The presence of genetic gradients across Europe has been described and discussed for more than 30 years. In the case of autosomal markers, a southeast to northwest gradual change in the distribution of the genetic diversity has been reported using principal component analysis (PCA) [1, 2]. Initially, this gradient was described from classical markers such as blood groups [1], and later was confirmed by genome-wide single nucleotide polymorphisms (SNPs) [3, 4]. This genetic diversity cline is traditionally explained by several major prehistoric demographic events in Europe: the first colonization of Europe by anatomically modern humans together with a postglacial re-expansion from the southern European refugee areas in Palaeolithic times, and the introduction of the Neolithic agricultural lifestyle by people from the Near East [1]. Theoretical studies using computer simulations [5] have shown that such major prehistoric demographic events can produce genetic gradients in autosomal markers that in particular situations resemble what is observed in real data from Europe. However, simulations tend to necessarily simplify the demographic history by ignoring more subtle demographic events that took place throughout history at a smaller geographical scale such as those in Europe [6]. Furthermore, it was suggested that caution should be taken when interpreting results from PCA analyses [7]. With this study we aim to investigate whether (similar) spatial patterns in genomic diversity can also be detected on a micro-geographic scale, within a European country like the Netherlands, and if so, whether these patterns could also be explained by more recent demographic events.

We chose the Dutch population as an example because results from geological, archaeological, and historical studies strongly indicate that during several prolonged periods of time different factors and events resulted in discontinuities of human populations on the current territory of the Netherlands. A summary of the population history of the Netherlands is provided in the supplementary material [see Additional file 1]. In brief, geological processes have been a major driving force shaping the Dutch demographic history. During prehistory, the Dutch landscape went through several significant transformations. Also in more recent times, mainly under influence of variable water levels of the North Sea and many rivers, the Dutch landscape underwent major changes. Humans additionally had a substantial direct impact on the Dutch landscape with major land-reclamation projects [8]. As a result of this, large parts of the country that are densely populated today, were not suitable for human habitation during several periods in both prehistoric and historic times [9, 10]. Figure 1 provides examples of the changing Dutch landscape and its suitability for human settlement from 500 Before Christ (BC) to the present day, and illustrates the changing conditions relevant for human habitation. Furthermore, archaeological and historical evidence provides several indications for cultural processes that additionally caused discontinuity of the Dutch population. Some examples are i) the fast population growth and subsequent decline during the Roman period (from 15-30,000 to 150,000 to less than 40,000 in just 400 years time) followed by substantial subsequent migrations [10–15], ii) the religious division that emerged in 1648, which has lasted to the present day [15], and iii) the substantial and complex migrations during the second half of the 20th century [16] [see Additional file 1 for more details].

Given the archaeological, geological and historical evidence for genetic discontinuity in Dutch population history, one might expect that the ancient genetic signatures from Palaeolithic and Neolithic times, such as the southeast to northwest cline seen across Europe, would not be detectable in the contemporary Dutch gene pool. To test this hypothesis via studying the spatial distribution of the Dutch genomic diversity, including computer simulations, and to investigate the overall genomic-geographic substructure of the Dutch population, we sampled 999 individuals at 54 sites across the Netherlands following a grid-like scheme. DNA samples were genotyped with the Genome-Wide Human SNP Array 5.0 (Affymetrix; http://www.affymetrix.com/estore/) from which 443,816 quality-controlled genome-wide autosomal SNPs were used in various spatial, cluster, and simulation analyses.

The locations of the 54 Dutch geographic subpopulations from which the 999 individuals were sampled are shown in Figure 2 and further explained in Table 1. As evident, most of the current Dutch territory was sampled evenly with an average sample size across subpopulations of 18 individuals (range between 1 and 65). Overall, about half of the genome-wide autosomal SNPs (53.75%) had a Weir and Cockerham’s Fst value [34] of zero (or smaller). The mean Fst value across all genome-wide autosomal SNPs used was only 0.003 (after setting negative values to zero) and the mean combined Fst value between pairs of subpopulations was even smaller at 0.00038. These results together demonstrate a very small overall genetic differentiation among the 54 Dutch subpopulations sampled across the entire country. In fact, genetic differentiation between geographic subpopulations from within the Netherlands as observed here is smaller than between geographic subpopulations from within other northern European countries studied thus far in a systematic fashion, such as Sweden [40]. Our genomic results are in agreement with expectations from human populations of small geographic areas, and suggest the absence of strong genetic barriers within the contemporary Dutch population (in addition to the nonexistence of strong geographic barriers). To investigate the spatial distribution of the Dutch genomic diversity as well as the genetic-geographic substructure of the Dutch population, we applied a combination of well-established and recently introduced approaches to the genomic data after stringent quality control on markers and individuals (see Methods for details on quality control).

First, we performed a classical multidimensional scaling (MDS) analysis on an identical-by-state (IBS) distance matrix between all pairs of 969 individuals (30 individuals were excluded during quality control via the Tukey’s outlier criterion, see Method section for details). By applying Mclust [41] to the first two dimensions of this MDS [see Additional file 1: Figure S2(A) for the two-dimensional plot], we identified three clusters of individuals. The first two clusters comprised of 98.25% of all individual samples, while the third cluster comprised of 17 individuals only [see Additional file 1: Figure S2(B)]. These 17 individuals mostly represent singletons from widely dispersed geographic subpopulations: 1 from ASS, 1 from DOK, 1 from MAA, 1 from MAS, 1 from OSS, 1 from PUR, 1 from ROE, 1 from SCH, 1 from WIN, 1 from WOE, 1 from ZIE, 2 from HIL, 2 from HOO and 2 from LEI (see Table 1 for explanations of subpopulation abbreviations). Notably, these 17 individuals were mostly found contributing to differences in the first dimension of the MDS. When excluding these individuals from the MDS, the first dimension of a two-dimensional plot (accounting for 0.296% of the total variance) tends to distribute the remaining 952 Dutch individuals according to a south to north axis (Figure 3A). The mean of the first dimension in each subpopulation correlates strongly with latitude (adjusted R squared = 0.676, P value = 1.455e-14) and somewhat less strongly with longitude (adjusted R-squared: 0.297, P value = 1.214e-05). The second dimension (accounting for 0.191% of the total variance) tends to differentiate individuals from the central-east region of the Netherlands (HAA, MAR, LOS, DEN and TUB) from the rest and correlates with longitude, albeit not very strongly (adjusted R-squared = 0.297, P value: 1.214e-05; adjusted R-squared with latitude = 0.083, P value: 0.019). When considering both dimensions at the same time, the correlation in a symmetric Procrustes rotation [25] between the mean value of each dimension and the geographic coordinates of each of the 54 subpopulation was high (r = 0.762, P value after 1,000 simulations <0.0005), suggesting that the proposed genetic map of the Netherlands fits the geographic map of sampling locations well.

Second, we performed an MDS analysis using an identical-by-descent (IBD) distance matrix between pairs of individuals (see Figure 3B for a plot of the first two dimensions). The mean of the first dimension for each subpopulation correlates with longitude (r = 0.496, P value: 0.0001348) while the second dimension correlates strongly with latitude (r = 0.89, P value: <2.2e-16). Both MDS dimensions together correlate with the geographic coordinates (Procrustes symmetric correlation: 0.679, P value <0.0005 based on 1,000 permutations). Furthermore, we observed a strong correlation between the MDS based on IBS and the one based on IBD (correlation in a symmetric Procrustes rotation: 0.844, P value = 0.001 after 1,000 permutations). Third, we carried out two genetic clustering analyses of the genomic data. In the first analysis, we used ADMIXTURE [26] allowing for K = 1 to 6 [see Additional file 1: Figure S3]. By performing a cross-validation error analysis [26] to distinguish the most sensible model choice, we found that at K = 1 the cross-validation error was smallest indicating the most sensible model and that this error increased until K = 4. At K = 5, however, the cross-validation error decayed compared to K = 4 and K = 6 [see Additional file 1: Figure S3(A)] suggesting that K = 5 could also be regarded as a sensible model. We therefore focused on the results of K = 5 in Figure 4 (the full results of K = 1 to 6 are available from [Additional file 1: Figure S3]). With K = 5, the consensus plot of ancestries (out of 10 independent ADMIXTURE replicates merged with CLUMPP [28]; H’: 1) suggests that three of the ancestral populations together describe almost all (total 97.47%: 24.53%, 30.54% and 42.4%) of the total genome-wide ancestry of the samples. The remaining two ancestral populations together represent only a very small (2.13% together, 2.07% and 0.06% separately) fraction of the average ancestry in the samples. The three main ancestry components together tend to divide the Dutch population into four main geographic groups (Figure 4C): a southern group, a northeastern group, a central-western group, and a central-northern group. In contrast, the two additional minor ancestral components appear in individuals sparsely distributed across the country (Figure 4A,B). In agreement with our MDS based on IBS and Mclust analyses, the 17 individuals classified by Mclust in the third group (and therefore removed from the subsequent MDS) tend to show a statistically significant larger ancestry component in either of these two minor ancestral populations than individuals classified into the other two groups (Wilcoxon rank sum test with continuity correction W = 815, P value = 2.551e-11). In the second analysis, we used FRAPPE [27], which provided a similar result to what we obtained with ADMIXTURE at K = 5 (H’ = 0.741 statistic from CLUMPP analysis [28]) using inferred ADMIXTURE (10 runs) and FRAPPE (one run) clustering at K = 5 [see Additional file 1: Figure S4 for FRAPPE results].

Fourth, and to further explore the geographic distribution of the genome-wide diversity across the Netherlands, we performed two different spatial analyses of the genomic data. In the first analysis, we carried out a spatial ancestry analysis (SPA) [18] on the same 952 individuals used after MDS-based outlier exclusion. As seen in Figure 5A, the Dutch individuals tend to be distributed according to a southeast to northwest gradient in a two-dimensional mapping (correlation in a symmetric Procrustes rotation between the geographic coordinates of each population and the mean value of each SPA dimension: 0.612, P value = 0.001 after 1,000 replications). Latitude seems to be more influenced by the second dimension of SPA (latitude = −0.578*SPA1 -7.279*SPA2 + 52.004; P value of SPA1 = 0.807, P value of SPA2 = 3.26e-06; Adjusted R-squared of the multiple linear regression: 0.655, P value: 6.138e-13), while both SPA dimensions seem to contribute to longitude (longitude = −11.877*SPA1 -10.616*SPA2 + 4.685; P value of SPA1 = 0.010025, P value of SPA2 = 0.000182; Adjusted R-squared of multiple regression: 0.244, P value: 0.0002973). We also found several regions dispersed throughout the genome with individual SNPs displaying stronger frequency gradients (P value <0.0005) across the Netherlands (3,627 SNPs) (Figure 5B). Of these, the most striking signal is observed at a particular region on chromosome 8 ((Figure 5B) and [see Additional file 2: Table S2]). Unfortunately, this region includes several genes so that it is difficult to conclude without additional data which of the genes may be responsible for the observed spatial pattern. Notably, we did not observe a strong SPA signal in the LCT gene region on chromosome 2, which is known to be involved in lactase persistency and positive selection in Europeans, or the OCA2-HERC2 region on chromosome 15, which is known to be involved in blue-brown eye color determination and positive selection in Europeans. Both phenotypes, and also the genotypes at various SNPs in these genomic regions, have been previously reported to show a north to south gradient across Europe [4, 42]. One explanation for why we did not pick-up these signals in our data might be that the genotype frequency gradients in these genomic regions are too small for detection on a micro-geographic level such as within the Netherlands using the methods we applied. In the second analysis, we performed a spatial autocorrelation analysis on individual relationships [30]. The obtained covariance matrix between individuals is indicative of a statistically significant clinal pattern across the Netherlands (Figure 6, P value of the autocorrelogram after 1,000 replications <0.0005). In order to infer the slope of this cline, we computed a pairwise Fst matrix between pairs of subpopulations and performed a Bearing correlogram analysis [37]. The angle at which the Mantel correlation between the pairwise Fst matrix, and the geographic-angle based distance reached its maximum was 110 degrees (r = 0.254, P value after 999 permutations = 0.001). This indicates a southeast to northwest orientation of the genomic cline within the Netherlands for the increase of genetic differentiation between the 54 Dutch subpopulations analyzed. A Mantel test between the geographic distance matrix and the Fst subpopulation pairwise matrix, without considering angle information, revealed a correlation of r = 0.165 (P value based on 999 replicates = 0.00914).

We additionally explored whether geographically restricted dialects of the Dutch language, which also show north–south gradients as reported elsewhere [43], could be associated with the genomic diversity pattern we observed across the country. We estimated the amount of genetic variation explained by classifying the 54 subpopulations according to the 6 main dialects (Frisian, Groningen, Overijssel, Southwest Limburg, Brabant and Central Dutch varieties) that were previously identified in a dendrogram analysis by Heeringa [43]. Analysis of Molecular Variance (AMOVA) showed that classifying the Dutch subpopulations by dialect explains a small and statistically nonsignificant proportion of only 0.2% (P(random value >observed value) = 0.99707 after 1,000 iterations) of the total genetic variance observed. This result indicates that dialects are unlikely to have influenced our genomic findings including the spatial distribution of genomic diversity across the Netherlands.

The genome-wide southeast to northwest cline in the distribution of the genomic diversity across the Netherlands observed here via different analyses could be interpreted as fitting the southeast to northwest genetic cline previously found for the whole of Europe [3, 4]. Without any prior knowledge about the geological and human settlement history of the sampled region, one may explain the observed genomic gradient across the Netherlands by the major prehistoric demographic events that were previously used to explain the cline seen across the whole of Europe [1]. However, taking into account the strong palaeogeographic and archaeological evidence for marked population discontinuities on the Dutch territory during several, including more recent, periods in the Dutch history, we regard it as rather unlikely that the Palaeolithic colonization together with postglacial re-colonization and the Neolithic transformation process are directly responsible for the genomic findings we obtained here for the Dutch population. To test if the observed genomic cline could also be explained by recent events in the Dutch history [see Additional file 1 for details], we ran two SPLATCHE2 [38] simulations. In the first simulation, we used the parameters of the Palaeolithic-Neolithic model previously proposed by Francois et al. [5] (see Methods for details). In the second simulation, we introduced a genetic discontinuity scenario around 250 Anno Domini (AD) (70 generations ago, assuming 25 years per generation) in the Netherlands, when most of the country close to the sea remained uninhabitable by humans (Figure 1) up to 35 generations ago, or until approximately 1250 AD. After this period, previously uninhabitable areas acquired the same carrying capacity as the rest of Europe and became populated by individuals from the surrounding populations in this model. For each simulation, we generated 1,000 SNPs at a minimum allele frequency (MAF) of 0.03 and computed the Fst distance between pairs of populations using Arlequin 3.1 [39], setting all negative Fst values to 0. The Fst matrix of each of the two demographic models was then used in a MDS analysis and compared by means of Procrustes analysis either with the geographic coordinates or with the MDS coordinates of the other model. We found that both models strongly correlate with geography (correlation with geography in a symmetric Procrustes rotation when using the genetic discontinuity model: 0.576, P value = 0.001; correlation in a symmetric Procrustes rotation of the Palaeolithic-Neolithic model: 0.62, P value = 0.001; both analyses based on 1,000 permutations). Furthermore, we observed that the outcomes of both model simulations are statistically significant in their correlation with each other (correlation in a symmetric Procrustes rotation: 0.446, P value = 0.003). The Bearing correlogram analysis using the Fst distance matrix obtained with the model of genetic discontinuity is highly similar to the one produced by considering genetic continuity (Adjusted R-squared: 0.93, P value <2.2e-16), which suggests that the genetic gradient produced by both models is virtually indistinguishable. This finding, together with the rich archaeological evidence for human genetic discontinuity on Dutch territory led us to propose that the observed genomic gradient across the Netherlands was not caused by ancient but rather by recent events in Dutch history.

Although it cannot be excluded that the observed genomic gradient across the Netherlands that we explain by recent events, by chance resembles the ancient genomic gradient seen across Europe, another explanation is that this gradient was re-introduced by immigration of people from outside regions carrying ancient genetic signatures. One prerequisite for this scenario would be that immigration did not occur by one major population (or a limited number of populations), described as elite-dominance, but by movements of several populations from adjacent areas of similar latitudes in a way that the northern parts of the Netherlands received immigrants from northern/northeastern neighboring regions, southern parts from southern/southeastern neighboring regions, and central parts from eastern neighboring regions. Also, the mainly south–north geographic orientation of the Dutch territory provides a suitable prerequisite for such a scenario given the south–north genomic cline observed across Europe. However, there is no clear evidence provided by the archaeological records that would support such a scenario. The observation that subpopulations from the central-east of the Netherlands appeared more diverse (within and between groups) on the genome-wide level compared to all other Dutch subpopulations tested, could be indicative of recent admixture with other genetically diverse subpopulations not analyzed in our study. It would require, however, more detailed archaeological and/or historical research in addition to similarly detailed genetic information from regions outside the current Dutch political borders to disentangle the exact demographic events that shaped the current genetic variation of the Dutch population.

Besides evolutionary implications, our findings of small but detectable genomic substructure in the Dutch population, particularly the detection of geographic groups of Dutch subpopulations that can be differentiated using genome-wide data, also is of relevance for epidemiology and forensics. For future epidemiological studies, this knowledge may be relevant for (disease) gene mapping on Dutch individuals to avoid confounding effects that in principle can reveal false-positive findings. For future forensic genetic studies, the implications are two-fold. First, the detected population substructure may be considered as a correction factor when estimating match probabilities of STR profiles obtained from crime scene and suspect materials in the Netherlands. Second, our data provide evidence that in case the large number of SNPs used here can be derived from a forensic DNA sample, inferring the subregion of biogeographic ancestry within the Netherlands of an unknown may be feasible, which can provide useful investigative information to find unknown perpetrators.

We have shown that despite the genetic differentiation between Dutch individuals and subpopulations sampled systematically across the country being very small, the overall genome-wide diversity tends to correlate statistically significantly with geography and that the genomic map of the Netherlands resembles the geographic map of sampling locations in all dedicated analyses we performed. Furthermore, we identified a significant southeast to northwest cline in the distribution of genomic diversity across the Netherlands, similar to earlier findings from across Europe. For the Netherlands however, the classical interpretation of the observed genetic gradient by Paleolithic-Neolithic processes is challenged by the geological, archaeological and historical evidence pointing towards population discontinuity on the Dutch territory through the ages. Our demographic simulations revealed that the expected Paleolithic-Neolithic pattern in autochthonous populations would be similar to the one produced by a recent colonization of a region from neighboring areas. Considering the evidence for population discontinuity we therefore believe that the genomic patterns we observe are caused by recent rather than ancient events in the Dutch population history. On a wider picture, our results indicate that local and more recent demographic events can produce genetic patterns strongly resembling those traditionally explained by the major prehistoric migrations. We therefore suggest that future studies pay more attention to local and more recent demographic events when explaining clinal distributions of genetic diversity. Ultimately, ancient DNA analysis of past populations in comparison with DNA analysis of contemporary populations from the same region should be used to elucidate the contribution of ancient versus recent populations to the current gene pool of the Netherlands.