If you are using this database, please, consider citing our paper: AmtDB: a database of ancient human mitochondrial genomes
As of 2018, we are starting the database, so the updates will be quite frequent, as we are adding more and more samples. After the initial operation phase (2019+), we plan to have two major updates annually (with possible additional smaller updates if needed). You can always check the database log.
Our database provides full mitochondrial sequences and descriptive metadata for samples coming from prehistoric and early historic populations. Generally speaking, our samples are from the first milliennium CE (Common Era, AD, after Christ) or older. Vast majority of our samples comes from the prehistoric populations of Neolithic and Bronze Age. Most of the samples originate in Europe, but we have also samples from central Asia, Middle East, Near East or Africa. In the future, we will bring you more non-European samples, as more of them will get analyzed and published.
Good job! It would help us if you could, please, report the bugs/errors using one of the e-mails on contact page. Thank you!
We provide the complete mitochondrial genome sequences in FASTA format, together with sample description metadata. Our data are only provided for research of human ancient population life-histories and their relations and connections with modern populations. Please note that we are not responsible for any use of provided data.
Sure, if you have ancient human full mtDNA sequences! Please, contact us by e-mail.
We are constatly adding new samples into the database, but if you want some specific samples added, please let us know! In an ideal case, those samples were already published and their sequences deposited in GenBank, ENA, SRA or similar database.
This information can be found in the Docs section.

AmtDB documentation

Here you can find the documentation for metadata provided and the mitochondrial genome sequences stored in the database.

  • id - primary identifier of the sample
  • id_alt - secondary identifier(s) of the sample (is case it was also published under other name(s))
  • country - country of origin
  • continent - continent of origin
  • geo_group - more general geographic group of the sample. So far we use these geo_groups: Altai, Anatolia, Balkans, Baltic, British Isles, Caucasus, Iberia, Middle East, Near East, Pannonia, Pontic steppe, Scandinavia, Siberia, central Europe, eastern Africa, southern Europe, western Europe.
  • culture - archaeological classification of the sample
  • epoch - rough time/culture identifier. Categories used: Aurignacian, Bronze Age, Copper Age, Epigravettian, Epipaleolithic, Gravettian, Iron Age, Mesolithic, Neolithic, and Upper Paleolithic.
  • group - we use these acronyms to classify samples into different groups. They are generally based on culture, ethnicity, epoch, geo_group or any combination thereof. It the acronym has 3 letters, then it is based predominately on culture. If the acronym has 4+ letters, then it contains information about epoch and location of the sample, and its meaning can be decoded as follows:
    XXYY, where XX = epoch (BA - Bronze Age, CA - Copper Age, EBA - Early Bronze Age, IA - Iron Age, LNE - Late Neolithic, MA - Middle Ages, ME - Mesolithic, NE - Neolithic), and YY = location, if used in capital letters - based on geo_location (AF - Africa, BA - Balkans, BI - British Isles, CA - Caucasus, IB - Iberia, ME - Middle East, NE - Near East, SC - Scandinavia); or if ending with lower case letter - based on the country of origin (Cz - Czech Republic, Ge - Germany, Gr - Greece, Hu - Hungary, It - Italy, Nl - Netherlands, Pl - Poland, Ru - Russia, Uk - Ukraine)
  • comment - place for general comments, can contain uncertainties, additional sample info, relationship between samples, etc.
  • latitude - geographical coordinates, decimal degrees
  • longitude - geographical coordinates, decimal degrees
  • sex - male (M)/female (F)/unknown (U), ideally based on the amount of Y chromosomal DNA from the sample, but we are not distinguishing between sex based on genetic, bone anthropology, or archaeological evidence.
  • site - archaeological site where the sample was excavated
  • site_detail - more details about the site, where available
  • mt_hg - mitochondrial haplogroup
  • ychr_hg - Y chromosomal haplogroup, where available
  • ychr_snps - mitochondrial haplogroup

Following variables all concern the dating of the sample. This kind information about each sample varies considerably in its completeness and available form. We are struggling to keep as much information in the database as possible and provide parsed data for quick and comfortable use. In general, we will use calibrated BCE or CE values, wherever possible. If the sample has radiocarbon dating (14C), we provide this information as well.

  • year_from - lower limit of sample age (of 95.4% probability interval for 14C samples), if starting with '-', then it refers to BCE, positive values refer to CE
  • year_to - upper limit of sample age (of 95.4% probability interval for 14C samples), if starting with '-', then it refers to BCE, positive values refer to CE
  • date_detail - string with information about the sample age, taken directly from the source publication
  • bp - uncalibrated age for 14C samples, numbers of years before 1950
  • c14_lab_code - for 14C samples, their radiocarbon laboratory code
  • reference_name - source reference of the sample
  • reference_link - DOI based link of the reference
  • data_link - GenBank, ENA, SRA, or other depository link, where the sequence is available
  • c14_sample_tag - 1 if sample was radiocarbon dated, 0 otherwise
  • c14_layer_tag - 1 if another sample in the same statum was radiocarbon dated, 0 otherwise. The sample can't have '1's in both c14_sample_tag AND c14_layer_tag. If both tags equal to 0, then the sample was dated by other methods only, most probably according to the archaeological culture

In our MitoPathoTool and database entries, we are using data and description of mitochondrial pathological mutations from MITOMAP project, although it is slightly adjusted for our needs.

  • mitopatho_alleles - Allele value is composed of position and change against the revised Cambridge reference sequence (Andrews et al., 1999). Generally, it has the shape of XXXY, where XXX are numbers (position of the allele on mtDNA sequence and Y is the changed sequence). Usually in the form of SNP (i.e. 5587C), or a small indel (letter d marks the deletion) (7472CA, 15944d, or 16021_16022dCT - dinucleotide deletion).
  • mitopatho_positions - Position of the pathological allele on mtDNA sequence, values between 1 and 16 569.
  • mitopatho_locus - Location of the pathological mutation according to functional composition of mtDNA. It can have these values: MT-CR, MT-ND1, MT-ND2, MT-CO1, MT-CO2, MT-ATP8, MT-ATP8/6, MT-ATP6, MT-CO3, MT-ND3, MT-ND4L, MT-ND4, MT-ND5, MT-ND6, MT-CYB, MT-TF, MT-RNR1, MT-TV, MT-RNR2, MT-TL1, MT-TI, MT-TQ, MT-NC2, MT-TM, MT-TW, MT-TA, MT-TN, MT-TC, MT-TY, MT-TS1 precursor, MT-TS1, MT-TD, MT-TK, MT-TG, MT-TR, MT-TH, MT-TS2, MT-TL2, MT-TE, MT-TT, MT-TP.
    MT-CR is the mitochondrial Control Region, the other values mark the two rRNA genes (MT-R*), 22 tRNA genes (MT-T*), and 13 protein-coding genes (rest of the codes).
  • mitopatho_diseases - Short description or characterization of the pathological phenotype/disease/disorders associated with the allele.
  • mitopatho_statuses - Status of the pathological allele. We keep the MITOMAP description here, so the values of this field can be: Cfrm, Reported, Conflicting reports, P.M.-possibly synergistic, Unclear, Possibly synergistic, ... The most important (and serious) result is Cfrm (confirmed) status - it indicates that at least two or more independent laboratories have published reports on the pathogenicity of a specific mutation. These mutations are generally accepted by the mitochondrial research community as being pathogenic. Reported status indicates that one or more publications have considered the mutation as possibly pathologic. Please, note that several Reported statuses exist, with more details about the nature of the reported allele. P.M. (point mutation/polymorphism) status indicates that some published reports have determined the mutation to be a non-pathogenic polymorphism.
  • mitopatho_homoplasms - Homoplasmy, pure mutant mtDNAs. Possible values:
    • + = True
    • - = False
    • nr = Not Reported
    • nan = Missing data, status unknown
  • mitopatho_heteroplasms - Heteroplasmy, mixture of mutant and normal mtDNAs. Possible values:
    • + = True
    • - = False
    • nr = Not Reported
    • nan = Missing data, status unknown

Whenever it is possible to download the full mtDNA sequence from GenBank or other repository, we prefer this option. When only BAM/SAM files are provided (usually in ENA or SRA archives), we will use the following pipeline:

BWA software package version 0.7.829 is used to map merged reads as single-end reads against the revised Cambridge Reference Sequence (rCRS)[1, 2] (GenBank: NC_012920), with the non-default parameters -l 16500 -n 0.01 -o 2 -t 2. The ratio of reads mapping to Y and X chromosomes (Ry) (with mapping quality greater than 30) is calculated to assign molecular sex of individuals sequenced on the Illumina platform[3].

FASTX-Toolkit is used to demultiplex sequences generated by PGM Ion Torrent, the scripts fastx_barcode_splitter.pl and fastx_trimmer (from the FASTX toolkit) are used to demultiplex the reads by barcode, using a one mismatch threshold. The Cutadapt v.1.8.133 is then used to remove the long (−M 110), short (−m 35), and low-quality sequences (−q 20). The filtered reads are analyzed with FastQC v0.11.334 using the options described previously by [4]. The sequences are mapped against the rCRS using TMAP v3.4.136. To collapse duplicate sequence reads with identical start and end coordinates (for both PGM and Illumina sequence data) we use FilterUniqueSAMCons.py script[5]. Misincorporation patterns are assessed with the use of mapDamage v2.0.537. Consensus sequences are build using ANGSD v0.91038. We accept only reads with mapping score of 30, a minimum base quality of 20, and a minimum coverage of 3 as in [4]. Where necessary, mitochondrial haplogroups (mt hgs) are assigned for each individual with the use of HAPLOFIND[6], the PhyloTree phylogenetic tree build 17[7] and Mitomaster[8].

References
  1. Anderson, S. et al. Sequence and organization of the human mitochondrial genome. Nature 290, 457–465 (1981).
  2. Andrews, R. M. et al. Reanalysis and revision of the Cambridge reference sequence for human mitochondrial DNA. Nat. Genet. 23, 147 (1999).
  3. Skoglund, P. et al. Accurate sex identification of ancient human remains using DNA shotgun sequencing. J. Archaeol. Sci. 40, 4477–4482 (2013).
  4. Chyleński, M. et al. Late Danubian mitochondrial genomes shed light into the Neolithisation of Central Europe in the 5th millennium BC. BMC Evol. Biol. 17, 80 (2017).
  5. Meyer, M. and Kircher, M. Illumina sequencing library preparation for highly multiplexed target capture and sequencing. Cold Spring Harb. Protoc. 2010, t5448 (2010).
  6. Vianello, D. et al. HAPLOFIND: a new method for high-throughput mtDNA haplogroup assignment. Hum. Mutat. 34, 1189–1194 (2013).
  7. van Oven, M. and Kayser, M. Updated comprehensive phylogenetic tree of global human mitochondrial DNA variation. Hum. Mutat. 30, E386–E394 (2009).
  8. Lott, M. T. et al. mtDNA Variation and Analysis Using Mitomap and Mitomaster. Curr. Protoc. Bioinforma. 44, 1.23.1-26 (2013).

Authors Year Title Journal Samples Links
Chyleński et al. 2019 Ancient Mitochondrial Genomes Reveal the Absence of Maternal Kinship in the Burials of Çatalhöyük People and Their Genetic Affinities Genes 10 paper
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González-Fortes et al. 2019 A western route of prehistoric human migration from Africa into the Iberian Peninsula Proceedings of the Royal Society B: Biological Sciences 17 paper
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Olalde et al. 2019 The genomic history of the Iberian Peninsula over the past 8000 years Science 271 paper
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Schroeder et al. 2019 Unraveling ancestry, kinship, and violence in a Late Neolithic mass grave Proceedings of the National Academy of Sciences 15 paper
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Vai et al. 2019 A genetic perspective on Longobard-Era migrations European Journal of Human Genetics 135 paper
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Amorim et al. 2018 Understanding 6th-century barbarian social organization and migration through paleogenomics Nature Communications 63 paper
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Damgaard et al. 2018 137 ancient human genomes from across the Eurasian steppes Nature 137 paper
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Emery et al. 2018 Ancient Roman mitochondrial genomes and isotopes reveal relationships and geographic origins at the local and pan-Mediterranean scales Journal of Archaeological Science: Reports 45 paper
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Harney et al. 2018 Ancient DNA from Chalcolithic Israel reveals the role of population mixture in cultural transformation Nature Communications 22 paper
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Jeong et al. 2018 Bronze Age population dynamics and the rise of dairy pastoralism on the eastern Eurasian steppe Proceedings of the National Academy of Sciences 22 paper
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Juras et al. 2018 Mitochondrial genomes reveal an east to west cline of steppe ancestry in Corded Ware populations Scientific Reports 24 paper
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Mathieson et al. 2018 The genomic history of southeastern Europe Nature 207 paper
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Mittnik et al. 2018 The genetic prehistory of the Baltic Sea region Nature Communications 37 paper
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Neparáczki et al. 2018 Mitogenomic data indicate admixture components of Central-Inner Asian and Srubnaya origin in the conquering Hungarians PLOS ONE 102 paper
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Olalde et al. 2018 The Beaker phenomenon and the genomic transformation of northwest Europe Nature 391 paper
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Stolarek et al. 2018 A mosaic genetic structure of the human population living in the South Baltic region during the Iron Age Scientific Reports 41 paper
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Veeramah et al. 2018 Population genomic analysis of elongated skulls reveals extensive female-biased immigration in Early Medieval Bavaria Proceedings of the National Academy of Sciences 39 paper
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Zalloua et al. 2018 Ancient DNA of Phoenician remains indicates discontinuity in the settlement history of Ibiza Scientific Reports 9 paper
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Chyleński et al. 2017 Late Danubian mitochondrial genomes shed light into the Neolithisation of Central Europe in the 5th millennium BC BMC Evolutionary Biology 6 paper
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Haber et al. 2017 Continuity and Admixture in the Last Five Millennia of Levantine History from Ancient Canaanite and Present-Day Lebanese Genome Sequences The American Journal of Human Genetics 5 paper
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Jones et al. 2017 The Neolithic Transition in the Baltic Was Not Driven by Admixture with Early European Farmers Current Biology 7 paper
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Juras et al. 2017 Investigating kinship of Neolithic post-LBK human remains from Krusza Zamkowa, Poland using ancient DNA Forensic Science International: Genetics 4 paper
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Juras et al. 2017 Diverse origin of mitochondrial lineages in Iron Age Black Sea Scythians Scientific Reports 19 paper
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Knipper et al. 2017 Female exogamy and gene pool diversification at the transition from the Final Neolithic to the Early Bronze Age in central Europe Proceedings of the National Academy of Sciences 84 paper
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Lazaridis et al. 2017 Genetic origins of the Minoans and Mycenaeans Nature 14 paper
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Lipson et al. 2017 Parallel palaeogenomic transects reveal complex genetic history of early European farmers Nature 54 paper
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Margaryan et al. 2017 Eight Millennia of Matrilineal Genetic Continuity in the South Caucasus Current Biology 44 paper
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Saag et al. 2017 Extensive Farming in Estonia Started through a Sex-Biased Migration from the Steppe Current Biology 10 paper
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Schuenemann et al. 2017 Ancient Egyptian mummy genomes suggest an increase of Sub-Saharan African ancestry in post-Roman periods Nature Communications 90 paper
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Tassi et al. 2017 Genome diversity in the Neolithic Globular Amphorae culture and the spread of Indo-European languages Proceedings of the Royal Society B: Biological Sciences 11 paper
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Unterländer et al. 2017 Ancestry and demography and descendants of Iron Age nomads of the Eurasian Steppe Nature Communications 97 paper
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Broushaki et al. 2016 Early Neolithic genomes from the eastern Fertile Crescent Science 5 paper
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Fu et al. 2016 The genetic history of Ice Age Europe Nature 10 paper
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Hofmanová et al. 2016 Early farmers from across Europe directly descended from Neolithic Aegeans Proceedings of the National Academy of Sciences 7 paper
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Jeong et al. 2016 Long-term genetic stability and a high-altitude East Asian origin for the peoples of the high valleys of the Himalayan arc Proceedings of the National Academy of Sciences 8 paper
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Kılınç et al. 2016 The Demographic Development of the First Farmers in Anatolia Current Biology 8 paper
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Lazaridis et al. 2016 Genomic insights into the origin of farming in the ancient Near East Nature 44 paper
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Omrak et al. 2016 Genomic Evidence Establishes Anatolia as the Source of the European Neolithic Gene Pool Current Biology 2 paper
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Ozga et al. 2016 Successful enrichment and recovery of whole mitochondrial genomes from ancient human dental calculus American Journal of Physical Anthropology 6 paper
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Allentoft et al. 2015 Population genomics of Bronze Age Eurasia Nature 110 paper
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Cassidy et al. 2015 Neolithic and Bronze Age migration to Ireland and establishment of the insular Atlantic genome Proceedings of the National Academy of Sciences 4 paper
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Günther et al. 2015 Ancient genomes link early farmers from Atapuerca in Spain to modern-day Basques Proceedings of the National Academy of Sciences 4 paper
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Haak et al. 2015 Massive migration from the steppe was a source for Indo-European languages in Europe Nature 85 paper
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Jones et al. 2015 Upper Palaeolithic genomes reveal deep roots of modern Eurasians Nature Communications 3 paper
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Llorente et al. 2015 Ancient Ethiopian genome reveals extensive Eurasian admixture in Eastern Africa Science 1 paper
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Mathieson et al. 2015 Genome-wide patterns of selection in 230 ancient Eurasians Nature 80 paper
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Olalde et al. 2015 A Common Genetic Origin for Early Farmers from Mediterranean Cardial and Central European LBK Cultures Molecular Biology and Evolution 1 paper
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Fu et al. 2014 Genome sequence of a 45,000-year-old modern human from western Siberia Nature 1 paper
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Gamba et al. 2014 Genome flux and stasis in a five millennium transect of European prehistory Nature Communications 12 paper
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Lazaridis et al. 2014 Ancient human genomes suggest three ancestral populations for present-day Europeans Nature 2 paper
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Olalde et al. 2014 Derived immune and ancestral pigmentation alleles in a 7,000-year-old Mesolithic European Nature 1 paper
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Skoglund et al. 2014 Genomic Diversity and Admixture Differs for Stone-Age Scandinavian Foragers and Farmers Science 4 paper
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Raghavan et al. 2013 Upper Palaeolithic Siberian genome reveals dual ancestry of Native Americans Nature 1 paper
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Keller et al. 2012 New insights into the Tyrolean Iceman's origin and phenotype as inferred by whole-genome sequencing Nature Communications 1 paper
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Krause et al. 2010 The complete mitochondrial DNA genome of an unknown hominin from southern Siberia Nature 1 paper
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AmtDB tools

MitoPathoTool

The MitoPathoTool is the result of our ongoing efforts to include more information about mitochondrial DNA functions, structure and its uses in our database. Today, pathological phenotypes caused by mutations in mtDNA genes and control region form a group of diseases and syndromes often called as mitochondrial diseases. The AmtDB content has been updated to include the information about pathological alleles in our samples' mtDNA sequences (you can check the sample details). MitoPathoTool is suited for analyzing your samples and annotating the pathological alleles. To realize these goals, we are using the list of mitochondrial pathological mutations published openly on the MITOMAP webpage (the current version r386 for the coding and control region mutations, and r382 for the rRNA/tRNA mutations). The list of alleles from MITOMAP database has been formatted for our needs, but the information content is unchanged.

How to use the MitoPathoTool
  1. Prepare your samples in HSD format. The easiest way is to run online version of the Haplogrep 2 software, where you can upload mtDNA sequences of your samples and get a HSD file. Another possibility is to install Haplogrep locally (e.g. from its GitHub repository) and run your HSD conversion offline.
  2. Copy-paste the file directly or upload it into our online version of the MitoPathoTool. Alternatively, you can use the offline version which can be downloaded here.
  3. The results (one line per found pathological locus) can be saved in several ways (Excel, PDF, CSV, copy to clipboard) or printed.
Certificate of maternal lineage

Our app can help you to search and compare your maternal lineage for locations and (pre-)historical epochs to find where and when the carriers of the same lineage have lived. To use this tool, you need to know your mtDNA haplogroup.

There are several commercial genealogical services/companies that you can find online, and that after receiving your biological sample, will sequence your mtDNA molecule and estimate your mitochondrial haplogroup. Mitochondrial haplogroups are classified into groups and sub-groups in several levels. The first level of classification is marked with major letter, i.e. haplogroup A, B, C, ... . In Europe, common major haplogroups are H, U, T, I, J. Sub-haplogroups are designated with subsequent numbers and lower letters. So haplogroup U can have sub-haplogroup U3, which again can have sub-haplogroup U3a, and again U3a1 -> U3a1a and so on. It is necessary to consider how detailed sub-haplogroup will be used to create the certificate. Let's say, your haplogroup is U3a1a3. For this detailed lineage, we don't have a direct hit in our database yet. You will need to use less detailed lineage. There are 29 U3 samples all over from Europe, north Africa and western Asia. If you choose U3a, you will find 12 samples ranging from Spain to Iran. For U3a1, there are 10 samples from south-western and central Europe. For haplogroup U3a1a the certificate app will find just 3 samples from Middle Ages Poland. As you can see, the level of detail can be adjusted to your needs, as should also help with interpretation of the results.

Please note that this search and certificate app will not find your direct ancestors/family. The results should be rather interpreted as places and times where the people from the same (or relative - depending on the haplogroup level used) maternal lineage lived and came from.

Chyleński, M., Ehler, E., Somel, M., Yaka, R., Krzewińska, M., Dabert, M., Juras, A. and Marciniak, A., 2019. Ancient mitochondrial genomes reveal the absence of maternal kinship in the burials of Çatalhöyük people and their genetic affinities. Genes, 10(3), p.207.

The article maps the important Anatolian archaeological site of Çatalhöyük in the Neolithic period and its connection to the Eastern Mediterranean on the one hand and the Levant on the other. Of interest is the great variability of local mtDNA haplogroups.

Juras, A., Makarowicz, P., Chyleński, M., Ehler, E., Malmström, H., Krzewińska, M., Pospieszny, Ł., Górski, J., Taras, H., Szczepanek, A. and Polańska, M., 2020. Mitochondrial genomes from Bronze Age Poland reveal genetic continuity from the Late Neolithic and additional genetic affinities with the steppe populations. American Journal of Physical Anthropology, 172(2), pp.176-188.

In this article we examined three archaeological cultures from Poland (Mierzanowice, Trzciniec, Strzyżow) and their mtDNA relationship to the original Neolithic population of central and eastern Poland and the whole of central Europe and to the migration wave from the Pontic Steppe, which came at the beginning of the Bronze Age. The populations associated with Mierzanowice culture and Trzciniec culture show a higher proportion of local (Neolithic) traits and are very similar, for example, to the populations of Corded Ware culture. People of Strzyżow culture, on the other hand, are more similar to eastern populations, such as the typically steppe culture of Yamnaya, which is now considered to be the main source of the steppe migration to the west.