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Genetic differentiation between upland and lowland populations shapes the Y-chromosomal landscape of West Asia.

Oleg BalanovskyM ChukhryaevaV ZaporozhchenkoV UrasinM ZhabaginA HovhannisyanA AgdzhoyanK DibirovaM KuznetsovaS KoshelE PocheshkhovaI AlborovaR SkhalyakhoO Utevskanull nullKh MustafinL YepiskoposyanC Tyler-SmithE Balanovska
Published in: Human genetics (2017)
Y-chromosomal variation in West Asian populations has so far been studied in less detail than in the neighboring Europe. Here, we analyzed 598 Y-chromosomes from two West Asian subregions-Transcaucasia and the Armenian plateau-using 40 Y-SNPs and 17 Y-STRs and combined them with previously published data from the region. The West Asian populations fell into two clusters: upland populations from the Anatolian, Armenian and Iranian plateaus, and lowland populations from the Levant, Mesopotamia and the Arabian Peninsula. This geographic subdivision corresponds with the linguistic difference between Indo-European and Turkic speakers, on the one hand, and Semitic speakers, on the other. This subdivision could be traced back to the Neolithic epoch, when upland populations from the Anatolian and Iranian plateaus carried similar haplogroup spectra but did not overlap with lowland populations from the Levant. We also found that the initial gene pool of the Armenian motherland population has been well preserved in most groups of the Armenian Diaspora. In view of the contribution of West Asians to the autosomal gene pool of the steppe Yamnaya archaeological culture, we sequenced a large portion of the Y-chromosome in haplogroup R1b samples from present-day East European steppe populations. The ancient Yamnaya samples are located on the "eastern" R-GG400 branch of haplogroup R1b-L23, showing that the paternal descendants of the Yamnaya still live in the Pontic steppe and that the ancient Yamnaya population was not an important source of paternal lineages in present-day West Europeans.
Keyphrases
  • copy number
  • mitochondrial dna
  • genome wide
  • genetic diversity
  • transcription factor
  • deep learning
  • single cell
  • meta analyses