MDLP K16c
MDLP K16c (Ancient) estimates proportions of 16 deep-time ancestral components using ancient reference samples. Designed for worldwide users—researchers and enthusiasts—it highlights Neolithic farmer, Steppe, Paleolithic hunter‑gatherer, Oceanian, and African signals. Use it to explore prehistoric migrations and population structure; results reflect long-term population history rather than recent genealogy.
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Información detallada sobre esta calculadora de mezcla genética
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MDLP K16c (Ancient) is a deep-time genetic admixture calculator that estimates the proportional contribution of 16 ancient source populations to a user’s genome. Designed for global users — researchers, students, genealogy enthusiasts and anyone curious about prehistoric ancestry — it focuses on broad ancient signals rather than recent family-level ancestry. The calculator compares your genotype to allele-frequency profiles drawn from ancient and deeply divergent reference groups: SEA (Southeast Asia), CHG (Caucasus Hunter‑Gatherers), Steppe_EMBA (Early–Middle Bronze Age Steppe pastoralists), Mota (ancient Ethiopian), ElMiron (Upper Paleolithic Western Europe), Anatolia_N (Neolithic Anatolian farmers), Papuan, Steppe_Eneolithic (earlier steppe groups), Onge (Andaman Islands), Europe_LNBA (Late Neolithic–Bronze Age Europe), ANE (Ancient North Eurasians), Europe_MNChL (Middle–Late Neolithic and Chalcolithic Europe), Paleoafrican (deep African lineages), Biaka (Central African hunter‑gatherers), EA (East Asia), and Villabruna (Western European hunter‑gatherers).
By estimating mixture proportions you can: identify signals of Neolithic farmer expansion, detect Steppe-related ancestry tied to Bronze Age migrations, see Oceanian and Southeast Asian components, and explore ancient African structure and Paleolithic hunter‑gatherer contributions. The model provides historical context — for example, the Anatolia_N and Europe_MNChL components reflect agricultural dispersals, CHG and Steppe components track movements from the Caucasus and the Pontic–Caspian steppe, and ANE/Villabruna/ElMiron capture earlier Pleistocene and Holocene hunter‑gatherer lineages.
MDLP K16c is valuable as an interpretive tool: it translates complex ancient DNA datasets into intuitive proportions, useful for comparative studies, hypothesis generation, and public engagement. Results are approximations constrained by available reference samples and statistical modeling; they are most informative about p
Poblaciones de referencia
Las poblaciones utilizadas como referencias genéticas en esta calculadora
16 Poblaciones de referencia
SEA (Southeast Asia)
- Populations in this region are diverse, with influences from Austronesian, Austroasiatic, and Sino-Tibetan ethnic groups.
CHG (Caucasus Hunter-Gatherers)
- Ancient populations from the Caucasus region, known to have contributed to the genetic makeup of various Eurasian groups.
Steppe_EMBA (Early to Middle Bronze Age Steppe)
- Nomadic pastoralist populations from the Eurasian Steppe during the Early to Middle Bronze Age, associated with the spread of Indo-European languages.
Mota
- An ancient African individual from Ethiopia, providing insights into the genetic history of East Africa.
ElMiron
- Represents populations from Upper Paleolithic Europe, specifically associated with the Magdalenian culture.
Anatolia_N (Neolithic Anatolia)
- Early farming populations from Anatolia, pivotal in the spread of agriculture into Europe.
Papuan
- Indigenous populations of Papua New Guinea, characterized by ancient lineages distinct from mainland Asians.
Steppe_Eneolithic (Eneolithic Steppe)
- Populations from the Eurasian Steppe during the Eneolithic (Copper Age), crucial in Bronze Age migrations.
Onge
- Indigenous people of the Andaman Islands, representing one of the earliest modern human groups in Southeast Asia.
Europe_LNBA (Late Neolithic to Bronze Age Europe)
- Populations in Europe during the transition from the Neolithic to the Bronze Age, marked by significant cultural and genetic changes.
ANE (Ancient North Eurasian)
- Ancient populations from Siberia, contributing to the genetic ancestry of Native Americans and other groups.
Europe_MNChL (Middle to Late Neolithic and Chalcolithic Europe)
- Farming communities in Europe during the Middle to Late Neolithic and Chalcolithic periods, prior to the arrival of Steppe migrants.
Paleoafrican
- Ancient African lineages with deep historical roots on the continent.
Biaka
- Indigenous populations of Central Africa, part of the Pygmy groups with a unique genetic heritage.
EA (East Asia)
- Diverse populations from East Asia, encompassing a variety of ethnic groups and genetic makeups.
Villabruna
- An Upper Paleolithic individual from Italy, representing European hunter-gatherer ancestry.
Grouped by Continent
Africa
- Mota, Paleoafrican, Biaka
Asia
- SEA, Papuan, Onge, EA
Europe
- ElMiron, Anatolia_N, Europe_LNBA, Europe_MNChL, Villabruna
Eurasian Steppe
- Steppe_EMBA, Steppe_Eneolithic, CHG
North Eurasia
- ANE
Cómo interpretar el análisis de mezcla genética
Descubre cómo funcionan las calculadoras de mezcla genética y cómo interpretar los resultados
¿Qué es el análisis de mezcla genética?
El análisis de mezcla genética estima la similitud genética comparando tu ADN con poblaciones de referencia de distintas partes del mundo. Los componentes del modelo representan esas referencias y dependen de los datos y supuestos utilizados.
Esta calculadora utiliza 16 cuidadosamente seleccionadas ancient poblaciones como referencias para describir con detalle la similitud genética.
Cómo funciona
- Tu ADN se compara con 16 poblaciones de referencia
- Ancient se utilizan como referencias genéticas
- Los resultados muestran tu similitud genética con estas poblaciones
- La diversidad del panel de referencia influye en las estimaciones
Cómo interpretar tus resultados
Los resultados muestran porcentajes de similitud genética con estas poblaciones de referencia. Ten presentes estos puntos:
- Los resultados reflejan similitud genética, no ascendencia directa demostrada
- Ancient se utilizan como referencias
- Los porcentajes indican contribuciones relativas en el modelo
- Los resultados son estimaciones basadas en los datos de referencia disponibles