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Nomadic groups of conquering Hungarians played a predominant role in Hungarian prehistory, but genetic data are available only from the immigrant elite strata. Most of the 10–11th century remains in the Carpathian Basin belong to common people, whose origin and relation to the immigrant elite have been widely debated. Mitogenome sequences were obtained from 202 individuals with next generation sequencing combined with hybridization capture. Median joining networks were used for phylogenetic analysis. The commoner population was compared to 87 ancient Eurasian populations with sequence-based (Fst) and haplogroup-based population genetic methods. The haplogroup composition of the commoner population markedly differs from that of the elite, and, in contrast to the elite, commoners cluster with European populations. Alongside this, detectable sub-haplogroup sharing indicates admixture between the elite and the commoners. The majority of the 10–11th century commoners most likely represent local populations of the Carpathian Basin, which admixed with the eastern immigrant groups (which included conquering Hungarians).

Background: Current state of art kinship analysis is capable to infer relatedness up to the 5-6th degree from deeply sequenced DNA if the proper reference population is known. Low coverage, partially genotyped, degraded archaic (or forensic) DNA and often unavailable or unknown reference population poses additional challenges, hence kinship analysis from low coverage archaic sequences so far has been possible up to the second degree with large uncertainties. Results: We performed extensive simulations to identify and correct the main factors of bias in kinship analysis from low coverage data. As a result, we introduce a new metric for correction and offer a guideline, which overcomes the difficulties associated with low coverage samples. We validated our methodology on experimental modern and archaic data with widely different genome coverages (0.12x-11.9x) using samples with known family relations and known or unknown population structure. Out of 2526 ancient individuals from the REICH data set we confirmed all 96 indicated, and identified 303 new relatives additionally. Conclusion: With the proposed workflow we provide the necessary additional tools to calculate the corrected kinship coefficient from the commonly used genome data formats. Our methodology allows to reliably identify relatedness up to the 4-5th degree from variable/low coverage archaic (or badly degraded forensic) WGS genome data.

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