About

The methods for the individual traits are systematically named after their sources. The following descriptors are also used: Exploratory admits more evaluable but less certain evidence; Strict uses the same source basis with tighter quality release; Minimal denotes deliberately small comparison models; PGS denotes a published polygenic score. All direct models use observed AADR markers without imputation; only the Deep SNP scan explicitly evaluates markers that are absent from the AADR panel.

For complex traits (hair colour, skin pigmentation and so on) GWAS (“genome-wide association studies”) are used above all. In doing so we observe SNPs (“single nucleotide polymorphisms”), the smallest possible coding genetic units, which are always defined exactly by the four nucleic bases A, T, C, G. Larger gene structures also have an influence, but SNPs make the main contribution and are the easiest and most unambiguous to trace.

Among all the larger genes and SNPs there are in each case some that have a stronger influence and some that are only very weakly related to the trait. Following the power law, the 5-20 SNPs with the strongest effects often carry more weight than all the other thousands of weaker-effect SNPs together, each of which pushes towards the trait only marginally. In contrast to PGS, it is therefore not thousands of weak signals that are evaluated here, but specifically the strongest known signals. Some characteristics such as height are, however, so complex that the weaker signals still make a relevant contribution. In these cases PGS models are used as a benchmark.

The general procedure for these direct SNP models was as follows:

•Es wurden aktuelle Publikationen nach relevanten genetischen Markern durchsucht und deren Effektstärke ermittelt.

•Es wurde geprüft, inwieweit die AADR-Datengrundlage diese Marker tatsächlich abbildet. Diejenigen die nicht abgebildet waren, wurden dokumentiert und in der SNP-Tiefensuche nochmals an den Rohdaten berechnet.

•Aus den in AADR vorhandenen Markern wurden die belastbarsten, gut reproduzierten und effektstarken Kandidaten herausgegriffen und je nach Merkmal zu einem transparenten Evidenzindex verrechnet. Bei der Berechnung wurde auf publizierte Effektstärken aus möglichst übertragbaren Quellen zurückgegriffen; Marker ohne sicher harmonisierbare Richtung oder Gewichtung wurden ausgeschlossen.

•Nicht beobachtete Loci werden nicht als Gegenbeweis gewertet. Zu schwache, nicht unabhängige oder widersprüchliche Aussagen werden blockiert, damit sie das Ergebnis nicht verzerren.

Direct models, minimal models, and PGS

“Genome Wide Association Studies” (GWAS) and “Polygenic Scores” (PGS) cannot be separated from one another. GWAS examine the genomes of thousands of modern subjects and compare them with the traits that can actually be observed. GWAS is therefore the study by which genetic markers are found and weighted. PGS is the computational model with which effect sizes for the trait are then calculated for single individuals on the basis of GWAS.

The effect sizes of the exploratory and strict direct models are produced in the same way. In the PGS methods, by contrast, the SNPs that enter the calculation each come from a jointly calibrated score source. This increases internal consistency, but it brings in many weak markers whose modern calibration cannot readily be transferred to populations thousands of years old. The source-based direct models concentrate on a few strong and transparently traceable markers; PGS work on a considerably broader basis that is, however, more sensitive in temporal and population terms.

Lona-Durazo/Sulem (Minimal) and Morgan 2018 (Minimal) are deliberately not named as PGS: both are small, GWAS-oriented direct-marker comparison models. The genuine polygenic models, by contrast, are called Tanigawa 2022 (PGS), Privé 2022 (PGS) and Yengo 2022 (PGS).

Hysi/Morgan/Lona-Durazo (Strict)

•Selection and availability: The Strict mode uses exactly the same current source, marker, and score basis as Hysi/Morgan/Lona-Durazo (Exploratory). It must not subsequently alter markers, weights, or class boundaries.

•Release: A larger share of the observed effect-weight basis, sufficient sequencing coverage, and a passing AADR quality assessment are additionally required. Marker counts are diagnostic only. This may block results but cannot shift a released score in another direction. The separate Hysi rank is reported as a consistency diagnostic and does not change the class.

Hysi/Morgan/Lona-Durazo (Exploratory)

•Sources: Hysi et al. 2018, Morgan et al. 2018, and the raw plus COJO-filtered CanPath/Lona-Durazo basis from 2021. Hysi provides a European ordinal hair-colour axis; Morgan and CanPath provide categorical hair-colour effect sizes.

•Selection: The backbone uses only the shared Morgan/Lona-Durazo contrast of blonde versus brown+black. Effects are aligned to the same AADR allele and combined with their standard errors; significant heterogeneity triggers a random-effects weight. Brown versus black is outside the target and is fully excluded. 250 kb locus groups prevent LD double counting.

•Available: The audit covers 2,111 source rows: 258 from Hysi, 293 from Morgan, 1,543 raw Lona-Durazo rows, and 17 Lona-Durazo COJO rows. Eighty-seven unique AADR markers are active: 40 Morgan/Lona-Durazo blonde-axis markers, seven functional MC1R red markers, and 50 Hysi consistency markers; some markers belong to more than one separate axis. Every source row has a documented inclusion or exclusion reason.

•Calculation and interpretation: Blond and red are not treated as genetically identical. The MC1R red branch and Morgan/Lona-Durazo blonde axis remain separate internally; only their empirical evidence ranks are combined as alternative routes to the final Blond/Red class. Hysi standardised betas are never added to log odds because the values are not directly compatible. One symmetric dark-blond colour band marks the area near the boundary without changing binary classes or correlations. This is internal aggregate calibration, not an individual phenotype probability.

Morgan/Lona-Durazo (Strict)

•Sources and SNPs: Identical to Morgan/Lona-Durazo (Exploratory): Morgan 2018 and CanPath/Lona-Durazo 2021, with 16 active effect rows across 15 unique SNPs. Of 62 reviewed hair markers, 28 are directly visible in AADR.

For Morgan/Lona-Durazo (strict) the source-weighted marker basis remains the same as for Morgan/Lona-Durazo (exploratory), but the release of results becomes narrower. Robust blond requires at least 15 per cent positive blond effect weight basis, a blond score of at least 0.50 and dark competition of at most 0.25. Robust red-blond follows the positive MC1R effect weight basis. Brown/dark requires positive weighted dark evidence of at least 0.50; where a competing light basis is present, dark must lie at least 0.25 above the blond score. A dark statement additionally requires at least 25 per cent of the light effect weight basis to be observable. Marker and locus counts are diagnostics only. In the current run, 2,127 profiles are blond, 1,266 red-blond, 4,511 brown/dark, 1,593 dark blond tending dark, 2,589 dark blond tending light and 5,499 unknown.

Morgan/Lona-Durazo (Exploratory)

•Quellen: Morgan et al. 2018 und CanPath/Lona-Durazo et al. 2021 für quellengewichtete Haarfarben-Effektgrößen, HIrisPlex, Guenther 2014, Zorina-Lichtenwalter 2019, Branicki 2011, Sulem 2007 und Wilde 2014 dienten zur Markerauswahl, Allelorientierung, funktionellen Begründung und Kontextualisierung

•Auswahl: Aktiv gewertet werden 16 Effektzeilen auf 15 eindeutigen SNPs. Blond/Hell: KITLG rs12821256 C, HERC2/OCA2 rs12913832 G, SLC24A4 rs12896399 T, TPCN2 rs3829241 A und rs35264875 T, TYR rs1042602 A und OCA2 rs1800407 T. Rotblond: MC1R rs1805006 A, rs11547464 A, rs1805007 T, rs1805008 T, rs1805009 C und rs1805005 T. Braun/Dunkel: SLC45A2 rs16891982 C, HERC2/OCA2 rs12913832 A und IRF4 rs12203592 T. rs1110400 und rs2228479 bleiben als MC1R-Kontext dokumentiert, werden aber nicht aktiv gezählt; rs28777 bleibt blockiert, weil es in diesem kleinen Modell nicht unabhängig genug von rs16891982 gezählt werden kann. Weitere sichtbare Kontextmarker werden nicht gezählt, wenn Richtung, Unabhängigkeit oder Effektgewicht nicht sicher genug sind.

•Vorhanden: Für Haarfarbe wurden 62 Marker geprüft. Davon sind 29 in der AADR-Datengrundlage direkt sichtbar, 33 sind aktuell nicht sichtbar. Aus der Effektallel-Basis gehen 16 sicher gerichtete und quellengewichtet scorebare Effektzeilen auf 15 eindeutigen SNPs aktiv in den Score ein; rs1110400, rs2228479 und rs28777 sind dokumentiert, aber blockiert. Die übrigen sichtbaren Marker bleiben Dokumentations- oder Kontextmarker, bis Richtung und Gewichtung sicher genug sind.

•Gewichtung: Pro Locusgruppe wird nur der stärkste beobachtete Kandidat verwendet, damit eng gekoppelte Marker nicht doppelt dasselbe Signal zählen. Die Gewichtung stammt aus der Master-SNP-Liste: Morgan-ORs werden als log(OR) geführt, CanPath-Betas als logOR-nahe Effektgrößen; gezählt wird jeweils abs(effect_size_value) für scoring_allowed-Zeilen. Der jeweilige Teilscore ist die quellengewichtete Zahl beobachteter Effektallelkopien geteilt durch die quellengewichtete beobachtbare Maximalbasis; fehlende Marker werden nicht als Gegenbeweis gezählt. Robust blond verlangt mindestens 15 Prozent positive Blond-Effektgewichtsbasis, einen Blondscore ab 0,50 und Dunkelkonkurrenz höchstens 0,25. Rotblond folgt der positiven MC1R-Effektgewichtsbasis und ihrer Dunkelkonkurrenz. Braun/Dunkel braucht positive gewichtete Dunkelevidenz und wird nicht aus fehlender Hellbasis abgeleitet. Marker- und Locuszahlen sind nur Diagnostik. Im aktuellen Lauf sind 2.407 Profile blond, 1.266 rotblond, 2.923 braun/dunkel, 2.536 dunkelblond eher hell, 3.953 dunkelblond eher dunkel und 4.500 unbekannt.

Morgan 2018 (Minimal)

Morgan et al. 2018 on hair colour in the UK Biobank is used. This mode is not a fully fledged large PGS, but a small GWAS-oriented comparison mode built from directly observed markers.

10 unique markers from 11 effect rows are actively usable; their number is thus considerably smaller than in the Tanigawa PGS. Several markers are deliberately not used if they are not sufficiently independent, have an unclear direction, or act too weakly or contradictorily in the small model.

The mapping onto AADR is correspondingly limited: the mode uses only the directly observable markers from this small GWAS model. In the current run, 11,286 profiles are calculated, 1,254 limited and 5,045 not evaluable.

Weighting follows the strength of the GWAS association, cautiously attenuated so that individual modern associations are not transferred too rigidly to ancient DNA. What is output is support for blond, dark and red, or a mixed status if no clear category dominates. The robustness is lower than with a large PGS and also lower than with robust individual markers. The Morgan mode is useful as an independent comparative indication, but not as a primary hair-colour method.

Tanigawa 2022 (PGS)

The hair-colour PGS family of Tanigawa et al. 2022 is used. It contains separate models for black, blond, brown, dark brown, light brown and red hair colour.

In the current runtime version, the Tanigawa PGS contains 6,896 unique active markers and 8,896 active score entries. By category, the following are active: 691 for black, 2,759 for blond, 1,642 for brown, 2,271 for dark brown, 866 for light brown and 667 for red.

Of the originally possible candidates, only the portion that is directly observable in the AADR data basis and can be harmonised reliably is usable. A particularly large number of candidates drop out because they are not visible in the AADR panel or cannot be matched reliably enough against the alleles there.

The map output adopts the existing intermediate PGS categories according to fixed builder thresholds: red is only output clearly if red Z reaches at least 1.10 and lies at least 0.35 above blond Z and dark Z. Blond or dark are only output clearly if the respective Z value reaches at least 0.75 and lies at least 0.30 above the two competing axes. Dark blond appears if blond Z or dark Z reaches at least -0.15 but no clear category dominates. The lighter dark-blond side applies if blond Z minus dark Z plus 0.35 times max(light-brown Z, -1.0) yields at least 0; otherwise dark blond is displayed towards the darker side.

For a calculated result, an individual needs at least 3.5 per cent of the total absolute effect-weight basis and at least 2 per cent of the high-weight basis. For a limited result, 0.4 and 0.2 per cent respectively are sufficient. Marker count and unweighted coverage are only diagnostics. In the current run, 15,136 profiles are calculated, 1,186 limited and 1,263 not evaluable.

The Tanigawa PGS is broad and internally consistent, but it is not a forensic hair-colour verdict for ancient DNA. It is particularly useful as a comparison model. Over very long periods it has to be interpreted with caution, because many weak modern effects may be noisier or weighted differently in ancient populations.

What the genotype cannot tell us here

The light-dark axis of hair colour is largely additive: every additional light effect allele shifts the value a little, and observing individual alleles is enough for that. The red axis works differently. Red hair arises mostly when two function-reducing MC1R variants meet - including when they are two different ones, that is, as a compound pair. Whether someone had red hair therefore does not depend on the sum of their markers but on the combination of both gene copies.

That combination is precisely what cannot be established reliably in ancient genotypes. The 1240K data are pseudo-haploid: usually only a single base call is available per marker, which makes a heterozygote look like a homozygote. A compound pair can therefore neither be demonstrated nor ruled out. The GeoGens red values should accordingly be read as observed allele evidence, not as a determined phenotype: for the dominance- and recessiveness-dependent components of hair colour, the genotype permits only an imprecise inference about the actual hair colour. What remains defensible are the broad tendencies of the light-dark axis and group patterns, not a red-hair diagnosis for a single individual.

Lona-Durazo/Sulem (Strict)

•Sources and SNPs: Identical to Lona-Durazo/Sulem (Exploratory): CanPath/Lona-Durazo Data S1 and Sulem/GWAS Catalog, with five active effect rows across four unique SNPs. All 15 reviewed eye markers are visible in AADR, but only the cleanly oriented and weighted GWAS basis is scored.

With this method the GWAS-based marker basis remains the same as for Lona-Durazo/Sulem (exploratory), but proxy-like and contradictory profiles are blocked more strongly. Blue requires rs12913832 G/G as its structural main anchor, at least 3 per cent independent blue effect weight basis outside that anchor, a blue value of at least 0.50 and brown competition below 0.35. Brown/dark requires rs12913832 A/A, a brown/dark value of at least 0.50 and both blue competition and conflict competition below 0.35. Green/mixed is released only where blue and brown/dark clearly compete; direct intermediate markers contribute no mixed value of their own. In the current run, 1,696 profiles are blue, 6,150 brown/dark, 372 green/mixed and 9,367 unknown.

Lona-Durazo/Sulem (Exploratory)

•Quellen: Verwendet wird für den SNP-Score ein kleines auditiertes Eye-GWAS-Modell aus Lona-Durazo et al. 2022/CanPath Data S1 und Sulem et al. 2007/GWAS-Catalog. IrisPlex/HIrisPlex-nahe Augenmarker, Chaitanya et al. 2014 und neuere Kontextbefunde aus Abbatangelo, Lona-Durazo, Edwards & Parra 2026 bleiben Review- und Rohsignal-Kontext, wenn Effektallel, Richtung oder Anwendung auf ein unstratifiziertes Scoremodell nicht sauber genug sind.

•Auswahl: Aktiv im SNP-Score sind 5 GWAS-Effektzeilen auf 4 eindeutigen SNPs: HERC2/OCA2 rs12913832 G als zentrale Blauachse und rs12913832 A als zentrale Braun/Dunkel-Gegenachse; IRF4 rs12203592 T, SLC24A4 rs12896399 T und TYR rs1393350 A als zusätzliche Blau-/Hellmarker. rs1129038, rs1800407 und rs16891982 werden für Score und Streng nicht verwendet; rs1800407 erzeugt auch kein direktes Grün/Gemischt-Rohsignal. Grün/Gemischt ist keine eigene Markerachse, sondern entsteht als Rest- beziehungsweise Konfliktkategorie aus konkurrierender Blau- und Braun/Dunkel-Evidenz.

•Vorhanden: Für Augenfarbe wurden 15 Marker geprüft, und alle 15 sind in der AADR-Datengrundlage sichtbar. Aktiv im Score gewertet werden daraus 5 GWAS-Effektzeilen auf 4 eindeutigen SNPs. Weitere sichtbare Effektzeilen bleiben dokumentiert, werden aber wegen Proxy-Charakter, Haplotyp-Kontext, stratifiziertem Kontext, richtungsunsicherer Kandidatur oder fehlender sauberer GWAS-Scorebasis nicht direkt in den Score aufgenommen.

•Gewichtung: Beta-Zeilen werden mit abs(beta) mal Safety-Factor 0,7 gewichtet; OR-Zeilen werden mit abs(log(OR)) mal Safety-Factor 0,7 gewichtet. Dadurch zählen rs12913832 G und A jeweils mit 0,885115, rs1393350 A mit 0,29309723, rs12203592 T mit 0,115157 und rs12896399 T mit 0,0844564. Pro Locusgruppe wird nur der stärkste beobachtete Beitrag verwendet. Blau wird ausgegeben, wenn rs12913832 G/G bei Braunwert unter 0,45 vorliegt oder die Blauachse mit Zusatzloci einen Blauwert ab 0,55 erreicht. Braun/Dunkel wird ausgegeben, wenn rs12913832 A/A ohne starke Blau- oder Konfliktkonkurrenz vorliegt oder die Braunachse mindestens 0,50 erreicht. Grün/Gemischt wird ausgegeben, wenn konkurrierende oder zu schmale GWAS-Hinweise keine robuste Blau- oder Braun/Dunkelentscheidung tragen; der Green/Mixed-Wert ist dabei ein abgeleiteter Konfliktwert ohne direkte Marker-Evidence. Fehlt eine ausreichende positive Augenfarbenbasis, wird blockiert. Im aktuellen Lauf sind 4.324 Profile blau, 6.433 braun/dunkel, 880 grün/gemischt und 5.948 unbekannt.

Simcoe/Lona-Durazo (Exploratory)

•Sources: Simcoe et al. 2021 and Lona-Durazo et al. 2022, both based on European-ancestry samples. The audit reviews 115 Simcoe rows, 401 Lona GWAS rows, and 35 additional Lona fine-mapping context rows.

•Selection: Eleven unique markers that can be harmonised directly in AADR are active, derived from 14 included source components. Reproducible Simcoe regional effects and independent Lona COJO signals are used; LD followers, unresolved palindromic variants, and findings without an accessible effect size remain excluded.

•Availability and calibration: The runtime model uses a rank-preserving internal reference calibration to Lona-Durazo/Sulem (Strict) for European samples from the first millennium CE as a geohistorical validation point. The green/middle band is deliberately very narrow. Calibration work is ongoing.

Simcoe/Lona-Durazo (Strict)

•Selection and availability: The Strict mode uses the same 11-marker score and the same internal calibration as Simcoe/Lona-Durazo (Exploratory). The genetic value and its category boundaries are not recalculated.

•Release: Exploratory and Strict use all 11 markers, the same effect weights, the same calibrated index, and the same category boundaries; Strict therefore does not alter any released value. The only difference is the evidence-release policy. Exploratory becomes evaluable from 8% observed absolute effect-weight basis, whereas Strict requires 18%. In both modes, a profile below 35% is classed as a limited basis and one at or above 35% as calculated. For AADR samples, Strict additionally requires a ‘Pass’ quality assessment, a reported sequencing coverage, and at least 0.1× coverage. If any of these conditions is missing, the profile is blocked as an insufficient basis despite observed markers. In the current run, Exploratory has 9,306 calculated, 3,312 limited, and 4,967 blocked profiles; Strict has 7,033 calculated, 1,512 limited, and 9,040 blocked profiles.

Lona-Durazo/Sulem (Minimal)

For eye colour there is currently no large active PGS. What is used here is a small GWAS indicative mode drawn from Lona-Durazo et al. 2022, Sulem et al. 2007 and a more recent stratified context source that has so far only been reviewed and is not yet fully activated as a score.

4 unique markers from 5 effect rows are active; they are restricted to directly observable GWAS markers whose direction is unambiguous at row level. The mode is therefore called Lona-Durazo/Sulem (Minimal) and not PGS.

The AADR mapping is sufficient for a portion of the individuals, but remains narrow. In the current run, 10,846 profiles are calculated, 513 limited and 6,226 not evaluable.

Weighting follows the reported GWAS effect. The output is Blue or Brown/dark where the support is unambiguous enough; otherwise the profile remains unknown or limited. Its robustness is limited. Eye colour is already strongly shaped by a few main markers, above all the HERC2/OCA2 axis. The other methods are therefore usually more comprehensible and more robust than this small minimal mode.

SNP deep search

Because the AADR panel contains only the most common SNPs, a multi-day intensive computation examined the readily accessible raw genomes for additional SNPs from the known sources. The run is not yet complete and will be expanded over time as the more difficult genomes are processed.

•Source: A dedicated damage-aware scan of raw ENA BAM data at 1,250 target positions. The scan covers 8,043 ancient genomes and also reaches markers outside the AADR 1240K panel.

•Selection: The underlying eye-colour weight table contains 34 blue and brown/dark axis rows across 28 unique SNPs. After runtime harmonisation, the active artifact combines 27 weighted markers into one eye-colour axis.

•Available: In the current runtime build, the eye axis contains 6,371 evaluable profiles: 3,218 calculated, 1,273 with a limited basis, and 1,880 with an insufficient basis.

•Calculation and interpretation: Each position is counted both in full and with clipped read ends; transition markers are assessed using the less damage-prone trimmed counts. Genotypes are treated as diploid or pseudo-haploid depending on depth. The result is a weighted allele index.

What the genotype cannot tell us here

Eye colour is the least additive of the pigmentation traits shown here. It depends predominantly on the HERC2/OCA2 region, and the light variant acts largely recessively there: only two copies reliably produce blue eyes, whereas heterozygotes usually have brown eyes and merely pass the light variant on. The phenotype therefore does not follow the sum of the markers but the interplay of both gene copies at a few positions.

Ancient genotypes cannot resolve that interplay reliably. The 1240K data are pseudo-haploid: usually only a single base call is available per marker, so a heterozygote cannot be distinguished from a homozygote. An observed light allele therefore evidences the disposition, not blue eye colour. The GeoGens eye values should accordingly be read as observed allele evidence, not as a determined phenotype: for traits that depend on dominance and recessiveness, the genotype permits only an imprecise inference about the actual eye colour. Group means and temporal patterns are considerably more defensible here than an attribution to a single individual.

Liu/Jacobs (Strict)

•Sources and SNPs: Identical to Liu/Jacobs (Exploratory): six active R²-weighted markers from Liu 2015 and Jacobs 2013 within a registry of 30 skin-marker rows. Four requested direct markers are absent from the AADR 1240K panel; the remaining inactive rows stay as review context.

For Liu/Jacobs (strict) the marker basis remains the same as for Liu/Jacobs (exploratory). In addition, all low confidences, mixed dosage modes, missing quality information and sequencing coverage below 0.1× are blocked. The genetic minimum basis remains effect-weighted: 45 per cent total weight and 40 per cent anchor weight for calculated, 25 and 20 per cent respectively for limited. In the current run, 6,860 profiles are calculated and 10,725 blocked.

Liu/Jacobs (Exploratory)

•Quellen: Verwendet wurden vor allem Liu et al. 2015 zu direkter europäischer Hautfarbvariation und Jacobs et al. 2013 zu quantitativen Hautfarbachsen.

•Auswahl: Aktiv sind 6 Marker: HERC2/OCA2 rs12913832 A als dunkler Ankermarker mit R2 5,376 und Gewicht 1,0; IRF4 rs12203592 T als heller Ankermarker mit R2 3,627 und Gewicht 0,82138; TYR rs1393350 A als heller Liu-Marker mit R2 0,573 und Gewicht 0,326473; OCA2 rs7495174 G als dunkler Jacobs-SD-Marker mit partiellem R2 0,48 und Gewicht 0,298807; ASIP rs4911442 G als heller Jacobs-Sättigungsmarker mit partiellem R2 0,35 und Gewicht 0,255155; SLC24A4 rs17128291 G als heller Liu-Marker mit R2 0,241 und Gewicht 0,211728. Nicht aktiv sind unter anderem rs1805007, rs1805008, rs376397, rs16891982, rs10756819, rs1540771, rs4911414, rs17426596, rs1800407, rs6742078, rs12896399, rs12821256, rs1408799, rs35264875, rs1426654, rs2675345, rs2470102, rs11637235, rs8028919 und rs6602666, weil sie fehlen, auf einer anderen Achse liegen, nicht auf die aktuelle R2-Skala übertragen sind, nicht ausreichend harmonisiert sind oder in dieser Runde nur Review-Kontext bleiben.

•Vorhanden: In der geprüften Hautmarkerbasis sind 30 Registry-Zeilen dokumentiert. 6 Marker sind aktiv sichtbar und werden direkt verwendet. 4 gewünschte direkte Marker fehlen im AADR-1240K-Panel (rs183671, rs4268748, rs6059655 und rs2924567). 20 weitere Hautzeilen sind sichtbar oder dokumentiert, aber derzeit nicht aktiv scorebar, weil sie nicht für diesen Score freigegeben sind oder ihre Achse, Richtung beziehungsweise Effektgrößenskala nicht sicher genug passt.

•Gewichtung: Die Gewichtung folgt dem erklärten Anteil am Pigmentierungsunterschied: R2- und partielle-R2-Werte werden über die Quadratwurzel skaliert und auf rs12913832 als Gewicht 1,0 normiert. Hellere Richtung wird auf der Skala negativ, dunklere Richtung positiv abgebildet. Alte AADR-0/2-Aufrufe werden als Einzelallel-Halbdosierung behandelt; fehlende Marker werden aus Zähler und Nenner ausgelassen. Für ein berechnetes Ergebnis müssen mindestens 45 Prozent der gesamten Effektgewichtsbasis und 40 Prozent der Anker-Effektgewichtsbasis beobachtet sein; limitiert reichen 25 beziehungsweise 20 Prozent. Markerzahl und ungewichtete Coverage sind nur Diagnostik. Werte bis -0,55 gelten als deutlich blasser, bis -0,2 als blasser, unter 0,25 als mittelhell, unter 0,6 als gebräunter und darüber als deutlich gebräunter. Im aktuellen Lauf sind 10.497 Profile berechnet, 1.355 limitiert und 5.733 nicht auswertbar.

Ju/Liu/Jacobs (Exploratory)

•Sources: The six active Liu/Jacobs markers are combined with independent European UK Biobank signals from Ju and Mathieson 2021 and one supplementary effect from Farré et al. 2023.

•Selection: A total of 171 source components are included — 164 from Ju/Mathieson, six from Liu/Jacobs, and one from Farré. After merging one overlap, 170 unique directly harmonised runtime markers remain. Findings that are not independent, are palindromically ambiguous, or cannot be transferred to a European light-to-dark axis are excluded.

•Available: In the current run, 11,105 profiles are calculated, 3,278 are limited, and 3,202 are blocked for insufficient basis. The six established Liu/Jacobs anchors remain part of the extended model.

•Calculation and interpretation: Effects are first normalised within each source; source blocks are then combined with equal weight. This prevents the numerically large Ju list from overwhelming the smaller Liu/Jacobs and Farré blocks merely through its row count. The value is an uncalibrated European evidence index, not a phenotype probability.

HIrisPlex-S

HIrisPlex-S is a prediction model that originates in forensics and mostly uses around 40 SNPs for pigmentation in modern populations. Its weaknesses lie above all in the fact that it is not tailored to European gene variants but was explicitly tuned for broad, global applicability. Around 30 populations underlie its calibration (African, East Asian, Middle East, Admixed, etc.). This makes the model highly error-prone to almost unusable for ancient European DNA. Here, HIrisPlex-S produces statistical noise or false results.

In terms of sources, the skin-pigmentation view used here rests on the Walsh line of global skin-colour prediction and on the HIrisPlex-S system developed from it, which was forensically validated by the Erasmus MC group around Susan Walsh, Manfred Kayser and their colleagues. From this, GeoGens does not use the full modern forensic probability calibration, but a direct marker index: only SNPs that are actually observed and unambiguously harmonised are counted, and missing markers are not substituted.

What is actively used is the skin-colour component of the HIrisPlex-S/Walsh model with five target directions: markedly paler, paler, medium-light, more tanned and markedly more tanned. The result is a pigmentation index, not a forensic diagnosis of an individual and not a modern probability output.

The model comprises 36 requested skin-pigmentation markers. Of these, 29 are visible and actively harmonised in the current AADR data basis; a further 7 desired model markers are currently not directly observable in this basis. The core markers include, among others, SLC24A5 rs1426654, SLC45A2 rs16891982, IRF4 rs12203592, OCA2 rs1800414 and rs1800407, HERC2/OCA2 rs12913832, HERC2 rs1129038 and rs1667394, ASIP rs6119471 as well as MC1R rs1805008.

The remaining active markers supplement these axes with further MC1R, TYR, SLC24A4, OCA2, HERC2, KITLG, ASIP/PIGU, RALY, DEF8, TYRP1, ANKRD11 and BNC2 signals. In the current run, 10,107 profiles are calculated, 1,997 limited and 5,481 not evaluable. 9,554 profiles reach moderate confidence; 8,031 remain low in confidence and are not treated as a robust map colour.

The weighting follows the ordinal coefficients of the Walsh/HIrisPlex-S skin model. For normal markers the centred dosage counts: one copy of the effect allele lies at the midpoint, two copies shift the index towards the coefficient, and zero copies shift it in the opposite direction. Rare MC1R variants are handled differently: only directly observed effect-allele copies are counted, so that the absence of a rare variant does not artificially act as an indication of dark pigmentation.

The raw value is capped to an index from -1 to +1. Negative values point towards paler pigmentation, positive values towards more tanned pigmentation. A calculated result requires at least 45 per cent of both the total and the core-marker effect-weight basis to be observed; a limited result requires 25 per cent of each. Moderate confidence requires 50 per cent weight basis in each case and a winning category with at least 0.5 support. Marker count and unweighted coverage remain purely diagnostic.

Over longer time spans, HIrisPlex-S is only of limited reliability. The model was developed for modern forensic application, not for 10,000-year-old genomes with strong missingness, a different population structure, and possible shifts in weak effect relationships. In European ancient samples in particular it can therefore produce signals that are useful as a rough pigmentation indication but must not be read as a reliable skin-colour diagnosis.

In GeoGens, HIrisPlex-S is therefore explicitly a supplementary direct-marker index. The more robust skin-pigmentation logic is covered by Liu/Jacobs (Exploratory) and in particular by Liu/Jacobs (Strict). HIrisPlex-S remains visible because it represents a well-known forensic reference, but its categories here are only cautious pigmentation indicators.

Privé 2022 (PGS)

A modern skin-pigmentation PGS from the PGS Catalog/UK Biobank line is used, which is based on the self-report, or rather the modern skin-colour classification, of the UK Biobank field for skin pigmentation. Just as with HIrisPlex-S, the PGS is retained as a negative example. calibrated. It incorporates very weak signals that are not robust across longer time spans, which makes it unusable for statements about ancient European DNA. The model's error-proneness is easy to recognise from the strong statistical noise and the numerous validation failures.

The active skin PGS contains 9,330 usable markers out of 9,342 candidates. It is therefore very broad, but also weak. The AADR mapping is broad enough for many individuals, but not for all. In the current run, 11,643 profiles are calculated and 5,942 not evaluable.

For a result, an individual needs at least 25 per cent of the observable absolute effect-weight basis and at least 20 per cent of the high-weight basis. Marker count and unweighted coverage are diagnostics only; missing markers are not counted as zero.

The ‘Light complexion’ mode rests on the interesting observation that light hair and light eyes are very strongly correlated in the present day (~0.8-0.9). The obvious question of whether the same held for past peoples could be tested very easily here. The answer was remarkably clearly affirmative. The correlation between blond hair and blue eyes is not only consistently high and stable, it even becomes slightly stronger the further back in time one can look.

The map mode therefore allows this relationship to be observed, a relationship long known to anthropology as the ‘light complexion’. It may thereby make it possible to trace the typical ‘Nordic phenotype’ back through time. Pale skin pigmentation also proved to be substantially correlated with light complexion, which is why the selection was extended in a further step to a three-way constellation. Light complexion in the broader sense can therefore mean:

blond hair x blue eyes

blond hair x pale skin

blue eyes x pale skin

blond hair x blue eyes x pale skin

All combinations have different coverage, but they nevertheless resemble one another, which again means that we are dealing here with an old, significant population-genetic relationship.

Light complexion continues to combine the three standard methods Morgan/Lona-Durazo (Strict), Lona-Durazo/Sulem (Strict), and Liu/Jacobs (Strict) with equal weight. Depending on the selected component, two or all three traits are combined; missing bases are not scored as zero.

Four component modes are active: hair x eyes, hair x skin, eyes x skin, and All 3 traits. For hair, the blond value from the hair strict profile is used; for eyes, the blue value from the eye strict profile; and for skin, the paler value from the skin strict profile. Each of these individual values lies between 0 and 1. Missing components are not counted as dark or low, but block the respective combination.

If all required components are present, their mean is formed. This mean, running from 0 to 1, is then transferred onto the map axis from -1 to +1: 0 becomes -1, 0.5 becomes 0, and 1 becomes +1. A high positive value therefore means a strong light complexion, a value around 0 a moderate or partial light complexion, and a negative value a low light complexion. In the band mode the following applies: from +0.5 very light, from 0 light, from -0.5 darker, and below that dark. The colours follow the same logic: green stands for low light complexion, yellow for partial light complexion, orange for stronger light complexion, red for high light complexion, and grey for missing component basis.

OCEAN denotes five broad personality dimensions: openness, conscientiousness, extraversion, agreeableness, and neuroticism. GeoGens provides a separate map method for each dimension. A high value only means that the directly observed genetic marker basis points more strongly in the named direction within this model. The value is neither a personality test nor a diagnosis and does not reveal how an individual actually behaved.

The basis consists of European-validated genome-wide significant associations from Gupta 2024 at p ≤ 5 × 10⁻⁸. Only SNPs whose effect and alternative alleles can be harmonised reliably in the raw data and directly observed in at least one sample are used. There is no imputation and sparse dimensions are not padded with markers from other studies. The current runtime model contains 209 active SNPs: 4 for openness, 24 for conscientiousness, 9 for extraversion, 3 for agreeableness, and 169 for neuroticism. This very uneven breadth must be kept in mind when comparing the dimensions.

How the value is calculated

For every observed SNP, GeoGens counts the fraction of damage-aware filtered reads carrying the effect allele. A fraction of 0 means that no read carries the effect allele, 0.5 means half, and 1 means all reads. This fraction is weighted by the beta direction reported in Gupta 2024. To keep betas of different magnitudes comparable within a dimension, each beta is divided by the median absolute beta for that dimension; an individual weight is capped at ±2. For conscientiousness, the display direction is reversed after weighting because the raw Gupta/MVP-GPC1 direction ran opposite to the reviewed map interpretation; the original source betas remain documented in the model.

The raw score is the sum of effect-allele fraction × signed weight, divided by the sum of the absolute weights of all markers actually observed for that individual. Missing SNPs therefore disappear from both numerator and denominator and are not treated as opposing alleles or neutral values. The observed weight basis is reported separately and states how much of the dimension's total possible effect weight is available for that individual.

At 35 per cent observed weight basis or more, the status is ‘calculated’; from 20 to below 35 per cent it is ‘limited basis’; below that the profile has no evaluable basis. At 50 per cent weight basis or more, computational confidence is at most moderate. Marker count, mean read depth, and single-read markers remain additional quality indicators and do not replace these weight thresholds.

To colour individuals comparably on the map, each dimension's raw score is centred relative to all evaluable GeoGens profiles. The median forms the midpoint and the median absolute deviation provides a robust measure of spread. The visible index is (raw score − median) / (3 × robust spread) and is clamped to −1 to +1 for colouring. Zero therefore means approximately the centre of the evaluable ancient sample, not the absence of the trait. Positive and negative values are relative deviations within GeoGens, not universal personality measurements.

The five dimensions

Openness comprises open-mindedness, curiosity, and imagination; lower values point within the model towards conventionality and closed-mindedness. The active basis contains only 4 SNPs and is therefore especially sparse.

Conscientiousness comprises dutifulness, order, achievement striving, self-discipline, precision, and prudence; lower values point more towards lack of planning and disorganisation. The active basis contains 24 SNPs.

Extraversion comprises sociability, outgoingness, activity, excitement seeking, and lively emotionality; lower values point more towards reserve and introversion. The active basis contains 9 SNPs.

Agreeableness comprises altruism, trust, modesty, cooperativeness, and sincerity; lower values point more towards competitiveness and lower cooperativeness. The active basis contains only 3 SNPs and is therefore especially weak.

Neuroticism comprises anxiety, irritability, emotional instability, and nervousness; lower values point more towards emotional stability and inner calm. The active basis contains 169 SNPs and is therefore much broader than the other four dimensions.

Interpretation

All five methods are exploratory genetic evidence indices. They are based on small effects from modern European samples and are applied to incomplete ancient genomes sequenced at uneven depth. Openness and agreeableness in particular can be shifted strongly by individual markers. Cautious group means, temporal patterns, and results that remain stable under bilateral inspection and different quality filters are therefore more defensible than interpreting a single individual.

Lifelong lactose tolerance is rare worldwide today and almost exclusively limited to (Northern) Europeans. In the past, this trait was far rarer still, and virtually non-existent during the Paleolithic era. Its spread can be traced so effectively because, in Europeans, it is linked to essentially a single SNP marker: rs4988235. The persistence allele acts dominantly; a single copy suffices—individuals carrying one or two copies are generally lactase-persistent, whereas only two copies of the alternative allele result in lactose intolerance. Consequently, detecting a persistence allele constitutes a reliable positive finding, whereas detecting the alternative allele does not rule out carrier status—meaning the map tends to underestimate persistence rather than overestimate it. Markers that are not detected remain omitted and do not count as a negative finding.

‘Metabolism’ is a rather exploratory method that examines two different SNPs, both of which have been linked in earlier studies to the difference between a more ‘hunter-like’, animal-food-based diet and a more ‘farming’, plant-food-based diet.

Only these two genes are therefore observed, and it is checked whether both plant variants, both animal variants, or one of each, that is a mixed type, are present. These labels are of course very popular-scientific, because what is actually involved are concrete metabolic control points:

The first gene, CLTCL1/CHC22, determines whether the body is geared to high carbohydrate consumption and can metabolise it quickly, or whether it stores the sugar in the cells for longer in order to maintain blood-sugar levels across periods of fasting as well. Making large amounts of carbohydrate rapidly available is presumably more adaptive in farming populations, whereas longer storage fits the carbohydrate-poor diet of hunting or herding peoples more closely.

The second gene, FADS1/FADS2, determines how intensively the body converts plant fatty acids into higher-value ones, which is less necessary on a diet rich in meat and fish, but is necessary on a strongly plant-based diet.

In detail, the genes act as follows:

CLTCL1 encodes CHC22, a clathrin heavy-chain variant involved in the control of GLUT4-containing vesicles. GLUT4 is the central insulin-dependent glucose transporter in muscle and adipose tissue. The variant observed here, rs1061325, marks M1316V: T/T corresponds to the ancestral M1316 profile, C/C to the V1316 profile. Functionally, this region is linked to differences in how quickly glucose is taken up from the blood into cells following an insulin signal, or retained in cellular stores.

FADS1/FADS2 control the desaturase steps by which the body can produce longer-chain polyunsaturated fatty acids from plant precursors. Such conversions are especially important when high-value animal or marine fatty acids are scarcer in the diet and have to be formed to a greater extent from plant precursors.

In active use, the FADS component is currently represented by rs174546 and rs174570. Both use C as the plant-food/farmer direction and T as the animal-food/hunter direction, and both enter the FADS component with equal weight. A further FADS context marker is documented but is currently not scored, because it is not directly observable in the basis used.

In the current implementation there are two equally weighted components: CLTCL1/CHC22 and FADS1/FADS2. CLTCL1 is calculated via rs1061325. FADS is formed from the observed active FADS markers rs174546 and rs174570; both markers enter the FADS component with a weight of 0.5. Every observed marker direction is placed on a scale from -1 to +1: -1 means the animal-food direction, +1 the plant-food direction, and 0 an intermediate state.

If both components are observed, their mean is returned; if only one component is observed, a limited value from that single basis is used. From +0.35 upwards plant-food metabolism is shown, down to -0.35 animal-food metabolism, and mixed type in between. If every evaluable component is missing, the individual remains unknown. In the current run, 4,937 profiles are plant-food metabolism, 4,312 animal-food metabolism, 3,821 mixed type, and 4,515 unknown. CLTCL1 is observed in 10,100 profiles, FADS in 12,156 profiles.

“Muscle type” is a rather exploratory mode that examines a single SNP, namely rs1815739. The gene influences the structure of the muscle fibres.

rs1815739 is the well-known ACTN3 R577X variant. The C allele corresponds to the R variant and preserves functional alpha-actinin-3 in fast, strength-oriented muscle fibres. C/C is therefore displayed as Strength type. On average this type is more compatible with sprinting, explosive power, rapid changes of direction, lifting, throwing and short bouts of high exertion. The drawback is not weakness in endurance, but rather a lower degree of specialisation for very efficient, prolonged aerobic exertion.

The T allele corresponds to the X variant. T/T means that no functional alpha-actinin-3 is produced; the musculature shifts more towards oxidative, endurance-oriented properties. T/T is therefore displayed as Stamina type. This can be advantageous for long, steady exertion, marching, running, repetitive work and energy efficiency, but it is not proof of actual athletic performance. C/T is displayed as mixed type. In the current run, 5,264 profiles are Strength type, 4,408 Stamina type, 594 mixed type and 7,319 unknown.

Type I was accordingly called “Stamina type”, type II “Strength type”. Since the Strength type becomes slightly, but discernibly, rarer over time, an actual adaptation to changed living conditions appears to be observable here as well.

The COMT gene became famous as the “Warrior/Worrier” variant, but this label is somewhat misleading. In fact it is a single SNP, which above all influences the dopamine concentration in the frontal cortex.

rs4680 is the COMT Val158Met variant. The G allele corresponds to Val and leads to higher COMT enzyme activity. As a result, dopamine is broken down more quickly in the prefrontal cortex. In GeoGens, G/G is displayed as fast-acting type: a tendency towards rapid reaction, robust readiness to act under strain, and a lower tendency to defer cognitive control for too long. The possible drawback is that very fine-grained planning, working memory and calm deliberation under low strain may be less favoured.

The A allele corresponds to Met and leads to lower COMT activity. As a result, dopamine remains available for longer in the prefrontal cortex. A/A is displayed as brooder type: rather favoured for working memory, anticipatory thinking, deliberation and stable cognitive control. The possible drawback is a greater sensitivity to stress or overload when there is too much dopamine or pressure in the system. A/G is displayed as mixed type. In the current run, 4,864 profiles are fast-acting type, 4,643 brooder type, 530 mixed type and 7,548 unknown.

We have therefore called the Met/Met variant “brooder type” and the Val/Val variant “fast-acting type”, because these terms capture the general effect of the gene more accurately. The brooder types appear to be associated to a small degree with hunter cultures, but more strongly still with northern origin. Even today, north-west Europeans show peak values here. In general, however, almost all populations display a relatively balanced ratio between brooder and fast-acting types, which is in itself an indication that the gene's real relevance for population-genetic adaptation must not be overestimated.

The Pairwise Mismatch Rate (PMR) measures allele similarity between two individuals exclusively at autosomal SNP positions actually observed in both genomes. Genotype data are harmonised to the positions and allele directions of the 1240K panel, and each position is encoded as the reference or alternative allele. For read-based data, one pseudo-haploid allele is drawn deterministically from quality-filtered base calls; the minimum mapping quality is 25 and the minimum base quality is 20. Positions that were not observed or cannot be harmonised reliably remain missing. No imputation is performed.

The complete source matrix currently contains 7,199 individuals and all 25,909,201 possible pairs. After identity resolution, matching to GeoGens, and quality filtering, the runtime view contains 6,412 individuals with 20,171,257 valid relationship records. Not all genomes have yet been evaluated because some require time-consuming conversion into suitable file formats. The selection is expanded continuously.

How the value is calculated

For two individuals A and B, let C be the set of SNPs at which an allele is available for both. At every position, the two alleles encoded as 0 or 1 are tested for equality. With N = |C| jointly observed SNPs and M = Σ 1(xA ≠ xB) mismatches, PMR(A,B) = M/N. A raw value of 0 means that the two individuals do not differ at any jointly observed position; the fraction of differing alleles increases with the value. The complementary allele-sharing value is 1 − PMR. Missing positions enter neither the numerator nor the denominator. A pair must have at least 50,000 jointly observed SNPs to enter the current GeoGens runtime.

The symmetric raw PMR remains unchanged in the detail window. For map display, colour, and sorting, it is additionally converted into a bilateral reciprocal rank. First, every individual's quality-valid partners are ordered by ascending PMR; smaller values are nearer. Ties receive the midrank. The rank position is then normalised from 0 to 1, where 1 denotes the nearest and 0 the most distant partner. For pair A–B, GeoGens calculates this rank position once within A's complete profile and once within B's complete profile. The bilateral pair score is the conservative minimum of the two perspectives.

This bilateral evaluation is necessary even though a pair's raw value is symmetric: differences in preservation, SNP coverage, and available comparison partners can shift one individual's entire PMR distribution relative to another's. A pair is therefore considered strongly similar only when B ranks correspondingly high in A's profile and A ranks correspondingly high in B's profile. This limits one-sided outliers and spurious universal proximity. The selectable, individual, or global colour scale only stretches this unchanged bilateral value and alters neither its percentage nor its ordering.

Interpretation

For a culture or cumulative selection, the unweighted median of all available bilateral pair scores is calculated for each target. Missing relationships are omitted and are not counted as zero. ‘Contributions n/N’ shows how many selected individuals actually contribute to that target; median SNP overlap additionally describes the shared data basis. Raw PMR, bilateral rank, and SNP overlap therefore answer different questions and must not be conflated.

PMR is a descriptive measure of genome-wide allele concordance on the basis observed jointly for each pair. It is neither an ancestry proportion nor a kinship coefficient and does not prove a direct genealogical relationship. A small raw value or high bilateral rank can arise from close kinship, shared population history, or broadly similar ancestry. SNP overlap is evidence for the breadth of the comparison but is not itself a similarity value; line width and quality information must therefore not be read as additional genetic proximity.

The Shared genetic drift (f3-outgroup) method measures how much population-history drift two individuals share relative to a fixed outgroup. The current calculation explicitly does not use Mbuti but four observed YRI reference individuals: NA18486, NA18488, NA18489, and NA18498. YRI stands for Yoruba in Ibadan, Nigeria, the West African reference population of the 1000 Genomes Project; it serves as the outgroup because it stands roughly equally distant from all Eurasian individuals compared here. Their calls derive from the published 1000 Genomes reference in the hapROH 1240K resource and are harmonised to the same autosomal 1240K positions and allele directions as the ancient individuals.

The materialised f3 source matrix is based on the same 7,199 individuals and 25,909,201 pairs as the PMR matrix. After identity resolution and quality filtering, 6,412 individuals with valid relationships were admitted to the GeoGens runtime. PMR and f3 therefore refer to the same controlled sample frame but remain conceptually different measures: PMR counts direct differences between A and B, whereas f3 counts allele states shared relative to the outgroup.

How the value is calculated

For pseudo-haploid alleles encoded as 0 or 1, the implemented formula is f3(A,B;O) = (1/N) × ΣO Σl (xA,l − xO,l)(xB,l − xO,l). The sum runs over all four YRI references O and all positions l at which A, B, and the respective O jointly have a call; N is the total number of these valid A–B–O comparisons. With binary single-allele calls, a position contributes exactly 1 to the numerator when A and B carry the same allele and the respective YRI individual carries the other allele. If all three agree, or A and B differ, the contribution is 0. The raw value is the mean across all valid comparisons.

The reported f3 overlap is therefore not simply the number of unique SNP positions. Each position can enter the denominator with up to four YRI individuals, so the number of valid A–B–O comparisons can exceed the number of SNPs in the panel. A pair enters the runtime only with at least 50,000 SNPs jointly observed in A and B. To form the bilateral f3 rank, the f3 comparison basis must additionally contain at least 50,000 valid A–B–O observations. If it falls below this threshold, an available raw value can remain documented but receives no bilateral f3 score.

Larger raw f3 values indicate more drift shared relative to YRI. Every individual's valid partners are therefore ranked by descending f3 value; ties receive the midrank. The position is normalised from 0 to 1, with 1 for the nearest and 0 for the most distant partner. As with PMR, B's rank in A's complete profile and A's rank in B's complete profile are calculated. The displayed bilateral score is the conservative minimum of the two perspectives. This check prevents shifted individual value distributions or one-sided outliers from appearing as generally strong proximity.

Interpretation

For group and cumulative selections, each target uses the unweighted median of the available bilateral f3 pair scores. Missing relationships are not inserted as zero. Contributor count and median f3 overlap show how many selected individuals and how many valid A–B–O comparisons support the summary. The three colour scales change only the visual stretch of the bilateral rank; raw f3, percentage, and ordering remain unchanged.

The f3 value is a relative measure of shared drift and is neither an ancestry proportion nor a date, migration direction, or definitive statement of kinship. Its numerical value depends on the fixed outgroup, the observable SNP basis, and the composition of the comparison matrix. The map method calculates f3 between individual pseudo-haploid samples; it does not estimate a population-level standard error or Z score. SNP overlap and bilateral stability document the data basis but do not replace a confidence interval. PMR and f3 must therefore be compared only within their respective scales and interpretations.

The cluster view divides the currently filtered map selection into two to twelve computational groups according to the selected features. It searches for similarities in a multidimensional feature space using a deterministic k-means procedure. The groups are derived entirely from the currently visible individuals and the selected settings. They are not predefined populations, ethnicities, or archaeological cultures and need not remain stable when the selection, period, features, weights, or number of clusters changes.

Hair colour uses the three raw axes blond, red, and dark from Morgan/Lona-Durazo (Strict); eye colour uses the blue, brown/dark, and green/mixed axes from Lona-Durazo/Sulem (Strict); skin colour uses the light and dark axes from Liu/Jacobs (Strict); and COMT uses the number of effect-allele copies normalised from 0 to 1. Numerical model values are used rather than the colour labels derived from them. Allele similarity (Pairwise Mismatch Rate) and Shared genetic drift (f3-outgroup), by contrast, enter as global profiles of the complete measured relationship network.

How the value is calculated

For PMR and f3, a weighted graph is first constructed for each method: every evaluable individual is a node, every positive bilateral relationship is an edge, and its bilateral rank is the edge weight. In the current build, both graphs contain 6,412 nodes; the PMR network contains 20,164,847 and the f3 network 20,164,848 positive measured edges. The adjacency matrix W is symmetrically normalised with weighted node degrees D as S = D⁻¹ᐟ²WD⁻¹ᐟ². The leading eigenvectors of S are determined, the trivial first eigenvector is discarded, and the following twelve axes are weighted by their eigenvalues. Each individual's resulting twelve-dimensional vector is normalised to length 1. This translates the complete bilateral relationship profile into comparable spectral coordinates without replacing individual missing pair values with invented raw values.

Within the current map selection, every included dimension is brought onto a 0-to-1 scale by min-max scaling. If all individuals have the same value in a dimension, a neutral value of 0.5 is assigned. The selected feature weight is distributed equally across that feature's dimensions and applied to the coordinates as a square-root factor. A three-axis colour model therefore does not automatically exert three times as much influence as COMT merely because it contains three numbers. Multiple selected features then form one joint feature vector per individual.

In ‘Exclude incomplete’ mode, only individuals with every selected dimension available are clustered. In ‘Tolerate gaps’ mode, no imputation is performed: the distance between two vectors is the mean squared difference across only their jointly available dimensions, and centroids are averaged per dimension from the member values available there. If an individual lacks every usable dimension, it remains grey and unclustered. The effective number of clusters also cannot exceed the number of evaluable individuals.

Starting centroids are selected reproducibly: first the evaluable vector with the greatest magnitude, then at each step the point farthest from its nearest already selected centroid. K-means then assigns every individual to the nearest centroid and recomputes each centroid as the dimension-wise mean of its members. Assignment and recomputation repeat until stable, but no more than 40 times. Thus, with an identical selection and identical settings, there is no random initialisation that produces different groups on every run.

Interpretation

After clustering, groups are ordered along the first principal axis of their feature vectors and only then receive numbers and colours. For geographic naming, a two-by-two contingency table of membership and non-membership is formed for every group and every defined geographic region. GeoGens calculates the phi coefficient and uses the region with the strongest positive association and greatest support as a descriptive name. Geography therefore does not enter the cluster calculation retrospectively; the name only summarises the group already formed.

Cluster distances are calculated from the distances between their centroids. The dendrogram then joins the groups or branches with the smallest mean pairwise centroid distance using average linkage. It visualises proximity among the already calculated clusters but changes neither their members nor the k-means assignment. Because k-means always forms the requested number of groups, a cluster alone is not evidence of a natural population-historical boundary. The most defensible patterns are those that remain stable with appropriate features, sufficient data, and plausible changes to filters, weights, and cluster count.

Y-DNA describes the paternal line and is therefore in principle only to be expected in male individuals; mtDNA describes the maternal line and can be present in both sexes. Y haplogroups and mt haplogroups are consequently observed separately on principle.

The current haplogroup map is based on the merged GeoGen sample universe. After spatial exclusions and deduplication, the current public export contains 17,585 find and analysis points. Y-DNA is published with two separate current values: a hierarchical designation and a terminal SNP node. Imported AADR/Poseidon values and approved TruHaplo reanalyses remain available as auditable provenance; curated evidence decisions can replace both current values together. For mtDNA, the mtHaplogroup label is used.

TruHaplo extends this dataset through intensive reanalysis of aligned Y-chromosome read evidence. The run finaler_pathphynder_y_run_20260703-071029 used PathPhynder 1.2.4 and BigTree.Y_GRCh37.V2.250606. The 1,163 initially released Step 2 calls from lineages I and R were subsequently checked against the derived and ancestral markers at their actual final call. 1,064 calls have a derived-marker majority and remain approved; 99 ties or ancestral majorities are rejected and cannot override the imported call. Review, no-call, and Step 1 results remain excluded.

PathPhynder/BigTree and the YFull/YTree tree used for relationship and TMRCA functions do not consistently share the same nomenclature. The hierarchical designation, terminal SNP, and internal calculation node therefore remain separate. Terminal filters use only the current terminal designation; internal calculation nodes appear neither as terminal filters nor in parentheses after the hierarchical designation. YFull age estimates never rewrite a haplogroup call automatically.

Tree resolution is not performed by string comparison alone. Y-DNA is checked against the YFull/YTree export 14.04.0 (43,261 nodes, 1,307,048 alias forms, CC BY 4.0); mtDNA is checked against HaploGrep/PhyloTree RSRS 17.1 (5,435 nodes, 102 alias forms). A label counts as usable if it can be resolved as an exact node or as an alias; for mtDNA, prefix nodes are additionally permitted provided they are not merely root-like. Very broad upper nodes such as R, R1, I, J, CT, F, or H for Y-DNA, and H, U, J, T, K, L, M, N, R, V, W, X, or I for mtDNA, are deliberately marked as broad_root. They can be visible for a coarse surface colour, but they are not treated like terminal lineages.

The count is therefore stricter than a mere label count. Across the entire dataset there are 8,855 Y-DNA calls by calculation priority; of these, 8,029 are usably resolved, 819 are broad root nodes only, 5 are invalid, and 2 cannot be resolved in the reviewed tree. 8,730 profiles have no Y call by calculation priority. For mtDNA there are 12,890 calls; 12,324 are usably resolved, 545 are broad root nodes only, 21 are invalid, and 4,695 are missing. Missing here means only this: no analysable haplogroup call is available for this axis. It is not a negative finding, and certainly not a population-genetic zero value.

The map surface for haplogroups is categorical. For Y-DNA, only sufficiently deep clades are admitted as a surface category, typically something like I1 or R1b rather than merely I or R; for mtDNA, subclades such as H1 or U5, and not merely H or U, are grouped accordingly. Every analysable point contributes its group colour with constant weight, within the current time window and after the active filters. Where several lineages overlap spatially, the weighted average colour of the competing groups results. The point colour can additionally be overridden by selected prefixes, so that sublineages such as I2a, R1b, or U5 can be traced specifically.

Clicking a data point switches the haplogroup display to lineage mode. This shows graphically: with which other individuals in the time window could a possible paternal or maternal lineage relationship theoretically exist in the reviewed tree? That is speculative, but it is a reasoned hypothesis.

For Y-DNA and mtDNA, the selection and the candidate are first resolved against the respective reviewed tree index. A candidate is marked as a possible Y ancestor or mtDNA ancestral lineage if its node lies above the selected node in the tree and its dating lies earlier under the same rule. Identical resolved nodes too, for example I2a1 to I2a1, can be shown as a possible ancestor given a clearly older dating, or as a possible descendant given a clearly younger dating; without a clear dating direction they remain visible as the same lineage without a direction. It counts as a possible Y descendant or mtDNA sublineage if its node lies below the selected node and is chronologically younger. Broad higher-level nodes and invalid or missing resolutions are blocked for such directed arrows, because no robust direct lineage follows from them. They are nevertheless still marked by colour! The arrows therefore do not show all connections that come into question, but only the most obvious ones.

The dating check runs strictly by default and always follows the active dating anchor. Earliest uses the older end, Mid the mean value, Latest the younger end; with Overall, the midpoint applies to lineage direction, arrows, and sorting, while visibility within the time window continues to use the entire span. Strict means: for a possible ancestor, the candidate anchor must genuinely be older than the selection anchor; for a possible descendant it must genuinely be younger. If the strict check is switched off, a tie at the same anchor may still count as a direction where the tree nodes differ.

Agnatic Y collateral lineages are calculated differently from direct ancestors. This requires a shared Y node that is neither the candidate node nor the selected node itself. Its TMRCA in years before present is taken from the tree. The older of the two samples is converted into years before present: olderSampleYbp = 1950 + dateBce. The following then applies: agnateDistance = tmrcaYbp - olderSampleYbp. If this value is negative, the shared lineage node would be younger than the older sample; the relationship is then not counted as agnatic. If the value is not negative and is at most as large as the chosen threshold, the relationship appears as a close Y collateral lineage. The threshold can be set to 1,000, 2,000, 3,000, 5,000, or 10,000 years; the default is 5,000 years.

In conservative mode, both Y values must be resolvable as exact, alias, or prefix, and the shared node must not be a very broad higher-level node. In exploratory mode, weaker signals are additionally made visible: Y collateral lineages up to 10,000 years beyond the close threshold appear as distant Y collateral lineages; very coarse identical major lineages can be shown as a broad identical Y major lineage. Such hits are explicitly less robust and are meant to be search leads rather than results. For mtDNA there is no analogous agnate calculation, because the mtDNA tree file used here does not supply comparable TMRCA distances for collateral lineages; what is shown there is direct mtDNA ancestral lineages, mtDNA sublineages, the same mtDNA lineage, and the same but not more precisely resolved mtDNA major clade.

Arrow display: the arrows are drawn according to fixed rules so that the map does not become unreadable when many possible relationships exist. By default, only the 5 geographically nearest ancestors and descendants are given an arrow. This can be regulated with the checkbox 'Show arrows only for nearest ancestors/descendants', and the number can also be changed. The small label along the arrow gives a rough indication of the generations lying between the two dating points. A generation is taken as 25 years here, and the value is rounded up in case of doubt. Dashed arrows indicate low or missing lineage confidence.

Competing ancestors are shown only in single-selection mode, and they are shown in green. For every visible descendant, other potential ancestors are searched for and ranked by geographic proximity, with the three geographically nearest ones being displayed. The condition is that they are not more than twice as far from the descendant as the selected point. Put loosely, these green arrows warn against assuming descent, because competing ancestors exist. If such a competing ancestor is too far away, it no longer carries an arrow.

In culture mode, a click first selects the sample's subgroup, where one exists. Clicking again within the same culture expands the selection to the entire culture group. The time window is then adjusted to this selection and widened to at least 3,000 years so that as many points as possible become visible. In cumulative mode, individual samples are added or removed by clicking. The point chosen most recently remains the primary anchor, again with a window width of at least 3,000 years. Both modes compute the lineage relationships separately for each selected anchor.

For the question of which colour a point should receive in a multiple selection, the following order applies: selection > ancestors/descendants > close Y collateral lineages > distant Y collateral lineages > broad Y major lineages > same unresolved root. The legend counts in multiple-selection modes, by contrast, count the relations across the individual contexts.

The AADR has no consistent archaeological culture assignments. That is regrettable, because archaeology positively lives off its culture concepts and off the contextualisation of finds. For more than ten years, effort has gone into collecting archaeogenetic data while at the same time almost no effort has been made to match that data against actual material cultures. A few of the AADR labels do contain a coarse “CordedWare” or “BellBeaker”, but the vast majority of individuals are labelled only laconically as “Spain EBA” (Spanish Early Bronze Age) or “UkraineN” (Ukrainian Neolithic). Because people from the same present-day national territory may well have lived, thousands of years ago, under starkly contrasting circumstances and in materially distinct culture groups, and because these culture groups probably often represented something like tribes or peoples, it matters a great deal which individual permits a clear assignment here and which does not.

In general, the culture-group classification must of course be treated with caution. We are by no means always certain whether an archaeological culture really corresponds to a shared sphere of custom, language and exchange. Often it does, and this can be demonstrated very well from the culture horizons of the Migration Period. But because archaeology today is often carried out separately in different countries, the culture-group classification is rarely uniform. One and the same group may be given different names. Very different cultures that are only loosely connected are sometimes subsumed under a single term. The chronological trajectories are not clear either. One and the same continuum of forms is sometimes sharply subdivided and given different names; sometimes proto- or post-cultures are invented that have little of substance to do with the culture in question. In short: the nomenclature is inconsistent and sometimes misleading. The observation of archaeogenetic data should not proceed from the culture group as a “people” alone, nor make itself dependent on the culture concept at all. But it would be just as insane to ignore the material circumstances of finds and to lump every hunter in Europe between 20,000 and 7,000 BCE under the stamp “WHG”, even though many material differences and also anthropological clusters are to be found within that span. Finally, the questions of the Indo-Europeanisation and Neolithisation of Europe can likewise only be resolved in the context of the culture groups.

That is why I have taken this task upon myself in painstaking detail work, researching the circumstances of the finds epoch by epoch and region by region in order to identify “culture groups” and “culture subgroups”. For the research I did of course occasionally use AI-assisted source searches, since otherwise the task would have been unthinkable from the outset, given that it involves more than 17,000 records. But because I checked the AI research strictly and repeatedly, it still amounted to a considerable effort.

Now to the technical side:

•Nicht alle Kulturguppen haben Kulturuntergruppen. Wenn sie aber Untergruppen haben, müssen alle ihre Individuen eine solche zugewiesen bekommen.

•In jeder Zeitstufe sind Individuen, deren Zuordnung unklar ist und die folglich keine Kulturgruppe haben. Bei diesen brauche ich noch Unterstützung. Manchmal ist eine Zuordnung aber aufgrund der Fundumstände generell nicht möglich (Moorleichen, Strandfunde, beigabenlose Skelette usw.). Es heißt dann nur z.B. „(Unsichere Zuordnung - Neolithikum)“

•Dann gab es Fundkontexte die mit einigermaßener Sicherheit einer bestimmten Kultur zugewiesen werden konnten, aber als „Unsichere Zuordnung“ markiet wurden. Ist das der Fall, erscheint im Echtzeitfenster ein Fragezeichen hinter der Kulturzuordnung. Beim rechten Anteilsanzeigefenster lässt sich sodann per Haken einstellen, ob diese unsicheren Zuordnungen berücksichtigt werden sollen oder nicht.

•Hinweis: Ich bin mit der Kulturgruppenzuweisung noch nicht ganz zufrieden. Sie ist an manchen Stellen grob und insbesondere in Zentralasien und dem nahen Osten sind viele Funde unkuratiert, die man mit entsprechender Quellenkenntnis garantiert zuweisen könnte. Es sind sicherlich auch noch einzelne Falschzuweisungen vorhanden. Ich bitte um Mithilfe und Hinweise auf Korrekturen und Ergänzungen!

The colouring of the map is of course not an attempt to mark out territories or to give exact descriptions of conditions. It is an aid to visualisation, allowing approximate tendencies to be read off from an otherwise confusing sea of data points. Every evaluable data point contributes to the surface at its geographic position with the same spatial influence kernel.

This visual aid remains a smoothed reading surface and not a population or territory map. If several evaluable individuals come from the same site, their statements are retained individually and are not aggregated in advance into a site-level majority. Opposing values therefore compete within the surface, without ties causing entire sites to disappear.

In addition, points surrounded by a large amount of land mass have a stronger effect than points located on islands such as Sicily or Gotland, which can therefore colour hardly any land mass. The haplogroup-arrow system is subject to a similar representational distortion, in that arrows spanning greater distances take up more space and catch the eye more quickly than small-scale relationships.

A further weakness lies in actually translating the trait values into colours and in equating the spectrum of trait variance uniformly with the spectrum of the colour gradient. Strictly speaking this is of course impossible, and all that remains here is to concede that the intensity of the colour gradient must be interpreted very cautiously and should wherever possible always be considered in relative terms. Relative to what? Above all relative to other finds of the same period. And relative to the method. A colour difference in trait A can mean considerably less than the same colour difference in trait B or method C.

For the bilateral A-or-B values (muscle type, lactase persistence, eye colour, etc.), colour bands were used. For numerical value gradients (light complexion, height, skin pigmentation), by contrast, gradients were used. For light complexion, the colour-band mode was also built for the sake of comparability.

In principle the map generates a circle of influence from every evaluable data point. For hair colour, eye colour, light-complexion bands, metabolism, muscle type, COMT, culture groups and haplogroups the radius is 3.0 degrees of latitude; for height, skin values and continuous light complexion likewise 3.0; for lactase persistence 3.8. The influence falls off sharply with distance. Below a minimum strength no surface is drawn: 0.32 for discrete colour bands, 0.28 for continuous values and 0.38 for lactase persistence. The minimum strength is governed by the total local kernel strength and not by how extreme the trait value is. Lactase persistence additionally has a small close range of 0.28 degrees, so that a positive signal remains visible immediately at the site.

Hair colour, eye colour, metabolism, muscle type, COMT and light-complexion bands are discrete colour bands. A point does not contribute a number on a gradient but a category. Every scientifically admitted point receives the same spatial kernel within the active trait; score and quality information determine the admission and the outcome respectively, not the geographic reach or the opacity.

Hair colour: the Morgan/Lona-Durazo, Hysi/Morgan/Lona-Durazo, Morgan 2018 and Tanigawa 2022 methods each take the winning category of their active model. Blond and red-blond run towards the light colour direction, brown/dark towards the dark one, dark-blond intermediate bands towards intermediate colours. Unknown or blocked profiles do not contribute to the surface.

Eye colour: the Lona-Durazo/Sulem, Simcoe/Lona-Durazo and minimal methods, as well as the SNP deep search, use the same map logic. Blue contributes towards the blue direction, brown/dark towards the brown direction, green/mixed towards an intermediate colour. What is decisive is the active status of the respective model; unknown profiles are not coloured.

Skin pigmentation and height are continuous gradients. Skin values are limited to -1 to +1 and coloured from pale to tanned pigmentation. Height values are likewise limited to -1 to +1; lower values run into blue, higher ones into red, the middle stays neutral. Yengo/Marouli/Hawkes (exploratory) and (strict) use their own centring around the active middle range, so that small real differences do not disappear visually. Insufficient profiles are excluded; every admitted point receives the same spatial kernel.

Light complexion exists in two map forms. The continuous mode averages the available strict light values of the selected components and maps them onto -1 to +1: green means low light complexion, yellow/orange medium to stronger, red high light complexion. Every admitted point receives the same spatial kernel irrespective of how extreme its value is; the value determines only the colour. Only sufficiently well-supported strict eye profiles are used as a component. The band mode uses the same values but applies fixed classes: from +0.5 very light, from 0 light, from -0.5 darker, below that dark. If a required or sufficiently well-supported component is missing, the point remains grey, that is, without any contribution to the surface.

Lactase persistence is bilateral. An observed rs4988235 LP allele colours green, an observed marker without an LP allele blue. Missing markers are not counted as absent lactase persistence and do not contribute to the positive or negative decision. Where both signals compete within a region, the stronger weighted neighbourhood prevails.

Metabolism, muscle type and COMT are small discrete modes. Metabolism colours animal-food metabolism, mixed type or plant-food metabolism. Muscle type colours Strength type, mixed type or Stamina type. COMT colours Doer type, mixed type or Ponderer type. Missing individual markers do not contribute to the surface.

Culture groups, culture subgroups, Y haplogroups and mt haplogroups are categorical surfaces. Every point contributes the group colour assigned to it with a constant weight; in overlapping regions the weighted average colour of the competing groups emerges. Uncertain, missing or neutral assignments are not asserted as a dominant colour of their own.

One important aspect of our data visualisation was that of contemporaneity. Comparing genetic data across long spans of time has repeatedly led to errors and inconsistencies in the past, and for various reasons it is generally hazardous. Hence our approach of always viewing the map within a concrete time window. The width of the observed window is adjustable; it can be chosen from 100 to 3,000 years (for culture groups only up to 1,000 years, for performance and labelling reasons). For haplogroups a range between 500 and 10,000 years can be selected, because here it is above all ancestor-descendant relationships that are examined, and less so contemporaneity. The current year shown at the right of the timeline always determines the midpoint of the time window, so that with a 1,000-year window all data points are displayed that lie up to 500 years before and up to 500 years after this midpoint.

Wo liegt nun ein Datenpunkt? Diese Frage ist schwieriger als sie zunächst scheint. Prähistorische Individuen haben selten einen Grabstein mit ihrem Todestag. Sie werden C14-datiert und dabei ist oft eine erhebliche Spanne möglich. Etwa kann ein Skelett auf den Zeitraum von 2400 to 1900 BCE datiert werden. Wo liegt dann dieser Datenpunkt? Dafür haben wir vier Auswahlmodi in dem Panel „Datierungsanker“, mit denen diese Frage beantwortet werden kann. Entweder der früheste Zeitpunkt der Datierung wird gewählt oder der späteste. Die pragmatischste dritte Möglichkeit besteht darin, den Mittelwert zu verwenden, in unserem Beispiel wäre das 2150 BCE Wer sich mit diesem rechnerischen „Zurechtstutzen“ nicht abfinden will, wählt die vierte Datierungsmethode „Gesamt“, bei der ein Individuum sowohl bei seinem frühesten als auch spätesten Datum angezeigt wird und natürlich auch im Zeitraum dazwischen. Im Extremfall gibt es aber altsteinzeitliche Funde, die ganz grob zwischen 20.000 und 10.000 BCE datiert sind. Mit dem „Gesamt“-Datierungsanker würde ein solcher Datenpunkt also sehr lange auf der Karte sichtbar sein und ggf. eine „ungerechte“ Dominanz und Sichtbarkeit erhalten, nur weil die Archäologie hier zufällig nichts genaueres angeben kann oder will.

The AADR data basis lists several analyses for some individuals. This is because individual finds were analysed at different times, in different laboratories, or with different degrees of stringency. An individual may therefore have been examined very strictly in analysis A and consequently show far fewer genes, but these genes come with far greater scientific robustness. In analysis B the search run may have been less strict, which permits more statements but gives those statements less weight.

The filter area therefore offers, under the basic filters, the option of displaying either the "most informative analysis" of each individual (relative to the trait currently under investigation), disregarding data quality. Alternatively, only the strictest analysis per individual can be displayed, which may cause data points to drop out but guarantees higher quality in return. The third option is "All analyses", with which such individuals show all of their data points on the map. This makes it possible to check where the analyses differ, but it may risk mistaking a single individual for several different ones if one is not paying close attention.

The right-hand share panel is a real-time ranking of the currently loaded map selection. It answers the question: in which culture groups, geographic regions, or haplogroups is the selected trait value highest? Only individuals for which the active map mode can supply a numerical value are used for this; unknown, grey, or blocked profiles are removed from this calculation and are not counted as zero.

Every evaluable point is first brought onto a uniform scale from 0 to 1. A value of 1 always denotes the positive direction of the heading, for example lactase persistence, animal-food metabolism, Strength type, worrier, blond/light hair, blue/light eyes, lighter skin, a higher height index, or high light complexion. For indices running from -1 to +1, (value + 1) / 2 is used; for skin values the direction is deliberately inverted, so that paler skin appears as the higher value: (1 - value) / 2.

For every possible group, the mean of its evaluable members is calculated. This mean appears in the bar as Ø in per cent. In addition, a point-biserial Pearson r is calculated: group membership is coded as 1, non-membership as 0, and the trait value remains the 0-1 value. Formally this is r = ((group mean - remainder mean) / standard deviation of all values) × sqrt(p × q), where p is the share of the group and q the share of the remainder. A group is only shown if it reaches the configured minimum sample size.

The two geographic gradients are calculated separately. Northing and westing are normalised coordinate values within the fixed map BBox; the current trait value is correlated against this coordinate using Pearson r.

The correlation page uses only individuals with a valid dateMidpointBce in the range 45,000 BCE to 1,000 CE, and only complete cases: both selected traits must be evaluable for the same individual. A missing marker basis is not counted as 0 but is removed from the pair count and the statistics.

All values are harmonised to a 0 to 1 scale. Blond hair and blue eyes use the respective strict evidence, skin pigmentation is read as the paler skin direction, and height is transferred from the index bounded to -1 to +1 via (value + 1) / 2. Lactase persistence is 1 for an observed LP allele and 0 for an observed non-LP; metabolism, muscle type, and COMT use 1/0.5/0 for the respective positive, mixed, and negative direction. Northing and westing are normalised coordinate values. Time stages, culture stages, and geographic regions are shown as group means in stage mode.

For two numerical traits, one pair (x, y) is formed per individual. GeoGens calculates Pearson r as the covariance divided by the product of the standard deviations; r lies between -1 and +1. Positive values mean trait expression in the same direction, negative values in opposite directions. Below n = 5, r remains locked; between n = 5 and n = 19 the block counts as limited, and from n = 20 as calculated. The time series divides the same complete cases into selectable blocks of 100, 200, 300, or 500 years; the overall r uses all cases, while the time-window r uses only the blocks visible in the graph.

Das 95%-Konfidenzintervall wird über die Fisher-z-Approximation berechnet: z = 0,5 × ln((1 + r) / (1 - r)), Standardfehler = 1 / sqrt(n - 3), Rücktransformation über tanh. p-Werte und Benjamini-Hochberg-q-Werte werden bewusst nicht ausgegeben. Die Datenpunkte sind nicht unabhängig: Fundorte sind räumlich autokorreliert, benachbarte Zeitblöcke zeitlich, Individuen desselben Gräberfelds häufig miteinander verwandt, und dasselbe Individuum kann über mehrere AADR-Analysen in dieselbe Rechnung eingehen. Der Standardfehler 1 / sqrt(n - 3) setzt aber unabhängige Beobachtungen voraus; er unterschätzt hier den wahren Fehler, ein daraus berechneter p-Wert wäre antikonservativ, und eine FDR-Korrektur darauf würde diesen Fehler erben und ihm zusätzlich den Anschein einer Multiplizitätskontrolle geben. Das Intervall bleibt, weil es als Breite gelesen wird: Es zeigt, wie wenig die Stichprobe r festlegt.

This view asks a targeted question: which traits are elevated or reduced within this group? And with which other groups does it overlap particularly strongly?.

A group only appears in the selection field once it reaches the configured minimum group size: 3, 5, 10, 30, or 100 individuals. For calculated values, a minimum sample size of 3, 5, or 10 evaluable cases additionally applies. Culture groups and subgroups are by default compared only with data points from their own date range! with "Compare with the entire period" the full correlation basis is used instead, which here means 45,000 BCE to 1,000 CE.

For each trait, group membership is coded as 1 and non-membership as 0; the trait value is again the harmonised 0-1 value. Pearson r is calculated from this. In addition, GeoGens shows the mean within the group, the mean outside the group, and the difference in percentage points. A positive delta means that the trait value is higher in the selected group than in the comparison basis. A missing trait basis, a missing culture or region basis, and a missing haplogroup resolution are excluded from the respective calculation.

The variance figure refers to the standard deviation of the analysable individual values within the selected group. It is not interpreted in absolute terms but ranked relative to other groups of the same category and the same trait: the lower roughly 35 per cent count as rather homogeneous, the upper roughly 35 per cent as rather heterogeneous; the middle values receive no strong classification. The intention is to show not only whether a group has, for example, high blond values, but also whether those values scatter widely within the group or occur strikingly tightly clustered.

Over time, the same calculations are repeated block by block. The blocks can be formed as thirds, fifths, millennia, or centuries; for thirds and fifths, the division can be made either by time span or by equal numbers of individuals. A time series requires a comparison window of at least 300 years. Each block must contain enough analysable overall cases and enough group cases; otherwise it remains locked. Y-DNA and mtDNA series additionally show the five most frequent haplogroup prefixes at the level selected above, for example I, I2, or I2a.

The top correlating groups are computed as an overlap analysis. For a candidate group, it is checked how often it occurs inside the selected group and how often outside it. Only positive enrichments are shown; sorting is by the difference of the rates, then by r, overlap, and candidate size. Here too only r, n, and the 95% confidence interval are reported; p and q values are omitted for the same reason as in the trait correlations.

To enable better filtering and classification, each individual was assigned a time stage and culture stage according to its dating and archaeological find circumstances. The time stage is absolute and based on exact calendar years (the midpoint of the individual's dating was used):

Palaeolithic (until 10,000 BCE)

Mesolithic (10,000-7,000 BCE)

Neolithic (7,000-3,500 BCE)

Chalcolithic (3,500-2,000 BCE)

Bronze Age (2,000-800 BCE)

Iron Age (800 BCE-300 CE)

Migration Period (300-700 CE)

Early Middle Ages (700-1,000 CE)

Middle Ages (1,000-1,500 CE)

Early Modern Period (1,500-1,800 CE)

Modern period (from 1,800 CE)

The culture stage, by contrast, is relative, meaning that it derives from the actual find circumstances. Mesolithic hunters still lived in Northern Europe when Neolithic farmers had long been living in Southern Europe; the same applies to the spread of bronze casting and ironworking. Culture stages shift gradually, and the question of the actual stage is therefore often more interesting than an absolute chronological classification, for example when comparing changes in metabolism or character traits between sedentary and non-sedentary peoples. It goes without saying that culture stages imply no value judgement regarding the people concerned.

To enable better filtering and to examine traits by geography, common geographic regions were introduced as categories. Some are very broad (Western Europe, Eastern Europe), others more localised (Baltic region, Italian islands). Each individual can therefore belong to several Geographic Regions. The geographic regions are defined as follows:

Italian Peninsula

Present-day Italy excluding the part north of Rimini.

Italian islands

Corsica, Sardinia, and Sicily.

British Isles

Present-day borders of Great Britain and Ireland.

Iberian Peninsula

Present-day borders of Spain, Portugal, and Andorra.

Gaul

France, Belgium, Luxembourg, Monaco, Channel Islands, the Federal Republic west of the Rhine, and the Dutch provinces of Limburg, North Brabant, and Zeeland.

Central Europe

Germany east of the Rhine, Lower Austria, Upper Austria, Vienna, Czechia, Slovakia, Poland, and Kaliningrad Oblast.

Scandinavia

Sweden, Denmark, and Norway up to the 65th parallel.

Finno-Karelia

Finland, European Russia north of the 60th parallel, and Norway/Sweden north of the 65th parallel; Siberian administrative units are excluded.

Baltic region

Estonia, Latvia, Lithuania, Kaliningrad Province, and Warmian-Masurian Voivodeship.

Northeastern Europe

Belarus, Ukraine excluding Odessa, Transcarpathia, and Crimea, the Central Federal District of Russia, and the Baltic region.

Balkan Peninsula

Bounded to the north by and including Slovenia, Hungary, Romania, and Moldova; includes Odessa and Transcarpathia in Ukraine.

Aegean

Greece, Eastern Thrace, and Turkish provinces 41, 77, 16, 10, 17, 45, 35, 9, 48, 7.

Adriatic

Slovenia, Croatia, Bosnia and Herzegovina, Montenegro, and Albania.

Anatolia

Turkey excluding Eastern Thrace and excluding the eastern provinces 8, 25, 75, 36, 76, 4, 49, 12, 21, 63, 47, 72, 56, 13, 30, and 73; including Van.

Pontic Steppe

Ukraine east of Kyiv and the Russian regions Krasnodar, Rostov, Volgograd, Adygea, and Crimea.

Caspian Steppe

Southern Russia excluding the Pontic Steppe, the North Caucasus, and the Kazakh regions West Kazakhstan, Atyrau, Mangystau, and Aktobe.

Pontic-Caspian Steppe

Pontic Steppe plus Caspian Steppe.

Carpathian Basin

Hungary, Serbia north of Belgrade, the Romanian planning regions Nord-Vest/Centru/Vest, southwestern Slovakia, and defined Croatian counties.

Lower Danube region

Northern Bulgaria, western/southern/eastern Romanian regions and Bucharest, and Serbia excluding Kosovo.

Alpine region

Everything within the polygon Linz-Munich-Zurich-Geneva-Grenoble-Grasse-Sanremo-Turin-Como-Verona-Udine-Maribor-Vienna-Linz.

Western Mediterranean

Land within 100 km of the western Mediterranean coast; west of Catanzaro and east of Gibraltar.

Eastern Mediterranean

Land within 100 km of the eastern Mediterranean coast; south of Istanbul and east of Catanzaro.

Mediterranean

Land within 100 km of the Mediterranean coast.

Levant

Lebanon, Syria, Israel, the West Bank, and Jordan.

Mesopotamia

Iraq excluding al-Anbar, selected Iranian provinces, the Syrian provinces ar-Raqqa, al-Hasakah, and Deir ez-Zor, and the Turkish provinces of Mardin, Diyarbakır, and Şanlıurfa.

Caucasus

Russia south of the Makhachkala-Krasnodar line, plus Georgia, Armenia, and Azerbaijan.

Central Asia

Kazakhstan, Turkmenistan, Uzbekistan, Kyrgyzstan, and Tajikistan.

Persian Highland

Iran, Afghanistan, and Balochistan.

India

India and Pakistan excluding Balochistan.

Tarim Basin

Xinjiang region.

Ural

Southern and western Ural: the Russian regions of Chelyabinsk, Bashkortostan, Orenburg, Udmurtia, Tatarstan, Kurgan, Sverdlovsk, Perm, and Samara.

Siberia

Siberian administrative units east of the Urals, including Western Siberia, Altai, Central Siberia, and Transbaikalia.

Himalaya

Nepal and the Tibet Autonomous Region.

Iceland

Iceland and the Faroe Islands.

Canary Islands

Canary Islands.

Arabian Peninsula

Saudi Arabia, Yemen, Oman, Kuwait, Bahrain, Qatar, the United Arab Emirates, and the Iraqi region al-Anbar.

Maghreb

Tunisia, Algeria, and Morocco.

Northeast Africa

Libya, Egypt, and Sudan.

Northern Europe

Europe north of the polygon line Nantes-Paris-Dortmund-Magdeburg-Warsaw-Vilnius-Saint Petersburg.

Southern Europe

Europe south of the line Bordeaux-Zurich-Budapest-Odessa, excluding Anatolia and Caucasus regions.

Western Europe

Europe west of the Trieste-Szczecin line, excluding Norway and Sweden.

Eastern Europe

Europe east of the Trieste-Szczecin line up to the longitude of Atyrau, excluding Finland, Norway, and Sweden.

North European Plain

Flanders, the Netherlands, Luxembourg, Lower Saxony, North Rhine-Westphalia, Schleswig-Holstein, Saxony-Anhalt, Mecklenburg-Western Pomerania, Brandenburg, Berlin, Hamburg, Bremen, and Poland excluding Opole, Silesia, Holy Cross, Lesser Poland, Subcarpathian, and Lublin.

At the outset, the GeoGens project had to define a spatial and temporal framework. Since the main focus was to be European prehistory, Europe and adjacent regions (North Africa and Western Asia), as well as the period from 45,000 BCE to 1,000 CE, were chosen. The exact map boundaries are 25° west longitude in the west, 85° east longitude in the east, 72.5° north latitude in the north, and 20° north latitude in the south. This area may be expanded in future, but curating individuals and cultural groups requires additional work that had to be weighed accordingly. In any event, the vast majority of AADR individuals are located in Europe.

With regard to the temporal framework, hardly any informative genomes survive from before 45,000 BCE. The timeline can be switched at the lower left to begin at 15,000 BCE, making the selection clearer. The limit of 1,000 CE was chosen to avoid having to curate the large mass of medieval and modern genomes. They are insignificant for questions of prehistory. However, extending the period to the present is under consideration: first, to allow better validation of the trait maps, and second, to enable future comparison with personal genomes and their individual origins.

The status box at the lower left is a snapshot of the current map view. The time window, date anchor, filters, analysis selection, and method immediately affect its numbers.

Basic information

Visible

Individuals remaining after the current time and content filters.

Window

The two boundaries of the active time window around the slider midpoint.

Imported

The size of the loaded AADR map core before current filters.

C14 provenance

Summary of detected direct, C14-like, calendar, and contextual dating evidence.

Active time window

The rows below depend on the selected method. “Calculated” passes the full quality gate; “Limited basis” is mappable but narrower; “No basis” is grey and is not counted as a biological negative.

Marker basis

Directly observed target-marker calls; this is a call sum, not automatically the number of distinct SNPs.

Diploid calls

Calls with enough depth for both alleles. Shallow pseudo-haploid calls do not count as true diploid calls.

Median index

The middle value of evaluable individuals; it is a relative model index, not a phenotype prediction.

Weight basis

The observed share of absolute effect weight. A strong marker therefore counts more than a weak one.

All status numbers describe the visible selection. They are neither a population-genetic estimate nor a smoothed prevalence estimate. Missing markers are not treated as counter-evidence in GeoGens.

A point represents a visible individual. The map uses the published find coordinates; point size, opacity, and colour are determined by the active method. Hovering shows a short summary; clicking opens the detail panel.

Multiple individuals at the same site

Individuals with identical coordinates are deterministically spread around a small ring. The order follows the sample ID; the ring grows with group size and is capped. This is only a readability aid. The detail panel retains the published coordinates.

There is no general colour hierarchy. In most views points share the same drawing rank, so residual overlaps follow technical draw order. Exceptions are LP-positive points in the lactase view and selected or stronger relationships in the genetic-similarity view.

Display modes

All points

All individuals in the current selection, including grey or non-evaluable cases.

Relevant points

Only points with an evaluable basis for the active method. Hidden points are not historically irrelevant.

Surface only

Point circles are hidden while an active surface remains visible. In genetic similarity, the selection may remain as an orientation.

Colours and timeline

Categorical traits use fixed class colours; continuous indices use a gradient. Grey means missing, blocked, or non-mappable basis and is not a negative class. Optional colouring override can show another trait than the active analysis, so the legend must always be read with the selected method.

The timeline shows filtered, dated individuals across the full available period. “Earliest”, “midpoint”, and “latest” choose an endpoint or midpoint of a range; “full range” uses the full range for visibility and its midpoint for point position. Dense periods are only visually softened, not aggregated.

Data sources

•Allen Ancient DNA Resource (AADR) v66.0, Dataverse DOI 10.7910/DVN/FFIDCW, Release 2026-04-13T04:33:11Z

•AADR v66.1240K.aadr.PUB.anno und v66.1240K.aadr.PUB Genotypbasis

•Poseidon Community Archive: 2025_Gretzinger_SlavicPeriod, 2020_Furtwaengler_Switzerland, 2025_SkourtaniotiJia_SCaucasus

•Poseidon Minotaur Archive: 2025_Schuetz_Sarmatians

•PGS Catalog: PGS001092, PGS001093, PGS001094, PGS001095, PGS001096, PGS001098, PGS001897, PGS002135, PGS002804

•UK Biobank Field 1717 Skin colour scale; Tran et al. 2026, UK Biobank Field 1717 distribution

Poseidon imports

•Gretzinger et al. 2025, Ancient DNA connects large-scale migration with the spread of Slavs - https://doi.org/10.1038/s41586-025-09437-6

•Furtwängler et al. 2020, Ancient genomes reveal social and genetic structure of Late Neolithic Switzerland - https://doi.org/10.1038/s41467-020-15560-x

•Skourtanioti et al. 2025, The genetic history of the Southern Caucasus from the Bronze Age to the Early Middle Ages - https://doi.org/10.1016/j.cell.2025.07.013

•Koptekin et al. 2023, Spatial and temporal heterogeneity in human mobility patterns in Holocene Southwest Asia and the East Mediterranean - https://doi.org/10.1016/j.cub.2022.11.034

•Schütz et al. 2025, Unveiling the origins and genetic makeup of the forgotten people: A study of the Sarmatian-period population in the Carpathian Basin - https://doi.org/10.1016/j.cell.2025.05.009

Haplogroups

•YFull/YTree 14.04.0, YFullTeam/YTree current_tree.json, CC BY 4.0

•HaploGrep/PhyloTree RSRS 17.1, phylotree-rsrs-17

•van Oven & Kayser 2009, Updated comprehensive phylogenetic tree of global human mitochondrial DNA variation - https://doi.org/10.1002/humu.20921

•Weissensteiner et al. 2016, HaploGrep 2: mitochondrial haplogroup classification in the era of high-throughput sequencing - https://doi.org/10.1093/nar/gkw233

Ancestry components

•Lazaridis et al. 2014, Ancient human genomes suggest three ancestral populations for present-day Europeans - https://doi.org/10.1038/nature13673

•Haak et al. 2015, Massive migration from the steppe was a source for Indo-European languages in Europe - https://doi.org/10.1038/nature14317

•Mathieson et al. 2015, Genome-wide patterns of selection in 230 ancient Eurasians - https://doi.org/10.1126/science.aaa2773

•Hofmanová et al. 2016, Early farmers from across Europe directly descended from Neolithic Aegeans - https://doi.org/10.1073/pnas.1523951113

•González-Fortes et al. 2017, Paleogenomic evidence for multi-generational continuity of ancient European hunter-gatherers - https://doi.org/10.1016/j.cub.2017.05.023

•Mathieson et al. 2018, The genomic history of southeastern Europe - https://doi.org/10.1126/science.aau5085

•Olalde et al. 2018, The Beaker phenomenon and the genomic transformation of northwest Europe - https://doi.org/10.1038/nature25738

•Lazaridis et al. 2022, The genetic history of the Southern Arc - https://doi.org/10.1126/science.abm4247

•Olalde et al. 2022, Large-scale migration into Britain during the Middle to Late Bronze Age - https://doi.org/10.1038/s41586-021-04287-4

•Ghalichi et al. 2024, The rise and transformation of Bronze Age pastoralists in the Caucasus - https://doi.org/10.1038/s41586-024-08113-5

•Lazaridis et al. 2025, The genetic origin of the Indo-Europeans - https://doi.org/10.1038/s41586-024-08531-5

•Nikitin et al. 2025, A genomic history of the North Pontic Region from the Neolithic to the Bronze Age - https://doi.org/10.1038/s41586-024-08372-2

Hair colour

•Walsh et al. 2013, HIrisPlex system for simultaneous prediction of hair and eye colour - https://doi.org/10.1016/j.fsigen.2012.07.005

•Branicki et al. 2011, Model-based prediction of human hair color using DNA variants - https://doi.org/10.1007/s00439-010-0939-8

•Guenther et al. 2014, A molecular basis for classic blond hair color in Europeans - https://doi.org/10.1038/ng.2991

•Wilde et al. 2014, Direct evidence for positive selection of skin, hair, and eye pigmentation in Europeans - https://doi.org/10.1073/pnas.1316513111

•Sulem et al. 2007, Genetic determinants of hair, eye and skin pigmentation in Europeans - https://doi.org/10.1038/ng.2007.13

•Zorina-Lichtenwalter et al. 2019, A study in scarlet: MC1R as the main predictor of red hair - https://doi.org/10.1093/hmg/ddz018

•Hysi et al. 2018, Genome-wide association meta-analysis of individuals of European ancestry identifies new loci explaining a substantial fraction of hair color variation and heritability - https://doi.org/10.1038/s41588-018-0100-5

•Morgan et al. 2018, Genome-wide study of hair colour in UK Biobank explains most of the SNP heritability - https://doi.org/10.1038/s41467-018-07691-z

•Lona-Durazo et al. 2021, CanPath hair-colour GWAS meta-analysis - https://doi.org/10.1038/s42003-021-02764-0

•Tanigawa et al. 2022, Components of genetic associations across 2,138 phenotypes in the UK Biobank highlight adipocyte biology - https://doi.org/10.1371/journal.pgen.1010105

Eye colour

•Walsh et al. 2013, HIrisPlex system for simultaneous prediction of hair and eye colour - https://doi.org/10.1016/j.fsigen.2012.07.005

•Chaitanya et al. 2014, The HIrisPlex system for simultaneous prediction of hair and eye colour from DNA - https://doi.org/10.1016/j.fsigen.2014.04.006

•Sulem et al. 2007, Genetic determinants of hair, eye and skin pigmentation in Europeans - https://doi.org/10.1038/ng.2007.13

•Sulem et al. 2008, Two newly identified genetic determinants of pigmentation in Europeans - https://doi.org/10.1038/ng.160

•Liu et al. 2010, Digital quantification of human eye color highlights genetic association of three new loci - https://doi.org/10.1371/journal.pgen.1000934

•Lona-Durazo et al. 2022, Investigating the genetic architecture of eye colour in a Canadian cohort - https://doi.org/10.1016/j.isci.2022.104485

•Simcoe et al. 2021, Genome-wide association study in almost 195,000 individuals identifies 50 previously unidentified genetic loci for eye color - https://doi.org/10.1126/sciadv.abd1239

•Farré et al. 2023, Skin Phototype and Disease: A Comprehensive Genetic Approach to Pigmentary Traits Pleiotropy Using PRS in the GCAT Cohort - https://doi.org/10.3390/genes14010149

•Abbatangelo, Lona-Durazo, Edwards & Parra 2026, A comparative GWAS of eye colour in light and dark eye genetic backgrounds defined by HERC2 rs12913832 polymorphism in a Canadian cohort of European ancestry - https://doi.org/10.1038/s41598-026-44580-8

Skin pigmentation

•Walsh et al. 2017, Global skin colour prediction from DNA - https://doi.org/10.1007/s00439-017-1808-5

•Walsh et al. 2017, Global skin colour prediction from DNA, Erratum - https://doi.org/10.1007/s00439-017-1817-4

•Chaitanya et al. 2018, The HIrisPlex-S system for eye, hair and skin colour prediction from DNA: introduction and forensic developmental validation - https://doi.org/10.1016/j.fsigen.2018.04.004

•Liu et al. 2015, Genetics of skin color variation in Europeans - https://doi.org/10.1007/s00439-015-1559-0

•Jacobs et al. 2013, Comprehensive candidate gene study highlights UGT1A and BNC2 as new genes determining continuous skin color variation in Europeans - https://doi.org/10.1007/s00439-012-1232-9

•Ju & Mathieson 2021, The evolution of skin pigmentation-associated variation in West Eurasia - https://doi.org/10.1073/pnas.2009227118

•Farré et al. 2023, Skin Phototype and Disease: A Comprehensive Genetic Approach to Pigmentary Traits Pleiotropy Using PRS in the GCAT Cohort - https://doi.org/10.3390/genes14010149

•Kim et al. 2024, ostasiatische Hautpigmentierungs-GWAS; auditiert, aber mangels reproduzierbarer europäischer Gewichtstabelle nicht im Ju/Liu/Jacobs-Modell verwendet - https://doi.org/10.1038/s41467-024-49031-4

•Privé et al. 2022, Portability of 245 polygenic scores when derived from the UK Biobank and applied to 9 ancestry groups from the same cohort - https://doi.org/10.1016/j.ajhg.2021.11.008

Lactase persistence, metabolism, muscle type and COMT

•Enattah et al. 2002, Identification of a variant associated with adult-type hypolactasia - https://doi.org/10.1038/ng826

•Fumagalli et al. 2019, Genetic diversity of CHC22 clathrin impacts its function in glucose metabolism - https://doi.org/10.7554/eLife.41517

•Ye et al. 2017, Dietary adaptation of FADS genes in Europe varied across time and geography - https://doi.org/10.1038/s41559-017-0167

•Irving-Pease et al. 2024, The selection landscape and genetic legacy of ancient Eurasians - https://doi.org/10.1038/s41586-023-06705-1

•Garcia-Retortillo et al. 2024, A systematic review and meta-analysis of the association between ACTN3 R577X genotypes and performance in endurance versus power athletes and non-athletes - https://doi.org/10.1186/s40798-024-00711-x

•Egan et al. 2001, Effect of COMT Val108/158 Met genotype on frontal lobe function and risk for schizophrenia - https://doi.org/10.1073/pnas.111134598

•Intelligenz (Quellenlage schlecht, bisherige Analysen sind an der Validierung gescheitert)

•Die 30 Facetten des Big Five/OCEAN-Modells

•Testosteron

•Risikobereitschaft

•Psychopathie

•Zeitpräferenz

•Feldunabhängigkeit

•Räumliche versus verbale Intelligenz

•Slow-Life-History

•Aspekte der K-Strategie (Knochendichte, Wachstumsgeschwindigkeit, Lebensdauer/Alterung, Sexuelle Aktivität, Elterliche Fürsorge, Kooperativität, Altruismus)

•Korruption

•Willenskraft/Impulskontrolle

•Agency

•Konformität/Kollektivismus

•Kreativität

•Autismus

•Agression

•Neuigkeitslust/Novelty Seeking

•Neanderthaler-Anteile

•Blutgruppen

•Kraniometrik (Schädelmaße)

•3D-Gesichtsrekonstruktion

•Erweiterung des Zeitraums bis in die Gegenwart

•Abfolgebäume archäologischer Kulturen

•Haplogruppen-Bäume und Kreisdiagramme für Kulturen

•Wichtige Ereignisse (Doggerlandflutung, Gründung Roms, Attila usw.) in einem Ereignisterminal ausgeben

•Kulturen als „gesichert indogermanisch“ und „vermutet indogermanisch“ kennzeichnen

•Kulturzusammenhänge abbilden, also Kulturgruppen bei eindeutiger Formennachfolge als verwandt kennzeichnen

•Möglichkeit, eigenes Genom hochzuladen und in den Kontext als modernes Referenzindividuum zu stellen

•GeoGens mit tatsächlichen archäologischen Fundkarten vereinen

•GeoGens mit tatsächlichen anthropologischen Befunden anreichern

Anyone with ideas or who wants to help can message me at any time: gg [at] geogens.eu

Support is especially welcome in curating the culture groups, in researching and integrating new genomes and publications, or in finding new SNPs and ideas for new traits.

You can also help to finance future research work. The email address above can be used for PayPal transfers.

Anyone who can permanently provide substantial computing power and high bandwidth can likewise be a great help to me!