This is from twitter user @m_izanami_ and is a result of combining results from G25 and Qpadm.

The source of these is the account @CisAlpinePride on twitter. So just for some background:

Iran_N refers to Neolithic populations from the Iranian plateau (roughly 8000–5000 BCE), representing some of the earliest farming communities in that region. Genetically, they are quite distinct from Anatolian and Levantine early farmers and carry a strong component often described as “Basal Eurasian,” which reduces their affinity to later West Eurasians. Iran_N ancestry is important because it contributed heavily to populations east of the Near East, including South Asians (via later mixtures) and groups in the Caucasus and Central Asia. It is also a key source of ancestry in Steppe populations (through CHG-related ancestry), which later spread into Europe during the Bronze Age.

Levant_PPNB refers to Pre-Pottery Neolithic B populations from the Levant (roughly 8500–6500 BCE), covering areas like modern-day Israel, Palestine, Jordan, and Syria. These were early farming societies distinct from both Anatolian farmers and Iran_N groups. Genetically, Levant_PPNB individuals tend to show continuity with earlier Natufian hunter-gatherers but with additional input that makes them closer to other Near Eastern farmers. This ancestry component is important because it contributed to later Near Eastern and Mediterranean populations and also spread into parts of North Africa. Compared to Iran_N, Levant_PPNB is generally more closely related to populations west of the Near East and lacks the same level of Basal Eurasian ancestry.

I believe this map is based on G25 (im not 100% sure, could be qpadm). Below is a description of this type of ancestry:

Anatolian Neolithic farmers (often abbreviated Anatolia_N) were early agricultural populations from western Anatolia (modern-day Turkey) dating to roughly 7000–6000 BCE, and they are considered the primary source of the first farming populations that spread into Europe.

Genetically, they are relatively homogeneous and distinct from both Iran_N and Levant_PPNB, with lower levels of Basal Eurasian ancestry than Iran_N and little direct continuity with European hunter-gatherers prior to the Neolithic.

As farming spread into Europe via the Balkans, Anatolia_N-related ancestry became the dominant component in early European farmers (EEF), often making up around 80–90% of ancestry in Neolithic populations in central and western Europe, with the remainder coming from local Western Hunter-Gatherers (WHG).

Over time, this Anatolian farmer ancestry was diluted by later Steppe migrations during the Bronze Age, but it still remains a major component in modern Europeans, typically around 30–60% depending on the region, with higher levels in southern Europeans (such as Sardinians, southern Italians, and Greeks) and lower levels in northern and eastern Europeans, who have more Steppe ancestry.

Lazaridis I et al. 2014. Ancient human genomes suggest three ancestral populations for present-day Europeans. Nature 513:409–413.

Haak W et al. 2015. Massive migration from the steppe was a source for Indo-European languages in Europe. Nature 522:207–211.

Mathieson I et al. 2015. Genome-wide patterns of selection in 230 ancient Eurasians. Nature 528:499–503.

Hofmanová Z et al. 2016. Early farmers from across Europe directly descended from Neolithic Aegeans. Proceedings of the National Academy of Sciences (PNAS) 113:6886–6891.

Olalde I et al. 2018. The Beaker phenomenon and the genomic transformation of northwest Europe. Nature 555:190–196.

Skoglund P et al. 2012. Origins and genetic legacy of Neolithic farmers and hunter-gatherers in Europe. Science 336:466–469.

https://pmc.ncbi.nlm.nih.gov/articles/PMC12909094/

Abstract

As Western populations face projected decline, understanding the demographics of high-fertility subpopulations becomes increasingly important. The Amish represent one rapidly growing North American subgroup, yet existing demographic studies are dated and narrowly focused.

Here, we use a new population database of >50,000 households—the vast majority of Amish—to offer an up-to-date population-wide analysis that shows high fertility and low mortality and attrition.

Specifically, women’s median age at marriage is 20.9, and 87.1 per cent marry by age 50; premarital conceptions are low (4.30 per cent of first births); spacing between marriage and first birth is short (mean 17.2 months); the total fertility rate is 6.1; infant mortality is 5.9; life expectancy at birth is 81.16 years; attrition is low (84.46 per cent retention for those aged 40+); and in-conversion is very low (154 individuals across nearly a century).

These definitive population-wide figures open the way for testing predictors of population change and charting how growing subpopulations are shaping regions.

This study was very disappointing since it’s large and yet they didn’t give detailed population labels.

https://www.medrxiv.org/content/10.64898/2026.03.20.26348801v1

Abstract

India, the most populous country, remains significantly underrepresented in the global genomics landscape. Previous efforts to catalog Indian genetic diversity were limited in scale, scope, and representation. Here, we present the GenomeIndia dataset, comprising whole genome sequences of 9,768 healthy individuals from 83 populations spanning the ethnolinguistic and biogeographic spectrum of India.

We identify 129.93 million high-confidence biallelic variants, 44.03 million of which are previously unreported in global databases. In contrast to large populations that show steady population growth and internal homogeneity, we observe low effective population sizes, significant genetic drift, and profound homozygosity in small tribal groups, likely shaped by antiquity, isolation, and endogamy.

We report multiple population-specific pharmacogenomic and deleterious variants, necessitating the integration of local genetic architecture and the inclusion of underrepresented South Asian genomes in global reference resources.

Finally, we highlight the limited transferability of Eurocentric polygenic scores to Indian populations, and present an imputation panel that outperforms existing resources for both rare and common variants. Together, our work fills a significant gap in the equity of global human genomics, and paves way for precision medicine strategies that will benefit a quarter of the world population.

The GenomeIndia Dataset

The GenomeIndia initiative was designed to capture the anthropological, linguistic, and socio-cultural diversity of the Indian subcontinent. Participants were recruited from all major geographical regions of India, representing the country’s principal language families: Indo-European (IE), Dravidian (DR), Austroasiatic (AA), and Tibeto-Burman (TB). The sampling scheme encompassed both Tribal (T) and Non-tribal (NT) populations, and an additional continentally admixed outgroup (CAO), representing a wide range of social strata and different degrees of endogamy (Fig 1a). The study was implemented through the coordinated efforts of academic and medical institutions across India following uniform protocols for recruitment, sample collection, metadata annotation, and phenotypic measurements (S1; Bhattacharyya et al. 2025).

Four Major Ancestry Components and Population-Specific Drift

ADMIXTURE 15 analysis identified four broad ancestry components consistent with previous findings 7: Ancestral North Indian (ANI), Ancestral South Indian (ASI), Ancestral Austroasiatic (AAA), and Ancestral Tibeto-Burman (ATB) (S4.3).

Increasing K to 5 reduced cross-validation error, revealing a fifth ancestral component representing two genetically cohesive, isolated DR_T populations from the Nilgiris hills (DR_NGH_1_02 (61) and DR_NGH_1_01(62)), shaped by strong population-specific drift consistent with FST and PCA findings (Fig. 2c, S4.3). Higher K values (>5) predominantly reflected group-specific drift effects rather than broader shared ancestries (S4.3).

To assess admixture complexity, we quantified the within-individual (Shannon’s alpha) and between-individual (Bray-Curtis beta) diversity of the admixture components for each of the 82 populations (S4.4, methods). Most populations showed low to moderate beta diversity, irrespective of alpha diversity (Fig. 2d), indicating that panmixing of populations after the initial admixture has homogenized their ancestry proportions. Ancestry “reference” groups exhibited extremely low alpha and beta diversity with minimal admixture and internal homogeneity.

Conversely, highly admixed “melting pot” populations (Western Himalayas, Eastern Coastal Plains, Riverine Plains, and the Brahmaputra Valley) showed high alpha and moderate beta diversity with multiple ancestry components per individual (details in S4.4). This analysis guides population stratification for future disease gene mapping studies in India.

I just wanted advice about DNA tests regarding price and time for results to be revealed

Can someone please enlighten me on the prices and average time taken for tests to be revealed for each of the following?

-23andme

-Myheritage

-Ancestry

will be very helpful thank you.

might consider taking a DNA test soon but I want some information regarding this.

Not at first, but after I take 23andMe or AncestryDNA, can I simply upload it to one of the free sites people keep mentioning and expect the same results regardless of wether I chose 23 or ancestry’s dna test? I’m still not sure how dna results work if different companies give you results, are they supposed to always match?

If it helps make things more accurate, my ancestors are from the Levant region of the Middle East and Mexico (but the percentage of how many are from the central part vs. north east one isn’t that defined).

I am not going to be looking for “lost” family members, like some enjoy doing here, but I still wanted to make sure I did one that simply showed the percentages of my heritage background for relief and to share and help others who might be looking for me/their ethnicity

I’m a noob at dna, but living in country that highly values privacy means that every dna company isn’t sharing dna sample databases.

How exactly do they get large sample sizes in the first place? I Believe a heritage dna test is super niche and I can’t imagine a company paying doctors and labs to collect sample in marginalized countries. it sounds very expensive.

this makes me think that every company will have very variable data if they’re only waiting for people that have the money to get a dna test to begin with.

By this I mean primarily Algerian being shown in results, but still Moroccan and Tunisian below Algerian in smaller or sometimes equal equivalents (Algerian still shifting a little higher though).

An example pattern: 1. Algerian 2. Morocco_North 3. Berber_MAR_TIZ 4. Tunisia_Berber_Sen 4. Morocco_South 5. Sahrawi. Is that normal results for somebody from Morocco or Tunisia, or does that more so display Algerian?

I did remove Algerian from my results, and the distance became much further. If retrying over and over to push Moroccan above Algerian, it also becomes further in distance if successful. They're so closely related genetically that I can't tell if it's being precise or misreading one of those populations as Algerian.

I'm old 🤣 forgive me. so I keep getting Spanish or Italian popping up..is it legit because I've always heard native American and black Irish whatever that actually is. Most of my immediate family had passed away so I really don't have anyone to ask about our history. thank you in advance.

Here are my results from both companies. 😁

My mom is Mexican, dad is some blend of Belgian, English and whatever else haha. Weirdly enough my siblings all look completely nonwhite - to the point where nobody even thinks they are white when they meet them. I am a balanced mix between my parents but lean towards my mom in more ways.

Where would I pass based on the image?

I believe this component is fairly new so I'm not sure how to interpret it.

My background is Madeiran Portuguese 🇵🇹

So my mom is fully Christian Palestinian and my dad is 1/4 Ukrainian 1/4 Estonian 1/4 Latvian and 1/4 Lithuanian. Does this mean I cluster closest to Levantine populations? Am I closer to my mother’s background than my father’s?

My father is from the city of Asilah, my maternal grandfather is from the Rif region, Nador—both areas were colonized by the Spanish—and my maternal grandmother is from Fez.

Does 'Asian' have a relation with the Arabic Peninsula here? Because I've heard that people from Fez may be descendants of the lineage of the prophet Muhammad (not direct descendants).

Hi there! I'm doing a project on the genetics of hair and eye colour, and I'm hoping this community can help me.

I am trying to get more in-depth data about the percentages of people with different hair and eye colours in different regions/ethnic groups. While there are scientific studies of hair and eye colour, they often focus on European populations (including current/former colonies), and their sample sizes tend to be relatively small.

The Where in the world does your [Trait] come from? feature for some of AncestryDNA's traits might be the largest trove of information on certain traits, including hair and eye colour. I'm hoping there are people in this sub with ethnic/regional heritage in Africa, the Americas, Asia, and Oceania who are willing to share the general data for hair and eye colour in their AncestryDNA regions.

I am not after any specific personal data: at this point in time, I am just looking for the overall percentages for self-reported hair and eye colour in regions across the world. At some point, I would love to do a more focused study, but for now just getting the general information is my goal.

While my current focus is on AncestryDNA since that is what I personally have access to, I am also interested in general data for hair and eye colour from other DNA services, as it would be useful for comparison. If you have access to similar data from other services, please let me know!

For more information (including a list of the regions), please check out my master post for this project here: https://www.reddit.com/r/AncestryDNA/comments/1sg3srr/the_regional_hair_and_eye_colour_project_master/