Yurchenko et al. BMC Medical Genetics 2019, 20(Suppl 1):51
https://doi.org/10.1186/s12881-019-0772-4

RESEARCH

Open Access

Exome-wide survey of the Siberian
Caucasian population
Andrey A. Yurchenko1*, Nikolai S. Yudin1,2 and Mikhail I. Voevoda1,2,3
From 11th International Multiconference “Bioinformatics of Genome Regulation and Structure\Systems Biology” - BGRS\SB2018
Novosibirsk, Russia. 20-25 August 2018

Abstract
Background: Population structure is an important factor in the genetic association studies but often remains
underexplored for many human populations. We identified exome variants in 39 Siberian Caucasian individuals
from Novosibirsk, Russia and compared their genetic allele frequencies with European populations from 1000
Genomes Project.
Methods: The study participants were from Novosibirsk and represented people with monogenic diabetes, healthy
individuals and a cohort from the tick-borne encephalitis study. Isolated DNA was enriched using Agilent SureSelectV5 kit and sequenced on Illumina HiSeq 4000 and genetic variants were identified using GATK pipeline. To estimate
the patterns of the population structure we used PCA and ADMIXTURE analysis. Pharmocogenetically and medicallyimportant variants were annotated based on PharmGKB and ClinVar databases.
Results: The analysis identified low, but highly significant population differentiation attributed to numerous locibetween the Siberian Caucasian population and other European population samples as well as a higher proportionof the Finnish genetic component in the studied sample. The medical and pharmacogenetic annotation of highly
significantly differentiated variants between the Novosibirsk and the combined European populations revealed anumber of important genetic polymorphisms located in such genes as FCGR3B, TYR, OCA2, FABP1, CHEK2 and
SLC4A1.
Conclusions: The study reports for the first time an exome-wide comparison of a population from Russia with
European samples and emphasizes the importance of population studies with medical annotation of variants.
Keywords: Exome sequencing, Population structure, Associations, Siberia, Russia

Background
Population structure is a very important factor in medical
genetic association studies which can compromise modern genomic methods not being properly accounted for.
In Russia, population studies were mainly conducted using
Y-chromosome or mitochondrial markers with the recent
application of microarray methods [1–3] and did notallow to estimate the functional role of variants. Some recent phylogeographic studies used whole-genome* Correspondence: andreyurch@gmail.com
1
The Federal Research Center Institute of Cytology and Genetics, The
Siberian Branch of the Russian Academy of Sciences, Lavrentieva 10 St,
Novosibirsk, Russia630090
Full list of author information is available at the end of the article

sequencing with samples from Russia to elucidate historyof migrations in Eurasia, but used small samples from diverse populations [4, 5]. In this study, we identified exome
genetic variants for 39 individuals from Novosibirsk,
Russia and compared them with the previously publishedgenome-wide data and exomes of European populations
from the 1000 Genomes Project to understand the level ofthe exome-wide divergence and the extent of the population stratification. The Novosibirsk population (NVSB) isof particular interest because it exhibits an example of amodern big city population affected by political and economic events of the twentieth century which changed thehistorical landscape of ethnic diversity of the former USSR
territory through increasing urbanization, mass migration© The Author(s). 2019 Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0
International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, andreproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to
the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver(http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

Yurchenko et al. BMC Medical Genetics 2019, 20(Suppl 1):51

across the country and rapid demographic growth. In thisstudy, we identified exome genetic variants for 39 individuals from Novosibirsk, Russia and compared them withthe previously published genome-wide data and exomesof European populations from the 1000 Genomes Projectto understand the level of the exome-wide divergence andthe extent of the population stratification. Additionally, wetested allele frequency differences between our sample
and combined European dataset for medically and pharmacogenetically important variants to identify loci which
can be important for national studies.

Methods
The study participants (n = 39) were from Novosibirsk
and represented people with monogenic diabetes (n = 10),
healthy individuals (n = 7) and a cohort from the tickborne encephalitis study (n = 22). The participants signed
an informed consent and defined themselves as ethnic
Russians. The ethnicity of the participants was additionallychecked prior the analysis with data from 1000 Genomes
Project and two samples identified as clear outliers (close
to the Asian populations) were excluded from the analysis.
Isolated DNA was enriched using Agilent SureSelect V5
kit in and sequenced on Illumina HiSeq 4000 with 150PE
reads. After the quality control with Trimmomatic [6] thereads were aligned with BWA mem [7] to Hg19 reference
genome and processed with SAMtools [8]. Single nucleotide variants (SNVs) and indels were identified using
GATK [9] according to the GATK Best Practices workflow
for germline variation with the sensitivity filter equal to99.9. The resulted VCF file was combined with 1000 Genomes Project genotypes [10] using bcftools [11] merge
and filtered with VCFtools [11] at maximum 10 missed genotypes (−-max-missing-count) keeping only biallelic sites.
We performed the analysis on the two levels: with the
Finnish (FIN) population for population genetic analysis(PCA, ADMIXTURE, Fst) and without FIN population
to test the allele frequency differences for clinically andpharmacogenetically important variants. The FIN population was excluded from the second analysis as the mostdivergent European population with unique history [12].
To reduce the influence of the tightly linked loci on thepatterns of population structure we applied the linkage-disequilibrium pruning using PLINK V1.93 software (Table 1).
To estimate the patterns of the population structure weused the Principal Component Analysis (PCA) realisedin SNPrelate [13] with European (1000 Genomes Project)and previously published Russian Siberian populations [2].
The proportions of genetic ancestry between populations
were estimated using ADMIXTURE [14] for K = 2–8(Table 1) and tested using Cross Validation Error estimation (CVE). To estimate and test statistically the levelof pairwise population differentiation (Fst, [15]) we used
smartpca software of the EIGENSTRAT package [16].

Page 30 of 56

Table 1 Number of variants and filters applied to them forvarious analysis
Analysis

Number ofvariants

Filters

PCA

5948

LD < 0.3, only autosomes,
MAF = 0.05, LD window = 100kbp

ADMIXTURE

55,669LD < 0.2, only autosomes,
LD window = 50kbp

Fst (smartpca)

65,436

only autosomes, MAF = 0.05

Allele frequency
difference

117,010 (SNV)
5989(INDEL)PLINK: --assoc fisher-midp
mperm = 1,000,000

We annotated the variants using ANNOVAR [17] andPharmGKB [18] databases and then tested medically (ClinVar, [19]) and pharmacogenetically relevant variants for thedifferences in allele frequencies between the NVSB population and the combined non-Finnish European (NFE) dataset with PLINK v1.93 [20] using 1 M permutations.
The average coverage of the studied exomes varied from
47.7X to 71.3X. In total, we identified 136,276 SNVs and14,464 indels in the studied dataset. Merging with datafrom 1000 Genomes Project produced a dataset of overlapped variants consisted of 117,010 SNVs and 5989 indels.

Results
During the population genetic analysis, the first principal
component accounted for 0.77% of the total variation andseparated all the populations (Fig. 1a) except closely related
American (CEU) and British (GBR). The second principal
component accounted for 0.36% of the total variation andseparated mostly Tuscan (TSI) and Spanish (IBS) samples.
Novosibirsk population (NVSB) was placed between the
Finnish (FIN) and CEU with GBR samples and was clearly
distinguished from them. The Russian Siberian populations
from a previous microarray-based study [2], represented bya similar Caucasian Siberian population (Russian_NSK)
and partially isolated Siberian Starovers (Old Believers,
Russian_STV) were not distinguished between each otherand samples from our study (NVSB).
In ADMIXTURE analysis, the lowest value of the Cross
Validation Error was attributed to the K = 2, which captured the divergence of FIN from other European populations. NVSB demonstrated a higher proportion of theancestral Finnish-related genetic component at K = 2 andat K = 3 relative to other populations. A new cluster (green)
consisted of TSI and IBS appeared at K = 3 and then at anadditional ancestral component emerged (K = 4) clearly
separating NVSB (Fig. 1c, purple). Lastly, at K = 5, the IBS
was separated from the rest of the samples.
The pairwise Fst values between all the populations
except the CEU and GBR (P-value = 0.048) werehighly significant (P-value < 1.1656e-11) albeit low(Fst = 0.002–0.013). The NVSB population demonstrated
the highest level of differentiation with TSI (Fst = 0.009)

Yurchenko et al. BMC Medical Genetics 2019, 20(Suppl 1):51

Page 31 of 56

Fig. 1 a Principal Component Analysis (Russian_NSK and Russian_STV are Russian from Novosibirsk and Siberian starovers respectively from [2]) b
Observed and expected P-value distribution for allele frequency differences between NVSB and combined NFE sample c Results of the
ADMIXTURE analysis for K = 2–5

and the lowest with GBR and CEU (Fst = 0.005). The
results of the test for allele frequency differences between NVSB and NFE populations demonstrated pervasive inflation of the P-values attributed to numerousloci (Fig. 1b).
Among the 452 pharmocogenetically and 210 medically important variants we found 3 and 7 variants
respectively (Table 2) which showed significant allele
frequency differences between the NVSB and NFE
population after the multiple testing correction (BHadjusted P-value < 0.05). The most significant differences in allele frequencies were attributed to suchgenes as FCGR3B, TYR, OCA2, FABP1 and SLC4A1
genes.

Discussion
In this study, we used an exome-wide dataset for the firsttime to study the population structure of the Caucasian
Siberian population from a big Russian city Novosibirsk.

The exome-wide survey of the Novosibirsk population
demonstrated its genetic congruence with the previously
published Russian dataset including the partially isolated
Siberian Starovers regardless of the dramatic migrationand demographic changes of the previous century. The
Caucasian Novosibirsk population is quite homogeneous(Fig. 1a) and significantly differentiated from other
European populations from 1000 Genomes Project
demonstrating a relatively higher Finnish component
which is presumably ancestral but not a result of recent
migrations according to the ADMIXTURE results (Fig.
1c). This genetic differentiation although low in absolute Fst values should be taken into account during association studies. We identified 10 medically relevant SNVs
with statistically significant allele differences between the
NVSB and NFE populations including rs2241883 in
FABP1 gene previously associated with polycystic syndrome [21] and toxicity of fenofibrate [22], rs1801274
variant in FCGR2A gene shown to be important for thers1105525

5:79950508

G/A

C/T

C/T

rs7572857

2:65296798

11:126162843 rs8177374

G/A

rs448740

rs1126809

rs74653330

rs201779890 G/C

rs45562031

rs17879961

rs1805008

7:150696111

1:161599693

11:89017961

15:28228553

1:145507765

17:42338993

22:29121087

16:89986144

C/T

A/G

C/T

C/T

G/A

T/C

T/G

rs6280rs1799983

3:113890815

A/G

rs2266782

rs699

1:230845794

G/T

A/G

C/T

G/A

T/C

T/C

0.16670.051280.11540.051280.0641

0.06410.974360.78210.80770.076920.30770.57690.5256

0.39740.32050.29490.29490.141

0.55130.061760.001188

0.021380.001188

0.001188

0.272

0.66270.6306

0.6615

0.19830.1746

0.40970.35870.247

0.50710.14250.50950.036820.31710.016046

FABP1

BH adjusted GeneP-value0.001211

0.0001025

8.30E-05

0.0001065

9.00E-06

4.00E-06

1.00E-06

0.008170.008071

0.004250.00565

0.0059780.004475

0.005383

0.002335

0.001166

TIRAP

CEP68

AGT

FMO3

CTH

0.03633

0.0037275

0.0037275

0.0037275

0.000630.00042

cyclophosphamide, doxorubicin,
fluorouracil, methotrexate (3)risperidone (3)ustekinumab (3)aspirin (3)atenolol, irbesartan (3)sulindac, itopride (3)busulfan, cyclophosphamide (3)Pathogenic/Likely pathogenic

Likely benign(2);Pathogenic(1);Uncertain significance(1)Benign(2);Likely benign(1);Pathogenic(3); Uncertain
significance(1)Radial aplasia-thrombocytopenia
syndrome

Tyrosinase-positive
oculocutaneous albinism

Albinism, melanoma

Neutrophil-specificantigens na1/na2

Breast Neoplasms

Autistic disorderPsoriasis

AsthmaHypertension

NA

Hemopoietic stem celltransplant

Breast Neoplasms

Precursor Cell Lymphoblastic
Leukemia-Lymphoma

Leukemia, Myelogenous,
Chronic, BCR-ABL Positive

Pancreatic Neoplasms

Hypertriglyceridemia

Phenotype

MC1R

CHEK2

Likely benign(2);Pathogenic(1)Likely pathogenic(4);Pathogenic(7);Uncertain significance(2)Skin conditions

Cancer of multiple types,
susceptibility

SLC4A1 Likely benign(2);Likely pathogenic(1);Spherocytosis type 4
Pathogenic(1); Uncertain significance(2)

RBM8A

OCA2

TYR

FCGR3B Pathogenic (1)0.307736667 NOS3
0.00021

methotrexate (3)imatinib (3)gemcitabine (3)fenofibrate (3)Drug (PharmGKB evidence
level)/Clinvar annotation(number of reports)

FCGR2A trastuzumab (2B)

DHFR

0.307736667 DRD3

0.2702056

0.2702056

0.2702056

0.2702056

0.2702056

0.211084

0.131758

0.0003185 0.047987333 ULK3

0.0002365 0.047987333 ESR2

3.55E-05

Reference allele/ Frequency Frequency P-Value
subsitution
in NVSB
in NFE

1:171076966

rs1801274

rs2290573

15:75129594

rs1021737

rs944050

14:64700045

1:70904800

rs2241883

2:88424066

1:161479745

SNP ID

Chr:Position

ClinVar variants

PharmGKB
variants

Table 2 Genetic variants from PharmGKB (P-value < 0.01) and ClinVar (BH adjusted P-value < 0.05) databases which demonstrated highly significant differences in allele
frequency between NVSB and NFE

Yurchenko et al. BMC Medical Genetics 2019, 20(Suppl 1):51
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Yurchenko et al. BMC Medical Genetics 2019, 20(Suppl 1):51

efficiency of trastuzumab in breast neoplasms [23], therare rs17879961 variant in CHEK2 gene reliably associatedwith predisposition to breast and colorectal cancer [24]and showed elevated frequency in NVSB. These variants
should be studied in future on an expanded dataset withassociated clinical data.

Conclusion
The study reports for the first time an exome-wide comparison of a population from Russia with European samples and emphasizes the importance of population studies
with medical annotation of variants.

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2.

3.

4.

5.

Acknowledgments
We acknowledge A. Bielhin for English language editing of the manuscript.

6.

Funding
This study and publication costs are funded by the grant from the Russian
Science Foundation (project no. 16–15-00127).

7.

8.
Availability of data and materials
The dataset (individual genetic variants) analysed during the current study isavailable from the corresponding author on reasonable request following
approval of the Institute of Cytology and Genetics (Siberian Branch, Russian
Academy of Sciences) authorities and the Bioethics Committee.
About this supplement
This article has been published as part of BMC Medical Genetics Volume
20 Supplement 1, 2019: Selected articles from BGRS\SB-2018: medical
genetics. The full contents of the supplement are available online athttps://bmcmedgenet.biomedcentral.com/articles/supplements/volume-20supplement-1.

9.

10.

11.

12.
Authors’ contributions
AY, NY and MV designed the study; AY performed data analysis; AY wrote thedraft of the manuscript; all authors read, edited and approved the manuscript.
13.
Ethics approval and consent to participate
This research was approved by the Bioethics Committee of the Federal
Research Centre Institute of Cytology and Genetics (Siberian Branch, Russian
Academy of Sciences). All the participants signed an informed consent.

14.

Consent for publication
Not applicable.

15.

Competing interests
The authors declare that they have no competing interests.

16.

Publisher’s NoteSpringer Nature remains neutral with regard to jurisdictional claims inpublished maps and institutional affiliations.

17.

Author details
1
The Federal Research Center Institute of Cytology and Genetics, The
Siberian Branch of the Russian Academy of Sciences, Lavrentieva 10 St,
Novosibirsk, Russia630090. 2Novosibirsk State University, Novosibirsk 630090,
Russia. 3Institute of Internal and Preventive Medicine-branch of Institute ofCytology and Genetics, Siberian Branch of Russian Academy of Sciences,175/1, B. Bogatkov Street, 630089 Novosibirsk, Russia.

18.

19.

20.
Published: 9 April 2019
References
1. Kushniarevich A, Utevska O, Chuhryaeva M, Agdzhoyan A, Dibirova K,
Uktveryte I, et al. Genetic Heritage of the Balto-Slavic Speaking Populations:
A Synthesis of Autosomal, Mitochondrial and Y-Chromosomal Data. PLoS

21.

One. 2015;10(9):e0135820. Available from: https://doi.org/10.1371/journal.
pone.0135820
Triska P, Chekanov N, Stepanov V, Khusnutdinova EK, Kumar GPA,
Akhmetova V, et al. Between Lake Baikal and the Baltic Sea: genomic
history of the gateway to Europe. BMC Genet. 2017;18(S1). Available
from: https://doi.org/10.1186/s12863-017-0578-3
Khrunin AV, Khokhrin DV, Filippova IN, Esko T, Nelis M, Bebyakova NA, et al.
A Genome-wide analysis of populations from European Russia reveals a
new pole of genetic Diversity in northern Europe. PLoS One. 2013;8(3):1–9.
Wong EHM, Khrunin A, Nichols L, Pushkarev D, Khokhrin D, Verbenko D, etal. Reconstructing genetic history of Siberian and northeastern European
populations. Genome Res. 2016;27(1):1–14. Available from:. https://doi.org/
10.1101/gr.202945.115.
Mallick S, Li H, Lipson M, Mathieson I, Gymrek M, Racimo F, et al. The
Simons Genome Diversity Project: 300 genomes from 142 diverse
populations. Nature . 2016 21;538:201. Available from: https://doi.org/10.
1038/nature18964.
Bolger AM, Lohse M, Usadel B. Trimmomatic: a flexible trimmer for Illumina
sequence data. Bioinformatics 2014;30(15):2114–2120. Available from:https://doi.org/10.1093/bioinformatics/btu170
Li H, Durbin R. Fast and accurate short read alignment with burrowswheeler transform. Bioinformatics [Internet]. 2009;25(14):1754–1760.
Available from: https://doi.org/10.1093/bioinformatics/btp324
Li H, Handsaker B, Wysoker A, Fennell T, Ruan J, Homer N, et al. The
sequence alignment/map format and SAMtools. Bioinformatics 2009;25(16):2078–2079. Available from: https://doi.org/10.1093/bioinformatics/btp352
McKenna A, Hanna M, Banks E, Sivachenko A, Cibulskis K, Kernytsky A, et al.
The Genome analysis toolkit: a MapReduce framework for analyzing nextgeneration DNA sequencing data. Genome Res 2010;20(9):1297–1303.
Available from: https://doi.org/10.1101/gr.107524.110
An integrated map of genetic variation from 1,092 human genomes.
Nature. 2012;491(7422):56–65. Available from: https://doi.org/10.1038/
nature11632
Danecek P, Auton A, Abecasis G, Albers CA, Banks E, DePristo MA, et al. The
variant call format and VCFtools. Bioinformatics 2011;27(15):2156–2158.
Available from: https://doi.org/10.1093/bioinformatics/btr330
Kääriäinen H, Muilu J, Perola M, Kristiansson K. Genetics in an isolated
population like Finland: a different basis for genomic medicine? J
Community Genet 2017;8(4):319–326. Available from: https://doi.org/10.
1007/s12687-017-0318-4
Zheng X, Levine D, Shen J, Gogarten SM, Laurie C, Weir BS. A highperformance computing toolset for relatedness and principal component
analysis of SNP data. Bioinformatics 2012;28(24):3326–3328. Available from:https://doi.org/10.1093/bioinformatics/bts606
Alexander DH, Novembre J, Lange K. Fast model-based estimation ofancestry in unrelated individuals. Genome Res 2009;19(9):1655–1664.
Available from: https://doi.org/10.1101/gr.094052.109
Hudson RR, Slatkin M, Maddison WP. Estimation of levels of gene flow fromDNA sequence data. Genetics. 1992;132(2):583 LP–589 Available from:http://www.genetics.org/content/132/2/583.abstract.
Price AL, Patterson NJ, Plenge RM, Weinblatt ME, Shadick NA, Reich D.
Principal components analysis corrects for stratification in genome-wide
association studies. Nat Genet 2006;38(8):904–909. Available from: https://doi.org/10.1038/ng1847
Wang K, Li M, Hakonarson H. ANNOVAR: functional annotation of genetic
variants from high-throughput sequencing data. Nucleic Acids Res 2010;
38(16):e164–e164. Available from: https://doi.org/10.1093/nar/gkq603
Hewett M. PharmGKB: the pharmacogenetics Knowledge Base. Nucleic
Acids Res [Internet]. 2002;30(1):163–165. Available from: https://doi.org/10.
1093/nar/30.1.163
Landrum MJ, Lee JM, Riley GR, Jang W, Rubinstein WS, Church DM, et al.
ClinVar: public archive of relationships among sequence variation andhuman phenotype. Nucleic Acids Res 2013;42(D1):D980–D985. Available
from: https://doi.org/10.1093/nar/gkt1113
Purcell S, Neale B, Todd-Brown K, Thomas L, Ferreira MAR, Bender D, et al.
PLINK: a tool set for whole-Genome association and population-based
linkage analyses. Am J Hum Genet 2007;81(3):559–575. Available from:https://doi.org/10.1086/519795
Xue H, Zhao H, Liu X, Zhao Y, Chen Z-J, Ma J. Association of singlenucleotide polymorphisms rs2197076 and rs2241883 of FABP1 gene with

Yurchenko et al. BMC Medical Genetics 2019, 20(Suppl 1):51

polycystic ovary syndrome. J Assist Reprod Genet 2015;33(1):75–83.
Available from: https://doi.org/10.1007/s10815-015-0626-8
22. Brouillette C, Bossé Y, Pérusse L, Gaudet D, Vohl M-C. Effect of liver fatty
acid binding protein (FABP) T94A missense mutation on plasma lipoprotein
responsiveness to treatment with fenofibrate. J Hum Genet. 2004;49(8).
Available from: https://doi.org/10.1007/s10038-004-0171-2
23. Tamura K, Shimizu C, Hojo T, Akashi-Tanaka S, Kinoshita T, Yonemori K, et al.
Fc R2A and 3A polymorphisms predict clinical outcome of trastuzumab inboth neoadjuvant and metastatic settings in patients with HER2-positive
breast cancer. Ann Oncol 2010;22(6):1302–1307. Available from: https://doi.
org/10.1093/annonc/mdq585
24. Han F, Guo C, Liu L. The effect of CHEK2 variant I157T on Cancer susceptibility:
evidence from a meta-analysis. DNA Cell Biol 2013;32(6):329–335. Available
from: https://doi.org/10.1089/dna.2013.1970

Page 34 of 56