A new self-learning computational method for footprints of early human migration processes

Abstract

We present a new self-learning computational method searching for footprints of early migration processes determining the genetic compositions of recent human populations. The data being analysed are 26- and 18-dimensional mitochondrial and Y-chromosomal haplogroup distributions representing 50 recent and 34 ancient populations in Eurasia and America. The algorithms search for associations of haplogroups jointly propagating in a significant subset of these populations. Joint propagations of Hgs are detected directly by similar ranking lists of populations derived from Hg frequencies of the 50 Hg distributions. The method provides us the most characteristic associations of mitochondrial and Y-chromosomal haplogroups, and the set of populations where these associations propagate jointly. In addition, the typical ranking lists characterizing these Hg associations show the geographical distribution, the probable place of origin and the paths of their protection. Comparison to ancient data verifies that these recent geographical distributions refer to the most important prehistoric migrations supported by archaeological evidences.

Keywords: Archaeogenetics; Artificial intelligence; Clustering; Rank correlation; Self-learning algorithm; Y-chromosomal and mtDNA haplogroups.