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DOI: 10.18413/2658-6533-2021-7-3-0-2
Integrated in-depth bioinformatic analysis suggests RELCH/KIAA1468, LINC02341, and AKAP11 as candidate genes for ages at menarche and menopause
Volodymyr Dvornyk
College of Science and General Studies, Alfaisal University, P.O.Box 50927, Riyadh, 11533, Kingdom of Saudi Arabia Corresponding author: Volodymyr Dvornyk (vdvornyk@alfaisal.edu)
Abstract
Background: Polymorphisms of the TNFRSF11A and TNFSF11 genes were reported for their association with age at menarche (AAM) and age at natural menopause (ANM). However, the biological mechanisms underlying this association remain largely unclear. The aim of the study: This study was to determine biological processes backing the observed genetic associations. Materials and methods: Forty-four SNPs were analyzed using in silico approach and ten publicly available online databases and tools. Results: TNFRSF11A and TNFSF11 are highly pleiotropic genes that play a role in many metabolic processes. However, among that variety, li-pid metabolism and cell survival and apoptosis seem the most biologically plausible mechanisms, through which these genes contribute to AAM and ANM. The analysis identified several mechanisms underlying the previously determined association of the TNFRSF11A and TNFSF11 genes with AAM and ANM and suggested RELCH/KIAA1468, LINC02341, and AKAP11 as new candidate genes for the traits. Conclusion: The in silico analysis is a powerful approach making it possible to uncover possible metabolic pathways underlying observed genetic associations. Keywords: bioinformatics; in silico analysis; age at menarche; age at menopause; TNFRSF11A ; TNFSF11
For citation: Dvornyk V. Integrated in-depth bioinformatic analysis suggests RELCH/KIAA1468, LINC02341, and AKAP11 as candidate genes for ages at menarche and menopause. Research Results in Biomedicine. 2021;7(3):220-231. DOI: 10.18413/2658-6533-2021-7-3-0-2
Introduction. Tumor necrosis factor receptor superfamily, member 11a (.TNFRSF11A), also known as receptor activator of nuclear factor-KB (NF-kB; RANK), and its ligand (TNFSF11 or RANKL) have been implicated in various cellular processes related to proliferation and death, immunity, and
tissue development. The
TNFRSF11A/TNFSF11 system is widely acknowledged as one of the key players in some primary postmenopausal disorders, such as osteoporosis [1] and cardiovascular diseases [2]. Also, these genes are expressed in mammary gland cells and were shown to con-
trol the development of a lactating mammary gland during pregnancy [3]; that is, they play a role in the reproductive system. Several candidate gene association studies suggested that TNFRSF11A and TNFSF11 were associated with ages at menarche and menopause in different ethnic populations [4-7]. However, biological mechanisms, which underlie these associations, remain largely unclear. The exponential growth of biomolecular data and its mining into databases have provided not only a possibility of more accurate and substantiated choice of genetic markers for a study but also tools for comprehensive analysis to get deeper insights into probable functional assignments of the candidate genetic variants and mechanisms of their contribution to traits [8-10]. I took advantage of the recent advances in bioinformatics and used several online genomic databases to conduct a comprehensive in silico analysis of the TNFRSF11A and TNFSF11 polymorphisms, which were re-
ported as associated with age at menarche and menopause. This bioinformatic analysis aimed to get insights into possible mechanisms of these associations. Materials and Methods Selection of polymorphisms Polymorphisms for the analysis were selected based on the published results of their association with ages at menarche and/or menopause. For this purpose, PubMed was screened using terms "TNFRSF11A", "TNFSF11", "RANK", "RANKL", "menar-che", and "menopause" in various combinations. The search returned four articles with relevant results. These articles reported in total 44 SNPs (reference polymorphisms hereafter) associated with ages at menarche and/or menopause in three ethnic samples: Caucasians, Chinese, and Mexicans. The list of the selected polymorphisms and the map of the genomic regions, in which they are located, are given in Table 1 and Figure 1.
Beginning of Table 1
Information about the analyzed SNPs __
Gene SNP ID Location in/around the gene Association Ethnicity Reference
AAM ANM
TNFRSF11A (RANK) rs3826620 Intron + + Caucasian [7]
+ Chinese [6]
rs8086340 Intron + Caucasian [7]
+ Chinese [6]
rs11665260 Intron + + Caucasian [7]
rs7239261 Intron + Chinese [6]
rs8094884 Intron + Chinese [6]
rs8089829 Intron + Chinese [6]
rs9956850 Intron + Chinese [6]
rs1805034 Exon Missense Ala/Val + Chinese [6]
+ Chinese [5]
rs4524034 Intron + Chinese [6]
rs4524035 Intron + Chinese [6]
rs12455775 Intron + Chinese [6]
rs17069904 Intron + Chinese [6]
rs12959396 Intron + Chinese [6]
rs2981003 5'-region, 5.8kb 3' of KIAA1468 + Chinese [6]
rs2981004 5'-region, 6.2kb 3' of KIAA1468 + Chinese [6]
rs6567263 5'-Region + Chinese [6]
rs7233197 Intron + Chinese [6]
rs4941125 Intron + Chinese [6]
rs4500848 Intron + Chinese [5]
End of Table 1
Information about the analyzed SNPs
Gene SNP ID Location in/around the gene Association Ethnicity Reference
AAM ANM
rs6567270 Intron + Chinese [5]
rs9962159 Intron + Chinese [5]
TNFSF11 (RANKL) rs12585014 5'-region + Mexican [4]
+ + Caucasian [7]
rs9525641 Intron + + Caucasian [7]
rs2200287 Intron + Caucasian [7]
rs1054016 3'-UTR + Caucasian [7]
rs346578 3'-UTR + Caucasian [7]
rs3742257 Intron + + Caucasian [7]
rs922996 Intron + + Caucasian [7]
rs7988338 Intron + + Caucasian [7]
rs2277438 Intron + + Caucasian [7]
rs9525645 Intron + + Caucasian [7]
rs2148073 Intron + + Caucasian [7]
LINC02341 rs12874142 5'-region + Chinese [6]
rs7326472 5'-region + Chinese [6]
rs11147871 5'-region + Chinese [6]
rs9590697 5'-region + Chinese [6]
rs727243 5'-region + Chinese [6]
rs12864265 Intron + Chinese [6]
rs7316953 Intron + Chinese [6]
rs1324005 Intron + Chinese [6]
rs9525625 Intron + Chinese [6]
rs720824 Intron + Chinese [6]
AKAP11 rs9525610 3'-UTR + Chinese [6]
rs238281 3'-UTR + Chinese [6]
rs9525613 3'-UTR + Chinese [6]
rs430586 3'-UTR + Chinese [6]
rs417768 3'-UTR + Chinese [6]
rs912100 3'-UTR + Chinese [6]
rs17063218 3'-UTR + Chinese [6]
rs17522044 3'-UTR + Chinese [6]
rs238270 3'-UTR + Chinese [6]
Fig. 1. Maps of the genomic regions where the analyzed SNPs and genes are located.
Bioinformatic analysis
In total ten bioinformatics tools were employed for the analyses.
The effect of non-synonymous SNPs on the protein function was analyzed using SIFT (https://sift.bii.a-star.edu.sg/) [11].
The integrated online tool, HaploReg v4.1 [12] was used to identify polymorphisms in strong linkage disequilibrium (LD) (r2 > 0.8) with the AAM- and/or ANM-associated ones and to analyze them for their functional significance (chromatin states, motifs changes, protein interactions, regulatory potential, and eQTLs). The analysis was conducted separately for Caucasian and Chinese ethnicities using the data of the European and Asian populations from the 1000 Genomes Project Phase.
In addition to HaploReg (v4.1), three other databases were used to analyze regulatory effects of the polymorphisms: Regu-lomeDB (Version 1.1)
(http://regulome.stanford .edu/) [13],
rSNPB ase (http://rsnp.psych.ac.cn/index.do) [14], and SNP Function Prediction (FuncPred)
(https://snpinfo.niehs.nih.gov/snpinfo/snpfunc .html) [15], and GeneCards (https://www.genecards.org/) [16].
The effect of the 44 candidate SNPs for AAM and ANM on gene expression level (cis- and trans-eQTL) was estimated in peripheral blood using the Blood eQTL browser (http://genenetwork.nl/bloodeqtlbrowser/) [17], and in other organs and tissues using the GTExportal data (http://www.gtexportal.org/) as of 07/27/2020. The false discovery rate (FDR) <0.05 was applied as the significance level.
The functional significance of the candidate genes for AAM in the various biological pathways was studied using the Gene Ontology Resource tools available at http://geneontology.org [18]. The results of multiple comparisons were adjusted with the FDR<0.05. The gene interaction networks were constructed using GeneMANIA (version 3.5.0) [19] available at http://genemania.org.
Results
Genomic location of the SNPs First of all, 21 out of the 44 reference SNPs previously annotated to the regions of the TNFRSF11A and TNFSF11 genes could also be mapped to the regions of the other genes (Table 1). Ten reference SNPs were located in the region of the LINC02341 gene, five of them in the introns. Nine variants were located in the 3'-UTR of the AKAP11 gene. Two SNPs, rs2981003 and rs2981004, were located in the 3'-UTR of the RELCH/KIAA1468 gene.
Non-synonymous SNPs Only one of all analyzed SNPs, rs1805034 in the TNFRSF11A gene, was missense. It results in an Ala/Val replacement in the respective protein. The replacement has SIFT Score = 1 and prediction value "tolerated".
SNPs in strong LD with the reference polymorphisms
The query against the HaploReg database returned in a total of 348 (224 unique) SNPs linked to the reference ones of the TNFRSF11A gene and 779 (322 unique) SNPs linked to the reference loci of the TNFSF11 gene (Supplementary Table 1). The SNP association and linkage patterns were quite different between European and Asian populations. Specifically, two SNPs of the TNFRSF11A gene, rs3826620 and rs8086340, were associated with AAM and/or ANM in both Caucasians and Chinese [6, 7]. However, the HaploReg analysis returned no SNPs linked to rs3826620 in Europeans vs eight SNPs in Asians. In total, six loci were linked to the three reference SNPs in the European population and 218 were linked to the 19 reference SNPs in the Asian population. Out of these 224 unique SNPs, only three were shared between the European and Asian populations. Quite a few SNPs in the Asian population were located at/near the RELCH/KIAA1468 and PIGN genes (Supplementary Table 1).
Even more striking ethnicity-related differences were observed for the TNFSF11 gene polymorphisms: no shared SNPs in Europeans and Asians. In the Asian population, more
than half reference and linked to them polymorphisms were located at/near the AKAP11 gene (Supplementary Table 1).
Regulatory effects
The results of the regulatory effect analysis are shown in Supplementary tables 1 and 2. They suggest that all reference SNPs can produce various regulatory effects, albeit
to a different extent. For example, rs8086340 of the TNFRSF11A gene displays histone marks associated with promoters in six tissues and enhancers in 14 tissues, located in the DNase-1 hypersensitive region in 21 tissues, binding region for six proteins, and altered motif for the Foxm1 transcription factor (Supplementary Table 1).
Table 2
Effect of the reference AAM- and ANM-associated SNPs on the gene expression (cis-eQTL) in peripheral blood according to the Blood eQTL browser [17]
SNP Gene/Region Gene Expressed P FDR*
rs3826620 TNFRSF11A RELCH/KIAA1468 4.7*10-9 0.00
rs8086340 TNFRSF11A RELCH/KIAA1468 6.9*10-5 0.03
rs7239261 TNFRSF11A RELCH/KIAA1468 3.9*10-5 0.02
rs7233197 TNFRSF11A RELCH/K1AA1468 3.1*10-9 0.00
rs4941125 TNFRSF11A RELCH/K1AA1468 3.7*10-6 0.00
rs9962159 TNFRSF11A RELCH/KIAA1468 7.3*10-5 0.03
rs12874142 80kb 3' of AKAP11 AKAP11 6.5*10-5 0.03
rs9525625 117kb 3' of AKAP11 AKAP11 1.1*10-6 0.00
rs238281 13kb 3' of AKAP11 AKAP11 1.9*10-32 0.00
rs9525613 21kb 3' of AKAP11 AKAP11 2.6*10-8 0.00
rs430586 23kb 3' of AKAP11 AKAP11 1.0*10-21 0.00
rs417768 23kb 3' of AKAP11 AKAP11 1.4*10-21 0.00
rs912100 24kb 3' of AKAP11 AKAP11 6.4*10-30 0.00
rs238270 36kb 3' of AKAP11 AKAP11 1.4*10-20 0.00
Note: * FDR, False Discovery Rate
Expression QTLs
Several reference SNPs appeared to have a significant cis-eQTL effect on the expression of five genes, RELCH/KIAA1468, PIGN, AKAP11, TNFRSF11A, and TNFSF11, in various tissues and organs (Tables 2 and 3).
Pathway analysis
This analysis was conducted for TNFRSF11A and TNFSF11 (because they were originally reported as associated with AAM and/or ANM), LINC02341 (because several reference polymorphisms were mapped to this gene), and RELCH/KIAA1468, PIGN, AKAP11 (because the expression of these genes might be affected by some reference SNPs according to the eQTL analysis).
According to the results of the PANTHER overrepresentation test,
the TNFRSF11A and TNFSF11 genes are involved in a broad range of biological processes, including regulation of ERK1 and ERK2 cascade, secretion of prostaglandins, bone remodeling, and mammary gland development (Supplementary Table 3). Apart from these two, AKAP11 was suggested to contribute to the organism's homeostasis (Supplementary Table 3). No data was found for RELCH/KIAA1468, PIGN., and LINC02341.
The gene-gene interaction network inferred using GeneMANIA (Figure 2) suggested that the major contribution (64.32%) came from physical interactions between the proteins, followed by co-expression (25.88%), co-localization (5.61%), and common pathways (4.19%).
Beginning of Table 3
Effect of the reference AAM- and ANM-associated SNPs on the gene expression (q's-eQTL) in various tissues according to the GTEx browser
SNP Gene/Region Gene Expressed Effect Tissue
rs3826620 TNFRSF11A RELCH/KIAA1468 Down Nerve-tibial
rs11665260 TNFRSF11A RELCH/KIAA1468 Down Skin - sun exposed (lower leg)
rs8094884 TNFRSF11A RELCH/KIAA1468 Up Skin - sun exposed (lower leg)
rs8089829 TNFRSF11A RELCH/KIAA1468 Down Testis, nerve - tibial
rs9956850 TNFRSF11A RELCH/KIAA1468 Up Adipose-subcutaneous
PIGN Down Aorta, coronary artery, adipose tissue, thyroid
rs17069904 23kb 3' of AKAP11 TNFRSF11A Down Esophagus - mucosa
PIGN Up Adipose-subcutaneous, lung, muscle-skeletal
rs12959396 24kb 3' of AKAP11 RELCH/KIAA1468 Down Testis
rs2981003 36kb 3' of AKAP11 TNFRSF11A Up Skin, esophagus-mucosa, brain, lung, mammary tissue, pancreas, pituitary, thyroid
PIGN Down Adipose-subcutaneous, esophagus-mucosa, brain, lung, muscle-skeletal, thyroid, artery-tibial, nerve-tibial
rs2981004 6.2kb 3' of KIAA1468 TNFRSF11A Up Skin, esophagus-mucosa, brain, lung, mammary tissue, pancreas, pituitary, thyroid
rs6567263 4.9kb 5' of TNFRSF11A PIGN Down Adipose-subcutaneous, esophagus-mucosa, brain, lung, muscle-skeletal, thyroid, artery-tibial, nerve-tibial
rs7233197 TNFRSF11A RELCH/KIAA1468 Down Skin, esophagus-mucosa, brain, lung, mammary tissue, pancreas, pituitary, thyroid, nerve-tibial
PIGN Down Adipose-subcutaneous, esophagus-mucosa, brain, lung, muscle-skeletal, thyroid, artery-tibial, nerve-tibial
rs4941125 TNFRSF11A TNFRSF11A Up Esophagus-mucosa, skin, thyroid
PIGN Down Skin, thyroid, adipose-subcutaneous, small intestine-ileum
rs4500848 TNFRSF11A RELCH/KIAA1468 Down Nerve-tibial, adipose-subcutaneous, testis
PIGN Down Skin, brain
rs9962159 TNFRSF11A TNFRSF11A Up Skin, thyroid
PIGN Down Skin, thyroid, adipose-subcutaneous
rs12874142 80kb 3' of AKAP11 TNFSF11 Up Esophagus-mucosa
AKAP11 Up Esophagus-muscularis, brain, skin
rs9590697 97kb 3' of AKAP11 TNFSF11 Up Esophagus-mucosa
AKAP11 Up Esophagus-muscularis, skin
rs727243 98kb 3' of AKAP11 TNFSF11 Up Esophagus-mucosa
AKAP11 Up Esophagus-muscularis
Dvornyk V. Integrated in-depth bioinformatic analysis .
Effect of the reference AAM- and ANM-associated SNPs on the gene expression
End of Table 3
SNP Gene/Region Gene Expressed Effect Tissue
rs12864265 117kb 3' of AKAP11 TNFSF11 Up Esophagus-mucosa
AKAP11 Up Esophagus-muscularis, skin
rs7316953 119kb 5' of TNFSF11 TNFSF11 Down Esophagus-mucosa
AKAP11 Down Esophagus-muscularis
rs1324005 119kb 5' of TNFSF11 TNFSF11 Up Esophagus-mucosa
AKAP11 Up Esophagus-muscularis, skin
rs9525625 119kb 5' of TNFSF11 AKAP11 Down Esophagus-mucosa, esophagus-muscularis, brain
rs720824 119kb 5' of TNFSF11 TNFSF11 Down Esophagus-mucosa
AKAP11 Down Esophagus-muscularis
rs238281 13kb 3' of AKAP11 AKAP11 Up Artery, brain, colon, esophagus-mucosa, esophagus-muscularis, nerve-tibial, adipose-subcutaneous
rs9525613 21kb 3' of AKAP11 AKAP11 Up Artery-tibial
rs430586 23kb 3' of AKAP11 AKAP11 Up/Down Esophagus-mucosa, esophagus-muscularis, brain, adipose-subcutaneous, heart, muscle-skeletal
rs417768 23kb 3' of AKAP11 AKAP11 Up/Down Esophagus-mucosa, esophagus-muscularis, brain, adipose-subcutaneous, heart, muscle-skeletal
rs912100 24kb 3' of AKAP11 AKAP11 Up/Down Esophagus-mucosa, esophagus-muscularis, brain, adipose-subcutaneous, heart, muscle-skeletal, vagina, lung, colon, nerve-tibial
rs17063218 25kb 3' of AKAP11 AKAP11 Up/Down Esophagus-mucosa, esophagus-muscularis, brain, adipose-subcutaneous, heart, muscle-skeletal, vagina, lung, colon, nerve-tibial
rs17522044 26kb 3' of AKAP11 AKAP11 Up Esophagus-mucosa, esophagus-muscularis, heart, muscle-skeletal, nerve-tibial, artery-tibial
rs238270 36kb 3' of AKAP11 AKAP11 Up/Down Esophagus-mucosa, esophagus-muscularis, brain, adipose-subcutaneous, muscle-skeletal, nerve-tibial
Fig. 2. The interaction networks of the candidate genes for age at menarche and natural menopause inferred using GeneMANIA. The candidate genes for the traits determined in the present study
are cross-shaded
Discussion. This study provides evidence that, in addition to the TNFRSF11A and TNFSF11 genes previously reported as associated with AAM and/or ANM, four other genes might be associated with these traits.
The LINC02341 gene belongs to the class of long non-coding RNAs. There is not much information about LINC02341 in public databases. Although long non-coding RNAs have not been studied well, there is a growing body of evidence that they are involved in transcriptional regulation [20]. Indeed, according to the GeneHancer database [21], LINC02341 harbors enhancers for six genes,
including TNFSF11 and AKAP11, and binding sites for 76 transcription factors. The expression of the gene is relatively low and was documented in several tissues and organs, including lymph nodes, kidneys, placenta, and others [22]. The reference SNP, rs9525625, which is an intronic variant of the gene, was reported as a risk factor of inflammatory bowel disease [23].
The regions of two genes, RELCH/KIAA1468 and AKAP11, also harbored several reference SNPs associated with AAM (Table 1). Besides, quite a few genetic variants in these genes were linked to the reference pol-
ymorphisms (Supplementary Table 1). These results suggested that the above genes might also contribute to the above trait.
RELCH (RAB11 binding and LisH domain, coiled-coil and HEAT repeat-containing, alias KIAA1468) encodes a protein playing a key role in intracellular cholesterol distribution [24]. The gene is ubiquitously expressed in human tissues and organs, including endocrine glands, endometrium, and ovaries [22]. The results of the GeneMANIA analysis suggested that this gene was co-expressed with TNFRSF11A, AKAP11, and PIGN (Figure 2).
A product of AKAP11, A-kinase anchoring protein 11, belongs to the protein family whose members, despite the diverse structure, have the same function of binding to the regulatory subunit of protein kinase A and targeting the enzyme to specific locations in the cell. It has similar to RELCH expression patterns [22] but is not co-expressed with TNFRSF11A and PIGN (Figure 2).
The PIGN gene encodes ethanolamine phosphate transferase, a key element of gly-cosylphosphatidylinositol-anchor biosynthesis. Mutations in the gene were associated with multiple congenital anomalies-hypotonia-seizures syndrome [25]. The gene is co-expressed with TNFRSF11A and RELCH (Figure 2).
RELCH and AKAP11 are pleiotropic genes and were associated with multiple traits, including those related to menarche and menopause (e.g., bone phenotypes, obesity, development, etc.) [26, 27]. There is ample evidence that the above phenotypes have a shared genetic basis with AAM and ANM (see e.g., [6, 7, 28]. Together with the results of the in silico analysis of the present study, it suggests that RELCH/KIAA1468, LINC02341, and AKAP11 may be candidate genes for AAM and/or ANM. This assumption is biologically plausible too.
A possible contribution of PIGN to AAM and/or ANM looks less obvious, largely due to the lack of data about the association of this gene with menarche- and menopause-related phenotypes. On the other hand, according to GeneHancer, this gene harbors
binding sites of multiple transcription factors targeting the expression of RELCH and TNFRSF11A. Furthermore, given the involvement of this gene in the basic cellular and developmental processes [29] and tight linkage to the AAM-associated loci (Supplementary Table 1), the above possibility could not be ruled out.
The results of the Gene Ontology and GeneMANIA analyses (Supplementary tables 3, 4, Figure 2) suggested that the contribution of TNFRSF11A and TNFSF11 to menarche and menopause timing is likely multifaceted. The TNFRSF11A/TNFSF11/TNFRSF11B (RANK/RANKL/OPG) signaling pathway has been widely acknowledged as a key player in bone remodeling [1]. Apart from this, the system plays an important role in the progesterone-driven proliferation of the mammary gland epithelium and the risk of breast cancer [30]. One of the possible ways through which TNFRSF11A can affect AAM and ANM is an interaction with TRAF2, a key element in the control of cell survival and apoptosis [31]. Involvement in the metabolism of lipids may be one more important biological mechanism of the AAM- and ANM-related role of TNFRSF11A. The relationship between obesity and AAM/ANM has been well documented [32, 33]. Arachidonic acid/prostaglandin E2 axis was implicated in uterine epithelium cell death induced by menopause [34]. The fatty acid composition was shown to be related to the menopausal status [35].
The lack of the GO Ontology data about RELCH/KIAA1468, PIGN,, and LINC02341 may suggest that their role in metabolic pathways is still poorly studied. On the other hand, there is extensive evidence about co-expression of RELCH/KIAA1468 and PIGN with many genes, including those involved in the control of the basic cellular processes, e.g., cell proliferation [36] (Supplementary Table 4).
In general, a degree of gene pleiotropy seems to be inversely related to the relative contribution of the gene to the trait. Given that most genes in the human genome are pleiotropic [37], the expected contribution of each of them to a particular trait is quite mod-
est. Therefore, highly pleiotropic genes have a small effect size and often yield false negative results in GWAS unless their contribution to a particular trait is above the average for other traits (e.g., TNFRSF11A/TNFSF11 contribution to bone remodeling).
The present study also sheds light on the frequently observed inconsistencies in associated polymorphisms and unsuccessful attempts to replicate candidate loci in different ethnic populations. Previous studies suggested that differences in population genetic structure might underlie the above disparities [38, 39]. The results of the present study suggest that, in addition to the allele frequencies, population-specific LD patterns are another important factor.
Conclusion. The in silico analysis of the TNFRSF11A and TNFSF11 polymorphisms previously reported for association with AAM and/or ANM suggested RELCH/KIAA1468, LINC02341, and AKAP11 genes as candidates for the traits. While this assumption is biologically plausible, candidate gene association studies are needed to verify it. In summary, the present study demonstrates that the in-depth analysis of rapidly expanding biological databases may provide new insights into possible factors and mechanisms underlying the observed association of genetic markers with a trait.
Financial support
No financial support has been provided for this work.
Conflict of interests
The author has no conflict of interest to declare.
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Received 15 April 2021 Revised 30 May 2021 Accepted 21 June 2021
Information about the author Volodymyr Dvornyk, PhD, Associate Professor at the Department of Life Sciences, College of Science and General Studies, Alfaisal University, Riyadh, Kingdom of Saudi Arabia, E-mail: vdvornyk@alfaisal.edu, ORCID:
https://orcid.org/0000-0002-2535-103X.
