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. 2021 Jan 13;10(1):giaa159.
doi: 10.1093/gigascience/giaa159.

Genome diversity in Ukraine

Taras K Oleksyk  1   2   3 , Walter W Wolfsberger  1   2   3 , Alexandra M Weber  4 , Khrystyna Shchubelka  2   3   5 , Olga T Oleksyk  6 , Olga Levchuk  7 , Alla Patrus  7 , Nelya Lazar  7 , Stephanie O Castro-Marquez  2   3 , Yaroslava Hasynets  1 , Patricia Boldyzhar  5 , Mikhailo Neymet  8 , Alina Urbanovych  9 , Viktoriya Stakhovska  10 , Kateryna Malyar  11 , Svitlana Chervyakova  12 , Olena Podoroha  13 , Natalia Kovalchuk  14 , Juan L Rodriguez-Flores  15 , Weichen Zhou  4 , Sarah Medley  2 , Fabia Battistuzzi  2 , Ryan Liu  16 , Yong Hou  16 , Siru Chen  16 , Huanming Yang  16 , Meredith Yeager  17 , Michael Dean  17 , Ryan E Mills  4   18 , Volodymyr Smolanka  5
Affiliations

Genome diversity in Ukraine

Taras K Oleksyk et al. Gigascience. .

Abstract

Background: The main goal of this collaborative effort is to provide genome-wide data for the previously underrepresented population in Eastern Europe, and to provide cross-validation of the data from genome sequences and genotypes of the same individuals acquired by different technologies. We collected 97 genome-grade DNA samples from consented individuals representing major regions of Ukraine that were consented for public data release. BGISEQ-500 sequence data and genotypes by an Illumina GWAS chip were cross-validated on multiple samples and additionally referenced to 1 sample that has been resequenced by Illumina NovaSeq6000 S4 at high coverage.

Results: The genome data have been searched for genomic variation represented in this population, and a number of variants have been reported: large structural variants, indels, copy number variations, single-nucletide polymorphisms, and microsatellites. To our knowledge, this study provides the largest to-date survey of genetic variation in Ukraine, creating a public reference resource aiming to provide data for medical research in a large understudied population.

Conclusions: Our results indicate that the genetic diversity of the Ukrainian population is uniquely shaped by evolutionary and demographic forces and cannot be ignored in future genetic and biomedical studies. These data will contribute a wealth of new information bringing forth a wealth of novel, endemic and medically related alleles.

Keywords: BGISEQ-500; CNV; DNBSEQ; Illumina; NGS; SNP; genomes; genotyping; indels; variant calling.

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Conflict of interest statement

Y.L. and H.Y. are employed by BGI, which owns DNBSEQ™ technology; Olga Levchuk, Alla Patrus, and Nelya Lazar represent AstraDIA (Ukraine), which collected and extracted DNA samples. All other authors declare that they have no competing interests.

Figures

Figure 1:
Figure 1:
Variant concordance across the 3 sequencing/genotype methods: (A) Left: Overlap of SNP positions identified in 1 sample (EG600036) using each of the 3 platforms. Right: Concordance of SNP genotypes in 1 sample derived from each of the 3 platforms. This only includes the subset of SNPs with alternate alleles included in the Illumina genotyping array (the smallest of the 3 variant sets). The variants indicated as belonging to none of the categories are variants whose genotypes differ between all 3 platforms. (B) Left: The percentage of concordance between the Illumina SNP array and BGISEQ-500 for all SNPs compared to the percentage concordance of only SNPs with non-reference alleles in the Illumina SNP array for the 86 samples genotyped on both platforms. Right: Concordance of SNP genotypes between BGISEQ-500 and Illumina SNP Array for 1 sample (EG600036). (C) Overlap within the numbers of the 3 major structural variants detected in 1 sample using the 2 whole-genome sequencing datasets. (D) Overlap within the numbers of the 3 major mobile element insertions detected in 1 sample using the 2 whole-genome sequencing datasets.
Figure 2:
Figure 2:
Principal component (PC) analysis of genetic merged dataset, containing European populations. Colors reflect prior population assignments from the European samples from the 1KG (Utah residents [CEU] with Northern and Western European ancestry, Toscani in Italy [TSI], Finnish in Finland [FIN], British in England and Scotland [GBR], Iberian population in Spain [IBS]) [14, 39] and French (FRA) and Russians (RUS) from HGDP (RUS) [40], as well as the relevant high-coverage human genomes Croatian (CRO), Czech (CZ), Estonian (EST), German (GER), Greek (GRE), Hungarian (HUN), Moldovan (MOL), Polish (POL), Russian Cossack (RUS), and Ukrainian (UKR) from the Estonian Biocentre Human Genome Diversity Panel (EGDP) [43] as well as Simons Genome Diversity Project [44]. The analysis was performed with Eigensoft [48].
Figure 3:
Figure 3:
Genetic structure of Ukrainian population in comparison to other European populations. Structure plot constructed using ADMIXTURE package [49] at K = 3 illustrates similarity and differences between genomes from this study as well as samples from the 1KG (Utah residents [CEU] with Northern and Western European ancestry, Toscani in Italy [TSI], Finnish in Finland [FIN], British in England and Scotland [GBR], and Iberian population in Spain [IBS]) [14, 39] and French (FRA) and Russians (RUS) from HGDP [40], as well as the relevant high-coverage human genomes Croatian (CRO), Czech (CZ), Estonian (EST), German (GER), Greek (GRE), Hungarian (HUN), Moldovan (MOL), Polish (POL), Russian Cossack (RUS), and Ukrainian (UKR) from the Estonian Biocentre Human Genome Diversity Panel (EGDP) [43] as well as Simons Genome Diversity Project [44]. For identification of the optimal K parameter, we evaluated a range from 2 to 8, with K = 3 resulting in the lowest error. Plots with K = 3 to K = 6 are presented in Supplementary Fig. S3.
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