HaploSample report: Moroccan, the Maghreb coast. Not yours.
MCA7

Your ancestry report

Your ancestry is mostly North African (65%), with some West Asian (17%) and European (11%) and a little from elsewhere.

Analysed 31 August 2026

North AfricanNF65%Northwest EuropeanEN8%BedouinBD7%Maghreb CoastMC7%LevantineLV7%

Tap any bar or region to see how sure we are.

Ancestry

Your direct lines and deeper ancestry.

Go to Ancestry

Health & traits

Your chip covers 3 of the 24 health variants we check. We found none. Of 23 trait variants, your chip covers 2; 1 found.

Go to Health & traits

Technical

Your file was graded A, with 573,669 markers read. Closest reference groups, your file and other calculators are here.

Go to Technical

Ancestry

Where you come from

Recent regions first, then your chromosomes, your mother's and father's lines, and thousands of years back.

North AfricanNF65%Northwest EuropeanEN8%BedouinBD7%Maghreb CoastMC7%LevantineLV7%

Where your ancestors lived

Each bar is the share of your DNA that matches people living in that region today.

North African65.4%
West Asian16.6%
Bedouin
7.3%
Levantine
7.0%
Arabian, Druze, Armenian Highland and the South Caucasus, Anatolia and Iran, Ashkenazi, North Caucasus, Dagestan and the Nakh highlands, Parsi and South Caucasus: in the model, but this file cannot tell them apart from zero.
European10.6%
Northwest European
7.9%
Iberian, Sardinia and Corsica, Basque Country, Southeast European, Finland and Karelia, Italian, Roma, Eastern European and Volga and Ural: in the model, but this file cannot tell them apart from zero.
African7.0%
Maghreb Coast
7.0%
Eastern and Southern African, Southern Bantu, Copts of the Nile, Lake Eyasi, Horn of Africa, Central Kalahari, Northern Kalahari, Southern Kalahari and the Karoo, Nile Valley, Nilotic Sudan, Congo Basin, Sierra Leone and the Upper Guinea coast, Swahili coast and the Rift, Western Congo Basin foragers, West African forest, West African savanna, Egypt and Libya, Maghreb Amazigh and The Mzab: in the model, but this file cannot tell them apart from zero.
5.5% could not be placed in one region. It belongs to regions this file cannot tell apart.
How we worked this out

Each percentage is a share of the 12,712 markers we could read in your file (100% of 12,770), compared with people sampled today by the 1000 Genomes Project. The solid part of a bar is the share we are sure of. The faint part is how much higher it might be. The line is our best estimate. The range is a 95% interval from resampling your markers. If a population is missing from our reference set, your ancestry from it is counted under the nearest region we do have.

Each region and its range. North African 65.4% (likely 60–70%); West Asian 16.6% (likely 8–25%); European 10.6% (likely 1–17%); African 7.0% (likely 5–10%).

African is one region. How much of your ancestry is from this region is measured from the whole panel at once. How it divides between the areas inside it is a second, harder question, fitted separately — so the total is the firmer number of the two. Its total (7.0%) is firmer than the split inside it. The parts drawn inside it add to 7.0%, because some are too small to tell from zero and are not drawn.

European is one region. How much of your ancestry is from this region is measured from the whole panel at once. How it divides between the areas inside it is a second, harder question, fitted separately — so the total is the firmer number of the two. Its total (10.6%) is firmer than the split inside it. The parts drawn inside it add to 7.9%, because some are too small to tell from zero and are not drawn.

West Asian is one region. How much of your ancestry is from this region is measured from the whole panel at once. How it divides between the areas inside it is a second, harder question, fitted separately — so the total is the firmer number of the two. Its total (16.6%) is firmer than the split inside it. The parts drawn inside it add to 14.3%, because some are too small to tell from zero and are not drawn.

What “could not be placed” means. A region whose range reaches zero is not drawn on its own. The ancestry is still yours; this file does not have enough markers to say which region it belongs to. Here the regions that could hold it are South Asian (up to 3%) and Oceanian (up to 2%).

The map. Shading marks where each ancestry lives, at your own share of it. The outlines are geographic regions and the numbers are genetic, so an edge is approximate: ancestry shades into its neighbours. A faint region is one this file cannot tell from zero.

RegionFitted fromEstimateRange
North AfricanThe Maghreb — Morocco, Algeria, Tunisia and Libya, fitted from Mozabite65.4%60–70%
Northwest EuropeanBritain, Ireland, the Low Countries, northern Germany, fitted from CEU, GBR7.9%1–15%
Bedouinindividuals recruited among Bedouin communities of the Negev and Sinai7.3%2–8%
Maghreb Coastindividuals recruited along the coasts of Morocco, Algeria and Tunisia7.0%6–7%
LevantinePalestine, Jordan, Lebanon, Syria and northern Iraq, fitted from Palestinian, Druze, Jordanian, Assyrian7.0%0–13%
IberianIberia, fitted from Spanish, Basquebelow resolution0–2%
ArabianThe Arabian peninsula and its desert margins, fitted from BedouinA, BedouinBbelow resolution0–10%
Sardinia and Corsicaindividuals recruited on Sardinia and Corsicabelow resolution0–7%
DruzeDruze individuals recruited in the Carmel and the Golanbelow resolution0–11%
Oceanian20 pooled Papuan and Bougainville individuals — the whole public supplybelow resolution0–2%
Basque CountryBasque individuals recruited in the western Pyreneesbelow resolution0–6%
Armenian Highland and the South Caucasusindividuals recruited in Armenia, Georgia and Abkhaziabelow resolution0–3%
Anatolia and Iranindividuals recruited in Turkey, Iran and northern Iraqbelow resolutionunder 0.01%
Indigenous AmericanPeruvian, Mexican, Colombian and Puerto Rican reference samplesbelow resolutionunder 0.01%
East AsianHan, Japanese, Dai and Kinh reference samplesbelow resolutionunder 0.01%
Indo-Gangetic Plainindividuals recruited in Punjab, Sindh, Kashmir, Rajasthan, Gujarat and along the Gangesbelow resolutionunder 0.01%
Southern Peninsulaindividuals recruited in Tamil Nadu, Andhra Pradesh, Telangana, Karnataka and Keralabelow resolution0.0–0.2%
Makran and the western rangesindividuals recruited across Balochistan and Makranbelow resolution0–1%
Bengal and the central beltindividuals recruited in Bengal, Madhya Pradesh, Chhattisgarh and Maharashtrabelow resolutionunder 0.01%
Himalayas and Northeast Indiaindividuals recruited in Nepal, the terai, and the northeastern hillsbelow resolutionunder 0.01%
Chota Nagpur Plateauindividuals recruited in Jharkhand, Odisha and the eastern Ghatsbelow resolution0.0–0.3%
Andaman and Nicobar IslandsOnge, Jarawa and Great Andamanese individualsbelow resolution0–1%
Siberian21 West Siberian, South Siberian and Amur populations — 498 individualsbelow resolutionunder 0.01%
Central Asian155 individuals from six Central Asian populations — the oases and the Kazakh steppebelow resolutionunder 0.01%
Eastern and Southern AfricanThe Great Lakes and southern Africa, fitted from Luhya, LWK, BantuKenya, BantuSA, Malawi_Chewa, Malawi_Tumbuka and othersbelow resolutionunder 0.01%
Southern BantuNguni, Sotho-Tswana, Herero and Ovambo individuals recruited in southern Africabelow resolutionunder 0.01%
Copts of the NileCoptic individuals recruited in Sudan and Egyptbelow resolutionunder 0.01%
Lake EyasiThe Lake Eyasi basin in Tanzania, fitted from Hadzabelow resolutionunder 0.01%
Horn of AfricaThe Horn of Africa, fitted from Somali, Ethiopians, Ethiopian_Jews, Bejabelow resolutionunder 0.01%
Central KalahariKhwe, Xun and Gui-Ghanakgal individuals recruited in the central Kalaharibelow resolutionunder 0.01%
Northern KalahariThe northern Kalahari and the Okavango, fitted from Ju_hoan_North, Juhoansi, Xun, Khwe, GuiGhanaKgalbelow resolutionunder 0.01%
Southern Kalahari and the KarooThe southern Kalahari, the Karoo and the Northern Cape, fitted from Khomani, KHM_SA, Karretjie, Namabelow resolutionunder 0.01%
Nile ValleyThe middle Nile and Nubia, fitted from Copt, Gaalien, Meseria, Nubian, Arakien, Zaghawa and othersbelow resolutionunder 0.01%
Nilotic SudanThe Nile swamps and the Nuba hills, fitted from Nuer, Shilluk, Dinka, Nubabelow resolutionunder 0.01%
Congo BasinThe Congo basin rainforest, fitted from Mbuti, Biakabelow resolutionunder 0.01%
Sierra Leone and the Upper Guinea coastindividuals recruited in Sierra Leonebelow resolutionunder 0.01%
Swahili coast and the RiftThe Swahili coast and the East African Rift, fitted from Faza_Bajun, Ndau_Bajun, Jomvu_Mjomvu, Tchundwa_Bajun, Masai, Luobelow resolutionunder 0.01%
Western Congo Basin foragersBiaka individuals recruited in the Central African Republicbelow resolutionunder 0.01%
West African forestThe Gulf of Guinea forest belt, fitted from Yoruba, YRI, ESNbelow resolutionunder 0.01%
West African savannaThe western Sahel and upper Niger, fitted from Mandenka, GWD, MSL, Mende, Gambianbelow resolutionunder 0.01%
Egypt and Libyaindividuals recruited in Egypt and Libyabelow resolutionunder 0.01%
Maghreb Amazighindividuals recruited in the Atlas, the Mzab and the western Saharabelow resolutionunder 0.01%
The MzabMozabite individuals recruited in the Mzab valley of the northern Saharabelow resolutionunder 0.01%
Southeast EuropeanThe Balkans and the Carpathian basin, fitted from Greek, Bulgarian, Croatian, Serbian_Serb, Romanian, Hungarian and othersbelow resolutionunder 0.01%
Finland and KareliaFinland and Karelia, fitted from Finnishbelow resolutionunder 0.01%
ItalianItaly and Sardinia, fitted from Italian_North, Sardinianbelow resolutionunder 0.01%
RomaRoma individuals recruited in Barcelona, Bilbao, Granada, Madrid and Portobelow resolutionunder 0.01%
Eastern EuropeanThe East European plain and the Baltic, fitted from Russian, Ukrainian, Belarusian, Czech, Estonian, Lithuanianbelow resolutionunder 0.01%
Volga and UralThe middle Volga and the southern Urals, fitted from Bashkir, Mordovian, Chuvash, Tatar_Kazan, Udmurt, Tatar_Mishar and othersbelow resolutionunder 0.01%
Ashkenaziindividuals of Ashkenazi Jewish descent, recruited in Europe, Israel and the United Statesbelow resolutionunder 0.01%
North CaucasusThe northern slope of the Caucasus, fitted from Ossetian, Lezgin, Karachai, Tabasaran, Lak, Ingushian and othersbelow resolutionunder 0.01%
Dagestan and the Nakh highlandsindividuals recruited in Dagestan, Chechnya and Ingushetiabelow resolutionunder 0.01%
ParsiParsi individuals recruited in Gujarat and Sindhbelow resolutionunder 0.01%
South Caucasusindividuals recruited in Georgia, Abkhazia and Azerbaijanbelow resolution0.0–0.5%

Your chromosomes, painted

Each chromosome coloured by which region its stretches match. Long stretches point to recent ancestors; this sees back about nine generations.

Not measured for this file

This genome has not been painted yet. Painting is computed separately from the rest of the report and is being rolled out across the demo board.

Your mother's line and your father's line

Two threads pass down almost unchanged: one from mother to child, one from father to son. Each follows a single ancestor across thousands of years.

Maternal line
Not measured for this file

This file has no mitochondrial positions, so we cannot read a mother's line from it.

Paternal line
Not measured for this file

the chip this file came from carries no Y markers at all. A chip with Y coverage would give a result

How we worked this out

Thousands of years back

The ancient peoples you come from

Scientists have read DNA from people who lived long ago. Here your DNA is fitted as a mix of those ancient groups.

Not measured for this file

The ancestry model we have does not fit this genome. That is a result rather than an error — it means these ancient sources cannot account for this person's ancestry, and any percentages we showed would be describing a model we have already rejected.

About fifty thousand years ago

Your Neanderthal and Denisovan DNA

When early humans left Africa they met Neanderthals and Denisovans and had children together. Almost everyone outside Africa carries a little of both.

Not measured for this file

We hold no archaic call set covering CLM, the reference population your genome most resembles, nor one for its region. We will not borrow an unrelated population's — a percentile measured against the wrong cohort looks like an answer but is not yours.

How we worked this out

This depends on how many of the SPrime archaic positions your file happens to carry. It says nothing about the quality of the rest of the file. Below a minimum count the rank among other people moves too much to print.

Health & traits

A few variants, found or not found

We only say whether your file carries each variant, and what that usually means. There is no risk score here, and nothing on this page is medical advice.

Traits

A handful of well-studied variants, whether your file carries them, and what each one usually means.

We check 23 trait positions. Your chip covers 2 of them: 1 found, 1 read and not carried, 21 it cannot read.

Skin pigmentation and freckling, TYR S192YTYR rs1042602
Found, one copy

You carry this variant.

A coding change in tyrosinase, the rate-limiting enzyme of melanin synthesis. The A allele is associated with lighter skin, freckling and sun sensitivity.

establisheduntested in South Asian studies

At 36% in Britain, 13% in Punjab and absent in the East Asian and African cohorts, this is largely a European-gradient variant. Pigmentation is polygenic; one tyrosinase variant shifts a tendency and does not set a tone.

BEB 2.9%CEU 39.9%CHB 0.00%GBR 35.7%GIH 11.2%ITU 2.9%
Genetic determinants of hair, eye and skin pigmentation in Europeans. Nature Genetics, 2007. Icelandic and Dutch cohorts PMID 17952075
1 read, variant not carried
Hair and eye colour, TYR upstreamTYR rs1393350
Not found

You do not carry this variant.

A regulatory variant upstream of tyrosinase, associated with red and blond hair, blue eyes and freckling in the same genome-wide scans that found the coding change above.

establisheduntested in South Asian studies

27% in Britain and effectively absent everywhere else we hold. It is on the panel because the report is global, and it will tell most South Asian readers only that they do not carry it.

BEB 4.1%CEU 24.2%CHB 0.00%GBR 27.5%GIH 11.2%ITU 0.49%
Genetic determinants of hair, eye and skin pigmentation in Europeans. Nature Genetics, 2007. Icelandic and Dutch cohorts PMID 17952075
21 this chip cannot read
HERC2 pigmentation variantHERC2 rs1667394
Not read: not on this chip

One of the pigmentation positions in the HERC2/OCA2 region reported for hair and eye colour in a genome-wide study of Europeans.

Your chip does not include this position. Not read is different from not found.

establisheduntested in South Asian studies

Genetic determinants of hair, eye and skin pigmentation in Europeans. Nat Genet, 2007. Icelandic and Dutch PMID 17952075
KITLG, hair colourKITLG rs642742
Not read: not on this chip

A regulatory change near KITLG associated with lighter hair colour.

Your chip does not include this position. Not read is different from not found.

establisheduntested in South Asian studies

cis-Regulatory changes in Kit ligand expression and parallel evolution of pigmentation in sticklebacks and humans. Cell, 2007. European and Asian PMID 18083106
MC1R R151C, red hair and fair skinMC1R rs1805007
Not read: not on this chip

One of the three MC1R variants most consistently reported with red hair and freckling.

Your chip does not include this position. Not read is different from not found.

establisheduntested in South Asian studies

Variants of the melanocyte-stimulating hormone receptor gene are associated with red hair and fair skin in humans. Nat Genet, 1995. British and Irish PMID 7581459
MC1R R160W, red hair and fair skinMC1R rs1805008
Not read: not on this chip

A second of the three MC1R variants reported with red hair and freckling, in the same 1995 study that named the first.

Your chip does not include this position. Not read is different from not found.

establisheduntested in South Asian studies

Variants of the melanocyte-stimulating hormone receptor gene are associated with red hair and fair skin in humans. Nat Genet, 1995. British and Irish PMID 7581459
OCA2 R419Q, eye colourOCA2 rs1800407
Not read: not on this chip

Associated with green and hazel eye colour, and reported to shift eye colour away from blue in people who otherwise carry the blue-eye haplotype at HERC2.

Your chip does not include this position. Not read is different from not found.

establisheduntested in South Asian studies

Allele variations in the OCA2 gene (pink-eyed-dilution locus) are associated with genetic susceptibility to melanoma. Eur J Hum Genet, 2005. European PMID 15889046
SLC45A2 L374F, skin and hair pigmentationSLC45A2 rs16891982
Not read: not on this chip

One of the largest single contributions to the difference in skin and hair pigmentation between European and non-European populations.

Your chip does not include this position. Not read is different from not found.

establisheduntested in South Asian studies

Variants of the MATP/SLC45A2 gene are protective for melanoma in the French population. Hum Mutat, 2008. French PMID 18683857New common variants affecting susceptibility to basal cell carcinoma. Nat Genet, 2009. European PMID 19578363
Earwax typeABCC11 rs17822931
Not read: not on this chip

The clearest single-variant trait known in human genetics.

Your chip does not include this position. Not read is different from not found.

establisheduntested in South Asian studies

A SNP in the ABCC11 gene is the determinant of human earwax type. Nature Genetics, 2006. Japanese, and a 33-population global survey PMID 16444273The impact of natural selection on an ABCC11 SNP determining earwax type. Molecular Biology and Evolution, 2011. Worldwide populations, selection analysis PMID 20937735
Fast-twitch muscle (ACTN3)ACTN3 rs1815739
Not read: not on this chip

Two copies of this variant produce no alpha-actinin-3 in fast-twitch muscle fibres.

Your chip does not include this position. Not read is different from not found.

moderateuntested in South Asian studies

ACTN3 genotype is associated with human elite athletic performance. American Journal of Human Genetics, 2003. Australian elite athletes and controls PMID 12879365
Alcohol flushALDH2 rs671
Not read: not on this chip

Reduces aldehyde dehydrogenase 2 activity; associated with facial flushing after alcohol.

Your chip does not include this position. Not read is different from not found.

establisheduntested in South Asian studies

The alcohol flushing response: an unrecognized risk factor for esophageal cancer from alcohol consumption. PLoS Medicine, 2009. Review, East Asian populations PMID 19320537
Hair thickness and shovel-shaped incisors, EDAR V370AEDAR rs3827760
Not read: not on this chip

The derived allele is associated with thicker, straighter hair shafts, more eccrine sweat glands and shovel-shaped upper incisors.

Your chip does not include this position. Not read is different from not found.

establisheduntested in South Asian studies

A scan for genetic determinants of human hair morphology: EDAR is associated with Asian hair thickness. Human Molecular Genetics, 2008. Asian populations PMID 18065779
Blue-eye variant (HERC2)HERC2 rs12913832
Not read: not on this chip

The single variant explaining most of the blue-brown eye colour difference in European populations, by regulating OCA2 expression.

Your chip does not include this position. Not read is different from not found.

establisheduntested in South Asian studies

A single SNP in an evolutionary conserved region within intron 86 of the HERC2 gene determines human blue-brown eye color. American Journal of Human Genetics, 2008. Australian and Dutch European-ancestry PMID 18252222
Lactase persistenceMCM6 rs4988235
Not read: not on this chip

The variant upstream of LCT most strongly associated with continued lactase production into adulthood.

Your chip does not include this position. Not read is different from not found.

establisheduntested in South Asian studies

Identification of a variant associated with adult-type hypolactasia. Nature Genetics, 2002. Finnish families PMID 11788828Herders of Indian and European cattle share their predominant allele for lactase persistence. Molecular Biology and Evolution, 2012. Indian populations PMID 21836184
Alcohol metabolism, ADH1B His48ArgADH1B rs1229984
Not read: not on this chip

The ADH1B*2 allele encodes an enzyme that converts alcohol to acetaldehyde far faster than the common form.

Your chip does not include this position. Not read is different from not found.

establisheduntested in South Asian studies

A global perspective on genetic variation at the ADH genes reveals unusual patterns of linkage disequilibrium and diversity. American Journal of Human Genetics, 2002. Global populations PMID 11774072
Caffeine metabolism, CYP1A2 -163C>ACYP1A2 rs762551
Not read: not on this chip

CYP1A2 clears about 95% of ingested caffeine.

Your chip does not include this position. Not read is different from not found.

establisheduntested in South Asian studies

Functional significance of a C-->A polymorphism in intron 1 of the cytochrome P450 CYP1A2 gene tested with caffeine. British Journal of Clinical Pharmacology, 1999. German cohort PMID 10233211
MTHFR C677TMTHFR rs1801133
Not read: not on this chip

Reduces the activity of methylenetetrahydrofolate reductase.

Your chip does not include this position. Not read is different from not found.

moderateuntested in South Asian studies

A candidate genetic risk factor for vascular disease: a common mutation in methylenetetrahydrofolate reductase. Nature Genetics, 1995. Canadian and Dutch PMID 7647779
Long-chain fatty acid conversion, FADS1FADS1 rs174537
Not read: not on this chip

Associated with the efficiency of converting plant-derived short-chain omega-3 and omega-6 fatty acids into the long-chain forms the body uses.

Your chip does not include this position. Not read is different from not found.

establisheduntested in South Asian studies

Genome-wide association study of plasma polyunsaturated fatty acids in the InCHIANTI study. PLoS Genetics, 2009. Italian cohort PMID 19851445
Freckling and hair colour, IRF4IRF4 rs12203592
Not read: not on this chip

Associated with freckling, lighter hair colour and skin sensitivity to sun.

Your chip does not include this position. Not read is different from not found.

establisheduntested in South Asian studies

A genome-wide association study identifies novel alleles associated with hair color and skin pigmentation. PLoS Genetics, 2008. European cohorts PMID 18483556
Skin pigmentation, OCA2 H615ROCA2 rs1800414
Not read: not on this chip

An East Asian-specific pigmentation variant.

Your chip does not include this position. Not read is different from not found.

establisheduntested in South Asian studies

A common variant in the OCA2 gene is associated with skin pigmentation in East Asians. Human Genetics, 2010. East Asian cohorts PMID 20049473
Hair and eye colour, SLC24A4SLC24A4 rs12896399
Not read: not on this chip

A potassium-dependent sodium-calcium exchanger locus associated with lighter hair and eye colour, and one of the markers in the published HIrisPlex eye and hair colour models.

Your chip does not include this position. Not read is different from not found.

establisheduntested in South Asian studies

Genetic determinants of hair, eye and skin pigmentation in Europeans. Nature Genetics, 2007. Icelandic and Dutch cohorts PMID 17952075
Skin pigmentation, SLC24A5 A111TSLC24A5 rs1426654
Not read: not on this chip

The single largest-effect common variant on skin pigmentation known.

Your chip does not include this position. Not read is different from not found.

establisheduntested in South Asian studies

SLC24A5, a putative cation exchanger, affects pigmentation in zebrafish and humans. Science, 2005. Human populations and zebrafish PMID 16357253
Bitter taste (PTC)TAS2R38 rs713598
Not read: not on this chip

One of three coding variants in the TAS2R38 bitter receptor that together determine sensitivity to phenylthiocarbamide and related compounds.

Your chip does not include this position. Not read is different from not found.

establisheduntested in South Asian studies

Positional cloning of the human quantitative trait locus underlying taste sensitivity to phenylthiocarbamide. Science, 2003. Utah families of European ancestry PMID 12595690
How we worked this out

Not found: we read the position and the variant is on neither copy. That is a measurement. Found, one copy or two copies: also a measurement. Not read: the chip does not type this position, or could not read it cleanly. Nobody looked, so it is not the same as not found. Could not be read: several variants share this position or the strand is unmeasured, so the row says what blocked it.

Most associations here were established in European or East Asian cohorts. Each row's tags say which population the association has been tested in. Catalogue v0.1.0. Nothing here is a prediction about you.

Health variants

Whether your file carries variants that studies have linked to health. Found or not found, with the studies behind each one.

We check 24 health positions. Your chip covers 3 of them: 0 found, 3 read and not carried, 21 it cannot read.

Not medical advice, and not a reason to start, stop or change any medicine. A variant we could not read may still be there, and a variant missing from this short list was never examined. If a doctor has prescribed something, keep taking it and show them this page.
HFE hereditary haemochromatosis2 variants
1 read, not found
HFE C282Y, hereditary haemochromatosisHFE rs1800562
Not found

You do not carry this variant.

The commonest cause of hereditary haemochromatosis, in which the body absorbs more iron than it needs. ClinVar classifies it Pathogenic.

establisheduntested in South Asian studies

Almost all HFE research is in European-ancestry cohorts, where this variant is commonest. It is rare in South Asians and its frequency there is not well measured, so an absence here says less than it would for a European genome.

A novel MHC class I-like gene is mutated in patients with hereditary haemochromatosis. Nat Genet, 1996. Families with haemochromatosis PMID 8696333
HFE H63DHFE rs1799945
Not read: not on this chip

A second HFE change described in the same 1996 paper as C282Y. It is common in many populations and, on its own, is not generally associated with iron overload; the combination most reported is one copy of this alongside one copy of C282Y.

moderateuntested in South Asian studies

ClinVar records conflicting classifications for this variant, and it is reported far more often than iron overload occurs, so it is shown as a finding rather than a conclusion. Its frequency in South Asians is not well measured.

A novel MHC class I-like gene is mutated in patients with hereditary haemochromatosis. Nat Genet, 1996. Families with haemochromatosis PMID 8696333
ABCG2 transporter function1 variant
1 read, not found
ABCG2 Q141K, transporter functionABCG2 rs2231142
Not found

You do not carry this variant.

The T allele reduces ABCG2 transporter function. It is associated with higher serum urate and with reduced response to allopurinol, and CPIC uses it in guidance on rosuvastatin dosing.

establisheduntested in South Asian studies

Urate and gout have large dietary and renal components that this variant does not read. The allopurinol association describes response at a given dose rather than whether the drug works.

BEB 12.2%CEU 11.6%CHB 31.1%GBR 14.3%GIH 6.8%ITU 10.8%
Identification of a urate transporter, ABCG2, with a common functional polymorphism causing gout. Proceedings of the National Academy of Sciences, 2009. European and African-American cohorts PMID 19506252
CYP3A5 tacrolimus metabolism1 variant
1 read, not found
CYP3A5*3, tacrolimus metabolismCYP3A5 rs776746
Not found

You do not carry this variant.

The commonest reason CYP3A5 produces no working enzyme. It is the main genetic factor in tacrolimus dosing after transplantation.

establisheduntested in South Asian studies

Frequencies for this variant differ sharply between populations, and the South Asian estimate is less well measured than the European and African ones.

Sequence diversity in CYP3A promoters and characterization of the genetic basis of polymorphic CYP3A5 expression. Nat Genet, 2001. Multi-population PMID 11279519CYP3A5 genotype predicts renal CYP3A activity and blood pressure in healthy adults. J Appl Physiol (1985), 2003. Healthy adults PMID 12754175
11 this chip cannot read
G6PD 3 of 225 chip-readable variants; 248 more no chip can see
3 not read
G6PD deficiency, A- variantG6PD rs1050828
Not read: not on this chip

The commonest G6PD-deficiency variant in populations of African ancestry. ClinVar classifies it Pathogenic/Likely pathogenic.

establisheduntested in South Asian studies

Carried here as a deliberate negative. In gnomAD v4 this variant is at 0.00030 in South Asians and 0.12277 in Africans — a 400-fold difference — while the Mediterranean variant on the row above runs the other way, 0.019 against 0.0002. A G6PD panel built on the well-known African variant would miss almost every deficient South Asian and would look like a working panel while doing it. That is the same failure as reporting lactase persistence from one European SNP, in a gene where the consequence is a drug reaction.

gnomAD afr 12.3%gnomAD eas 0.00%gnomAD nfe 0.01%gnomAD sas 0.03%
Glucose-6-phosphate dehydrogenase deficiency. Lancet, 2008. Review PMID 18177777
G6PD deficiency, Mediterranean variantG6PD rs5030868
Not read: not on this chip

This variant reduces glucose-6-phosphate dehydrogenase activity and is the commonest cause of G6PD deficiency reported in Indian populations. ClinVar classifies it Pathogenic/Likely pathogenic for G6PD deficiency.

establishedreplicated in South Asian studies

G6PD deficiency is diagnosed by an enzyme activity test, not by a genotype. This tells you a variant is present. It is also not the only cause of G6PD deficiency, and the others are not read by this file. Measured as readable: the GSA probe here is [A/G] on the plus strand, which interrogates the correct alternate, and the site is present in every real 23andMe and AncestryDNA file tested.

gnomAD afr 0.02%gnomAD eas 0.00%gnomAD mid 4.2%gnomAD nfe 0.02%gnomAD sas 1.9%
Glucose-6-phosphate dehydrogenase deficiency. Lancet, 2008. Review PMID 18177777Prevalence and spectrum of mutations causing G6PD deficiency in Indian populations. Infection, Genetics and Evolution, 2020. Indian populations PMID 33069889
G6PD deficiency, Orissa variantG6PD rs78478128
Not read: not on this chip

A variant reducing glucose-6-phosphate dehydrogenase activity, classified Pathogenic/Likely pathogenic in ClinVar, and the most frequently reported cause of G6PD deficiency in Indian series.

establishedreplicated in South Asian studies

The measurement worth quoting precisely, because it is easy to misread. Among 350 molecularly characterised G6PD-deficient individuals drawn from a screen of 20,896 people across India, this variant accounted for 56.5% of deleterious alleles and the Mediterranean variant for 23.6%. That is a share of the alleles found IN DEFICIENT PEOPLE, not a frequency in the population — overall deficiency prevalence in the same screen was 1.9%, ranging 0.8 to 6.3% by region. The two numbers answer different questions and only the second says how common deficiency is.

Prevalence and spectrum of mutations causing G6PD deficiency in Indian populations. Infection, Genetics and Evolution, 2020. 20,896 individuals screened across India; 350 deficient individuals characterised PMID 33069889Glucose-6-phosphate dehydrogenase deficiency. Lancet, 2008. Review PMID 18177777
HBB Beta-thalassaemia and sickle cell6 of 223 chip-readable variants; 272 more no chip can see
6 not read
Beta-thalassemia, Cap+1 A>CHBB rs34305195
Not read: not on this chip

A variant in the HBB transcription start region, classified Pathogenic/Likely pathogenic in ClinVar, and reported in Indian series as a mild beta-thalassemia allele.

establishedreplicated in South Asian studies

Reported in the literature as producing a milder phenotype than the splice-site and nonsense alleles, which is a statement about published series and not a prediction about any individual.

Genetic Heterogeneity of Beta Globin Mutations among Asian-Indians and Importance in Genetic Counselling and Diagnosis. Mediterranean Journal of Hematology and Infectious Diseases, 2013. Asian-Indian PMID 23350016Spectrum of beta-thalassemia mutations and their association with allelic sequence polymorphisms at the beta-globin gene cluster in an Eastern Indian population. American Journal of Hematology, 2002. Eastern Indian PMID 12210807
Beta-thalassemia, codon 15 G>AHBB rs33986703
Not read: not on this chip

A nonsense variant in HBB, classified Pathogenic in ClinVar, and one of the beta-thalassemia alleles reported in Indian series.

establishedreplicated in South Asian studies

Two limits. This site is absent from the base Illumina GSA manifest and present in all three real 23andMe v5 files, because 23andMe adds custom content on top of the GSA platform and does not publish its site list — so the probe alleles here are unknown and unknowable from any source we are willing to use. It is also a T/A pair, which is strand-ambiguous, and until now we discarded such sites rather than resolve them — our policy rather than a limit of the array, whose three designs state the strand unanimously here. The row declines rather than reporting an absence.

gnomAD eas 0.04%gnomAD sas 0.00%
Genetic Heterogeneity of Beta Globin Mutations among Asian-Indians and Importance in Genetic Counselling and Diagnosis. Mediterranean Journal of Hematology and Infectious Diseases, 2013. Asian-Indian PMID 23350016
Beta-thalassemia, codon 30 G>CHBB rs33960103
Not read: not on this chip

A variant at the codon 30 splice junction of HBB, classified Pathogenic in ClinVar and reported in Indian beta-thalassemia series.

establishedreplicated in South Asian studies

Two independent limits. ClinVar records two further pathogenic alleles at this coordinate, so an array whose probe reads one of those has said nothing about ours, and the row never reports an absence here. It is also a C/G pair, which is strand-ambiguous; whether the probe designs at this position state a strand unanimously has not been measured, so the drop is attributed to our own policy rather than to the array until it is.

Spectrum of beta-thalassemia mutations and their association with allelic sequence polymorphisms at the beta-globin gene cluster in an Eastern Indian population. American Journal of Hematology, 2002. Eastern Indian PMID 12210807
Beta-thalassemia, IVS1-1 G>THBB rs33971440
Not read: not on this chip

A splice-donor variant in HBB, classified Pathogenic in ClinVar, and among the beta-thalassemia alleles most frequently reported in Indian series after IVS1-5.

establishedreplicated in South Asian studies

Three separate pathogenic alleles sit at this coordinate, so an array whose probe reads a different one has said nothing about this variant. The row therefore never reports an absence here.

The phenotypic and molecular diversity of hemoglobinopathies in India: A review of 15 years of experience. International Journal of Laboratory Hematology, 2019. Indian, 15-year single-centre series PMID 30489691Genetic Heterogeneity of Beta Globin Mutations among Asian-Indians and Importance in Genetic Counselling and Diagnosis. Mediterranean Journal of Hematology and Infectious Diseases, 2013. Asian-Indian PMID 23350016
Beta-thalassemia, IVS1-5 G>CHBB rs33915217
Not read: not on this chip

A splice-site variant in HBB, classified Pathogenic in ClinVar, and the most frequently reported beta-thalassemia allele in Indian series.

establishedreplicated in South Asian studies

Whether this variant can be read from a consumer array is unknown, and that is the honest answer rather than a hedge. Three different pathogenic alleles share this rsID at chr11:5226925 — C>A, C>G and C>T are separate ClinVar records — and the Illumina GSA manifest carries four probe designs at the position, one of which does interrogate C>G. Which design a vendor actually shipped is not stated in the file, which names the marker plainly with no suffix. So the site is typed, and what was typed cannot be determined.

The phenotypic and molecular diversity of hemoglobinopathies in India: A review of 15 years of experience. International Journal of Laboratory Hematology, 2019. Indian, 15-year single-centre series PMID 30489691Genetic Heterogeneity of Beta Globin Mutations among Asian-Indians and Importance in Genetic Counselling and Diagnosis. Mediterranean Journal of Hematology and Infectious Diseases, 2013. Asian-Indian PMID 23350016Spectrum of beta-thalassemia mutations and their association with allelic sequence polymorphisms at the beta-globin gene cluster in an Eastern Indian population. American Journal of Hematology, 2002. Eastern Indian PMID 12210807
Sickle cell variantHBB rs334
Not read: not on this chip

The HBB variant that produces haemoglobin S, classified Pathogenic in ClinVar for sickle cell disease.

establishedreplicated in South Asian studies

Not typed by 23andMe v5 at all — absent from all three real files tested, while present in a 2023 AncestryDNA file. So whether this row can be answered depends on which vendor and which chip version produced the upload, and the answer has to be computed per file rather than stated per vendor. It is also a T/A pair, which is strand-ambiguous, and unlike the other rows here the three probe designs at this position disagree about which strand they read, so no declaration exists to resolve it against. That is a limit of the array rather than a policy of ours.

The phenotypic and molecular diversity of hemoglobinopathies in India: A review of 15 years of experience. International Journal of Laboratory Hematology, 2019. Indian, 15-year single-centre series PMID 30489691
CYP2C19 Clopidogrel and other CYP2C19-activated drugs2 of 585 chip-readable variants
3 not read
CYP2C19*2, the commonest no-function alleleCYP2C19 rs4244285
Not read: not on this chip

A splice-site change that produces no working CYP2C19 enzyme from the affected copy. CYP2C19 converts several drugs into their active form, so carrying two copies means the enzyme activity is absent rather than reduced.

establishedreplicated in South Asian studies

This is not medical advice, and not a reason to change any medicine. If a doctor has prescribed something, keep taking it and show them this page. Metaboliser status is assigned from a person's full CYP2C19 star-allele diplotype; this file reads two of those alleles, so a result here is partial. Genotype is also not a measurement of enzyme activity, which is what a clinical test would give you.

BEB 32.6%GBR 14.3%GIH 33.0%ITU 37.3%PJL 34.4%STU 41.2%
Clinical Pharmacogenetics Implementation Consortium Guideline for CYP2C19 Genotype and Clopidogrel Therapy: 2022 Update. Clinical Pharmacology and Therapeutics, 2022. Guideline, systematic literature review PMID 35034351Prevalence of CYP2C19 Poor Metabolisers Among South Indian Psychiatric Patients. Annals of Neurosciences, 2025. South Indian patients PMID 41063924
CYP2C19*3, a second no-function alleleCYP2C19 rs4986893
Not read: not on this chip

A premature stop codon that truncates the CYP2C19 protein. Same consequence as *2 -- no working enzyme from that copy -- by a different mechanism.

establishedreplicated in South Asian studies

Not medical advice. Much rarer than *2 in South Asians -- gnomAD puts it at 0.5% against 33% -- so for most readers the *2 row is the informative one. A full diplotype needs more alleles than this file reads.

BEB 2.3%GIH 0.48%ITU 0.49%PJL 1.6%STU 1.5%gnomAD afr 0.04%
Clinical Pharmacogenetics Implementation Consortium Guideline for CYP2C19 Genotype and Clopidogrel Therapy: 2022 Update. Clinical Pharmacology and Therapeutics, 2022. Guideline, systematic literature review PMID 35034351Prevalence of CYP2C19 Poor Metabolisers Among South Indian Psychiatric Patients. Annals of Neurosciences, 2025. South Indian patients PMID 41063924
CYP2C19*17, faster metabolismCYP2C19 rs12248560
Not read: not on this chip

Increases CYP2C19 activity, the opposite direction to the *2 and *3 variants already in this report. The Clinical Pharmacogenetics Implementation Consortium publishes prescribing guidance based on the combination of these variants; this report shows what was found and does not adjust any dose.

establisheduntested in South Asian studies

The prescribing guidance behind this variant is built mainly on European and East Asian cohorts. Its frequency and effect in South Asians are less well measured.

Clinical Pharmacogenetics Implementation Consortium guidelines for cytochrome P450-2C19 (CYP2C19) genotype and clopidogrel therapy. Clin Pharmacol Ther, 2011. CPIC guideline PMID 21716271
NUDT15 Thiopurine sensitivity1 of 3 chip-readable variants
1 not read
NUDT15 R139C, reduced enzyme activityNUDT15 rs116855232
Not read: not on this chip

The R139C change reduces NUDT15 enzyme activity, so an active metabolite the enzyme normally clears persists longer. CPIC assigns metaboliser status from NUDT15 and TPMT together.

establishedreplicated in South Asian studies

Not medical advice, and not a reason to stop or change any medicine. Stopping a prescribed treatment is more dangerous than any genotype on this page. Show this to the prescribing doctor and let them decide. This variant is roughly 24 times commoner in South Asians than in Europeans, which is why it is here and why guidance derived from European cohorts has historically under-served this population.

BEB 6.4%GIH 3.9%ITU 7.8%PJL 8.3%STU 8.3%gnomAD afr 0.09%
Clinical Pharmacogenetics Implementation Consortium (CPIC) Guideline for Thiopurine Dosing Based on TPMT and NUDT15 Genotypes. Clinical Pharmacology and Therapeutics, 2026. Guideline, systematic literature review PMID 41618934Optimizing mercaptopurine therapy in indian pediatric ALL: The role of TPMT and NUDT15 genotyping. Cancer Treatment and Research Communications, 2026. Indian paediatric acute lymphoblastic leukaemia PMID 41819030
TPMT Thiopurine sensitivity1 of 9 chip-readable variants
1 not read
TPMT*3C, thiopurine S-methyltransferase activityTPMT rs1142345
Not read: not on this chip

TPMT is the second enzyme CPIC reads alongside NUDT15. The *3C allele reduces its activity.

establisheduntested in South Asian studies

Not medical advice. TPMT*3C is one of several TPMT alleles and this file reads one of them, so a normal result here does not establish normal TPMT activity. Enzyme activity is measurable directly and that test, not this one, is what a clinic would use. No South Asian cohort study of this allele's effect is cited here -- the frequency data below is South Asian, the effect evidence is not.

BEB 2.9%GIH 2.4%ITU 2.0%PJL 1.0%STU 0.49%gnomAD afr 5.5%
Clinical Pharmacogenetics Implementation Consortium (CPIC) Guideline for Thiopurine Dosing Based on TPMT and NUDT15 Genotypes. Clinical Pharmacology and Therapeutics, 2026. Guideline, systematic literature review PMID 41618934
SLCO1B1 Statin transport1 of 1 chip-readable variants
1 not read
SLCO1B1 V174A, reduced hepatic transportSLCO1B1 rs4149056
Not read: not on this chip

SLCO1B1 is a liver transporter: it moves certain compounds out of the blood and into hepatocytes, where they act and are cleared. The V174A change reduces that transport, so what the transporter handles stays in circulation longer.

establisheduntested in South Asian studies

Not medical advice, and not a reason to stop or change any medicine. Stopping a prescribed treatment on the basis of a web page is more dangerous than anything this row describes. The variant is LESS common in South Asians than in Europeans -- 4.9% against 15.9% -- so for most readers here this row will be uninformative. Effect evidence is European; no South Asian cohort is cited.

BEB 5.2%GBR 14.3%GIH 1.9%ITU 6.4%PJL 3.6%STU 4.4%
The Clinical Pharmacogenetics Implementation Consortium Guideline for SLCO1B1, ABCG2, and CYP2C9 genotypes and Statin-Associated Musculoskeletal Symptoms. Clinical Pharmacology and Therapeutics, 2022. Guideline, systematic literature review PMID 35152405
VKORC1 Warfarin sensitivity1 of 9 chip-readable variants
1 not read
VKORC1 -1639, reduced enzyme expressionVKORC1 rs9923231
Not read: not on this chip

This promoter variant lowers how much VKORC1 enzyme the liver produces. VKORC1 is the target of one class of anticoagulant, so the amount present matters to anyone taking one.

establisheduntested in South Asian studies

Not medical advice. Anticoagulant dosing is managed by blood tests that measure the actual effect in the actual person, which is far more informative than any genotype. Never change a dose on the basis of this page. Effect evidence is largely European and East Asian; no South Asian cohort is cited here.

BEB 15.7%GBR 35.7%GIH 17.5%ITU 9.3%PJL 19.8%STU 10.8%
Clinical Pharmacogenetics Implementation Consortium (CPIC) Guideline for Pharmacogenetics-Guided Warfarin Dosing: 2017 Update. Clinical Pharmacology and Therapeutics, 2017. Guideline, systematic literature review PMID 28198005
ASPA Canavan disease carrier1 variant
1 not read
ASPA E285A, Canavan disease carrierASPA rs28940279
Not read: not on this chip

The commonest ASPA variant in Canavan disease, a recessive condition affecting the white matter of the brain. ClinVar classifies it Pathogenic/Likely pathogenic.

establisheduntested in South Asian studies

This variant's frequency has not been measured in South Asian populations. The Canavan literature is largely Ashkenazi Jewish, where it is commonest; what carrier frequency looks like in South Asia is not something this report can tell you.

Cloning of the human aspartoacylase cDNA and a common missense mutation in Canavan disease. Nat Genet, 1993. Canavan families PMID 8252036The frequency of the C854 mutation in the aspartoacylase gene in Ashkenazi Jews in Israel. Am J Hum Genet, 1994. Ashkenazi Jewish PMID 8037206
ACKR1 1 variant
1 not read
Duffy-null blood groupACKR1 rs2814778
Not read: not on this chip

The C allele abolishes Duffy antigen expression on red cells. It confers resistance to Plasmodium vivax malaria and is associated with a lower baseline neutrophil count that is benign -- 'benign ethnic neutropenia'.

establisheduntested in South Asian studies

The clinically important part is the neutrophil count, because a normal result for a Duffy-null person can be read as abnormal against a reference range built on people who are not. This is close to fixed in West African populations, absent in the South Asian and East Asian cohorts, and is on the panel because the report is global.

BEB 0.00%CEU 0.00%CHB 0.00%GBR 0.00%GIH 0.00%ITU 0.00%
Disruption of a GATA motif in the Duffy gene promoter abolishes erythroid gene expression in Duffy-negative individuals. Nature Genetics, 1995. African populations PMID 7663520
CYP2C9 reduced-function allele1 variant
1 not read
CYP2C9*3, reduced-function alleleCYP2C9 rs1057910
Not read: not on this chip

CYP2C9*3 substantially reduces enzyme activity. CPIC guidelines use CYP2C9 genotype in dosing recommendations for warfarin and for phenytoin.

establisheduntested in South Asian studies

A genotype is not a dose. CPIC recommendations combine CYP2C9 with VKORC1 and with clinical factors, and warfarin is titrated on INR measurement whatever the genotype says. Carrying *3 is a reason to expect a lower dose requirement, not a reason to change one.

BEB 11.6%CEU 6.6%CHB 3.9%GBR 7.1%GIH 13.1%ITU 10.3%
The role of the CYP2C9-Leu359 allelic variant in the tolbutamide polymorphism. Pharmacogenetics, 1996. Human liver samples PMID 8946472
IFNL4 hepatitis C treatment response1 variant
1 not read
IFNL4 (IL28B), hepatitis C treatment responseIFNL4 rs12979860
Not read: not on this chip

Genotype at this locus predicts response to interferon-based hepatitis C therapy and spontaneous clearance of the virus. The C allele is the favourable one; the T allele is associated with poorer response.

establisheduntested in South Asian studies

This is a variant whose clinical relevance has largely PASSED. Interferon-based regimens have been superseded by direct-acting antivirals, which cure across genotypes, and it is reported here as a well-established association rather than as a treatment decision anybody should now make.

BEB 19.8%CEU 27.3%CHB 6.3%GBR 30.8%GIH 23.8%ITU 23.5%
Genetic variation in IL28B predicts hepatitis C treatment-induced viral clearance. Nature, 2009. Multi-ancestry cohort PMID 19684573
How we worked this out

Rows report variant presence against published work. The catalogue (v0.1.0) is a small selection of the variants known in each gene: ClinVar records hundreds more, and roughly half of those are deletions, duplications or repeat expansions that no genotyping chip can detect at all. Metaboliser status for CYP2C19 or TPMT needs a full star-allele diplotype and an enzyme activity test, which this file cannot give. G6PD deficiency is diagnosed by an enzyme test, not a genotype.

Whether a position can be read depends on the vendor and chip version that produced the upload, and it is measured for each file. Frequencies shown are from gnomAD v4 and the 1000 Genomes Project. The evidence for an effect often comes from European studies, and each row's tags say where it has been tested.

Technical

For the curious

The detail behind the numbers: the reference groups, your file, and the same file through other tools.

Reference groups closest to you

How close your DNA sits to each group in our reference set.

Closest areas

MozabiteEgyptianLebanese

The areas whose reference samples your DNA is nearest to. No order, and no ranking.

typical rangefar outside
Colombian
Puerto Rican
Peruvian
African ancestry
Mexican ancestry
7 further groups, none of them close
African Caribbean
Balochi
Makrani
Iberian
Brahui
Finnish
British
Inside the group's rangeClose to the groupOutsideColour is the group's region.
Groups are drawn where they are from. Several were sampled abroad; hover a row for its sampling place.
How we worked this out

Each file and every individual reference genome is projected into the first ten principal components of our panel. The chart is Mahalanobis distance in that space, so it scales each group by its own spread. The green band is where a typical member sits, the amber band is close but outside it, and a marker farther right is less like that group. Hover or tap a row for its distance. The closest areas come from the nearest three individual references, coarsened and left unranked.

On the same 289 people and 54 groups, this distance placed the correct group first 60.2% of the time against 35.3% for G25, and in the top three 74.0% against 63.0%. The G25 coordinates in this report are an export for other tools.

Present-day map

Where your file falls among living reference groups

Each dot is one person in our reference panel, placed by their genome on the first two axes of a principal component analysis. The ringed point marked You is your file, placed the same way.

2,000 reference individuals across 16 regions, on the first two principal componentsEastern ChineseWest African forestDeccan and the Tamil PlainsWest African savannaIberianThe Ganges Plain and GujaratEastern and Southern AfricanMainland Southeast AsiaFinland and KareliaBengal and the central beltYou

2,000 reference individuals, the same cloud a report draws, on the panel fitted today. The first two components carry 13.9% and 5.4% of total variance. Every point is one published reference individual and names itself on hover. Clusters are labelled with the region each cohort is pooled into and coloured by the continental component that region sits in. 504 individuals from cohorts pooled into no region are not drawn.

The picture is turned a quarter clockwise and mirrored, which is why it resembles a map: Europe upper left, East Asia upper right, Africa along the bottom, South Asia between them. That is a rotation and a reflection of the plane, so no point has moved relative to any other and every distance is what it was. It is a reading aid and nothing more. The axes have no meaning of their own: they are directions of maximum variance, their signs are arbitrary, and a region's position depends on which other regions are in the panel. The first component runs vertically here and the second horizontally.

Every region carries a numbered disc at its centre and an outline around its members, and the key below repeats the number — so a region too crowded to spell out on the plot can still be found on it. The outline is the convex hull of that region's own individuals with the furthest 8% trimmed off, which keeps one stray from dragging a boundary across the chart; it encloses people, not territory. Spelled out only in the key at this size: 4 Northwest European, 14 Tai and Kadai, 13 Punjab and Kashmir, 16 Sierra Leone and the Upper Guinea coast, 8 Japanese, 7 Italian.

African

  • 2West African forest207
  • 5West African savanna113
  • 10Eastern and Southern African99
  • 16Sierra Leone and the Upper Guinea coast85

Bengal and the central belt

  • 15Bengal and the central belt86

East Asian

  • 1Eastern Chinese208
  • 8Japanese104
  • 11Mainland Southeast Asia99
  • 14Tai and Kadai93

European

  • 4Northwest European190
  • 6Iberian107
  • 7Italian107
  • 12Finland and Karelia99

Indo-Gangetic Plain

  • 9The Ganges Plain and Gujarat103
  • 13Punjab and Kashmir96

Southern Peninsula

  • 3Deccan and the Tamil Plains204

12,713 of 12,770 panel markers placed you on the map.

The groups nearest your position, closest first:

  1. Colombian
  2. Puerto Rican
  3. Peruvian

This is a position on a plot. Sitting near a group means your genome resembles that group on these axes. It does not say who your ancestors were.

How we worked this out

The axes come from the reference panel alone. Your file is projected onto them using the markers it shares with the panel, so it moves no point on the map.

Nearest is measured over more axes than the two drawn, to the middle of each group, so a group that looks close here can rank lower in the list.

Ancient source map

Where your file falls among ancient genomes

Each dot is one ancient person, placed by their genome on the first two axes of a principal component analysis. The ringed point marked You is your file, placed the same way.

558 ancient individuals in 14 source groups, on the first two principal componentsChina, c. 2,000 BPChina, c. 4,500 BPTurkey, c. 8,500 BPRussia, c. 8,500 BPPapua New Guinea, c. 500 BPIran, c. 10,000 BPChina, c. 1,500 BPTaiwan, c. 1,500 BPSweden, c. 7,500 BPSteppe and Siberia, c. 5,500 BPRussia, c. 32,500 BPCameroon, c. 8,000 BPSouth Africa, c. 2,500 BPYou

558 excavated individuals with at least 100,000 1240K SNPs, on the first two principal components (5.1% and 1.7% of variance). 41 individuals sit far from their group's centroid and are left out of every outline and centre; they are not drawn here, and the methods page shows them. Axes have no meaning of their own, and a group's position depends on which other groups are in the map. 4 sources pooled from several regions are not drawn: a period is not a people.

Named only in the key at this size: 9 Japan, c. 4,500 BP.

Before 10,000 BP

  • 1Russia, c. 32,500 BP12

10,000 to 7,000 BP

  • 2Iran, c. 10,000 BP26 +2
  • 3Turkey, c. 8,500 BP85 +9
  • 4Russia, c. 8,500 BP48 +3
  • 5Cameroon, c. 8,000 BP8
  • 6Sweden, c. 7,500 BP17 +2

7,000 to 5,000 BP

  • 7Steppe and Siberia, c. 5,500 BP15 +1

5,000 to 3,500 BP

  • 8China, c. 4,500 BP113 +3
  • 9Japan, c. 4,500 BP14 +1

After 3,500 BP

  • 10South Africa, c. 2,500 BP7
  • 11China, c. 2,000 BP136 +14
  • 12Taiwan, c. 1,500 BP21 +4
  • 13China, c. 1,500 BP24
  • 14Papua New Guinea, c. 500 BP32 +2

Counts are individuals kept; +n is individuals dropped. Thin: fewer than 5 usable individuals. An outline is drawn around each excavation group of six or more people, not around a source.

50,462 of 138,630 sites in your file are on the map.

The sources nearest your position, closest first:

  1. Iran, c. 10,000 BP
  2. Turkey, c. 8,500 BP
  3. Russia, c. 32,500 BP

This is a position on a plot. Sitting near a source means your genome resembles that group on these axes. It does not say who your ancestors were.

How we worked this out

The axes come from the ancient individuals alone. Your file is projected onto them using the sites it shares with them, so it moves no point on the map.

Nearest is measured over the first four axes, to the middle of each source. The plot shows two of them, so a source that looks close here can rank lower in the list.

On eleven test genomes the nearest source fell in the same region as the qpAdm fit. Inside a region, the order of the nearest sources does not track the qpAdm proportions.

The same ancestor, twice

Stretches inherited twice

Places where both copies of a chromosome came from the same ancestor. A long stretch points to a recent shared ancestor.

2stretches
0.07%of your genome
1.4 cMlongest
2.5 cM
25–50 generations700–1,400 years ago
2 stretches
none
12.5–25 generations350–700 years ago
0 stretches
none
5–12.5 generations140–350 years ago
0 stretches
none
under 5 generationswithin 140 years
0 stretches

Oldest first. Bar heights compare your own bands with each other.

Where they sit

Each bar is one chromosome, drawn to length. A mark is a stretch where both of your copies match, coloured by how far back the shared ancestor sits.

1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
1-2 cM · 700–1,400 years2-4 cM · 350–700 years4-10 cM · 140–350 years10 cM and longer · under 140 years
How we worked this out

Runs of homozygosity need no reference panel. The question is whether your two copies match, so this runs on 573,095 of your own markers, more than the 12,770 that reach the ancestry estimate. It is the one chapter in this report that our reference data does not limit. In total the stretches cover 2.5 cM of 3,545 cM.

Two different detectors were run over your file and they agree on 99% of the length. They fail in different ways, so where they agree the result is not an artefact of either one.

Lengths are in centimorgans, read off a recombination map, not in megabases. Near the middle of a chromosome a megabase can be a fifth of a centimorgan and near the end it can be two, so a generation count computed from physical distance is wrong by a factor that changes along every chromosome. Generations follow from genetic length as roughly 50 divided by the length in centimorgans, and a year figure uses 28 years to a generation.

Each stretch is shown as a range of generations. A 20 cM stretch averages about two and a half generations back, but over a very wide spread, and one stretch is a single draw from it. A range is what the evidence supports.

What this does not say. It says nothing about whether your parents are related. That question needs a comparison against the population you descend from, and we do not publish it. In a community that has married within itself for centuries the background is high with no close relatedness at all, so a fixed threshold would tell many people something untrue about their own family.

Your file

How complete your raw DNA file was. This sets how precise every other chapter can be.

A

A: complete enough for every chapter.

573,669markers in the file
100.0%read successfully
23andMe, build 37recognised from the file's contents; no Y chromosome
Checks: This file carries no X or Y markers, so we cannot infer sex from it and there is no paternal (Y) line to trace. Affects: sex inference and the paternal-line chapter only.

The checks above are the ones that flagged; the rest passed. Nothing was sequenced by us. We read the export you already had.

How we worked this out

Heterozygosity is 25.1%: the share of read positions where your two copies differ. A consumer chip usually reads between 24% and 31%; well outside that range points to a file problem rather than to anything about you.

Your file is kept and used to build future reference panels, under a Creative Commons licence. We never sell or release an individual genome, and no genetic data is ever written to a log.

Who you were compared against

524 groups, 10,922 people. The reference behind every number in this report.

Every study behind the panel, and its regions

People alive today: 6,745 people, 18 studies. The reference this report measures your ancestry against.

The1000GenomesProjectConsortiumNature2015
2,320
unrecorded
1,544
PattersonReichGenetics2012
1,003
NakatsukaReichNatGenet2017
619
LazaridisKrauseNature2014
494
WangReichNature2021
207
BiaginiCalafellEJHG2019
120
LazaridisReichNature2016
83
EBC India 2023
81
YangZhangJHumGenet2018
80
MondalBertranpetitNatGenet2016
70
JeongKrauseNatEcolEvol2019
65
MallickReichNature2016
21
BergströmTyler-SmithScience2020
15
EBC Munda 2023
10
DamgaardWillerslevScience2018
10
RaghavanWillerslevNature2014
2
RasmussenWillerslevNature2014
1

Excavated individuals: 13,454 people, 309 studies. Used by the ancient peoples chapters. The region percentages do not use them.

PattersonReichNature2021
670
LazaridisReichScience2022
602
WangHofmanováNature2025
543
NarasimhanReichScience2019
476
Gnecchi-RusconeHofmanováNature2024
399
MargaryanWillerslevNature2020
394
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Behind the South Asian regions: 2,148 people across 11 studies, counted by the study that collected them.

NIBMG Indian population panel
557
1000 Genomes Project
472
Coorg and South Indian cohorts
424
South Indian cohort (GSE242813)
181
Allen Ancient DNA Resource
162
Estonian Biocentre, India
140
India-recruited (mixed sources)
89
Indian arrays (GSE93037)
65
Estonian Biocentre, Munda
26
Xing et al. worldwide panel
24
Khasi (northeast India)
8
South Asia 489Europe 503East Asia 504The Americas 347Africa 504Recently mixed 157
100%correct on 120 people held out of the fit
100%correct at the marker density of a typical consumer file
How we worked this out

Accuracy is the share of held-out people whose largest region came out right: 120 people from 24 groups at the full panel of 12,770 markers, and again at 10,317 markers, the density a real 23andMe export reaches. The people tested are part of the reference set they are scored against, so their own data helped define the groups. Someone uploading a file gets no such help, so the real-world rate is a little lower. Five individuals per population. A perfect score on 120 people is consistent with a true rate above about 97%. The Indigenous American component is built from four samples, three of them majority European, so a correct call there is an easier test than it sounds. It measures which region comes out largest. Whether the printed range covers the true value is a separate measurement we have not run. 10 of the held-out set are not scored (African Caribbean and African American samples, which define no component). Measured 2026-09-09 against the 12,770-marker panel this report used. A rebuilt panel re-runs it.

A few dozen people per group is what makes our ranges as wide as they are. A narrower range would need more people. West Asia is fitted from Bedouin, Palestinian and Druze samples, and Oceania from pooled Papuan and Bougainville individuals. Central Asia is not yet a region of its own.

Coordinates for other tools

Twenty-five numbers you can paste into community ancestry tools.

Not measured for this file

this map was fitted and checked on South Asian genomes, and this file is 0% South Asian. Placing it would mean extrapolating a map nobody has verified there

Other calculators

The same file run through popular community calculators. Where they agree with our result, the answer does not depend on the model.

Not measured for this file

Community calculators need more of their own markers than this file carries, so none were run.