Exploring the distribution of single nucleotide polymorphisms across human exons and introns

Exploring the distribution of single nucleotide polymorphisms across human exons and introns | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Exploring the distribution of single nucleotide polymorphisms across human exons and introns Magdalena Fraszczak, Jakub Liu, Magda Mielczarek, Paula Dobosz, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4356248/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Among all types of mutations, single nucleotide polymorphisms are the most common type of genomic variation. In our study, we explored the counts of single nucleotide polymorphisms in consecutive exons and introns of the human genome based on the data set of 1,222 individuals of Polish origin that comprises 41,836,187 polymorphisms. Chromosomes 1 and 22 were considered to be representatives of two markedly different DNA molecules, since HSA01 represents the longest chromosome and HSA22 is one of the shortest chromosomes. Therefore, the SNP count analysis was based on 1,705,575 variants located within 6,490 genes. The distribution of single nucleotide polymorphisms among introns and exons appeared to be not only highly nonuniform but also exhibited a very consistent pattern. On HSA01, a significant excess of SNPs was observed in the first and last exons, with the first exons always containing fewer SNPs than in the last. The same pattern was also true for HSA22, except for genes represented by 7 and 8 exons. Pairwise comparisons of SNP count between introns also yielded a very consistent pattern. In HSA01 significantly higher numbers of SNPs were harboured by the first intron. On HSA22 the same pattern was observed, although it was less consistent. This observation reflects the distinct functional role of these genomic units. exons humans introns SNP count Figures Figure 1 Figure 2 Figure 3 INTRODUCTION Single nucleotide polymorphisms (SNPs) are the most common type of genomic variation not only in humans but also in several other species. Still, their genomic distribution is not random (Amos 2010 ; Neininger et al. 2019 ). Although they are located in all functional genomic elements (promoters, exons, introns, 5' and 3' UTR, intergenic regions) their density varies between regions, with exons and splice sites (defined by exon-intron boundaries) being the most conservative, that is, SNP-sparse (Deng et al. 2017 ). But even within functional genomic units with a sequential structure, such as introns and exons, the density of SNPs is highly non-uniform (Castle 2011 ), with clusters of adjacent SNPs being an often-observed characteristic of the human genome. In particular, Hodgkinson and Eyre-Walker (Hodgkinson and Eyre-Walker 2010 ) and Prendergast et al. ( 2019 ) estimated an excess of intronic SNPs located in a single-bp proximity to each other Matsushita and Kano-Sueoka (Matsushita and Kano-Sueoka 2023 ) recently reported differential clustering of synonymous and non-synonymous SNPs among consecutive exons of the human HLA-A gene, while in the whole genome scope Back and Walther ( 2021 ) observed in Arabidopsis thaliana a higher density of SNPs located in the first intron than in the next introns. It has been widely agreed in the literature that such non-random distribution of SNPs must have evolutionary impactions and is the result of mutational hotspots, or that SNP clusters arise due to structural properties of DNA that mechanically promote the accumulation of such point mutations. It is important to note that SNP density and SNP count shall be regarded as two non-equivalent measures of SNP genomic distribution. The SNP count expresses a raw number of polymorphisms identified within a functional unit regardless of the unit length and distances between SNPs, while the SNP density is represented by various descriptive statistics (such as the mean or median) of pairwise distances between adjacent SNPs and is only indirectly related to the number of polymorphisms. In our analysis, we explored the number of SNPs in the human genome, focusing on differences in SNP counts among consecutive introns and exons of a given gene. The underlying hypothesis that we aim to verify is that there are differences in the numbers of SNPs among particular exons and introns that may reflect the differential role of particular exons and introns in the formation of the final product of a gene (mRNA). For this purpose, we used a large data set of whole genome sequences of 1,222 persons representing the 1000 Polish Genomes database (Kaja et al. 2022 ). MATERIAL AND METHODS Population studied The cohort analysed consisted of 41,836,187 SNPs identified in genomes of 1,222 individuals of Polish origin, recruited during the “Search for Genomic Markers Predicting the Severity of the Response to COVID-19” project. The sample consisted of 697 men and 525 women of age by sampling between 2 and 99 years old, with a mean age of 45 years. All samples were collected between April 2020 and April 2021. Details on subject ascertainment, whole genome sequencing, and variant calling were described by (Kaja et al. 2022 ). Variant filtering and genomic annotation Filtering of the initial set of 41,836,187 SNPs was performed using Vcftools software (Danecek et al. 2011 ) that was used to compose the subsets of SNPs located in HSA01 and HSA22. These two chromosomes were selected for downstream analysis as representatives of two markedly different DNA molecules in the human, since HSA01 represents the longest and HSA22 is one of the shortest chromosomes. In particular, HSA01 consists of 248.96 Mbp, which represents almost 8.04% of the genome and contains 5,485 genes. In contrast, HSA22 consists of only 50.82 Mbp, representing 1.64% of the genome, and contains 1,258 genes (GRCh38.p14 assembly GCA_000001405.29). Furthermore, variants with a mapping quality score of at least 20 were discarded. The remaining variants were genomically annotated using the Ensembl Variant Effect Predictor tool (McLaren et al. 2016 ). The final selection of SNPs that were subjected to downstream analysis consisted of variants located in introns or exons of canonical transcripts of each gene. Data exploration The statistical analysis pipeline was set up to follow the hypothesis testing scheme of increasing biological specificity, that was applied separately for exons and introns, as well as separately for each group of genes defined by the same numbers of exons/introns. At each testing step, the null-hypothesis was rejected based on the nominal type I error rate ≤ 0.05. The null-hypothesis of the total number of SNPs being equal among genes was tested using the 2 goodness of fit test calculated using the chisq.test function implemented in the R stats library. The null-hypothesis of the number of SNPs being equal in each exon/intron was tested using the Friedman test (Friedman 1937 ) implemented using the PMCMRplus package in R. For the groups of genes with significant differences in SNP numbers tested in step 2), the null-hypothesis of no differences in SNP numbers between each possible pairs of exons/introns was tested using the Conover test (Conover and Iman 1979 ) implemented using the PMCMRplus package in R. RESULTS Genomic distribution of SNPs on HSA01 and HSA22 Among the 41,836,187 SNPs identified in our data set of 1,222 persons, 1,705,575 SNPs were located in genes (i.e. exons or introns) on HSA01 and HSA22, which made up 4.06% of all SNPs. 5,177 of genes on HSA01 and 1,313 of genes on HSA22 contained at least one SNP, what resulted in the number of SNPs per gene ranging from 1 to 13,124 (HSA01) and from 1 to 8,083 (HSA22). The average number of SNPs per gene amounted to 303 ± 779 on HSA01 and 225 ± 571 on HSA22, with AGBL4 (ENSG00000186094) being the most SNP-rich gene on HSA01 that contained 13,124 SNPs, most of which was located in the third intron. Most of the genes, that is, 1,681 on HSA01 and 461 on HSA22 contained only one exon (Supplementary Fig. 1). In all downstream analyses, to maintain class counts that allow a reasonable estimation of type I and type II errors, genes with 3 to 12 exons were considered for further analysis. As expected biologically, in both considered chromosomes, exons contained fewer SNPs than introns, the average number of SNPs per exon was the highest for genes with a low number of exons, while the average number of SNPs per intron, that varied between 20 and 90 SNPs, did not depend on the number of introns in a gene (Fig. 1 ). 1,074 of those SNPs represented the HIGH impact class as defined by the Sequence Ontology. On HSA01, 507 of them were located in exons and 370 in introns, but a reverse pattern was observed on HSA22 with 97 high-impact SNPs in exons and 104 SNPs in introns. Differences in the number of SNPs located in exons Within all gene groups, defined by their exon counts, the total numbers of SNPs in exons were highly significantly different. Also, the differences in SNP count tested between exons were highly significant for all gene groups and both chromosomes (Supplementary Table 1). Pairwise differences in SNP counts between exons were visualised in Fig. 2 . For HSA01, a very consistent pattern emerged, showing the significant excess of SNPs in the first and the last exon, but the first exons always contained fewer SNPs than in the last. The same pattern was also true for HSA22, except genes represented by 7 and 8 exons. Considering only the subset of HIGH impact SNPs (defined by Sequence Ontology), 29% of them (HSA01) and 35% (HSA22) were assigned to the last exon. Differences in the number of SNPs located in introns Within all gene groups, the total numbers of SNPs in introns were highly significantly different. The differences in SNP count tested between introns were highly significant for all gene groups on HSA01, while on HSA22 there were no significant differences between introns for genes representing groups of 3, 5, and 6 introns (Supplementary Table 1). Pairwise comparisons between introns yielded a very consistent pattern for HSA01, showing significantly higher numbers of SNPs harboured by the first intron. On HSA22 this pattern was also observed, albeit being somewhat less consistent across all possible pairwise comparisons (Fig. 3 ). 26% and 27%of HIGH impact SNPs on HSA01 and HSA22 respectively were assigned to the 1st intron, while 27% (HSA01) and 31% (HSA22) were located in the last intron. DISCUSSION Today, very large data sets of SNPs identified from whole genome sequencing are available, such as e.g. the huge resource provided by the UK Biobank (ukbiobank.ac.uk). Still, the data set analysed in our study possesses characteristics that make it advantageous for the SNP count analysis. In particular, all 1,222 individuals were ascertained and processed as a single cohort and therefore underwent identical methodology of variant calling, including the genotyping platform and sequence pre-processing, which allowed for minimising the technical bias of SNP calling. Moreover, this data set represents a timely and geographically uniform group of individuals of Polish origin (Kaja et al. 2022 ) and therefore excludes the ascertainment bias of SNP frequency due to population stratification and selection. SNP distribution In our study, we deliberately focused on SNP count instead of SNP density, even though the length of particular exons and introns varies considerably. However, while some studies have suggested a correlation between gene length or exon/intron count and SNP density (Lopes et al. 2021 ), the relationship is not always straightforward. Gene function, selective pressures, and genomic context can influence SNP counts (Deng et al. 2017 ). Therefore, introns and exons were in our study regarded as functional genomic units, and not as a mechanical sequence of nucleotides. The functional role of the genomic region strongly determines the localization of polymorphisms, since SNPs in exons have a potential impact on gene products that on a further scope may be the cause of a disease or may alter quantitative phenotypes (Nair et al. 2021 ). Since introns exhibit various regulatory roles, the presence of polymorphism in introns may indirectly impact gene products or their expression levels (Mukherjee et al. 2018 ). Still, due to the generally more severe potential consequences of polymorphisms in exons, the expectation is that exons contain fewer SNPs than introns (Frigola et al. 2017 ), which was confirmed by our study. Moreover, genes with a low number of exons had the highest mean number of SNPs per exon, what was also observed in this study. This may be related to the fact that smaller genes are frequently expressed during an individual's lifetime because they are typically involved in functions that require fast responses, such as the immune system. These specific functions contribute to a higher variation which facilitates the response to and interaction with changing environment (Lopes et al. 2021 ). SNP counts in introns The biological role of introns is manyfold. They allow for alternative splicing (Bush et al. 2017 ) but additionally influence the stability of mRNA molecules (Gupta et al. 2013 ) and themselves contain noncoding RNA genes (Chorev and Carmel 2012 ) and regulatory elements, especially enhancers that affect the rate of transcription, known as the phenomenon of Intron Mediated Enhancement (Clancy and Hannah 2002 ; David-Assael et al. 2006 ). In our study, the significant excess of SNPs was observed in first introns. Of all the introns, the first one has been recognized as having special features and functions including, among others, correcting cytoplasmic localization of some mRNAs as well as transcriptional and translational regulation (Jo and Choi 2015 ). The important role of genetic variation in the first introns can also be anticipated by observing a very high number (over 120 since 2001) of publications reporting associations of SNPs located in the first intron with a variety of phenotypes measured in humans, animals, and plants (based on PubMed access on 10.01.2024). These specific roles may explain why the human DNA's first intronic sequence is considered the longest and highly dense of regulatory chromatin marks (Park et al. 2014 ; Jo and Choi 2019 ) Considering the criterion of SNP density, the above-mentioned studies identified the first introns as the most conserved regions, that however, provided a highly non-uniform distribution of SNPs along the intronic sequence (Majewski and Ott 2002 ) does not directly translate to the total low number of SNPs. Interestingly, in Arabidopsis thaliana , Back and Walther ( 2021 ) observed that the first introns harbour more SNPs than the subsequent introns. SNP counts in exons In the aforementioned study of Back and Walther ( 2021 ) that used Arabidopsis thaliana as the model genome, a high positive correlation was estimated between the sequence variation in the first exons and gene expression, which would explain higher genomic variability of the first exon observed in our study. Also, in a slightly different context of across-species comparison based on the reference genome sequence, Castle ( 2011 ) observed higher variability of coding regions in the proximity of the start and stop codons, so the ones typically corresponding to the first and the last exons, that is in line with our observation of SNP excess in the first and the last exons. Moreover, first and last exons include not only the protein-coding sequence but also the 5′- and 3′-untranslated regions (UTRs). The 5′UTR is the RNA sequence immediately upstream of the coding RNA. It is generally not a translated region but, due to the importance of the sequence for RNA transcription, stability, and translation, genetic variants modifying these elements are likely to have a profound effect. Analogously, the 3′UTR is located downstream of the coding sequence, and it is involved in regulatory processes, including RNA stability, mRNA translation, and localization. The 3′ UTR is characterized by binding sites for microRNAs and RNA-binding proteins and thus any variation of this region may lead to a change in gene expression (Steri et al. 2018 ). Although, UTRs are considered to have approximately the same genomic footprint as protein-coding regions still, polymorphisms within coding sequences may directly affect proteins and thus affect their function. This explains that higher SNP number in the first and last exons may be related to the presence of UTRs. Conclusions The distribution of single nucleotide polymorphisms among introns and exons is not only highly non uniform, but also exhibits a very consistent pattern of first introns, first exons and last exons harbouring significantly more polymorphisms. This observation reflects the distinct functional role of those genomic units. Declarations DATA AVAILABILITY Summary statistics of single nucleotide polymorphisms characterized for the whole genomes of the individuals were provided by Kaja et al. (2022) on https://github.com/MNMdiagnostics/NaszeGenomy. AUTHOR CONTRIBUTIONS Magdalena Fraszczak: Formal Analysis, Methodology, Visualization. Jakub Liu: Data curation, Formal Analysis, Software. Magda Mielczarek: Supervision, Writing. Paula Dobosz: Funding acquisition, Resources, Writing. Joanna Szyda: Conceptualization, Methodology, Writing. ACKNOWLEDGEMENTS The computational power was provided by Poznan Supercomputing and Networking Centre. The Authors would like to thank all sample donors that participated in the study, as well as the medical personnel of the Central Clinical Hospital of the Ministry of the Interior and Administration in Warsaw for their active support. The idea for this study was raised in August 2022, bioinformatics analysis performed in autumn 2022 and the manuscript written in 2023. FUNDING The dataset of the repository has been collected during the research partially funded by the Polish National Science Centre grant No. SZPITALE JEDNOIMIENNE/2/2020 and by the Medical Research Agency grant No 2020/ABM /COVID19/0022. CONFLICT OF INTEREST The authors declare no competing interests. References Amos W (2010) Even small SNP clusters are non-randomly distributed: is this evidence of mutational non-independence? Proceedings of the Royal Society B: Biological Sciences 277:1443–1449. https://doi.org/10.1098/rspb.2009.1757 Back G, Walther D (2021) Identification of cis-regulatory motifs in first introns and the prediction of intron-mediated enhancement of gene expression in Arabidopsis thaliana. BMC Genomics 22:390. https://doi.org/10.1186/s12864-021-07711-1 Bush SJ, Chen L, Tovar-Corona JM, Urrutia AO (2017) Alternative splicing and the evolution of phenotypic novelty. Philosophical Trans Royal Soc B: Biol Sci 372:20150474. https://doi.org/10.1098/rstb.2015.0474 Castle JC (2011) SNPs Occur in Regions with Less Genomic Sequence Conservation. PLoS ONE 6:e20660 Chorev M, Carmel L (2012) The function of introns. Front Genet 3:55. https://doi.org/10.3389/fgene.2012.00055 Clancy M, Hannah LC (2002) Splicing of the Maize Sh1 First Intron Is Essential for Enhancement of Gene Expression, and a T-Rich Motif Increases Expression without Affecting Splicing. Plant Physiol 130:918–929. https://doi.org/10.1104/pp.008235 Conover WJ, Iman RL (1979) Multiple-comparisons procedures. Informal report. United States Danecek P, Auton A, Abecasis G et al (2011) The variant call format and VCFtools. Bioinf (Oxford England) 27:2156–2158. https://doi.org/10.1093/bioinformatics/btr330 David-Assael O, Berezin I, Shoshani-Knaani N et al (2006) AtMHX is an auxin and ABA-regulated transporter whose expression pattern suggests a role in metal homeostasis in tissues with photosynthetic potential. Funct Plant Biol 33:661–672 Deng N, Zhou H, Fan H, Yuan Y (2017) Single nucleotide polymorphisms and cancer susceptibility. Oncotarget 8:110635–110649. https://doi.org/10.18632/oncotarget.22372 Friedman M (1937) The Use of Ranks to Avoid the Assumption of Normality Implicit in the Analysis of Variance. J Am Stat Assoc 32:675–701. https://doi.org/10.1080/01621459.1937.10503522 Frigola J, Sabarinathan R, Mularoni L et al (2017) Reduced mutation rate in exons due to differential mismatch repair. Nat Genet 49:1684–1692. https://doi.org/10.1038/ng.3991 Gupta SK, Carmi S, Ben-Asher HW et al (2013) Basal Splicing Factors Regulate the Stability of Mature mRNAs in Trypanosomes *. J Biol Chem 288:4991–5006. https://doi.org/10.1074/jbc.M112.416578 Hodgkinson A, Eyre-Walker A (2010) Human Triallelic Sites: Evidence for a New Mutational. Mechanism? Genet 184:233–241. https://doi.org/10.1534/genetics.109.110510 Jo B-S, Choi SS (2015) Introns: The Functional Benefits of Introns in Genomes. Genomics Inf 13:112–118. https://doi.org/10.5808/GI.2015.13.4.112 Jo S-S, Choi SS (2019) Analysis of the Functional Relevance of Epigenetic Chromatin Marks in the First Intron Associated with Specific Gene Expression Patterns. Genome Biol Evol 11:786–797. https://doi.org/10.1093/gbe/evz033 Kaja E, Lejman A, Sielski D et al (2022) The Thousand Polish Genomes—A Database of Polish Variant Allele Frequencies. Int J Mol Sci 23 Lopes I, Altab G, Raina P, de Magalhães JP (2021) Gene Size Matters: An Analysis of Gene Length in the Human Genome. Front Genet 12:559998. https://doi.org/10.3389/fgene.2021.559998 Majewski J, Ott J (2002) Distribution and characterization of regulatory elements in the human genome. Genome Res 12:1827–1836. https://doi.org/10.1101/gr.606402 Matsushita T, Kano-Sueoka T (2023) Non-random Codon Usage of Synonymous and Non-synonymous Mutations in the Human HLA-A Gene. J Mol Evol 91:169–191. https://doi.org/10.1007/s00239-023-10093-5 McLaren W, Gil L, Hunt SE et al (2016) The Ensembl Variant Effect Predictor. Genome Biol 17:122. https://doi.org/10.1186/s13059-016-0974-4 Mukherjee D, Saha D, Acharya D et al (2018) The role of introns in the conservation of the metabolic genes of Arabidopsis thaliana. Genomics 110:310–317. https://doi.org/10.1016/j.ygeno.2017.12.003 Nair V, Sankaranarayanan R, Vasavada AR (2021) Deciphering the association of intronic single nucleotide polymorphisms of crystallin gene family with congenital cataract. Indian J Ophthalmol 69:2064–2070. https://doi.org/10.4103/ijo.IJO_3062_20 Neininger K, Marschall T, Helms V (2019) SNP and indel frequencies at transcription start sites and at canonical and alternative translation initiation sites in the human genome. PLoS ONE 14:e0214816 Park SG, Hannenhalli S, Choi SS (2014) Conservation in first introns is positively associated with the number of exons within genes and the presence of regulatory epigenetic signals. BMC Genomics 15:526. https://doi.org/10.1186/1471-2164-15-526 Prendergast JGD, Pugh C, Harris SE et al (2019) Linked Mutations at Adjacent Nucleotides Have Shaped Human Population Differentiation and Protein Evolution. Genome Biol Evol 11:759–775. https://doi.org/10.1093/gbe/evz014 Steri M, Idda ML, Whalen MB, Orrù V (2018) Genetic variants in mRNA untranslated regions. Wiley interdisciplinary reviews RNA 9:e1474. https://doi.org/10.1002/wrna.1474 Supplementary Files Supplementary1.pdf Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4356248","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":300745824,"identity":"7addc6df-da15-4f40-94f5-01ef8f1e0024","order_by":0,"name":"Magdalena Fraszczak","email":"","orcid":"","institution":"Wroclaw University of Environmental and Life Sciences: Uniwersytet Przyrodniczy we Wroclawiu","correspondingAuthor":false,"prefix":"","firstName":"Magdalena","middleName":"","lastName":"Fraszczak","suffix":""},{"id":300745825,"identity":"05fdb37c-d167-4c14-89ba-a55367e35d5a","order_by":1,"name":"Jakub Liu","email":"","orcid":"","institution":"Wrocław University of Environmental and Life Sciences: Uniwersytet Przyrodniczy we Wroclawiu","correspondingAuthor":false,"prefix":"","firstName":"Jakub","middleName":"","lastName":"Liu","suffix":""},{"id":300745826,"identity":"45457a05-56e6-40e4-bf56-0d4c9a5c7d2e","order_by":2,"name":"Magda Mielczarek","email":"","orcid":"","institution":"Wroclaw University of Environmental and Life Sciences: Uniwersytet Przyrodniczy we Wroclawiu","correspondingAuthor":false,"prefix":"","firstName":"Magda","middleName":"","lastName":"Mielczarek","suffix":""},{"id":300745827,"identity":"df3f3837-e57e-4a6f-969a-c8fb350564a0","order_by":3,"name":"Paula Dobosz","email":"","orcid":"","institution":"Poznan University of Medical Sciences: Uniwersytet Medyczny imienia Karola Marcinkowskiego w Poznaniu","correspondingAuthor":false,"prefix":"","firstName":"Paula","middleName":"","lastName":"Dobosz","suffix":""},{"id":300745828,"identity":"42ae1cd2-d5f0-4267-8c61-783f6ef206c0","order_by":4,"name":"Joanna Szyda","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABBUlEQVRIiWNgGAWjYBACxgYE24DhA5QlAcQyRGlhnIGkhYcYGw2YYcrwamFub372uKCGwV5+RvLGz7ZtdxK3NzAfvM3DcAenFsaeY+bGM44xJDbOSCuWzm17ljjnAFuyNQ/DM9xaZiSYSfOwMSQwS+QYALUczp3BwAMUYTiMR0v6N2mefwz2bBI5xr8twVr4vxHQkmMmzdsGdKAEkMEIsYUNv5aeM2XSvH0SiTN4npVZ9px7Vj+Dmc3Yco4Bbr8Ytrdvk+b5ZmMv3568+caPsjvGEuzND2+8qbgjh1NLA5gCRoRAAohxABjuINrgAC4dDPJwFv8BqBYIwK1lFIyCUTAKRhwAAKTETxp8XAKHAAAAAElFTkSuQmCC","orcid":"https://orcid.org/0000-0001-9688-0193","institution":"Uniwersytet Przyrodniczy we Wroclawiu","correspondingAuthor":true,"prefix":"","firstName":"Joanna","middleName":"","lastName":"Szyda","suffix":""}],"badges":[],"createdAt":"2024-05-02 00:56:22","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4356248/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4356248/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":56811334,"identity":"455e0ee7-3544-4351-95da-d0e0e2ccd1ed","added_by":"auto","created_at":"2024-05-20 19:02:24","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":81608,"visible":true,"origin":"","legend":"\u003cp\u003eThe average number of SNPs per exon on HSA01 and HSA22 located in exons or introns.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-4356248/v1/18a6385362def9d5809f6382.png"},{"id":56811336,"identity":"e69ea565-03f1-44f0-a049-edef2d6f09a3","added_by":"auto","created_at":"2024-05-20 19:02:24","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":39666,"visible":true,"origin":"","legend":"\u003cp\u003eVisual representation of the significance of pairwise comparisons of SNP counts in particular exons. P values correspond to testing the alternative hypothesis of a lower number of SNPs within the i-th exon represented by the Y-axis (rows) than within the j-th exon represented by the X-axis (columns). Top HSA01, bottom HSA22.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-4356248/v1/16a81b2bab96657035c9561a.png"},{"id":56811335,"identity":"caab7a86-5666-4278-8b00-a9f70dc17296","added_by":"auto","created_at":"2024-05-20 19:02:24","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":40668,"visible":true,"origin":"","legend":"\u003cp\u003eVisual representation of the significance of pairwise comparisons of SNP counts in particular introns. P values correspond to testing the alternative hypothesis of a lower number of SNPs within the i-th exon represented by the Y-axis (rows) than within the j-th exon represented by the X-axis (columns). Top HSA01, bottom HSA22.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-4356248/v1/4c52a4bc9e70e1ec28522947.png"},{"id":58652568,"identity":"690b7264-fd3a-4357-bb90-397baf59e139","added_by":"auto","created_at":"2024-06-19 10:31:27","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":479096,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4356248/v1/3a075266-96f8-4aea-aee6-41288743cb24.pdf"},{"id":56811338,"identity":"f0d80050-5efa-45b5-a345-43d348e6c3f0","added_by":"auto","created_at":"2024-05-20 19:02:24","extension":"pdf","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":334499,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementary1.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4356248/v1/35c02251ed8fb637c92eaeda.pdf"}],"financialInterests":"","formattedTitle":"Exploring the distribution of single nucleotide polymorphisms across human exons and introns","fulltext":[{"header":"INTRODUCTION","content":"\u003cp\u003eSingle nucleotide polymorphisms (SNPs) are the most common type of genomic variation not only in humans but also in several other species. Still, their genomic distribution is not random (Amos \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Neininger et al. \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Although they are located in all functional genomic elements (promoters, exons, introns, 5' and 3' UTR, intergenic regions) their density varies between regions, with exons and splice sites (defined by exon-intron boundaries) being the most conservative, that is, SNP-sparse (Deng et al. \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). But even within functional genomic units with a sequential structure, such as introns and exons, the density of SNPs is highly non-uniform (Castle \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2011\u003c/span\u003e), with clusters of adjacent SNPs being an often-observed characteristic of the human genome. In particular, Hodgkinson and Eyre-Walker (Hodgkinson and Eyre-Walker \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2010\u003c/span\u003e) and Prendergast et al. (\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) estimated an excess of intronic SNPs located in a single-bp proximity to each other Matsushita and Kano-Sueoka (Matsushita and Kano-Sueoka \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) recently reported differential clustering of synonymous and non-synonymous SNPs among consecutive exons of the human \u003cem\u003eHLA-A\u003c/em\u003e gene, while in the whole genome scope Back and Walther (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) observed in \u003cem\u003eArabidopsis thaliana\u003c/em\u003e a higher density of SNPs located in the first intron than in the next introns. It has been widely agreed in the literature that such non-random distribution of SNPs must have evolutionary impactions and is the result of mutational hotspots, or that SNP clusters arise due to structural properties of DNA that mechanically promote the accumulation of such point mutations.\u003c/p\u003e \u003cp\u003eIt is important to note that SNP density and SNP count shall be regarded as two non-equivalent measures of SNP genomic distribution. The SNP count expresses a raw number of polymorphisms identified within a functional unit regardless of the unit length and distances between SNPs, while the SNP density is represented by various descriptive statistics (such as the mean or median) of pairwise distances between adjacent SNPs and is only indirectly related to the number of polymorphisms. In our analysis, we explored the number of SNPs in the human genome, focusing on differences in SNP counts among consecutive introns and exons of a given gene. The underlying hypothesis that we aim to verify is that there are differences in the numbers of SNPs among particular exons and introns that may reflect the differential role of particular exons and introns in the formation of the final product of a gene (mRNA). For this purpose, we used a large data set of whole genome sequences of 1,222 persons representing the 1000 Polish Genomes database (Kaja et al. \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e"},{"header":"MATERIAL AND METHODS","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003ePopulation studied\u003c/h2\u003e \u003cp\u003eThe cohort analysed consisted of 41,836,187 SNPs identified in genomes of 1,222 individuals of Polish origin, recruited during the \u0026ldquo;Search for Genomic Markers Predicting the Severity of the Response to COVID-19\u0026rdquo; project. The sample consisted of 697 men and 525 women of age by sampling between 2 and 99 years old, with a mean age of 45 years. All samples were collected between April 2020 and April 2021. Details on subject ascertainment, whole genome sequencing, and variant calling were described by (Kaja et al. \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eVariant filtering and genomic annotation\u003c/h2\u003e \u003cp\u003eFiltering of the initial set of 41,836,187 SNPs was performed using Vcftools software (Danecek et al. \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2011\u003c/span\u003e) that was used to compose the subsets of SNPs located in HSA01 and HSA22. These two chromosomes were selected for downstream analysis as representatives of two markedly different DNA molecules in the human, since HSA01 represents the longest and HSA22 is one of the shortest chromosomes. In particular, HSA01 consists of 248.96 Mbp, which represents almost 8.04% of the genome and contains 5,485 genes. In contrast, HSA22 consists of only 50.82 Mbp, representing 1.64% of the genome, and contains 1,258 genes (GRCh38.p14 assembly GCA_000001405.29). Furthermore, variants with a mapping quality score of at least 20 were discarded. The remaining variants were genomically annotated using the Ensembl Variant Effect Predictor tool (McLaren et al. \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). The final selection of SNPs that were subjected to downstream analysis consisted of variants located in introns or exons of canonical transcripts of each gene.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eData exploration\u003c/h2\u003e \u003cp\u003eThe statistical analysis pipeline was set up to follow the hypothesis testing scheme of increasing biological specificity, that was applied separately for exons and introns, as well as separately for each group of genes defined by the same numbers of exons/introns. At each testing step, the null-hypothesis was rejected based on the nominal type I error rate\u0026thinsp;\u0026le;\u0026thinsp;0.05.\u003c/p\u003e \u003cp\u003e \u003col\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eThe null-hypothesis of the total number of SNPs being equal among genes was tested using the 2 goodness of fit test calculated using the \u003cem\u003echisq.test\u003c/em\u003e function implemented in the R \u003cem\u003estats\u003c/em\u003e library.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eThe null-hypothesis of the number of SNPs being equal in each exon/intron was tested using the Friedman test (Friedman \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e1937\u003c/span\u003e) implemented using the \u003cem\u003ePMCMRplus\u003c/em\u003e package in R.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eFor the groups of genes with significant differences in SNP numbers tested in step 2), the null-hypothesis of no differences in SNP numbers between each possible pairs of exons/introns was tested using the Conover test (Conover and Iman \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e1979\u003c/span\u003e) implemented using the \u003cem\u003ePMCMRplus\u003c/em\u003e package in R.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003c/ol\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"RESULTS","content":"\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eGenomic distribution of SNPs on HSA01 and HSA22\u003c/h2\u003e \u003cp\u003eAmong the 41,836,187 SNPs identified in our data set of 1,222 persons, 1,705,575 SNPs were located in genes (i.e. exons or introns) on HSA01 and HSA22, which made up 4.06% of all SNPs. 5,177 of genes on HSA01 and 1,313 of genes on HSA22 contained at least one SNP, what resulted in the number of SNPs per gene ranging from 1 to 13,124 (HSA01) and from 1 to 8,083 (HSA22). The average number of SNPs per gene amounted to 303\u0026thinsp;\u0026plusmn;\u0026thinsp;779 on HSA01 and 225\u0026thinsp;\u0026plusmn;\u0026thinsp;571 on HSA22, with AGBL4 (ENSG00000186094) being the most SNP-rich gene on HSA01 that contained 13,124 SNPs, most of which was located in the third intron. Most of the genes, that is, 1,681 on HSA01 and 461 on HSA22 contained only one exon (Supplementary Fig.\u0026nbsp;1). In all downstream analyses, to maintain class counts that allow a reasonable estimation of type I and type II errors, genes with 3 to 12 exons were considered for further analysis. As expected biologically, in both considered chromosomes, exons contained fewer SNPs than introns, the average number of SNPs per exon was the highest for genes with a low number of exons, while the average number of SNPs per intron, that varied between 20 and 90 SNPs, did not depend on the number of introns in a gene (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). 1,074 of those SNPs represented the HIGH impact class as defined by the Sequence Ontology. On HSA01, 507 of them were located in exons and 370 in introns, but a reverse pattern was observed on HSA22 with 97 high-impact SNPs in exons and 104 SNPs in introns.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eDifferences in the number of SNPs located in exons\u003c/h2\u003e \u003cp\u003eWithin all gene groups, defined by their exon counts, the total numbers of SNPs in exons were highly significantly different. Also, the differences in SNP count tested between exons were highly significant for all gene groups and both chromosomes (Supplementary Table\u0026nbsp;1). Pairwise differences in SNP counts between exons were visualised in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. For HSA01, a very consistent pattern emerged, showing the significant excess of SNPs in the first and the last exon, but the first exons always contained fewer SNPs than in the last. The same pattern was also true for HSA22, except genes represented by 7 and 8 exons. Considering only the subset of HIGH impact SNPs (defined by Sequence Ontology), 29% of them (HSA01) and 35% (HSA22) were assigned to the last exon.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eDifferences in the number of SNPs located in introns\u003c/h2\u003e \u003cp\u003eWithin all gene groups, the total numbers of SNPs in introns were highly significantly different. The differences in SNP count tested between introns were highly significant for all gene groups on HSA01, while on HSA22 there were no significant differences between introns for genes representing groups of 3, 5, and 6 introns (Supplementary Table\u0026nbsp;1). Pairwise comparisons between introns yielded a very consistent pattern for HSA01, showing significantly higher numbers of SNPs harboured by the first intron. On HSA22 this pattern was also observed, albeit being somewhat less consistent across all possible pairwise comparisons (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). 26% and 27%of HIGH impact SNPs on HSA01 and HSA22 respectively were assigned to the 1st intron, while 27% (HSA01) and 31% (HSA22) were located in the last intron.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"DISCUSSION","content":"\u003cp\u003eToday, very large data sets of SNPs identified from whole genome sequencing are available, such as e.g. the huge resource provided by the UK Biobank (ukbiobank.ac.uk). Still, the data set analysed in our study possesses characteristics that make it advantageous for the SNP count analysis. In particular, all 1,222 individuals were ascertained and processed as a single cohort and therefore underwent identical methodology of variant calling, including the genotyping platform and sequence pre-processing, which allowed for minimising the technical bias of SNP calling. Moreover, this data set represents a timely and geographically uniform group of individuals of Polish origin (Kaja et al. \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) and therefore excludes the ascertainment bias of SNP frequency due to population stratification and selection.\u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eSNP distribution\u003c/h2\u003e \u003cp\u003eIn our study, we deliberately focused on SNP count instead of SNP density, even though the length of particular exons and introns varies considerably. However, while some studies have suggested a correlation between gene length or exon/intron count and SNP density (Lopes et al. \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), the relationship is not always straightforward. Gene function, selective pressures, and genomic context can influence SNP counts (Deng et al. \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Therefore, introns and exons were in our study regarded as functional genomic units, and not as a mechanical sequence of nucleotides.\u003c/p\u003e \u003cp\u003eThe functional role of the genomic region strongly determines the localization of polymorphisms, since SNPs in exons have a potential impact on gene products that on a further scope may be the cause of a disease or may alter quantitative phenotypes (Nair et al. \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Since introns exhibit various regulatory roles, the presence of polymorphism in introns may indirectly impact gene products or their expression levels (Mukherjee et al. \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Still, due to the generally more severe potential consequences of polymorphisms in exons, the expectation is that exons contain fewer SNPs than introns (Frigola et al. \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2017\u003c/span\u003e), which was confirmed by our study. Moreover, genes with a low number of exons had the highest mean number of SNPs per exon, what was also observed in this study. This may be related to the fact that smaller genes are frequently expressed during an individual's lifetime because they are typically involved in functions that require fast responses, such as the immune system. These specific functions contribute to a higher variation which facilitates the response to and interaction with changing environment (Lopes et al. \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eSNP counts in introns\u003c/h2\u003e \u003cp\u003eThe biological role of introns is manyfold. They allow for alternative splicing (Bush et al. \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) but additionally influence the stability of mRNA molecules (Gupta et al. \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2013\u003c/span\u003e) and themselves contain noncoding RNA genes (Chorev and Carmel \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2012\u003c/span\u003e) and regulatory elements, especially enhancers that affect the rate of transcription, known as the phenomenon of Intron Mediated Enhancement (Clancy and Hannah \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2002\u003c/span\u003e; David-Assael et al. \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2006\u003c/span\u003e). In our study, the significant excess of SNPs was observed in first introns. Of all the introns, the first one has been recognized as having special features and functions including, among others, correcting cytoplasmic localization of some mRNAs as well as transcriptional and translational regulation (Jo and Choi \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). The important role of genetic variation in the first introns can also be anticipated by observing a very high number (over 120 since 2001) of publications reporting associations of SNPs located in the first intron with a variety of phenotypes measured in humans, animals, and plants (based on PubMed access on 10.01.2024). These specific roles may explain why the human DNA's first intronic sequence is considered the longest and highly dense of regulatory chromatin marks (Park et al. \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Jo and Choi \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) Considering the criterion of SNP density, the above-mentioned studies identified the first introns as the most conserved regions, that however, provided a highly non-uniform distribution of SNPs along the intronic sequence (Majewski and Ott \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2002\u003c/span\u003e) does not directly translate to the total low number of SNPs. Interestingly, in \u003cem\u003eArabidopsis thaliana\u003c/em\u003e, Back and Walther (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) observed that the first introns harbour more SNPs than the subsequent introns.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eSNP counts in exons\u003c/h2\u003e \u003cp\u003eIn the aforementioned study of Back and Walther (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) that used \u003cem\u003eArabidopsis thaliana\u003c/em\u003e as the model genome, a high positive correlation was estimated between the sequence variation in the first exons and gene expression, which would explain higher genomic variability of the first exon observed in our study. Also, in a slightly different context of across-species comparison based on the reference genome sequence, Castle (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2011\u003c/span\u003e) observed higher variability of coding regions in the proximity of the start and stop codons, so the ones typically corresponding to the first and the last exons, that is in line with our observation of SNP excess in the first and the last exons. Moreover, first and last exons include not only the protein-coding sequence but also the 5\u0026prime;- and 3\u0026prime;-untranslated regions (UTRs). The 5\u0026prime;UTR is the RNA sequence immediately upstream of the coding RNA. It is generally not a translated region but, due to the importance of the sequence for RNA transcription, stability, and translation, genetic variants modifying these elements are likely to have a profound effect. Analogously, the 3\u0026prime;UTR is located downstream of the coding sequence, and it is involved in regulatory processes, including RNA stability, mRNA translation, and localization. The 3\u0026prime; UTR is characterized by binding sites for microRNAs and RNA-binding proteins and thus any variation of this region may lead to a change in gene expression (Steri et al. \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Although, UTRs are considered to have approximately the same genomic footprint as protein-coding regions still, polymorphisms within coding sequences may directly affect proteins and thus affect their function. This explains that higher SNP number in the first and last exons may be related to the presence of UTRs.\u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusions","content":"\u003cp\u003eThe distribution of single nucleotide polymorphisms among introns and exons is not only highly non uniform, but also exhibits a very consistent pattern of first introns, first exons and last exons harbouring significantly more polymorphisms. This observation reflects the distinct functional role of those genomic units.\u003c/p\u003e "},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eDATA AVAILABILITY\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSummary statistics of single nucleotide polymorphisms characterized for the whole genomes of the individuals were provided by Kaja et al.\u0026nbsp;(2022)\u0026nbsp;on https://github.com/MNMdiagnostics/NaszeGenomy.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAUTHOR CONTRIBUTIONS\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eMagdalena Fraszczak: Formal Analysis, Methodology, Visualization. Jakub Liu: Data curation, Formal Analysis, Software. Magda Mielczarek: Supervision, Writing. Paula Dobosz: Funding acquisition, Resources, Writing. Joanna Szyda: Conceptualization, Methodology, Writing.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eACKNOWLEDGEMENTS\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe computational power was provided by Poznan Supercomputing and Networking Centre. The Authors would like to thank all sample donors that participated in the study, as well as the medical personnel of the Central Clinical Hospital of the Ministry of the Interior and Administration in Warsaw for their active support. The idea for this study was raised in August 2022, bioinformatics analysis performed in autumn 2022 and the manuscript written in 2023.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFUNDING\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe dataset of the repository has been collected during the research partially funded by the Polish National Science Centre grant No. SZPITALE JEDNOIMIENNE/2/2020 and by the Medical Research Agency grant No 2020/ABM /COVID19/0022.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCONFLICT OF INTEREST\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAmos W (2010) Even small SNP clusters are non-randomly distributed: is this evidence of mutational non-independence? Proceedings of the Royal Society B: Biological Sciences 277:1443\u0026ndash;1449. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1098/rspb.2009.1757\u003c/span\u003e\u003cspan address=\"10.1098/rspb.2009.1757\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBack G, Walther D (2021) Identification of cis-regulatory motifs in first introns and the prediction of intron-mediated enhancement of gene expression in Arabidopsis thaliana. BMC Genomics 22:390. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1186/s12864-021-07711-1\u003c/span\u003e\u003cspan address=\"10.1186/s12864-021-07711-1\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBush SJ, Chen L, Tovar-Corona JM, Urrutia AO (2017) Alternative splicing and the evolution of phenotypic novelty. Philosophical Trans Royal Soc B: Biol Sci 372:20150474. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1098/rstb.2015.0474\u003c/span\u003e\u003cspan address=\"10.1098/rstb.2015.0474\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCastle JC (2011) SNPs Occur in Regions with Less Genomic Sequence Conservation. PLoS ONE 6:e20660\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChorev M, Carmel L (2012) The function of introns. Front Genet 3:55. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3389/fgene.2012.00055\u003c/span\u003e\u003cspan address=\"10.3389/fgene.2012.00055\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eClancy M, Hannah LC (2002) Splicing of the Maize Sh1 First Intron Is Essential for Enhancement of Gene Expression, and a T-Rich Motif Increases Expression without Affecting Splicing. Plant Physiol 130:918\u0026ndash;929. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1104/pp.008235\u003c/span\u003e\u003cspan address=\"10.1104/pp.008235\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eConover WJ, Iman RL (1979) Multiple-comparisons procedures. Informal report. United States\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDanecek P, Auton A, Abecasis G et al (2011) The variant call format and VCFtools. Bioinf (Oxford England) 27:2156\u0026ndash;2158. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1093/bioinformatics/btr330\u003c/span\u003e\u003cspan address=\"10.1093/bioinformatics/btr330\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDavid-Assael O, Berezin I, Shoshani-Knaani N et al (2006) AtMHX is an auxin and ABA-regulated transporter whose expression pattern suggests a role in metal homeostasis in tissues with photosynthetic potential. Funct Plant Biol 33:661\u0026ndash;672\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDeng N, Zhou H, Fan H, Yuan Y (2017) Single nucleotide polymorphisms and cancer susceptibility. Oncotarget 8:110635\u0026ndash;110649. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.18632/oncotarget.22372\u003c/span\u003e\u003cspan address=\"10.18632/oncotarget.22372\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFriedman M (1937) The Use of Ranks to Avoid the Assumption of Normality Implicit in the Analysis of Variance. J Am Stat Assoc 32:675\u0026ndash;701. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1080/01621459.1937.10503522\u003c/span\u003e\u003cspan address=\"10.1080/01621459.1937.10503522\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFrigola J, Sabarinathan R, Mularoni L et al (2017) Reduced mutation rate in exons due to differential mismatch repair. Nat Genet 49:1684\u0026ndash;1692. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1038/ng.3991\u003c/span\u003e\u003cspan address=\"10.1038/ng.3991\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGupta SK, Carmi S, Ben-Asher HW et al (2013) Basal Splicing Factors Regulate the Stability of Mature mRNAs in Trypanosomes *. J Biol Chem 288:4991\u0026ndash;5006. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1074/jbc.M112.416578\u003c/span\u003e\u003cspan address=\"10.1074/jbc.M112.416578\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHodgkinson A, Eyre-Walker A (2010) Human Triallelic Sites: Evidence for a New Mutational. Mechanism? Genet 184:233\u0026ndash;241. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1534/genetics.109.110510\u003c/span\u003e\u003cspan address=\"10.1534/genetics.109.110510\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJo B-S, Choi SS (2015) Introns: The Functional Benefits of Introns in Genomes. Genomics Inf 13:112\u0026ndash;118. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.5808/GI.2015.13.4.112\u003c/span\u003e\u003cspan address=\"10.5808/GI.2015.13.4.112\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJo S-S, Choi SS (2019) Analysis of the Functional Relevance of Epigenetic Chromatin Marks in the First Intron Associated with Specific Gene Expression Patterns. Genome Biol Evol 11:786\u0026ndash;797. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1093/gbe/evz033\u003c/span\u003e\u003cspan address=\"10.1093/gbe/evz033\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKaja E, Lejman A, Sielski D et al (2022) The Thousand Polish Genomes\u0026mdash;A Database of Polish Variant Allele Frequencies. Int J Mol Sci 23\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLopes I, Altab G, Raina P, de Magalh\u0026atilde;es JP (2021) Gene Size Matters: An Analysis of Gene Length in the Human Genome. Front Genet 12:559998. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3389/fgene.2021.559998\u003c/span\u003e\u003cspan address=\"10.3389/fgene.2021.559998\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMajewski J, Ott J (2002) Distribution and characterization of regulatory elements in the human genome. Genome Res 12:1827\u0026ndash;1836. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1101/gr.606402\u003c/span\u003e\u003cspan address=\"10.1101/gr.606402\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMatsushita T, Kano-Sueoka T (2023) Non-random Codon Usage of Synonymous and Non-synonymous Mutations in the Human HLA-A Gene. J Mol Evol 91:169\u0026ndash;191. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s00239-023-10093-5\u003c/span\u003e\u003cspan address=\"10.1007/s00239-023-10093-5\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMcLaren W, Gil L, Hunt SE et al (2016) The Ensembl Variant Effect Predictor. Genome Biol 17:122. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1186/s13059-016-0974-4\u003c/span\u003e\u003cspan address=\"10.1186/s13059-016-0974-4\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMukherjee D, Saha D, Acharya D et al (2018) The role of introns in the conservation of the metabolic genes of Arabidopsis thaliana. Genomics 110:310\u0026ndash;317. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.ygeno.2017.12.003\u003c/span\u003e\u003cspan address=\"10.1016/j.ygeno.2017.12.003\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNair V, Sankaranarayanan R, Vasavada AR (2021) Deciphering the association of intronic single nucleotide polymorphisms of crystallin gene family with congenital cataract. Indian J Ophthalmol 69:2064\u0026ndash;2070. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.4103/ijo.IJO_3062_20\u003c/span\u003e\u003cspan address=\"10.4103/ijo.IJO_3062_20\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNeininger K, Marschall T, Helms V (2019) SNP and indel frequencies at transcription start sites and at canonical and alternative translation initiation sites in the human genome. PLoS ONE 14:e0214816\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePark SG, Hannenhalli S, Choi SS (2014) Conservation in first introns is positively associated with the number of exons within genes and the presence of regulatory epigenetic signals. BMC Genomics 15:526. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1186/1471-2164-15-526\u003c/span\u003e\u003cspan address=\"10.1186/1471-2164-15-526\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePrendergast JGD, Pugh C, Harris SE et al (2019) Linked Mutations at Adjacent Nucleotides Have Shaped Human Population Differentiation and Protein Evolution. Genome Biol Evol 11:759\u0026ndash;775. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1093/gbe/evz014\u003c/span\u003e\u003cspan address=\"10.1093/gbe/evz014\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSteri M, Idda ML, Whalen MB, Orr\u0026ugrave; V (2018) Genetic variants in mRNA untranslated regions. Wiley interdisciplinary reviews RNA 9:e1474. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1002/wrna.1474\u003c/span\u003e\u003cspan address=\"10.1002/wrna.1474\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"exons, humans, introns, SNP count","lastPublishedDoi":"10.21203/rs.3.rs-4356248/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4356248/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eAmong all types of mutations, single nucleotide polymorphisms are the most common type of genomic variation. In our study, we explored the counts of single nucleotide polymorphisms in consecutive exons and introns of the human genome based on the data set of 1,222 individuals of Polish origin that comprises 41,836,187 polymorphisms. Chromosomes 1 and 22 were considered to be representatives of two markedly different DNA molecules, since HSA01 represents the longest chromosome and HSA22 is one of the shortest chromosomes. Therefore, the SNP count analysis was based on 1,705,575 variants located within 6,490 genes. The distribution of single nucleotide polymorphisms among introns and exons appeared to be not only highly nonuniform but also exhibited a very consistent pattern. On HSA01, a significant excess of SNPs was observed in the first and last exons, with the first exons always containing fewer SNPs than in the last. The same pattern was also true for HSA22, except for genes represented by 7 and 8 exons. Pairwise comparisons of SNP count between introns also yielded a very consistent pattern. In HSA01 significantly higher numbers of SNPs were harboured by the first intron. On HSA22 the same pattern was observed, although it was less consistent. This observation reflects the distinct functional role of these genomic units.\u003c/p\u003e","manuscriptTitle":"Exploring the distribution of single nucleotide polymorphisms across human exons and introns","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-05-20 19:02:19","doi":"10.21203/rs.3.rs-4356248/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"dda836cb-e446-445e-80ff-9e0ab9961714","owner":[],"postedDate":"May 20th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-06-19T10:23:21+00:00","versionOfRecord":[],"versionCreatedAt":"2024-05-20 19:02:19","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4356248","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4356248","identity":"rs-4356248","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}