Ancient mitogenomes show plateau populations from last 5200 years partially contributed to present-day Tibetans

Abstract

The clarification of the genetic origins of present-day Tibetans requires an understanding of their past relationships with the ancient populations of the Tibetan Plateau. Here we successfully sequenced 67 complete mitochondrial DNA genomes of 5200 to 300-year-old humans from the plateau. Apart from identifying two ancient plateau lineages (haplogroups D4j1b and M9a1a1c1b1a) that suggest some ancestors of Tibetans came from low-altitude areas 4750 to 2775 years ago and that some were involved in an expansion of people moving between high-altitude areas 2125 to 1100 years ago, we found limited evidence of recent matrilineal continuity on the plateau. Furthermore, deep learning of the ancient data incorporated into simulation models with an accuracy of 97% supports that present-day Tibetan matrilineal ancestry received partial contribution rather than complete continuity from the plateau populations of the last 5200 years.

Keywords: Tibetan prehistory; ancient DNA; population genetics of humans.

Figure 1.

Genetic relationships between the ancient and present-day populations of the Tibetan Plateau. (a) Ancient humans were sampled from 27 archeological sites from the higher altitude core areas (HTP, red) and lower margins (LTP, blue) of the Tibetan Plateau. The base map of China chart no. GS(2016)1550 was acquired from the National Administration of Surveying, Mapping and Geoinformation. (b) Multidimensional scaling (MDS) plot of genetic distance ΦST shows HTP and LTP mainly clustered between the Chinese and Tibetans. (c) Haplogroup correlation shows HTP and LTP share the most similar haplogroups with the Chinese and Tibetans (number indicates sample size per group). (d) Pairwise genetic distance ΦST shows the HTP–LTP pair and HTP–Tibetan pair are not significantly differentiated against each other, indicating the pairs could potentially be grouped together (numbers are ΦST values, red colour indicates p-value > 0.05 after 10 000 permutations). (e) Haplogroup sharing shows HTP and LTP share more haplogroups with Xinjiang people, Chinese, Tibetan, Nepalese and northeast Indians (proportion of shared and private haplogroups is denoted by the colour of other groups and the group itself). (f) Haplotype sharing shows HTP and LTP share about 2% haplotypes with the Tibetans. (g) Network of D4j1b, black arrows show the successive changes from a dominant 4750-year-old LTP source (blue) to a 2775-year-old HTP haplotype (red) and then to a present-day Tibetan (light orange); it also spread to nearby areas of the plateau (grey) among the Xinjiang people of China, northeast India and Thailand. The population of the D4j1b lineage first expanded approximately 10 508 years ago, as shown by the Bayesian skyline plot (y-axis; N_eτ is the product of female effective size and generation length in years). (h) Network of M9a1a1c1b1a, black arrows show a dominant 2125-year-old HTP mitogenome in Nepal (red) radiated into another younger 1500-year-old HTP mitogenome in Nepal (red) as well as into a 1100-year-old HTP mitogenome in Tibet (red) and other present-day Tibetan mitogenomes (orange). The population of the M9a1a1c1b1a lineage expanded approximately 6048 years ago. (Online version in colour.)

Figure 2.

Modelling the formation of the Tibetan Plateau populations. (a) Model 1 (red) describes Tibetans as recipients of maternal ancestry from a more diverse third population, but potentially also receiving maternal ancestry from populations related to LTP and/or HTP at different times during prehistory (the model also allows HTP to contribute before LTP); model 2 (green) and model 3 (blue) describe Tibetans as descendants of ancient plateau populations, where the arrow indicates direction of contribution and is fixed to occur within the last 5200 years. (b) Model 1 is the superior model, as 95% of its simulation envelope includes the projected observed data (cross, +), the corresponding density plots (below) show the first two principal components (PCs) with the projected observed data (dashes). (c) Prediction accuracy was first determined to be consistently high using 1–2 hidden layers. After testing a combination of nodes, a total of 90 nodes were optimal. Shown is the approximate outline of the final trained neural network that comprised an input layer (19 variables), two hidden layers (50, 40 nodes each) and an output layer (three responses), where the red and grey lines represent different node weights. The top two important variables that helped differentiate models were the pairwise genetic distance ΦST and total polymorphisms. Error rates in misclassifying true models were less than 1%, less than 2% and less than 2% for models 1, 2 and 3, respectively. With an accuracy of 97.2%, kappa of 0.96 and F1-score of 97.2%, the classifier verified the observed data to best-fit model 1. (Online version in colour.)