The Tutsi live mainly in Rwanda and Burundi, speaking the same Kinyarwanda and Kirundi languages as the Hutu majority and sharing most of their culture; historically, Tutsi identity was closely tied to cattle keeping. In our model, the Tutsi average is 70.7% Nilotic East African, 14.6% Natufian and 6.0% Levantine Neolithic, with several small shares, and the fit (0.0139) is close. The Near Eastern-related share resembles that of East African herding peoples such as the Maasai (19.0% Natufian). The neighbours point the same way: after other Tutsi samples, the closest modern average is the Hima of Uganda (0.0218), another cattle-keeping people of the Great Lakes, followed by the Ogiek, the Elmolo and the Maasai. The Hutu average (50.2% West African) differs clearly from this one, yet people in both groups vary widely and overlap, so no one's identity can be read from these figures. Kenya's Iron Age pastoralists are the nearest ancient group, at a fair distance (0.0612).

  • Largest ancient components in our model: Nilotic East African 70.7%, Natufian hunter-gatherers (Levant) 14.6%, Levantine Neolithic farmers 6.0%
  • Closest other population in our data: Hima, distance 0.0218
  • Closest ancient group in our data: Kenya Pastoral IA, distance 0.0612

Tutsi is one of the 335 modern populations in our population genetics atlas. The numbers on this page describe the average Global25 (G25) genome of the Tutsi individuals sampled in Rwanda and Burundi, listed in the G25 sheet as "Tutsi_Rwanda". Like every population average, it smooths over a lot of variation between individuals.

Ancient make-up of the Tutsi average

Nilotic East African
70.7%
Natufian hunter-gatherers (Levant)
14.6%
Levantine Neolithic farmers
6.0%
Western rainforest foragers (BiAka)
2.8%
Southern African foragers (San)
2.2%
North African hunter-gatherers (Iberomaurusian)
1.4%
West African
1.2%
Iranian Neolithic farmers
1.1%

In a model built from deep ancestral reference populations, the largest share of the Tutsi average, 70.7%, goes to Nilotic East African, ahead of Natufian hunter-gatherers (Levant) (14.6%) and Levantine Neolithic farmers (6.0%). The Nilotic East African component reflects ancestry found in East African populations. With well over half of the total, this single source dominates the profile.

Smaller traces of Western rainforest foragers (BiAka), Southern African foragers (San), North African hunter-gatherers (Iberomaurusian) and West African (under 5% each) also appear. At that level they can reflect real minor ancestry, but also simple model noise, so they should not be over-read. The model fits well (fit distance 0.014), so these proportions are a reasonable summary. This population was modelled with a global set of reference populations (ancient genomes, plus modern stand-ins where no suitable ancient genome exists), because West Eurasian sources alone cannot describe it.

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Sankofa – African Ancestry Report

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Closest modern populations to Tutsi

Ranking every modern population by genetic distance puts Tutsi (Burundi) first for Tutsi, at 0.016, then Tutsi (other sample set) and Hima. Leaving aside the other regional samples listed under the same name in the G25 sheet, the nearest other populations are Hima (0.022), Ogiek (0.034) and Elmolo (0.038). By the tenth closest (Baggara Arab (Chad B)) the distance grows to 0.056, a common pattern for a population that shares ancestry with its neighbours while keeping a profile of its own.

#PopulationDistanceCloseness
1 Tutsi (Burundi) 0.0159 Very close
2 Tutsi (other sample set) 0.0202 Very close
3 Hima 0.0218 Very close
4 Ogiek 0.0336 Close
5 Elmolo 0.0375 Close
6 Baggara Arab (Sudan) 0.0438 Close
7 Maasai 0.0486 Close
8 Masai (Kenya) 0.0495 Close
9 Baggara (Chad B) 0.0501 Moderate
10 Baggara Arab (Chad B) 0.0560 Moderate

Distances are Euclidean distances between averaged G25 coordinates. On our scale, below 0.025 is very close, below 0.050 close, below 0.080 moderate, and beyond that distant.

Closest ancient populations to Tutsi

Among ancient genomes, the closest match to the modern Tutsi average is Kenya Pastoral IA (Iron Age), at 0.061, followed by Tanzania Early Modern Songo Mnara Swahili (Southern Nilotic Profile) and Kenya Early Modern Kilifi 400BP. That is only a moderate match: part of the modern profile was already present then, but later movements of people added substantial layers. A close ancient match is not proof of direct descent: it means those individuals carried a similar overall mix of ancestry.

#Ancient sample or groupPeriodDistance
1 Kenya Pastoral IA Iron Age 0.0612
2 Tanzania Early Modern Songo Mnara Swahili (Southern Nilotic Profile) Early Modern, c. 1500-1800 AD 0.0728
3 Kenya Early Modern Kilifi 400BP c. 1550 AD 0.1014
4 Kenya High Medieval Kakapel 900BP c. 1050 AD 0.1161
5 Libya N Saharan Pastoral Takarkori Neolithic 0.1228
6 Tanzania Late Medieval-Early Modern Kilwa Swahili (Swahili-Mixed Profile) Late Medieval to Early Modern, c. 1300-1800 AD 0.1277
7 Kenya IA Deloraine Iron Age 0.1293
8 Ethiopia LSA Mota 4500BP c. 2550 BC 0.1300
9 Kenya Pastoral N Molo Cave 1500BP c. 450 AD 0.1356
10 Tanzania Early Modern Songo Mnara Swahili (Swahili-Mixed Profile) Early Modern, c. 1500-1800 AD 0.1361

Compare yourself with Tutsi

Paste your G25 coordinates (scaled, one line, with or without a name in front) and we compute your genetic distance to the Tutsi average and to its closest neighbours, right in your browser. Nothing is uploaded or stored.

No G25 coordinates yet? Get a free simulated G25 from your raw DNA file, or order G25 coordinates.

About these numbers

Keep in mind that a population average is a statistical summary of a limited number of sampled individuals. Real people inside any group vary, some carry more of one ancestry and some less, and no genetic profile decides who is or is not Tutsi. Use these numbers as a map of deep ancestry, not as a label.

Method: averaged G25 coordinates, Euclidean distances to other modern and ancient averages, and a non-negative least-squares model against deep ancestral reference populations (data generated 2026-10-01). Read the full method.

Go deeper than the average

This page describes the Tutsi average. Your own DNA has its own story: Sankofa – African Ancestry Report (15€) models your genome against the ancient sources and modern communities of the region, era by era, in a personal PDF report.

A report built for people of predominantly Sub-Saharan African (SSA) ancestry: modelling against 230+ regional African populations (West, Central, East & Southern Africa, the Horn of Africa, Madagascar) plus the European, Amerindian and...

Frequently asked questions

What is the ancient genetic make-up of Tutsi?

Modelled with deep ancestral reference populations, the Tutsi average is about 70.7% Nilotic East African, 14.6% Natufian hunter-gatherers (Levant) and 6.0% Levantine Neolithic farmers. These proportions are model estimates for a group average, not exact values for any one person.

Which populations are genetically closest to Tutsi?

Leaving aside other regional samples listed under the same name in the G25 sheet (such as Tutsi (Burundi)), the closest modern populations to the Tutsi average are Hima, Ogiek and Elmolo. The closest ancient matches are Kenya Pastoral IA, Tanzania Early Modern Songo Mnara Swahili (Southern Nilotic Profile) and Kenya Early Modern Kilifi 400BP.

Can a DNA test tell me if I am Tutsi?

No DNA test can confirm an ethnicity or a nationality. What DNA can show is how similar your genome is to the sampled Tutsi average and to its neighbours. If you have G25 coordinates, paste them in the comparison box on this page to check that for free.

Which ExploreYourDNA report suits Tutsi ancestry?

Sankofa – African Ancestry Report is the report built for this heritage, and its reference panel fits the Tutsi average closely. Your own DNA may differ from the average, so the free Report Finder checks every report against your file before you buy.

Where does the data on this page come from?

From the averaged Global25 (G25) coordinates of the sampled individuals: genetic distances to other modern and ancient population averages, and a non-negative least-squares model against deep ancestral reference populations. The full method is described at https://www.exploreyourdna.com/populations#method.

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