“Diachronic Investigations of Mitochondrial and Y-Chromosomal Genetic Markers in Pre-Columbian...

doi: 10.1111/j.1469-1809.2010.00620.x

Diachronic Investigations of Mitochondrial andY-Chromosomal Genetic Markers in Pre-Columbian AndeanHighlanders from South Peru

Lars Fehren-Schmitz1∗, Ole Warnberg1, Markus Reindel2, Verena Seidenberg1,Elsa Tomasto-Cagigao3, Johny Isla-Cuadrado4, Susanne Hummel1 and Bernd Herrmann1

1Historic Anthropology and Human Ecology, Johann-Friedrich-Blumenbach Department of Zoology and Anthropology, UniversityGoettingen, Goettingen 37073, Germany2German Archaeological Institute (DAI), Commission for Archaeology of Non-European Cultures (KAAK), Bonn 53173, Germany3Departmento de Humanidades, Pontificia Universidad Catolica del Peru, Lima 32, Peru4Instituto Andino de Estudios Arqueologicos (INDEA), Lima 11, Peru

Summary

This study examines the reciprocal effects of cultural evolution, and population dynamics in pre-Columbian southernPeru by the analysis of DNA from pre-Columbian populations that lived in the fringe area between the Andean highlandsand the Pacific coast. The main objective is to reveal whether the transition from the Middle Horizon (MH: 650–1000 AD) to the Late Intermediate Period (LIP: 1000–1400 AD) was accompanied or influenced by population dynamicprocesses. Tooth samples from 90 individuals from several archaeological sites, dating to the MH and LIP, in the researcharea were collected to analyse mitochodrial, and Y-chromosomal genetic markers. Coding region polymorphisms weresuccessfully analysed and replicated for 72 individuals, as were control region sequences for 65 individuals and Y-chromosomal single nucleotide polymorphisms (SNPs) for 19 individuals, and these were compared to a large set ofancient and modern indigenous South American populations. The diachronic comparison of the upper valley samplesfrom both time periods reveals no genetic discontinuities accompanying the cultural dynamic processes. A high geneticaffinity for other ancient and modern highland populations can be observed, suggesting genetic continuity in the Andeanhighlands at the latest from the MH. A significant matrilineal differentiation to ancient Peruvian coastal populations canbe observed suggesting a differential population history.

Keywords: Ancient DNA, Peru, Wari, cultural change, molecular genetics, mitochondrial DNA, y-chromosomalHaplogroups

Introduction

The pre-Columbian cultural landscape of the western CentralAndean region is characterised by two adjacent main culturalareas with extremely differing ecological conditions: the Pa-cific Coast including the western slopes of the Andes, and theAndean Highlands. In a time span of approximately 13,000

∗Corresponding author: Dr. Lars Fehren-Schmitz, Historic An-thropology and Human Ecology, Johann-Friedrich-BlumenbachDepartment of Zoology and Anthropology, University of Goettin-gen, Buergerstrasse 50, 37073 Goettingen. Tel: +49 551/39-22249;Fax: +49 551/39-3645; E-mail: [email protected] sponsorship: German Federal Ministry of Education and Re-search (BMBF); Grant number: 01UA0804B.

years (Dixon, 2001; Dillehay, 2009) the areas faced the tran-sition from small nomadic groups to sedentism, the evolutionof complex societies and even states. These developments areresults of complex patterns of interaction between the areaswith constantly altering reciprocal interferences, and changesin intensity and direction of cultural transmission. The ques-tion, to what extent these cultural processes were accom-panied or influenced by population dynamic processes andalterations of mobility patterns and even gene flow remainsmostly unanswered.

The understanding of human genetic diversity in SouthAmerica and the processes involved in the peopling of thecontinent has greatly improved in the last years throughseveral population genetic studies analysing mitochondrial,

266 Annals of Human Genetics (2011) 75,266–283 C© 2010 The AuthorsAnnals of Human Genetics C© 2010 Blackwell Publishing Ltd/University College London

DNA from ancient Peruvian highlanders

Y-chromosomal, and autosomal markers in contemporary in-digenous populations (e.g., Fuselli et al., 2003; Wang et al.,2007; Lewis & Long, 2008; Lewis, 2009b). However, to revealprehistoric population affinities and gene flow between pre-Columbian populations and the basis of modern diversity, it isnecessary to consider the diachronic, microevolutionary per-spective through the employment of palaeogenetic methods.The analysis of DNA from prehistoric human remains provedto be a powerful tool to reveal such processes when used ap-propriately (Paabo et al., 2004; Bramanti et al., 2009). ForSouth America the number of palaeogenetic studies is stillsmall. Only recently there has been an increased interest instudies of pre-Columbian Central Andean populations fromthe coast (Shimada et al., 2004; Moraga et al., 2005; Fehren-Schmitz et al., 2009, 2010) and the highlands (Shinoda et al.,2006; Lewis et al., 2007a; Kemp et al., 2009; Carnese et al.,2010).

Current archaeological data indicates that the initial devel-opment of social, technological, and demographic complex-ity in both main Central Andean cultural areas did not occursimultaneously (Dillehay et al., 2004). The level of mutualexchange increases with the Initial Period (1800–900 BC)and becomes apparent with the emergence of the Chavinstyle in the Early Horizon (800–200 BC) (Burger, 1981;Kembel & Rick, 2004). Especially for the central and southernareas there is no evidence that this spread was accompaniedby significant population biological processes (Burger, 2008).This assumption is supported by palaeogenetic studies thatverify a significant genetic differentiation between the pre-historic coastal and highland populations in Southern Peruat least for a period ranging from 800 BC to 650 AD eventhough cultural influences of coastal cultures are evident inthe adjacent highlands (Shimada et al., 2004; Shinoda et al.,2006; Fehren-Schmitz, 2008; Fehren-Schmitz et al., 2009,2010). With the transition to the Middle Horizon (600–1000AD) the first highland state societies like the Wari occur thatspread their cultural traditions intentionally throughout thecentral Andean area through partially aggressive expansion,state influenced migration, and relocation (Schreiber, 1992;Tung, 2007; Heggarty, 2008). This processes supposedly hadinfluence on migrational behaviour, and on the demogra-phy and genetic composition of the Central Andean popula-tions through urbanisation and alterations of social complexity(DAltroy & Schreiber, 2004; Tung, 2008). With the Late In-termediate Period more locally restricted cultural groups withvarying social complexity arose (1000–1400) altering the re-lation and spatial dominancy between coast and highlands andprobably the directions and intensity of human mobility andtherewith gene flow. However, recent palaeogenetic investi-gations on highland populations suggest genetic continuityin the Central Andean highlands at the latest from the MHon (Lewis et al., 2007a; Kemp et al., 2009). Considering the

distinctness of the earlier pre-Columbian coastal and highlandpopulations it can be assumed that if there were any signifi-cant population biological changes resulting from the culturalinteractions of the main cultural domains they would have lefttheir imprint in the direct fringe area of both major culturalzones.

In the course of a joint interdisciplinary project of Peru-vian and German archaeologists and natural scientists we hadthe opportunity to collect samples for a DNA analysis fromsix archaeological sites in South Peru situated in the uppervalleys of the Rio Palpa and Rio Viscas directly adjacent tothe Altiplano (Fig. 1), thus representing a fringe area as de-scribed above. Additionally, the investigation area is in theproximity of the coastal sites analysed in a previous archaeo-genetic study (Fehren-Schmitz et al., 2010). Here we anal-yse mitochondrial and Y-chromosomal genetic markers fromchronologically succeeding skeletal collectives to characterisethe pre-Columbian human genetic diversity and to reveal ifthere are diachronic changes in the genetic population com-position. The comparison of the diversity observed in theupper valley populations with modern and pre-Columbiancoastal and highland populations will permit us to determineto what extent cultural interactions between the coastal andthe highland areas were accompanied or influenced by humanpopulation dynamic processes. Since types of human migra-tion and mobility can be sex specific processes (Sanjek, 2003)matrilineal and patrilineal genetic markers are observed.

Material and Methods

Sites and Samples

The area of investigation is formed by the upper valleysof the Rio Palpa and Rio Viscas directly adjacent to theAndean Puna at an altitude of approximately 3000–4000m—Quechua and Suni ecological zones (Pulgar-Vidal,1979)—surrounding the modern town of Laramate, LucanasProvince, Region Ayacucho (Fig. 1). The area is situatedabout 50 km to the east of the archaeological sites in thenorthern Rio Grande de Nasca drainage—Chala ecologicalzone—investigated in the previous project (Reindel & Wag-ner, 2009; Fehren-Schmitz et al., 2010). In two field cam-paigns (2008, 2009) bone and teeth samples from 74 humanindividuals were collected by the main author (LF). The sam-pled individuals derive from settlement associated burial cavesand Chullpas, typical stone build burial structures in the high-lands, dating to the Middle Horizon and the Late Intermedi-ate Period (Table 1). Additionally, 16 individuals from the siteof Pacapaccari were already sampled in the course of a previ-ous project (Fehren-Schmitz et al., 2010). The largest skeletalseries was collected in the Cueva Yacotogia. The burial cave

Annals of Human Genetics (2011) 75,266–283 267C© 2010 The AuthorsAnnals of Human Genetics C© 2010 Blackwell Publishing Ltd/University College London

L. Fehren-Schmitz et al.

Figure 1 Map of the investigation area in South Peru. The crosses mark the sampledarchaeological sites (cf. Table 1).

Table 1 Sampled archaeological sites, dates and coordinates

Burial Time 14C AgeSite n1 type period2 (years BP)3 Altitude

Yacotogia 31 Cave MH 1187 ± 26 3420 mOcoro 10 Chullpa MH n.d. 3350 mPacapaccari 16 Chullpa LIP 820 ± 24 3012 mBotigiriayocc 15 Cave LIP n.d. 3535 mHuayuncalla 8 Chullpa LIP 978 ± 26 3091 mLayuni 10 Abri LIP-LH n.d. 3557 m

1n = number of sampled individuals.2MH = Middle Horizon, LIP = Late Intermediate Period, LH =Late Horizon.3only listed when dated from bone material.

contained human remains from about 60 individuals with aregular sex and age distribution. The archaeological site couldbe dated to the M H trough associated findings belonging tothe Wari culture and direct Accelerator Mass Spectrometry

(AMS) 14C dating of bones revealing an age of 1187 ± 26 BP,σ1 cal. AD 782–883 (cal. trough INTCAL04, Lab-NumberHd-28475, Environmental Physics – University Heidelberg).All information regarding the other sites is found in Table 1.The assigned dates of the sampled individuals are based onthe archaeological context (closed association) in the gravesand secondary on the stratigraphic information and generalarchaeological association of the cemeteries. Although thereare absolute numerical dates for most sites (Table 1), only rel-ative chronological dates—cultural periods—are applied herebecause most of the absolute dates have not been obtainedfrom the bones themselves.

Since most individuals were found disarticulated in thegraves we only collected teeth from mandibles or a Pars petrosato ensure that no individual is sampled twice. In general, twoindependent samples per individual were collected. They wereinstantly sealed to prevent contamination and then exportedfrom Peru to Germany with the permission of the Instituteof Peruvian Cultural Heritage (Instituto Nacional de Culturadel Peru).



Contamination Prevention

Previously, no modern DNA-based studies had been per-formed in our laboratories, which are dedicated to ancientDNA analysis. All analyses were carried out according tocommonly applied precautions for the analysis of ancientDNA (e.g., Hummel, 2003), including a strict separation ofpre- and post- PCR laboratories and the use of disposableprotective clothing, glasses, and disposable laboratory glovesto prevent contamination by the staff handling the material.Experiments were carried out with disposable laboratory waresuch as pipette tips and cups. Workbenches and all other labo-ratory equipment were treated with detergents (Alconox De-tergent, Aldrich, Germany) bidistillated water and ethanol forDNA contamination removal. All disposable ware and mostsolutions, buffers, and MgCl2 were irradiated with ultravioletlight employing aluminium foil coating (Tamariz et al., 2006).Negative extraction controls and negative PCR controls wereemployed in this study.

DNA Samples were taken from every person that had con-tact with the sample material. A database was establishedcontaining the genetic data of all excavation helpers, archae-ologists, project members in Peru, and all people working inour laboratories in Germany as well as information on whichpeople had contact with which samples. This procedure al-lows a gapless documentation of people who might possiblycontaminate the samples. Samples where a contamination byone of the people could not be ruled out were excluded fromfurther analyses.

Sample Preparation and DNA Extraction

The preparation of the bone and tooth samples for DNAextraction followed a standardised protocol (Hummel, 2003).After the outer surfaces of the samples were amply removedwith an electric drill with diamond tipped saw blade (K10,KaVo, Bieberbach, Germany), the fragments were mechan-ically pulverised. Decalcification of the samples and DNAextraction followed a previously described protocol (Fehren-Schmitz et al., 2010) employing automated DNA purificationin a BioRobot EZ1 (Qiagen, Hilden, Germany). The DNAextract was afterward stored at −20◦C.

For each individual we made two or more independentDNA extracts. In most cases, the different extracts from oneindividual originated from different skeletal samples, to allowan authentication of analysis results by comparison.

Mitochondrial HVR1 Analysis

To determine the mitochondrial haplotypes of the individ-uals we analysed a 388 bp fragment of the mitochondrialhypervariable region 1 (HVR1) (nucleotide positions 16,021–

16,408, relative to the revised Cambridge reference sequence(rCRS); (Andrews et al., 1999). For this purpose we used aset of four primer pairs generating overlapping PCR productswith a length range of 157 – 180bp. Primer sequences forthe amplification and the description of PCR conditions arefound in Fehren-Schmitz et al. (2010). PCR success and PCRproduct quantity were checked by gel electrophoresis on 2.5%Agarose-Gels. Following this, the products were purified witha MiniElute PCR Purification Kit (Qiagen) and subjected todirect sequencing. Sequence reactions were prepared using theHPLC-grade PCR primers and the BigDye Terminator v1.1Kit (Applied Biosystems, Carlsbad, CA, USA). Taq Cycle-Sequencing conditions followed the instructions of the pro-ducer. Consequently, sequencing products were purified withthe NucleoSeq Kit (Macherey-Nagel, Dueren, Germany) andanalysed using an ABI Prism 310 Genetic Analyzer and sup-plied software (Applied Biosystems).

Each of the four overlapping segments of the HVR1 wasamplified at least two times per extract. When the results ofthese replications were highly inconsistent the samples werediscarded from further analysis. Punctual sequence aberrationsfrom the consensus were classified as potential postmortemsequence degradations (Gilbert et al., 2003a, 2003b). In thiscase we performed additional amplifications of the conspicu-ous DNA segment and additionally sequenced the light-strand(Paabo et al., 2004). If inconsistencies could not be excludedand no consensus was found, the samples were also discardedfrom the dataset.

Mitochondrial Haplogroup Analysis

The HVR1 sequences—haplotypes—can be tentatively as-signed to respective haplogroups according to specific muta-tions, but there is a high risk of false determination throughfast evolving mutational hotspots and postmortal sequenceaberrations (Meyer et al., 1999; Stoneking, 2000; Gilbertet al., 2003b; Bandelt, 2005). Therefore, we additionally anal-ysed the specific coding region polymorphisms for the fourNative American haplogroups A, B, C, and D (Torroni et al.,2006). To determine the haplogroups, we analysed three sin-gle nucleotide polymorphisms (SNPs) characterising groupsA (np663 A to G), C (np13263 A to G), D (np 5178 Cto A), and a 9 bp deletion between (np) 8272–8280 in thenoncoding cytochrome oxidase II/tRNALys intergenic regioncharacterising B (Schurr et al., 1990; Torroni et al., 1993;Merriwether et al., 1995). Additionally, we analysed a singlenucleotide polymorphism (SNP) characterising macrogroupR∗(np12705 C to T) involving the most frequent Europeanmt-haplogroups (van Oven & Kayser, 2009) to discriminateagainst contamination from the German archaeologists andlab-members.



Table 2 Primers used for the multiplexamplification of the haplogroup specificSNPs and 9bp deletion

Conc.System Primer Sequence (5´ −3´) (μM)1

np663 mt_H_628_HgA CTCACATCACCCCATAAACAA 0.24mt_L_707_HgA GGGATGCTTGCATGTGTAAT 0.24

9bp Del. mt_H_8184_HgB GTGGAGCAAACCACAGTTTC 0.20mt_L_8321_HgB GGTTAATGCTAAGTTAGCTTTACAGT 0.20

np13263 mt_H_13177_HgC GGCGCTATCACCACTCTGT 0.20mt_L_13289_HgC CCGATTGTAACTATTATGAGTCCTA 0.20

np5178 mt_H_5112_HgD GCATTCCTACTACTCAACTTAAACTC 0.20mt_L_5207_HgD GGATGGAATTAAGGGTGTTAGTC 0.20

np12705 mt_H_12621_HgR CGTTACATGGTCCATCATAGAA 0.16mt_L_12771_HgR CTCTCAGCCGATGAACAGTT 0.16

1conc. = Primer concentration used in the primer mix for the multiplex amplification.

Table 3 Primers used for the multiplex amplification of the Y-Chromosomal SNPs

HG1 Mutation2 Primer Sequence (5´ −3´) Conc. (μM)3

Q1a3a∗ M3 Y_M3_up CTGCCAGGGCTTTCAAATAG 0.24Y_M3_low AATTTAAGGGCATCTTTCATTTTAG 0.24

Q1a3a1 M19 Y_M19_up GGTTTGTTAAGTGGCCAAACTAT 0.20Y_M19_low AGGCTTTTCATAAAATTTAACTCACTA 0.20

C M130 Y_M130_up TCCTCCCTTTCTTTCTGTACTTACT 0.20Y_M130_low TGGTGGCCAGCCTCTTATC 0.20

Q1a3a2 M194 Y_M194_up AAGGATTTTTGAATACTTTAAACATACAG 0.20Y_M194_low GGTTGCAAACGGTAGTATTTCTA 0.20

Q1a3a3 M199 Y_M199_up CAGGCTTTAATTTAATGTAGATTCATACT 0.24Y_M199_low CTGTGTGATTTCAAGGATTTGTTAGT 0.24

Q∗ M242 Y_M242_up TCATGTAATGTGATAGTATCTTGAACTTATAT 0.20Y_M242_low GAAAAAACCATGTACGTTAAAATAGAT 0.20

1HG = Y-chromosomal Haplogroup.2Haplogroup determining mutation following the Y-Chromosome Consortium nomenclature.3conc. = Primer concentration used in the primer mix for the multiplex amplification.

For the analysis of the five coding region polymorphisms,we developed a multiplex Single Base Extension (SBE)assay allowing a simultaneous amplification of the target se-quences. All Primers for the amplification (Table 2) and theminisequencing (Table 3) reactions were designed using thePrimer Select software (Lasergene 8.0 package, DNAstar).The minisequencing /SBE primers were designed one basecontiguous to the polymorphic site of interest in either theforward or reverse orientation. Additionally, variable lengthpolymeric-A tails were added to the 5´end of the primer inorder to ensure an effective separation of the products duringelectrophoresis (Nelson et al., 2007).

The multiplex amplification of the five amplicons was car-ried out in a total volume of 50 μl containing 2.5 unitsAmpliTaqGold DNA hot start Polymerase, 2 mM MgCl2(both: Applied Biosystems), 200 μM dNTP-Mix (Roche,Mannheim, Germany), 5 μl multiplex primer set (concen-trations Tab. 2), and 1μl BSA (20 g/ml, Roche) in 10 ×Buffer II (Applied Biosystems). As template we added 5–10

μl DNA extract and completed the final reaction volumewith DNA and DNase free HPLC H2O (Merck). PCR am-plification took place in a Mastercycler (Eppendorf, Ham-burg, Germany) under the following conditions: initialisationat 94◦C for 11 min; 35–40 cycles at 94◦C for 1 min, 56◦C for 1 min, and 72◦C for 1.5 min; final elongation 60◦Cfor 30 min. PCR success and PCR product quantity werechecked by gel electrophoresis. We used extra thin, highlyconcentrated 3.75% agarose gels poured on a slope allowingseparation down to 3–4 bp in the 50–120 bp range (Hummel,2003) and allowing direct identification of the 9 bp deletionbetween (np) 8272–8280 characterising haplogroup B. Fol-lowing this, the products were purified with the MiniElutePCR Purification Kit (Qiagen). To determine, the polymor-phisms for haplogroups A,C,D, and R single base extensionwas carried out using 2.5 μl SNaPshot Ready Reaction Mix(Applied Biosystems), 1 μl purified PCR product, 0.37 μlpooled SBE primer mix (final concentrations Table 3), and1.13 μl HPLC H2O (Merck, Darmstadt, Germany) in a 10 μl



final volume. The thermocycling conditions were as follows:25 cycles at 96◦C for 10 sec, 50◦C for 5 sec, and 60◦C for 30sec. Afterwards the SBE products were purified through incu-bation with 1 μl alkaline Phosphatase (rAPid, Roche) at 37◦Cfor 1 h. Analysis followed using an ABI Prism 310 GeneticAnalyzer (Applied Biosystems). The samples were preparedfor capillary electrophoresis following the instructions of themanufacturer.

Y-Chromosomal Haplogroup Analysis

To reveal the patrilineal population dynamics, we analysed sixSNPs from the nonrecombining portion of the Y chromo-some (NRY-DNA) characterising the NRY-haplogroups C∗

(M130), Q∗ (M242), Q1a3a∗ (M3), Q1a3a1 (M199), Q1a3a2(M19), Q1a3a3 (M194) (Underhill et al., 1997; Bergen et al.,1999; Ruiz-Linares et al., 1999; Underhill et al., 2001; Seiel-stad et al., 2003; Karafet et al., 2008) following the nomen-clature of the Y Chromosome Consortium (2002). The hap-logroup Q1a3a∗ (formerly Q-M3) is the most frequent inNative South American males with a frequency of 77% (Bor-tolini et al., 2003). The haplogroups Q1a3a1, −2 and −3downstream to Q1a3a∗ are specific to South America (Karafetet al., 2008). Q1a3a2 has been found mostly in indigenouspopulations of the northern Amazonian regions (Bortoliniet al., 2003) while there is no detailed information for thedistributions of both other Q1a3a groups. Haplogroup Q∗

upstream to Q1a3a∗ is the second most frequent group whileC∗ was found only in very few indigenous South Ameri-can individuals on the northern coast (Bortolini et al., 2003;Bailliet et al., 2009).

For the analysis of the six polymorphisms, we devel-oped a multiplex SBE assay following the same principlesas described for the mitochondrial Coding Region polymor-phisms. Primers for the amplification are found in Table 4and minisequencing primers in Table 3. The multiplex am-plification of the six amplicons was carried out in a total

volume of 50 μl containing 2.5 units AmpliTaqGold DNAhot start Polymerase, 2 mM MgCl2 (both: Applied Biosys-tems), 200 μM dNTP-Mix (Roche), 3.2 μl multiplex primerset (concentrations Table 4) and 1 μl BSA (20g/ml, Roche)in 10x Buffer II (Applied Biosystems). As a template we added10 μl DNA extract and completed the final reaction volumewith DNA and DNase free HPLC H2O (Merck). Afterwards,PCR success was checked by gel electrophoresis and productswere purified as described before. The minisequencing reac-tion and all following processes equal the processes describedfor the mt-SBE assay.

Data Analysis

All sequence data that have been obtained were manuallyverified with the SeqMan Software (Lasergene Software 8,DNAStar, Madison, WI, USA) to avoid analysis- and readingerrors of the sequencer software (Salas et al., 2005). Align-ments of the HVR1 sequences were performed using theMegAlign software (DNAStar) employing the Clustal V algo-rithm. The analysed individuals were assigned to mitochon-drial haplogroups primarily using the coding-region data andthen evaluation of consistency with the HVR1 sequence datafollowed.

Several statistical parameters were computed at thehaplogroup- and the haplotype (sequence) level. Compara-tive haplogroup frequency and sequence data were obtainedfrom contemporary populations (Ward et al., 1996; Moragaet al., 2000; Williams et al., 2002; Fuselli et al., 2003; Lewiset al., 2005, 2007b; Cabana et al., 2006; Melton et al., 2007)and pre-Columbian populations (Shimada et al., 2004; Shin-oda et al., 2006; Lewis et al., 2007a; Kemp et al., 2009;Carnese et al., 2010; Fehren-Schmitz et al., 2010). To com-pare mtDNA haplogroup frequencies, we employed a cor-respondence analysis using Statistica 8 (StatSoft, Hamburg,Germany). For the haplotype/sequence data, diversity in-dices within populations and geographical groups (cf. Table 5)

Table 4 Single Base Extension Primers used in the multiplex minisequencing reactions for the determination of Mitochondrial and Y-Chromosomal haplogroups

HG Polymorphisms Primer Orientation Sequence (5´ −3´) Conc. (μM)

A np663 SBE_mt_663 R (A)12GTAATCTTACTAAGAGCTAATAGAAAGGC 0.20C np13263 SBE_mt_13263 R (A)8GCCGATTGTAACTATTATGAGTCCTAG 0.20D np5178 SBE_mt_5178 F CTACTATCTCGCACCTGAAACAAG 0.20R np12705 SBE_mt_12705 R GGTAACTAAGATTAGTATGGTAATTAGGAA 0.14Q1a3a∗ M3 SBE_M3 F AAGGGTCACCTCTGGGACTGA 0.20Q1a3a1 M19 SBE_M19 F (A)13CAAACTATTTTTGTGAAGACTGTTGTA 0.20C M130 SBE_M130 F (A)14GGGCAATAAACCTTGGATTTC 0.20Q1a3a2 M194 SBE_M194 R GCCTTCTCGGGGGGAA 0.24Q1a3a3 M199 SBE_M199 F (A)7TGCATTGAAATGTTAAAATGGCTTACACTTG 0.20Q∗ M242 SBE_M242 R (A)29CACGTTAAGACCAATGCCAA 0.20



Table 5 Distribution of mtDNA haplogroups and HVR1-based Ddversity parameters of the analysed prehistoric populations and thecontemporary reference populations

HaplogroupFrequencies

Group Population n1 H2 A B C D Hd3 π 4 Reference

Ancient Upper Yacotogia (MH)5 25 12 0.04 0.56 0.40 0.00 0.9100 0.0151 this studyValleys Ocoro (MH) 2 (5) 2 0.20 0.60 0.20 0.00 1.0000 0.0282 this study

Total MH 27 (30) 13 0.07 0.57 0.37 0.00 0.9145 0.0158Pacapaccari (LIP) 12 (16) 6 0.00 0.69 0.31 0.00 0.8636 0.0174 (Fehren-Schmitz et al., 2010)Botigiriayocc (LIP) 12 9 0.08 0.42 0.25 0.25 0.9394 0.0178 this studyHuayuncalla (LIP) 5 4 0.20 0.40 0.40 0.00 0.9000 0.0215 this studyLayuni (LIP) 9 8 0.00 0.56 0.33 0.11 0.9722 0.0191 this studyTotal LIP 38 (42) 25 0.05 0.55 0.31 0.10 0.9730 0.0184

Ancient Conchapata (MH) 10 9 0.29 0.50 0.14 0.07 0.9778 0.0227 (Kemp et al., 2009)Highlands Chen Chen (MH) (23) n.d. 0.39 0.39 0.17 0.04 n.d. n.d. (Lewis et al., 2007b)

Huari (LIP) 17 12 0.17 0.22 0.55 0.06 0.9412 0.0183 (Kemp et al., 2009)Ancient Highlands (LH) (35) n.d. 0.09 0.66 0.23 0.03 n.d. n.d. (Shinoda et al., 2006)Ancient NW-Argentina 19 9 0.11 0.47 0.00 0.42 0.9123 0.0177 (Carnese et al., 2010)

(EIP-MH)

Ancient Ancient North Coast (MH) (36) n.d. 0.19 0.22 0.06 0.31 n.d. n.d. (Shimada et al., 2004)Peruvian Coast Ancient South Coast (EH) 31 (38) 17 0.05 0.00 0.19 0.76 0.9527 0.0097 (Fehren-Schmitz et al., 2010)

Ancient South Coast (EIP) 61 (65) 43 0.02 0.14 0.30 0.54 0.9852 0.0140 (Fehren-Schmitz et al., 2010)

Northern Chibchan 80 7 0.81 0.01 0.18 0.00 0.6807 0.0098 (Melton et al., 2007)Colombia Arawaken 29 4 0.28 0.28 0.44 0.00 0.7734 0.0172 (Melton et al., 2007)

Total 109 10 0.67 0.08 0.25 0.00 0.8116 0.0144

Northern Ancash 33 27 0.09 0.52 0.18 0.21 0.9811 0.0168 (Lewis et al., 2005)Peru San Martin 21 14 0.10 0.57 0.05 0.29 0.9333 0.0211 (Fuselli et al., 2003)

Tupe 16 9 0.00 0.69 0.31 0.00 0.8667 0.0158 (Lewis et al., 2007b)Yungay 36 20 0.03 0.47 0.36 0.14 0.9540 0.0158 (Lewis et al., 2007b)Total 106 68 0.06 0.55 0.23 0.17 0.9842 0.0205

Southern Arequipa 22 18 0.09 0.68 0.14 0.09 0.9784 0.0164 (Fuselli et al., 2003)Peru Puno (Quechua) 30 22 0.07 0.60 0.23 0.10 0.9724 0.0152 (Lewis et al., 2007b)

Puno (Aymara) 14 11 0.00 0.71 0.14 0.14 0.9670 0.0146 (Lewis et al., 2007b)Tayacaja 59 40 0.22 0.34 0.31 0.13 0.9655 0.0195 (Fuselli et al., 2003)Total 125 84 0.14 0.50 0.24 0.12 0.9857 0.0196

South Middle Mapuche 34 (111) 9 0.00 0.07 0.44 0.49 0.8378 0.0156 (Moraga et al., 2000)Chile Pehuenche 24 (105) 13 0.03 0.11 0.41 0.46 0.9022 0.0164 (Moraga et al., 2000)

Total 58 20 0.01 0.09 0.43 0.47 0.8748 0.0159

Tierra del Fuego Yaghan 15 (21) 7 0.00 0.00 0.48 0.52 0.8857 0.0151 (Moraga et al., 2000)Amazonia Gaviao 27 7 0.15 0.15 0.00 0.70 0.8661 0.0117 (Ward et al., 1996)

Xavante 25 4 0.16 0.84 0.00 0.00 0.6767 0.0082 (Ward et al., 1996)Yanomamo 155 6 0.00 0.56 0.32 0.12 0.6566 0.0122 (Williams et al., 2002)Zoro 30 9 0.20 0.07 0.13 0.60 0.7747 0.0108 (Ward et al., 1996)Total 240 20 0.06 0.49 0.22 0.23 0.8405 0.0140

Bolivian Lowlands Bolivian Lowlands (Pool) 53 32 0.18 0.24 0.50 0.06 0.9710 0.0167 (Bert et al., 2004)Gran Chaco Gran Chaco (Pool) 204 46 0.16 0.42 0.13 0.28 0.9371 0.0197 (Cabana et al., 2006)

1n = number of typed individuals. Numbers in brackets are for individuals with only mt-haplogroup information.2H = number of different haplotypes in population.3Hd = haplotype diversity.4π = nucleotide diversity.



were calculated (Tajima, 1983; Nei, 1987) and biologicaldistances between the populations and groups were esti-mated from mtDNA sequence data. Pairwise FST values,derived Slatkin distances for populations with short diver-gence times (Slatkin, 1995), Nei’s genetic distance D (Nei& Li, 1979), and also the diversity indices were calcu-lated using Arlequin 3.11 (Excoffier et al., 2005). Geneticdistances between haplotypes and mean distances betweengroups were calculated employing the Tamura and Nei dis-tance model with gamma correction (Tamura & Nei, 1993)with the suggested gamma value of 0.26 for mitochon-drial HVR1 data (Meyer et al., 1999). To visualise ge-netic relationships, we performed Multidimensional Scal-ing (MDS) based on the distance matrices using Statistica8 (StatSoft). Exact tests of population differentiation (Goudetet al., 1996), and an analysis of molecular variance (AMOVA)simulating different groupings, were also performed with Ar-lequin. All described tests were performed with at least 10,000permutations.

Results

Mitochondrial DNA

From a total of 90 individuals (incl. Pacpapaccari) it was pos-sible to reproducibly determine the mt-haplogroups of 72individuals (65%) through the analysis of coding region poly-morphisms and to obtain the mt-haplotypes (complete 388bp HVR1 sequence) of 65 individuals (59%). The success-

fully analysed samples distribute over all archaeological sitesthat have been sampled. The preservation of mtDNA in thesamples from Yacotogia, Pacapaccari, and Layuni proved tobe very good. In general, if results could not be reproduced,the individuals were discarded from the data set. Through-out the study, negative extraction and negative PCR controlsconsistently showed negative results.

Haplogroup distribution for the total upper valley samplewas as follows: 5.6% A, 55.6% B, 33.3% C, and 5.6% D.The detailed mt-haplogroup frequencies for the ancient pop-ulations analysed and the consulted reference populations,grouped into major chronological and spatial divisions, arefound in Table 5. In all six populations analysed haplogroupB is predominant followed by haplogroup C. There are over-all low frequencies of haplogroup A except in Ocoro andHuayuncalla (20%) but the frequencies here are biased throughthe very low sample size (n = 5). Haplogroup D is missing inthe MH sample but present in moderate to low frequenciesin the LIP individuals from Botigiriayocc and Layuni (25%,11%). The plot of the correspondence analysis among theanalysed and the ancient and contemporary reference popu-lations (Table 5) is found in Figure 2. Two clusters are formed,one consisting of the analysed highland sites and other ancientand contemporary Central Andean populations but also theGran Chaco and Amazonia populations. The other cluster isformed by the three ancient coastal populations from Peruand the contemporary populations of southern and southern-most Chile. The populations from Northern Columbia forma distant outgroup.

Figure 2 Correspondence analysis plot based on mitochondrial haplogroup frequencies.



The HVR1 sequence data from the 65 successfully typedindividuals could be assigned to 33 mitochondrial haplo-types (H) segregated through 39 polymorphic sites (S). Allhaplotypes could be assigned to one of the four NativeAmerican founding haplogroups A, B, C, and D indepen-dently confirmed through the coding region polymorphisms.A summary of the determined mt-haplotypes spanning 388bp of the HVR1 (np 16,021–16,408) and their distributionover the different sites and chronological periods is found inTable 6. The Native American founding haplotypes (Tamm etal., 2007) are the most frequent for every haplogroup (A2 =AT-A1; B2 = AT-B1/2; C1 = AT-C1; D1 = AT-D1). Thereare 19 singleton haplotypes and eight typed for one site inmore than one individual. Six haplotypes—four of them dis-tinct haplotypes different from the founder haplotypes—areshared between different archaeological sites, and five (two)of them are also shared between the two time periods MHand LIP. The comparison with the whole dataset (cf. Ta-ble 5 for citations) reveals exact haplotype matches betweenAT-B13 and one individual from the Gran Chaco, AT-C5and three individuals from Amazonia, AT-C7 and individualsfrom the Bolivian lowlands, Gran Chaco, and South MiddleChile, and between AT-C11 and one individual from Ama-zonia. Only the distinct haplotype AT-B12 is shared with theEarly Intermediate Period (EIP) coastal population at the An-dean foothills downstream of the river valleys. All obtainedhaplotypes were also cross-checked with the available datasetsof Native American and Asian populations and the Geno-graphic Project mitochondrial database (Behar et al., 2007).The haplotype AT-D2 is missing the haplogroup characteris-tic C-T transition at np16223 typical for haplogroup D. Thehaplogroup status was independently confirmed by codingregion.

The genetic diversity measures calculated from theHVR1 sequence data are found in Table 5. All anal-ysed pre-Columbian samples from the Central Andean re-gion show a high level of genetic diversity (Hd = MH0.9145; LIP 0.9730) congruent with those from the anal-ysed contemporary Andean populations (Hd = 0.8667 –0.9811).

Interpopulation genetic distances (sequence data) were cal-culated on two levels: comparing the single analysed pre-Columbian populations with other ancient and contemporaryCentral Andean populations (Table 7) and comparing ancientand contemporary populations grouped by chronological andmajor geographical origin on the continental scale (Table 8).Different distance calculations have been performed showingconcordant results (not all data shown). All sampled archaeo-logical sites exhibit a low genetic distance between each otherand the ancient and modern Central Andean populations. Anexact test of differentiation between each pair of populations

revealed that most distances between the analysed upper valleysites and the other populations are not significant (significancelevel: 0.0500) except with the ancient populations from thesouth coast, e.g., Yacotogia to Ancient South Coast—EIP(P = 0.00098 ± 0.0010 SD). However, it has to be consid-ered that the nonsignificance especially for sites like Ocoro,Huayuncalla, and Layuni is due to small sample size (cf. Table5). An MDS plot employing the pairwise FST matrix of thegroup comparison (Table 8) is shown in Figure 3. The plothas a reasonable stress level of 0.082, falling below the upperbound of 0.199 (Sturrock & Rocha, 2000), indicating that theplot is an accurate representation of these data with the orig-inal distance matrix. As in the single site comparison there isa very low and nonsignificant genetic distance between thepooled MH and LIP populations (pairwise FST = 0.0018,P = 0.32617 ± 0.0140 SD) and low distances between theancient and the contemporary Central Andean populations,except with the ancient coastal populations (Table 8). Thesepopulations exhibit the lowest distances to the contemporarypopulations of Southern Chile and Tierra del Fuego but onlythe latter cluster together with the ancient coastal populationsin the MDS plot (Fig. 3).

The AMOVA calculations show lower variation betweenthe ancient highland groups and contemporary Peruviangroups than between the ancient coastal groups and the an-cient highlands and contemporary Peruvians (Table 9) Whenthe contemporary Peruvian datasets and the ancient high-land datasets in aggregate are compared to the Amazoniandatasets in aggregate, the percentage between them is nearzero and �CT is nonsignificant. The percentage betweenancient highlands and the aggregated populations of southernand southernmost Chile is only slightly higher than betweenthe ancient coastal populations and the Chileans. Howeverthe associated �CT estimate for the ancient coast and Chilecomparison is nonsignificant (P = 0.11327 ± 0.00280) after10,000 permutations.

Y-Chromosomal DNA

It was possible to reproducibly determine Y-chromosomalhaplogroups for 19 individuals (Pacapaccari = 5; Botigiriay-occ = 5; Layuni = 3, Yacotogia = 6). All individuals belongto haplogroup Q1a3a∗. Only individuals where it was possi-ble to determine the full profile of six SNPs are consideredhere. There is a high number of individuals showing allelicdropout, presumably due to DNA degradation, for one ormore SNPs. For two individuals with realised polymorphismsin M242 and M3, only M19 could not be typed, so there isa chance that these individuals could belong to haplogroupQ1a3a2 and not Q1a3a∗.



Tab

le6

Mito

chon

dria

lHV

R1

hapl

otyp

esan

dth

eir

chro

nolo

gica

ldist

ribu

tion

obse

rved

inth

ean

cien

tpe

ruvi

anpo

pula

tions

anal

ysed

inth

isst

udy



Tab

le7

Pair

wise

F ST

valu

esfr

omH

VR

1se

quen

ceda

tabe

twee

nea

chsin

gle

pair

ofan

cien

tan

dm

oder

npe

ruvi

anpo

pula

tions

12

34

56

78

910

1112

1314

1516

17

1La

yuni

(LIP

)2

Bot

igir

iayo

cc(L

IP)

0.00

00∗

3Pa

capa

ccar

i(LI

P)0.

0062

∗0.

0399

∗

4H

uari

(LIP

)0.

0593

∗0.

0069

∗0.

1399

5Y

acot

ogia

(MH

)0.

0000

∗0.

0054

∗0.

0059

0.07

47∗

6C

onch

apat

a(M

H)

0.00

00∗

0.00

00∗

0.00

00∗

0.05

50∗

0.00

00∗

7O

corr

o(M

H)

0.00

00∗

0.00

00∗

0.00

00∗

0.07

47∗

0.04

68∗

0.00

00∗

8H

uayu

ncal

la(L

IP)

0.00

05∗

0.00

00∗

0.00

00∗

0.00

00∗

0.02

47∗

0.00

00∗

0.00

00∗

9A

requ

ipa

0.07

37∗

0.07

02∗

0.12

890.

1802

0.10

450.

0617

∗0.

0374

∗0.

1346

∗

10Sa

nM

artin

0.14

910.

0712

∗0.

2507

0.15

410.

2346

0.10

980.

0000

∗0.

1059

∗0.

1212

11T

ayac

aja

0.09

730.

0196

∗0.

1914

0.06

840.

1475

0.07

180.

0000

∗0.

0524

∗0.

0736

0.01

77∗

12A

ncas

h0.

0409

∗0.

0084

∗0.

1243

0.07

430.

0806

0.03

38∗

0.00

00∗

0.05

89∗

0.00

79∗

0.06

640.

0137

13Pu

no(Q

echu

a)0.

0553

∗0.

0356

∗0.

1024

0.08

840.

0614

0.05

25∗

0.06

22∗

0.07

10∗

0.01

60∗

0.13

120.

0659

0.00

57∗

14Y

unga

y0.

0431

∗0.

0129

∗0.

1467

0.05

750.

0801

0.07

330.

1142

∗0.

0851

∗0.

0516

0.11

190.

0402

0.00

57∗

0.03

08∗

15Tu

pe0.

0693

∗0.

0808

∗0.

0885

∗0.

1346

0.07

23∗

0.08

57∗

0.14

98∗

0.11

93∗

0.02

86∗

0.19

090.

1205

0.03

91∗

0.01

33∗

0.04

53∗

16Pu

no(A

ymar

a)0.

0496

∗0.

0434

∗0.

1345

0.12

140.

0935

0.05

93∗

0.06

84∗

0.08

92∗

0.01

350.

1325

0.06

950.

0000

∗0.

0000

∗0.

0457

∗0.

0410

∗

17A

ncie

ntSo

uth

0.46

280.

2333

0.70

790.

2125

0.52

710.

4502

0.53

720.

4148

0.53

730.

2425

0.14

560.

2833

0.43

300.

2684

0.61

960.

4831

Coa

st(E

H)

18A

ncie

ntSo

uth

0.19

960.

0757

0.37

620.

0679

0.26

230.

2271

0.25

720.

1752

0.30

170.

1603

0.08

110.

1465

0.23

150.

1216

0.30

910.

2451

0.02

37C

oast

(EIP

)

∗ FST

P-v

alue

indi

cate

sth

atge

netic

dist

ance

inth

eco

mpa

riso

nis

not

stat

istic

ally

signi

fican

t.



Table 8 Pairwise FST values from HVR1 sequence data between each pair of chronologically and geographically grouped population

1 2 3 4 5 6 7 8 9 10 11 12

1 North Peru2 South Peru 0.0000∗

3 Ancient PeruvianHighlands (LIP)

0.0455 0.0514

4 Ancient PeruvianHighlands (MH)

0.0646 0.0690 0.0018∗

5 Bolivian Lowlands 0.0775 0.0889 0.0327 0.09366 Gran Chaco 0.0632 0.0548 0.0429 0.0490 0.09667 South Middle Chile 0.0549 0.0628 0.0700 0.1247 0.0524 0.08618 Tierra del Fuego 0.2060 0.2347 0.1995 0.3157 0.1264 0.2067 0.08029 Amazonia 0.0277 0.0298 0.0196 0.0295 0.0836 0.0435 0.0756 0.250710 Northern Colombia 0.3234 0.2980 0.3800 0.4726 0.2836 0.2889 0.3406 0.4980 0.414211 Ancient South Coast

(EH)0.2499 0.2636 0.2834 0.4714 0.1829 0.2102 0.1769 0.1205 0.3002 0.4052

12 Ancient South Coast(EIP)

0.1381 0.1565 0.1378 0.2558 0.0668 0.1380 0.0866 0.0626 0.1602 0.3805 0.0228∗

13 AncientNW-Argentina

0.1014 0.1025 0.1057 0.1150 0.1953 0.0721 0.1702 0.3143 0.0951 0.4484 0.3763 0.2410

∗ FST P-value indicates that genetic distance in the comparison is not statistically significant.

Figure 3 MDS plot based on pairwise FST values derived from mitochondrial HVR1sequences. Raw stress was 0.082. NC = Northern Columbia; AH–MH = AncientHighlands –Middle Horizon; AH–LIP = Ancient Highlands–Late Intermediate Period;NP = North Peru; SP = South Peru; GC = Gran Chaco; AA–EIP = AncientArgentina; LB = Bolivian lowlands; SC = southern Chile; TdF = Tierra del Fuego;ASC–EIP = Ancient South Coast–Early Intermediate Period; ASC–EH = AncientSouth Coast–Early Horizon.



Table 9 AMOVA percentage of variation and fixation indexes

Molecular variation (%) Fixation index

Variation Variation among Variationbetween populations within within

Comparison groups groups populations �CT �SC �ST

Ancient Highlands to Ancient Coast 17.61 2.68 79.71 0.176 0.032∗ 0.203Ancient Highlands to Peru 5.08 3.67 91.25 0.051 0.037 0.087Ancient Coast to Peru 13.40 4.04 82.56 0.134 0.047 0.174Ancient Highlands to Southern Chile 9.23 2.45 88.32 0.092 0.027∗ 0.117Peru to Southern Chile 6.13 4.27 89.60 0.061 0.046 0.104Ancient Coast to Southern Chile 6.83 4.01 89.16 0.068∗ 0.043 0.108Ancient Highlands to Amazonia −3.87 19.47 84.40 −0.039∗ 0.187 0.156Peru to Amazonia −1.22 14.38 86.83 −0.012∗ 0.142 0.132Ancient Coast to Amazonia 8.66 19.67 71.68 0.087∗ 0.215 0.283

∗ Fixation indexes found to be not-significant (P values < 0.05) after 10.000 permutations

Discussion

DNA preservation for most studied sites proved to be good.This is consistent with other aDNA studies on Andean high-land populations and differs from data obtained at soil burialsfrom coastal sites. The relatively high success rate of DNAextraction was likely related to the burial environment. Mostsamples derive from caves and Chullpas lined of stone withno soil contact and stable microclimatic conditions (humid-ity and low temperatures). Such cave-like burial conditionsproved to be best for DNA preservation (Burger et al., 1999).The samples from Ocoro derive from a collapsed Chullpa.Samples were covered by soil and were not imbedded inthe microclimatic conditions described above, explaining thelower success rate.

The MH and LIP populations from the upper valley can-not be statistically differentiated at the mitochondrial hap-logroup or haplotype level. At the haplogroup level the onlydiachronic difference can be seen in the lack of haplogroupD in the MH. But since it occurs in LIP also only in a verylow frequency (10%) this difference could be due to samplingartefacts or may be biased due to the low sample size. Onthe haplotype level distances are very low and nonsignificantand MH and LIP sites share unique haplotypes. Thus, theMH populations cannot be excluded as being matrilineallydirectly ancestral to the LIP populations. Since all individu-als successfully typed for Y-chromosomal haplogroups belongto Q1a3a∗ also no patrilineal discontinuity can be observed.When compared to other populations of similar antiquity inthe Andes from the Ayacucho Basin (Kemp et al., 2009),Chen Chen in South Peru (Lewis et al., 2007a), and theCusco area (Shinoda et al., 2006) and modern Peruvian pop-ulations on the haplogroup level (cf. Table 5), homogeneity ofthe Central Andean populations at least from the MH on canbe suggested. This conclusion is validated through the inter-

population comparison of HVR1 sequence data with ancientand modern Peruvian populations mostly showing low FST

values (cf. Tables 7 & 8). For the low genetic distances be-tween ancient and modern populations genetic drift cannot berejected as causal (Lewis, 2009a). Since there are no earlier ge-netic dates from the highlands, preceding population dynamicevents or population replacements cannot be disproved. Themitochondrial data can be interpreted as evidence for spatialmatrilineal continuity throughout the Andes, including theupper valley of the western slopes, from the MH to moderntimes as suggested before (Lewis et al., 2007a; Kemp et al.,2009; Lewis, 2009a), although it is possible that the observedhomogeneity throughout the Andes makes it impossible todetect discontinuities without much higher sample sizes andhigher resolution genetic data.

The upper valley populations dating to the MH and LIPdiffer significantly from the EH and EIP coastal populations(Fehren-Schmitz et al., 2010) on mitochondrial haplogroupand haplotype level (cf. Tables 5 & 7) even if there is only a lowgeographic distance of 50 km. AMOVA calculations show avariation between the groups of 17.61%. Even though thereis evidence from archaeology for strong cultural links betweenboth areas (Reindel & Gruen, 2006; Reindel, 2009a, 2009b)population biological exchange and accordingly gene flowseems to be marginal, but it has to be taken into account thatthis assumption results from an asynchronous comparison. Ifwe assume coastal and highland populations as distinct ac-cording to the major cultural zones, the pre-Columbian pop-ulations of the upper valleys belong to the highland sphereof population biological influence. There are no diachronicchanges accompanying the cultural changeover in the uppervalley research area from Wari highland influence in the MHto coastal influence in the LIP that can be seen in the mi-tochondrial data. However, to date there is no genetic datafor the LIP and only insufficient data for the MH from the



coast, so there is no opportunity for direct synchronous com-parisons. Since the comparisons of the MH and LIP highlandpopulations to modern Peruvian populations show only lowgenetic distances (Table 7) and there is no detectable geneticdistinction of modern Peruvian coastal and highland popula-tions there must have been population dynamic processes thatled to the homogenisation of the Central Andean populationstarting at the earliest in the LIP. A possible factor could be theexpansions of the vast reaching highland empires, primarilythe Incas. However, to validate such hypotheses more geneticdata from coastal populations dating to the LIP and LH wouldbe needed.

A possible model of explanation for the phenomenon thatthe upper valley populations show no signs of intermixturewith the proximate coastal populations, even if there weremigrations from the coast to the highlands in the late EIP, canbe based on physical stressors affecting unadapted humans ataltitudes above 2500 m (Fehren-Schmitz et al., 2009). Thereis evidence from historical sources and high altitude medicinethat unadapted women coming to the highlands have a signif-icant higher chance of stillbirth than women adapted to highaltitude habitats (Moran, 2000; Moore et al., 2004; Gonzaleset al., 2008). Even if the coastal women adapt to the physicalstressors in the highlands after a period of time it could besuggested that they have a quantitative reproductive disadvan-tage compared to the women living in the highlands sincebirth (Gonzales & Tapia, 2009). Thus, the demographic ma-ternal influence on the populations above 2500 m is too lowto have a significant impact on the mitochondrial haplogroupfrequencies. But to validate this hypothesis exact numericaldata on the possible reproductive disadvantage and comparabledemographic data for the pre-Columbian coastal and uppervalley populations would be needed to do statistical simula-tions for the possible scenarios. Neither, exact informationregarding the reproductive chance of unadapted women inhigh altitude habitats, nor exact demographic information isaccessible at the moment.

When compared on the continental scale at the hap-logroup level the ancient coastal populations show a highaffinity—high frequencies of haplogroup D and low fre-quencies of haplogroup B—to modern indigenous pop-ulations of southern Chile and Tierra del Fuego (Mor-aga et al., 2000). This could be interpreted in termsof the hypothesis that the South American West Coastand the Andean highlands have a differential coloniza-tion history (Rothhammer et al., 2001). Rothhammer andcolleagues postulated that the Andean Highlands mighthave been colonized from the eastern Amazonian regions,while the coastal populations derive from another routedirectly following the coastline. When Amova and FST(cf. Tables 8 & 9) calculations based on sequence data areconsidered the ancient and modern Andean populations ex-

hibit a higher affinity to the modern Amazonian populations(FST = 0.0196–0.0298) than to the ancient coastal popu-lations (FST = 0.2499–0.4714). However, the variation be-tween modern Peru and the Chilean populations and ancientCoast and the latter are nearly exact (Table 8) as are thepairwise FST values between ancient coast and south-middleChile and between modern Peru and the latter. Thus onthe haplotype level there is little or no higher affinity of theancient coastal populations to the Chilean populations thanin the modern Peru-Chile comparison. The higher affinityin the Central Andean-Amazonia comparison is likely to bebiased through the highly heterogenic character of the em-ployed Amazonian population pool. The single populationsintegrated in the pool are geographically and culturally dis-tinct and show significant genetic differentiation if comparedto each other (Ward et al., 1996; Williams et al., 2002). On thebasis of the existing data a distinct colonization of the AndeanHighlands and the Pacific Coast cannot be validated as modelof explanation for the genetic differentiation of our coastal andupper valley populations. However, the route along the coast issupported through the distribution of the mt-haplotype D4h3found only in western South America in Ecuador and coastalnorth Peru and the indigenous populations of southern Chile(Perego et al., 2009) similar to the observed haplogroup pat-terns described before. That the differentiation between coastand highlands is not only a local phenomenon is also sup-ported through ancient DNA data from a MH populationof the Peruvian north coast (Shimada et al., 2004) showingsimilar haplogroup frequencies and clustering together withthe south coast populations in the correspondence analysis(Fig. 2). Unfortunately only mitochondrial haplogroup fre-quencies are known for this site. On the other hand genomewide STR comparison between western and eastern SouthAmerica are inconsistent with an initial west coast only mi-gration into the continent (Lewis, 2009b) correlating witha possible colonisation of the Andean region from the east.The encountered genetic variability in the continent can bebest explained with a single founding population (Fuselli etal., 2003; Lewis et al., 2007b; Wang et al., 2007; Lewis &Long, 2008). The previously formulated possibility that west-ern and eastern populations result from two founding pop-ulations entering South America separately and at differenttimes (Tarazona-Santos et al., 2001) can be rejected on thebasis of recent data. Populations from eastern and westernSouth America show nearly similar degrees of genetic vari-ability and the overall variability is too low to be explainedby several migrational waves (Lewis & Long, 2008; Lewis,2009b). But problems for the evaluation of population dy-namic processes can also result from the simplified geograph-ical and cultural assignments of populations to groups, as inthe simplified east-west distinction. For the east Rothham-mer et al. (2001) already doubted that the populations form a



cohesive group. The results of this study suggest that the westhas not always been as genetically homogenous as it is seen tobe in modern central Andean populations. Therefore it hasto be taken into account that results from studies compar-ing modern indigenous populations from South America toreveal the initial processes of peopling are biased through pre-historic population dynamic processes resulting in significantpalimpsest effects. Thus, the higher affinity of pre-Columbianwest coast populations and the distinction to ancient highlandand modern Central Andean populations must be founded inearlier population dynamic processes, e.g., the initial peopling,and the modern overall homogeneity observed in Central An-dean populations (coast and highlands) is most likely to be aproduct of later cultural and political processes like the ex-pansions of the great highland empires (Shinoda et al., 2006;Fehren-Schmitz et al., 2009). Correlating the genetic infor-mation and the archaeological data, a scenario suggesting asingle population entering the continent from the Isthmus ofPanama and then splitting up into two or more main routes isconceivable. One group might have followed a route along thewest coast, and another moved along the north coast and theninto the Amazonian Basin or maybe along the eastern slopesof the Andes, subsequently peopling the Andean highlands.This model, similar to the one suggested by Rothhammer etal. (2001), seems plausible but is statistically incapable of proofon the basis of recent data.

Conclusion

Based on the genetic comparisons there are no signs for matri-lineal and patrilineal discontinuities in the upper valley pop-ulations investigated in this study. Cultural dynamic processesand the change of cultural influence spheres in the transitionfrom MH to LIP did not visibly affect the populations liv-ing in the fringe area between the coast and the highlands.The examined populations group together with ancient andmodern Central Andean highland populations. At all the di-achronic comparisons suggest a very homogenous picture ofgenetic variability in the highland populations. The signifi-cant difference to the ancient coastal populations cannot besatisfactorily explained to date. Models are only based on plau-sibility but there is no chance for statistical validation. Overallit can be stated that the cultural and demographic history ofSouth America is too complex to be reconstructed only frommodern indigenous populations. More aDNA data from pop-ulations dating to the earliest periods of human presence inSouth America deriving from coastal and highland sites areneeded to get a gapless diachronic portrait of genetic vari-ability. With the existence of this data it would be possible toperform genetic modelling under consideration of diverse de-mographic scenarios (Anderson et al., 2005) helping to make

definite decisions regarding the processes involved in the peo-pling of the continent and the impact of subsequent culturallyand ecologically dynamic processes on human genetic distri-bution patterns.

Acknowledgements

The authors greatly thank the German Federal Ministry of Ed-ucation and Research for funding (Grant number: 01UA0804B)our research and all Peruvian and German colleagues in theresearch group “Andentransekt – Climate sensitivity of pre-Columbian Men-Environment-Systems” for the scientific col-laboration, as well as Mr. Matthew Rector for proofreading themanuscript. We also thank the Peruvian Institute for CulturalHeritage (Instituto Nacional de Cultura del Peru) for granting usthe exportation permission for the sample material (ResolucionDirectoral Nacional No. 1346).

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Received: 15 May 2010Accepted: 17 August 2010


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