Against Lynn's Doctrine

October 1, 2014

Here's another refutation of Richard Lynn's flawed study on Italian IQ:

Lynn's study uses regional differences in the performance of Italian secondary school children on Organisation for Economic Co-operation and Development tests of educational achievement to assess regional IQ differences. However, data on Italian regional differences in educational achievement obtained in a much larger INVALSI study of 2,089,829 Italian schoolchildren provide unequivocal evidence that Lynn's educational achievement measure is not a valid index of IQ differences. More generally, the lengthy literature review in Lynn's article reveals uncritical acceptance of reported correlations between any putative index of IQ and socio-economic variables. Any measure of cognitive performance that is correlated with IQ is considered a measure of IQ, even if there is only a weak correlation. All correlations between such measures and socio-economic or public health variables are viewed as evidence of direct causal relationships. In all cases, causality is assumed to be in the direction that supports Lynn's doctrine when it would be equally valid to argue that socio-economic and public health differences cause differences in the performance of IQ tests. In addition to these fundamental logical and statistical errors the present report records numerous other data processing, methodological, and conceptual errors.

Robinson et al. "The case against Lynn's doctrine that population IQ determines levels of socio-economic development and public health status". J Pub Ment Health, 2011.

Overestimated Admixture in Brisighelli (2012)

September 8, 2014

This study tries to quantify individual ancestry in Italians by using a small panel of autosomal markers that are known to produce errors and overestimate admixture compared with using the full SNP data set, and as a result it comes up with estimates of Sub-Saharan African admixture in Italy and elsewhere in Europe that are much higher than in any other study:

A panel of 52 AIMs was genotyped in 435 Italian individuals in order to estimate the proportion of ancestry from a three-way differentiation: sub-Saharan Africa, Europe and Asia. Structure analyses allowed us to infer membership proportions in population samples, and these proportions can be graphically displayed, as in Figure 2. This analysis indicated that Italians have a basal proportion of sub-Saharan ancestry that is higher (9.2%, on average) than other central or northern European populations (1.5%, on average). The amount of African ancestry in Italians is however more comparable to (but slightly higher than) the average in other Mediterranean countries (7.1%).

The authors go on to say that Sub-Saharan African admixture in Italy is also evident in uniparental markers, but they should've been suspicious of their high AIM-based estimate when they found only 1.2% mtDNA haplogroup L and no A, B or E(xE3b) Y-DNA, which leads to an admixture estimate (0.6%) that's 15 times lower.

They also should've been puzzled by other bizarre results in their structure analysis, like African admixture appearing higher in Northern and Central Italy than in Southern Italy, with an equally high amount in Britain and Japan, and a very high Asian component in Central and Southern Italians that other Europeans don't have:

52 AIMs

Indeed, when we look at many of the same and similar populations tested using 291,184 SNPs in Lazaridis et al. (2014), we can see that the pattern is totally different and fits much better with the uniparental data and known reality. Italians only have a drop (<1%) of African admixture, which is highest in the south, while the British and Japanese don't have any at all, and Asian admixture is expectedly higher in Northern and Eastern Europe than it is in Southern Europe:

291,184 SNPs

These two very different results highlight the importance of using all genome-wide data when estimating individual ancestry and admixture proportions.

Brisighelli et al. "Uniparental Markers of Contemporary Italian Population Reveals Details on Its Pre-Roman Heritage". PLoS One, 2012.

Mediterranean Sea as Genetic Barrier

June 16, 2014

This new study confirms that the Mediterranean Sea has acted as a strong barrier to gene flow through geographic isolation following initial settlements. Samples from (Northern) Italy, Tuscany, Sicily and Sardinia are closest to other Southern Europeans from Iberia, the Balkans and Greece, who are in turn closest to the Neolithic migrants that spread farming throughout Europe, represented here by the Cappadocian sample from Anatolia. But there hasn't been any significant admixture from the Middle East or North Africa into Southern Europe since then.

Genes Mirror Geography Across the Mediterranean Basin

We first used principal components analysis (PCA) to visualize the genotypic distances between studied populations. Populations on the southern and northern coasts of the Mediterranean appear to be separated by the geographic barrier of the Mediterranean Sea. The role of the Mediterranean Sea as a barrier for gene flow among populations was also supported by our analysis using the BARRIER software, which implements Monmonier's maximum difference algorithm. In accordance with this finding...the PCA distribution of the populations closely resembles the geographic map of the countries circling the Mediterranean Sea. On this PCA "map" of populations, the east coast of the Mediterranean Sea is appropriately occupied by the Palestinians and the Lebanese Druze. Yemenites and Bedouins branch out from the Mediterranean populations and are closer to the populations of the Near East.


Seljuk Turks settled in Anatolia in the 12th century AD; however, the Anatolian Cappadocians we included in this study belong to the population that have kept the religion and the language of the pre-Seljuk Cappadocians and, therefore, most likely carry the genetic makeup of the ancient Anatolians. The only important gene flows from Near East to Europe must have occurred in prehistoric times and, as genetic evidence suggests, the most prominent migrations should have occurred during the Neolithic.


Although the Southeastern Mediterranean islands seem to have acted as a bridge from Anatolia to Southern Europe, the relatively small degree of gene flow between the African and the European coasts shows that the Mediterranean Sea also had a barrier function as also suggested with studies of mtDNA polymorphisms. Thus, the Mediterranean seems to have facilitated the migrations of Neolithic farmers along its Southern European coast but it mostly acted as an isolating factor between its European and African coasts.

Paschou et al. "Maritime route of colonization of Europe". PNAS, 2014.

Italian Ancestry of Ashkenazi Jews

March 8, 2014

Genetic similarities between Italians and Ashkenazi Jews are due to the fact that about half of Jews' ancestry is European, a lot of which came from Italy when diaspora males migrated to Rome and found wives among local women who then converted to Judaism. The same process happened again to a lesser degree in other parts of Europe as Jews migrated further north, west and then east, but according to genome-wide autosomal DNA, their highest European admixture is Italian.

Overall, it seems that at least 80% of Ashkenazi maternal ancestry is due to the assimilation of mtDNAs indigenous to Europe, most likely through conversion. The phylogenetic nesting patterns suggest that the most frequent of the Ashkenazi mtDNA lineages were assimilated in Western Europe, ~2 ka or slightly earlier. Some in particular, including N1b2, M1a1b, K1a9 and perhaps even the major K1a1b1, point to a north Mediterranean source. It seems likely that the major founders were the result of the earliest and presumably most profound wave of founder effects, from the Mediterranean northwards into central Europe, and that most of the minor founders were assimilated in west/central Europe within the last 1,500 years. The sharing of rarer lineages with Eastern European populations may indicate further assimilation in some cases, but can often be explained by exchange via intermarriage in the reverse direction.

The Ashkenazim therefore resemble Jewish communities in Eastern Africa and India, and possibly also others across the Near East, Caucasus and Central Asia, which also carry a substantial fraction of maternal lineages from their 'host' communities. Despite widely differing interpretations of autosomal data, these results in fact fit well with genome-wide studies, which imply a significant European component, with particularly close relationships to Italians. As might be expected from the autosomal picture, Y-chromosome studies generally show the opposite trend to mtDNA (with a predominantly Near Eastern source) with the exception of the large fraction of European ancestry seen in Ashkenazi Levites.

Evidence for haplotype sharing with non-Ashkenazi Jews for each of the three main haplogroup K founders may imply a partial common ancestry in Mediterranean Europe for Ashkenazi and Spanish-exile Sephardic Jews, but may also, at least in part, be due to subsequent gene flow, especially into Bulgaria and Turkey, both of which witnessed substantial immigration from Ashkenazi communities in the fourteenth and fifteenth centuries. Gene flow could have been substantial in some cases—ongoing intermarriage is likely when these communities began living in closer proximity after the Spanish exile. A partial common ancestry for all European Jews—both Ashkenazi and Sephardic—is again strongly supported by the autosomal results.

Jewish communities were already spread across the Graeco-Roman and Persian world >2,000 years ago. It is thought that a substantial Jewish community was present in Rome from at least the mid-second century BCE, maintaining links to Jerusalem and numbering 30,000-50,000 by the first half of the first century CE. By the end of the first millennium CE, Ashkenazi communities were historically visible along the Rhine valley in Germany. After the wave of expulsions in Western Europe during the fifteenth century, they began to disperse once more, into Eastern Europe.

These analyses suggest that the first major wave of assimilation probably took place in Mediterranean Europe, most likely in the Italian peninsula ~2 ka, with substantial further assimilation of minor founders in west/central Europe. There is less evidence for assimilation in Eastern Europe, and almost none for a source in the North Caucasus/Chuvashia, as would be predicted by the Khazar hypothesis—rather, the results show strong genetic continuities between west and east European Ashkenazi communities, albeit with gradual clines of frequency of founders between east and west.

Costa et al. "A substantial prehistoric European ancestry amongst Ashkenazi maternal lineages". Nature Communications, 2013.

Admixture between previously diverged populations yields patterns of genetic variation that can aid in understanding migrations and natural selection. An understanding of individual admixture (IA) is also important when conducting association studies in admixed populations. However, genetic drift, in combination with shallow allele frequency differences between ancestral populations, can make admixture estimation by the usual methods challenging. We have, therefore, developed a simple but robust method for ancestry estimation using a linear model to estimate allele frequencies in the admixed individual or sample as a function of ancestral allele frequencies. The model works well because it allows for random fluctuation in the observed allele frequencies from the expected frequencies based on the admixture estimation. We present results involving 3,366 Ashkenazi Jews (AJ) who are part of the Kaiser Permanente Genetic Epidemiology Research on Adult Health and Aging (GERA) cohort and genotyped at 674,000 SNPs, and compare them to the results of identical analyses for 2,768 GERA African Americans (AA). For the analysis of the AJ, we included surrogate Middle Eastern, Italian, French, Russian, and Caucasus subgroups to represent the ancestral populations. For the African Americans, we used surrogate Africans and Northern Europeans as ancestors. For the AJ, we estimated mean ancestral proportions of 0.380, 0.305, 0.113, 0.041 and 0.148 for Middle Eastern, Italian, French, Russian and Caucasus ancestry, respectively. For the African Americans, we obtained estimated means of 0.745 and 0.248 for African and European ancestry, respectively. We also noted considerably less variation in the individual admixture proportions for the AJ (s.d. = .02 to .05) compared to the AA (s.d.= .15), consistent with an older age of admixture for the former. From the linear model regression analysis on the entire population, we also obtain estimates of goodness of fit by r2. For the analysis of AJ, the r2 was 0.977; for the analysis of the AA, the r2 was 0.994, suggesting that genetic drift has played a more prominent role in determining the AJ allele frequencies. This was confirmed by examination of the distribution of differences for the observed versus predicted allele frequencies. As compared to the African Americans, the AJ differences were significantly larger, and presented some outliers which may have been the target of selection (e.g. in the HLA region on chromosome 6p).

Banda et al. "Admixture Estimation in a Founder Population". Am Soc Hum Genet, 2013.

Two major differences among the populations in this study were the high degree of European admixture (30%-60%) among the Ashkenazi, Sephardic, Italian, and Syrian Jews and the genetic proximity of these populations to each other compared to their proximity to Iranian and Iraqi Jews. This time of a split between Middle Eastern Iraqi and Iranian Jews and European/Syrian Jews, calculated by simulation and comparison of length distributions of IBD segments, is 100–150 generations, compatible with a historical divide that is reported to have occurred more than 2500 years ago. The Middle Eastern populations were formed by Jews in the Babylonian and Persian empires who are thought to have remained geographically continuous in those locales. In contrast, the other Jewish populations were formed more recently from Jews who migrated or were expelled from Palestine and from individuals who were converted to Judaism during Hellenic-Hasmonean times, when proselytism was a common Jewish practice. During Greco-Roman times, recorded mass conversions led to 6 million people practicing Judaism in Roman times or up to 10% of the population of the Roman Empire. Thus, the genetic proximity of these European/Syrian Jewish populations, including Ashkenazi Jews, to each other and to French, Northern Italian, and Sardinian populations favors the idea of non-Semitic Mediterranean ancestry in the formation of the European/Syrian Jewish groups and is incompatible with theories that Ashkenazi Jews are for the most part the direct lineal descendants of converted Khazars or Slavs. The genetic proximity of Ashkenazi Jews to southern European populations has been observed in several other recent studies.

Atzmon et al. "Abraham's Children in the Genome Era: Major Jewish Diaspora Populations Comprise Distinct Genetic Clusters with Shared Middle Eastern Ancestry". Am J Hum Genet, 2010.

Some Perspective on Italian IQ

July 15, 2013

A new paper tries to evaluate the evidence in the recent debate about Italian IQ. The author has a little too much faith in Richard Lynn and the validity of his data, but still challenges his simplistic genetic explanations for north-south disparities, and urges caution when interpreting correlations between IQ and other variables.

The present study was intended to provide perspective, albeit less than unequivocal, on the research of Lynn (2010) who reported higher IQs in the northern than southern Italian regions. He attributes this to northern Italians having a greater genetic similarity to middle Europeans and southern Italians to Mediterranean people. Higher regional IQ was associated with biological variables more characteristic of middle European than Mediterranean populations (cephalic index, eye color, hair color, multiple sclerosis rates, schizophrenia rates). It was maintained, however, that very confident and definitive inferences regarding genetic regional differences in IQ are not warranted. Social conceptualized variables also correlated significantly with IQ so as to suggest the importance of nutrition and economic developmental status more generally.


One should also bear in mind that the correlations are ecological correlations and have the limitations associated with such. Prince (1998) succinctly described three problems with ecological correlations. One problem, the "ecological fallacy," is that people who are high or low in one variable are not necessarily the same people who are high or low on the other variable. In the present study the people in a region who are high in cephalic index are not necessarily the same people who are high in IQ. The second problem is that a third variable may be responsible for the correlation between the other two. In the present study, temperature, precipitation, constituents of drinking water, constituents of soil, health, genetic predisposition to medical disorder, nutrition, and medical care are some of the variables that could conceivably influence the correlation of IQ with schizophrenia and multiple sclerosis. The third problem is that cause and effect cannot be determined.

4. Discussion

It is apparent that regions that have at least some biological characteristics more common in middle European than Mediterranean populations (higher cephalic index, lighter eye color and hair, and higher rates of multiple sclerosis and schizophrenia) tend to have higher IQs. This could be viewed consistent with Lynn's (2010) assertion of a genetically based explanation of north-south IQ differences. Great caution, however, is urged regarding such inferences. Since these are ecological correlations, the persons high or low in these biological variables may not be the same persons in that region high or low in IQ. Because some characteristics are different does not mean that all characteristics are different. East Asians have different facial features than Europeans and Africans. These differences, however, may be only remotely related or not related at all to IQ differences. Also, there are notable exceptions to generalizations about IQ and coloration. Most East Asians and Jewish persons are darker than Scandinavians and yet have higher mean IQs. Furthermore, social variables could account, at least in part, for the north-south IQ differences. Nevertheless, examination and discussion of the biological variable findings are warranted.


The social correlations with IQ and latitude were also substantial and could be viewed as indicating social explanations of the north-south IQ differences. As reasoned above, the massive illiteracy of the south (and even in the north to a lesser extent) could not be explained mainly by genetically determined intelligence. The positive correlation between IQ and literacy suggests that the lower developmental stature of the southern region contributes to the lower IQ. Such an interpretation is also suggested by, as hypothesized, the negative correlation between IQ and percentage increase in stature and negative correlation between income and latitude. This correlation also shows that those regions with the greatest history of malnutrition have lower IQs. As pointed out by Lynn (1990), the secular increase in IQ and stature parallel each other and both seem to be a function of improved nutrition.

Donald I. Templer. "Biological correlates of northern-southern Italy differences in IQ". Intelligence, 2012.

Italians Are Getting Taller

June 14, 2013

Height is increasing throughout Europe because of improved living standards, but this is happening at a faster rate in the south (including Italy) where poverty and poor health were much worse, and people were shorter as a result.

This paper presents new evidence on the evolution of adult height in 10 European countries for cohorts born between 1950 and 1980 using the European Community Household Panel (ECHP), which collects height data from Austria, Belgium, Denmark, Finland, Greece, Ireland, Italy, Portugal, Spain and Sweden. Our findings show a gradual increase in adult height across all countries. However, countries from Southern Europe (Greece, Italy, Portugal, and Spain) experienced greater gains in stature than those located in Northern Europe (Austria, Belgium, Denmark, Finland, Ireland, and Sweden).


Three main features of these data stand out. First, we find that heights in all countries increased during this period. Second, the average stature in the Northern European countries is higher than in the Southern ones for all the cohorts and for both males and females. Third, the intensity of such a growth is heterogeneous: Northern versus Southern differences are visible. For instance, looking at Table 1, we see that Finnish men born in the first half of the 50’s were 177.8 cm tall, while those born in the late 70’s achieved 178.7 cm. The less than 1 cm increase by Finnish males contrasts sharply with the growth experienced by Spanish males: from 171.3 cm to 176.1 cm, almost 5 cm. In Table 2, we note that there are also huge differences between the growth experienced by Italian and Spanish women, more than 5 cm, in comparison to that of Danish women, only 1.4 cm.

This pattern of higher growth rates for both males and females in the Southern European countries becomes more evident when considering Table 3, where annual growth rates between the 1950-55 and the 1976-80 cohorts are reported (0.10% for Southern countries, 0.05% for Northern countries, and the total mean growth is 0.07%). Also we can point out that height growth rates are almost equal for males and females according to this geographical classification. There does not seem to be a clear pattern in terms of gender across countries. Some countries have experienced higher absolute gains for women (Belgium, Finland, Italy, Spain and Sweden) whereas some others have experienced greater gains for men (Austria, Denmark, Greece, Ireland and Portugal).

Considering the evolution of heights separately for the Northern and Southern European countries (Figures 1 - 4) some generalizations are evident. First, for the Northern countries, the cohorts of Danish males are always the tallest: 180.3 cm at the beginning and 183.7 cm at the end of the period. Second, the reverse situation is shown by the Irish males, who are the shortest in the Northern Europe sample during the whole period, 174.9 cm for those born in 1950-1955 and 177.4 cm in 1976-1980. Similar qualitative results are found for females.

From the evidence in Figure 3 and Figure 4 we can conclude for the Southern European countries that Greeks are the tallest for both males and females and Portuguese are the shortest ones in both cases. Both countries show a similar evolution profile in the period under consideration. At contrast, Spanish males and females for the last cohorts are growing more significantly than those in the other Southern European countries.


Trying to measure wellbeing in a society using only one measure is a challenging task, if not an impossible one. Usually, economists consider Gross Domestic Product (GDP) per capita or Gross National Product (GNP) per capita as conventional measures of living standards. Consumption per capita is also used. However, the use of these indicators is not without its shortcomings. [...] Stature is a measure that can help us to circumvent these caveats, but even more important, stature is interesting in its own right: it is a useful summary measure of biological wellbeing, as emphasized by Komlos and Baur (2004). First, stature is a measure that incorporates or adjusts for individual nutritional needs (Steckel, 1995). Second, it also meets satisfactorily the criteria set forth by Morris (1979) for an international standard of physical quality of life. Third, stature is a welfare measure that satisfies the approach to the standard of living suggested by Sen (1987): functionings and capabilities should be balanced. Fourth, it generally correlates positively with many health outcomes throughout the life course, and in particular, it correlates negatively with mortality (Waaler, 1984; Barker et al. 1990). Hence, physical stature can be used as a proxy for health, which as any inherently multidimensional concept is difficult to measure.

Garcia and Quintana-Domeque. "The Evolution of Adult Height in Europe: A Brief Note". Econ Hum Biol, 2007.

Pope Francis: Just Another Italian

March 18, 2013

The media is calling him the first "non-European" Pope in 1300 years and the first "Latin American" Pope ever, and Latinos and Hispanics in the U.S. are starting to identify with him as a fellow "minority" and "one of us". His name may be Jorge, he may speak Spanish, and he may have been born in South America, but his ancestry is 100% Italian and the country he's from isn't very representative of the region.

But the first Latin American pope also represents a cultural bridge between two worlds — the son of Italian immigrants in a country regarded by some as the New World colony Italy never had. For many Italians, his heritage makes him the next best thing to the return of an Italian pope.


It remains unclear whether even Latin Americans will respond with newfound energy to Bergoglio's ascension to the throne of St. Peter. Among many of its neighbors, Argentina is seen as a nation apart — a country that fancies itself more European than Latin American, with many likely to see the rise of an Italian Argentine as largely unrepresentative of the region as a whole.

"Argentina is so secular today, a more Eurocentric Latin country," said Joseph M. Palacios, a specialist in religion and society in Latin America at Georgetown University. "They are Catholic by culture but not by practice. Geopolitically it makes sense in terms of bridging Europe to Latin America or the Third World, but Argentines don't see themselves as being Third World."

Anthony Faiola. "Jorge Mario Bergoglio, now Pope Francis, known for simplicity and conservatism". The Washington Post, March 13, 2013.

Pope Francis
Parents Regina Maria Sivori
and Mario Jose Bergoglio

Back: brother Alberto Horacio, Francis, brother Oscar Adrian, and sister Marta Regina.
Front: sister Maria Elena, mother, and father.

PISA Test Score Gap Closing

December 27, 2012

More evidence against Richard Lynn's Italian "IQ" study. The achievement test scores he used to "measure" intelligence have since improved dramatically in Southern Italy, which is rapidly catching up to the North.

Recent results of international assessment programs (e.g., PISA) have shown a large difference in high school students' performance between northern and southern Italy. On this basis, it has been argued that the discrepancy reflects differences in average intelligence of the inhabitants of regions and is associated with genetic factors (Lynn, 2010a, 2012). This paper provides evidence in contrast to this conclusion by arguing that the use of PISA data to make inferences about regional differences in intelligence is questionable, and in any case, both PISA and other recent surveys on achievement of North and South Italy students offer some results that do not support Lynn's conclusions. In particular, a 2006-2009 PISA data comparison shows a relevant decrease in the North-South difference in only three years, particularly evident in the case of a single region (Apulia). Other large surveys (including INVALSI-2011) offer different results; age differences suggest that schooling could have an important role.

Cornoldi et al. "Problems in deriving Italian regional differences in intelligence from 2009 PISA data". Intelligence, 2013.

Population Structure Within Italy

October 29, 2012

The good news is there's a new study on population structure in Italy. The bad news is that it's not very well done. The authors failed to sample populations from elsewhere in Southern Europe (like Iberia and the Balkans), which Italians are most related to in other studies, and they didn't collect 4-grandparental information on each individual, resulting in several outliers apparently with recent origins in different parts of the country. They also describe genetic components that are probably very old and have a wide distribution as "Northern European ancestry" and "Middle Eastern ancestry" as if they came from modern populations. Besides all that, the results aren't too surprising.

According to the study, Italians have similar proportions of the same genetic components, varying slightly from North to South, but distinct from both Northern/ Central Europe and the Middle East/North Africa. The island of Sardinia is unique in having an excess amount of one of those components.

In terms of PCA, Italians plot expectedly according to geography between the Western and Eastern Mediterranean (the authors say "France" and "the Middle East" because of the lack of Southern European reference samples), with Sardinians out to the side.

The position of the Italian population samples suggests that genetic distances between these populations and other European and Middle East populations has a good correlation with geographic distances. At the same time, Sardinia was confirmed to be a genetic "outlier".


The relative position of the samples reflected their geographic location: the close correlation between PC and geography was previously reported by several authors. When compared to other European populations, Sardinia was confirmed to be a genetic "outlier", whereas the Northern Italian population was genetically close to the French population, and the Southern Italians had some similarities with other Mediterranean populations such as those from Middle East. Unfortunately, lack of data from other relevant reference populations from the South-East Europe, e.g. from the Balkan peninsula, made it impossible to fully analyze the extent of the Eastern contribution in Italian populations.

Our main goal was to investigate the genetic structure of the Italian population considering four main macro-areas (Northern, Central, Southern Italy and Sardinia). We carried out PC analysis on the Italian samples and plotted the eigenvectors 1 and 2 in Figure 2. Most samples fell within a main cluster which seems to be indicative of Italian peninsula individuals. The first PC divided Italian populations in two clusters, one for Sardinia and the other for the remaining three Italian macro-areas. The Sardinian population is highly dispersed along the first eigenvector.

The second PC divided Italian mainland population into two clusters, with a certain degree of overlapping between Northern and Central Italy, and a separate cluster for Southern Italy, suggesting that genetic variation is generally continuous rather than discrete, at least within Italian mainland.

The overlap of Northern and Central Italy, and the gap between Central and Southern Italy, is explained by the uneven distribution of the samples.

ADMIXTURE analysis confirms that there was no clear separation between Northern and Central Italy, at least as considered as macro-areas. Additional comparison of the distribution of pair-wise identity-by-state within each of the four populations and ADMIXTURE analysis clarified that this is not an artifact of the PC analysis. However, the PC and ADMIXTURE analysis results could be due to the sparse geographical coverage of our samples, especially for the Central and Northern macro-areas. In fact, many of the individuals (N = 413) in the North Italian sample analyzed in this study were from Piedmont — a North West Italian region that has historically been affected by intense migration. At the same time, many individuals in the Central Italy macro-area (113 samples) are settled in Tuscany, an administrative region which is at the border with northern regions.

Within each macro-area, there isn't much substructure, meaning that a Sicilian, e.g., is not particularly differentiated from a Campanian or a Puglian.

A finer view of the Italian substructure, can be seen in Figure S2 where the hidden population structure within the Italian dataset is appreciable. Subjects are labeled by municipality, or in the case of the Sardinian samples, by the main linguistic area. In this figure we can appreciate the lack of clustering at the municipality level, also within Sardinia. Individuals seem to cluster within the main macro-area, but the geographic patterning is less obvious for the municipality (or in the case of Sardinia, linguistic) division, and in our opinion this pattern indicates no substructure within regions among municipalities, while the structuring between regions can be easily detected. It is also possible to appreciate a certain genetic homogeneity within Sardinia.

Di Gaetano et al. "An Overview of the Genetic Structure within the Italian Population from Genome-Wide Data". PLoS One, 2012.