Lactase persistence in Tunisia as a result of admixture with other Mediterranean populations
Ben Halima et al. Genes & Nutrition
Lactase persistence in Tunisia as a result of admixture with other Mediterranean populations
Yosra Ben Halima 0 1
Rym Kefi 0 1
Sara De Fanti
Chokri Nouali 0 1
Majdi Nagara 1
Sahar Elouej 1
Abdelmajid Abid 1
Henda Jamoussi 1
Sonia Abdelhak 1
0 Université de Tunis El Manar , 2092 El Manar I, Tunis , Tunisia
1 Laboratory of Biomedical Genomics and Oncogenetics, Institut Pasteur de Tunis , BP 74, 13 Place Pasteur, 1002 Tunis , Tunisia
Background: The ability to digest lactose after weaning, namely, lactase persistence (LP), is encoded by polymorphisms in the MCM6 gene and varies widely in frequency among different human populations. Although, evolution of LP-related genetic variants was investigated in many groups of Sub-Saharan African, Middle Eastern, and European ancestry, only few studies have focused on populations from North Africa and no data are especially available from the Tunisian one. For this reason, there is an urgent need to investigate the frequency patterns at these loci in Tunisia since this adaptive trait is implicated in health. Methods: Forty SNPs covering the LCT/MCM6 genes and including the two functional variants − 13,910 C > T and − 22,018 G > A were genotyped in 117 Tunisian individuals using the Sequenom Mass Array technology. The observed nucleotide and haplotype patterns of variation were then compared with those of several African, European, and Mediterranean human groups for which comparable data were publicly available. Admixture analysis on a 5 Mb genomic region surrounding the LCT/MCM6 loci was also performed by extracting genotypes from a previously generated genome-wide dataset in order to deepen the reconstruction of the evolutionary history of these loci. Results: We found that lactase non-persistence (LNP)-related alleles and haplotypes were predominantly present in the examined population. A clear differentiation between Tunisian, African, and North European/North Italian samples was found, while the Tunisian population showed more genetic affinity to Central and South Italian groups. Conclusions: Our study provided a first report of LP-associated alleles and haplotypes in the Tunisian population. We highlighted a gradient followed by LP diffusion from Europe to North Africa. Based on the rich historic background of Tunisia, we suggest that this adaptive trait was introduced in that geographic region by a relatively recent gene flow.
Lactase persistence; North Africa; Admixture; Tunisia; Natural selection; LCT; MCM6
Lactase persistence (LP) or non-persistence (LNP) is a
genetically determined trait related to the capacity of
maintaining lactase activity till adulthood. LNP or lactase
intolerance (LI) represents the ancestral state
characterized by the downregulation of lactase activity [
]. Due to
the continued expression of the LCT gene in some groups
of North European descent, pastoralists from Africa, the
Arabian Peninsula, and Central Asia [
], it is known that
the lactase activity may persist even after weaning [
In fact, variants at the intronic regions in the neighboring
MCM6 gene were described to regulate transcriptional
activity of the LCT promoter and to confer therefore LP .
Several reports from previous studies emphasized that
the worldwide prevalence of the LP phenotype is highly
variable between different ethnic groups and is positively
correlated with the importance of milk in their diet. LP
shows a highly structured global geographic distribution,
it is indeed common in Europe, particularly in the
northwestern regions [
], in parts of the Indian
subcontinent and in many African and Middle Eastern
populations that traditionally practice pastoralism and regularly
consume milk [
6, 9, 10
]. It was thus hypothesized that
LP evolved because it confers a selective advantage. For
instance, the consumption of fresh milk and other dairy
products is supposed to allow efficient caloric intake
], calcium assimilation even at high latitude [
to increase water absorption from milk in arid
Different LP-associated variants arose in several
populations independently due to convergent evolution. In
particular, the − 13,910 T (rs4988235) and − 22,018 A
(rs182549) alleles were found to be 100 and 97%
associated with LP, respectively, in the Finnish population [
Furthermore, the − 13,910 T allele is ~86–98%
associated with LP in other European populations [
However, in the Middle East and in most regions of
Africa three other variants are commonly found in LP
individuals (i.e., − 13,915 G, − 13,907 G, and − 14,010 C) [
], as reported for instance for Ethiopian pastoralists
]. Variation at LP-related loci was recently
investigated in other groups from the South of Europe (i.e.,
Italy) and the Arabian Peninsula [
], but not from
the North of Africa. In fact, there are only few studies
that reported the frequencies of LP-related alleles in
Berber groups from Algeria and Morocco [
Several hypotheses were suggested to explain the
acquisition of the LP adaptive trait in North Africa. The first
one is that the expansion of pastoralists from the Middle
East into North Africa would presumably have resulted in
the spread of lactose tolerance [
]. The distribution
of the − 13,910 T allele may thus provide useful answers
concerning the origin of the spread of dairying .
Indeed, the age estimate for the − 13,910 T variant ranges
from 12,300 to 5000 years ago [
4, 20, 21, 24, 25
broadly coincides with the origins of cattle domestication
in North Africa and Levant around 10,000 years ago [
Indeed, the origin of the pastoral movement from the East
and West of Africa to Tunisia was dated to the trading of
gold, salt, and slaves across the Sahara [
studies conducted on populations from this area and
performed using mitochondrial DNA (mtDNA) and nuclear
markers confirmed also a correlation between genetic and
geographic structure and/or distances [
] and a
predominantly east-west structure [
]. Another hypothesis
suggested by Myles and colleagues is that the − 13,910 T
allele was introduced in North Africa from Europe via the
Gibraltar Strait [
]. However, several studies showed that
the Gibraltar Strait represented a strong boundary for
gene flow [
]. In addition, archeological evidence
indicates that the change to pastoralism in coastal North
Africa was abrupt and not developed locally over a long
period of time [
Tunisia has a geostrategic location since it represents a
crossroad between Europe, the Middle East and
SubSaharan Africa and a stepping-stone for recent human
migrations. Therefore, the genetic background of the present
Tunisian population may have been influenced by these
migrations and the successive invasions of the country
]. The main focus of this study is thus to explore
variation patterns of a large number of single nucleotide
polymorphisms (SNPs) located in the LCT/MCM6 region,
potentially related to the LP phenotype. Accordingly, we
compared newly generated data with those from
worldwide human populations to test whether in Tunisia the
same mutations as those found in the other parts of the
world are observable and, finally, to determine if the
distribution of allele frequencies underlying this trait are
due to de novo-mutations or admixture with populations
that were already characterized by LP (i.e., gene flow).
Sample collections and SNPs genotyping
The present study was carried out on 117 subjects
collected from three different Tunisian geographic
macroareas: 61 samples from the North of Tunisia (NT), 29
samples from the center of Tunisia (CT), and 27
samples from the south of Tunisia (ST). The collection of
blood samples was achieved with the collaboration of
the National Institute of Nutrition (Tunis, Tunisia). The
study was approved by the Ethics Committee of the
Institut Pasteur de Tunis (Tunis, Tunisia-Registration
numbers IRB00005445, FWA00010074), and all
participants provided written informed consent.
DNA was extracted from blood samples using a salting
out method, as described previously [
], and was used
to genotype 40 informative SNPs selected over a wide
genomic interval encompassing the LCT/MCM6 loci
and covering 3 Mb [
The Sequenom’s MassARRAY Designer software
(Sequenom, Inc., San Diego, CA, USA) was used to design PCR
and extension primers for the multiplex-PCR, with the total
number of SNPs being divided in two multiplex. The first
one was composed of 21 SNPs and the second one of 19
Genotyping was performed using the iPlex Gold
Genotyping Assay and Sequenom MassArray DNA analysis [
Matrix-assisted laser desorption/ionization time-of-flight
(MALDI-TOF) mass spectrometry (Sequenom, Inc., San
Diego, CA, USA) at the Centre for Applied Biomedical
Research (CRBA) of the Bologna S. Orsola University Hospital.
The obtained data for the Tunisian population (TN,
N = 117) were compared with published datasets
including 453 healthy Italian subjects [
] from North-Western
and Central-Western Italy (NCWI, N = 105),
NorthEastern Italy (NEI, N = 139), Central-Eastern and
Southern Italy (CESI, N = 159), and from Sardinia (SARD,
N = 47).Then, we merged these data with those for 10
populations of African and European ancestries from the
1000 Genomes Project [
]. Namely, 99 Utah residents,
with North and West European ancestry (CEU), 99 Esan
in Nigeria (ESN), 99 Finnish in Finland (FIN), 91 British
in England and Scotland (GBR), 113 Gambian in Western
Division (GWD), 107 Iberian populations in Spain (IBS),
99 Luhya in Webuye, Kenya (LWK), 85 Mende in Sierra
Leone (MSL), 107 Tuscany in Italy (TSI), 108 Yoruba in
Ibadan, Nigeria (YRI). The final merged dataset contained
38 common SNPs (one SNP was not found in the 1000
genomes data). Further information about the studied
populations were reported in Additional file 1.
Summary statistics and allele frequencies analyses
Summary statistics, such as nucleotide (π) and haplotype
(H) diversity at the examined genomic regions and the
number of haplotypes (k), were calculated for all the
studied groups using the Arlequin package v.3.5.2 [
Allele frequencies for the common genotyped SNPs
were calculated and compared between the examined
population samples by applying a Chi-square test with
the PLINK software package beta release 1.9 [
significance level was set at 5%. Bonferroni’s correction was
applied to the obtained asymptotic p values to account
for the adopted multiple testing procedures.
Population structure and differentiation analyses
To test the genetic structure of our samples, we
computed principal component analysis (PCA) by using the
adegenet and ade4 packages implemented in the R
software [http://www.R-project.org/]. For this purpose, SNPs
pairs that showed r2 > 0.2 calculated on sliding window
of 5 SNPs were removed from the dataset with PLINK
by maintaining only one SNP per pair. After such a
LDpruning, 20 SNPs remained for the Tunisian dataset.
The Arlequin software was also used to calculate
genetic diversity and pairwise Fst genetic distances between
Tunisian samples and the other studied populations.
Generated genetic distances (Fst matrix) were plotted to
visualize data in multidimensional scaling (MDS) using
the statistical package for the social science (SPSS,
version 20.0, Chicago, IL, USA).
Discriminant analysis of principal components (DAPC)
To further corroborate results from population structure
analyses, we evaluated the cluster membership
probabilities for each subject by applying DAPC to the three
identified Tunisian groups using the adegenet R package
]. This approach allowed to provide assignment of
individuals to different groups and to assess relationships
To better understand the genetic background and
population structure of the LP-related genomic region, we
extracted SNPs data for 5 Mb encompassing the LCT and
MCM6 loci and the surrounding genes from a dataset
generated by means of the Genome-Wide Human SNP
Array 6.0 (Affymetrix, Santa Clara, CA, USA) on 135
Tunisian healthy individuals that were recruited from
the middle coast of Tunisia as described previously [
The study was conducted according to the declaration of
Helsinki principles and approved by the Institutional review
board (under the reference PV09/06, IRB#0000000044).
These data were merged with the HapMap3 [
] and other
publicly available data [
] to produce a dataset
composed of 1560 variants for 1677 individuals (Additional file
1). The same quality control (QC) procedures used for
filtering the Sequenom data (see Results section) were applied
also to that dataset, which showed a mean genotyping rate
of 77%. To avoid bias due to LD, variants that showed
r2 > 0.8 were filtered by pruning one SNP per five using the
sliding windows approach implemented in the PLINK
package and as a result 347 SNPs were removed.
Admixture proportions in the LCT/MCM6 region
were inferred for each subject included in the pruned
dataset by means of the algorithm implemented in the
software ADMIXTURE [
], which provides a maximum
likelihood estimation of population structure. Individual
genotypes were clustered by running an unsupervised
analysis, testing numbers of potential ancestral
populations (K) from two to eight, and by performing five
iterations for each K.
Linkage disequilibrium and haplotype analyses
Patterns of LD at the genotyped loci were investigated
using the software Haploview 4.2 [
]. Haplotypes were
statistically inferred by considering all SNPs located
within high LD blocks and through the Bayesian
algorithm implemented in the PHASE software v.2.1 [
Evolutionary relationships of the reconstructed
haplotypes were finally visualized by means of a median
joining network [
] using the Network package v.18.104.22.168
Summary statistics of nucleotide and haplotype variation
Genotyping failed for four of the collected samples and
individuals with more than 30% missing genotypes were
excluded. One SNP (rs749017) for which experimental
problems occurred during PCR reaction was also
removed. The remaining 39 multiplexed SNPs showed call
rates higher than 99% and minor allele frequencies
(MAF) >5%. No loci showed a significant departure from
Hardy-Weinberg equilibrium (HWE).
Allele frequencies for the typed functional SNPs
−13,910 C/T and −22,018 G/A were calculated for all
populations (Tables 1 and 2). The frequencies of reference
alleles C and G and LNP associated genotype C/C were
high in TN (Tables 1 and 2) and reached almost the same
values in TSI, CESI, NEI, and IBS (Tables 1 and 2). The
highest values were observed instead in ESN, GWD,
LWK, MSL, and YRI (Tables 1 and 2), while the lowest
ones were found, as expected, in Northern European
populations, such as FIN, GBR, and CEU (Tables 1 and 2).
Since population structure analyses did not show
appreciable differentiation patterns within TN (see next
section), nucleotide diversity was calculated for the overall
Tunisian population showing high values of diversity
(0.400 ± 0.211) (Additional file 2: Table S1). We then
compared Tunisian patterns of variation at the examined loci
with those characterizing African and European
populations. The obtained values for the TN population were
comparable to those obtained for CESI, GBR, SARD, TSI,
and CEU populations (0.399 ± 0.211, 0.392 ± 0.208,
0.390 ± 0.208, 0.366 ± 0.195, and 0.335 ± 0.180,
respectively). In combination with the previously published data,
Northern Italian groups, such as NEI and NCWI, showed
the highest variability (0.472 ± 0.246 and 0.485 ± 0.252,
respectively), with values similar to FIN and IBS
(0.458 ± 0.239 and 0.489 ± 0.254, respectively). However,
lower diversity was observed for ESN, GWD, LWK, YRI,
and MSL (0.207 ± 0.119, 0.217 ± 0.124, 0.196 ± 0.114,
0.116 ± 0.075, and 0.167 ± 0.100, respectively). Summary
statistics of nucleotide and haplotype variation for each
group were reported in Additional file 2: Table S1.
Population structure analyses
PCA was applied to the generated Tunisian genotypes and
showed no clear genetic clustering of the different
examined groups (Additional file 2: Figure S1). In addition, we
performed DAPC that confirmed the absence of
welldefined population clusters (Additional file 2: Figure S2).
Observed patterns of Tunisian variation were thus
compared with those specific to African and European
populations using Fst estimates computed for the 15 SNPs
spanning the longest identified LD block (see LD block
analysis, Additional file 2: Figure S3) and plotted using
MDS (Fig. 1). Since PCA and DAPC applied to the
Tunisian dataset showed no appreciable internal structure, the
overall Tunisian population is considered as a single
sample (TN). Accordingly, the first dimension differentiates
mainly populations of African and European ancestry. TN
was positioned near to the intersection of the axes in close
proximity to the Southern European aggregate including
CESI, TSI, and SARD, suggesting a higher European
influence in TN than in the remaining African samples.
African populations then appear to cluster in the lower left
quadrant described by the second dimension.
In order to further explore patterns of population
differentiation, allele frequencies for both functional SNPs were
compared between TN and all populations by applying a
Chi-square test (Additional file 2: Table S2). Tunisian
Allelic frequencies of functional SNPs in the studied populations are reported
in Table 1. Frequencies for both reference and adaptive alleles
Derived Stimulus Configuration
Euclidean distance model
groups were again pooled together since no significant
results were obtained when considering the single
subgroups. Unadjusted and adjusted differences corresponding
to allelic frequencies for the typed SNPs are reported in
Additional file 2: Table S2.
The most significant differences were observed mainly
between TN and Northern European(i.e. CEU, GBR, and
FIN) and Western European populations (i.e., IBS),
followed by those compared to the African groups (i.e.,
GWD, YRI, MSL, ESN, and LWK). In contrast, the lowest
differences were observed when we compared TN with
Northern Italian samples (i.e., NEI and NCWI).Finally, no
significant differences were found when Italian groups with
a more Southern European ancestry (i.e., CESI, SARD, and
TSI) were considered (Additional file 2: Table S2).
We applied the unsupervised ancestry inference algorithm
implemented in the ADMIXTURE software on the
obtained pruned datasets. As the number of ancestral
clusters increased, we observed the emergence of specific
population clusters (Fig. 2). At K = 2, the ancestry
assignment mainly differentiates an African-like (red) and a
non-African-like (green) component. K = 3 further
distinguishes an Asian-like (green) ancestry fraction from a
European-like one (blue), while K = 4 identifies an
additional component (yellow) that is mainly shared among
East, Sub-Saharan, and North African clusters.
Remarkably, the Tunisian population, and at a lower extent, the
Algerian samples appeared to be clearly differentiated
from the other African populations. They showed more
than 95% contribution from different ancestral
populations along different K values. When using the cross
validation error, the mean log probability for the successive
increase of K levels continues to decrease substantially as
K increases. Although, higher values of K reveal additional
population-specific ancestries until K = 4. For this reason,
we focused on K = 2 through K = 4.
LD block analysis
The analysis of LD patterns that was carried out on the
Tunisian dataset showed the existence of a long block of
relatively high linkage disequilibrium (266 kb), which
includes the two functional variants − 13,910 C/T and
−22,810 G/A (Additional file 2: Figure S3a). Indeed, the
NT group presented a slightly shorter block (157 kb)
that contains only six SNPs of the 15 variants observed
when the total population was considered (Additional
file 2: Figure S3b). However, in CT, the same long
haplotype (266 kb) was observed and includes a different SNP
(rs1438307) (Additional file 2: Figure S3c). Interestingly,
this region of high LD appeared to be split into two
different blocks spanning respectively (12 kb) and (185 kb)
in the ST group (Additional file 2: Figure S3d).
We statistically inferred the haplotypes in all the samples
that belong to the Tunisian population by considering
the 15 SNPs located in the region of overall high LD that
is shared among the examined NT, CT, and ST groups.
We thus observed a total of 23 different haplotypes, 21
of which carried the − 13,910 C allele and two of them
(H19 and H23) showed the functional allele − 13,910 T
(Additional file 2: Table S3). The most frequent
haplotype (H1) carried the − 13,910 C and the −22,080 G
reference alleles, being remarkably represented in NT, CT,
and ST groups (0.57, 0.64, and 0.54, respectively). The
second most frequent haplotype (H19) carried instead
the adaptive alleles, being mainly represented in NT
(0.14) and showing instead lower frequencies in CT and
ST samples (0.10 and 0.10, respectively).
When we compared the Tunisian population to the other
populations, we found that it presented several private
haplotypes (e.g., H33, H34, and H35) that carry the reference
− 13,910 C and − 22,080 G alleles. The most frequent
haplotype (H1) in the merged dataset (Additional file 2:
Table S4) carried the − 13,910 C and the − 22,080 G alleles
and was represented in all populations. It reaches the
highest frequencies in YRI, TN, TSI, MSL, GWD, LWK, CESI,
SARD, NEI, ESN, and NCWI, (0.66, 0.58, 0.57, 0.56, 0.56,
0.53, 0.53, 0.51, 0.44, 0.49, and 0.41, respectively). In
contrast, the second most common haplotype (H19)which
carries the adaptive alleles − 13,910 T and − 22,080 A was
highly represented in GBR, CEU, and FIN (0.71,0.70, and
0.55, respectively) and absent in ESN, LWK, MSL, and YRI.
The remaining haplotypes finally appeared to be rare and,
in some cases, private. Evolutionary relationships among
the haplotypes inferred in the studied populations were
further explored using a median joining network that
highlighted the presence of two clearly distinct groups of
allelic combinations separated by several mutational steps.
Thus, the haplotype carrying the derived alleles (H19) was
segregated into a single cluster and was separated from
those carrying the ancestral alleles (H1) (Fig. 3).
In the present study, we reported the frequencies and
genotypes of the − 13,910 C/T and − 22,810 G/A functional
SNPs related to LP [
]. We further described the
distribution patterns of LCT/MCM6 haplotypes in the Tunisian
population, and we compared them to those observed in
several groups of African and European ancestry that
could have intervened in the formation of the Tunisian
genetic background as recently hypothesized [
The obtained results were discussed in the light of the
relatively recent historical relationships between
Tunisian and the abovementioned populations [
27, 53, 54
the attempt to clarify the routes followed by LP diffusion
and to explore the potential processes of convergent
evolution of LP in European and in some non-European
populations, such as the Tunisian one.
Investigation of the impact of admixture on a large
genomic region related to LP showed that the Tunisian
population is highly mixed and displays three major
ancestral contributions ascribable to European, African,
and Asian ancestry components (Fig. 2). This is in
agreement with previous studies that examined uniparental
and autosomal markers and showed a multi-ethnic
origin of North Africans [
27, 37, 46
Moreover, the study of LP-related genetic diversity
revealed that even within the relatively small region
represented by Tunisia, a high nucleotide diversity
(0.400 ± 0.212) at MCM6/LCT region could be observed
(Additional file 2: Table S1). Many studies demonstrated
that diversity varied substantially within and between
Tunisian and other populations [
according to our admixture analysis, the Tunisian
population showed higher European proportion compared to
other African groups. This was expected if we consider
the roots of human migration in Africa. These results
corroborate findings from several recent studies [
and suggest that the TN population has plausibly
acquired the LP adaptive variants mainly through
admixture with populations of European ancestry.
Our study highlighted an important prevalence of alleles
and genotypes related to LI in the Tunisian population, as
well as in South European and African groups (Tables 1 and
2). As expected, we found that the North European
populations showed the lowest frequencies for these alleles and
these findings were consistent with what was found
8, 18, 56, 57
]. Furthermore, the allele frequencies of
the typed SNPs exhibited great variation between Tunisians
versus North European and North Italian groups (Additional
file 2: Table S2). On the contrary, no differences were
detected when we compared different groups from the
Mediterranean regions. This distribution of LP-associated −
13,910 T and − 22,018 A alleles is in agreement with the
north-south decreasing pattern observed by Anagnostou
et al.  and De Fanti et al. [
] along the Italian peninsula.
The present study further highlighted the close affinity
of the Tunisian population to the South European and
Mediterranean populations than to African ones if we
consider the LP-related genetic variants. Our data thus
support the hypothesis that this adaptive trait was
introduced to Tunisia by a relatively recent gene flow and did
not evolve locally and independently as occurred for the
bulk of Sub-Saharan African groups.
To draw a first picture of the potentially LP-related
patterns in the Tunisian population, LD analysis (Additional
file 2: Figure S3a, b, c, and d) was also carried out. The
presence of a long haplotype block can be interpreted as
evidence of a more recent introduction of LP especially in
the CT subgroup with respect to those of NT and ST. Our
data showed that the − 22,810 A allele was in strong LD
with the LP-associated − 13,910 T variant in TN, which is
in agreement with prior studies conducted on European
8, 18, 24
] and to the hypothesis that positive
selection at the − 13,910 T allele led to the concomitant
rise in frequency of other polymorphisms near to the
functional SNP determining the creation of a conserved
haplotype block, as observed by Bersaglieri et al. [
Finally, we investigated the patterns of Tunisian
haplotype variation. We found that the haplotype (H1)
carrying the reference LI alleles was the most frequent in all
the Tunisian and Italian subgroups, as well as in most
African populations. However, the second most frequent
haplotype (H19) that carries the adaptive alleles showed
the highest frequencies in Northern European populations
(i.e., FIN, GBR, and CEU) (Additional file 2: Table S4).
Our findings suggest that the distribution of the observed
haplotype variation might be due to gene flow that
occurred over time from outside or within Africa possibly
during key historical events, such as the settlement of the
Roman Empire in parts of North Africa. These migration
events are also reported in studies based on mtDNA, Y
chromosome, and autosomal genetic variation [
7, 27, 31,
]. On the other hand, population structure analyses,
such as the MDS plot (Fig. 1), showed that the Tunisian
sample was relatively close to the Italian groups.
Furthermore, it is differentiated from African populations and
especially clustered with samples from Central-Eastern and
Southern Italy, supporting the occurrence of appreciable
gene flow from Southern Europe to North Africa.
Accordingly, the present study provided a very first
view of the genetic structure of the Tunisian population
considering the genomic region that modulates the LP
phenotype by analyzing subpopulations distributed
geographically along the Tunisian territory. Moreover,
the investigation of diversity at a wide genomic interval
surrounding the LCT gene could potentially help to
detect genetic variants that may contribute to the
development of certain diseases [
]. Indeed, LP was
previously shown to be able to confer susceptibility to
certain diseases, such as obesity or metabolic disorders
]. The LP alleles may thus contribute to the
complexity of the currently novel and obesogenic environment
also in Tunisia.
Our study was the first to report the distribution of
LPassociated alleles and haplotypes in the Tunisian
population. We thus described the gradient followed by LP
diffusion from Europe to Northern Africa. Based on the rich
history of gene flow documented for this population, we
support the hypothesis of introgression of the
LPassociated alleles from outside the region. For this reason,
we suppose that the current patterns of diversity observed
at this locus in Tunisia may be the result of the interaction
of a large number of evolutionary factors, such as genetic
drift, demographic processes, people migrations, and
Further molecular investigation and dissection of
the underlying evolutionary forces are needed to fully
understand this phenomenon. It could be possible in
the future to identify other loci than those on the
LCT/MCM6 that have undergone recent positive
selection and that contributed to the modulation of
the LP phenotype, thus leading to new insights into
Additional file 1: Supplementary information about the studied
populations. (ZIP 22 kb)
Additional file 2: Figure S1. Principal component analysis (PCA) plot of
genetic variation in Tunisian groups. Distribution of northern, central,
and southern samples in the space of two first component of LP-related
genotypes. Figure S2. Discriminant analysis of principal component
(DAPC) in Tunisian groups. DAPC analysis confirmed results of PCA.
Figure S3. Linkage disequilibrium patterns (LD) of the investigated
genomic region in Tunisian population. Each number in squares indicates
the r2 index of LD between the corresponding SNPs. a Tunisian
population. b Northern Tunisian group. c Central Tunisian group. d
Southern Tunisian group. Table S1. Summary statistics for the studied
populations. Table includes number of chromosome, nucleotide diversity,
number of haplotypes, and haplotype diversity. Table S2. Results of
allelic frequencies comparison between Tunisia and African and European
populations. A chi-square test was used to test allelic differences; to
reduce the false discovery rate of multiple testing, Bonferroni’s multiple
comparison adjustment was performed. The statistical significance
reached a p value less or equal to 5%. Table S3. Haplotype list in
Tunisian population. A list of 23 different haplotypes generated by PHASE
software, distributed between Northern, Central, and Southern Tunisian
subgroups. Table S4. Haplotype list in the merged dataset. A list of 45
different haplotypes generated when comparing our data with other
populations. (ZIP 608 kb)
We thank the Centre for Applied Biomedical Research (CRBA) of the Bologna
S. Orsola University Hospital and in particular Dr. Vilma Mantovani and Dr.
Elena Marasco for their technical support.
This work was supported in part by Italian National Grant PRIN
2010EL8TXP_006 to DL, the Tunisian Ministry of Public Health, the Ministry of
Higher Education and Scientific Research (LR11IPT05) and the E.C. Grant
agreement no. 295097 for FP7 project GM-NCD-Inco (www.genomedika.org).
Availability of data and materials
The datasets supporting the conclusions of this article are included within
the article and its Additional files.
RK, SA, and DL conceived the study. YBH performed the statistical analysis
and drafted the manuscript. AA and HJ contributed to the collection of
biological material during their consultations. SE assisted to the collection of
biological material and extraction of DNA. SDF performed the design of
primers. CG, GM, and YBH performed the genotyping analysis. MS, CN, and
MN assisted with the statistical analysis. RK, SA, and DL controlled the work
progress. RK, MS, GR, SA, and DL critically reviewed the manuscript. All
authors approved the final version of the manuscript.
Ethics approval and consent to participate
The study was approved by the Ethics Committee of the Institut Pasteur de
Tunisin Tunis, Tunisia under the registration numbers IRB00005445,
FWA00010074, and PV09/06, IRB# 0000000044. All participants provided
written informed consent.
Consent for publication
All authors have agreed to its publication in Genes and Nutrition.
The authors declare that they have no competing interests.
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1Laboratory of Biomedical Genomics and Oncogenetics, Institut Pasteur de
Tunis, BP 74, 13 Place Pasteur, 1002 Tunis, Tunisia. 2Université de Tunis El
Manar, 2092 El Manar I, Tunis, Tunisia. 3Laboratory of Molecular Anthropology
and Centre for Genome Biology, Department of Biological, Geological and
Environmental Sciences (BiGeA), University of Bologna, 40126 Bologna, Italy.
4Department of external consultation, National Institute of Nutrition and
Food Technology, Tunis, Tunisia. 5Laboratory of Genetic Medicine and
Immunology, Weill Cornell Medical College in Qatar, Qatar Foundation, Doha,
Qatar. 6Medical Genetics Unit, Department of Medical and Surgical Sciences,
Polyclinic S. Orsola-Malpighi, University of Bologna, Bologna, Italy.
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