OL Oncology Letters 1792-1074 1792-1082 D.A. Spandidos 10.3892/ol.2021.13040 OL-0-0-13040 Articles Repurposing non-invasive prenatal testing data: Population study of single nucleotide variants associated with colorectal cancer and Lynch syndrome ForgacovaNatalia 1 2 GazdaricaJuraj 2 3 4 BudisJaroslav 1 3 4 RadvanszkyJan 1 2 5 SzemesTomas 1 2 3 Comenius University Science Park, Comenius University, 841 04 Bratislava, Slovakia Department of Molecular Biology, Faculty of Natural Sciences, Comenius University, 841 04 Bratislava, Slovakia Geneton Ltd., 841 04 Bratislava, Slovakia Science Support Section, Slovak Centre of Scientific and Technical Information, 811 04 Bratislava, Slovakia Institute for Clinical and Translational Research, Biomedical Research Center, Slovak Academy of Sciences, 845 05 Bratislava, Slovakia Correspondence to: Miss Natalia Forgacova, Comenius University Science Park, Comenius University, Ilkovicova 8, Karlova Ves, 841 04 Bratislava, Slovakia, E-mail: natali.forgacova@gmail.com 11 2021 13 09 2021 22 5 779 29042021 16072021 Copyright: © Forgacova et al. 2021 This is an open access article distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non-commercial and no modifications or adaptations are made.

In our previous work, genomic data generated through non-invasive prenatal testing (NIPT) based on low-coverage massively parallel whole-genome sequencing of total plasma DNA of pregnant women in Slovakia was described as a valuable source of population specific data. In the present study, these data were used to determine the population allele frequency of common risk variants located in genes associated with colorectal cancer (CRC) and Lynch syndrome (LS). Allele frequencies of identified variants were compared with six world populations to detect significant differences between populations. Finally, variants were interpreted, functional consequences were searched for and clinical significance of variants was investigated using publicly available databases. Although the present study did not identify any pathogenic variants associated with CRC or LS in the Slovak population using NIPT data, significant differences were observed in the allelic frequency of risk CRC variants previously reported in genome-wide association studies and common variants located in genes associated with LS. As Slovakia is one of the leading countries with the highest incidence of CRC among male patients in the world, there is a need for studies dedicated to investigating the cause of such a high incidence of CRC in Slovakia. The present study also assumed that extensive cross-country data aggregation of NIPT results would represent an unprecedented source of information concerning human genome variation in cancer research.

 colorectal cancer Lynch syndrome non-invasive prenatal testing low-coverage massively parallel whole genome sequencing PANGAIA project H2020-MSCA-RISE-2019 872539 OP Integrated Infrastructure for the project ‘Long term strategic research and development focused on the occurrence of Lynch syndrome in the Slovak population and possibilities of prevention of tumors associated with this syndrome’ 313011V578 European Regional Development Fund (ERDF) Operational Program Integrated Infrastructure 313011F988 ERDF Scientific Grant Agency of the Ministry of Education, Science, Research and Sport of the Slovak Republic and the Slovak Academy of Sciences 1/0305/19 The present study was supported by the PANGAIA project H2020-MSCA-RISE-2019 (grant no. 872539) funded under H2020-EU.1.3.3. Programme, the OP Integrated Infrastructure for the project ‘Long term strategic research and development focused on the occurrence of Lynch syndrome in the Slovak population and possibilities of prevention of tumors associated with this syndrome’ (grant no. 313011V578) co-financed by the European Regional Development Fund (ERDF), the Operational Program Integrated Infrastructure (grant no. 313011F988) co-financed by the ERDF, and the Scientific Grant Agency of the Ministry of Education, Science, Research and Sport of the Slovak Republic and the Slovak Academy of Sciences (grant no. 1/0305/19). Introduction

Colorectal cancer (CRC) is the third most common cancer worldwide and the second most common cancer in Europe, causing an estimated 9.4% of all cancer deaths in Europe. CRC is also a serious societal problem in Slovakia, with an incidence rate of 15,7% and a mortality rate of 15,6% [(1,2) GLOBOCAN 2020 data]. Many risk factors and causes are associated with the likelihood of developing CRC, but the main reason is still not fully understood. The considerable geographical variability suggests that CRC is a complex polygenic disease caused by genetic and environmental factors and their interactions. Age, sex, lifestyle, and dietary habits (3,4), including meat and alcohol consumption (5,6), tobacco smoking (7–9), obesity and lack of physical activity (4,10,11) play a major role in the pathogenesis of CRC. Other well-known risk factors may also be inflammatory bowel diseases, acromegaly, renal transplantation with long-term immunosuppression, diabetes mellitus and insulin resistance, cholecystectomy, or androgen deprivation therapy (3,4).

Beside these, inherited susceptibility plays a significant role in the etiology of CRC because it can be responsible for about 35% of all cases of colorectal cancer. However, high-penetrance germline variants in known genes (APC, BRCA2, KRAS, NTS, SMAD4, POLE, BRAF, BMPR1A, POLD1, STK11, MUTYH and DNA mismatch repair genes), which are associated with severe hereditary syndromes, such as familial adenomatous polyposis and Lynch syndrome (also called hereditary non-polyposis colorectal cancer), account for only 5–7% of total CRC cases (4,12). Therefore, the remaining unknown heritability is probably explained by the interaction of common, low-penetrance variants identified through genome-wide association studies (GWAS). GWAS in ethnic/racial minority populations offers the opportunity to uncover genetic susceptibility factors and discover new genomic regions and loci that contribute risk for CRC development. Since 2007, more than 100 common risk variants have been successfully identified in GWAS, which have helped to elucidate the etiology of CRC (13–18).

Lynch syndrome (LS) is an autosomal dominant hereditary cancer syndrome that accounts for approximately 3% of all colorectal cancer cases (19). From a clinical point of view, 10–82% (20) of LS cases are associated with a lifetime risk of developing CRC, unless the risk is significantly lower in other types of cancer (21,22). LS is caused by pathogenic germline mutations in a class of genes called DNA mismatch repair (MMR) genes, mainly MLH1, located in 3p22.2 chromosome, and MSH2, located in 2p21 chromosome (23), which represent 70–85% of cases of LS (24). Mutations found in MSH6 (2p16.3), PMS2 (7p22.1) (25) and MLH3 genes have lower incidence (26). Molecular investigations have also shown that MSH3 (27) and germline 3′deletions of the EPCAM gene, which lead to epigenetic silencing of MSH2 (28), are also implicated in the pathogenesis of LS. As a consequence of MMR pathway inactivation and loss of expression of MMR proteins, DNA replication errors accumulate typically resulting in microsatellite instability (MSI), which is generally detected in LS patients' tumor tissues (29). The diagnosis of LS involves three main steps, identification of patients and their familial history that meet the Amsterdam or Bethesda guidelines, presence of MSI in tumors and immunohistochemical analysis (IHC) of MMR protein expression. A definitive diagnosis of LS must be confirmed by detecting the germline mutations in MMR genes (30).

Non-invasive prenatal testing (NIPT) based on low-coverage massively parallel whole-genome sequencing of plasma DNA from pregnant women generates a large amount of data that provides the resources to investigate human genetic variations in the population. In our previous studies, we described the re-use of the data from NIPT for genome-scale population specific frequency determination of small DNA variants (31) and CNVs (32). Since pregnant women represent a relatively standard sample of the local female population, we assumed this NIPT data could also be used in the population study of CRC, the most common cancer in Slovakia. Some research concerning on genomic analysis of plasma from NIPT has also demonstrated NIPT data's efficiency and utility for viral genetic studies (33), genetic profiling of Vietnamese population (34) or detection of CNV aberrations (32,35).

The main aim of our study was a detailed analysis of common variants (MAF >0,05) that showed evidence of association with CRC in GWAS datasets and characterization of population variability from data generated by NIPT. We assumed that the genetic factors, mainly the increased specific population frequency of CRC and LS variants could be responsible for the high incidence of CRC in Slovakia. To test this hypothesis, allele frequencies of risk CRC variants identified in the Slovak population were compared with allele frequencies of risk CRC variants in 6 worldwide populations. As LS is among the most common hereditary CRC syndromes, the aim of our study was also to analyze population allele frequencies and describe clinical impacts of relevant variants located in known LS predisposing genes. To our knowledge, this was the first population study of CRC using NIPT data conducted exclusively in the Slovak population.

 Materials and methods <sec> <title>Data source

The laboratory procedure used, to generate the NIPT data, were as follows: DNA from plasma of peripheral maternal blood was isolated for NIPT analysis from 1,501 pregnant women after obtaining a written informed consent consistent with the Helsinki declaration from the subjects. The population cohort consisted from women in reproductive age between 17–48 years with a median of 35 years. Genomic information from a sample consisted of maternal and fetal DNA fragments. Each included individual agreed to use their genomic data in an anonymized form for general biomedical research. The NIPT study (study ID 35900_2015) was approved by the Ethical Committee of the Bratislava Self-Governing Region (Sabinovska ul.16, 820 05 Bratislava) on 30th April of 2015 under the decision ID 03899_2015. Blood samples were collected to EDTA tubes and plasma was separated in dual centrifugation procedure. DNA was isolated from 700 µl of plasma using DNA Blood Mini kit (Qiagen) according to standard protocol. Sequencing libraries were prepared from each sample using TruSeq Nano kit HT (Illumina) following standard protocol with omission of DNA fragmentation step. Each sample was normalized to 4 nm library and the final concentration of libraries was 2,8 pM. Individual barcode labelled libraries were pooled and sequenced using low-coverage whole-genome sequencing on an Illumina NextSeq500 platform (Illumina) by performing paired end sequencing of 2×35 bases (36).

 Data analysis

The datasets generated and/or analyzed during the current study are available in the DSpace repository, https://dspace.uniba.sk/xmlui/handle/123456789/27 (31).

 Analyses of common variants previously reported to be risk variants for CRC

We combined genotype data from all previously reported GWAS studies available online (https://www.gwascentral.org/) for the years 2007–2020, specifically 66 GWAS studies of CRC risk variants that included individuals with European, Asian and African American ancestry. Using data from these GWAS datasets, we identified 116 risk variants associated with CRC, which were then merged with our data of identified variants from NIPT. Risk variants that were not found in NIPT data were excluded from the analysis. All identified variants in the Slovak population used for further analyses were common (MAFs >0.05). Subsequently, allele frequencies of CRC risk variants for each population (East Asian, South Asian, African, American, Finnish European and non-Finnish European) were extracted from the gnomAD database available online (v3.0, downloaded from http://gnomad.broadinstitute.org/downloads) and compared with our frequencies determined for the Slovak population from NIPT data. Allele frequency in each population and allele frequency differences were plotted using boxplots. Outliers of boxplots that represent variants with highly different frequencies between Slovak and non-Finnish populations were annotated via published literature and studies [in dbSNP (https://www.ncbi.nlm.nih.gov/snp/) and GWAS (https://www.gwascentral.org/)]. To assess the relations between allele frequency of CRC risk variants in each population, we also used Principal Component Analysis (PCA) using matplotlib.pyplot library, which reduces the dimension of the data to a graphically interpretable 2D or 3D dimension. Consequently, we obtained information on which populations have similar or different allele frequencies of the identified CRC risk variants.

 Analyses of variants located in genes associated with LS

After analyzing variants associated with CRC, we focused on the study of variants associated with LS. First, we filtered out a group of variants located in 7 genes known to be associated with LS (MLH1, PMS2, MSH6, MLH3, MSH2, TGFBR2, EPCAM). The genomic locations of genes were determined by the GeneCards database (https://www.genecards.org/). From the dataset of identified variants in LS associated genes, we excluded variants in low complexity genomic regions (soft masked in the reference FASTA file), eliminating the variants that could represent sequencing artifacts or repetitive regions. All variants that were used for further analysis were annotated using Ensembl Variant Effect Predictor (VEP, version 101_GRCh38). In our dataset, based on ClinVar database annotation of the most common types of pathogenic and likely pathogenic variants associated with LS (https://www.ncbi.nlm.nih.gov/clinvar), we selected variants including frameshift, missense, nonsense, splice site, non-coding and UTR variants. After this filtering, allele frequencies for both groups of variants (all variants identified in LS genes and selected types of variants) for each population (East Asian, South Asian, African, American, Finnish European and non-Finnish European) were extracted from the gnomAD database (v3.0, downloaded from http://gnomad.broadinstitute.org/downloads) and compared with our frequencies determined for the Slovak population from NIPT data. Allele frequency in each population and allele frequency differences were plotted using boxplots and PCA analysis using matplotlib.pyplot library. Outliers of boxplots representing variants with allele frequency differences more than 10% were annotated via published literature and studies [in dbSNP (https://www.ncbi.nlm.nih.gov/snp/) and GWAS (https://www.gwascentral.org/].

 Results <sec> <title>Analyses of common variants previously reported to be risk variants for CRC

In the analysis of the 66 GWAS studies that included all identified risk variants associated with colorectal carcinogenesis from 2007–2020, we investigated 116 variants. There were 25 independent CRC risk variants in Asian population, 62 risk variants found in European population, 27 risk variants in both European and Asian population and 2 risk variants located in African American population that were previously reported in GWAS (Table I).

After merging all identified variants from GWAS (116 risk variants) with our NIPT data, we identified 106 common risk CRC variants (Table SI), while 10 risk variants that were not called in the Slovak population were excluded from further analysis. The allele frequencies of 106 variants identified in our population sample (Slovak population) and the allele frequencies of variants for 6 world populations (East Asian, South Asian, African, American, Finnish European and non-Finnish European) obtained by gnomAD database (Table SII) are shown in graphical comparison by Boxplots (Fig. 1) and principal component analysis (PCA) (Fig. 2). As shown in Fig. 1, the MAF ranged from 0.0–0.963109 in 6 world populations that is comparable with the Slovak population (0.0521–0.931). The median allele frequency for the Slovak population reached the value of 0.4072, which is closest to the value of the median of the American population (MED=0.3991) and Finnish population (MED=0.4166). PCA placed our sample set most closely to the two gnomAD population sample sets, i.e., to the Finnish and non-Finnish European population.

Next, we compared known allele frequencies of 106 CRC risk variants in our sample set from the Slovak population to allele frequencies of CRC variants in six world populations. The final findings of allele frequency differences are shown in Fig. 3. The median allele frequency for comparing the Slovak population and non-Finnish European population reached the value of 0.002285. Together, we identified 14 outliers in Fig. 3 (3 of 14 variants reached a similar value and overlapped, so they are not clearly visible in Fig. 3). Since the same variant rs4246215 was identified in 4 different population comparisons (Slovak-American, Slovak-Finnish European, Slovak-non-Finnish European, and also identified in Slovak-East Asian population comparison) and the rs3131043 variant in 2 population comparisons (Slovak-Finnish European and Slovak-non-Finnish European population comparison), we identified a total of 10 variants, whose difference in population allele frequency was more than 10%. Table II includes annotation information about these variants by dbSNP NCBI, ClinVar database, and population comparison in which they were identified.

 Analyses of variants located in genes associated with LS

In the analysis of LS, we identified 1212 variants in our sample set from NIPT that were located in genes known to be associated with LS, i.e., MLH1, PMS2, MSH6, TGFBR2, MLH3, MSH2 and EPCAM. After excluding variants from low complexity regions, we obtained 648 variants that were finally annotated by VEP and used for further analysis. The allele frequencies of 648 variants identified in our population sample (Slovak population) and the allele frequencies of variants for 6 world populations (East Asian, South Asian, African, American, Finnish European and non-Finnish European) obtained by gnomAD database (Table SIII) are shown in graphical comparison by Boxplots (Fig. 4) and principal component analysis (PCA) (Fig. 5). As shown in Fig. 4, the MAF ranged from 0.0–1.0 in 6 world populations. In the Slovak population, all variants were with MAF>0.05 (0.0502–1.0). The median allele frequency for the Slovak population reached the value of 0.2204, which is closest to the value of the median of the South Asian population (MED=0.221274). PCA placed our sample set most closely to the non-Finnish European population (Fig. 5).

In the next step, to identify variants having significantly different frequencies, we compared known allele frequencies of 648 variants located in genes associated with LS identified in our sample set from the Slovak population to allele frequencies of these variants in six gnomAD world populations. The final findings of allele frequency differences are shown in Fig. 6. The median allele frequency for the comparison of the Slovak population and non-Finnish European population reached the value of −0.01093. By comparing the allele frequency of variants of the Slovak and non-Finnish populations, we identified a total of 64 outliers. Most outliers were found in the MSH2 gene, others in MSH6, TGFBR2, PMS2, MLH1 and EPCAM. We did not identify any outlying variant in the MLH3 gene. The variation type of all outliers was ‘intronic variant’ and the clinical significance of all outliers was not reported in ClinVar. All this annotation information, also including chromosome position, variants ID, reference and alternative allele, genes, is available in Table SIV.

Our analysis also included allele frequency comparison of selected variants from 648 variants identified in our sample set of NIPT data. We focused on frameshift, missense, nonsense, splice site, non-coding and UTR variants, annotated in the ClinVar database as the most common types of pathogenic and likely pathogenic variants associated with LS. However, from these selected types of variants, we found only UTR and non-coding variants in our dataset of 648 variants. Other types of variants (downstream, upstream, and intron) were excluded from further analysis. Finally, we selected 18 variants, 10 UTR and 8 non-coding variants (all selected variants with annotation information by VEP and ClinVar are available in Table III). We compared known allele frequencies of these 18 selected variants identified in our population sample (Slovak population) to the six gnomAD world populations. The final findings of allele frequency differences are shown in Fig. 7. The median allele frequency for the comparison of the Slovak population and non-Finnish European population reached the value of 0.014215, which is closest to the value of the median allele frequency comparison of the South Asian and Slovak population (MED=0.014186). By comparing the allele frequency of variants of the Slovak and six gnomAD world population, we identified a total of 4 outliers-rs10951973, rs10951972 (identified in Slovak-American population comparison), rs6791557 (in Slovak-American and Slovak-non-Finnish European population comparison) and rs9852378 (in Slovak-South Asian population comparison). All outliers were non-coding variants, rs10951973 and rs10951972 located in the PMS2 and rs6791557 located in the TGFBR2 were not reported in ClinVar. The rs9852378 SNP, detected in the MLH1, was reported as benign by ClinVar.

Finally, we analyzed allele frequencies of pathogenic and likely pathogenic variants associated with Lynch syndrome annotated in the ClinVar database. From 229 SNPs with pathogenic and likely pathogenic clinical significance, only 15 have non-zero AF records in the gnomAD database. As shown in Fig. 8, all found AF are significantly below 5% (Table IV and Fig. 8).

 Discussion

Population genetic studies currently have a huge impact on the study of genomics (37). The detection of risk variants in a population and identifying their genetic relationships have advanced our understanding of the human genome's variability and led to the elucidation of many factors that influence cancer risk. In recent years, NGS technologies have played a key role in colorectal cancer research and have become a useful tool for cancer diagnostics and screening (38–41). Due to the high incidence of colorectal cancer in the Slovak population, it is crucial to determine the possible causes of the high incidence of this disease in Slovakia.

Non-invasive prenatal testing of common fetal chromosomal aberrations, using low-coverage massively parallel whole-genome sequencing of maternal plasma cfDNA of pregnant women, has become the fastest low-cost genomic DNA test that is rapidly implemented in clinical practice. Currently, more than 3 million NIPT tests are carried out worldwide each year, and the large amount of data generated during NIPT provides the resources to investigate human genetic variations in the population (31). In our study, we analyzed low-coverage massively parallel whole-genome sequencing data of total plasma DNA from pregnant women generated for NIPT screening to characterize the variants in genes associated with CRC and LS in the Slovak population. To our knowledge, the present study is the first population analysis of CRC and LS variants worldwide and also in the Slovak population using NIPT data. We illustrate the utility of these genomic data for clinical genetics and population studies.

Over the past two decades, GWAS offer the opportunity to uncover genetic susceptibility factors for CRC and provide insights into the biological basis of CRC etiology. These studies have demonstrated that only a fraction of CRC heritability is explained by known risk-conferring genetic variation, whereas the remaining genetic risk of CRC may be accounted for by a combination of high-prevalence and low-penetrance of common genetic variants. To date, a large number of common genetic variants have been identified by the GWAS approach, which has intimately connected to the onset of CRC (13,18,42–46).

By pooling GWAS data of risk variants associated with colorectal carcinogenesis from 2007–2020 and data variants in our population sample from NIPT, we have identified 106 common risk CRC variants. When we compared allele frequencies of these variants to allele frequencies in six gnomAD world population, finally 13 common risk variants were found that showed statistically significant differences in population allele frequencies-rs5934683, rs7252505, rs4779584, rs1535, rs174550, rs4246215, rs11196172, rs10904849, rs6928864, rs3131043, rs1476570, rs12659017, rs397775554.

The SNP rs5934683 is located on chromosome Xp22.2 between two genes, GPR143 (G protein-coupled receptor 143), which is expressed by melanocytes and retinal pigment epithelium and SHROOM2 (shroom family member 2), a human homolog of the Xenopus laevis APX gene that has important functions in cell morphogenesis including endothelial and epithelial tissue development (44). Missense mutations in this gene have been detected in large-scale screens for recurring mutations in cancer cell lines. Both GPR143 and SHROOM2 play a role in melanosome biogenesis and retinal pigmentation. It is known that abnormal retinal pigmentation, similar to the congenital hypertrophy of retinal pigment epithelium lesions, are typical of the familial adenomatous polyposis syndrome (FAP), one of the inherited syndromes of CRC (47). The relationship between Xp22.2 and CRC risk represents the first evidence for the role of X-chromosome variation in predisposition to non-sex-specific cancer (48).

The SNP rs7252505, located in the 19q13 risk locus, is in an intron of the gene GPATCH1 (G-patch domain containing 1). Although GPATCH1 is expressed in the colon, little is known about its function other than the fact that it contains a G-patch domain, a domain typically associated with RNA processing. One study found that rs7252505 was associated with CRC in African Americans (46,49).

Intergenic variant rs4779584 in chromosomal region 15q13.3 lies between SCG5 and GREM1, and the association between this SNP to CRC has been identified in several GWAS studies (13,50).

The rs4246215 polymorphism is located in the FEN1 in the long arm of chromosome 11 (11q12.2). The association between this SNP and the potential risk of different types of cancers, including esophageal, lung, gastrointestinal, gallbladder, breast cancer in Chinese and Iran populations, glioma and childhood leukemia, has been previously studied. The rs4246215 variant was also associated with colorectal cancer in East Asians and the Chinese population (51,52).

To identify variants that may predispose to LS and may cause the high incidence of CRC in Slovakia, we used NIPT data, including variants with at least 5% AF and coverage at least 100 reads per variant. To verify the reliability of the found variants using NIPT, we selected 15 variants with AF below 5% and validated them using Sanger sequencing. For this reason, it is not possible to find rare variants with AF under 5%. Initially, we selected gene variants known to be associated with LS and we focused on their population AF in gnomAD database and as well as on pathogenicity as reported in public database ClinVar. No publications are available for all variants showing statistically significant differences in population allele frequencies and selected 18 variants. The rs9852378 SNP was reported as benign by ClinVar, and other variants were not reported in ClinVar.

Our study has several key shortcomings. None of the variants identified in this study are pathogenic or likely pathogenic due to their extremely low frequency in the general population (Fig. 8). From the total number of pathogenic or likely pathogenic variants annotated in the ClinVar database, we could determine the population frequency of only 15 variants even when using the gnomAD database (Table IV). Second, the sample size was relatively small and it is strongly biased towards females. We assume that even larger sample sets will also not offer opportunities to detect such low frequencies of LS variants in the population using NIPT data. Third, a substantial portion of identified variants was removed from analyses due to technical limitations, mainly because of their location in low complexity regions. Although these could be technical artifacts (53), they could also be real variants having biological effects that are yet generally hardly determinable, but likely existing (54). Moreover, colorectal cancer is a disease caused by a combination of multiple genes and environmental factors. To assess the relationship between the variants identified in population and CRC development, it is very important in future research to study the interaction between genes and also the environment on the colorectal cancer risk. Although suitable for the determination of general population frequencies of independent variants, NIPT data are unsuitable for calculations (such as polygenic risk score determinations) based on exact combinations of these variants in individuals, which may de facto determine the risk of individuals to develop certain diseases.

The underlying mechanism for a high incidence of CRC in the Slovak population is still unclear at the moment; however, it is possible that genetic factors, like the most common inherited syndrome-LS, play a crucial role in colorectal etiology. We have performed a literature search in PubMed focused on population studies of CRC and LS in Slovakia from 2010–2020 using next-generation sequencing. In the Slovak population, only a few population studies of risk variants have been conducted to elucidate the etiology of CRC (55–57). In general, little is known about risk variants associated with CRC or LS in the Slovak population.

Identifying mutations associated with CRC in populations with high mortality rate, such as the Slovak population, is important to reduce the incidence of this multifactor disorder. The findings from these studies suggest a lack of understanding of the mechanism of many risk variants of CRC. Due to study limitations, we could not identify any pathogenic variants associated with LS in the Slovak population using NIPT data. On the other hand, NIPT data is not a major obstacle to better results, as pathogenic variants have extremely low frequencies in the general population. Even in most cases, the frequencies are not known. However, we identified several promising common risk variants associated with CRC previously reported in GWAS studies that represent variants with highly different frequencies between Slovak and non-Finnish populations in boxplots. Since NIPT expands rapidly to millions of individuals each year, the reuse of these data reduces the cost of large-scale population studies and likely provides an acceptable background for information about genomic variation. Finally, future population studies on larger sample sets with various types of mutations are needed to reveal new mechanisms of pathogenicity and links to new biological pathways, which may be useful in designing preventive strategies and treatment of CRC.

 Supplementary Material Supporting Data Acknowledgements

Not applicable.

 Funding

The present study was supported by the PANGAIA project H2020-MSCA-RISE-2019 (grant no. 872539) funded under H2020-EU.1.3.3. Programme, the OP Integrated Infrastructure for the project ‘Long term strategic research and development focused on the occurrence of Lynch syndrome in the Slovak population and possibilities of prevention of tumors associated with this syndrome’ (grant no. 313011V578) co-financed by the European Regional Development Fund (ERDF), the Operational Program Integrated Infrastructure (grant no. 313011F988) co-financed by the ERDF, and the Scientific Grant Agency of the Ministry of Education, Science, Research and Sport of the Slovak Republic and the Slovak Academy of Sciences (grant no. 1/0305/19).

 Availability of data and materials

The datasets generated and/or analyzed during the current study are available in the DSpace repository, https://dspace.uniba.sk/xmlui/handle/123456789/27.

 Authors' contributions

NF and JG performed data analysis. NF was responsible for the literature search and manuscript writing. JG, JB and JR were responsible for designing the study and supervising the work. TS conceived the idea of the project and TS, JB and JR performed proofreading of the manuscript. JG and JB confirm the authenticity of all the raw data. All authors have read and approved the final manuscript.

 Ethics approval and consent to participate

The non-invasive prenatal testing study (study ID. 35900_2015) was approved by the Ethical Committee of the Bratislava Self-Governing Region (Sabinovska ul.16, 820 05 Bratislava) on 30th April 2015 (approval no. 03899_2015).

 Patient consent for publication

Not applicable.

 Competing interests

The authors declare that they have no competing interests.

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Boxplots show allele frequency of 106 risk colorectal cancer variants identified from genome-wide association studies for the Slovak and other six world populations. AFR, African population; AMR, American population; EAS, East Asian population; FIN, European (Finnish) population; NFE, European (non-Finnish) population; SAS, South Asian population; SVK, Slovak population.

Principal Component Analysis plot illustrates the allele frequency of 106 risk colorectal cancer variants identified from genome-wide association studies for the Slovak and other six world populations. PC1, Principal Component 1; PC2, Principal Component 2; AFR, African population; AMR, American population; EAS, East Asian population; FIN, European (Finnish) population; NFE, European (non-Finnish) population; SAS, South Asian population; SVK, Slovak population.

Boxplots show allele frequency differences of Slovak and other six world populations for 106 risk colorectal cancer variants identified from genome-wide association studies. AFR, African population; AMR, American population; EAS, East Asian population; FIN, European (Finnish) population; NFE, European (non-Finnish) population; SAS, South Asian population.

Boxplots show allele frequency of 648 variants located in seven Lynch syndrome genes for the Slovak and other six world populations. AFR, African population; AMR, American population; EAS, East Asian population; FIN, European (Finnish) population; NFE, European (non-Finnish) population; SAS, South Asian population; SVK, Slovak population.

Principal Component Analysis plot illustrates the allele frequency of 648 variants located in genes associated with Lynch syndrome for the Slovak and other six world populations. PC1, Principal Component 1; PC2, Principal Component 2; AFR, African population; AMR, American population; EAS, East Asian population; FIN, European (Finnish) population; NFE, European (non-Finnish) population; SAS, South Asian population; SVK, Slovak population.

Boxplots show differences of Slovak and the other six world populations in allele frequency for 648 variants located in genes associated with Lynch syndrome. AFR, African population; AMR, American population; EAS, East Asian population; FIN, European (Finnish) population; NFE, European (non-Finnish) population; SAS, South Asian population.

Boxplots show differences of Slovak and the other six gnomAD world populations in allele frequency for 18 selected variants (UTR and non-coding variants) from 648 identified variants located in Lynch syndrome risk genes. AFR, African population; AMR, American population; EAS, East Asian population; FIN, European (Finnish) population; NFE, European (non-Finnish) population; SAS, South Asian population.

Boxplots show allele frequency of 15 single nucleotide polymorphisms of Lynch syndrome genes with pathogenic and likely pathogenic clinical significance for six world populations. It was found that allele frequency was <5%. AFR, African population; AMR, American population; EAS, East Asian population; FIN, European (Finnish) population; NFE, European (non-Finnish) population; SAS, South Asian population.

Identification of 116 risk variants associated with colorectal cancer from 66 genome-wide association studies between 2007 and 2020.

First author, year rs_ID Chr POS POP Gene PMID (Refs.)
Wang et al, 2017 rs7252505 19q13 33,084,158 AFR A GPATCH1 28295283 (46)
Wang et al, 2017 rs56848936 19q13.3 46,321,507 AFR A SYMPK 28295283 (46)
Lu et al, 2019 rs7542665 1p31.3 62,673,037 ASN L1TD1 30529582 (13)
Law et al, 2019 rs12143541 1p32.3 55,247,852 ASN TTC22   31089142 (43)
Lu et al, 2019 rs201395236 1q44 245,181,421 ASN EFCAB2 30529582 (13)
Lu et al, 2019 rs7606562 2p16.3 48,686,695 ASN PPP1R21 30529582 (13)
Lu et al, 2019 rs113569514 3q22.2 133,748,789 ASN SLCO2A1 30529582 (13)
Lu et al, 2019 rs12659017 5q23.2 125,988,175 ASN ALDH7A1, PHAX 30529582 (13)
Law et al, 2019 rs639933 5q31.1 134,467,751 ASN C5orf66, LOC105379188 31089142 (43)
Jia et al, 2013 rs647161 5q31.1 134,499,092 ASN PITX1 23263487 (58)
Law et al, 2019 rs6933790 6p21.1 41,672,769 ASN TFEB 31089142 (43)
Zeng et al, 2016 rs4711689 6p21.1 41,692,812 ASN TFEB 26965516 (14)
Schmit et al, 2018 rs6906359 6p21.31 35,528,378 ASN FKBP5 29917119 (18)
Lu et al, 2019 rs3830041 6p21.32 32,191,339 ASN NOTCH4 30529582 (13)
Lu et al, 2019 rs6584283 10q24.2 101,290,301 ASN NKX2-3 30529582 (13)
Lu et al, 2019 rs77969132 12p11.21 31,594,813 ASN DENND5B 30529582 (13)
Zeng et al, 2016 rs11064437 12p13.31 6,982,162 ASN SPSB2 26965516 (14)
Lu et al, 2019 rs2730985 12q12 43,130,624 ASN PRICKLE1 30529582 (13)
Lu et al, 2019 rs1886450 13q22.1 73,986,628 ASN KLF5, KLF12 30529582 (13)
Lu et al, 2019 rs4341754 16q23.2 80,039,621 ASN WWOX, MAF 30529582 (13)
Lu et al, 2019 rs1078643 17p12 10,707,241 ASN PIRT 30529582 (13)
Law et al, 2019 rs73975588 17p13.3 816,741 ASN NXN 31089142 (43)
Law et al, 2019 rs9797885 19q13.2 41,873,001 ASN TMEM91 31089142 (43)
Law et al, 2019 rs6055286 20p12.3 7,718,045 ASN None 31089142 (43)
Jia et al, 2008 rs2423279 20p12.3 7,812,350 ASN HAO1 23263487 (58)
Law et al, 2019 rs2179593 20q13.12 42,660,286 ASN TOX2 31089142 (43)
Lu et al, 2019 rs13831 20q13.32 57,475,191 ASN GNAS 30529582 (13)
Law et al, 2019 rs61776719 1p34.3 38,461,319 EUR None 31089142 (43)
Peters et al, 2013 rs10911251 1q25.3 183,112,059 EUR LAMC1 23266556 (59)
Whiffin et al, 2014 rs10911251 1q25.3 183,112,059 EUR LAMC1 24737748 (60)
Houlston et al, 2010 rs6687758 1q41 222,164,948 EUR None 20972440 (61)
Houlston et al, 2010 rs6691170 1q41 222,045,446 EUR DUSP10 20972440 (61)
Law et al, 2019 rs11692435 2q11.2 98,275,354 EUR ACTR1B 31089142 (43)
Law et al, 2019 rs11893063 2q33.1 199,601,925 EUR LOC105373831 31089142 (43)
Law et al, 2019 rs7593422 2q33.1 200,131,695 EUR None 31089142 (43)
Orlando et al, 2016 rs992157 2q35 219,154,781 EUR TMBIM1 27005424 (62)
Law et al, 2019 rs9831861 3p21.1 53,088,285 EUR None 31089142 (43)
Law et al, 2019 rs12635946 3q13.2 112,916,918 EUR None 31089142 (43)
Houlston et al, 2010 rs10936599 3q26.2 169,774,313 EUR MYNN 20972440 (61)
Schmit et al, 2018 rs1370821 4q22.2 94,943,383 EUR None 29917119 (18)
Law et al, 2019 rs17035289 4q24 106,048,291 EUR None 31089142 (43)
Law et al, 2019 rs75686861 4q31.21 145,621,328 EUR HHIP 31089142 (43)
Schmit et al, 2014 rs35509282 4q32.2 163,333,405 EUR FSTL5 25023989 (63)
Schmit et al, 2018 rs58791712 5p13.1 40,281,797 EUR PTGER4 29917119 (18)
Schmit et al, 2018 rs2735940 5p15.33 1,296,486 EUR TERT 29917119 (18)
Peters et al, 2012 rs2853668 5p15.33 1299910 EUR TERT 21761138 (64)
Schmit et al, 2018 rs62404968 6p12.1 55,714,314 EUR BMP5 29917119 (18)
Dunlop et al, 2012 rs1321311 6p21.2 36,622,900 EUR CDKN1A 22634755 (48)
Law et al, 2019 rs9271770 6p21.32 32,594,248 EUR LOC107987449 31089142 (43)
Law et al, 2019 rs3131043 6p21.33 30,758,466 EUR HCG20 31089142 (43)
Law et al, 2019 rs2070699 6p24.1 12,292,772 EUR EDN1 31089142 (43)
Law et al, 2019 rs6928864 6q21 105,966,894 EUR None 31089142 (43)
Law et al, 2019 rs10951878 7p12.3 46,926,695 EUR None 31089142 (43)
Law et al, 2019 rs3801081 7p12.3 47,511,161 EUR TNS3 31089142 (43)
Tomlinson et al, 2008 rs16892766 8q23.3 117,630,683 EUR EIF3H 18372905 (64)
Law et al, 2019 rs1412834 9p21.3 22,110,131 EUR CDKN2B-AS1 31089142 (43)
Schmit et al, 2018 rs10994860 10q11.23 52,645,424 EUR A1CF 29917119 (18)
Al Tassan et al, 2015 rs10904849 10p13 16,955,267 EUR CUBN 25990418 (15)
Law et al, 2019 rs4450168 11p15.4 10,286,755 EUR SBF2 31089142 (43)
Dunlop et al, 2012 rs3824999 11q13.4 74,345,550 EUR POLD3 22634755 (48)
Tenesa et al, 2008 rs3802842 11q23.1 111,171,709 EUR COLCA2 18372901 (66)
Houlston et al, 2010 rs7136702 12q13.12 50,880,216 EUR LARP4 20972440 (61)
Law et al, 2019 rs7398375 12q13.3 57,540,848 EUR LRP1   31089142 (43)
Schmit et al, 2018 rs72013726 12q24.21 115,890,835 EUR MED13L 29917119 (18)
Schmit et al, 2018 rs10161980 13q13.2 34,093,518 EUR STARD13 29917119 (18)
Law et al, 2019 rs12427600 13q13.3 37,460,648 EUR SMAD9 31089142 (43)
Law et al, 2019 rs45597035 13q22.1 73,649,152 EUR KLF5 31089142 (43)
Law et al, 2019 rs1330889 13q22.3 78,609,615 EUR LINC00446 31089142 (43)
Law et al, 2019 rs7993934 13q34 111,074,915 EUR COL4A2 31089142 (43)
Tomlinson et al, 2011 rs1957636 14q22.2 54,560,018 EUR BMP4 21655089 (67)
Houlston et al, 2008 rs4444235 14q22.2 54,410,919 EUR BMP4 19011631 (68)
Tomlinson et al, 2011 rs4444235 14q22.2 54,410,919 EUR BMP4 21655089 (67)
Tomlinson et al, 2008 rs11632715 15q13.3 33,004,247 EUR None 18372905 (65)
Tomlinson et al, 2008 rs16969681 15q13.3 32,993,111 EUR SCG5 18372905 (65)
Tomlinson et al, 2011 rs16969681 15q13.3 32,993,111 EUR SCG5 21655089 (67)
Tomlinson et al, 2008 rs4779584 15q13.3 32,994,756 EUR CRAC1 18372905 (65)
Law et al, 2019 rs4776316 15q22.31 67,007,813 EUR SMAD6 31089142 (43)
Law et al, 2019 rs10152518 15q23 68,177,162 EUR None 31089142 (43)
Law et al, 2019 rs7495132 15q26.1 91,172,901 EUR CRTC3 31089142 (43)
Houlston et al, 2008 rs9929218 16q22.1 68,820,946 EUR CDH1 19011631 (68)
Law et al, 2019 rs61336918 16q23.2 80,007,266 EUR None 31089142 (43)
Schmit et al, 2018 rs2696839 16q24.1 86,340,448 EUR FOXF1 29917119 (18)
Broderick et al, 2007 rs4939827 18q21 46,453,463 EUR SMAD7 17934461 (69)
Tenesa et al, 2008 rs4939827 18q21 46,453,463 EUR SMAD7 18372901 (66)
Law et al, 2019 rs285245 19p13.11 16,420,817 EUR None 31089142 (43)
Houlston et al, 2008 rs10411210 19q13.11 33,532,300 EUR RHPN2 19011631 (68)
Law et al, 2019 rs12979278 19q13.33 49,218,602 EUR MAMSTR 31089142 (43)
Tomlinson et al, 2011 rs4813802 20p12.3 6,699,595 EUR BMP2 21655089 (67)
Peters et al, 2012 rs4813802 20p12.3 6,699,595 EUR BMP2 21761138 (64)
Houlston et al, 2008 rs961253 20p12.3 6,404,281 EUR BMP2 19011631 (68)
Schmit et al, 2018 rs2295444 20q11.22 33,173,883 EUR PIGU 29917119 (18)
Schmit et al, 2018 rs1810502 20q13.13 49,057,488 EUR PTPN1 29917119 (18)
Law et al, 2019 rs3787089 20q13.33 62,316,630 EUR RTEL1 31089142 (42)
Houlston et al, 2010 rs4925386 20q13.33 60,921,044 EUR LAMA5 20972440 (61)
Schumacher et al, 2015 rs8124813 3p14.1 43,476,841 EUR, ASN LRIG1 26151821 (70)
Schumacher et al, 2015 rs35360328 3p22.1 40,924,962 EUR, ASN CTNNB1 26151821 (70)
Lu et al, 2019 rs1476570 6p22.1 29,809,860 EUR, ASN HLA-G 30529582 (13)
Tomlinson et al, 2008 rs2450115 8q23.3 117,624,093 EUR, ASN EIF3H 18372905 (67)
Tomlinson et al, 2008 rs6469656 8q23.3 117,647,788 EUR, ASN EIF3H 18372905 (67)
Haiman et al, 2007 rs6983267 8q24.21 128,413,305 EUR, ASN POU5F1B 17618282 (71)
Tomlinson et al, 2007 rs6983267 8q24.21 128,413,305 EUR, ASN POU5F1B 17618284 (72)
Hutter et al, 2010 rs6983267 8q24.21 128,413,305 EUR, ASN POU5F1B 21129217 (73)
Cui et al, 2011 rs6983267 8q24.21 128,413,305 EUR, ASN POU5F1B 21242260 (4)
Law et al, 2019 rs12255141 10q25.2 114,294,892 EUR, ASN VTI1A   31089142 (43)
Zhang et al, 2014 rs704017 10q22.3 80,819,132 EUR, ASN ZMIZ1 24836286 (16)
Whiffin et al, 2014 rs1035209 10q24.2 101,345,366 EUR, ASN SLC25A28 24737748 (60)
Zeng et al, 2016 rs4919687 10q24.32 104,595,248 EUR, ASN CYP17A1 26965516 (14)
Zhang et al, 2014 rs11196172 10q25.2 114,726,843 EUR, ASN TCF7L2 24836286 (16)
Wang et al, 2014 rs12241008 10q25.2 114,280,702 EUR, ASN VTI1A 25105248 (75)
Zhang et al, 2014 rs1535 11q12.2 61,597,972 EUR, ASN FADS2 24836286 (16)
Zhang et al, 2014 rs174550 11q12.2 61,571,478 EUR, ASN FADS1 24836286 (16)
Zhang et al, 2014 rs4246215 11q12.2 61,564,299 EUR, ASN FEN1 24836286 (16)
Zhang et al, 2014 rs174537 11q12.2 61,552,680 EUR, ASN MYRF 24836286 (16)
Law et al, 2019 rs10849438 12p13.31 6,412,036 EUR, ASN None   31089142 (43)
Zhang et al, 2014 rs10849432 12p13.31 6,385,727 EUR, ASN PLEKHG6 24836286 (16)
Peters et al, 2013 rs3217810 12p13.32 4,388,271 EUR, ASN CCND2 23266556 (59)
Whiffin et al, 2014 rs3217810 12p13.32 4,388,271 EUR, ASN CCND2 24737748 (60)
Jia et al, 2013 rs10774214 12p13.32 4,368,352 EUR, ASN CCND2 23263487 (58)
Zhang et al, 2014 rs12603526 17p13.3 800,593 EUR, ASN NXN 24836286 (16)
Zhang et al, 2014 rs7229639 18q21.1 46,450,976 EUR, ASN SMAD7 24836286 (16)
Zhang et al, 2014 rs1800469 19q13.2 41,860,296 EUR, ASN TMEM91 24836286 (16)
Zhang et al, 2014 rs2241714 19q13.2 41,869,392 EUR, ASN B9D2 24836286 (16)
Schumacher et al, 2015 rs606682520 20q13.13 897,353 EUR, ASN PREX1 26498495 (70)
Schumacher et al, 2015 rs6066825 20q13.13 47,340,117 EUR, ASN PREX1 26151821 (70)
Dunlop et al, 2012 rs5934683 Xp22.2 9,751,474 EUR, ASN SHROOM2 22634755 (48)

Chr, chromosome; POS, position; POP, population; AFR A, African American; ASN, Asian; EUR, European.

Outliers identified in boxplots that show allele frequency differences of Slovak and the other six world populations for 106 risk colorectal cancer variants identified from genome-wide association studies.

rs_ID Population comparison Chr POS Variant type Gene Consequence Clinical significance
rs5934683 Slovak-East Asian chrX 9783434 SNV GPR143 Intron Variant Not Reported in ClinVar
rs7252505 Slovak-African chr19 33084158 SNV GPATCH1 Intron Variant Not Reported in ClinVar
rs4779584 Slovak-East Asian chr15 32702555 SNV None None Not Reported in ClinVar
rs174550 Slovak-American chr11 61804006 SNV FADS1 Intron Variant Not Reported in ClinVar
rs4246215 Slovak-American chr11 61796827 SNV FEN1 3′ UTR Variant Not Reported in ClinVar
Slovak-European (Finnish) chr11 61796827 SNV FEN1 3′ UTR Variant Not Reported in ClinVar
Slovak-European (non-Finnish) chr11 61796827 SNV FEN1 3′ UTR Variant Not Reported in ClinVar
Slovak-East Asian chr11 61796827 SNV FEN1 3′ UTR Variant Not Reported in ClinVar
rs10904849 Slovak-European (non-Finnish) chr10 16955267 SNV CUBN Intron Variant Not Reported in ClinVar
rs6928864 Slovak-African chr6 105519019 SNV None None Not Reported in ClinVar
rs3131043 Slovak-European (non-Finnish) chr6 30790689 SNV HCG20 Intron Variant Not Reported in ClinVar
Slovak-European (Finnish) chr6 30790689 SNV HCG20 Intron Variant Not Reported in ClinVar
rs12659017 Slovak-East Asian chr5 126652483 SNV None None Not Reported in ClinVar
rs397775554 Slovak-European (non-Finnish) chr5 40281696-40281704 Indel None None Not Reported in ClinVar

Chr, chromosome; POS, position; SNV, single nucleotide variant.

Identification of 18 selected variants (UTR and non-coding) from all 648 variants in genes associated with Lynch syndrome from non-invasive prenatal testing data in the Slovak population.

rs_ID Chr POS Variant type Gene Consequence Clinical significance
rs11901645 chr2 47510079 SNV MSH2 3′ UTR variant Not reported in ClinVar
rs11891189 chr2 47510259 SNV MSH2 3′ UTR variant Not reported in ClinVar
rs876936 chr2 47513059 SNV MSH2 3′ UTR variant Not reported in ClinVar
rs72872839 chr2 47565070 SNV MSH2 3′ UTR variant Not reported in ClinVar
rs17036769 chr2 47633503 SNV MSH2 3′ UTR variant Not reported in ClinVar
rs2969774 chr2 47661684 SNV MSH2 3′ UTR variant Not reported in ClinVar
rs2952372 chr2 47661919 SNV MSH2 3′ UTR variant Not reported in ClinVar
rs2969773 chr2 47662141 SNV MSH2 3′ UTR variant Not reported in ClinVar
rs2705765 chr2 47662389 SNV MSH2 3′ UTR variant Not reported in ClinVar
rs12328344 chr2 47662542 SNV MSH2 3′ UTR variant Not reported in ClinVar
rs10427209 chr2 47709297 SNV MSH6 Non-coding transcript exon variant Not reported in ClinVar
rs10427344 chr2 47709476 SNV MSH6 Non-coding transcript exon variant Not reported in ClinVar
rs3136240 chr2 47784947 SNV MSH6 Non-coding transcript exon variant Not reported in ClinVar
rs6791557 chr3 30614676 SNV TGFBR2 Non-coding transcript exon variant Not reported in ClinVar
rs1817338 chr3 30631239 SNV TGFBR2 Non-coding transcript exon variant Not reported in ClinVar
rs9852378 chr3 36997280 SNV MLH1 Non-coding transcript exon variant Benign in ClinVar
rs10951972 chr7 6002187 SNV PMS2 Non-coding transcript exon variant Not reported in ClinVar
rs10951973 chr7 6002205 SNV PMS2 Non-coding transcript exon variant Not reported in ClinVar

Chr, chromosome; POS, position; SNV, single nucleotide variant.

Identification of 15 pathogenic and likely pathogenic variants associated with Lynch syndrome with non-zero allele frequency in gnomAD database.

Allele frequency in population
rs_ID Chr POS REF Allele ALT Allele African American East Asian European (Finnish) European (non-Finnish) South Asian
rs63750615 2 47403333 G T 0 0 0 0 0 3.28×10−5
rs1194793421 2 47414417 AG A 0 0 0 0 2.75×10−5 0
rs63750636 2 47476492 C T 0 0 0 0 1.55×10−5 0
rs63749873 2 47795903 C G 0 0 0 0 3.10×10−5 0
rs587783056 2 47799684 GTT G 4.76×10−5 0 0 0 0 0
rs63751017 2 47800714 C T 0 0 0 0 3.10×10−5 0
rs876660943 2 47806359 G T 0 0 0 0 1.55×10−5 0
rs63751221 3 37001045 C T 2.38×10−5 0 0 0 0 0
rs587779338 7 5977589 G A 4.86×10−5 0 0 0 1.58×10−5 0
rs267608161 7 5982885 C T 4.80×10−5 0 0 9.61×10−5 1.55×10−5 0
rs63751422 7 5986838 G A 2.38×10−5 0 0 0 0 0
rs63750250 7 5986933 A AT 0 0 0 0 9.30×10−5 0
rs200640585 7 5992018 G A 0 0 0 0 1.55×10−5 0
rs267608154 7 5995572 ACTGT A 0 0 0 0 1.55×10−5 0
rs63750871 7 6002590 G A 0 7.33×10−5 0 0 0 0

Chr, chromosome; POS, position; REF, reference; ALT, alternative.