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Edited* by Charles A. Dinarello, University of Colorado Denver, Aurora, CO, and approved January 2, 2014 (received for review September 19, 2013)
Significance
This article gives a unique perspective on the impact of evolution on the immune system under pressure by infections, using the special demographic history of Europe in which two populations with different genetic ancestry, Europeans and Rroma (Gypsies), have lived in the same geographic area and have been exposed to similar environmental hazards, including infections. We identified convergent evolution signals in genes from different human populations. Reconstruction of evolutionary history of European populations has identified Toll-like receptor 1 (TLR1)/TLR6/TLR10 as a pattern recognition pathway shaped by convergent evolution by infections, among which plague is a likely cause, influencing the survival of these populations during the infection.
Abstract
Recent historical periods in Europe have been characterized by severe epidemic events such as plague, smallpox, or influenza that shaped the immune system of modern populations. This study aims to identify signals of convergent evolution of the immune system, based on the peculiar demographic history in which two populations with different genetic ancestry, Europeans and Rroma (Gypsies), have lived in the same geographic area and have been exposed to similar environments, including infections, during the last millennium. We identified several genes under evolutionary pressure in European/Romanian and Rroma/Gipsy populations, but not in a Northwest Indian population, the geographic origin of the Rroma. Genes in the immune system were highly represented among those under strong evolutionary pressures in Europeans, and infections are likely to have played an important role. For example, Toll-like receptor 1 (TLR1)/TLR6/TLR10 gene cluster showed a strong signal of adaptive selection. Their gene products are functional receptors for Yersinia pestis, the agent of plague, as shown by overexpression studies showing induction of proinflammatory cytokines such as TNF, IL-1β, and IL-6 as one possible infection that may have exerted evolutionary pressures. Immunogenetic analysis showed that TLR1, TLR6, and TLR10 single-nucleotide polymorphisms modulate Y. pestis–induced cytokine responses. Other infections may also have played an important role. Thus, reconstruction of evolutionary history of European populations has identified several immune pathways, among them TLR1/TLR6/TLR10, as being shaped by convergent evolution in two human populations with different origins under the same infectious environment.
By recognition and elimination of pathogenic microorganisms during infection, the immune system has allowed mankind to survive. Genetic variation in the immune system is a major factor influencing susceptibility to infections. Subsequently, genes of the immune system are under constant evolutionary pressure (1), and this pressure can change based on local conditions and migration routes of human populations (2).
In time, changes induced in the immune system by infectious pressures can shape not only the host defense and susceptibility to infections but also susceptibility to autoimmune or inflammatory diseases of modern human populations (2), with balancing selection proposed as a main force shaping the innate immunity reaction (3). It has been suggested that a predominantly proinflammatory profile in the immune system, induced by infections, predisposes modern human populations to autoimmune diseases (4, 5), whereas selection of certain genetic variants during epidemics [e.g., selection of C-C chemokine receptor type 5 (CCR5) variants presumably by plague] reduces susceptibility to HIV infection in modern Europeans compared with Africans (6). All these studies have investigated candidate genes selected on the basis of biological assumptions, but comprehensive genome-wide approaches to identify the immune pathways under evolutionary pressure by infections are missing.
In this study, we make use of the opportunity that a special historical demographic situation is present in Europe—that is, ancient European populations living together with Rroma in the same geographic locations. Rroma (traditionally called Gypsies) are a population from Northwest India that has migrated in Europe one millennium ago (7). We hypothesized that despite their different ethnic and genetic backgrounds, the strong infectious pressure exerted by the major epidemics of the last millennium (of which epidemics of plague are probably the most significant) has led to convergent evolution: specific immune genes, selected during these European epidemics, become signatures that differ from those found in the Northwest Indian populations from whom the Rroma have derived (7). These signatures would enable us to detect recent adaptations and could lead to the understanding of susceptibility to infections (and other immune-mediated diseases) in modern European populations.
Results
Populations.
The population of Romania is comprised mainly of Indo-European populations, among which Romanian speakers represent 88% of the population, whereas 3.2% of inhabitants are of Rroma ethnic background (www.recensamantromania.ro). After ethical approval by the Ethics Committee of the University of Craiova, Romania, informed consent was obtained for all volunteers and DNA samples were collected from individuals of European/Romanian or Rroma ethnic background. A population of individuals of Northwestern Indian descent, representing the geographic origin of the Rroma group (Fig. 1A), was also recruited.
Fig. 1.
Geographic origin of the three populations studied. (A) European/Romanians and Rroma/Gipsy share the same location, even if the origin of the latter is in North India. (B) Plot of the populations under analysis according to the coordinates to the two main eigenvectors of smartpca (Eigensoft) analysis, in which each dot represents an individual. Individuals within the circles and the same color have been considered for the study; those of different colors represent false population allocation and those intermediate represent admixed individuals. ROM, nongypsy Romanians; INDI, individuals from North India; GYP, Rroma/Gypsies living in Romania.
We assayed 196,524 single-nucleotide polymorphisms (SNPs) using the Illumina immunochip array (8) in all three populations. Analysis of genetic distance and principal component analysis between these populations based on nongenic, and thus presumably neutral, SNPs show clear differences between the three populations studied. Admixed individuals and erroneous self-assigned ancestry was examined using principal components analysis (PCA) implemented in eigensoft program (9) and plotted using multidimensional scaling (Fig. 1B). Individuals showing admixture ancestry or false allocation were excluded from further analysis. A plot of the first versus the second eigenvectors (Fig. 1B) shows a clear differentiation of the Rroma cluster of individuals from the Romanian and the Indian populations. However, Rroma are very close to Indians across eigenvector 1, in agreement with their evolutionary history. This indicates these population labels have a genetic basis and are not merely social constructs.
Evolutionary Analysis Identifies Innate Immune Pathways and TLR1/TLR6/TLR10 Among Genes Under Common Selection Pressure in Europeans/Romanians and Rroma.
To identify signals of positive selection shared between Europeans and Rroma but not present in the Indian population, we looked for shared signals of important genetic differentiation between these two populations with the Indian population, accompanied by the absence of genetic differentiation between them. Two tests were used: (i) Cross-Population Composite Likelihood Ratio (XP-CLR) (10), which is a test that aims to identify selective sweeps in a population by detecting important genetic differentiation in an extended genomic region by including information about linkage disequilibrium without requiring haplotype information, and (ii) TreeSelect test (11), which is a tree-based method that incorporates allele frequency information from all populations analyzed to increase power to detect selection and distinguishes which population has been under positive selection. A window was considered to show an extreme score if its summary statistic (maximum in the case of XP-CLR, mean in case of TreeSelect statistic) belonged to the 1% upper tail of the genome-wide summary statistic distribution. Therefore, for XP-CLR, we were interested in windows with the extreme 1% signal of population differentiation both between Rroma and Indians and between Europeans and Indians, as long as these windows did not belong to the 5% extreme distribution for the Rroma versus European comparison. For TreeSelect, we listed the windows belonging to the 1% upper tail of the distribution for Rromas and Romanians as long as they do not belong to the 5% upper tail of the distribution in Indians. Table 1 lists the genes contained in windows that fulfill these criteria, along with other genes highly significant in any of the tests in any of the three populations analyzed. Manhattan plots for XP-CLR and TreeSelect statistics are shown in Fig. 2 A and B, respectively, where the strong concordance between both tests can be seen.
View this table:
Table 1.
Genes with extreme values of XP-CLR statistic and TreeSelect test, indicative of putative signals of positive selection
Fig. 2.
Manhattan plot of results of selection tests in Rroma, Romanians, and Indians using TreeSelect statistic (A) and XP-CLR statistic (B). Chromosomes ordered from chromosome 1 to chromosome 22.
We investigated the overrepresentation of categories of genes detected to show similar selection signals in Rroma and Romanians and not in Indians, using Protein Analysis Through Evolutionary Relationships (PANTHER) (12) analysis. Table 2 shows the overrepresented molecular functions and biological processes with the contributing genes. The Toll-like receptor (TLR)/cytokine–mediated signaling pathway group, which comprises the genes TLR1, TLR6, and TLR10 (in the second cluster of Table 1), appears at the top of groups overrepresented with a P value = 0.00381.
View this table:
Table 2.
Statistical overrepresentation test of PANTHER analysis
The finding of the TLR2 gene cluster as under positive selection is of great relevance in looking for convergent selection in Rromas and Romanians. To overcome a possible lack of power of detecting selection in Indians for this cluster, we sought derived allele frequency (DAF) of SNPs that shows signals of positive selection in this study. SNP rs4833103 has a DAF in Rroma of 0.3, in Romanians 0.5, and in Indians 0.02. For SNP imm_4_38475934, the DAF in Rroma is 0.05, in Romanians 0.04, and in Indians 0.007. This result suggests that the signals of positive selection can be attributed only to Rroma and Romanians. Moreover, population differentiation estimated by FST statistic shows that most of the SNPs within this cluster have high differentiation between Rroma and Indians and between Romanians and Indians but not between Rroma and Romanian. The case of SNP rs4833103 is of special interest; this SNP shows an FST between Rroma and Indians of 0.49, between Romanians and Indians 0.69, and between Rroma and Romanians 0.04 (Fig. S1), values of undoutable value for the present framework. Notably, this SNP (intergenic between TLR1 and TLR6) was reported to be associated with an expression quantitative trait loci of the expression of three genes, TLR1, TLR6, and TLR10, in lymphoblastoid cell lines (LCLs) (13).
We also performed an additional analysis using genotype data from the Illumina Omni 2.5M Chip for the 1000 Genome Project for individuals (14) in an Indian (Gujarati) and European (Northern Europeans from Utah, CEU) population. XP-CLR statistic was used to detect selection in this Indian population. Results show that there is clear signal of selection in the European population (CEU) compared with the Indian (Gujarati) population, but no signal of selection was detected in this Indian population compared with the European population (Fig. S2 A and B).
Interestingly, the other gene cluster detected (first row in Table 1), with four genes in chromosome 5, contains the well-known gene SLC45A2, described as being under positive selection in relation to skin pigmentation in Europeans (14). Other strong signals are for the BTNL2 gene locus in chromosome 6 coming from the TreeSelect test in Rroma and Romanian populations. This gene is highly polymorphic, with homology to the butyrophilin gene family, and is located at the border of the major histocompatibility complex (MHC) class I and class II regions in humans. This signal of positive selection may be due to the role of MHC in adaptation to pathogens in human history. Many other strong signals are shown in Fig. 2 A and B, however these signals are specific to one single population or show differentiation between Rroma and Romanians and cannot be caused by a convergent adaptation of the same evolutionary process in these two populations.
Most of the signals found in this study cluster in regions of the genome with a high linkage disequilibrium (Fig. S3 A–C for TLR group, cluster containing SLC45A2 gene and cluster containing the BTLN2 gene). This finding makes it difficult to pinpoint the exact target of selection in each case, a general problem of selection studies (15). Clearly, genes in the TLR1/6/10 cluster are of special interest for the present study.
TLR2 Cluster Genes Are Involved in the Recognition of Yersinia pestis.
TLR2 recognition of V-antigen and LcrV of Y. pestis is the main recognition mechanism during plague. TLR2 forms heterodimers with receptors of the same gene cluster (TLR1/TLR6) for recognition of bacterial lipopeptides (16), but it is not known whether TLR2 also collaborates with TLR10 for the recognition of Y. pestis. We transfected HEK cells (that normally express TLR1 and TLR6) with TLR2, TLR10, or TLR2 and TLR10. The HEK cells transfected with TLR2 alone release significantly more cytokines than untransfected cells: twofold more for Y. pestis and fivefold more for Yersinia pseudotuberculosis, the microorganisms from which Y. pestis evolved (Fig. 3A). Although TLR10 by itself is not able to induce cytokine production, cotransfection of TLR10 with TLR2 completely abrogates the stimulatory effect of TLR2 (Fig. 3A). These data were supported by blocking TLR2 in monocytes using monoclonal antibodies (Fig. 3 B–D). Interestingly, blocking TLR10 resulted in an increase in cytokine production (Fig. 3 B–D), supporting the observation that TLR10 has a modulatory effect, thus corroborating the overexpression experiments.
Fig. 3.
The role of TLR10 for the recognition of Y. pestis and Y. pseudotuberculosis. (A) HEK293 transiently transfected with TLR2, TLR10, or TLR2/10, and stimulated with 1 × 105 heat-inactivated Y. pestis or Y. pseudotuberculosis, respectively. Bars represent the means ± SEM of at least three separate experiments. (B) PBMCs stimulated with Y. pestis or Y. pseudotuberculosis per mL. n = 6; means ± SEM; *P = 0.05, **P = 0.01. (C) TNF-α production after PBMCs stimulated with Y. pestis or Y. pseudotuberculosis in the presence or absence of 10 µg/mL antibody. (D) IL-1β production after 24 h of stimulation. Means ± SEM; *P = 0.05, **P = 0.01. The data shown are from three independent experiments each performed in duplicate.
The modulatory effects of TLR10 seem to be exerted specifically on TLR2 signaling, as anti-TLR10 antibodies modulated cytokine production induced by palmitoyl-3-cysteine-serine-lysine-4, but not by the TLR4 agonist LPS (Fig. S4). Moreover, when cells of individuals carrying the SNP in TLR10 were exposed to either LPS, Poly I:C, CpG, or flagellin, no differences between the groups could be detected (Fig. S5). Interestingly, however, cross-linking of TLR10 receptors inhibited the IL-6 induction by IL-1 (Fig. S6), suggesting that TLR10 may exert inhibitory effects on the IL-1 family of cytokines (17).
Common TLR1, TLR6, and TLR10 Polymorphisms in European Populations Modulate Cytokine Responses to Y. pestis.
To demonstrate that TLR1, TLR6, and TLR10 genetic variation in the population modulates the response to Y. pestis, we isolated peripheral blood mononuclear cells (PBMCs) from a group of 101 individuals of European descent and exposed them to the pathogen. SNPs in TLR1, TLR6, and TLR10 significantly influenced cytokine production induced by Y. pestis and Y. pseudotuberculosis (Fig.4 and Fig. S7). In contrast, known polymorphisms in TLR4 (Asp299Gly and Thre399Ile) did not influence the response of PBMCs to Y. pestis or Y. pseudotuberculosis (Fig. S8).
Fig. 4.
Functional consequences of human TLR1/TLR6/TLR10 SNPs for Y. pestis–stimulated cytokine production. PBMCs from healthy volunteers stimulated with different stimuli, including Y. pestis (1 × 105/mL). Volunteers were separated into three groups: one group did not display the SNP in either TLR1 (A/B), TLR6 (C/D), or TLR10 (E/F; wt, wild-type); one group was heterozygous for the polymorphism (He); and one group was homozygous (Ho). Data are means ± SEM. *P = 0.05, **P = 0.01, ***P = 0.001.
Discussion
In this study, we identified a set of genes evolving under positive selection in populations of different ethnic ancestry living in Europe, but not in Northwest India. Among these genes, the region encompassing TLR1, TLR6, and TLR10 is under selection in Europeans/Romanians and Rroma/Gypsies, but not in a population from Northwest India. The common selection pressures in the Romanians and Rroma may be interpreted as the same evolutionary process induced by local infectious conditions in two European populations of different genetic backgrounds. To look for more evidence on positive selection in European populations, we analyzed sequence data from the 1000 Genome Project (18). These data show a clear selective sweep in Europeans using two methods based on genetic differentiation and extended linkage disequilibrium haplotype [cross-population extended haplotype homozogysity (XP-EHH) and XP-CLR]. This signal was specific in Europeans and absent in an African population (Yoruba) and in a Chinese population (Fig. S9).
Besides the TLR2 gene cluster, other genes of interest include (i) a gene cluster with four genes in chromosome 5 that contains the well-known gene SLC45A2 being under positive selection in relation to skin pigmentation; (ii) FBXL19, a gene known to be involved in the modulation of inflammation (19) in a cluster comprising three genes; and (iii) ADAMTS12 gene, which is associated with susceptibility to autoimmune diseases (20). In the same cluster as the SLC45A2 gene, other genes (Table 1) may be of special interest to be analyzed functionally in the future.
Linguistic and genetic studies suggested that the Rroma population left India in the 5–10th centuries and started to settle in Europe during the 11th century (21). Genetic studies, focused on uniparental and Mendelian disease markers, confirmed Rroma as an isolated population of Indian origin among the European majority (7). We pose that after the Rroma migration, the infectious pressures to which the Rroma were exposed were the same as for the Europeans, whereas for the ancestral North Indian population, they remain linked to their geographical location in India. This peculiar demographic situation in Europe, in which populations with different genetic backgrounds have been exposed for a long period to similar infection pressures, gave us the opportunity to attempt the reconstruction of recent evolutionary events acting on the immune system of populations living in Europe.
An important question is which evolutionary pressures were common to the Romanian and Rroma populations. Infections are likely to have been one of the most important evolutionary forces shaping the immune system in both Europe and India, and several candidates may be considered. An infection often associated with evolutionary effects in Europeans is plague, responsible for several large epidemics with death rates of up to 30–50% of the European population and lingering thereafter in Europe for several centuries (22), thus allowing for the exertion of selective sweeps. Based on this extreme burden of mortality, it is rational to hypothesize that plague had major evolutionary effects on the immune system of European populations. The TLR/IL-1 functional cluster is crucial for host defense against Y. pestis: TLR2 and its coreceptors TLR1, TLR6, and TLR10 are the main pattern recognition receptors for Y. pestis—all localized in a single gene cluster in chromosome 4 (23), whereas Y. pestis Caf1 protein is an inhibitor of IL-1β (24). Decreased IL-1 responses, either through defective TLR signaling or release of Caf1, are likely to have deleterious effects on host survival. The data presented here show that the TLR1/TLR6/TLR10 receptor cluster has been under positive selection in both Romanians and Rroma, and suggest that plague is a potential infection that has exerted this selection. Our data are also supported by an earlier study that identified the TLR1/TLR6/TLR10 gene cluster as a target of recent positive selection in non-Africans (25). We confirmed the functional impact of TLR1, TLR6, and TLR10 polymorphisms currently present in Europeans for the immune responses to Y. pestis.
Although evolutionary pressure exerted by plague is a plausible cause of adaptive selection, it should be emphasized that other infections in which the receptors of the TLR2 cluster play a central role, such as tuberculosis, leprosy, or common Gram-positive pathogens, could have also contributed to the genetic pattern observed here. Nevertheless, these infections have a generally less restricted geographical pattern as common in India as in Europe. Importantly, the impact of historical plagues in India has been a matter of debate. Out of the three main outbreaks of plague (6–7th centuries, 14th century, and turn of 19–20th century), by far the most devastating is the second, called the Black Death. This outbreak is known not to have affected India (26) and took place after the settlement of Rroma in Europe. Indeed, the Indian subcontinent may have been the only part of Eurasia to have experienced steady population growth during the last half of the 14th century, and the first reports of plague are from the 17th century, with much less impact than the Black Death. During the epidemics in the Indian subcontinent, the disease behaved differentially than plague in the 14th century in Europe, with less than 5% human mortality. It is likely that the absence of the flea Xenopsylla cheopis due to tropical environment and the distance and geographical barriers could have prevented the entrance of the devastating outbreak of the Middle Ages into India (26).
The identification of the immune pathways and genetic variants that were specifically selected in Europe not only helps us to understand the evolutionary history of European populations, but also contributes to our understanding of the differences in susceptibility between European and other populations to modern human diseases. Evolutionary pressure exerted by plague or smallpox has been previously proposed to partly explain the increased resistance to HIV in Europeans (6). In addition, the evolution toward a proinflammatory profile induced by infections during history might explain the burden of autoimmune diseases in modern human populations (27). Genetic variation in TLR7 and TLR8 has been shown to protect against viral infections (25), while predisposing some to autoimmune diseases (4). Similarly, TLR1 or TLR10 polymorphisms can protect against infections, while being associated with autoinflammatory diseases such as sarcoidosis (28) and Crohn’s disease (29). Although the differences in cytokine production induced by Y. pestis in individuals with various TLR1, TLR6, or TLR10 polymorphisms are moderate from an immunological point of view, they are large from an evolutionary perspective, and can lead in the long term to significant shifts in the population. It should be realized that we may not have detected other genes relevant for host defense that may be under selective pressure, as they have not been included in the Illumina immunochip array, and only future studies using genome-wide sequencing have the capacity to provide an exhaustive analysis of the entire genome.
In conclusion, by comparing genes under selection in European/Romanian and Rroma/Gipsy populations, we identified several immunological pathways specifically shaped by evolutionary processes in populations living together in Europe during the last millennium. It is likely that the selection pressure at least on some of these genes has been exerted by plague epidemics, and we identify the TLR1/TLR6/TLR10 pattern recognition system as a likely candidate.
Methods
Populations.
After informed consent was obtained, blood was collected from 100 individuals of European/Romanian descent and 100 individuals of a Rroma/Gipsy ethnic background. A population of 500 individuals of North Indian descent, representing the geographic origin of the Rroma/Gipsy group, was also recruited. Healthy Dutch individuals were recruited for cytokine stimulations (21–73 y old, 73% males and 27% females).
Immunochip Arrays and Analysis of Genetic Distances Between Populations.
Samples were genotyped on immunochip custom array at the Department of Genetics, University Medical Center Groningen, The Netherlands (8). To explore genetic relationships among the populations, we used PCA as implemented in the Eigensoft package (9). For a detailed description of the methods, see SI Methods.
Evolutionary Models.
A selective sweep induces a fast spread of the beneficial allele through the population until it reaches fixation. Through hitchhiking, the selected allele carries with it neutral alleles in the linked genomic region. Thus, in comparison with the neutral expectation, one expects to observe within a region that has evolved recently under positive selection a dramatic pattern of genetic differentiation among populations within an extended genomic region. Taking advantage of these theoretical expectations, we applied two methodologies, XP-CLR (10) and TreeSelect (11) tests, to identify the genomic region under putative selection in European/Romanian and the Rroma/Gipsy populations, but not in the population from North India. We focused our study on population differentiation because it has been described as the best molecular pattern to study very recent events of positive selection after haplotype structure (30). However, the design of the immunochip with very variable SNP density across the genome does not allow us to properly study the haplotype structure (for phasing issues and haplotype informativeness differences among regions with different SNP density). We used tests that are amenable to SNP data (and thus with ascertainment bias). For an extensive description of the XP-CLR and TreeSelect tests, please consult SI Methods.
TLR Cloning and Transfection.
TLR cloning and transfection of human embryonic kidney 293 cells that were stably transfected with hTLR2 (293-hTLR2; kindly provided by Dr. D. T. Golenbock, University of Massachusetts Medical Center, Worcester, MA) are described in detail in SI Methods.
Cytokine Stimulation.
PBMCs were isolated after obtaining informed consent (31). PBMCs (5 × 105) in 100 µL volume were added to round-bottom 96-well plates (Greiner) and incubated with stimuli for 24 h at 37 °C and 5% CO2. Cytokines were measured using specific sandwich ELISA kits for IL-1β and TNF-α (R&D Systems). IL-6, IL-8, and IL-10 were measured using PeliKine Compact ELISA kits (Sanquin).
Immunogenetic Studies.
DNA was isolated from whole blood using the Gentra Pure Gene Blood kit (Qiagen), and genotype assessments of the TLR10-N241H, TLR1-N248S, and TLR6-S249P SNPs were performed using a predesigned TaqMan SNP genotyping assay (Applied Biosystems). The software automatically plotted genotypes based on a two-parameter plot with an overall success rate of >95%. Cycling conditions were 2 min at 50 °C and 10 min at 95 °C, followed by 40 cycles of 95 °C for 15 s and 1 min at 60 °C. Fluorescence intensities were corrected using a postread/preread method for 1 min at 60 °C before and after the amplification.
Acknowledgments
We thank Dr. Vandana Midha for recruitment of the Indian study cohort. We also thank the National Institute of Bioinformatics (www.inab.org) for computational support. M.G.N. and C.W. were supported by Vici grants of the Netherlands Organization of Scientific Research. This work was funded by Grant BFU2010-19443 (to J.B.) from the Ministerio de Ciencia y Tecnología (Spain) and the Direccío General de Recerca, Generalitat de Catalunya (Grup de Recerca Consolidat 2009 SGR 1101). P.L. was supported by a PhD fellowship from “Acción Estratégica de Salud, en el Marco del Plan Nacional de Investigación Científica, Desarrollo e Innovación Tecnológica 2008–2011” from Instituto de Salud Carlos III. B.K.T. was supported by Grant BT/01/COE/07/UDSC from the Department of Biotechnology, Government of India, New Delhi.
Footnotes
↵1H.L. and M.O. contributed equally to this work.
↵2Present address: Division of Genetics, Department of Medicine, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA 02115; and Program in Medical and Population Genetics, Broad Institute of Harvard and Massachusetts Institute of Technology, Cambridge, MA 02142.
↵3J.B. and M.G.N. share senior authorship.
- ↵4To whom correspondence should be addressed. E-mail: Mihai.Netea{at}radboudumc.nl.
Author contributions: H.L., J.W.M.v.d.M., A.S., B.K.T., C.W., L.A.B.J., J.B., and M.G.N. designed research; H.L., M.O., P.L., M.I., S.A., I.R.-P., G.T., A.Z., T.S.P., S.-C.C., R.P., A.S., and L.A.B.J. performed research; M.O., P.L., M.I., S.A., I.R.-P., G.T., A.Z., T.S.P., S.-C.C., and R.P. contributed new reagents/analytic tools; H.L., M.O., P.L., M.I., S.A., I.R.-P., G.T., A.Z., T.S.P., S.-C.C., R.P., and M.G.N. analyzed data; and H.L., M.O., M.I., S.A., J.W.M.v.d.M., A.S., B.K.T., C.W., L.A.B.J., J.B., and M.G.N. wrote the paper.
The authors declare no conflict of interest.
↵*This Direct Submission article had a prearranged editor.
This article contains supporting information online at www.pnas.org/lookup/suppl/doi:10.1073/pnas.1317723111/-/DCSupplemental.
References
Convergent evolution in Europe
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