Reply With QuoteHere are the samples arranged by their study in the 3D PCA.
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Dodecad Globe 13 Ancient & Modern
Christmas update!:
I have 2,441 aDNA coordinates for Dodecad Globe 13, from 41 academic studies.
I have also included the Modern Dodecad Globe 13 populations, with an expanded version for West Eurasians, with the individual HGDP samples.
Download the zip file here
Paste them into Vahaduo source tabs for analysis.
I have arranged them in three different formats.
One for the 3D PCA, all grouped as "aDNA" for clarity.
One divided by the study the samples are from.
One arranged by individual sample numbers.
Last edited by Jovialis; 25-12-20 at 16:49.
Here are the samples arranged by their study in the 3D PCA.
Dod K13
individual sample arrangement
... Thanks Jovialis :)
My Dod k13 (individual sample arrangement)
Distance to: Dodecad_Glob_13Stuvanè 4.08461749 I12221:Fernandes_et_al_2020 4.34099067 R1:Antonio_M_et_al_2019 5.09472276 R33:Antonio_M_et_al_2019 5.18898834 I6491:Olalde_et_al_2019 5.19982692 R55:Antonio_M_et_al_2019 5.21557284 scy300:Krzewińska_et_al_2018 5.21999042 scy305:Krzewińska_et_al_2018 5.62549553 scy192:Krzewińska_et_al_2018 5.62836566 I7041:Olalde_et_al._2018 5.67967429 scy197:Krzewińska_et_al_2018 6.18153703 I3499:Mathieson_et_al_2018 6.45676390 I3313:Mathieson_et_al_2018 6.72580850 I4331:Mathieson_et_al_2018 7.03241068 I4332:Mathieson_et_al_2018 7.23624903 R105:Antonio_M_et_al_2019 7.48278691 R36:Antonio_M_et_al_2019 7.69515432 R111:Antonio_M_et_al_2019 7.69551818 I3574:Olalde_et_al_2019 7.72903616 R1285:Antonio_M_et_al_2019 7.79855756 R1283:Antonio_M_et_al_2019 8.25667609 I7043:Olalde_et_al._2018 8.35907890 R120:Antonio_M_et_al_2019 8.41250260 I8475:Olalde_et_al_2019 8.59768574 R1287:Antonio_M_et_al_2019 8.63588444 R474:Antonio_M_et_al_2019
Distance: 0.9094% / 0.90942080
Target: Dodecad_Glob_13Stuvanè | ADC: 0.25x RC30.4 R55 24.7 R120 23.1 I12221 17.3 R1 2.9 PSS4693 1.6 scy300
FYI,
You can obtain your coordinates for Dodecad Globe 13 from Admixture Studio. The calculator is not on GEDmatch or GEDmatch Genesis.
Thanks Jovialis
Distance to: Duarte 5.65621782 I12514:Olalde_et_al_2019 6.13415031 I10895:Olalde_et_al_2019 6.24398110 I10866:Olalde_et_al_2019 6.26891538 I10853:Olalde_et_al_2019 6.39570168 I10892:Olalde_et_al_2019 6.55540235 I1313:Olalde_et_al_2019 6.74273683 R110:Antonio_M_et_al_2019 6.89459208 I12516:Olalde_et_al_2019 6.91129510 R109:Antonio_M_et_al_2019 6.92582125 I10852:Olalde_et_al_2019 6.93939479 I3584:Olalde_et_al_2019 6.94108061 I6490:Olalde_et_al_2019 7.29715013 por003:C_Valdiosera_et_al._2018 7.49508506 I3585:Olalde_et_al_2019 7.59223946 ERS88:S_Brunel_et_al._2020 7.61400683 R1289:Antonio_M_et_al_2019 7.67329786 R474:Antonio_M_et_al_2019 7.71966321 R63:Antonio_M_et_al_2019 7.71978627 I4890:Olalde_et_al._2018 7.78762480 I7673:Olalde_et_al_2019 7.79531911 I5524:Olalde_et_al._2018 7.85466740 I7675:Olalde_et_al_2019 7.96866363 R61:Antonio_M_et_al_2019 7.99977500 R473:Antonio_M_et_al_2019 8.00427386 I12649:Olalde_et_al_2019
Distance: 0.6767% / 0.67667683
Target: Duarte | ADC: 0.5x RC43.3 I10866 24.9 I12648 13.5 I12516 8.1 I7427 5.3 I3574 4.9 I3584
Distance: 0.6133% / 0.61333680
Target: Duarte | ADC: 0.25x RC37.6 I10866 21.3 I12648 13.4 I12516 11.0 I3584 8.9 I7427 7.7 R474 0.1 I5019
... from the website:
Iberian samples grouped. Olalde et al’s PCA.
alas, even Dodecad Globe 13 is not 100% accurate in recreating the PCA from the study, as some samples are a tad out of place. Nevertheless, it is a good approximation.Originally Posted by Salento
One day, there will be a calculator that will more accurately project the samples, and I will be there with these files, ready to instantaneously roll out coordinates.
R53 and R54 are related (first cousins or great grandparents, or...), I ran each sample on various calculators, ... more or less relative similar distances from each other, but they’re closer to me than to each other, R54 in particular that just like me, on dodk13 gets R58 as his top sample, ... so out of curiosity, I added on SOURCE couple of extra kits of mine:
... no close relatives among the 134 ancient Italian individuals (including 127 from central Italian peninsula and 7 from Sardinia). All pairs have kinship coefficients below 0.035, except for R53 and R54, who have an estimated kinship coefficient of 0.0639 corresponding to third-degree relationships (e.g., great- grandparents, first cousins). Both of these two individuals were found in Villa Magna, dated to late Medieval (1280-1430 CE) and inferred to be male ...
https://science.sciencemag.org/conte...Antonio_SM.pdf
... the two cousins on my results:
Big update just in time for Christmas! This is my gift to you all.
I have my completed my endeavor. I was able to yield 2441 aDNA samples from 41 academic studies:
Download
Many thanks Jovialis. You are a gentleman. A very Merry Christmas to you too. I am disabled to vote in this moment, but I am very happy with your gift. Later I will check it carefully.
Thank you Duarte! You as well, Merry Christmas!
Thanks Jovialis, already upvoted, Merry Christmas and Thanks for your hard work.
Merry Christmas to you Duarte as well and when Salento shows up Buon Natale as well (that goes to Jovialis as well!)
God bless all. Thank you PTrapani
Thanks, Buon Natale Palermo!
Merry Christmas to All ... :)
Here are the studies I was able to obtain aDNA from:
Population genomics of Bronze Age Eurasia https://web.archive.org/web/20200423055125/https://genetic-genealogy-tools.blogspot.com/ Ancient Rome: A genetic crossroads of Europe and the Mediterranean https://www.ebi.ac.uk/ena/browser/view/PRJEB32566 Late Pleistocene human genome suggests a local origin for the first farmers of central Anatolia https://www.ebi.ac.uk/ena/browser/view/PRJEB24794 Ancient DNA sheds light on the genetic origins of early Iron Age Philistines https://www.ebi.ac.uk/ena/browser/view/PRJEB31035 The genetic history of Ice Age Europe https://www.ebi.ac.uk/ena/browser/view/PRJEB13123 Ancient DNA from Chalcolithic Israel reveals the role of population mixture in cultural transformation https://www.ebi.ac.uk/ena/browser/view/PRJEB27215 Ancient genomes suggest the eastern Pontic-Caspian steppe as the source of western Iron Age nomads https://www.ebi.ac.uk/ena/browser/view/PRJEB27628 Ancient Fennoscandian genomes reveal origin and spread of Siberian ancestry in Europe https://www.ebi.ac.uk/ena/browser/view/PRJEB29360 Genomic insights into the origin of farming in the ancient Near East https://www.ebi.ac.uk/ena/browser/view/PRJEB14455 Genetic origins of the Minoans and Mycenaeans https://www.ebi.ac.uk/ena/browser/view/PRJEB20914 The genomic history of southeastern Europe https://www.ebi.ac.uk/ena/browser/view/PRJEB22652 The genomic history of the Iberian Peninsula over the past 8000 years https://www.ebi.ac.uk/ena/browser/view/PRJEB30874 Genome flux and stasis in a five millennium transect of European prehistory https://web.archive.org/web/20200423055125/https://genetic-genealogy-tools.blogspot.com/ Ancient Fennoscandian genomes reveal origin and spread of Siberian ancestry in Europe https://www.ebi.ac.uk/ena/browser/view/PRJEB29360 Ancient genomes reveal social and genetic structure of Late Neolithic Switzerland https://trace.ncbi.nlm.nih.gov/Trace...tudy=SRP250694 Genomic History of Neolithic to Bronze Age Anatolia, Northern Levant, and Southern Caucasus https://www.ebi.ac.uk/ena/browser/view/PRJEB37213 Massive migration from the steppe was a source for Indo-European languages in Europe https://www.ebi.ac.uk/ena/browser/view/PRJEB8448 Genomic Evidence Establishes Anatolia as the Source of the European Neolithic Gene Pool https://www.ebi.ac.uk/ena/browser/view/PRJEB12155 Ancient genomes from present-day France unveil 7,000 years of its demographic history https://www.ebi.ac.uk/ena/browser/view/PRJEB36529 The Beaker Phenomenon and the Genomic Transformation of Northwest Europe https://www.ebi.ac.uk/ena/browser/view/PRJEB23635 Genomic analysis of pre-conquest human remains from the Canary Islands reveal close affinity to modern North Africans https://www.ebi.ac.uk/ena/browser/view/PRJEB86458 Early farmers from across Europe descended directly from Neolithic Aegeans https://www.ebi.ac.uk/ena/browser/view/PRJEB11848 An early modern human from Romania with a recent Neanderthal ancestor https://www.ebi.ac.uk/ena/browser/view/PRJEB8987 Early Neolithic genomes from the eastern Fertile Crescent https://www.ebi.ac.uk/ena/browser/view/PRJEB14180 Interactions between earliest Linearbandkeramik farmers and central European hunter gatherers at the dawn of European Neolithization https://www.ebi.ac.uk/ena/browser/view/PRJEB33001 Genomics of Mesolithic Scandinavia https://www.ebi.ac.uk/ena/browser/view/PRJEB21940 The Demographic Development of the First Farmers in Anatolia https://www.ebi.ac.uk/ena/browser/view/PRJEB14675 Ancestry and demography and descendants of Iron Age nomads of the Eurasian Steppe https://www.ebi.ac.uk/ena/browser/view/PRJEB18686 Four millennia of Iberian biomolecular prehistory illustrate the impact of prehistoric migrations at the far end of Eurasia https://www.ebi.ac.uk/ena/browser/view/PRJEB23467 Genomic and strontium isotope variation reveal immigration patterns in a Viking Age town https://www.ebi.ac.uk/ena/browser/view/PRJEB27220 The genomic ancestry of the Scandinavian Battle Axe culture and its relation to the broader Corded Ware horizon https://www.ebi.ac.uk/ena/browser/view/PRJEB32786 The spread of steppe and Iranian-related ancestry in the islands of the western Mediterranean https://www.ebi.ac.uk/ena/browser/view/PRJEB35980 Ancient genomes indicate population replacement in Early Neolithic Britain https://www.ebi.ac.uk/ena/browser/view/PRJEB31249 Ancient human genome-wide data from a 3000-year interval in the Caucasus corresponds with eco-geographic regions https://www.ebi.ac.uk/ena/browser/view/PRJEB29603 Genome-wide patterns of selection in 230 ancient Eurasians https://www.ebi.ac.uk/ena/browser/view/PRJEB11450 Parallel paleogenomic transects reveal complex genetic history of early European farmers https://www.ebi.ac.uk/ena/browser/view/PRJEB22629 Population genomic analysis of elongated skulls reveals extensive female-biased immigration in Early Medieval Bavaria https://www.ebi.ac.uk/ena/browser/view/PRJEB23079 Ancient genomes link early farmers from Atapuerca in Spain to modern-day Basques https://www.ebi.ac.uk/ena/browser/view/PRJEB9783 Shifts in the genetic landscape of the western Eurasian Steppe associated with the beginning and end of the Scythian dominance https://www.ebi.ac.uk/ena/browser/view/PRJEB32764 Megalithic tombs in western and northern Neolithic Europe were linked to a kindred society https://www.ebi.ac.uk/ena/browser/view/PRJEB31045 The arrival of Siberian ancestry connecting the Eastern Baltic to Uralic speakers further east https://www.ebi.ac.uk/ena/browser/view/PRJEB31893
Here are the studies I am unable to get aDNA from. The columns on the right indicate the reason:
New insights into the Tyrolean Iceman's origin and phenotype as inferred by whole-genome sequencing https://www.ebi.ac.uk/ena/browser/view/PRJEB2830 1 *Unclear sample arrangement ANGSD: Analysis of Next Generation Sequencing Data ? ? *Cannot locate samples Ancient admixture in human history ? ? *Cannot locate samples A Late Bronze Age II clay coffin from Tel Shaddudin the Central Jezreel Valley, Israel: context andhistorical implications ? ? *Cannot locate samples Testing support for the northern and southern dispersal routes out of Africa: an analysis of Levantine and southern Arabian populations ? ? *Cannot locate samples Ancient DNA of Phoenician remains indicates discontinuity in the settlement history of Ibiza accession numbers: MH43585-43559 ? Author to upload bams Upper Palaeolithic genomes reveal deep roots of modern Eurasians https://www.ebi.ac.uk/ena/browser/view/PRJEB11364 3 BAMs do not work with WGSextract The Neolithic Transition in the Baltic Was Not Driven by Admixture with Early European Farmers https://www.ebi.ac.uk/ena/browser/view/PRJEB18067 9 BAMs do not work with WGSextract The genetic history of admixture across inner Eurasia https://www.ebi.ac.uk/ena/browser/view/PRJEB31152 6 BAMs do not work with WGSextract Paleogenomic Evidence for Multi-generational Mixing between Neolithic Farmers and Mesolithic Hunter-Gatherers in the Lower Danube Basin https://www.ebi.ac.uk/ena/browser/view/PRJEB20616 6 BAMs do not work with WGSextract Extensive farming in Estonia started through a sex-biased migration from the Steppe https://www.ebi.ac.uk/ena/browser/view/PRJEB21037 9 BAMs do not work with WGSextract Genomic signals of migration and continuity in Britain before the Anglo-Saxons https://www.ebi.ac.uk/ena/browser/view/PRJEB11004 14 BAMs do not work with WGSextract The population genomics of archaeological transition in west Iberia: Investigation of ancient substructure using imputation and haplotype-based methods https://www.ebi.ac.uk/ena/browser/view/PRJEB14737 14 BAMs do not work with WGSextract A western route of prehistoric human migration from Africa into the Iberian Peninsula https://www.ebi.ac.uk/ena/browser/view/PRJEB29189 21 BAMs do not work with WGSextract The first horse herders and the impact of early Bronze Age steppe expansions into Asia https://www.ebi.ac.uk/ena/browser/view/PRJEB26349 111 BAMs do not work with WGSextract Paleogenomics. Genomic structure in Europeans dating back at least 36,200 years https://www.ebi.ac.uk/ena/browser/view/PRJEB7618 1 BAMs do not work with WGSextract Unraveling ancestry, kinship, and violence in a Late Neolithic mass grave https://www.ebi.ac.uk/ena/browser/view/PRJEB28451 24 BAMs do not work with WGSextract Kinship-based social inequality in Bronze Age Europe https://www.ebi.ac.uk/ena/browser/view/PRJEB34400 104 BAMs do not work with WGSextract 137 ancient human genomes from across the Eurasian steppes https://www.ebi.ac.uk/ena/browser/view/PRJEB20658 137 BAMs do not work with WGSextract Ancient genomes from Iceland reveal the making of a human population https://www.ebi.ac.uk/ena/browser/view/PRJEB26760 27 BAMs do not work with WGSextract Deep genome sequencing for diverse human populations from around the world https://www.ebi.ac.uk/ena/browser/view/PRJEB9586 116 Files too big Understanding 6th-century barbarian social organization and migration through paleogenomics https://www.ebi.ac.uk/ena/browser/view/PRJEB27220 53 No FTP BAMs Neolithic and Bronze Age migration to Ireland and establishment of the insular Atlantic genome https://www.ebi.ac.uk/ena/browser/view/PRJEB11995 28 No FTP BAMs A genomic Neolithic time transect of hunter-farmer admixture in central Poland https://www.ebi.ac.uk/ena/browser/view/PRJNA318237 17 No FTP BAMs Ancient genomes from North Africa evidence prehistoric migrations to the Maghreb from both the Levant and Europe https://www.ebi.ac.uk/ena/browser/view/PRJEB22699 23 No FTP BAMs Continuity and Admixture in the Last Five Millennia of Levantine History from Ancient Canaanite and Present-Day Lebanese Genome Sequences https://www.ebi.ac.uk/ena/browser/view/PRJEB21330 100 No FTP BAMs A Transient Pulse of Genetic Admixture from the Crusaders in the Near East Identified from Ancient Genome Sequences https://www.ebi.ac.uk/ena/browser/view/PRJEB31618 116 No FTP BAMs The genetic prehistory of the Baltic Sea region https://www.ebi.ac.uk/ena/browser/view/PRJNA421333 80 No FTP BAMs Ancient Genomes Reveal Yamnaya-Related Ancestry and a Potential Source of Indo-European Speakers in Iron Age Tianshan https://www.ebi.ac.uk/ena/browser/view/PRJEB32336 10 No FTP BAMs A Common Genetic Origin for Early Farmers from Mediterranean Cardial and Central European LBK Cultures https://www.ebi.ac.uk/ena/browser/view/PRJNA280812 12 No FTP BAMs Derived immune and ancestral pigmentation alleles in a 7,000-year-old Mesolithic European https://www.ebi.ac.uk/ena/browser/view/PRJNA230689 1 No FTP BAMs Iron Age and Anglo-Saxon genomes from East England reveal British migration history (Hinxton) https://www.ebi.ac.uk/ena/browser/view/PRJEB4604 92 No FTP BAMs Iron Age and Anglo-Saxon genomes from East England reveal British migration history (Linton and Oakington) https://www.ebi.ac.uk/ena/browser/view/PRJEB6915 18 No FTP BAMs Genomic diversity and admixture differs for Stone-Age Scandinavian foragers and farmers https://www.ebi.ac.uk/ena/browser/view/PRJEB6090 35 No FTP BAMs Ancient Egyptian mummy genomes suggest an increase of Sub-Saharan African ancestry in post-Roman periods https://www.ebi.ac.uk/ena/browser/view/PRJEB15464 93 Raw data files are too small
amazing ! thank you very much for your hard work! but aren't these (the ancients at least) already on the vahaduo global k13 page ?
anyway, the top50 lists of a Greek friend and his mother (with the latter having a possible big influence from a west asian Greek pop like Cappadocians and/or Pontics, that's probably why she has quite bigger distances in general)
Code:
Distance to: Chris 3.21121472 R64:Antonio_M_et_al_2019 3.57491259 R57:Antonio_M_et_al_2019 3.68766864 R836:Antonio_M_et_al_2019 3.79159597 R436:Antonio_M_et_al_2019 4.24333595 R973:Antonio_M_et_al_2019 4.30155786 R65:Antonio_M_et_al_2019 4.45313373 R1544:Antonio_M_et_al_2019 4.71823060 R54:Antonio_M_et_al_2019 4.80814933 R49:Antonio_M_et_al_2019 5.03709242 R117:Antonio_M_et_al_2019 5.13353679 R835:Antonio_M_et_al_2019 5.14076843 R136:Antonio_M_et_al_2019 5.28363511 R53:Antonio_M_et_al_2019 5.34635390 R58:Antonio_M_et_al_2019 5.90803690 R52:Antonio_M_et_al_2019 6.20847002 R131:Antonio_M_et_al_2019 6.23215853 R59:Antonio_M_et_al_2019 6.40630939 R47:Antonio_M_et_al_2019 6.46419369 R122:Antonio_M_et_al_2019 6.60056816 STR502b:KR_Veeramah_et_al._2018 6.68869943 R970:Antonio_M_et_al_2019 6.73218390 R32:Antonio_M_et_al_2019 6.85363407 R114:Antonio_M_et_al_2019 6.88179482 R35:Antonio_M_et_al_2019 7.02950923 R56:Antonio_M_et_al_2019 7.21467255 R1548:Antonio_M_et_al_2019 7.23346390 R118:Antonio_M_et_al_2019 7.29195447 R60:Antonio_M_et_al_2019 7.30406051 R1290:Antonio_M_et_al_2019 7.43123812 R121:Antonio_M_et_al_2019 7.46420123 R125:Antonio_M_et_al_2019 7.47283748 R137:Antonio_M_et_al_2019 7.58721293 R50:Antonio_M_et_al_2019 7.70285012 R969:Antonio_M_et_al_2019 7.94594236 R1549:Antonio_M_et_al_2019 8.58492865 R51:Antonio_M_et_al_2019 8.61013356 R437:Antonio_M_et_al_2019 8.71775774 R113:Antonio_M_et_al_2019 9.01173124 R69:Antonio_M_et_al_2019 9.06444703 R30:Antonio_M_et_al_2019 9.46790368 R107:Antonio_M_et_al_2019 9.70455563 R850:Antonio_M_et_al_2019 10.01113880 R36:Antonio_M_et_al_2019 10.33894095 R123:Antonio_M_et_al_2019 10.47804371 R133:Antonio_M_et_al_2019 10.56867541 R1545:Antonio_M_et_al_2019 10.67855327 R1283:Antonio_M_et_al_2019 10.82845788 R115:Antonio_M_et_al_2019 11.21325555 R34:Antonio_M_et_al_2019 11.32976611 AEHIb:KR_Veeramah_et_al._2018 Code:
Distance to: Chris_Mom 2.87167199 R1548:Antonio_M_et_al_2019 5.24008588 R114:Antonio_M_et_al_2019 5.41761940 R50:Antonio_M_et_al_2019 6.40588011 R136:Antonio_M_et_al_2019 6.43958073 R436:Antonio_M_et_al_2019 6.70587802 R1544:Antonio_M_et_al_2019 6.78897636 R850:Antonio_M_et_al_2019 7.19549859 R53:Antonio_M_et_al_2019 7.40632838 STR502b:KR_Veeramah_et_al._2018 7.44746266 R1545:Antonio_M_et_al_2019 7.53745978 R64:Antonio_M_et_al_2019 7.56463482 R123:Antonio_M_et_al_2019 7.61334355 R65:Antonio_M_et_al_2019 7.75233513 R137:Antonio_M_et_al_2019 7.95448930 R115:Antonio_M_et_al_2019 8.03191135 R51:Antonio_M_et_al_2019 8.60324357 R30:Antonio_M_et_al_2019 8.63240986 R125:Antonio_M_et_al_2019 8.67361516 R128:Antonio_M_et_al_2019 8.68377222 R54:Antonio_M_et_al_2019 8.77722621 R1543:Antonio_M_et_al_2019 8.96292921 R34:Antonio_M_et_al_2019 9.01214181 R117:Antonio_M_et_al_2019 9.02131919 R133:Antonio_M_et_al_2019 9.13192203 R66:Antonio_M_et_al_2019 9.32612996 R58:Antonio_M_et_al_2019 9.48902524 R57:Antonio_M_et_al_2019 9.49352938 R39:Antonio_M_et_al_2019 9.66421751 R973:Antonio_M_et_al_2019 9.72639193 R69:Antonio_M_et_al_2019 9.84931470 R134:Antonio_M_et_al_2019 10.03665781 R836:Antonio_M_et_al_2019 10.06340897 TIT003:Skourtanioti_et_al_2020 10.10539955 R49:Antonio_M_et_al_2019 10.16548572 R35:Antonio_M_et_al_2019 10.18058937 I8205:Olalde_et_al_2019 10.34281393 R81:Antonio_M_et_al_2019 10.34383875 I1631:Lazaridis_et_al_2016 10.50172843 I1634:Lazaridis_et_al_2016 10.60666771 R47:Antonio_M_et_al_2019 10.73297256 I1632:Lazaridis_et_al_2016 10.80243954 I12223:Fernandes_et_al_2020 11.07870028 R56:Antonio_M_et_al_2019 11.25611834 R835:Antonio_M_et_al_2019 11.36892695 R52:Antonio_M_et_al_2019 11.42575599 R40:Antonio_M_et_al_2019 11.43946240 R131:Antonio_M_et_al_2019 11.46836518 R122:Antonio_M_et_al_2019 11.68729652 IKI024:Skourtanioti_et_al_2020 11.75794200 R59:Antonio_M_et_al_2019
You can paste the coordinates here to see it in the 3D PCA
https://vahaduo.github.io/custompca/
Took the coordinates of their top25 ancient matches (obviously some are shared ) but I don't know how to "read" a 3D PCA.. I can sort of understand by the placements in the 3d space if something is a part of a cluster of similar samples or not but I don't know how to read the trends that show up in the placements.
Can someone help me by providing a commentary of the results ? thanks in advance
Code:Chris,0,0.41,0,0.8,14.55,0.82,35.86,0.15,0.05,26.67,19.87,0.82,0 Chris_Mom,0,0.16,0,0,17.18,0.81,32.19,0.06,0.26,30.46,16.31,1.25,1.32 aDNA:Antonio_M_et_al_2019:R1548,0,0,0.6,0,18.94,0,33.55,0,0,29.96,16.03,0.91,0 aDNA:Antonio_M_et_al_2019:R114,0,0.64,0.78,0.45,19.35,0,35.43,0,0,27.94,15.4,0,0 aDNA:Antonio_M_et_al_2019:R50,0,0,0.96,0.17,20.26,0,34.3,0.68,0.17,27.19,15.42,0.85,0 aDNA:Antonio_M_et_al_2019:R136,0,0.2,0.59,0,18.43,0.33,36.19,0.09,0.38,25.85,16.92,0.41,0.58 aDNA:Antonio_M_et_al_2019:R436,0,0,0.77,0.05,17.39,0.8,36.25,0,0.49,26.08,17.87,0.3,0 aDNA:Antonio_M_et_al_2019:R1544,0.82,0,0.4,0,16.67,0,37.33,1.01,0,26.96,16.81,0,0 aDNA:Antonio_M_et_al_2019:R850,0,0,0.69,0.33,21.44,0.46,35.17,0,0.5,27.91,13.33,0,0.17 aDNA:Antonio_M_et_al_2019:R53,0,0.8,0.56,0,17.58,0.5,37.23,0.55,0,25.63,16.27,0,0.88 aDNA:KR_Veeramah_et_al._2018:STR502b,0,0.23,2.37,1.34,15.52,0,33.73,0,0.21,25.34,16.32,4.95,0 aDNA:Antonio_M_et_al_2019:R1545,0,0,0.95,0,20.13,0,36.54,1,0,29.71,11.68,0,0 aDNA:Antonio_M_et_al_2019:R64,0.55,0.32,0.65,0.09,16.01,0,36.15,0.75,1.11,25.08,18.76,0,0.52 aDNA:Antonio_M_et_al_2019:R123,0,0,0.35,0.09,19.12,0.83,37.2,1,0,29.98,11.44,0,0 aDNA:Antonio_M_et_al_2019:R65,0,0.58,0.66,0,16.96,0.32,37.85,0.3,0,25.94,17.39,0,0 aDNA:Antonio_M_et_al_2019:R137,0,0.1,0.42,0.68,17.76,0,38.75,0.06,0.36,27.83,14.03,0,0 aDNA:Antonio_M_et_al_2019:R115,0.91,0,0.58,0,21.38,0,36.37,0.28,0,28.58,11.91,0,0 aDNA:Antonio_M_et_al_2019:R51,0,0,0.34,0,18.28,0.77,38.4,1.56,0,27.75,12.9,0,0 aDNA:Antonio_M_et_al_2019:R30,0,0,0.74,0.27,17.73,1.82,38.59,0.57,0,27.08,12.1,0,1.1 aDNA:Antonio_M_et_al_2019:R125,0,0.15,0.53,0.3,16.76,0,39.93,0,0,27.98,14.34,0,0 aDNA:Antonio_M_et_al_2019:R128,0,0.33,0.33,0.12,20.07,0,35.84,0.5,0,32.87,9.68,0.26,0 aDNA:Antonio_M_et_al_2019:R54,0,0,0.15,0,17.43,0.82,35.01,0.39,0.76,23.63,20.64,0,1.16 aDNA:Antonio_M_et_al_2019:R1543,0,1.03,0.92,0.61,22.37,0.26,35.18,0,0,28.28,10.65,0.67,0.02 aDNA:Antonio_M_et_al_2019:R34,0.49,0,1.02,0.09,19.67,0.27,37.95,0.17,0,28.39,10.51,0.09,1.34 aDNA:Antonio_M_et_al_2019:R117,0,0.54,0.44,0.3,15.92,0,39.14,0,0.82,25.2,16.97,0.29,0.37 aDNA:Antonio_M_et_al_2019:R133,0.77,0,0.97,0,19.87,0.6,38.3,0.24,0,27.59,11.39,0.21,0.06 aDNA:Antonio_M_et_al_2019:R66,0,0,0.19,0,22.02,0.7,36.59,1.2,0,28.72,10.58,0,0 aDNA:Antonio_M_et_al_2019:R57,0,0,0.85,0,15.66,0.66,37.55,0.74,0,24.18,20.22,0.14,0 aDNA:Antonio_M_et_al_2019:R836,0,0.42,0,0.3,14.11,0,39.04,0,0.4,25.48,20.24,0,0 aDNA:Antonio_M_et_al_2019:R973,0.33,0.37,1.54,0.11,14.12,0,39.17,0.04,0,25.63,18.66,0,0.04 aDNA:Antonio_M_et_al_2019:R49,0,0,0.81,0.18,14.9,0.03,39.53,0.72,0,24.59,18.51,0,0.72 aDNA:Antonio_M_et_al_2019:R835,0,0.73,0,0,15.13,0.84,37.51,0.61,0.8,22.38,21.4,0,0.6 aDNA:Antonio_M_et_al_2019:R58,0,0.22,0.81,0,18.28,0.45,36.38,0,0,23.24,19.95,0,0.68 aDNA:Antonio_M_et_al_2019:R52,0,0.91,1.24,0.12,16.19,0.47,39.09,0,0.2,22.37,19.42,0,0 aDNA:Antonio_M_et_al_2019:R131,0,0,1.09,0,16.51,0.84,39.33,0,0,22.19,19.12,0.91,0 aDNA:Antonio_M_et_al_2019:R59,0.03,0,1.66,0,16.4,1.28,38.29,0.17,0,21.7,20.47,0,0 aDNA:Antonio_M_et_al_2019:R47,0.28,0,0.72,0,14.49,0,40.47,0.36,1.27,24.75,16.6,0,1.06 aDNA:Antonio_M_et_al_2019:R122,0,0,0.57,0.48,14.83,0.14,40.55,0.24,0.62,23.31,17.8,0,1.44 aDNA:Antonio_M_et_al_2019:R970,0,0.86,1.39,0.17,13.47,0,37.8,0,0.5,22.1,23.72,0,0 aDNA:Antonio_M_et_al_2019:R32,0,0.28,1.33,0,14.79,0.39,40.92,0.07,0.34,23.28,17.82,0,0.77 aDNA:Antonio_M_et_al_2019:R35,0,0,0.83,0,18.49,0,38.14,1.43,0,22.72,17.46,0.03,0.89 aDNA:Antonio_M_et_al_2019:R56,0,0,0.09,0,16.51,0,39.8,0,1.87,22.81,17.25,0,1.67
