be found, in
the online version, at doi:10.1016/j.fsigen.2010.08.020.
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