Vahaduo is one of the most powerful online tools to explore your deep ancestry using G25 coordinates — a set of principal components developed by Davidski from the Eurogenes project. In this tutorial, we’ll walk you through how to use Vahaduo to model your ancestry in just a few simple steps.
What You Need Before You Start
- Your G25 coordinates (target)
- A list of reference samples (sources) in G25 format
- Access to Vahaduo
Step 1: Open the Right Vahaduo Tool
Go to https://vahaduo.github.io/vahaduo/.
Step 2: Paste Your Target Coordinates
Here are two examples you can use as target individuals:
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individual1_scaled,0.119514,0.111708,0.05242,0.025194,0.026466,0.007251,-0.020446,-0.024461,0.008385,0.006743,-0.000812,0.002398,-0.005798,-0.003578,0.010858,0.003182,-0.004303,0.000507,0.00264,-0.005002,0.003369,-0.001113,-0.008381,0.005663,-0.001916
individual2_scaled,0.09675,0.07718,0.029415,-0.004845,0.00277,-0.01255,-0.043242,-0.055613,0.009204,0.015126,0.003897,-0.002548,-0.002081,-0.002064,-0.0057,0.01127,0.011735,0.0019,0.003017,0.001501,-0.003743,0.004081,-0.003204,0.003012,0.002634
Step 3: Load the Source Populations
In the Source tab, paste a list of populations in G25 format. Each line must follow the same structure:
Here is an example you can use directly, it comes from the Modern World calculator by Joshua, available at Explore Your DNA:
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Levant_South,0.085367,0.148674,-0.057398,-0.092508,-0.009602,-0.035029,-0.002209,-0.008169,0.016321,0.008237,0.009159,-0.009921,0.020485,0.011863,-0.005537,0.002121,-0.011239,0.001014,0.002112,-0.003652,-0.000524,0.003116,0.000567,-0.00294,0.004646
Levant_North,0.088149,0.145785,-0.048858,-0.078848,-0.012891,-0.03074,-0.000496,-0.004282,0.005181,0.010225,0.006658,-0.004529,0.01039,0,-0.010315,0.008471,-0.002101,0.001788,0.002542,-0.000125,0.000763,-0.000302,-0.00141,0.002356,-0.001211
Jordan_Plateau,0.048944,0.131206,-0.047442,-0.07442,-0.012618,-0.027276,-0.004935,-0.003231,0.014889,0.000838,0.006463,-0.008722,0.015996,-0.000936,-0.001113,0.009812,0.000104,0.000887,-0.002011,0.004227,0.001622,0.002893,-0.001134,0.002506,-0.002443
Syria_Interior,0.063236,0.127167,-0.048649,-0.062806,-0.01773,-0.020127,-0.003486,-0.002269,-0.000568,-0.001053,0.002806,-0.005603,0.011422,0.000986,-0.002217,0.004773,-0.004665,-0.000739,0.00192,0.001438,0.001608,0.000927,-0.002232,-0.000522,-0.000246
Kurdistan_Highlands,0.092295,0.112234,-0.062164,-0.038063,-0.037469,-0.004055,0.004428,-0.005603,-0.025112,-0.012856,0.002509,-0.000688,0.005105,0.000122,0.001582,0.010425,-0.005015,0.001254,0.004117,-0.009066,-0.000427,-0.001068,-0.00016,-0.001486,0.004059
Italy_North,0.12384,0.150705,0.030698,-0.011757,0.036191,-0.002677,0.001081,0.001708,0.010758,0.027663,-0.001397,0.006804,-0.012844,-0.006276,-0.003474,-0.004163,-0.000365,0.000659,0.005154,-0.005553,-0.001048,-0.000025,-0.00419,0.003784,0.000958
Italy_South,0.101682,0.146066,-0.003834,-0.038330,0.018618,-0.012597,-0.001293,0.000307,0.010464,0.021808,0.005359,0.003497,-0.004188,0.002500,-0.004094,-0.004840,-0.002368,-0.000591,0.001801,-0.005003,0.000395,0.001752,-0.002547,-0.000603,0.000319
Italy_Central,0.118810,0.147784,0.013882,-0.021011,0.025294,-0.008938,-0.001063,-0.001626,0.005649,0.023239,-0.000309,0.006066,-0.011475,-0.003703,-0.002243,-0.000814,0.002154,0.000290,0.003334,-0.003829,-0.001967,0.001407,-0.000734,0.003196,-0.001226
Arabian_Gulf,0.053393,0.141712,-0.064899,-0.114607,-0.012422,-0.046930,-0.012242,-0.008727,0.050424,-0.005169,0.017405,-0.032426,0.063830,0.003140,0.003924,0.029037,-0.022568,0.004814,-0.001394,0.031481,0.013023,0.018154,-0.007462,0.008884,-0.010113
Maghreb_East,-0.048517,0.131384,-0.015085,-0.075138,0.019619,-0.030678,-0.022091,0.005019,0.054378,0.019795,0.005440,-0.008037,0.026740,-0.010184,0.013046,-0.007143,-0.003716,-0.012922,-0.029005,0.005174,-0.010450,-0.022829,0.018117,-0.003344,0.000210
Maghreb_West,-0.085367,0.130213,-0.012822,-0.070881,0.021132,-0.032940,-0.027783,0.009769,0.055403,0.024399,0.009509,-0.009025,0.026990,-0.014466,0.016302,-0.006188,-0.002767,-0.017033,-0.038422,0.009435,-0.009844,-0.029512,0.020911,-0.001125,0.005921
India_Bengal_Delta,0.041713,-0.119503,-0.145613,0.099722,-0.052951,0.061758,-0.001611,0.011769,0.041885,0.025695,-0.004585,0.001389,-0.000044,0.000069,-0.006151,-0.006579,0.004556,-0.000693,-0.002910,0.004521,0.000907,0.002811,0.001316,0.004852,0.000257
Italy_Sardinia,0.121687,0.167285,0.028490,-0.050652,0.060151,-0.022134,-0.003952,0.002496,0.041574,0.077351,-0.000059,0.016649,-0.028664,-0.012974,-0.013572,-0.003001,0.011403,-0.001347,0.001680,-0.012995,-0.002121,-0.001102,-0.010084,-0.021383,0.000337
Iberia_Basque,0.126424,0.148739,0.055679,0.008790,0.055450,0.000249,-0.001754,0.000025,0.029539,0.042923,-0.005214,0.010801,-0.024986,-0.019493,0.015807,0.002614,-0.005630,0.003172,-0.002375,-0.001791,0.008819,0.002204,-0.006479,-0.008654,0.001150
Scotland_Highlands,0.131466,0.134232,0.062265,0.047043,0.039106,0.017281,0.003550,0.005176,0.003572,0.002825,-0.005614,0.005047,-0.011680,-0.011973,0.023024,0.003125,-0.011255,0.002774,0.002550,0.001452,0.004225,0.003308,-0.000656,0.013556,-0.000770
Ireland_Midlands,0.133361,0.134098,0.061169,0.048864,0.037788,0.019352,0.003293,0.004713,0.003568,0.002927,-0.006971,0.005840,-0.014189,-0.014076,0.025935,0.005215,-0.011145,0.001894,0.000597,0.001776,0.005114,0.001279,0.000293,0.014407,0.000661
Fennoscandia_Saami_North,0.111262,-0.030212,0.111722,0.079781,-0.009329,0.008297,0.008137,0.013860,0.002966,-0.032096,0.022288,-0.007475,0.018071,-0.019826,-0.004004,0.002693,0.001157,-0.001489,-0.005986,0.004494,0.015816,0.001345,-0.002565,0.002658,0.000344
Baltic_Region,0.134197,0.118309,0.088265,0.086387,0.042993,0.031892,0.011986,0.014261,-0.000879,-0.032666,-0.001754,-0.013084,0.021340,0.026369,-0.008612,0.002883,0.001467,-0.001432,0.001477,0.003258,-0.000705,-0.003790,0.006667,-0.004290,0.002844
Slavic_West,0.131500,0.130566,0.065176,0.052498,0.039667,0.020318,0.007511,0.009763,0.001870,-0.013720,-0.003017,-0.005158,0.009571,0.015200,-0.002641,0.000220,0.000452,-0.000110,0.003131,0.002092,-0.002198,-0.001640,0.006059,0.000299,0.000753
Slavic_East,0.132262,0.123623,0.075022,0.069596,0.040233,0.026569,0.010732,0.013322,-0.000273,-0.024602,-0.002696,-0.009212,0.019326,0.027231,-0.011265,-0.003801,-0.000965,0.000372,0.004140,-0.001051,-0.004234,-0.004608,0.007724,-0.006860,0.000695
Slavic_South,0.126878,0.136504,0.038596,0.016325,0.030209,0.006395,0.005480,0.005030,-0.000013,0.000819,-0.001097,-0.001143,0.003581,0.012310,-0.012595,-0.001277,0.006066,0.000329,0.003998,-0.001652,-0.006851,-0.001614,0.004753,-0.000709,-0.000888
Scandinavia_Main,0.131844,0.130135,0.069024,0.055314,0.040630,0.020039,0.005233,0.007596,0.004412,-0.003836,-0.004735,0.002763,-0.006929,-0.006865,0.017838,0.006180,-0.006275,0.002326,0.003069,0.004490,0.006031,0.003017,0.000944,0.013158,0.000115
Central_Asian,0.079169,-0.074084,-0.004858,0.001093,-0.038165,0.000947,0.007008,0.004693,-0.016117,-0.010328,-0.013142,-0.002249,0.001782,-0.003628,0.004269,0.004946,-0.001990,-0.000068,0.000811,-0.001974,-0.007839,-0.002260,-0.003832,-0.000914,0.001899
Africa_East,-0.296623,0.093429,-0.026851,-0.069736,0.000954,-0.034080,-0.020493,0.005769,0.115392,-0.079674,-0.008948,-0.006669,0.005218,-0.001665,0.024728,-0.018616,0.014864,-0.001976,0.009980,-0.003839,0.000599,0.004946,-0.002736,-0.001241,-0.002790
Africa_West,-0.618275,0.064848,0.020924,0.014087,0.001303,0.009026,-0.040128,0.043268,-0.040308,0.028458,0.005017,-0.001429,0.019643,-0.000017,0.011769,-0.012471,0.008384,-0.000367,0.001953,-0.003110,-0.000044,0.000251,0.001318,-0.000286,0.000476
China_South,0.018970,-0.448694,-0.026231,-0.065103,0.102497,0.047659,0.000685,-0.005468,-0.016731,-0.007887,-0.020989,-0.004601,0.003667,-0.002760,-0.001195,0.002342,0.001239,-0.001481,-0.004165,-0.011669,0.013653,0.009438,0.014930,-0.000288,0.003906
China_North,0.025285,-0.446253,0.010344,-0.063747,0.047152,0.020917,0.006026,0.003115,-0.012344,0.003046,-0.071346,-0.007868,0.007922,-0.007235,-0.006873,0.000464,-0.002412,0.000136,-0.003187,-0.006065,0.010580,0.003604,0.010062,0.000361,-0.002164
Japan_Korea,0.022788,-0.450483,0.015616,-0.061619,0.037520,0.010473,0.004311,0.002038,-0.008017,0.008647,-0.074547,-0.008060,0.010837,-0.006437,-0.010035,-0.003481,0.000597,0.002734,0.000754,-0.008281,0.021635,-0.010477,0.006157,0.002184,-0.029733
Aegean,0.106977,0.145820,-0.019480,-0.050973,0.004481,-0.016813,0.003041,-0.003036,-0.002691,0.014768,0.003389,0.001825,-0.003125,0.001705,-0.011953,-0.001788,0.003985,0.002985,0.004857,-0.004822,-0.004379,-0.000032,0.000495,0.000287,-0.002553
Caucasus_North,0.110689,0.100057,-0.032791,-0.021497,-0.035116,-0.000369,0.009279,-0.004390,-0.054759,-0.025349,-0.001210,0.007732,-0.019783,0.001725,0.004796,-0.015229,0.008029,-0.005061,-0.009802,0.017975,0.008991,0.001495,0.002149,-0.004110,-0.003012
Caucasus_South,0.105774,0.134032,-0.055219,-0.054578,-0.032641,-0.012760,0.006014,-0.005239,-0.040846,-0.010136,0.002464,0.005701,-0.010135,0.003165,-0.001831,-0.008953,0.003382,-0.001319,-0.002189,0.003696,0.004721,0.002156,-0.000175,-0.003806,-0.000038
Iran_Highlands,0.089920,0.101959,-0.078969,-0.021189,-0.058749,0.005912,0.007614,-0.005054,-0.041375,-0.021267,-0.001364,-0.001888,-0.000074,0.001156,0.005592,0.010713,-0.006376,0.003281,0.004198,-0.014019,-0.000911,-0.007691,-0.001553,-0.005374,0.005041
Vietnam_Cambodia,0.013924,-0.394296,-0.071055,-0.030270,0.121971,0.063192,-0.003231,-0.008204,-0.004650,-0.012717,0.054398,0.004311,-0.004274,0.001239,0.001375,-0.003799,-0.000799,-0.003068,-0.004033,0.007158,-0.008749,0.011337,-0.001939,0.002655,0.027786
Balkan_General,0.126371,0.137956,0.037429,0.012397,0.030784,0.005491,0.004819,0.004905,0.000309,0.002821,-0.000739,-0.000617,0.001478,0.011618,-0.013483,-0.000552,0.007324,-0.000194,0.005223,-0.002003,-0.007094,-0.000814,0.004881,0.000656,-0.000934
Iran_Central,0.087661,0.094267,-0.069210,-0.017737,-0.048842,0.003918,0.004946,-0.003495,-0.030018,-0.017584,-0.000123,-0.001944,0.003852,-0.003345,0.006556,0.014096,-0.003763,0.002059,0.002284,-0.010313,-0.000816,-0.004195,-0.000479,-0.004484,0.005108
Iraq_Mesopotamia,0.059507,0.117923,-0.062546,-0.057779,-0.028827,-0.015882,-0.002720,-0.004616,-0.001845,-0.006626,0.005535,-0.006638,0.015714,0.000690,0.001925,0.009210,-0.009024,0.003238,0.002785,-0.001127,0.000481,0.000388,-0.001176,0.000377,0.000824
Anatolia_Central,0.106588,0.125382,-0.012526,-0.020130,-0.008925,0.000757,0.004347,-0.000404,-0.014901,-0.004764,-0.000934,0.000626,-0.003106,0.004055,-0.002734,-0.000999,-0.005109,-0.001294,0.003484,-0.004596,-0.002197,0.003670,0.004296,-0.001188,0.003071
Horn_Africa,-0.259101,0.100063,-0.031103,-0.077263,0.001212,-0.036875,-0.014521,-0.000175,0.106946,-0.069999,-0.004610,-0.010118,0.012227,0.000066,0.022763,-0.014639,0.012763,0.001056,0.007802,0.000304,0.002361,0.005730,-0.003537,0.001916,-0.003129
South_Asia_Interior,0.062004,-0.033422,-0.122184,0.088524,-0.069736,0.054054,0.001392,0.007429,0.014921,0.007249,-0.006843,-0.000216,-0.000681,-0.003418,0.004907,0.003220,-0.002445,-0.000158,0.000440,-0.004797,0.000057,-0.002572,0.001733,-0.000245,-0.000003
Tibetan_Himalayas,0.028024,-0.342713,-0.024163,-0.009564,0.003836,0.018216,0.005478,0.006533,0.007485,0.012508,-0.064812,-0.007506,0.008052,-0.002405,-0.007222,-0.004168,0.000450,-0.001811,-0.006811,0.002671,0.000668,0.018559,0.005071,0.000696,0.025524
Southeast_Asia,0.012830,-0.352039,-0.091323,-0.003500,0.092988,0.058393,-0.003671,-0.005084,0.007170,-0.002043,0.047778,0.004667,-0.005592,0.004102,0.000052,-0.002126,0.000456,-0.000973,-0.000003,0.007962,-0.004814,0.004841,-0.006108,0.000392,0.009558
Native_America,0.054741,-0.302734,0.112213,0.088447,-0.105646,-0.016245,-0.264462,-0.316927,-0.009644,-0.016155,-0.001006,-0.002249,0.001469,0.017441,-0.009351,0.003670,0.005187,0.001061,0.002602,0.001071,-0.001045,0.002976,0.001001,0.002281,0.000333
Oceania_Island,-0.019024,-0.216536,-0.189635,0.220962,0.174225,-0.359067,-0.001856,0.003571,-0.034284,-0.013469,-0.007158,0.001304,0.000277,-0.004577,0.004112,0.000000,-0.003888,0.000693,0.001421,-0.004285,0.003305,-0.001179,-0.000196,-0.000428,-0.006271
Iberia_Main,0.126690,0.150851,0.035253,0.005179,0.048573,-0.002410,-0.001640,0.000799,0.027414,0.039412,-0.003037,0.010125,-0.021799,-0.017713,0.013424,0.002840,-0.005301,0.002990,-0.000560,-0.001404,0.007108,0.001349,-0.005866,-0.006632,0.001175
France_South,0.123777,0.152449,0.030754,0.000456,0.045128,-0.001755,-0.001038,0.000975,0.024667,0.036681,-0.002915,0.009838,-0.020888,-0.016918,0.012065,0.002737,-0.004918,0.002767,-0.000797,-0.001692,0.006479,0.001243,-0.005505,-0.006097,0.001113
France_North,0.130248,0.144172,0.046672,0.025342,0.042005,0.009144,0.003632,0.004508,0.012086,0.013814,-0.004601,0.004116,-0.010319,-0.011225,0.018950,0.003943,-0.009148,0.002140,0.002150,0.001140,0.004174,0.002532,-0.001645,0.008721,-0.000061
Germany_Main,0.130376,0.135369,0.054563,0.037280,0.041414,0.014738,0.005178,0.006681,0.006947,0.006015,-0.005271,0.002440,-0.007557,-0.008601,0.020931,0.004099,-0.008752,0.002073,0.002978,0.002219,0.004787,0.002600,-0.001017,0.010299,-0.000236
You can find additional source files here: G25 Downloads or Explore Your DNA Reference Panels.
Step 4: Distance Mode – Find Closest Matching Populations
Once your target and source data are entered, switch to the Distance tab and click "Run" to generate your ancestry model. This will show you the closest reference populations to your target based on Euclidean genetic distance.
In the output, the most important metric is the distance. A low distance (typically below 0.02) indicates a good fit. The value is usually displayed in green when it's considered acceptable.
In this example, both individual1_scaled and individual2_scaled are distant from their first matching population. This typically means either:
- There is no population in the source panel that represents their true ancestry well (e.g., their ethnicity is not covered), or
- They are genetically admixed, even if they identify with a single ethnic label.
This situation is common and does not invalidate the model — it simply suggests refining the reference list or exploring deeper ancestry layers.
Interpreting Distance Values
-
0.00 – 0.02: Excellent match (close reference population)
-
0.02 – 0.05: Acceptable match (moderate proximity)
-
0.05+: Distant match (likely mixed or underrepresented)
Step 5: Single Mode – Explore Ancestry Composition
After reviewing the overall model, we now switch to Single Mode. This mode allows us to evaluate each target’s ancestry as a composition of the selected source populations. It does not look for a single closest match, but instead provides a more nuanced breakdown of ancestral components.
We will now model both Individual 1 and Individual 2 using Single Mode. This step will reveal what their genetic makeup is composed of by estimating the proportions contributed by each source population.
Interpretation tip: Even if the overall results point to mixed ancestry, Single Mode helps you understand what proportions of each reference population contribute to the individual's genetic makeup.
This mode doesn't find the single closest match — instead, it reveals how different populations combine to form the target's profile. It’s especially helpful for detecting subtle or complex ancestries that aren't obvious in Distance Mode.
Advanced Tip: If you lower the distance threshold (e.g. to 0.05), Vahaduo will try to force a fit using only the closest populations. While this might seem cleaner, it can actually introduce noise or overfitting, especially if no good matches exist. If you increase the threshold (e.g. to 0.5), you give Vahaduo more flexibility to include distant but meaningful sources — which often produces a more realistic and balanced model, especially for complex or mixed ancestries.
Step 6: Multi Mode – Compare Multiple Individuals
Multi Mode works just like Single Mode, but allows you to run multiple individuals at once. It applies the same list of source populations to each target and displays their results side-by-side in a comparative format.
This is especially useful when you want to:
- Compare members of the same family or population
- Analyze how different individuals relate to the same source panel
- Visualize group patterns (e.g., shared Steppe or WHG ancestry)
Step 7: Interpretation
Given all the results obtained through Distance Mode, Single Mode, and Multi Mode, we can draw the following conclusions:
- Both Individual 1 and Individual 2 possess mixed Amerindian and Western Eurasian ancestry.
- Individual 2 has approximately twice as much Amerindian ancestry as Individual 1, suggesting a closer recent link to Indigenous populations.
- Individual 1 shows European ancestry primarily from Northern Europe, with minor contributions from Southern Europe and the Middle East.
- Individual 2 has a significant portion of ancestry from Southern Europe and the Middle East, indicating a different ancestral makeup within the Western Eurasian component.
Why were distances high in Distance Mode?
The relatively high distances both individuals had in Distance Mode are primarily due to their mixed ancestry. Since Distance Mode is optimized for finding close matches to single population sources, it performs poorly when the target is admixed or does not have a good direct reference. This explains why individual1 and individual2 did not show low (green) distances to any one group in that mode — their diverse background requires more refined modeling using Single or Multi Mode.
These distinctions illustrate how tools like Vahaduo G25 can uncover both shared ancestry and nuanced individual differences, making them valuable for personal exploration and population studies.
Conclusion
Vahaduo is a simple yet powerful tool to visualize your ancestry using G25 coordinates. By testing different ancient and modern populations, you can build detailed models of your genetic past.
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