www.sciencemag.org/cgi/content/full/336/6080/466/DC1 Supplementary Materials for Origins and Genetic Legacy of Neolithic Farmers and Hunter-Gatherers in Europe Pontus Skoglund,* Helena Malmström, Maanasa Raghavan, Jan Storå, Per Hall, Eske Willerslev, M. Thomas P. Gilbert, Anders Götherström,* Mattias Jakobsson* *To whom correspondence should be addressed. E-mail: [email protected] (P.S.); [email protected] (A.G.); [email protected] (M.J.) Published 27 April 2012, Science 336, 466 (2012) DOI: 10.1126/science.1216304 This PDF file includes: Materials and Methods Supplementary Text Figs. S1 to S22 Tables S1 to S15 References (33–85)
Origins and Genetic Legacy of Neolithic Farmers and Hunter-Gatherers in Europe
Pontus Skoglund,* Helena Malmström, Maanasa Raghavan, Jan Storå, Per Hall, Eske Willerslev, M. Thomas P. Gilbert, Anders Götherström,* Mattias Jakobsson*
*To whom correspondence should be addressed. E-mail: [email protected] (P.S.);
12 Estimation of population divergence time from complete genomes 1612.1 Divergence from genomes of Northwest European ancestry . . . . . . . . . . 1712.2 Divergence from genomes of East Asian and West African ancestry . . . . . 18
13 Modelling European population history 1813.1 Testing population topology using genealogical concordance . . . . . . . . . 1813.2 Gene flow between a population related to Gok4 and extant European populations 1913.3 Estimation of Neolithic farmer ancestry in European populations . . . . . . . 20
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14 The Neolithic hunter-gatherer gene pool is not fully represented in modernEuropeans 21
The process of Neolithization in Scandinavia starts around 6,000 before present (BP) whenthe oldest traces of farming, husbandry and material culture expressions of the Funnel BeakerCulture (TRB; after the German term Trichterbecher Kultur) appear in the archaeologicalrecord (33, 34). The TRB culture of Scandinavia represents the northernmost distribution ofa large Western European complex (e.g. 35). The oldest TRB finds in the Southern parts ofScandinavia are roughly contemporary with the end-phase of the Mesolithic hunter-gathererErtebolle Culture (33). In Sweden, the introduction of TRB marks the transition to the Ne-olithic Stone Age despite the fact that hunter-gatherer groups are present in large parts of thearea (12). The TRB complex is visible in the archaeological record until approximately 5,000BP (12, 15). In the coastal areas of Southern and Eastern Middle Sweden the Pitted WareCulture (PWC), representing a local hunter-gatherer complex, appears between the time span5,300-4,400/4,000 BP (12). Material culture expressions such as pottery style and generalsubsistence patterns bear the greatest similarities with groups East of the Baltic Sea (e.g. 36).The PWC culture represents the youngest (last) hunter-gatherer culture in the area and iscontemporary with TRB groups in the early phase, but later the PWC appears side-by-sidewith the Corded Ware Culture (or Battle Axe Culture; 12).
The relationship between the farming and the hunter-gatherer complexes has been one ofthe most debated questions in Scandinavian archaeology (e.g. 12) and the question is ofgeneral relevance for archaeology and more specifically for the Neolithisation of the Northernparts of Europe (e.g. 35). The key question has been whether the TRB culture appear as aresult of migrations to the area or whether the change represent adoption of new traditionsand material culture expressions by local groups (12). Similar questions have been debatedfor much of the European continent (3, 5)
Osteological studies have revealed a certain degree of morphological differences in the skeletonbetween individuals from TRB and PWC contexts. It has been shown that PWC individualsexhibit skeletal traits characteristic of cold-adaptation, such as certain facial features and limbproportions (crural index) which are absent in TRB individuals (37,38). Traits in the dentitionexhibit both differences (39) and simlarities between the two groups (40). The mean statureof the two groups is roughly similar for males (164.0 cm for TRB and 164.6 cm for PWC)but there is a larger difference for females (154.6 cm for TRB and 157.7 for PWC) (38). Theosteological observations are affected by small sample sizes and especially the TRB samplesare affected by the collective burial custom. The bones are normally recovered commingledand few skeletal elements may be associated to articulated skeletons. We also note the gen-eral difficulties involved in interpreting the reasons for morphological differences in skeletalmaterial. Thus, it has not been possible to make conclusive interpretations of the relationshipbetween the TRB and PWC individuals based on osteological data. However, chemical andbiomolecular analyses of human bones have in recent years shed new light on the relationshipbetween material culture units and human groups in general and more specifically the relation-ship between the TRB and PWC complexes. Isotopic studies (Strontium, Sr) indicate thatsome level of mobility occurred of those buried in the megaliths in Falbygden, Vastergotland,and that as much as 25% of the population may have been non-local (by birth) (41). Inter-estingly, the data indicate that the most likely area of origin of those interpreted as non-locals
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was to the southwest of the Falbygden area, i.e., areas with abundant TRB remains and arte-facts but with few traces of PWC. The analyses indicate a low level of mobility between thegeographical areas of PWC and TRB.
The relationship between the TRB and PWC has further been investigated by stable analysisof dietary patterns (Carbon, C and Nitrogen, N). Such analyses have shown that on the islandof Oland in the Baltic Sea, the two groups apparently lived side by side but kept distinctand different dietary patterns (42). TRB individuals buried in megalithic structures exhibita dietary pattern with mainly terrestrial proteins while PWC individuals exhibit a diet basedon marine proteins. The dietary pattern at Ajvide and Ire on the island of Gotland is similarto that of the Pitted Ware Culture individuals on Oland indicating a high intake of marineproteins (43–45). Stable isotope analyses of human remains from several megalithic structuresin Vastergotland, among them samples from the same parish as the TRB individual (Gok4)analysed in the present study, show a dietary pattern including proteins mainly of terrestrialorigin (44, 46, 47). The TRB and PWC are two distinct archaeological cultural units andthe differences in material culture expressions between them may be regarded as fundamentaland includes artifact types, burial constructions and burial customs, settlement pattern andsubsistence strategies as well as dietary patterns.
1.1 Burial sites
In the present study, aDNA of four individuals from the Stone Age were successfully analysed(Table S1). Three of the individuals originate from two Pitted Ware Culture cemetery andsettlement sites on the island of Gotland (Ajv52, Ajv70 and Ire8). One of these sites, Ajvide,is located in the southwestern part of the of Gotland while Ire is located in the northern partof the island. The material culture expressions of the two Gotland sites are characteristicof the PWC and show striking resemblances. One individual (Gok4) originates from themegalithic burial structure (Gokhem 94) characteristic of the Funnel Beaker Culture in Gokhemparish, Vastergotland, Sweden. The fifth sample (Ste7) that was excluded from the analyses(see sections 4 and 7) originates from the same megalithic structure. Gokhem 94 containedmore than 9,000 fragments of commingled human bones, together with the remains of fourarticulated skeletons. The megalith was in (primary) use between approximately 3,400-2,900BC.
1.2 Dating
The sample from Gokhem (Gok4) has been 14C-dated to 4,921 ± 50 calBP (approx. 3100-2900 BC; 15 . The three Neolithic hunter-gatherer samples from Gotland have not been dateddirectly but several radiocarbon dates of other burials from the two cemeteries indicate arougly similar age as the individual from Gokhem. Radiocarbon dates of several burials fromthe sites fall roughly between 4,360-4,005 BP at Ajvide (48) and between 4,280-3,850 BP forIre (49). This corresponds roughly to 3,300-2,350 BC and 3,100-2,150 BC in calibrated years.Note that the dates for Ire were done with conventional dating methods while the Ajvide dateswere obtained using accelerator mass spectrometry (AMS) and are therefore more reliable.
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1.3 Osteoarchaeology of the analysed samples
• Ajv52 (Ajvide 52; ID:146): Child, 7 years old. Grave 52 at Ajvide contained skeletonsof two children, one aged approximately 7 years and another 1.5-2 years (50). The olderindividual was sampled for the present study. Grave goods associated with the olderchild were bone points, beads made of seal teeth and bird bones, a worked pig tusk,and stone implements.
• Ajv70 (Ajvide 70; ID:164): Male aged 20-25 years (50 ; Fig. S1). Burial goods recoveredwere the base of a ceramic pot (PWC) and a worked bone artefact.
• Ire8 : Female, aged 40-50 years. Close to the skeleton one shard of pottery and a fewfish bones were recovered (49).
• Gok4 : Female aged approximately 20 years recovered in the megalithic structure Gokhem94 (39, 41). The skeleton was not complete. Analyses of Sr in teeth (87Sr/86Sr =0.7171) indicate that the individual was born within an area between 50-100 km southor southwest from the megalithic structure (17).
• Ste7 : Analysed tooth found as strayfind in the megalithic structure Gokhem 94. Noosteological data available.
2 Sample selection and DNA extraction
DNA extracts from seven Neolithic samples, five of which human (Ajv52, Ajv70, Ire8, Ste7and Gok4), were chosen from a previous study (8) based on several authenticity criteria thatindicated suitable preservation and low levels of contamination:
• Quantitative real-time PCR (qPCR) had shown that the extracts contained more than1,000 template copies of an 80 bp mtDNA fragment (between 47,334 and 1,497 copiesfor Ajv70 and Gok4, Table S4A in (8))
• qPCR had revealed an inverse correlation between fragment length and DNA yield (8).The degradation ratio was between 9.07 and 3.23 for Ajv70 and Ste7, see Table S4Ain (8).
• Roche GS FLX deep sequencing of short overlapping d-loop amplicons produced vastamount of synthetic clones that gave consistent results between clones deriving fromindependent DNA extractions (8).
• Consensus mtDNA sequences comprising 341 bp of the d-loop yielded phylogeneticallyconsistent results (8).
• The samples Ajv52, Ajv70 and Ire8 had also yielded nuclear DNA in an earlier study (14).
Two animal samples, a Harp seal (Pagophilus groenlandicus) from Ajvide (GSAj15) and adomestic dog from Linkoping (CFBe3a), that had been handled in parallel with the humansamples were used as negative controls. The GSAj15 sample was conservatively chosen basedon it being the non-human sample with the most evidence of human contamination in a
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previous study (197 template copies of the same 80 bp human mtDNA fragment referred toabove; 8). The CFBe3a sample had 11 template copies of the human mtDNA fragment (8).Thus, while both animal samples had previously shown evidence of a low degree of humanmtDNA contamination, the amount of human mtDNA in the animal samples was much lowercompared to all human samples used in this study (8).
Bones and teeth from the seven samples had been decontaminated (13) and DNA extracted (8)according to previous protocols. The outer surfaces of bones were removed by sandpaper pol-ishing and teeth were wiped with 0.1M HCl. All samples were soaked in 0.1M HCl, rinsed withddH2O (DNA free ELGA grade) and dried. Between 50-200mg bone powder was obtainedper sample with a Dremel automated drill and soaked in 0.5% sodium hypochlorite for 15 minand rinsed three times with LiChrosolv water (Merck, Darmstadt, Germany). The powder wassubsequently incubated for 24 h at 55◦C in 1.6 ml buffer containing 0.5M EDTA (pH 8.0),1M urea and 100g/ml proteinase K. The sample was then briefly spun at 2,000rpm for 5 minbefore transferring the supernatant to an Amicon Ultra-15 centrifugal filter (Millipore, MA,USA) which was centrifuged at 4,000 g for 15 minutes, or until 50-100µl remained in thecolumn. The extract was then purified using QIAquickTM silica-based spin columns (Qiagen,Hilden, Germany) based on the method of (51). First, the extract was added to a spin col-umn together with approximately 5 times its volume of PB buffer. Following centrifugationat 13,000rpm for 1 minute, 750µl PE buffer was added and the column was centrifuged asbefore until there was no visible liquid left. DNA was then eluded by adding 2 X 45µl EBbuffer to the column membrane and centrifuging for 1 minute at 13,000 rpm.
3 Library preparation and sequencing
Paired-end Illumina libraries were built on all samples following the manufacturer’s protocol(Illumina Paired-End Sample Prep Kit), with the given modifications. The nebulization stepwas bypassed due to the fragmented nature of ancient DNA. Adaptor ligation was performedusing 1µl of Index PE Adaptor Oligo Mix (Illumina Multiplexing Sample Preparation Oligonu-cleotide Kit). Libraries were amplified using both Phusion polymerase (Finnzymes, Espoo,Finland) and Platinum Taq DNA Polymerase High Fidelity (HiFi) (Invitrogen, Carlsbad, CA),the former of which is known to not faithfully amplify molecules that contain uracil residues.
The set-up for the first round of amplification using Phusion polymerase was as follows: 5µlDNA library, 1x Phusion HF buffer, 200µM dNTP each (Invitrogen, Carlsbad, CA), 500nMMultiplexing PCR primer 1.0, 10nM Multiplexing PCR primer 2.0, 500nM Index PCR primer,0.02U/µl Phusion DNA Polymerase and water up to 50µl (primers obtained from the IlluminaMultiplexing Sample Preparation Oligonucleotide Kit). Cycling conditions were: 98◦C for 30seconds; 18 cycles of 98◦C for 10 seconds, 65◦C for 30 seconds, 72◦C for 30 seconds; and, afinal extension at 72◦C for 5 minutes.
The Hifi PCR was set up in two successive amplification rounds, with the first amplifica-tion protocol as follows: 5µl DNA library, 1x High Fidelity PCR buffer, 2mM MgSO4, 200µMdNTP each (Invitrogen, Carlsbad, CA), 500nM Multiplexing PCR primer 1.0, 10nM Multi-plexing PCR primer 2.0, 500nM Index PCR primer, 1 unit of Platinum Taq High Fidelity andup to 50µl water. Cycling conditions for the HiFi amplification were: 94◦C for 4 minutes; 18
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cycles of 94◦C for 30 seconds, 60◦C for 30 seconds, 68◦C for 30 seconds; and, a final extensionat 72◦C for 7 minutes. PCR products from this first amplification round were purified throughQIAquick spin columns (Qiagen, Hilden, Germany), from which 5µl of library was used to setup a second HiFi amplification round using the same PCR set-up as above and similar cyclingconditions except that the number of cycles was increased to 22.
All Phusion and HiFi (second round) PCR products were purified using QIAquick Gel Ex-traction Kit (Qiagen, Hilden, Germany) with no modifications to the provided protocol andeluted in a final volume of 30µl. Libraries were sequenced on at least one lane each on theIllumina GAIIx platform.
4 Data preprocessing and alignment
Raw data was analyzed using 1.7.0 of CASAVA and 1.8.0 of the OLB (offline basecaller). Toavoid possible contamination that could have entered the library outside clean room facilities,we stringently discarded all reads that did not have the exact correct index sequence (Ta-ble S2). The retained fraction was generally 96%-99% in all libraries except the one fromthe seal, for which the fraction was only 48%. In addition, we trimmed remaining adaptorsequences, again requiring an exact match of a portion of the adaptor sequence and that itwas located in the 5’ end of the sequence read. Sequencing errors can lead to untrimmedadaptor sequences when only exact matches are identified and trimmed, but these should haveminimal effect on our analysis due to further filtering criteria applied below.
4.1 Mapping
We mapped all sequence reads to the human reference genome (UCSC hg18) with the mito-chondrion replaced by the Cambridge reference sequence (rCRS). We used Stampy-1.0.10
(18), an accurate mapper for Illumina sequence reads used e.g. for indel discovery in the 1000genomes project (25). We ran Stampy in ’sensitive’ mode under default parameters. Duplicatereads were filtered based on mapping positions (’rmdup’) using the samtools package (52).
The fraction of reads mapping to the human genome (Table 1 in the main text) is simi-lar to the fraction obtained from the best preserved Neandertal fossils (53,54), but lower thana permafrost preserved modern human of similar age (11).
4.2 Stampy performance for ancient DNA
To assess Stampy’s applicability to ancient genomic data that is affected by e.g. postmortemnucleotide misincorporations (19), we compared the fraction of mapped reads obtained byStampy and 50,000 randomly subsampled reads from the same library that were aligned us-ing megablast (55) to an extensive database (hg18, panTro2, NCBI ”other genomic” and”ref seq”). We used a word size of 12 and required an e-value of less than 0.001. Aligningagainst a large database with genomic information for several vertebrates closely related tohumans together with the high sensitivity of megablast allows distinguishing between se-quences that have their absolute best match to humans from sequences that align to humansas well as other organisms. We find that the fraction of human reads obtained for the 5 human
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samples sequenced using both Hifi and Phusion polymerase is highly similar when mappingwith Stampy and megablast (Fig. S2).
4.3 Ste7 resequencing
One of the five human samples, Ste7, had a significantly lower fraction of reads mapping tothe human genome than the other four samples. Attempting to remedy this, a new library wasbuilt for this sample using the GS Rapid Library Prep Kit (Roche, Branford, CO) and followingthe provided protocol with some modifications. 1µl of a 5µM dilution of the Index PE AdaptorOligo Mix was added to the end-repaired and A-tailed DNA fragments with 1µl of RL ligase,and the adaptor ligation reaction was carried out at 25◦C for 15 minutes. Adaptor-ligatedlibraries were purified using MinElute spin columns (Qiagen, Hilden, Germany) and eluted in30µl of EB buffer. The HiFi PCR protocol and cycling conditions outlined in section 3 werefollowed for the new Ste7 library with the exception that two reactions with 3µl DNA librarywere set up for each of the two PCR rounds, and, shorter primers than the ones providedby Illumina were used in both the amplification rounds in order to minimize the formationof primer dimers (see [56] for primer sequences, Index 2). The two PCR reactions afterthe second amplification round were pooled together and gel purified using the E.Z.N.A gelpurification kit (Omega Bio-Tek) and eluted with 30µl elution buffer. The gel-purified librarywas sequenced on six additional Illumina GAIIx lanes. However, we found that saturation inthe number of unique sequences had been reached, as the total number of unique humanreads did not increase proportional to the amount of sequencing (Table S3). This was alsoreflected in the number of new SNPs obtained as data from each sequencing lane was added.On the basis of these observations and the increased error rate in Ste7 data (see section 7),this individual was excluded from all population genetic analyses.
5 mtDNA analysis
The 5 Neolithic samples had previously been assigned to mtDNA haplogroups based on 341bp of the mitochondrial d-loop (8). To authenticate our data and, if possible, obtain betterresolution of the haplogroups, we assembled mitochondrial genome sequences and comparedthe results with those obtained previously.
5.1 Assembly
We extracted all reads that aligned to the human mitochondrion (rCRS), and called a consen-sus sequence using the samtools package with default parameters assuming a haploid model.We find that the mtDNA genome coverage shows little correlation with the total amount ofhuman DNA obtained from each sample (Table S4), which is in line with other observationsof highly variable mtDNA-nuDNA ratios in ancient DNA extracts (57).
5.2 Haplogroup assignment
The haplogroup affiliation of each sample was assigned using HaploGrep (58) and PhyloTree
version 12 (http://www.phylotree.org; 59). The mutations are described relative to therCRS (60). The haplogroup affiliation of all samples corresponded to earlier results. The
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increased amount of sequence data added further support, and in three cases, the place ofthe sample in mtDNA gene tree was refined down to subhaplogroup level.
Two of the Neolithic hunter-gatherer samples, Ire8 and Ajv70, were previously classified asU4/H1b based on substitutions at np 16093 and 16356 (8). The mitochondrial genome ofIre8 displays 22 of the 26 mutations expected for subhaplogroup U4d (Table S5). The missingmutations are T195C (that define U4’9 together with G499A and T5999C), T4646C (that de-fine U4 together with A6047G, C11332T, C14620T, T15693C, T16356C) and 2405.1C (thatdefine U4d together with T629C). The consensus sequence lacks coverage for A8860G (whichtogether with A263G and A15326G define H2a2a). The mutational pattern for the Ajv70 mi-tochondrial genome also corresponds to that of haplogroup U4, either within subhaplogroupU4d or U4a2a. In both cases, Ajv70 displays 23 of the 26 expected mutations but lacks,as do Ire8, the T195C and 2405.1C mutations and sequence data for A8860G. The missingmutations leading to U4a2 are T195C and C8818T (defining U4a). However, the sample doesdisplay the T310C substitution that defines U4a2. The third Neolithic hunter-gatherer sample,Ajv52, was previously assigned to haplogroup V based on a transition at nucleotide position(np) 16298 (8). The new data further provide 10 of 12 mutations leading to V (Table S5).The two expected transitions that are missing are A8860G, for which there is no sequencedata, and A4769G (defining H2a).
The Neolithic farmer sample, Gok4, was previously classified as belonging to haplogroupH as no d-loop mutations were observed (8) and the shotgun sequence assembly providedfour out of six expected mutations that further supported the classification. The transitionat np 8860 is missing as there is no sequence data for that position and the transition at np4769 display the ancestral allele state. The second Neolithic farmer sample, Ste7, had previ-ously been assigned to haplogroup T as it displayed transitions at np 16126-16294-16304 (8).The new assembly could further place the sample within subhaplogroup T2b3 as 28 of 32expected mutations were present. The ancestral allele state was present for A8860G, A4769Gand C16296T (that defines T2 together with A11812G and A14233G). No sequence data wereavailable for mutation G5147A (defining T2b together with G930A and T16304C). Further,Ste7 seems related to T2b3a, as the sample displays one (C151T) of four (151T, 152C, 5656Gand C16292T) polymorphisms that define the subhaplogroup.
The number of non-diagnostic mutations (mutations not observed in PhyloTree) were con-siderably higher in assemblies with lower coverage (Gok4 had 92 such mutations comparedto 10, 19, 21 and 26 for Ajv70, Ajv52, Ste7 and Ire8 respectively) (Table S6). Note thatthe relative sequence error estimates presented for the autosomal data (see section 7) arefor positions that are already known to be polymorphic, and thus the total fraction of errorsfor low-coverage data when taking all possible positions into account is much greater. Themajority of the mtDNA polymorphisms that have not been documented previously are likelyerroneous, and they display the expected relationship to coverage, with the mtDNA genomeassembly with the lowest coverage (Gok4) also having the highest number of this type ofpolymorphisms (Table S6).
In addition, the mtDNA genomes display a third category of polymorphisms: mutations thatare observed in association with other haplogroups (Table S5). The samples displayed between
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zero and nine such mutations. These were all transitions, found between one and 36 times atwidely dispersed positions in the phylogenetic tree. However, some of these could representtrue polymorphisms in the Neolithic individuals. For example, both Ire8 and Ajv70 displayedtransitions at np 16093 in the previous study (8). Other polymorphisms in this category arelikely due to errors, for example the transitions at np 16145 and 16167 in the Gok4 mtDNA,for which the previous study showed no support.
6 Autosomal data processing
For our population genetic analyses, we obtained haploid genotypes for all autosomal positionsthat overlapped with SNPs in the particular reference dataset (see below and Fig. S3) withthe following criteria:
• We retained reads with mapping quality greater than 30.
• We retained bases with quality greater than 15.
• We excluded positions where a T was found in the sequencing read and a C in thereference human SNP data set.
• We excluded positions where a A was found in the sequencing read and a G in thereference human SNP data set.
• We excluded positions with more than two alleles (see Table S7 and Table S8).
• We excluded positions with gaps or an N in the sequencing read.
• We randomly sampled one read from positions covered by multiple reads.
We base all autosomal population genetic analyses on known human single nucleotide poly-morphisms (SNPs) from five different reference sets. Compared to sequence-based analyses,the advantage is that most sequencing errors (which are prevalent in second-generation se-quencing data, in particular from ancient DNA) will be weeded out and therefore have a muchsmaller impact on the analyses. Haplotypes and missing genotypes were estimated for eachdataset of extant humans using fastPHASE (61) version 1.4.5 (except for the data that werealready provided with phase information, see below). The number of haplotype clusters wasset to 25, and we used 25 runs of the EM algorithm. This analysis was used to generate a”best guess” estimate of the true underlying patterns of haplotype structure. When multipledata sets were to be combined, we merged them separately based on position, excluded A/Tand G/C SNPs, and flipped genotypes according to data sets that were provided with hg18strand orientation.
6.1 HM3+FINHM+HGDP
We obtained phased Finnish HapMap (FINHM) data from 81 individuals in two groups (FINand LSFIN) (ftp.fimm.fi/pub/FIN HAPMAP3/; 22), phased hapmap3 (HM3) genotypesfrom 7 populations (CEU; TSI; GIH; JPT+CHB; MKK; LWK; YRI; ftp.ncbi.nlm.nih.gov/hapmap/phasing/2009-02 phaseIII/HapMap3 r2/; downloaded 30 nov 2010, 23), and
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phased genotypes for 938 individuals from the Human Genome Diversity Project (HGDP;http://hgdp.uchicago.edu/Phased data/; 21, 62). To resolve strand issues in the datasets, we flipped SNPs to hg18 strand orientation for HGDP, FINHM, and HM3 data separately,and intersected the 1,163,280 SNPs common to HM3 and FINHM with the HDGP IlluminaSNPs. We excluded SNPs with MAF≤0.5%, leaving a total of 519,951 SNPs.
6.2 FINHM+POPRES
We merged the FINHM data set with a data set consisting of 1385 individuals from thePOPRES collection that had been genotyped on Affymetrix 6.0 arrays (24, 63). We used2 outlier iterations in EIGENSOFT with pairwise r2 limit of 0.2 to exclude 7 individuals inthe FINHM data that were identified as outliers on the first or second eigenvector (LSFIN2,LSFIN22, FIN12, LSFIN10, LSFIN30, LSFIN40, LSFIN21, LSFIN28). We excluded SNPs withMAF≤0.5% to a final total of 279,344 SNPs.
6.3 1KGPomni
We used 2,379,175 autosomal SNPs genotyped on Illumina 2.5 Omni arrays (25) from 241individuals from 5 European populations (CEU, TSI, FIN, GBR, IBS). We performed 5 outlieridentification iterations in EIGENSOFT 4.0 with pairwise r2 limit of 0.2, and exluded 3 CEUoutliers (NA12329, NA06989, NA06984) based on visual inspection of the PCA results. Weexcluded SNPs with MAF≤0.5%.
6.4 1KGPomni+Sweden
We phased genotype data from 353,565 autosomal SNP typed in 1,525 Swedish individuals(28) as described above. This data set was then merged with 1KGPomni (see above) in orderto flip to hg18 strand orientation, excluding all G/C and A/T SNPs, resulting in 250,725autosomal SNPs that were present in both data sets. We excluded SNPs with MAF≤0.5%.
6.5 Complete genomes
SNP data genotyped using arrays suffers from ascertainment bias, and reports a limited num-ber of polymorphisms for each individual. We attempted to circumvent these issues by iden-tifying SNPs between 9 human genomes sequenced to high-coverage. These consisted ofthe 7.5X genome of Craig Venter (64) obtained with capillary seqeuncing technology to-gether with 30X genome assemblies obtained with Illumina sequencing technology from 2individuals from Utah of European descent (25), one individual of Chinese descent (65), oneindividual of Korean descent (66) and two Yoruba individuals from Ibadan, Nigeria (25), andfinally the genome of an individual of Northern European ancestry sequenced with Helicostechnology (67). We added data on the chimpanzee allele (panTro2; 68, 69 , and excludedhypermutable CpG sites to a total data set comprising 6,001,729 SNPs. To avoid biasesfrom using different sequencing platforms and genotyping methodologies, we restricted allpopulation genetic analyses to the seven genomes sequenced by Illumina technology (theVenter (64) and Patient 0 (67) genomes were excluded), and used genotypes that had been
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called by the 1000 genomes project and filtered for mapping uniqueness and genotyping qual-ity (25 ; ftp://ftp.sanger.ac.uk/pub/rd/humanSequences/; retrieved April 2011). Weextracted genotypes from the Neolithic data separately for each sample as described above.
We note that ascertainment bias also affects this data since we only use sites that havebeen found to be polymorphic among the 9 diploid genomes. We therefore ignore potentialsites where the Neolithic data has an ancestral allele (shared with the chimpanzee) that isderived in all the 18 chromosomes from the modern genomes. This would lead to an under-estimation of population divergence time between Neolithic individuals and modern genomes(see section 12). However, these sites are rare, and are more susceptible to sequencing errorscompared to sites known to be polymorphic in the human population.
7 Relative sequencing/alignment error rates
To investigate relative sequencing error rates in sequences from different Neolithic samples,we counted the number of sites in which the Neolithic individual had a third allele that hadnot been seen in the 9 high-coverage human genomes and the chimpanzee. This fraction ofsites comprises positions in which there is a sequencing or alignment error in the Neolithicdata, and possibly also mismatches caused by sequencing and alignment errors in the moderndata. Regardless, these sites should be informative on the relative rates of sequencing andalignment errors between the different Neolithic data sets.
We find that Ste7 has an approximately 3 to 7 times inflated error rate compared to theother 4 samples (Table S7). This could possibly be due to the lower fraction of reads map-ping to the human genome, which could cause a proportionally higher number of reads withinthis fraction to be the result of mapping error. We also find similar results when computingthe fraction of triallelic sites using the 1KGPomni genotype data (Table S8), with Ste7 havingapproximately 2 to 3 times as high error rate as the other individuals. Error rates for theremaining 4 Neolithic individuals are rather homogeneous, but slightly inflated for the Ire8and Gok4 individuals.
8 PCA and Procrustes analysis
A central problem for ancient genomic projects is the large fraction (usually >95%) of retainedshotgun sequences that are comprised of non-endogenous sequences (e.g. from microbes inbone material [57 ]). This has the consequence that it is very difficult to obtain high-coveragedata even from relatively well-preserved material (53, 54). The data from the four differentNeolithic individuals presented here cover only about 1%-3.5% of the human genome each,resulting in few overlapping positions that could be used for direct population genetic com-parisons between individuals. However, the vast amount of reference data for modern humangenetic variation offers an opportunity of indirect comparison, where each individual is analyzedtogether with a modern reference data set separately, whereafter the individual analyses areconsidered jointly. Here we present such an approach based on Procrustes analysis (70) thatis tailored for joint analysis of non-overlapping data from different individuals using principalcomponent analysis or other methods based on the same conceptual framework (71).
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8.1 Implementation
Under the assumption that the genealogical history of each genetic locus–although non-overlapping–represents a random outcome of the same ancestral process, the relationshipbetween non-overlapping data from different individuals from the same population (i.e. indi-viduals with similar ancestry) and individuals from other populations is largely independent ofwhich loci are used. We transformed PC1 and PC2 from each individual analysis to matchthe PC1 and PC2 configuration obtained using only the reference individuals (using all SNPsin the data set, and where each ancient (Neolithic) individual was given coordinates (0, 0))(Fig. S4). As long as the ancient individual does not heavily influence the loadings of the refer-ence individuals, the PC1 and PC2 loadings for a given reference individual in an analyses of asubset of SNPs from the complete set should just reflect the sampling variance in the numberof SNPs. Therefore, we take the mean of PC1 and PC2 obtained in each separate analysis foreach reference individual, but retain the transformed PC1 and PC2 configuration for ancientindividuals. All principal component analyses were performed using EIGENSOFT 4.0 (20),with one SNP from each pair with r2 > 0.2 excluded. Results are shown in Figs. S5-S9.
8.2 Validation using Saqqaq data
As a proof of concept, we analyzed sequence reads from two Illumina libraries prepared froma paleo-eskimo hair sample (”Saqqaq”; 11) that had been amplified by both Hifi polymeraseand Phusion polymerase by (72) and mapped to the human genome using BWA (73) byAmhed Missael Vargas Velazquez and Ludovic Orlando. We used the same genotype filtersas described above and obtained 23,587 SNPs from the Hifi data and 38,107 SNPs fromthe Phusion data that overlapped with the HM3+FINHM+HGDP data set. We used 48non-African populations in the data set to generate a PC1-PC2 projection as in Figure 3Bin (11), and used Procrustes analysis to obtain a joint analysis of the non-overlapping datafrom the two different libraries as described above. The results were highly consistent betweenthe haploid low-coverage data analyzed using our pipeline and the diploid high-coverage dataanalyzed by (11), as well as highly consistent between the two libraries amplified by Hifi andPhusion (Fig. S10).
9 Model-based clustering
Each Neolithic individual was clustered together with extant European individuals from HapMap,Finnish HapMap, and HGDP (HM3+FINHM+HGDP) plus the Levantine Druze populationusing an unsupervised clustering algorithm implemented in ADMIXTURE (26). For each Ne-olithic individual was the clustering performed based on SNPs that overlapped between theNeolithic individual and the set of extant individual (see Table 1 in main text). All runs ofADMIXTURE were based on the ’admixture’ model, in which each individual is assumed to haveancestry in multiple clusters. Default settings were used (except for generating the randomseed). For a given value of K, the number of clusters considered, 10 replicate analyses wereperformed for each dataset comprising of one Neolithic individual and 384 extant individuals.The 10 replicate runs of ADMIXTURE (for each set of individuals and each K) were analyzedwith CLUMPP version 1.1.2 (27) to identify common modes among replicates. The CLUMPP
analysis used the fullSearch algorithm for K = 2 and 3, and the Greedy algorithm with 10,000
14
random permutations for K = 4. The 10 runs of ADMIXTURE produced very similar solutions,the average similarity coefficient H ′ was greater than 0.98 for all choices of K (2, 3 and 4) andfor all four Neolithic individuals. The clustering results were visualized with Distruct (74).
Fig. S11 shows the clustering solutions for the four Neolithic individuals assuming 2, 3 and 4clusters (each plot shows the average across 10 replicate ADMIXTURE runs). Figure 1D in themain text was generated by extracting the (averaged) ancestry matrix of the extant individu-als from each analysis containing one Neolithic individual and averaging them using CLUMPP.These four ancestry matrices were similar (H ′ = 0.795) and the four Neolithic individualswere merged to the plot so that the original clustering solutions were respected (comparewith Fig. S11).
10 Authenticity
Several observations, such as high fraction of sequences mapping to the human genomecompared to animal controls and matching mitochondrial genome assemblies to previouslyobtained mtDNA data, point to the presence of endogenous DNA in the human sequencedata. To investigate authenticity more closely we characterized two physical features that havepreviously been shown to differ between authentic ancient DNA and contaminating modernhuman molecules (19,29): (i) increased rate of thymine residues at positions where a cytosineis found in the reference sequence due to nucleotide misincorporation, and (ii) increased rate ofpurine residues to the 5’ end of the mapped sequence reads. However, increased rate of purinesin the 5’ end can also be caused by bleach treatment (75), which was used to decontaminatethe samples. We therefore base our conclusions and method for assessing authenticity in theautosomal data (see below) only on the nucleotide misincorporation pattern. Since Phusionpolymerase does not faithfully replicate Uracil (11, 72, 76), these analyses only utilized datafrom libraries amplified by Hifi.
10.1 Patterns of molecular degradation
In each alignment to the human genome, we extracted the positions covered by the read plus5 bp in the 5’ and 3’ from the hg18 reference sequence, and locally realigned the read and thereference sequence using MUSCLE3.8 (77). We then computed the average base frequencyat different positions of both the sequence read and the sequence at and around the alignedregion in the reference genome. We required reads to have a mapping quality of at least 30and averaged the frequency over all analyzed positions. We find clear evidence of patterns (i)and (ii), which were highly homogenous among samples (Fig. S12).
10.2 Testing for autosomal contamination
The presence of endogenous DNA does not preclude that a small fraction of contaminationcould influence the population genetic analyses and comparisons with modern populations. Totest whether contamination could be present in sufficient amounts to affect our conclusions, wedevised a new approach that relies on the expectation that C→T nucleotide misincorporationsare many times more frequent in endogenous molecules than contaminating molecules. Bydividing the sequences into those that have and those that do not have evidence of nucleotide
15
misincorporation we can create fractions that are highly enriched for endogenous and putativecontaminating molecules, respectively. Assuming significant (but not overwhelming) presenceof contamination, the latter category without evidence for misincorporation is expected tostill contain a portion of endogenous molecules that do not have misincorporations, but theformer category with misincorporations is expected to be composed mostly of endogenoussequences. All population genetic analyses that could be subject to contamination can thenbe assessed for different behaviour between the two fractions.
To implement this idea, we created additional sets of genotypes where we only consideredsequence reads that either had, or did not have a T where the reference base was a C withinthe first 10 bp (in the 5’ end) (Table S9). We used the same method for local realignment asabove. We performed PCA and Procrustes analysis on each separate fraction (with- and with-out nucleotide misincorporations) separately, together with the TSI (Tuscans from Italy) andFIN (Finnish from Finland) in the 1KGPomni reference data set, since differentiation betweenthese two populations summarizes the main component of differentiation between Neolithichunter-gatherers and the Neolithic farmer in our analyses (Fig. 1 in the main text). We findno evidence suggesting that contamination could influence our analyses and conclusions, withthe separation and different population genetic affinities between the Neolithic farmer and theNeolithic hunter-gatherers remaining evident in all analyses (Figs. 2, S13 and S14).
11 Allele sharing analysis
We computed the mean frequency of the allele observed in Neolithic farmer data (Gok4), andNeolithic hunter-gatherer data (Ajv52+Ajv70+Ire8) separately in various extant populations,summing over the total number of SNPs. To create a data set representing all three Ne-olithic hunter-gatherers we pooled the sequence reads from all individuals and genotyped allSNPs using the procedure described in section 6, resulting in 12,609 SNPs that overlappedwith the FINHM+POPRES data. We restricted the analysis to the 25 populations in theFINHM+POPRES data set with sample size of at least 8 chromosomes, and computed thefrequency of the allele observed in either Neolithic hunter-gatherers or the Neolithic farmerin extant populations by averaging over 8 randomly sampled chromosomes (without replace-ment). This procedure was replicated 100 times (for each SNP) and the mean across the 100repititions is reported in Table S10. Spatial interpolations of Neolithic allele frequencies wereperformed using the Krig function in the R package ”fields” (78). Coordinates for POPRESpopulations were obtained with Geonames (http://www.geonames.org/). For some pop-ulations (Sweden, Russia, United Kingdom) the coordinates were placed in the capital (63).FINHM coordinates were placed in Helsinki (FIN) and Oulo (LSFIN).
12 Estimation of population divergence time from com-plete genomes
To provide an alternative view of the genetic relationship between the Neolithic individualsand modern humans, we computed the population divergence time between each Neolithicindividual and one of seven complete genomes from extant humans. We used the same frame-work for estimation of population divergence time using reference genomes as in (32), with the
16
main difference being we estimate population divergence time to each modern diploid genomeseparately, using the chimpanzee as an outgroup. To implement the method, we compare thetwo gene copies in a modern genome to the Neolithic gene copy and the chimpanzee. Weinfer a genealogical topology only in those cases where two copies are identical, but differfrom the other two (one of which is the chimpanzee–representing the ancestral state), whichare also identical. We then count sites where the two copies from the modern genome share aderived allele not found in the Neolithic sample as ’concordant’ (assuming a model where theNeolithic individual splits from the modern population). The remaining two configurationsresult in a ’discordant’ topology. When two copies from the same population (in this casea single individual) are sampled in this way, the internode time T in the framework outlinedin (32) becomes equal to the total divergence time between the modern population and theNeolithic population.
Since the expected fraction of concordant topologies increases proportional to the time sincepopulation divergence, we can estimate T (measured in units of 2Ne generations where Ne
is the effective population size) and obtain confidence intervals in a maximum likelihoodframework using a coalescent model (e.g. 79). We have the log-likelihood function
Log(L(T )) = Nconc ×(
1− 2e−T
3
)−Ndisc ×
2e−T
3
where Nconc is the number of concordant genealogies, and Ndisc is the number of discordantgenealogies (the sum of the of two types) (79).
For estimating a joint population divergence times for all the three Neolithic hunter-gatherers,we pooled the sequence reads from the three individuals (Ajv52+Ajv70+Ire8) and obtainedgenotypes as outlined above.
12.1 Divergence from genomes of Northwest European ancestry
We find that the estimated population divergence time T between all Neolithic humans andmodern individuals of Northwest European ancestry (CEU) is on the order of 0.01, with pointestimates from 0.0069 to 0.0241. Comparing estimates for the Neolithic farmer to the pooledNeolithic hunter-gatherer data obtained using NA12891 we obtain T = 0.0150 (95% ML CI:0.003-0.027) and T = 0.0155 (95% ML CI: 0.008-0.023), respectively (Table S11). The esti-mates obtained using NA12892 are more disparate but with overlapping confidence intervals,specifically we obtain T = 0.0241 (95% ML CI: 0.012-0.037) for the Neolithic farmer and T= 0.0103 (95% ML CI: 0.003-0.018) for the Neolithic hunter-gatherers.
Assuming a recent European effective population size of 2Ne=10,000 (80) and a 25 yeargeneration time, the estimates obtained for NA12891 would translate to a divergence time of0.015× 10, 000× 2× 25 = 7, 500 years (95% CI:1,500-13,500) for the Neolithic farmer and0.0155 × 10, 000 × 2 × 25 = 7, 750 years (95% CI:4,000-11,500) for the Neolithic hunter-gatherers. The estimates for NA12892 are 12,050 years (95% ML CI: 6,000-18,500) for theNeolithic farmer and 5,150 years (95% ML CI: 1,500-9,000) for the Neolithic hunter-gatherers.The maximum and minimum values for each Neolithic group obtained using the two differentCEU genomes are reported in the main text, together with the mean of the two point estimates.
17
If the history between the modern genome and a Neolithic individual was more complexthan an instantanuous divergence model (79), the interpretation of these estimates wouldhave to be modified accordingly. If, for example, the modern genome would be the result ofadmixture between the Neolithic individual and some other population, T would be a functionpartly of genetic drift in the two source populations since their divergence, as well as therespective fractions they contributed to the ancestry of the Neolithic individual.
With this in regard, these estimaed divergence times are in line with the age of the spec-imens, and the similarity of the estimates for the Neolithic farmer and the Neolithic huntergatherers in the comparison with the NA12891 genome is in line with an estimated fraction of51.7% Neolithic farmer ancestry in the CEU population (Table S15). The difference betweenthe two genomes, where NA12892 appear to be non-significantly closer to Neolithic hunter-gatherers than the Neolithic farmer, could possibly be due to different ancestry in NA12892compared to NA12891. This difference is however within the confidence intervals of the esti-mates (Fig. S15).
While these estimates can be inflated by sequencing errors (32), the estimated error ratesfor Gok4 are not widely different from the Neolithic hunter-gatherer data (see section 7). Onthe other hand, it has been shown that the ancestral effective population size influences thismethod when single SNPs are used to infer genealogical topologies, but this bias is small fordivergence times on the order of 0.01 (32).
12.2 Divergence from genomes of East Asian and West African an-cestry
In contrast to the results obtained using extant European genomes, we find that the inferredpopulation divergence times from East Asians is significantly higher (0.12-0.18; Fig. S16;Table S12). This likely reflects both a more ancient divergence between ancestors of Europeansand East Asians as well as greater genetic drift in East Asians than Europeans (81). However,we are still unable to detect any significant difference between the Neolithic farmer Gok4 andthe three Neolithic hunter-gatherers. We obtained similar results when comparing the Neolithicdata to three genomes from individuals of West African ancestry (Fig. S16, Table S12).
13 Modelling European population history
Here, we use the observed patterns of differentiation between Neolithic farmers and hunter-gatherers to investigate models of population history relating the Neolithic individuals tomodern populations.
13.1 Testing population topology using genealogical concordance
To build a tree-like framework relating the Neolithic individuals to modern populations, wetake a similar approach as (82), testing the support of individual four taxon topologies andconsidering the results jointly. Consider a case where a single gene copy is sampled from
18
each of four populations A, B, C and D as for the divergence time estimation above, thereare only three possible genealogical topologies, of which the one that mirrors the populationtopology (i.e. the ’concordant’ topology) is expected to be most frequent. By scoring whichgenealogical topology is supported by the configuration of alleles at each site (assuming aninfinite sites model of mutation), we use a ’concordance’ test of the hypothesis that thetopology (A,B), (C,D) is significantly more frequent than the topologies (A,C), (B,D) and(A,D), (B,C). We conduct this test by computing the excess of the putatively ’concordant’category over the second most frequent category (the most frequent ’discordant’ category,denoted disc1), as
C =Nconc −Ndisc1
Nconc +Ndisc1
To obtain standard errors (SEs) for this statistic, we use a block jackknife procedure wherewe divide the genome in 130-140 contiguous blocks, and obtain Z-scores by normalizing withthe SE. If the C-statistic is significantly positive (> 3 SEs from 0), we consider the topologysupported.
We use European and Levantine (Druze) populations in the HM3+FINHM+HGDP data set,and add San and Yoruba as outgroups. Most tests support the results obtained from the pre-vious analysis, suggesting a close relation between Neolithic hunter-gatherers and NorthernEuropean populations (such as Finnish) on one hand, and Neolithic farmers and SouthernEuropean populations (such as Sardinians), on the other hand (Table S13). Based on all the1,271 significant four-wise combinations of tests in the data (108 that included the Neolithichunter-gatherers and 97 that included the Neolithic farmer), we constructed a neighbour-joining tree using an algorithm in the clann software package (83), and find that the resultsof this clustering is largely similar to the results based on PCA and ADMIXTURE analyses(Fig. S17).
This approach provides a means for clustering haploid data from populations and representingthe result as a topology, but we note that the neighbour-joining algorithm allows for conflictsbetween inferred topologies, and it utilizes information such as how many times two groupsappear in the same significant concordance test (for example in tests including the outgroup)rather than explicitly testing if there is support for each node in the inferred topology. In thefuture, more sophisticated algorithms for constructing the supertree might allow populationtopologies to be investigated in a more formal setting that is directly related to populationgenetic models of divergence with- or without gene flow.
13.2 Gene flow between a population related to Gok4 and extantEuropean populations
While the concordance tests are designed to portray the major pattern underlying the data,human population history often comprises more complex events such as migration and admix-ture. The observation that a Neolithic farmer individual in Scandinavia 5,000 years ago hasa similar genetic makeup as modern-day Southern Europeans suggest that it belonged to apopulation that expanded in concert with agriculture, and as such may have interacted andadmixed with resident populations. Observing that Neolithic hunter-gatherers tend to cluster
19
in the vicinity of Finnish individuals and the Neolithic farmer clusters with individuals fromsoutheastern Europe, we considered the possibility that other European populations in theHM3+FINHM+HGDP data set are derived from admixture between resident hunter-gatherergroups and expanding farming populations related to Gok4. Using the topology inferred aboveas a framework, we tested the hypothesis of no gene flow in the unrooted population topology(Outgroup,Gok4), (X, LSFIN), were Outgroup is either Papuans, Native Americans (Su-rui, Karitiana and Colombians; 21,84), Yoruba, or Levantine Druze. X is one of 9 Europeanpopulations in the HM3+FINHM+HGDP data set. We restrict the analysis to the 7,712SNPs not affected by putative nucleotide misincorporations. We compute the 4-populationtest based on the estimated allele frequencies in each population as in (31, 82).
D =
∑ni=1[p
iOutgroup(1− pi
Gok4)piX(1− pi
LSFIN)]− [(1− piOutgroup)p
iGok4p
iX(1− pi
LSFIN)]∑ni=1[p
iOutgroup(1− pi
Gok4)piX(1− pi
LSFIN)] + [(1− piOutgroup)p
iGok4p
iX(1− pi
LSFIN)]
where pi is the allele frequency at locus i. The statistic is summed over all n loci and wecompute a standard error using a block jackknife procedure over 132 windows with 50 SNPsin each.
We find evidence of a violation of the basic population topology using all outgroups (Ta-ble S14). The signal is consistently (but not always significantly) negatively skewed regardlessof which outgroup is used. This suggests that gene flow from Asia into Northern Europe orfrom Africa into Southern Europe is not enough to explain the data, since the signal is seenacross all populations when using outgroups from both these regions.
13.3 Estimation of Neolithic farmer ancestry in European popula-tions
Based on the 4-population tests and the available data, we considered a model (Fig. S18)where the late-settlement Finnish founder population LSFIN (22) has received a negligableamount of Neolithic farmer-related gene flow since the Neolithic expansion. Secondly, we as-sume that Gok4 instantanuosly split from the ancestors of extant Druze in the Levant (Druzewas chosen over Palestinians due to low degree of recent African admixture (85). Using threeNative American populations with no evidence of European admixture (Surui, Karitiana andColombians; (21, 84) as an outgroup, we estimate the fraction of Gok4-related ancestry inan extant European population X, as a mixture between populations related to LSFIN andGok4, using the f4-ancestry estimation framework (31, 85). Using the notation of (85), wecompute the statistic
f4(Outgroup,Druze;Gok4, LSFIN) =
∑ni=1(p
iOutgroup − pi
Druze)(piX − pi
LSFIN)∑ni=1(p
iOutgroup − pi
Druze)(piGok4 − pi
LSFIN)
where pi is the allele frequency at locus i in each population, and we compute the statisticsumming over all n loci. To obtain standard errors, we used a block jackknife, with 132 con-tiguous windows with the same number of SNPs. To also estimate ancestry in the Swedishpopulation, we merged the HM3+FINHM+HGDP data with the Swedish data, which resulted
20
in 4,817 SNPs that could be used in the analysis. We divided the Swedish data into individ-uals from Southern- (743 individuals), Central- (545 individuals), and Northern Sweden (237individuals).
A number of possible model violations could influence these estimates. Most notably, geneflow from East Asia into the European populations (e.g. the Finnish, Russian, Adygei andDruze populations) in the model. To assess the robustness of the model we replaced theNative Americans used as outgroup with Papuans, JPT+CHB (Japanese and Chinese ances-try) or Uygur (Central Asian), and obtained correlations with the original estimates of 0.99,0.98 and 0.99 respectively (Fig. S20). While the absolute values of ancestry varied with theoutgroup, we found that using Native Americans was the only case when estimates did notexceed 100%.
14 The Neolithic hunter-gatherer gene pool is not fullyrepresented in modern Europeans
To maximize the population genetic resolution of the genetic profile of the Neolithic hunter-gatherers, with special regard to their relationship with modern Scandinavians, we analyzed14,113 SNPs obtained by merging the Swedish and HM3+FINHM+HGDP data, and callinggenotypes based on all sequences available from the available Neolithic hunter-gatherer data(Ajv52, Ajv70 and Ire8), with the same criteria as above. To avoid the issue of a much largersample size in the Swedish data, we randomly sampled 20 individuals from each of Southern,Central, and Northern Sweden (Gotaland, Svealand, and Norrland; 28). We performed PCAand model-based clustering using ADMIXTURE as above but without Procrustes transformationor CLUMPP analysis. We find that Neolithic hunter-gatherers fall outside the variation of extantEuropeans in both these analyses (Figs. S21 and S22).
21
Figure S1: The Ajvide 70 (Ajv70) individual. Photograph by Johan Norderang
22
2 4 6 8 10 12 14
24
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1012
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% Human reads (Stampy)
% H
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rea
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meg
abla
st)
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R = 0.96, P < 1e−5
Figure S2: Benchmarking of Stampy by comparison with megablast.
Figure S3: Sample locations of all data sets used in the study. Some sampling locations arerepresented in multiple data sets (e.g. Spain), and thus have overlapping symbols.
24
−40 −20 0 20 40 60
−20
020
40Procrustes errors
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Figure S4: Procrustes transformation of (PC1,PC2) coordinates from two different data sets.Arrows represent the residuals between reference (”target”) and the coordinates for rotationafter transformation.
Figure S5: PCA and Procrustes analysis using individuals sampled worldwide in theHM3+FINHM+HGDP data set. A) PCA of modern individuals only. B) Procrustes-transformed and combined PCAs of all 4 Neolithic individuals. (C-F) Procrustes-transformedPCAs of each Neolithic individual separately.
Figure S6: PCA and Procrustes analysis using individuals sampled from Europe and theLevant in the HM3+FINHM+HGDP data set. A) PCA of modern individuals only. B)Procrustes-transformed and combined PCAs of all 4 Neolithic individuals. (C-F) Procrustes-transformed PCAs of each Neolithic individual separately.
Figure S7: PCA and Procrustes analysis using individuals in the FINHM+POPRES dataset. A) PCA of modern individuals only. B) Procrustes-transformed and combined PCAs ofall 4 Neolithic individuals. (C-F) Procrustes-transformed PCAs of each Neolithic individualseparately.
28
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Modern individuals
PC2
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1
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PC2
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PC2
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PC2
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1
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Ire8
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FIN (Finland)GBR+CEU (Gr. Britain/Utah)IBS (Spain)TSI (Italy)
Neolithic hunter−gatherer: Ajv52Ajv70Ire8
Neolithic farmer:Gök4
Figure S8: PCA and Procrustes analysis using individuals sampled from Europe in the1KGPomni data set. A) PCA of modern individuals only. B) Procrustes-transformed andcombined PCAs of all 4 Neolithic individuals. (C-F) Procrustes-transformed PCAs of eachNeolithic individual separately.
29
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1
Northern SwedenCentral Sweden
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FIN
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Northern SwedenCentral SwedenSouthern Sweden
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1
Northern SwedenCentral SwedenSouthern Sweden
F
FIN (Finland)GBR+CEU (Gr. Britain/Utah)IBS (Spain)TSI (Italy)Northern SwedenCentral SwedenSouthern SwedenNeolithic hunter−gatherer: Ajv52Ajv70Ire8Neolithic farmer:Gök4
Figure S9: PCA and Procrustes analysis using individuals sampled from Europe in the1KGPomni+Sweden data set. A) PCA of modern individuals only. B) Procrustes-transformedand combined PCAs of all 4 Neolithic individuals. (C-F) Procrustes-transformed PCAs ofeach Neolithic individual separately. Centroids for Southern-, Central and Northern Swedenare indicated.
30
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Figure S10: PCA and Procrustes analysis using data from two libraries prepared from apaleo-eskimo hair sample (Saqqaq; 11,72) using Phusion (denoted ”P”) and Hifi polymerase(denoted ”H”) comprising 38, 107 and 23, 587 SNPs, respectively, together with non-Africanindividuals in the HM3+FINHM+HGDP data set.
31
Ajv52 (hunter-gatherer)
Ajv70 (hunter-gatherer)
Ire8 (hunter-gatherer)
Gok4 (farmer)
Finl
and
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sian
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adia
n
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opea
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ry (C
EU)
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que
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th It
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)
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an
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inia
n
Ady
gei
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ze
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Ajv70 (hunter-gatherer)
Ire8 (hunter-gatherer)
Gok4 (farmer)
K = 2
K = 3
K = 4
Figure S11: Model-based clustering of Neolithic and extant Europeans (plus the levantine Druze)assuming 2, 3 and 4 clusters
32
0 2 4 6 8 10 12 14
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Figure S12: Patterns of nucleotide misincorporation and fragmentation in libraries preparedfrom Neolithic samples and amplified by Hifi polymerase. A to D shows average base frequencyin sequence reads at sites where a C is found in the reference sequence (hg18) for Ajv52, Ajv70,Ire8 and Gok4, respectively. E to H shows base frequencies in the reference around the 5’-endof the sequence reads.
33
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FIN (Finland)TSI (Italy)
Neolithic hunter−gatherer: Ajv52Ajv70Ire8
Neolithic farmer:Gök4
Figure S13: PCA and Procrustes analysis using individuals of Italian (TSI) and Finnish(FIN) ancestry in the 1KGPomni data set and Neolithic sequences with evidence of cytosinedeamination in the first 10 bp. A) PCA of modern individuals only. B) Procrustes-transformedand combined PCAs of all 4 Neolithic individuals. (C-F) Procrustes-transformed PCAs of eachNeolithic individual separately.
34
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FIN (Finland)TSI (Italy)
Neolithic hunter−gatherer: Ajv52Ajv70Ire8
Neolithic farmer:Gök4
Figure S14: PCA and Procrustes analysis using individuals of Italian (TSI) and Finnish(FIN) ancestry in the 1KGPomni data set and Neolithic sequences without evidence of cytosinedeamination in the first 10 bp. A) PCA of modern individuals only. B) Procrustes-transformedand combined PCAs of all 4 Neolithic individuals. (C-F) Procrustes-transformed PCAs of eachNeolithic individual separately.
Figure S16: Relative log-likelihood curves for population divergence time T between Neolithicindividuals and complete genomes from East Asian and African individuals.
37
SanYoruba Druze Neolithic farmer
TSI
Sardinian
NorthItalian
FrenchBasqueFrench
CEU
OrcadianRussian
FINLSFIN
Neolithic hunter−gatherers
Figure S17: Neighbour-joining supertree of 1,271 four-taxa topologies inferred by concor-dance tests.
38
Gök4
DruzePopulation XLSFINColombianKaritiana
Surui
Figure S18: Illustration of the model used for estimating the fraction of Neolithic farmerancestry in extant European populations.
39
Figure S19: Estimated Neolithic farmer ancestry (red) in European populations.
40
0.2 0.4 0.6 0.8 1.0
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oup:
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French
NorthItalian
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Russian
Tuscan
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Adygei
1
32
R = 0.99, P < 1e−13
C
Figure S20: Substituting Native Americans as an outgroup yields highly correlated estimatesof Neolithic ancestry in European populations. The numbers 1, 2 and 3 in the plots refer toNorthern, Central, and Southern Sweden, respectively. In all panels, estimates using NativeAmericans are displayed on the x-axis, and estimates using a different outgroup are on they-axis. Alternative outgroups: A) Papuans, B) JPT+CHB (Japanese and Chinese), and C)Uygur (Central Asia).
41
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Figure S21: Principal component analysis of combined data (14,113 SNPs) from the threeNeolithic hunter-gatherers (Ajv52, Ajv70 and Ire8) and extant Europeans (including Swedes)and the Levantine Druze.
42
GR
K (h
unte
r-ga
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sian
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eden
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eden
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th It
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Italy
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)
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Sard
inia
n
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gei
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ze
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Figure S22: Model-based clustering of combined data (14,113 SNPs) from the three Ne-olithic hunter-gatherers (Ajv52, Ajv70 and Ire8) and extant Europeans (including Swedes) andthe Levantine Druze assuming 2, 3 and 4 clusters.
43
Table S1: Summary of samples. *Pagophilus groenlandicus, #Canis familiaris.
Sample Species Context Age (years) Sex LocalityAjv52 Human PWC ca. 7 - AjvideAjv70 ” PWC 20-25 Male AjvideIre8 ” PWC 40-50 Female IreGok4 ” TRB ca. 20 Female Gokhem 94:1Ste7 ” TRB - - Gokhem 94:1GSAj15 Harp seal* - - - AjvideCFBe3a Domestic dog# - - - Linkoping
Table S3: Summary of initial quality control and mapping. In the main text we reportfraction of human reads based on Phusion-amplified libraries to allow direct comparison withthe negative animal controls.
Sample Indexed reads Hits to hg18 (%) Hits after rmdup (%)
Table S7: Triallelic site frequency in genome sequence data.
Total number of SNPs Neolithic data has Neolithic data hasSample (deamination excluded) third allele (count) third allele (%)Ajv52 94,650 2,276 2.46%Ajv70 153,973 3,833 2.55%Ire8 49,744 2,248 4.73%Gok4 105,274 2,929 2.86%Ste7 8,794 1,303 17.39%
50
Table S8: Triallelic site frequency in genotype array data.
Total number of SNPs Neolithic data has Neolithic data hasSample (deamination excluded) third allele (count) third allele (%)Ajv52 26,495 435 1.61%Ajv70 44,788 837 1.83%Ire8 11,763 279 2.32%Gok4 27,910 763 2.66%Ste7 1,348 68 4.80%
51
Table S9: Number of SNPs called using sequences with (’Degraded’) and without (’Notdegraded’) evidence of cytosine deamination in the the 5’ end.
Table S15: Estimated Neolithic farmer ancestry in European populations.
Population Gok4-related ancestry (c) Standard errorSardinian 95.3% 13.4%North Italian 78.9% 12.1%TSI 77.4% 11.1%Tuscan 69.8% 11.6%French Basque 65.0% 10.2%French 55.3% 8.5%CEU 51.7% 7.8%Orcadian 46.9% 7.8%Adygei 46.4% 7.7%Southern Sweden 41.1% 7.8%Central Sweden 35.8% 7.1%Northern Sweden 31.1% 6.3%FIN 14.0% 3.4%Russian 11.3% 4.1%
58
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