1. bookVolume 81 (2018): Issue 3 (September 2018)
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24 Jan 2008
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access type Open Access

A glance of genetic relations in the Balkan populations utilizing network analysis based on in silico assigned Y-DNA haplogroups

Published Online: 31 Oct 2018
Page range: 252 - 268
Received: 10 Apr 2018
Accepted: 17 Aug 2018
Journal Details
License
Format
Journal
First Published
24 Jan 2008
Publication timeframe
4 times per year
Languages
English

The aim of this study is to provide an insight into Balkan populations’ genetic relations utilizing in silico analysis of Y-STR haplotypes and performing haplogroup predictions together with network analysis of the same haplotypes for visualization of the relations between chosen haplotypes and Balkan populations in general. The population dataset used in this study was obtained using 23, 17, 12, 9 and 7 Y-STR loci for 13 populations. The 13 populations include: Bosnia and Herzegovina (B&H), Croatia, Macedonia, Slovenia, Greece, Romany (Hungary), Hungary, Serbia, Montenegro, Albania, Kosovo, Romania and Bulgaria. The overall dataset contains a total of 2179 samples with 1878 different haplotypes. I2a was detected as the major haplogroup in four out of thirteen analysed Balkan populations. The four populations (B&H, Croatia, Montenegro and Serbia) which had I2a as the most prevalent haplogroup were all from the former Yugoslavian republic. The remaining two major populations from former Yugoslavia, Macedonia and Slovenia, had E1b1b and R1a haplogroups as the most prevalent, respectively. The populations with E1b1b haplogroup as the most prevalent one are Macedonian, Romanian, as well as Albanian populations from Kosovo and Albania. The I2a haplogroup cluster is more compact when compared to E1b1b and R1b haplogroup clusters, indicating a larger degree of homogeneity within the haplotypes that belong to the I2a haplogroup. Our study demonstrates that a combination of haplogroup prediction and network analysis represents an effective approach to utilize publicly available Y-STR datasets for population genetics.

Keywords

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