253
Documenta Praehistorica XLIII (2016)
Genomic approaches and their contributions
to understanding the European Neolithisation
Cristina Gamba
Centre for GeoGenetics, Natural History Museum of Denmark, Copenhagen, DK
cristinagamba@gmail.com
Introduction
The field of ancient DNA was 30 years old in 2014,
and the anniversary was celebrated with an inter-
national conference at the Royal Society of London
entitled “Ancient DNA: the first three decades” (Ha-
gelberg et al. 2015). This conference was dominated
by the most recent results in the field, including an
extensive genome-wide proof of Neanderthal inter-
breeding with modern humans (Sankararaman et
al. 2014). However, ancient DNA has entered the ‘ge-
nomic era’ only very recently, with the first ancient
human genome published in 2010 (Rasmussen et
al. 2010), the first scattered genomic data from a
Neolithic sample in 2012 (Skoglund et al. 2012),
and the first Near Eastern Neolithic genomes only
very recently (Mathieson et al. 2015).
Before the ‘genomic era’, the field advanced thanks
to the complicated and low-success recovery-rate of
small DNA fragments, mainly from mitochondrial
DNA (mtDNA). The mitochondria are cell organelles
that govern the energy supply, in several copies per
cell. They contain one or more small circular mole-
cules of DNA; therefore, 1000 to 10 000 copies of
mtDNA can be counted in a cell, as compared to only
two copies of maternally and paternally inherited
nuclear DNA (nuDNA) (Robin, Wong 1988). This has
made mtDNA the preferred target for ancient DNA
studies, given >1000 higher probability of recover-
ing DNA after the environmental degradation that
affects cells and molecules after death.
The first mtDNA sequence from ancient material was
isolated in 1984 from the dried muscle of an early
1900s quagga, an extinct equid (Higuchi et al. 1984).
Since then, a wide range of other ancient samples
and tissues have been analysed, especially ancient
bones and teeth, the most frequently preserved tis-
sues in ancient samples, with the seminal work of
ABSTRACT – The contribution of ancient DNA to the understanding of past events has been increas-
ing exponentially in recent years. This is mainly due to the synergy of technical advances, such as
the molecular technique of high-throughput DNA sequencing, which has allowed for the reconstruc-
tion of complete genomes as old as 750 000 years. Another step toward the cost-effective characte-
risation of ancient genomes is the sampling of petrous bone, which has allowed sequencing of the
first ancient African genome. Here I review the significant contribution of ancient genomics to our
understanding of the European Neolithisation process.
IZVLE∞EK – V zadnji letih se je eksponentno pove≠al prispevek stare DNK k razumevanju preteklo-
sti, kar je predvsem posledica sinergije tehni≠nih napredkov, npr. molekularne diagnostike visoke
zahtevnosti DNK sekvence, s katero lahko rekonstruiramo celoten genom tudi do 750 000 let starih
vzorcev. Drug korak k stro∏kovno u≠inkovitej∏i karakterizaciji starega genoma je vzor≠enje kosti
skalnice v lobanji, s katero smo lahko dobili sekvenco prvega starega afri∏kega genoma. V ≠lanku
nudim pregled nad glavnimi prispevki raziskave starega genoma k razumevanju evropskega proce-
sa neolitizacije.
KEY WORDS – ancient DNA; palaeogenomics; Neolithic
DOI> 10.4312\dp.43.12 
Cristina Gamba
254
Hagelberg and colleagues (Hagelberg et al. 1989).
Other tissues such as hair (Gilbert et al. 2004), cop-
rolites (Poinar et al. 1998), and arctic sediments
(Willerslev et al. 2003) were also successfully inve-
stigated in the first two decades of the aDNA field.
In the specific research on the Neolithic transition in
Europe, several studies based on short mtDNA se-
quences were published, with the pioneering work
of Wolfgang Haak and colleagues (Haak et al. 2005)
on Neolithic LBK (samples from Central Europe).
This study was the first population survey of an an-
cient Neolithic group to help us understand the Neo-
lithisation process. The authors showed an ancient
Neolithic genetic composition very different from
the current European population, including variants
virtually absent nowadays (i.e. haplogroup N1a). Se-
veral other mtDNA works followed, including a com-
parison of contemporaneous farmers and hunter-ga-
therers in Northern Europe ion the 3rd millennium
BC (Malmström et al. 2009), revealing a genetic
break. For the Iberian Peninsula, Cristina Gamba and
colleagues (Gamba et al. 2011) also showed an im-
portant component of new incomers at the begin-
ning of the Neolithic.
The jump to the second-generation or the ‘genomic
era’ has allowed a quantum leap in retrieving gene-
tic information from ancient specimens. After the
first ancient human genome to be sequenced, a 4000
years old specimen from Greenland called ‘Saqqaq’
after his culture (Rasmussen et al. 2010), many
other ancient genomes followed. Most of the stud-
ies included one or very few samples, due to the high
cost of high-throughput DNA sequencing (HTS) of
whole genomes (3 billion base pairs for the human
genome – around 200 000 times longer than the
whole mtDNA). This is especially true for degraded
samples with endogenous DNA below 5%, the other
95% being mostly co-purified microbial DNA from
the environment and, unfortunately, as costly as the
endogenous DNA fraction. Basically, to sequence the
genome of an ancient sample costs at least 20 times
more than a modern one, restricting the whole-ge-
nome sequencing of ancient sample to a few highly
funded laboratories and to very well-preserved spe-
cimens.
Ancient DNA challenges and validation
In order to understand aDNA data, it is essential to
understand the challenges involved in performing
a molecular analysis based on degraded material.
Firstly, the DNA starts to degrade immediately after
the organism dies due to the action of microorga-
nisms in the depositional environment, enzymes re-
leased from the cells themselves and chemical reac-
tions due to the presence of oxygen and water (Hof-
reiter et al. 2001). The main outcomes are DNA frag-
mentation and chemical changes. The first implies
that only short sequences can be retrieved from an-
cient samples, and a sequence is informative only
above 25–30 base pairs, because below this length
the probability of a precise match between the DNA
fragment in different locations of a genome or even
in different genomes significantly increases, making
the genetic data uninformative.
The main chemical DNA modification known to date
is the deamination at cytosines, one of the four nu-
cleotide bases composing the DNA, which implies
their transformation into uraciles (usually only found
in RNA and not in the DNA of living organisms), and
ultimately into thymines through the amplification
process, which is necessary for sequencing DNA
(Hofreiter et al. 2001). This means that we detect a
thymine, which was originally a cytosine, which ren-
ders misleading the genetic information retrieved.
This happens mainly around DNA breaks, thus at the
beginning or end of DNA fragments. Consequently,
the deamination might lead to confusion concern-
ing the correct retrieval of genetic information, un-
less many fragments from the same genomic region
are analysed and compared (high coverage), given
that deamination should happen randomly.
On the other hand, detecting deamination provides
a unique signature of DNA degradation and has been
largely used as a criterion from ancient DNA (Ginol-
hac et al. 2011). Interestingly, there is an experimen-
tal way to eliminate these miscoding lesions from
the DNA strands, by using an enzyme (UDG, Uracil-
DNA-Glycosylase) that can remove the majority of
the uracils from ancient DNA before amplification,
making downstream analysis less noisy (see e.g.,
Rohland et al. 2015).
Another significant weakness of aDNA, especially
before the HTS era, is its susceptibility to contamina-
tion from fresh genetic material, either from the de-
positional environment or introduced after an exca-
vation during manipulation of the sample. This un-
dermined many of the first aDNA studies, which
claimed to recover sequences from material millions
of years old, such as a magnolia leaf claimed to be
17–20 million years old (Golenberg et al. 1990), am-
ber-preserved bees, termites and plants (Cano et al.
1993; DeSalle et al. 1992; Poinar et al. 1993), and
Genomic approaches and their contributions to understanding the European Neolithisation
255
even fossil bacteria (Cano et al. 1994). These stud-
ies were later shown to be fallacious, as only conta-
minant DNA of modern origin was actually retrieved
(Poinar et al. 1998; Yousten, Rippere 1997).
All these million-year studies revealed the high risk
of combining a highly sensitive technique, such as
PCR, with the study of scarce and degraded ancient
molecules. This led to outsiders taking a very scep-
tical view of the field, especially archaeologist and
palaeontologists unwilling to destroying precious
samples in order to obtain unreliable results.
However, this was an important turning point in the
aDNA field, highlighting the importance of authen-
ticating and validating results. The so-called ‘authe-
nticity criteria’ were formalised for the first time by
Matthias Krings and colleagues through the valida-
tion of the DNA results obtained from the first Ne-
anderthal specimen analysed (Krings et al. 1997),
followed by several comprehensive reviews of all
the criteria needed to validate results (Cooper, Poi-
nar 2000; Pääbo et al. 2004). Despite its controver-
sial beginnings, ancient DNA can now be considered
a well-established scientific discipline, with greatly
improved standards, especially since the develop-
ment of HTS.
A highly valuable piece: the petrous bone
Recently, Gamba et alii (2014) demonstrated that
the densest area of the petrous bone, part of the
temporal bone, could provide very high levels of
endogenous DNA, up to 180-times more than other
bone pieces from the same individual, preserving up
to more than 80% of the ancient endogenous DNA
of the specimen. This implies a sequencing cost only
slightly higher than modern genomes, which are
currently systematically generated by the hundreds.
This is linked to the high density of this bone in
mammals (Lam et al. 1999), which might be respon-
sible for the low penetration of bacteria during the
decomposition processes of the body. After this se-
minal study, different parts of the petrous bone were
shown to provide lower amounts of DNA (Pinhasi
et al. 2015) than the densest part chosen in the first
case (Gamba et al. 2014).
In the latter study, the closest samples to the petrous
bone in terms of DNA preservation were the molar
roots, only 3-times less efficient (Gamba et al. 2014).
In another study, Peter B. Damgaard and colleagues
demonstrated that the cementum of teeth is where
the best-preserved genetic material is harboured (Da-
mgaard et al. 2015). However, although especially
well-preserved teeth can also have very high levels
of endogenous DNA, on average they do not perform
nearly as well as petrous bone (Fig. 1).
Thanks to the specific sampling of the petrous bone,
it became possible to retrieve and sequence the ge-
nome of a sample from an especially degrading en-
vironment such as Africa for the first time (Gallego-
Llorente et al. 2015), and more recently, even a hand-
ful of the first farmers from the Near East (Brousha-
ki et al. 2016; Gallego-Llorente et al. in press; Laza-
ridis et al. 2016)
Palaeogenomics and the Neolithic transition in
Europe
Ancient genomes from prehistoric Europeans have
provided highly valuable insights into the under-
standing of the population dynamics of the past. This
Fig. 1. Boxplot representing the per-
centage of endogenous human DNA
retrieved from the petrous bone ver-
sus other skeletal elements retriev-
ed from ancient specimens. The bot-
tom and top of the box refer to the
first and third quartiles; the band
inside the box is the median, while
the ends of the whiskers represent
the 5th and 95th percentiles. Outliers
are represented as points. Database
from Mathieson and colleagues
(2015), which also includes data
(Allentoft et al. 2015; Gamba et al.
2014; Haak et al. 2015; Keller et al.
2012; Lazaridis et al. 2014; Olalde et al. 2014) completed with these data (Cassidy et al. 2016; Günther et
al. 2015; Jones et al. 2015; Omrak et al. 2016; Skoglund et al. 2014; Broushaki et al. 2016; Gallego-Lloren-
te et al. in press). Only data at a minimum coverage of 0.025x were included. Plot realised in R (Core
Team 2014) using the library ggplot2 (Wickham 2010).
Cristina Gamba
256
is especially true for the Neolithic period, for which
many complete genomes or genome-wide data are
now available (Fig. 2).
The Neolithisation of Europe has been thoroughly
investigated by different disciplines (i.e. Bocquet-
Appel, Bar-Yosef 2008; Pinhasi, Stock 2011) which
showed the relative contribution of demographic
movements and the transmission of ideas and new
practices that accompanied the economic revolu-
tion (Zvelebil 2001).
Investigations of this process with modern DNA
data has yielded contradictory results (see Jobling
et al. 2013.Ch. 12 and references therein) because
of the specificity of the genetic markers studied and
the important genetic reshaping of the European ge-
netic pool after the Neolithic, recently directly detect-
ed through ancient genomics (see Mathieson et al.
2015 and references therein). However, it seems that
now this long-lasting debate on the mechanisms of
Neolithic expansion has been resolved thanks to
ancient genetics and genomics. Ancient genomes sup-
port the first studies based on mtDNA (Bramanti et
al. 2009; Haak et al. 2010; Brandt et al. 2013; Szé-
csényi-Nagy et al. 2015), pointing to a genetic break
between the Mesolithic and the Neolithic periods in
Europe, with the arrival of new incomers from the
Near East.
The first clue to the genomic landscape of Neolithic
Europeans was provided from the complete sequen-
cing of a frozen mummy from the Copper Age (5300
BP) found in the Italian Alps (Keller et al. 2012). The
so-called Iceman surprisingly showed high genetic
affinities with modern-day Sardinians, despite their
geographic distance. These data suggested for the
first time that (1) major genomic reshaping occurred
after the Neolithic and (2) Sardinians might be relics
of the original Neolithic population, because of the
late peopling of the island and the implicit geogra-
phic isolation.
Molecular approaches
Palaeogenomic analyses start in the laboratory,
where a sample, usually a piece of bone, is ground
and the DNA extracted. The short DNA molecules
retrieved are then built into so-called libraries by
attaching to each side of the DNA strand two se-
quences, the adapters, which contain well-known
DNA stretches compatible with further processing.
The library is then amplified and (usually at this
stage) short unique DNA, including unique indexes,
are attached to the adapters to tag each sample
differently and to allow the sequencing machine
to distinguish between samples. The amplified li-
braries are then either sequenced or enriched. In
the first case, the whole DNA molecules extracted
from the sample are sequenced. This method is
called shotgun sequencing, because there is no se-
lection of the DNA to be sequenced, and then, apart
from the endogenous DNA of interest, also DNA
from the environment (contaminant DNA) is co-
sequenced. In the second case, the amplified library
is firstly combined with a set of DNA sequences, so-
called probes, which capture regions of interest,
while all the other molecules are washed away. In
this case, principally these regions are selected, en-
riched and then sequenced, reducing sequencing
costs while increasing the coverage of these posi-
tions of interest. The drawback of this second ap-
proach is that the genome to be sequenced needs to
be well characterised in order to produce specific
probes for DNA enrichment.
Analytical approaches
Given the massive amount of sequences retrievable
from HTS machines, bioinformatic tools should be
applied to the data analysis and interpretation. The
first step is to align the sequence retrieved to a ref-
erence genome, whenever available. Sometimes, a
reference genome is not available, e.g., in the case
of species not characterised or extinct; therefore, the
genome should be assembled from scratch. For hu-
man samples, a very well characterised genome is
available and constantly improved. So the first step
consists of aligning the sequences to the human ref-
erence genome, followed by quality filtering, which
takes into account how well a genome is aligned to
the reference (mapping quality) and how confident-
ly each base was identified during the sequencing
(base quality). This is followed by genotyping, main-
ly focusing on the identification and analysis of
punctual genomic variants, also called SNPs (Single
Nucleotide Polymorphism), which are positions in
the genome that vary among different individuals
and populations. Further filters can be applied, such
as the setting of a minimum coverage (how many
times a position has been sequenced), which reflects
Understanding palaeogenomics
Genomic approaches and their contributions to understanding the European Neolithisation
257
Further genomic data from northern European far-
mers and hunter-gatherers extended to much higher
latitudes and much later periods (~5000 years old)
showed the affinity of ancient farmers with modern-
day southern Europeans (Skoglund et al. 2012). On
the other hand, northern hunter-gatherers showed
genetic discontinuity with farmers, falling outside
the genomic variability of modern Europeans (Skog-
lund et al. 2012). Despite the very low amount of
genomic data retrieved, covering only ~10% of the
genome, this study allowed for the correlation of
two different genomic backgrounds with two diffe-
rentiated cultural groups: farmers and hunter-gathe-
rers, suggesting the arrival of new incomers with the
advent of the Neolithic.
Later studies showed the same pattern at lower lati-
tudes, such as the characterisation of two high-qua-
lity genomes, one Neolithic farmer from Germany
and one Mesolithic hunter-gatherer from Luxem-
bourg, further supported by eight lower-quality ge-
nomes from Swedish hunter-gatherers (Lazaridis
et al. 2014).
The study of a time series from the Early Neolithic
to the Iron Age in Eastern Hungary (Gamba et al.
2014), provided a pool of nine new Neolithic ge-
nomes (one high-quality, NE1), including two early
Neolithic individuals associated with the Körös cul-
ture. One of them, KO2, showed affinities with the
Neolithic pool, close to modern-day Sardinians, with
some Near-Eastern influence. Surprisingly, the other
Körös sample, KO1, fell outside modern-day variabi-
lity, clustering together with hunter-gatherers’ geno-
mes, despite the Neolithic cultural context. This find-
ing points to possible admixture between hunter-ga-
therers and farmers at the beginning of the Neolithic.
The following investigations mainly focused on ge-
nome-wide informative markers (Haak et al. 2015;
Mathieson et al. 2015), allowing for the characteri-
sation of more than one hundred Neolithic genomes
to date.
how confidently the genotyping was assigned. High-
quality or high-coverage genomes usually refer to
genomes that have been sequenced with an average
coverage of the whole genome of 20x and above.
Specific filters that take into account post-mortem
deamination reactions are frequently applied to an-
cient genomic data, i.e. considering only SNPs that
involve transversions (from C or G to T or A and vice
versa), which cannot derive from deamination events.
The genomic variability can be summarised and com-
pared to other individuals with Principal Compo-
nent Analyses (PCA), relying on those components
that best explain the diversity between samples.
So-called admixture plots are also frequently used to
visualise with stacked ancestry components in bar-
plots. The number of these components is set a pri-
ori and can identify portions of ancestry related to
geographic distributions, temporal periods or ethni-
cities shared among different individuals.
Relationships between individuals can also be drawn
by adopting phylogenetic approaches. One of the
most interesting approaches used in palaeogenomics
relies on whole genome data and allows for the re-
presentation of a phylogenetic tree including arrows
between branches, pointing to possible admixture
events (e.g. interbreeding between species or inbre-
eding between unrelated populations).
Many methods have been specifically developed
to identify admixture events among populations
or species including ancient DNA. These rely on
different statistical tests, such as so-called D-sta-
tistics and the f-statistics.
Adaptation to the environment and the selection
of advantageous variants (positive selection) can
also be tested at a population or species level, and
rely on the comparison between patterns of genet-
ic variability (such as FST, which provides a mea-
sure of population differentiation).
All the genomic analyses described above can also
be implemented on either genotype likelihood or
imputed genotypes (genotype likelihood incorpo-
rating information from available databases), which
provide genotype probabilities instead of observ-
ed genotypes. This is especially interesting for an-
cient genomes, usually showing few data mined by
molecular damage, which are not suitable for ex-
tensive genotyping.
Further reading
For a review of aDNA molecular methods see Sha-
piro, Hofreiter (2012), and for an updated and com-
prehensive review of aDNA analytical tools see
Leonardi et al. (2016). 
Cristina Gamba
258
Within the Neolithic pool, there is a Southeast-North-
west cline, with a decreasing Near-Eastern affinity,
pointing to a dilution of the original gene pool along
with the expansion. The same trend can be detected
in modern European populations, overlapping with
an East-West gradient due to the influence of Bronze
Age incomers from the Steppe (Mathieson et al.
2015) at later stages.
It is worth noting that the Neolithic cluster also in-
cludes a Spanish genome associated with the Cardial
Neolithic cultural expansion, the first Neolithic inco-
mers into Mediterranean regions, suggesting a com-
mon origin with the LBK Neolithic culture, which
spread in parallel into Central Europe (Olalde et al.
2015). Moreover, it was possible to identify an east-
ern European hunter-gatherer component in this
Cardial genome (ibid.), reinforcing the hypothesis of
a certain admixture of hunter-gatherers and farm-
ers at the beginning of the Neolithic expansion.
Similarly, Zuzana Hofmanová and colleagues (2016)
detected a low-level admixture of migrating farmers
and local hunter-gatherers in the earliest stages of
the Neolithic, consistent with sporadic occurrences.
The admixture with local hunter-gatherers increas-
ed substantially at later stages (Haak et al. 2015;
Hofmanová et al. 2016) at the transition to the Mid-
dle Neolithic across Europe, while Late Neolithic and
Bronze Age periods were characterised by increasing
input from steppe populations (Haak et al. 2015).
The analysis of north-western Anatolian Neolithic
samples from the Marmara region in Turkey (Ma-
thieson et al. 2015), which also clearly cluster with-
in the ancient Europeans’ Neolithic pool, confirmed
that the source of the agricultural incomers reached
Europe through northern Anatolia, and probably fol-
lowed a route across Greece to Europe (Hofmanová
et al. 2016).
However, until very recently, there were no geno-
mic data directly linked to the first appearance of
Neolithic culture in the Fertile Crescent. Only short
sequences from mitochondrial DNA were available
from Pre-Pottery Neolithic samples (Fernández et
al. 2014). However, recently, whole genomes and
genome-wide data from the Fertile Crescent region
have become available, giving a first glimpse of the
Near-Eastern genetic pool through time and space
(Broushaki et al. 2016; Lazaridis et al. 2016; Gal-
lego-Llorente et al. in press). The analysis of sam-
ples from such warm areas has now become possi-
ble thanks to the recovery of relatively high endo-
genous content from the petrous bone, used as the
main DNA source in all three studies (see section
above).
Four Early Neolithic (EN) genomes from Zagros in
Iran show a distinct genetic signature from both
European hunter-gatherers and farmers, close to mo-
dern Pakistanis and Afghans (Broushaki et al. 2016).
In this study, the authors suggested that the affini-
ties of Zagros Neolithic individuals to modern pop-
Fig. 2. Location of ancient hu-
man genomes sequenced at a
minimum coverage of 0.025x
(database Mathieson et al.
2015 which also includes data
from Allentoft et al. 2015;
Gamba et al. 2014; Haak et
al. 2015; Keller et al. 2012;
Lazaridis et al. 2014; Olalde
et al. 2014; this database is
completed with data from
Cassidy et al. 2016; Günther
et al. 2015; Jones et al. 2015;
Omrak et al. 2016; Skoglund
et al. 2014; Broushaki et al.
2016; Gallego-Llorente et al.
in press; Lazaridis et al.
2016). Plot realised in R
(Core Team 2014) using the
libraries ggmap (Kahle, Wick-
ham 2013) and ggplot2 (Wick-
ham 2010).
Genomic approaches and their contributions to understanding the European Neolithisation
259
ulations of southern Asia can be related to the spread
of Indo-Iranian languages or Dravidian languages,
along with the demographic expansion of farming
into the region (Broushaki et al. 2016). This study
also pointed out that the European Neolithic migra-
tion probably had a different genetic source than
the eastern Fertile Crescent.
Iosif Lazaridis and colleagues (2016) tried to answer
to this question by analysing serial samples from
different regions of the Near East, mainly from two
areas, the Levant (Israel and Jordan) and Iran, for
a total of 44 samples (also including some from Ar-
menia in the Caucasus, and one from Turkey) span-
ning from the Epi-Paleolithic and Natufian (pre-Neo-
lithic) to the Chalcolithic periods. The results retriev-
ed from the Iranian samples supported the study by
Farnaz Broushaki and colleagues (2016) and high-
lighted the high genetic differentiation of those sam-
ples not only with respect to the European Neolithic,
but also to the Levantine pool. Interestingly, they
also detected genetic continuity in both regions from
pre-Neolithic to Neolithic periods, suggesting a major
cultural spread of the Neolithic throughout the Near
East (Lazaridis et al. 2016).
The populations of the Levant in the Neolithic are
genetically closer to the European and Anatolian
Neolithic pool than the Iranian Neolithic, but never-
theless cluster separately (Lazaridis et al. 2016).
The authors identify the Levantine population as a
good proxy for East African ancestry, pointing to the
fact that the source population of the Neolithic ex-
pansion into Europe still remains to be identified
(Lazaridis et al. 2016). Further analysis from such
crucial areas and periods will improve our under-
standing of the dynamics that influenced the initial
development of the Neolithic period and its subse-
quent multidirectional expansion.
Although palaeogenomics data have significantly
contributed to deciphering the mechanisms involv-
ed in European Neolithisation, this field would bene-
fit from deeper interaction and integration with re-
lated disciplines, such as archaeology and anthropo-
logy.
Positive selection and phenotypes during the
Neolithic
Genomic research is sufficiently advanced to allow
the identification of the genes involved in several
phenotypic attributes – including complex traits
such as eye and hair colour – controlled by several
genes. European hunter-gatherers had quite dark
skin, dark hair and, interestingly, light-coloured eyes,
while the incoming farmers typically had lighter skin
and dark eyes (Gamba et al. 2014; Lazaridis et al.
2014; Mathieson et al. 2015; Olalde et al. 2014).
A recent study specifically focused on detecting the
positive selection of phenotypes through time (Ma-
thieson et al. 2015) identified several candidates.
The authors investigated the temporal progression
of allele frequency of those genes for spotting the
selection timing and consequences.
A strong signal of selection was detected for the
light skin pigmentation variant of the gene SLC45A2
(SNP rs16891982), now almost fixed in Europeans,
significantly increasing in frequency through time.
Another gene, SLC24A5, also associated with light
skin pigmentation, was not identified as under se-
lection in the analysis, but the selected allele fre-
quency increased at the beginning of the Neolithic in
Europe, probably due to the migration pattern from
the Near East.
The primary determinant of light eye colour is link-
ed to the gene HERC2/OCA2 (SNP rs12913832).
This has been found in all European hunter-gathe-
rers, while at later stages up to the present, the allele
associated with light eye colour increases with high
latitudes, suggesting selection due to the environ-
ment.
Interestingly, the derived allele of the SNP rs3827760
in the gene EDAR, almost absent in present-day Euro-
peans, was detected with high frequency in Scandina-
vian hunter-gatherers from Motala in Sweden (5898–
5531 cal BC). This gene, which affects tooth morpho-
logy and hair thickness, is highly frequent in East
Asian and Native Americans and has previously been
suggested to have originated in East Asia (Kambe-
rov et al. 2013), but a different scenario emerge from
these ancient data.
However, the strongest signal of selection was re-
trieved from the LCT gene (SNP rs4988235), associ-
ated with lactase persistence, which allows to adults
to digest milk. The authors confirmed previous re-
sults (Gamba et al. 2014; Burger et al. 2007; Allen-
toft et al. 2015) pointing out the late occurrence of
this allele, which appeared for the first time only
around 4000 years ago, much later than the advent
of the Neolithic. The earliest date for lactase persi-
stence is from a central European Bell Beaker sam-
ple dated to 2457–2142 cal BC (Mathieson et al.
Cristina Gamba
260
2015). Other variants in genes associated with diet
have been identified, suggesting components of adap-
tation to a variety of diets (fatty acid), different food
sources and environments (vitamin D) and others
possibly linked to coeliac disease (Mathieson et al.
2015).
In this study, Iain Mathieson and colleagues (2015)
were also able to investigate an even more complex
trait which depends on hundreds of variants: height.
A North-South cline in Europe is evident, with height
decreasing in modern Europeans, probably linked to
selection processes that occurred in the past re-
flecting better adaptation to the environment. Based
on 169 genomic variants, the authors explained this
gradient by detecting a significant signal of selection
of reduced height in Iberian Neolithic and Chalco-
lithic samples, as well as increased height in steppe
populations relative to the central European Neoli-
thic. This suggests that the height gradient detected
now is mainly due to the increased steppe ancestry
of northern Europeans and selection for lower height
in Southern Europe.
New perspectives in ancient genomics
Despite the short history of the aDNA field, the ge-
nomic boom has resulted in an explosion of ancient
genomes from many species, including humans, ar-
chaic hominids, animals, and plants. Also, other sa-
tellite approaches are attracting more and more at-
tention, providing new ways to study ancient geno-
mics, such as ancient metagenomic and ancient epi-
genomic analysis. The first refers to the analysis of
the whole sequencing output, the exogenous DNA,
that vast amount of unused sequencing obtained
through shotgun approaches, especially vast in
those samples with very low amounts of endoge-
nous DNA. Why should we be interested in such
DNA portion? Because it might contain other inter-
esting organisms, which might come from the depo-
sitional environment, from manipulation, or from
other organisms, such as pathogens, that were inha-
biting the specimen. Recent work has demonstrat-
ed that it is possible to collaterally detect and fully
characterise Yersinia pestis, the agent of the plague,
in ancient samples (Rasmussen et al. 2015), push-
ing back the presence of this pathogen at least 3000
years before any historical record.
A very interesting substrate for metagenomic ana-
lysis is the dental calculus (Adler et al. 2013; Wa-
rinner et al. 2014), allowing for the investigation of
ancient oral microbiomes, clearly detecting shifts
correlating with dietary changes during the Neoli-
thic and the Industrial revolutions (Adler et al. 2013).
On the other hand, epigenomics studies DNA modi-
fications that do not imply changes in the sequence,
but only reversible modifications to the genetic ma-
terial that mainly influence gene expression (i.e.
how the genetic information coded in the genes is
differently expressed in cells). One of the most com-
mon epigenetic modifications is DNA methylation,
which is the addition of a methyl group to a nitro-
genous base in specific genomic contexts. Recent stu-
dies have demonstrated that it is possible to retrieve
this information from ancient genomes, by using
either direct techniques (bisulphite sequencing,
which is difficult to apply to ancient specimens, as
it requires a large amount of DNA) (Llamas et al.
2012), or by analysing patterns of molecular dam-
age directly linked to methylation rates (Gokhman
et al. 2014; Pedersen et al. 2014).
The applications of both epigenomics and metage-
nomics to ancient substrates pinpoint the progres-
sive incorporation of new approaches to palaeogeno-
mics, the result of a continuously updated multidi-
sciplinary field.
I would like to thank the reviewers for their help in
improving this manuscript and all my colleagues for
their support. My contract was funded by the Marie-
Curie Intra-European Fellowship program (FP7-IEF-
328024).
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