Method
Imagine a historical study that claimed that a general north-south
division was visible in, for the sake of argument, the prologues of medieval
charters and that this model had predictive value, such that the geographical
origin of a charter could be accurately discerned from the sequence, appearance,
or non-appearance of particular phrases. This would be quite an assertion, if not
necessarily implausible. Imagine, however, that this model had been constructed
from a sample of no fewer than 2,233 charters from Switzerland and southern
Europe but of only 179 from northern Europe. The claim would – at best – be
regarded as shaky. Yet, the geographical distribution of the modern DNA samples
against which the aDNA extracted at Szólád and Collegno were compared takes
exactly this form. The POPRES (POPulation REference Sample) data base,
the largest of those used to establish the distribution of genetic types across
modern Europe, contains 1349 subjects from Switzerland, 599 from the United
Kingdom, 147 from France, 131 Portuguese subjects, 114 from Italy and 92 from
Spain. For the variable ‘Country of Father’, the uneven distribution was skewed
further: 1,404 samples from Switzerland, 310 from Italy, 184 from Spain, 177
from France and 158 from Portugal, compared with 91 from Germany, 50 from
Belgium and 38 from England.
That latter pattern was repeated across the other country of parent or
grandparent variables. Every other nation represented, and from which regional
characteristics were constructed, contained a few dozen individuals at most. Remember,
too, that that the Swiss and southern European subjects were drawn from a
population of 193.185 million people: they represented, in other words, just
over a thousandth of one percent of the modern population. Even the largest
sample, which happened to be taken from the smallest population (that of
Switzerland), represents only a hundredth of 1% of the latter. The whole POPRES
reference population totals only 5,886 subjects reduced, after quality-control
procedures, to 3,082. These are infinitesimally small samples.
The POPRES data was compiled from ten collections with the overall
aim of providing useful material ‘for population, disease, and pharmacological
genetics research’.
Its UK data came overwhelmingly from a sample of 431 South Asian and 938
Northern European subjects, aged between thirty-five and seventy-five,
collected from fifty-eight GPs in west London for the purposes of research into
cardiovascular illness. The largest population (2,809 subjects) was assembled
from the Centre Hospitalier Universitaire Vaudois in Lausanne (hence the
dominance of Swiss and southern European samples). Other collections included only
healthy subjects. Much of the European genetic data was assembled from the
declarations of US, Canadian and Australian subjects of their paternal and
maternal ancestry. According to the publication of the POPRES project, ‘[t]he
second round of quality control included further PCA [Principal Components
Analysis] to identify subjects with ... misreported genetic ancestry’.
What this means is unclear but it gives the impression that ‘misreporting’ was
established upon the basis of perceived statistical anomaly. Another statement
of method is worth quoting in full:
Based on this
information, we first attributed a best-guess geographic label to each of the
family members based on the following rules: 1) missing data was ignored; 2) if
ethnicity conflicted with birthplace or first language data, only ethnicity was
considered; 3) if birthplace and first language disagreed, a higher level
container label was chosen (e.g. an individual who was born in France but
reported his first language to be Norwegian was labeled European); and 4) white
individuals born in the US or Canada were attributed according to the first
language information alone, if other than English.
Such methods and assumptions may be fair
enough, especially for the purposes for which the database was assembled, but
they might all be subject to discussion if employed to study historical
population movement and ethnicity: not a purpose for which the data were
collected. Furthermore, there are serious differences in the reliability of
these data between their use at the level of population and their employment at
the level of individuals.
The other reference populations were smaller
than the POPRES sample.
The ‘1000 Genome Project’, from which the genetic ancestry of the
Szólád-Collegno individuals was estimated, used populations of only 100-200
subjects.
Those that were most significant in the discussion of the results were ‘Central
Europeans in Utah’ (CEU: 184 samples); ‘Toscani’ project (TSI, from a small town
near Florence: 117 samples); Great Britain (GBR: 107 samples); and ‘Iberians in
Spain’ (IBS: 162 samples). Statistically we are looking at fragments of droplets
in oceans.
The small modern DNA sample was compared with
a still smaller (435 subjects)
sample of aDNA collected from archaeologically-recovered skeletons of Bronze
Age date ‘or more recent’
and the conclusion drawn that, over the past three and a half millennia in
Europe, there has been only the barest drift of population, generally to the
south.
It is significant, however, that the distribution of the Bronze Age sample was
almost the diametrical opposite of that of the modern reference population: ninety-three
subjects from north of the Alps (mostly from Germany), compared with thirty-three
from south of the mountains (including only four from Italy).
Some areas heavily represented in the modern sample (Switzerland; France, the
UK) featured barely or not at all in the Bronze Age reference set. We have no
idea what the population of Bronze Age northern Europe was but one imagines
that eighty-eight would be a small fraction of one percent of it. This should
raise all sorts of red flags. On this statistical basis, the claims made by
Amorim and his fellow authors are, to be generous, bold indeed.
The kindreds were illustrated through a
comparison of their genetic ancestry, expressed in terms of the admixture of
seven types, of which the most important were labelled ‘CEU+GBR’, ‘TSI’ and
‘IBS’ (see above). It might seem fair to refer to ‘CEU+GBR’ as ‘northern’ and
‘TSI’ and ‘IBS’ as ‘southern’. It should be noted though that the analyses of
the aDNA were incapable of clearly separating ‘GBR’ and ‘CEU’. The ‘CEU’
population, as will have been noted, was of modern Americans of European
descent. How accurate and precise are its results likely to be in a European
context? Even without the problems of sample and method, combining these
ancestries covers a very broad region of Europe, inside and outside the Roman
frontiers, one unlikely to sustain the very precise but sweeping claims made in
the article.
This experiment takes data suggesting
immobility, constructed at the level of populations, and compares it against
data drawn from individuals and putatively showing migration. If, against the
backdrop of a 3,500-year-long history of supposedly general population
immobility, aDNA taken from sixty-three burials at two different cemeteries
revealed, at both sites, evidence of the arrival of genetically distinct
populations, this must have been the equivalent of randomly locating the
proverbial needle in a haystack, worthy of a media ‘splash’ in itself. The
other implication ought to be that – given the supposed genetic difference of
the incomers at Collegno from modern north Italians – whatever its scale, this
population movement turned out to be a genetic dead-end, leaving no significant
trace in the region’s modern population. If so, the value of this research for
the study of the Völkerwanderung should be quite the opposite of that which has
been supposed.
Finally, it is worth mentioning that one set
of results, which lay outside the expected range, was rejected on hypothetical
grounds: ‘this sample showed high levels of contamination (which we hypothesize
is the result of plastic wares produced in China that were utilized in DNA
extraction) and thus the results are unreliable.’
If that were the case, surely that whole body of data should be thrown out of
the experiment, not just selected results that did not ‘fit’.
Results
We must assume that the laboratory analyses
and subsequent mathematical modelling were flawless but there are strong
reasons to discount the experiment’s results on the grounds of its set-up and
the problems with its samples. Let us nonetheless treat the results on their
own terms. My first point concerns the geographical plotting of different
genotypes. The SPA (Spatial Ancestry Analysis) plots geographical coordinates
for each allele within a Single Nucleotide Polymorphism (SNP) according to the
location of the individual from whom the DNA sample was taken.
This data can then be used to predict the location of individuals according to
the frequency of particular alleles within the SNPs of their genome. After
running a series of SPA analyses, the geographical location of the individuals
whose DNA was collected could be represented on a graph, using x and y coordinates,
in such a way that the means of samples from different regions stood in a
spatial relationship to each other that more or less replicated the
geographical relationships between those regions. Thus the mean of samples
from, say, the Republic of Ireland, United Kingdom and the Netherlands would be
located near each other in the top left quadrant of the graph, above and
perhaps to the left of the mean for France, and so on. Now, as Yang et al.
illustrate, a very similar result can be produced using Principal Components
Analysis.
If that is so, it must also be the case that geographical coordinates describe
(that is to say represent the variation within) the data to a greater degree
than variables within the genotypes. Otherwise, the means of samples taken from
particular countries or regions would be pulled into clusters according to
those genetic variables rather than their geographical relationships. Alternatively
the genetic variables have been represented in such a way as to describe the
data less well than the geographical coordinates and so allow the latter to
determine the plot to a greater extent. Now, for the medical purposes for which
SPA or other genetic models were created, this need not be an issue; indeed it
might be desirable. For the discussion of historical genetics, however, one is
left wondering exactly how significantly the genotypes differ from one another. [I am no longer sure that I have expressed (or got) this quite right, but there’s something very problematic about this mapping and its implications, and the assumptions made about it.] The plotting of individual samples against these geographically-driven plots
seems to produce anomalies.
The presentation of the experiment’s results
steers the reader towards a particular interpretation. At both Szólád and
Collegno analyses suggested the existence of two genetically distinct groups.
The argument is that one such group represents ‘northerners’, implicitly
immigrants, and the other ‘locals’ (we can bracket the question of whether
these assumptions are valid). On the published diagrams the former is coloured
blue; the latter red. There is no good reason to have used exactly the same
colour-coding at both sites or, alternatively, to have overlaid the results
from both sites on the same figures,
especially when we might suppose that they represent significantly different
populations. Clearly, the reader is intended to associate the two groups at the
two sites and to see them as parts of two larger, generally distinct
populations – of incoming Longobards and local provincial Romans, respectively.
This hampers any critical reading of the data.
The analyses strongly suggested that there
were genetically distinct kindreds present at both Szólád and Collegno. They
also showed, however, that the two sites’ populations were quite similar
overall.
Both included people with genetic ancestry of predominantly ‘CEU+GBR’ (‘northern’)
type and others with ancestry that was overwhelmingly of ‘TSI’ (‘southern’) type,
although most individuals showed combinations of the two. ‘IBS’ ancestry at
both was only found in subjects who showed ‘TSI’ ancestry (although in most
cases ‘CEU+GBR’ was also present). Both sites contained some people with
entirely ‘northern’ and others with entirely ‘southern’ genetic ancestry. On
the basis of the genetic data, however, there would be as much reason to
suppose that, at least in in Szólád, the people with ‘southern’ ancestry were
the incomers, and those with ‘northern’ ancestry the locals, rather than vice
versa. That might superficially seem less likely at Collegno but the lack of
significant Italian aDNA comparanda means it is possible there too. The –
hardly numerous – prehistoric Italian aDNA subjects clustered in a quite
different part of the SPA diagram from the Collegno ‘southerners’.
That the kindred with ‘northern’ ancestry were newer to the region of Collegno
than the other kindreds was only revealed by the isotopic analyses which were,
overall, more interesting than the genetic studies. At Szólád those analyses suggested
that kindreds of both ancestry types had moved there quite recently.
Two of the analysts’ presuppositions come
into play here, neither of which emerges from the data themselves. The first is
that the pattern illustrates a specific episode of demographic movement;
the similarity results from one population moving to the area of the other. The
second is that, more specifically, this episode was the Longobard migration from
Pannonia to Italy. Without these, one could argue that the profiles of the two
sites revealed that, genetically, populations in sixth-century northern Italy
and in Pannonia were fairly similar and attested to continuous movement back
and forth between the two regions as one might expect on historical grounds. This
is why a control, or other comparanda, was (or were) essential. How likely is
it that a sample of any cemetery in
the Po Valley, dating to any period
between the ‘Celtic’ settlement of Cisalpine Gaul and now, would –
like Collegno – contain at least some people with genetic make-up
suggestive of comparatively recent origins north of the Alps? I would propose
that the answer is ‘very likely’.
While difficult and dangerous, population
movement across the Alps has been constant since Ötzi the Iceman.
The Celtic migration into northern Italy has been mentioned; later, the different
regions were part of the same imperial state for the best part of five
centuries; Carolingian Italy was politically connected with the Rhine valley, Germany,
and Provence and many armies (and doubtless countless individuals) moved back
and forth over the mountains. Those contacts continued in the period of the
‘Holy Roman Empire’, bringing French and German troops into the peninsula, as
happened again in the sixteenth-century Italian Wars. The ensuing Hapsburg dominance
of northern Italy strengthened the already significant ties between that region,
southern Germany, and Hungary up to the late nineteenth century. The idea that
genetic similarities between Italian and transalpine populations at any point
in history can (let alone must) be explained by then recent, discrete
large-scale events lacks empirical basis. In other words, while the
similarities between the populations of Szólád and Collegno surely attest to
individual movement across the Alps, there is no good reason to suppose that
they must testify to any particular, large-scale ‘migration event’, or to
change rather than stasis in patterns of population movement. None of that (or
indeed any of the arguments proposed here) means there was no Longobard
migration or that that movement did not involve a large number of people: both
facts are incontestable. What they do mean is that the evidence from
these sites is not necessarily evidence of that migration, and that traces of
that migration need not be expected to be especially clear in the genomes of
late antique northern Italians. In many regards the experiment was
fundamentally ill-conceived.
Overall, the analyses suggested a generally
mixed population of Szólád. The results of Principal Coordinates Analysis (PCA)
of Hungarian aDNA samples, when overlaid (using ‘Procrustes’)
with the Szólád-Collegno and modern reference samples revealed a distribution
that overlapped with the ‘northern’ and ‘southern’ Szólád kindreds.
If the authors’ assumptions about long-term population stability between the
Bronze Age and the present day, and about their methodology, were correct
this evidence would surely not show very conclusively that either group had
moved into the region from a significantly distinct area. Analysis
of the strontium content of the teeth at Szólád did not suggest that the
‘northern’ group were necessarily more likely to be outsiders than the
‘southern’ group. They were, however, evidently more heterogeneous in origin
than the latter. According to the ‘narrative’ the study was supposed to be
‘testing’, barbarian immigrants were heterogeneous but on what basis would one
assume that the population of late Roman Pannonia was not? From historical
sources we know that it was a frontier province in which garrisons of diverse
origins were stationed; in the late fourth century, Goths passed through the
region more than once; the fifth century saw several groups, not least the Huns
and Ostrogoths, resident there. The latter, of course, later moved to Italy and
established a kingdom there.
The results of the analyses, as presented in
the diagrams in Nature, seem less than convincing when examined closely.
Subjects with different genetic ancestry are plotted against modern and Bronze
Age subjects, as mentioned earlier. However, their grouping raises critical
issues as the Principal Components Analysis, for whatever reason, pulled the
data in such a way as to reveal, in some cases, a greater range within
the groups defined by their ancestry than between them. For example, the
Szólád ‘northerner’ and the two Szólád ‘southerners plotted nearest the origin lie
closer to each other than they do to the members of their groups plotted farthest
from the origin. It is also clear that some Principal Components Analyses have
described the data far less clearly than others. Something in the data gives us
grounds to wonder about the combination of different analyses of different data
sets. Is the PCA calling the ‘Admixture’ analyses into question? This
especially muddles the results at Szólád. That issue is further obfuscated by
the overlaying of the results from both sites on the same plot and the use of
the same colours in their representation, discussed earlier.
The ‘northern’ group at Collegno is in fact clustered
more compactly, in a different region of the plot from the Szólád
‘northerners’. Indeed we see that the different genetic kindreds at Collegno
are far more significantly separated on that plot, something that might support
the conclusions the authors wished to draw. However, we also perceive a third
group clearly distinguished from both: those with over 50% ‘TSI’ and ‘IBS’
(Tuscan and Iberian) ancestry who, one would have thought, ought to be plotted
much further towards the ‘south’ or ‘south-east’ (or lower left-hand) quadrant on
the PCA plot rather than in the region where modern Central European subjects
cluster. This must question some of the experiment’s assumptions. Ultimately, though, while
there are nine ‘northerners’ (with over 70% ‘GBR+CEU’ ancestry) plotted, there
are only four with over 70% Tuscan ancestry and four with Tuscan/Iberian. We may
wonder why the authors chose to emphasise only the group with Tuscan ancestry
as locals when the ‘TSI + IBS’ group could just as easily be called
‘southerners’, unless it was because this was inconvenient for the narrative
that they had decided their results should present.
Finally, given the stress laid upon ancestry
in the article’s conclusions, the cautionary note sounded recently by Mathieson
and Scally is important:
Another source of
confusion is that three distinct concepts – genealogical ancestry, genetic
ancestry, and genetic similarity – are frequently conflated. ... but note that
only the first two are explicitly forms of ancestry, and that genetic data are
surprisingly uninformative about either of them.
Nelson MR, et al., ‘The Population
Reference Sample, POPRES: a resource for population, disease, and
pharmacological genetics research.’ Am J Hum Genet. 2008
Sep;83(3):347-58. doi: 10.1016/j.ajhg.2008.08.005. Epub 2008 Aug 28. PMID:
18760391; PMCID: PMC2556436.
The next largest reference population was
that created by G. Hellenthal, G.B.J.
Busby, et al., ‘A genetic atlas of human admixture history.’ Science.
2014 Feb 14;343(6172):747-751. doi: 10.1126/science.1243518. PMID: 24531965;
PMCID: PMC4209567. This contained 1,490 subjects from ninety-five genotyped
population groups worldwide (thus an average of fifteen subjects each).
