Original Paper
Hum Hered 2007;64:123–135
DOI: 10.1159/000101964
Received: September 18, 2006
Accepted: February 14, 2007
Published online: May 2, 2007
Campora: A Young Genetic Isolate in
South Italy
Vincenza Colonna a Teresa Nutile a Maria Astore a Ombretta Guardiola a
Giuliano Antoniol b, c Marina Ciullo a M.Graziella Persico a
a
Institute of Genetics and Biophysics ‘A. Buzzati-Traverso’, CNR Naples, Naples, and b Università degli Studi del
Sannio, Benevento, Italy; c Département de Génie Informatique École Polytechnique de Montréal,
Montréal, Qué, Canada
Key Words
Genetic isolates ⴢ Genealogy ⴢ Haplotype analysis ⴢ
Bottleneck
Abstract
Genetic isolates have been successfully used in the study of
complex traits, mainly because due to their features, they allow a reduction in the complexity of the genetic models underlying the trait. The aim of the present study is to describe
the population of Campora, a village in the South of Italy,
highlighting its properties of a genetic isolate. Both historical evidence and multi-locus genetic data (genomic and
mitochondrial DNA polymorphisms) have been taken into
account in the analyses. The extension of linkage disequilibrium (LD) regions has been evaluated on autosomes and on
a region of the X chromosome. We defined a study sample
population on the basis of the genealogy and exogamy data.
We found in this population a few different mitochondrial
and Y chromosome haplotypes and we ascertained that,
similarly to other isolated populations, in Campora LD extends over wider region compared to large and genetically
heterogeneous populations. These findings indicate a conspicuous genetic homogeneity in the genome. Finally, we
found evidence for a recent population bottleneck that we
propose to interpret as a demographic crisis determined by
the plague of the 17th century. Overall our findings demonstrate that Campora displays the genetic characteristics of a
young isolate.
Copyright © 2007 S. Karger AG, Basel
Introduction
Recently, there has been wide-spread discussion about
the use of isolated human populations for the identification of genes responsible for complex traits [1, 2]. The
usefulness of isolated populations in these studies has
been widely shown [3–9]. Isolated populations originate
from a restricted number of founders due either to a past
migration event or to a past reduction in the population
size (e.g. a bottleneck). Because of this founder effect and
limited gene flow, it is possible that fewer risk alleles
might underlie complex disorders in those populations.
Moreover, the effect of genetic drift can be considerable
[10, 11], causing an increase in the frequency and the
attributable risk of particular alleles. The presence of
inbreeding and extended genomic regions in linkage
disequilibrium (LD), together with previous mentioned
factors, contribute to make the genetic background extremely homogeneous. Such genetic homogeneity is a
great advantage in the initial approach to gene mapping,
even though it could be a disadvantage in the subsequent
step of refining the identified genomic regions.
M. Ciullo and M.G. Persico contributed equally to this work.
© 2007 S. Karger AG, Basel
0001–5652/07/0642–0123$23.50/0
Fax +41 61 306 12 34
E-Mail karger@karger.ch
www.karger.com
Accessible online at:
www.karger.com/hhe
Vincenza Colonna
Institute of Genetics and Biophysics ‘A. Buzzati-Traverso’, CNR
Via Pietro Castellino, 111
IT–80131 Naples (Italy)
Tel. +39 081 613 2294, Fax +39 081 613 2595, E-Mail colonna@igb.cnr.it
Naples
Campora
village
Region of Campania
National Park of
“Cilento e Vallo di Diano”
Fig. 1. Geographical location of Campora. The village is part of
the National Park of ‘Cilento e Vallo di Diano’ of the region Campania, in the South of Italy.
Members of isolated populations share a common environment and a very similar life-style, thus the environmental diversity is greatly reduced. The availability of extensive genealogical records can provide large genealogies, potentially highly informative for linkage analysis.
Therefore, in these genetically and culturally homogeneous populations, a large proportion of individuals presenting a given trait are likely to share the same trait-predisposing gene inherited from a common ancestor. Finally, additional features such as the presence of extensive
genealogical records, and possibility of standardized
phenotypes [12] enhance the value of these populations
for the studies of complex traits.
The main features of isolated populations have been
extensively reviewed [13]. It is clear that every isolated
population carries the signs of its own demographic history. Knowledge of the underlying population structure
is essential to design studies for gene identification and
the choice of statistical methods critically depends on features of the population [14, 15] such as the degree of isolation (ranging from ‘extreme’ to ‘mild’), the length of
time that the population has remained isolated and the
size of the founding nucleus. In the current literature
about isolated populations, such demographic characteristics have been primarily evaluated for those representing extreme cases of isolation, such as the Amish or the
Hutterites. On the other hand, the structure of only a
handful of ‘mild’ isolated populations has been characterized, although a number of such isolates have been
identified [16–18].
124
Hum Hered 2007;64:123–135
Here we describe the population in the village of Campora in South Italy, which suffered a bottleneck in the
17th century and has remained geographically isolated
until the last century. In this population we have recently
identified a locus associated with hypertension [9]. Moreover, our preliminary data indicate that linkage studies
in the Campora population will also be a powerful tool to
detect QTLs. In this paper, we trace the genetic history of
Campora and establish its degree of isolation, applying
both genealogy-based and genetic-based strategies. Our
analysis provides a description of the Campora population as a model of a mild genetic isolate.
Historical Background
The area that today corresponds to the National Park
of ‘Cilento and Vallo di Diano’, within which Campora is
located (see map in fig. 1), was originally occupied by
Greeks during the 8th century BC. In the middle of the
5th century BC, the Lucanians conquered the internal
area without reaching the coast. Subsequently Lucanians
were chased from this territory by the Greeks. The community of Campora was already present at the time of
Lucanians but no further historical information is available until the 11th century [19].
In the 8th century, groups of monks coming from the
Byzantine Empire reached the coast of the area that today
corresponds to the park. In the 10th century, the monks
were forced to move to the internal hilly region to elude
the coastal invasions of Saraceni coming from the middle
East. Once there, the monks organized groups of local
people into villages. Among those was Campora, for
which the arrival of monks has been dated at the beginning of the 11th century.
Presumably, the first nucleus of inhabitants was made
of individuals of Greek and Lucanian origin employed in
the agricultural activities of the monastery [19]. Subsequently, despite different dominations affecting the village after its foundation, none of them contributed to the
population in terms of individuals. In the second half of
the 16th century, there was a general scarcity of food in
the area surrounding Campora. The famine lasted about
one century and was followed by a severe epidemic of bubonic plague. The first registered case of plague in the
area of Campora dates to the year 1656 in the nearby village of Novi Velia.
According to historical sources and owing to its geographical position, Campora experienced isolation from
its foundation until the end of the World War II. A first
wave of emigrants went to America at the end of 19th
century while a second one, mainly directed to big cities
Colonna /Nutile /Astore /Guardiola /
Antoniol/Ciullo /Persico
1,600
cording to historical and civil census data
from the 16th century to recent time. A
strong reduction in population size is evident across one century (dashed line) since
the middle of 16th century (due to famine
and the 1656 plague epidemic) followed by
a period of population expansion. It is also
possible to notice a minor reduction at the
end of the 19th century most likely corresponding to the first migration wave. Finally, since the beginning of 20th century
migration diminished the number of
individuals currently living in the village to 500 (not shown).
1,200
Number of individuals
Fig. 2. Population trend of Campora ac-
800
400
0
1500
in Italy, moved after World War II and is still occurring
(fig. 2). The first wave was compensated by a high birth
rate. However since the second half of the 20th century,
births have decreased (data not shown) while emigration
has been constant and has gradually reduced the number
of individuals currently living in the village from 1,300
in 1880 to only 500 at present.
Subjects and Methods
Subjects and Genealogy
Extensive genealogical data from the 16th century up to the
present day have been collected by consulting the Registry Office
and the Parish archives. Additional information about emigrated
people was obtained by directly querying inhabitants about their
relatives. Comparison and integration of this information led to
the accumulation of 10,737 individual records of which 1,719 are
of living individuals.
Demographic data on Campora from the 16th to 18th century
result from ‘stati delle anime’, a type of religious census present at
that time. For later centuries, data derive from civil census. Exogamic marriages were counted from the registries of the Parish.
Matrilinear and patrilinear genealogical lines (GLs) were built
by scanning the pedigree with a Perl algorithm to connect each
individual with the parent of the same sex, proceeding until no
further connections were possible.
The mean generation time (MGT) was calculated as the average age of individuals at the birth of his/her children. Individuals
in the whole genealogical dataset were considered. We found a
value of MGT = 32.5 8 8.0 years (females MGT = 30.9 8 5.5
years; males MGT = 34.1 8 5.8 years).
All individuals participating in the study, recruited among
both resident and immigrants, signed an informed consent in ac-
A Genetic Isolate in South Italy
1600
1700
1800
1900
2000
Year
cordance with the Declaration of Helsinki (World Medical Association). The study was approved by the Ethics Committee of
Azienda Sanitaria Locale Napoli 1.
DNA Preparation and Genotyping of Microsatellite Markers
Genomic DNA was extracted from 10 ml of peripheral blood
using a Flexigene kit (Qiagen) following the manufacturer’s instructions. Genotyping at 1,122 autosomal microsatellites (average marker spacing of 3.6 cM and mean marker heterozygosity of
0.70) was performed by deCODE genotyping service on 584 individuals. Mendelian inheritance inconsistencies were identified
using the Pedcheck program [20].
On the Y chromosome, a set of seven microsatellites (DYS19,
DYS385, DYS389, DYS390, DYS391, DYS392, DYS393) was analyzed. Primer sequences were obtained from the Genome Database (http://www.gdb.org). Polymerase chain reaction (PCR) cycling conditions were 95 ° C for 10 min, then thirty cycles of 95 ° C
for 30ⴖ, annealing at 55 ° C (for DYS19, DYS390, DYS392, DYS393)
or 57 ° C (for DYS391) or 62 ° C (for DYS385, DYS389) for 30ⴕ, then
synthesis at 72 ° C for 30ⴕ, and finally 72 ° C for 7ⴕ. PCR products
were loaded on a MegaBACE1000Flexi (Amersham) and genotype data were analyzed using Fragment Profiler software.
Six microsatellites in the Xq13 region (DXS983, DXS8092,
DXS8037, DXS1225, DXS8082, DXS986) were considered for
Linkage Disequilibrium (LD) analysis testing. Primer sequences
were obtained from the Genome Database. PCR cycling conditions were 94 ° C for 2ⴕ, then 94 ° C for 30ⴕ, 60 ° C to 65 ° C (–0,5 ° C/
cycle) for 30ⴕ, then 72 ° C for 30ⴕ over 15 cycles; 96 ° C for 15ⴕ, 65 ° C
for 30ⴕ, 72 ° C for 30ⴕ over 20 cycles and finally 72 ° C for 4ⴕ. PCR
products were loaded on a MegaBACE1000Flexi (Amersham) and
genotype data were analyzed using Fragment Profiler software.
mtDNA Analysis
Seven fragments were amplified from mtDNA. For each fragment, the position of the first base of the primer on the light (L)
strand and on the heavy (H) strand, according to the reference
Hum Hered 2007;64:123–135
125
sequence [21, 22] is: L15996-H16401; L1643-H1874; L6909-H7115;
L8845-H9163; L10290-H10557; L9932-H10088; L15428-H15682.
Haplogroups of mtDNA [23, 24] were determined through the
analysis of restriction polymorphisms (in brackets) as follow: H
(–10394 DdeI; –7025 AluI); T (–10394 DdeI; –15925 MspI; +15606
AluI; +13366BamHI); U (–10394 DdeI; +12308 Hin); V (–10394
DdeI; –4577 NlaIII); W (–10394 DdeI; –8994 HaeIII; +8249
AvaII); X (–10394 DdeI; –1715DdeI); I (–1715DdeI; –4529 HaeII;
+8249 AvaII; +10028 AluI; +16389 BamHI); K (+12308 HinfI;
–9052 HaeII); J (–16065 HinfI; –13704 BstNI); M (+10397 AluI);
L (+3592 HpaI); preH (–10394 DdeI; +7025 AluI; +16517 HaeIII).
Enzymatic reactions were carried out at 37 ° C for 90ⴕ in a reaction
volume of 20 l using 6–8 l purified PCR product/reaction.
Polymorphisms in the Hypervariable Region I (HVR-I) were
determined by sequencing from nucleotide 15940 to 16383. Sequencing was done using BigDye Terminator Cycle Sequencing
Ready Reaction (Applied Biosystems, Warrington, UK) and loaded on an ABI PRISM 377 DNA analyzer (PE Biosystems). Sequences were analysed using AutoAssembler software (Applied
Biosystems, Warrington, UK).
Statistical Analyses
Coefficients of inbreeding ( f ) were evaluated from the genealogy using two different algorithms: the one proposed by Karigl
[25] implemented in the KinInbCoef [26] (http://galton.uchicago.
edu/⬃mcpeek/software/CCtests) and the Stevens-Boyce algorithm [27] implemented in the KINSHIP module of the PEDSYS
software (http://www.sfbr.org/pedsys/pedsys.html).
The Fisher test associated p value for the evaluation of LD on
the X chromosome was determined using the Haploxt module in
the GOLD package [28] (http://www.sph.umich.edu/csg/abecasis/GOLD/) in a sample of 63 unrelated males.
To assess disequilibrium between alleles from autosomal
markers, we inferred haplotypes using Merlin (http://www.sph.
umich.edu/csg/abecasis/Merlin/). We manage to infer the haplotype of 635 individuals, belonging to a 2,947-member pedigree.
Among those 635 individuals, we chose 73 whose coefficient of
kinship was ! 0.0625 (first cousin) using KinSamp, an algorithm
that we developed that take into account the kinship matrix obtained by KinInbCoef to choose a sample of individuals allowing
a degree of kinship defined by the user. We analyzed pairwise
disequilibrium on haplotype data using the software miLD-2.1
(http://www.geneticepi.com/Research/software/software.html),
which implements the calculation of a corrected Dⴕ value (Dadj)
[29]. Dadj is based on the traditional Lewontin’s multiallelic measure of LD, the multiallelic Dⴕ, but it is corrected in order to minimize the effect of sample size and allele frequencies, allowing the
comparison between samples with different sizes. The miLD-2.1
software also allows the estimation of the significance of LD
through the MLD programme [30].
Intermarker distances were established on the basis of the DeCode sex-averaged maps using the Haldane map function.
Temporary excess of heterozygosity compared to the expected
one in relation to the number of alleles at each locus was tested
using BOTTLENECK [31] (http://www.montpellier.inra.fr/
URLB/bottleneck). The 1,072 autosomal microsatellites available
were tested for Hardy-Weinberg (HW) equilibrium using a test
analogous to Fisher’s exact test implemented in the Arlequin
package (http://cmpg.unibe.ch/software/arlequin3/). HW equilibrium was tested in a sample of 80 individuals (assembled with
126
Hum Hered 2007;64:123–135
KinSamp) whose coefficient of kinship was ! 0.0625 to avoid inference of relatedness in the calculation [32]. HW equilibrium
was ascertained for 1,012 microsatellites that were grouped in 5
datasets and used in the successive calculations. Average marker
spacing in each dataset is 17.5 cM. This marker spacing avoids
overrepresentation in genomic regions that are in linkage disequilibrium, as recommended by the authors of BOTTLENECK.
Allele frequencies at microsatellite loci were estimated using the
BLUE estimator [33] and used as input for BOTTLENECK. The
analysis was carried out under the Two-phased Model of Mutation (TPM) as model of microsatellites evolution. In the software
the TPM model combines the Stepwise Mutation Model (SMM)
and the Infinite Allele Model (IAM) in a percentage defined by
the user. For the former model, the heterozygosity excess after a
bottleneck has been demonstrated to be present for a consistent
period of time [34], while under the SMM model the decline of
heterozygosity is more rapid and thus not detectable by the software. Although the SMM model is considered to more faithfully
represent the true process of evolution of microsatellites compared to the IAM model [35], most microsatellite data sets fit the
TPM more better than the SMM or IAM model [36]. In our case,
the SMM component in the TPM model was set to 90%.
Results
Identification of the Study Sample in the Population of
Campora
Using data of the parish archive from the 19th century
to the present, we estimated the percentage of exogamic
marriages (marriages with individuals from different villages) per generation (fig. 3), considering a mean generation time of 32 years. According to the data, exogamy in
Campora through the 19th century remained below 20%
but has increased since the beginning of the 20th century
(last three generations). Exogamy values are consistent
with those observed in another Italian isolate Talana [17].
We want to take into account exogamy in assembling a
study sample and thus we considered the analysis of exogamic marriages as a rough estimate of the gene flow. We
assembled the study sample among all living individuals
for whom genealogical records are available (n = 1,719)
including in it all those individuals deriving from ancestors that entered the pedigree before the last three generations, that is before exogamy started to break the isolation.
Matrilinear and patrilinear genealogical lineages
(GLs) were determined on the whole genealogy, which
includes 10,737 members distributed over four centuries.
Each line begins with an ancestor and includes related
descendents of the same sex. There is no overlapping
among individuals of different lines. Each GL has been
dated with the birth year of the ancestor. Table 1 shows
Colonna /Nutile /Astore /Guardiola /
Antoniol/Ciullo /Persico
0.007
1985
1955
1925
1925
1895
1865
1835
1805
0.001
0
1775
0.002
10
1745
0.003
20
1715
0.004
30
1685
0.005
40
1655
0.006
50
1625
60
the number of GLs starting in each of the four centuries
included in the pedigree. Notably there has been a greater turnover of females compared to males due to patrilocal behaviour (tendency of females to move to the native
village of the male after marriage) common in this
area.
In concordance with exogamy data, most of the matrilinear and patrilinear lineages with still living descendents began in the last century and only 46 female GLs
and 70 male GLs were present before 1890 (table 1). These
lines are represented today by 576 living females and 608
living males. These 1,184 living individuals, with at least
one ancestor that entered the pedigree before 1890, constitute our study sample. Those lineages that do not have
living descendants could have terminated because of emigration or because only descendants of the opposite sex
were generated or because there were no descendants at
all.
Individuals in the study sample (n = 1,184) represent
69% of the living individuals (n = 1,719) included in the
genealogical data. The remaining portion of living individuals (n = 535) includes: subjects for whom a genderspecific parent-sib lineage was missed (15%) and immigrants that recently joined the village (16%).
The study sample was assembled considering matrilinear and patrilinear lineages with the aim of investigating about the founding nucleus of the village through the
analyses of mtDNA and Y chromosome. However, although only these two special lines were used, we found
out that they catch almost all the information of all the
possible ascending genealogical lines (data not shown)
and thus we used the same study sample also for the examination of the demographic structure.
A Genetic Isolate in South Italy
0.008
Marriages
Coefficient of inbreeding
0
Average coefficient of inbreeding
70
1595
and inbreeding trend over time. Classes of
30 years have been considered and the
midpoint of each class is represented on
the x axis. Marriages were counted from
marriage register in the church archive,
while inbreeding was determined from the
genealogy.
Percent of exogamic marriages
Fig. 3. Percentage of exogamic marriages
80
Years
Table 1. Distribution of genealogical lines through centuries. In
bold are indicated the lineages from which derives the study sample population.
Century
17th
18th
19th
17–19th
20th
Matrilineages
Patrilineages
total
with living
descendant
total
with living
descendant
39
111
59
209
101
13
18
18
49
97
22
23
37
82
165
20
21
29
70
147
A Few Founding Lineages Gave Rise to the Current
Population
The genealogical information is limited to four centuries and therefore we cannot investigate the coalescence of GLs before the 17th century. We thus analyzed
mitochondrial and Y chromosome DNA to verify the
coalescence of GLs by grouping those who present the
same haplotype in what we refer to as a ‘founding lineage’ (FL). It is important to notice that each lineage can
include more than one individual, and that is why we
talk about lineages and not in terms of individuals. Samples for the analysis were chosen according to GLs in
order to sample almost all possible different haplotypes.
In fact due to the high degree of relatedness among individuals, a ‘random’ sampling could easily lead to underestimation of the number of different haplotypes.
For each GL, at least two individuals were chosen (if
available) to assure the concordance of results within it.
The number of female FLs was determined from the
Hum Hered 2007;64:123–135
127
128
Table 2. Distribution of the HVR-I types into the examined haplogroups and polymorphisms of the HVR-I sequence
16129
16136
16145
16146
16148
16153
16163
16184
16186
16187
16192
16193
16194
16195
16215
16225
16244
16249
16250
16257
16261
16262
16279
16285
16287
16294
16295
16299
16301
16312
16344
16353
16357
16363
16369
–
–
–
–
–
–
–
–
–
–
–
T A –
–
–
T –
–
d
–
–
– C –
–
–
–
–
–
–
–
–
–
–
–
–
–
–
– C –
–
–
–
–
–
–
–
–
–
–
–
–
–
–
–
–
T A –
–
–
T –
–
d
–
–
– C –
–
–
–
T –
–
–
–
–
–
–
–
–
–
– C –
–
– C –
3
8.0
–
–
–
–
–
–
–
–
–
– A –
–
–
–
T A –
–
–
T –
–
d
–
–
– C –
–
–
–
–
–
–
–
–
–
–
–
–
–
–
– C –
–
–
–
4
7.6
–
–
–
–
–
–
–
–
–
–
–
–
T –
–
T A –
–
–
T –
–
d
–
–
– C –
–
–
–
T –
–
–
–
–
–
–
–
– C A C –
–
–
–
–
H
5
4.2
– G –
–
–
–
–
–
–
–
–
–
–
–
T A –
–
–
T –
–
d
–
–
– C –
–
–
–
–
–
–
–
–
–
–
–
–
–
–
–
–
–
(61.6)
6
0.7
–
–
–
– C –
–
–
–
–
–
–
–
–
–
T A –
–
–
T –
–
d
–
–
– C –
–
–
–
–
–
–
–
–
–
–
–
–
–
–
– C –
–
–
–
–
7
0.5
–
–
–
–
–
–
–
–
–
–
–
–
–
–
–
T A –
–
–
–
–
d
–
–
– C –
–
–
–
–
–
–
–
–
–
–
–
– G –
– C –
–
– C –
8
0.5
–
–
–
–
–
–
–
–
–
–
–
–
–
–
–
T –
–
–
–
–
–
–
–
–
–
– C –
–
–
–
–
–
–
–
–
–
–
–
–
–
–
– C –
– C –
9
0.5
–
–
–
–
–
–
–
–
–
–
–
–
–
–
–
–
–
–
–
–
–
–
–
–
–
– C –
–
–
–
T –
–
–
–
– G –
–
–
–
– C – C –
–
–
10
0.3
–
–
– C –
–
–
–
–
–
–
–
–
–
–
– A –
–
T –
–
–
d
–
– C –
–
–
–
–
–
–
–
–
–
–
–
–
–
–
– C –
–
–
–
–
–
–
–
–
–
16321
16126
–
–
16320
16111
–
–
16224
16104
–
–
16218
16093
–
9.4
16189
16086
29.9
2
16183
16069
1
16172
% a,b
16037
Hum Hered 2007;64:123–135
HVR–ty
pe
16017
Haplogroup
(% a)
– C –
–
–
T
11
1.7
–
–
–
–
–
–
– C –
–
–
–
–
– G T A –
–
–
–
–
–
d
–
–
– C – C –
–
–
–
–
–
–
–
–
T –
– C – C –
–
–
–
(2.6)
12
0.7
–
–
–
–
–
–
– C –
–
–
–
–
– G T A –
–
–
–
–
–
d
–
–
– C – C –
–
T –
–
–
–
–
–
T –
– C – C –
–
–
–
–
13
0.2
–
–
–
–
–
–
– C –
–
–
–
–
– G T A –
T –
–
–
–
–
–
–
– C –
–
–
–
–
–
–
–
–
–
–
T –
–
–
– C –
– C –
–
14
1.4
–
–
–
–
–
T –
–
–
–
–
–
T A –
–
–
–
–
–
d
–
–
– C –
–
–
–
–
–
–
–
–
–
–
–
–
–
– C G –
–
–
–
preH
15
12.8
G –
–
– C –
–
–
–
–
–
–
–
–
–
T A –
–
–
T –
–
d
–
–
– C –
–
–
–
–
–
–
–
–
–
–
– C –
–
– C –
–
–
–
–
(17.7)
16
4.9
–
–
–
–
–
–
–
–
–
–
–
–
T A –
–
–
T –
–
d
–
–
– C –
–
–
–
–
–
–
–
–
–
–
– C –
–
– C –
–
–
–
–
–
U
(1.4)
–
–
–
–
–
–
–
X
17
2.1
–
–
–
–
–
–
–
–
–
–
–
–
–
–
–
T A A –
–
–
–
–
–
–
–
–
–
–
T –
–
–
–
T –
–
–
–
–
–
–
– C –
–
–
–
–
(4.2)
18
2.1
–
–
–
–
–
–
–
–
–
–
–
–
–
–
–
T –
–
–
–
–
– C –
–
–
–
–
T –
–
–
–
T –
–
–
–
–
–
–
– C –
–
–
–
–
–
–
Colonna /Nutile /Astore /Guardiola /
Antoniol/Ciullo /Persico
J
19
1.4
–
–
T –
–
–
– C –
–
–
–
–
–
–
T A –
–
–
T –
T d
–
–
– C –
–
–
–
–
–
–
–
–
–
–
–
– G C – C –
–
–
–
–
(2.9)
20
1.2
–
–
T –
–
–
– C –
–
–
–
–
–
–
T A –
–
–
T –
–
d
–
–
– C –
–
–
–
–
–
–
–
–
–
–
–
–
–
–
– C –
–
–
–
–
21
0.3
–
–
T –
–
–
– C –
– A –
–
–
–
T A –
–
–
T –
–
d
–
–
– C –
–
–
–
–
–
T –
–
–
–
–
–
–
–
– C –
–
–
– C
–
–
K
22
1.9
–
–
–
–
–
–
–
–
–
–
–
–
–
–
–
T A –
–
–
T –
–
d
–
–
– C C –
–
–
–
–
–
–
–
–
–
– C – C –
–
–
–
(4.2)
24
1.4
–
–
–
–
–
–
–
–
–
–
–
–
–
–
–
T A –
–
–
T T –
d
–
–
–
– C –
–
–
–
–
–
– G G –
–
–
– C – C –
–
–
–
–
23
0.9
–
–
–
– C –
–
–
–
–
–
–
–
–
–
T A –
–
–
T –
–
d
–
–
– C C –
–
–
–
–
–
–
–
–
–
–
–
– C – C –
–
–
–
–
25
2.1
–
–
–
–
–
– A –
–
–
–
–
–
T –
–
–
–
–
–
–
–
–
T –
–
–
–
– C –
–
–
–
–
–
–
–
–
– C – C –
–
–
–
–
– A –
–
–
T –
–
–
–
–
–
–
–
–
–
–
–
–
–
– C –
–
–
–
–
–
–
–
–
– C – C –
–
–
–
–
T A –
–
–
T –
–
d
–
–
–
–
–
–
–
–
–
–
–
–
–
–
–
–
–
–
–
–
M
(2.1)
–
–
n.d.
26
1.0
–
–
–
–
–
–
–
(2.3)
27
0.3
–
–
–
–
–
–
T –
–
–
–
–
–
–
–
– A –
The position of polymorphisms are relative to the reference sequence. n.d. = not determined; d = deletion.
a Referred to the female population in the study sample (n = 567).
b Relative to HVR-I type.
–
–
– A C –
–
Table 3. Number of different haplotypes
in the HVR-I region in different
populations
Population
Features
Number of
individuals
HVR-I type
number
Campora
Croatian-Italiansb
Abruzzo-Moliseb
Campaniab
Laziob
Pugliab
genetic isolate
linguistic minority
open population
open population
open population
open population
46a
41
73
48
52
26
27
29
51
41
37
24
a
Here we report the number of different GLs because the sampling has been done
according to GLs. The actual number of individuals is 92.
b From Babalini et al. 2005.
number of different mitochondrial DNA (mtDNA) haplotypes present in the sample. By the analysis of 92 sequences of individuals belonging to 46 GLs (2 for each
female GL) of the HVR-I region, we described 27 different HVR-I types (table 2). In a similar study [37], Babalini and colleagues observed a similar paucity of haplotypes in a group of Croatian-Italians constituting a linguistic minority, compared to samples coming from
open populations as indicated in table 3, where we have
added Campora for comparison. It is worth mentioning
that Campora is located within the open population of
Campania reported in table 3.
Polymorphisms characterizing each HVR-I type are
reported in table 2 where their position according to the
reference sequence [21, 22] is indicated together with the
percentage of living females that each type comprises. We
interpret the presence of 27 different HVR-I types as evidence of 27 different FLs, with the most common including 29.9% of living females.
We also performed a study to ascertain which haplogroups, according to the main classification [23, 24]
contain the different HVR-I types. We characterized a set
of polymorphisms describing the haplogroups typical of
European, Asian and African populations and we found
nine different groups in the study sample (table 2). There
was no African contribution, and only a minor Asian
contribution (haplogroup M). As expected, the majority
of females (61.4%) belongs to the H haplogroup, the most
frequent in Europe. A small percentage indicated as ‘n.d.’
was of uncertain classification.
As was done for the female FLs, the male FLs were determined by counting the number of different haplotypes
on the non-recombinant Y chromosome region in the
male sample. We used an informative Y-STR core set
(DYS19, DYS389I, DYS390, DYS391, DYS392, DYS393,
A Genetic Isolate in South Italy
m17
1%
m19
m18 1%
1%
m20
1%
<1%
2%
n.s.
8%
m16
1%
m15
1%
m1
15%
m14
2%
m13
2%
m12
3%
m11
3%
m10
4%
m2
15%
m3
7%
m9
4% m8
4% m7
5%
m6
6%
m5
7%
m4
7%
Fig. 4. Male founding lineages (FLs). The percentage of the population in the study sample that each FL represents is indicated. The
category ‘!1%’ includes all those lineages whose descendents represent less than 1% of the male population. n.s. = not sampled.
DYS385) to define the haplotypes. We found 24 different
haplotypes that suggest the presence of 24 FLs (fig. 4).
Linkage Disequilibrium in Campora
It has been well demonstrated that isolated populations show extended regions of LD [38, 39]. Within the
Campora population, we analysed a low recombination
rate and non-coding DNA segment located on the Xq13
chromosome region [40] that has been previously charHum Hered 2007;64:123–135
129
Table 4. LD on the X chromosome
Marker pairs
DXS8092
DXS8092
DXS1225
DXS1225
DXS8037
DXS8092
DXS8037
DXS8092
DXS983
DXS983
DXS8037
DXS8092
DXS983
DXS983
DXS983
Markers distance
DXS8037
DXS986
DXS986
DXS8092
DXS1225
DXS1225
DXS8092
DXS8092
DXS8092
DXS8037
DXS986
DXS986
DXS1225
DXS8092
DXS986
Mb
cMa
0.00
1.01
1.17
1.62
3.98
3.98
4.14
4.14
4.64
4.68
5.15
5.15
8.65
8.82
9.82
0.40
0.20
0.50
0.30
0.00
0.40
0.30
0.10
1.60
2.00
0.50
0.10
2.00
1.70
1.50
SAAMIa
(n = 54)
GAVOIb
(n = 73)
Campora
(n = 53)
Swedena
(n = 41)
Sardiniab
(n = 73)
UKb
(n = 73)
Finland a
(n = 80)
Estoniaa
(n = 45)
0.000
0.000
0.000
0.000
0.091
0.000
0.012
0.000
0.000
0.300
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.008
0.000
0.004
0.000
0.000
0.000
0.003
0.000
0.170
0.245
0.003
0.045
0.001
0.104
0.000
0.009
0.008
0.002
0.001
0.000
0.407
0.119
0.006
0.001
0.050
0.042
0.028
0.618
0.448
0.000
0.242
0.676
0.033
0.102
0.746
0.924
0.256
0.332
0.480
0.082
0.400
0.280
0.322
0.166
0.000
0.710
0.921
0.630
0.319
0.876
0.036
0.302
0.125
0.169
0.142
0.825
0.620
0.884
0.703
0.000
0.647
0.320
0.002
0.492
0.974
0.149
0.975
0.940
0.338
0.243
0.253
0.180
0.092
0.393
0.000
0.836
0.283
0.238
0.044
0.314
0.683
0.620
0.331
0.630
0.565
0.829
0.072
0.143
0.688
0.000
0.488
0.120
0.625
0.065
0.153
0.104
0.739
0.100
0.520
0.730
0.468
Marker pairs are ordered according to the distance between markers. Significant LD associated p-values are in bold.
a
From Laan and Paabo, 1997. b From Zavattari et al., 2000.
Table 5. Genomewide LD in Campora compared to the isolated populations of Palau and GRIP
Recombination interval
Number of marker pairs
% Significant p-values
Campora GRIP
Palau
Campora GRIP
Palau
Campora
GRIP
Palau
<0.02
0.02–0.05
0.05–0.1
<0.1
0.1–0.2
0.2–0.3
0.3–0.4
>0.4
325
940
1,827
3,092
4,206
5,142
6,827
9,139
–
–
–
–
–
–
–
–
64.6
50.9
27.1
38.3
12.0
6.9
6.8
0.6
–
–
–
16.2
11.6
11.6
7.1
4.4
0.11380.077
0.07680.069
0.04380.063
0.06080.07
0.01580.055
0.00480.053
0.00380.053
0.00380.054
0.05080.008
0.03780.003
0.02480.002
0.03080.001
0.01080.001
0.00380.001
0.00080.001
0.00180.001
–
–
–
0.031
0.019
0.017
0.012
0.009
65
393
775
1,233
1,705
2,124
2,720
3,520
35.4
24.7
17.7
20.8
9.0
6.4
4.3
5.1
Average Dadj 8 SD
Data on Palau from Devlin B et al. (2001) and on GRIP from Aulchenko YS et al. (2004).
acterized in both large and isolated populations [41–44].
In this region, tests for disequilibrium among six STRs
spanning about 10 Mb were carried out in a sample of 63
unrelated males. Although in the Campora sample, the
average number of alleles at each locus is not significantly reduced (data not shown), only 44 different haplotypes
in 63 individuals were found. The resulting LD-associated
p-values relative to the 15 possible pairs among the six
STRs grouped according to the distance between markers
are shown in table 4. The p-values from similar studies
on other isolated (Saami, Gavoi) and large populations
are also reported [41, 42]. It is evident that in Campora,
130
Hum Hered 2007;64:123–135
like in the other isolated populations, a consistent number of marker pairs (12 out of 15) are in significant LD.
Results obtained on the X chromosome have been
confirmed also in the analysis relative to the autosomal
part of the genome. LD-associated p-value and Dadj were
evaluated among all possible pairs of syntenic markers
and then marker pairs were grouped according to their
recombination interval in classes as shown in table 5. For
each class the average of the LD-associated measures is
reported and compared with two other isolated populations: the GRIP from the South West Netherland [18] and
the one of Palau from Oceania [45]. As shown in the table,
Colonna /Nutile /Astore /Guardiola /
Antoniol/Ciullo /Persico
Table 6. Detection of heterozygosity excess caused by the bottleneck
Dataset
Sardiniaa
Campora
a
b
c
d
e
Sample size (2n)
Number of loci
Average observed heterozygosity 8 SD
Average number of alleles 8 SD
584
584
584
584
584
204
202
203
201
203
0.72780.117 0.72580.110 0.72780.109 0.72380.121 0.71980.121
982
882
982
882
982
TPM
SSM = 90%
Sign test
exp
obs
p value
excess
120.29
137
0.00984
excess
119.19
147
0.00003
excess
119.46
145
0.00013
excess
118.13
142
0.00033
excess
118.86
135
0.01195
S.D.T.
IT2I
p value
excess
3.062
0.00110
excess
3.805
0.00007
excess
4.381
0.00001
excess
4.290
0.00001
excess
3.226
0.00063
–b
Wilcoxon
p value
excess
0.00001
excess
<10e-5
excess
<10e-5
excess
<10e-5
excess
<10e-5
–b
Sign test
exp
obs
p value
excess
117.99
199
<10e-5
excess
116.95
198
<10e-5
excess
111.28
199
<10e-5
excess
115.77
198
<10e-5
excess
116
197
<10e-5
0.001
S.D.T.
⌱T2⌱
p value
excess
17.0
<10e-4
excess
17.3
<10e-5
excess
17.6
<10e-5
excess
17.4
<10e-5
excess
17.0
<10e-5
deficiency
15.1
<10e-5
Wilcoxon
p value
excess
<10e-4
excess
<10e-5
excess
<10e-5
excess
<10e-5
excess
<10e-5
deficiency
<10e-5
IAM
23
10
–b
–b
–b
deficiency
–b
exp/obs = Expected/observed number of loci with heterozygosity excess; excess/deficiency refers to the number of loci in heterozygosity.
a Data from Cornuet and Luikart 1996. b Data not available.
when recombination intervals are !0.1, both the percentage of pairs in significant disequilibrium and the Dadj in
Campora are doubled compared to the GRIP population
and more than doubled compared to Palau. In classes of
greater recombination interval, the trend changes and
values become comparable among populations.
Overall, these results indicate that in the genome of
the population of Campora, extended regions show significant LD as in other isolated and sub-isolated populations.
The Population of Campora Experienced a Bottleneck
in the Past
The famine of the 16th century almost halved the population and the plague of 1656 halved it again so that in
1669 the population of Campora consisted of only 140
plague survivors (fig. 2) [19].
A temporary excess of heterozygosity, relative to that
expected on the basis of the number of alleles, takes place
when a bottleneck occurs. Such an excess is caused by the
more rapid decline of the number of alleles compared to
the decline of gene diversity (heterozygosity), as rare alleles are lost more quickly. The period of time during
which it is possible to estimate the heterozygosity excess
depends on the effective population size and on the extent
of the population reduction at the bottleneck [34]. We assessed heterozygosity excess in the genome of the Campora population using 1,012 microsatellites in a sample of
584 individuals. The 1,012 loci were ascertained to be in
Hardy-Weinberg equilibrium in a sample of 80 individuals taking into account for their relatedness. In table 6, we
show the results of the bottleneck analysis under the TPM
model in which the SMM component has been set to 90%
and under the IAM. This last option is not representative
A Genetic Isolate in South Italy
Hum Hered 2007;64:123–135
131
Table 7. Inbreeding evaluated from the genealogy in different isolates
Pedigree
Founding lineages
Generations
Members
Sample population size
Inbreeding
Mean 8 SD
Median
1st quartile
3rd quartile
Campora
Perdasdefogu
Talana
S-leut Hutteritesa
53
17
3,906
1,184
–b
15d
2,506d
821d
44c
16d
5,219d
876e
64
13
1,623
806
0.00680.009
0.004
0.001
0.008
0.01080.021e
0.005 d
0.001 d
0.010 d
0.01880.022e
0.015e
0.007e
0.021e
0.03480.015
–b
–b
–b
Features of the pedigree used in the calculations are reported.
a
From Weiss et al., 2005; b data not available; c from Angius et al., 2001; d from Falchi et al., 2004; e Angius
A., personal communication.
of the model of microsatellites evolution but it has been
considered only for the purpose of comparing datasets of
Campora with the only other data available about human.
These data belong to an expanding population, far removed from a bottleneck [31]. In Campora, an excess of
heterozygosity is detected under both the TPM and the
IAM models, suggesting that a bottleneck has occurred.
The results of the genetic analysis were matched by the
study of the genealogy. We estimated the percentage of the
living population derived from FLs originating during the
plague period. This period was defined as one generation
after the first documented case of plague in the nearby
village of Novi Velia (i.e. 32 years after the year 1656). FLs
were dated according to the date of the most elderly GLs.
We found that 82% of the 1,184 individuals in the study
sample belong to ‘plague FLs’. In other words, it is legitimate to consider the Campora population as derived from
the survivors of the bottleneck, some 13 generations ago.
those of other populations in table 7 where the genealogy
structure of the sample used in the analysis is also reported [46–48]. In Campora, the average value of the inbreeding coefficient is modest compared to that of populations that have experienced extreme isolation like the
Hutterites [49]. The distribution of f in Campora is instead comparable to those of the two isolates from Sardinia. In addition, similar values of the inbreeding coefficients were estimated in other European genetic isolates, such as Wurtenburg and Val di Parma [50].
We then calculated the average f per generation and
plotted it together with the exogamic marriages as shown
in figure 3. From the graph, it is evident that, since the
bottleneck had occurred, f increased throughout the period of isolation but this trend changed when the recent
gene flow occurred.
Discussion
Inbreeding in the Population of Campora
Average inbreeding ( f ) in the study sample (n = 1,184)
was evaluated from the genealogy using a 3,906-member
sub-pedigree that included all ancestors of living individuals and that was distributed over 17 generations and
over four centuries. Two different computational methods were used and gave consistent results: 82% of the living population have a value of f different from zero; the
average inbreeding is 0.00651 8 0.00915. Furthermore,
0.93% of the population show a value of f 1 0.0625 (first
cousin), and 9.44% show f 1 0.0156 (second cousin). The
f value in the Campora population is compared with
132
Hum Hered 2007;64:123–135
In this study, we traced the genetic history of the population of Campora and we found evidence suggesting
that Campora can be defined as a genetic isolate. The
population features and the extent of isolation have been
determined on the basis of consistent information coming from the analysis of historical, demographic and genetic data, as well as through comparisons with other
populations.
According to historical data, the first considerable nucleus of the population appeared around the 11th century
and was of Greek and Lucanian origin. Accurate demoColonna /Nutile /Astore /Guardiola /
Antoniol/Ciullo /Persico
graphic information is available for the last four centuries.
On the basis of this information, we evaluated exogamy in
the last three centuries. We observed that exogamy was
present through the 18th and 19th centuries, although it
has never been consistent (less than 20%) indicating that
overall, the population remained subject to a constant but
weak gene flow during its growth (mainly due to exogamic marriages from nearby villages). However, as exogamy
had conspicuously increased in the last century, we decided to take it into account. Thus we planned a strategy to
define a study sample, tracing back matrilineages and
patrilineages in order to exclude those individuals whose
ancestors entered the pedigree after 1890. We performed
this study on the genealogy not only to define our study
sample, but also to show that a ‘core’ population can be
obtained from complex genealogies of populations in
which isolation starts to decline. In fact, many populations, like Campora, have experienced periods of geographical isolation in the past and only recent exposure to
migration. Such populations are probably going to lose
their characteristic features in the near future, and therefore they deserve critical attention in the present day [51].
We estimated that in 96.7% of the study sample population there are only 17 and 20 female and male sex-specific
haplotypes, respectively (10 out of 27 female FLs and 4 out
of 24 male FLs have a number of descendents !1% of the
total population) thus indicating that the population of
Campora is genetically homogeneous. Moreover, there is a
striking difference in the number of living descendents
among FLs, most probably because of random sorting of
alleles through genetic drift. The same difference has been
found in a similar study in the village of Talana in Sardinia, where only eight Y chromosome haplotypes represent
70% of current males and ten mtDNA haplogroups represent 77% of current females [17]. Correspondingly, in
Campora, 74% of the living males are represented by ten
different haplotypes and seven mitochondrial haplotypes
account for 76.6% of living females.
Evidence of genetic homogeneity also comes from the
LD analysis. We have demonstrated that LD extends over
wide regions in the genome of the population of Campora. Moreover the comparison with the GRIP population,
whose founding nucleus is dated to the middle of the 18th
century [18], suggests that the founding nucleus of Campora must be earlier to it. This is consistent with the historical hypothesis of the first settlement of Campora in
the 11th century.
Using a dense map of microsatellite markers, we observed that the population recently experienced a bottleneck. To our knowledge, this is the first example of bot-
tleneck evaluation in a human population based on such
a large set of genetic markers. Most likely the bottleneck
coincides with historical reports about the plague of the
17th century through which almost all the ancestors of
the living individuals have passed. This is also suggested
by the ‘dating’ of FLs, which shows that the living individuals derive from ancestors who were already present
in the village before the plague and thus survived it. Thus
due to the bottleneck, Campora can be considered a
young isolate (! 20 generations) according to the Heutink
and Oostra classification [51], despite the fact that its
founding nucleus seems to be more ancient.
Inbreeding is present in the population (mean inbreeding coefficient is 0.006), but is not as high as in ‘extreme’ human isolates, like the S-leu Hutterites [49],
which could be considered close to the upper limit for human populations. Campora instead can be considered a
‘mild’ isolate, like two other isolates from Sardinia [17,
47], where the average inbreeding in the population is
also moderate. These kinds of isolates are certainly more
common in humans and their usefulness in complex trait
mapping has already been demonstrated.
A reduced number of founders and the presence of inbreeding provide evidence that isolation has occurred.
Further, the progressive increase of inbreeding since the
bottleneck suggests that mating was occurring mainly
between individuals who were becoming more and more
similar genetically; apparently, people from outside the
village were only marginally participating in the mating.
Hence, this inbreeding trend provides evidence that the
population expanded under conditions of isolation, even
though it is possible that, because of a partial availability
of genealogical data in the 17th century, inbreeding in the
first generations is slightly underestimated. We note that
for the 19th and 20th centuries, inbreeding sharply decreases when exogamy rises, which is an argument in favour of the completeness of the genealogical data.
The determination of GLs from the genealogy was crucial to achieve many of the results in our present study.
Due to the presence of the Catholic Church in Italy, written records of births, marriages, and deaths have been produced since the 17th century. Consequently, wide ranging
genealogical information is available for many villages
and provides a valuable resource for population genetics.
We want to emphasize the role that genealogical information has played in our study, showing how it can integrate
and support the genetic analyses. In fact, matrilinear and
patrilinear GLs allowed us to assemble the study sample
and played a key role in sampling for mtDNA and Y chromosome haplotype analyses. In addition, GLs tell us how
A Genetic Isolate in South Italy
Hum Hered 2007;64:123–135
133
successful was the DNA sampling. According to genealogical data, we managed to sample almost all the population. We found that the 9% of males for which no DNA is
available are grouped in 25 different lineages, each lineage
thus contributing to a very small number of males. In contrast, the female sampling was more successful: only about
2% of the females, corresponding only to three different
lineages, could not be sampled.
With this work, we have demonstrated that Campora
is a young and homogeneous isolate. We also proved the
usefulness of the comparison of genealogical and genetic
information to investigate the structure of human populations. These characteristics, together with the environmental uniformity and an accurate phenotype description, make this population a valuable resource for the
study of complex traits.
Acknowledgments
We thank the population of the village of Campora for their
kind cooperation, Don Guglielmo Manna for helping in the interaction with the population and the Institutions. We thank Lucio
Luzzatto, Guido Barbujani, Claudia Angelini, Catherine Bourgain, Yurii S. Aulchenko, Mario Aversano, Francesco Cucca and
Patrizia Zavattari for valuable comments and suggestions; Jim
McGhee for reviewing the manuscript; Mrs. M. Terracciano for
technical assistance. We also want to thank two anonymous Reviewer whose suggestions were helpful for the improvement of the
manuscript. This work was supported by grants from Ente Parco
Nazionale del Cilento e Vallo di Diano, the Associazione Italiana
per la Ricerca sul Cancro (AIRC), the Assessorato Ricerca Regione Campania, the Fondazione Banco di Napoli to MGP.
References
1 Varilo T, Peltonen L: Isolates and their potential use in complex gene mapping efforts.
Curr Opin Genet Dev 2004; 14:316–323.
2 Sheffield VC, Stone EM, Carmi R: Use of isolated inbred human populations for identification of disease genes. Trends Genet 1998;
14:391–396.
3 Arcos-Burgos M, Palacio G, Sanchez JL,
Londono AC, Uribe CS, Jimenez M, Villa A,
Anaya JM, Bravo ML, Jaramillo N, Espinal
C, Builes JJ, Moreno M, Jimenez I: Multiple
sclerosis: Association to HLA DQalpha in a
tropical population. Exp Clin Immunogenet
1999;16:131–138.
4 Gianfrancesco F, Esposito T, Ombra MN,
Forabosco P, Maninchedda G, Fattorini M,
Casula S, Vaccargiu S, Casu G, Cardia F,
Deiana I, Melis P, Falchi M, Pirastu M: Identification of a novel gene and a common variant associated with uric acid nephrolithiasis
in a Sardinian genetic isolate. Am J Hum
Genet 2003;72:1479–1491.
5 Peltonen L, Jalanko A, Varilo T: Molecular
genetics of the Finnish disease heritage.
Hum Mol Genet 1999;8:1913–1923.
6 Sheffield VC: Use of isolated populations in
the study of a human obesity syndrome, the
Bardet-Biedl syndrome. Pediatr Res 2004;
55:908–911.
7 Anaya JM, Correa PA, Mantilla RD, ArcosBurgos M: Rheumatoid arthritis association
in Colombian population is restricted to
HLA-DRB1*04 QRRAA alleles. Genes Immun 2002;3:56–58.
8 Anderson SL, Coli R, Daly IW, Kichula EA,
Rork MJ, Volpi SA, Ekstein J, Rubin BY: Familial dysautonomia is caused by mutations
of the IKAP gene. Am J Hum Genet 2001;68:
753–758.
134
9 Ciullo M, Bellenguez C, Colonna V, Nutile T,
Calabria A, Pacente R, Iovino G, Trimarco B,
Bourgain C, Persico MG: New susceptibility
locus for hypertension on chromosome 8q by
efficient pedigree-breaking in an Italian isolate. Hum Mol Genet 2006;15:1735–1743.
10 Pardo LM, MacKay I, Oostra B, van Duijn
CM, Aulchenko YS: The effect of genetic
drift in a young genetically isolated population. Ann Hum Genet 2005; 69(Pt 3):288–
295.
11 Patton MA: Genetic studies in the Amish
community. Ann Hum Biol 2005; 32: 163–
167.
12 Peltonen L, Palotie A, Lange K: Use of population isolates for mapping complex traits.
Nat Rev Genet 2000;1:182–190.
13 Arcos-Burgos M, Muenke M: Genetics of
population isolates. Clin Genet 2002; 61:
233–247.
14 Bourgain C, Genin E: Complex trait mapping in isolated populations: Are specific
statistical methods required? Eur J Hum
Genet 2005;13:698–706.
15 Wright AF, Carothers AD, Pirastu M: Population choice in mapping genes for complex
diseases. Nat Genet 1999;23:397–404.
16 Vitart V, Biloglav Z, Hayward C, Janicijevic
B, Smolej-Narancic N, Barac L, Pericic M,
Klaric IM, Skaric-Juric T, Barbalic M, Polasek O, Kolcic I, Carothers A, Rudan P, Hastie
N, Wright A, Campbell H, Rudan I: 3000
years of solitude: extreme differentiation in
the island isolates of Dalmatia, Croatia. Eur
J Hum Genet 2006;14:478–487.
Hum Hered 2007;64:123–135
17 Angius A, Melis PM, Morelli L, Petretto E,
Casu G, Maestrale GB, Fraumene C, Bebbere
D, Forabosco P, Pirastu M: Archival, demographic and genetic studies define a Sardinian sub-isolate as a suitable model for mapping complex traits. Hum Genet 2001; 109:
198–209.
18 Aulchenko YS, Heutink P, Mackay I, BertoliAvella AM, Pullen J, Vaessen N, Rademaker
TA, Sandkuijl LA, Cardon L, Oostra B, van
Duijn CM: Linkage disequilibrium in young
genetically isolated Dutch population. Eur J
Hum Genet 2004;12:527–534.
19 Del Mercato P, Infante A: Cilento, uomini e
vicende. Salerno, Reggiani Editore, 1980.
20 O’Connell JR, Weeks DE: PedCheck: A program for identification of genotype incompatibilities in linkage analysis. Am J Hum
Genet 1998;63:259–266.
21 Anderson S, Bankier AT, Barrell BG, de
Bruijn MH, Coulson AR, Drouin J, Eperon
IC, Nierlich DP, Roe BA, Sanger F, Schreier
PH, Smith AJ, Staden R, Young IG: Sequence
and organization of the human mitochondrial genome. Nature 1981;290:457–465.
22 Ingman M, Kaessmann H, Paabo S, Gyllensten U: Mitochondrial genome variation and
the origin of modern humans. Nature 2000;
408:708–713.
23 Macaulay V, Richards M, Hickey E, Vega E,
Cruciani F, Guida V, Scozzari R, BonneTamir B, Sykes B, Torroni A: The emerging
tree of West Eurasian mtDNAs: a synthesis
of control-region sequences and RFLPs. Am
J Hum Genet 1999;64:232–249.
Colonna /Nutile /Astore /Guardiola /
Antoniol/Ciullo /Persico
24 Torroni A, Huoponen K, Francalacci P,
Petrozzi M, Morelli L, Scozzari R, Obinu D,
Savontaus ML, Wallace DC: Classification of
European mtDNAs from an analysis of three
European populations. Genetics 1996; 144:
1835–1850.
25 Karigl G: A recursive algorithm for the calculation of identity coefficients. Ann Hum
Genet 1981;45(Pt 3):299–305.
26 Bourgain C, Hoffjan S, Nicolae R, Newman
D, Steiner L, Walker K, Reynolds R, Ober C,
McPeek MS: Novel case-control test in a
founder population identifies P-selectin as
an atopy-susceptibility locus. Am J Hum
Genet 2003;73:612–626.
27 Swedlund AC, Boyce AJ: Mating structure in
historical populations: estimation by analysis of surnames. Hum Biol 1983;55:251–262.
28 Abecasis GR, Cookson WO: GOLD – graphical overview of linkage disequilibrium. Bioinformatics 2000;16:182–183.
29 Aulchenko YS, Axenovich TI, Mackay I, van
Duijn CM: miLD and booLD programs for
calculation and analysis of corrected linkage
disequilibrium. Ann Hum Genet 2003;67(Pt
4):372–375.
30 Zaykin D, Zhivotovsky L, Weir BS: Exact
tests for association between alleles at arbitrary numbers of loci. Genetica 1995;96:169–
178.
31 Cornuet JM, Luikart G: Description and
power analysis of two tests for detecting recent population bottlenecks from allele frequency data. Genetics 1996; 144:2001–2014.
32 Bourgain C, Abney M, Schneider D, Ober C,
McPeek MS: Testing for Hardy-Weinberg
equilibrium in samples with related individuals. Genetics 2004;168:2349–2361.
33 McPeek MS, Wu X, Ober C: Best linear unbiased allele-frequency estimation in complex pedigrees. Biometrics 2004; 60: 359–
367.
34 Maruyama T, Fuerst PA: Population bottlenecks and nonequilibrium models in population genetics. II. Number of alleles in a
small population that was formed by a recent
bottleneck. Genetics 1985;111:675–689.
A Genetic Isolate in South Italy
35 Cornuet J, Luikart G: Empirical evaluation
of a test for identifying recently bottlenecked
populations from allele frequency data. Conservation Biol 1998; 12:228 -237.
36 Di Rienzo A, Peterson AC, Garza JC, Valdes
AM, Slatkin M, Freimer NB: Mutational processes of simple-sequence repeat loci in human populations. Proc Natl Acad Sci USA
1994;91:3166–3170.
37 Babalini C, Martinez-Labarga C, Tolk HV,
Kivisild T, Giampaolo R, Tarsi T, Contini I,
Barac L, Janicijevic B, Martinovic Klaric I,
Pericic M, Sujoldzic A, Villems R, Biondi G,
Rudan P, Rickards O: The population history
of the Croatian linguistic minority of Molise
(Southern Italy): A maternal view. Eur J
Hum Genet 2005;13:902–912.
38 Ardlie KG, Kruglyak L, Seielstad M: Patterns
of linkage disequilibrium in the human genome. Nat Rev Genet 2002;3:299–309.
39 Varilo T, Paunio T, Parker A, Perola M, Meyer J, Terwilliger JD, Peltonen L: The interval
of linkage disequilibrium (LD) detected with
microsatellite and SNP markers in chromosomes of Finnish populations with different
histories. Hum Mol Genet 2003;12:51–59.
40 Kaessmann H, Heissig F, von Haeseler A,
Paabo S: DNA sequence variation in a noncoding region of low recombination on the
human X chromosome. Nat Genet 1999; 22:
78–81.
41 Laan M, Paabo S: Demographic history and
linkage disequilibrium in human populations. Nat Genet 1997;17:435–438.
42 Zavattari P, Deidda E, Whalen M, Lampis R,
Mulargia A, Loddo M, Eaves I, Mastio G,
Todd JA, Cucca F: Major factors influencing
linkage disequilibrium by analysis of different chromosome regions in distinct populations: demography, chromosome recombination frequency and selection. Hum Mol
Genet 2000;9:2947–2957.
43 Latini V, Sole G, Doratiotto S, Poddie D,
Memmi M, Varesi L, Vona G, Cao A, Ristaldi
MS: Genetic isolates in Corsica (France):
linkage disequilibrium extension analysis
on the Xq13 region. Eur J Hum Genet 2004;
12:613–619.
44 Laan M, Wiebe V, Khusnutdinova E, Remm
M, Paabo S: X-chromosome as a marker for
population history: linkage disequilibrium
and haplotype study in Eurasian populations. Eur J Hum Genet 2005;13:452–462.
45 Devlin B, Roeder K, Otto C, Tiobech S, Byerley W: Genome-wide distribution of linkage
disequilibrium in the population of Palau
and its implications for gene flow in Remote
Oceania. Hum Genet 2001;108:521–528.
46 Fraumene C, Petretto E, Angius A, Pirastu
M: Striking differentiation of sub-populations within a genetically homogeneous isolate (Ogliastra) in Sardinia as revealed by
mtDNA analysis. Hum Genet 2003; 114: 1–
10.
47 Falchi M, Forabosco P, Mocci E, Borlino CC,
Picciau A, Virdis E, Persico I, Parracciani D,
Angius A, Pirastu M: A genomewide search
using an original pairwise sampling approach for large genealogies identifies a new
locus for total and low-density lipoprotein
cholesterol in two genetically differentiated
isolates of Sardinia. Am J Hum Genet 2004;
75:1015–1031.
48 Weiss LA, Abney M, Cook EH, Jr, Ober C:
Sex-specific genetic architecture of whole
blood serotonin levels. Am J Hum Genet
2005;76:33–41.
49 Weiss LA, Abney M, Parry R, Scanu AM,
Cook EH, Jr, Ober C: Variation in ITGB3 has
sex-specific associations with plasma lipoprotein(a) and whole blood serotonin levels
in a population-based sample. Hum Genet
2005;117:81–87.
50 Crawford MH, Mielke JH, Morton NE (1982)
Kinship and inbreeding in populations of
Middle Eastern origin and controls pp 449–
466, in Current Developments in Anthropological Genetics. Vol. II. Ecology and Population Structure., Press P, Editor. 1982:New
York.
51 Heutink P, Oostra BA: Gene finding in genetically isolated populations. Hum Mol
Genet 2002;11:2507–2515.
Hum Hered 2007;64:123–135
135