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Page 4 of 13 Smetanina et al. Vessel Plus 2021;5:19 https://dx.doi.org/10.20517/2574-1209.2021.17

(ABO), rs966562 (XKR5, ANGPT2, AGPAT5), rs7111987 (ADM, AMPD3), rs11121615 (CASZ1),
rs111434909 (ANGPT1), rs145218303 (ARGHAP6), rs6905288 (VEGFA), rs4516218 (PIEZO1), rs4463578 (
ZIC3, FGF13), rs6062618 (SOX18, TCEA2), rs6712038 (PPP3R1, CNRIP1) and rs79607156 (THEG5,
[21]
ZNF507) . However, those associations were not replicated. Another genome‐wide association analysis
(combining the discovery and replication stages) for chronic venous disease performed by a German group
revealed robust associations within the two loci and genes corresponding to them, namely rs17278665 (
EFEMP1) and rs727139 (KCNH8), and suggestive association within rs2030136 (SKAP2) . Noteworthy, we
[22]
[23]
found the EFEMP1 gene is upregulated in VVs compared to non-VVs . The first large-scale genetic
association study for primary varicose veins performed in our laboratory on the Russian population using
exome genotyping identified a promising association signal at chromosome 6 within classical major
histocompatibility complex class III subregion, with the most statistically significant association being
shown in a combined analysis (discovery and replication stages) for polymorphism rs4151657 in the CFB
(complement factor B) gene , which points to immune system involvement in VV pathogenesis.
[24]

Quite a few works have been done to replicate top associations from GWASs for VVD using independent
[21]
datasets. One of them, performed in our laboratory, aimed to verify the associations revealed by Bell et al.
and Ellinghaus et al. using two independent groups of patients: (1) ethnic Russian individuals; and (2)
[22]
genetic data on a large population-based cohort of British residents obtained from UK Biobank. We also
aimed to perform a meta-analysis . After combining the original GWAS results and replication studies by
[25]
a meta‐analysis, the following polymorphisms passed a genome‐wide significant threshold: rs11121615,
rs6712038, rs507666, rs966562, rs7111987, rs6062618, rs6905288, rs111434909, rs4463578, rs111434909 and
rs4463578. Most of them are located near or within the genes involved in vascular development, remodeling
and inflammation, which implicates these processes in VV pathogenesis. None of those SNPs were from the
study of Ellinghaus et al. , and the only one that reached a nominal significance level (P < 0.05) in the
[22]
Russian cohort was rs11121615 (CASZ1). Thus, the set of polymorphic variants of genes is indeed not the
same in different populations.

Recently, two fairly large bioinformatics (non-experimental) works using modern approaches to big data
analysis were published almost in parallel by American and Swedish scientists in 2018 in Circulation and
[26]
[27]
Russian scientists in 2019 in PLoS Genetics . After analyzing data from the UK Biobank, the authors of
these papers presented a comprehensive genetic and epidemiological study on VVs and identified new
clinical and genetic risk factors. Fukaya et al. demonstrated that greater height has a causal role in
[26]
varicose vein development and discovered a strong genetic correlation between varicose veins and deep vein
thrombosis (which, in our opinion, could be because some VVs in their study were not primary since VVD
and DVT directly and inversely correlate with each other). Their large-scale GWA of VVs among 337,536
individuals (9577 cases and 327,959 controls) identified the top 30 genetic loci: rs11121615, rs2911463,
rs2861819, rs28558138, rs8053350, rs3101725, rs11135046, rs7773004, rs12625547, rs236597, rs7614922,
rs73107980, rs7469817, rs2241173, rs816943, rs1061539, rs1549063, rs16828263, rs9719461, rs2263321,
rs247749, rs75522736, rs553399706, rs62512472, rs584768, not available SNP on chr6 position 127452639
(build37), rs2089657, rs12594708, rs186005582 and rs192647746. They denoted the genes in closest
proximity to the variants (and not only those genes): CASZ1, LBH, PPP3R1, IGSF11, GATA2-AS1, STIM2,
TEMN3-AS1, AGGF1, EBF1, SLC12A2, HISTIH3G, HCG9, HLA-B, LINC02549, RSPO3, ALDH8A1,
PRKAR1B, CNGB3, DEC1, HDAC7, DAOA, LINC00924, GLG1, CTU2, PIEZO1, KCNJ2, LINC01152,
MAMSTR, NFATC2,ZNF512B . Shadrina et al. used genetic association data on VVs for 337,199
[27]
[26]
individuals (6958 cases and 330,241 controls) and demonstrated direct causal effects of the anthropometric
traits, such as height and weight (and not only those traits), as well as plasma levels of immune-related
proteins MICB and CD209. Their large-scale GWA identified 12 reliably associated loci that explain 13% of