Epigenetic Analysis of ST3GAL3 and other Sialic Acid Metabolism Genes in ADHD

Epigenetic Analysis of ST3GAL3 and other Sialic Acid Metabolism Genes in ADHD | 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 Article Epigenetic Analysis of ST3GAL3 and other Sialic Acid Metabolism Genes in ADHD Lillian Dipnall, Mark Ziemann, Peter Fransquet, Jo Wrigglesworth, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4519315/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 Research indicates that the underlying neurobiology of Attention Deficit/Hyperactivity Disorder (ADHD) may stem from a combination of genetic and environmental contributions. Genetic and epigenetic research have highlighted the potential role of the sialtransferase gene ST3GAL3 in this process. Adopting a pathways approach, this study sought to examine the role that ST3GAL3 and other sialic acid metabolism (SAM) genes play in ADHD. Peripheral measures of DNA methylation (Illumina 850k EPIC; saliva samples) and clinical data were collected as part of a community-based pediatric cohort consisting of 90 children with ADHD [ m age = 10.40 (0.49); 66% male] and 50 non-ADHD controls [ m age = 10.40 (0.45); 48% male]. Using Reactome, 33 SAM genes were defined and resulted in a total of 1419 probes which included associated promotor/enhancer regions. Linear regression analysis was undertaken to explore differences in SAM probe DNA methylation between children with and without ADHD. The relationship with ADHD symptom severity was also examined. Analysis found 38 probes in the group-regression, and 64 probes in the symptom severity regression reached significance at an uncorrected level (a = 0.05). No probes survived correction for multiple comparisons. Enrichment analysis revealed an overall pattern of hypermethylation across the SAM pathway for the ADHD group, with 84% of nominally significant probes being annotated to sialyltransferase genes. These results suggest that ST3GAL3 and the broader SAM pathway could contribute to subtly disrupted epigenetic regulation in ADHD. However, extensive longitudinal research, across broad developmental age ranges, is necessary to further explore these findings. Biological sciences/Neuroscience/Epigenetics in the nervous system Health sciences/Pathogenesis Figures Figure 1 Figure 2 Introduction Attention Deficit Hyperactivity Disorder (ADHD) is a neurodevelopmental disorder primarily characterized by difficulties with inattention and/or impulsivity and hyperactivity with neuroimaging research highlighting broad structural and functional brain differences between those with ADHD and neurotypical individuals ( 1 , 2 ). Although genetic studies suggest substantial heritability ( 3 ), understanding of ADHD pathophysiology is still lacking. Genome-wide association studies (GWAS) indicate complex underlying polygenic architecture ( 4 – 6 ) potentially contributing to aberrant brain structure and function in ADHD, while neuroimaging research of brain white matter microstructure have reported differences in ADHD, with suggestion that this could be attributed to differences in myelination ( 1 , 6 – 9 ). In addition to this, broader work suggests potential contribution of environmental factors ( 7 ). Examination of epigenetic modifications to gene expression, such as DNA methylation, could aid in unravelling the complex underpinnings of ADHD as it presents a window through which to examine interactions between gene and environment. This highlights a need for epigenetic exploration of ADHD-implicated genes. Recently, the largest genome-wide association study (GWAS) to date was conducted, with 20,183 diagnosed ADHD cases and 35,191 controls ( 5 ). The highest-ranked of twelve loci associated with ADHD was annotated to ST3GAL3 , whose function is implicated in myelination ( 5 ), a key structural component of brain tissue. Aligning with this gene’s role, neuroimaging research of brain white matter microstructure have reported differences in ADHD, with suggestion that this could be attributed to differences in myelination ( 1 , 8 – 11 ). ST3GAL3 , located on chromosome 1p34.1 encodes β-galactoside-α2,3-sialyltransferase-III (ST3Gal-III), which is actively involved in sialylation, an integral part of the glycocalyx of glycoproteins and glycolipids ( 12 ). With glycolipids forming a fundamental component of myelin, any disruption to intercellular communication through differential glycocalyx formation could ultimately disrupt the formation and function of neural white matter. Human neural sialic acid concentrations appear two to four times higher than other mammals, and in comparison to non-neural cellular membranes, sialic acid is up to twenty times higher in neural cellular membranes ( 13 ). In addition to mediating sialylation, in the mouse brain, St3gal3 has also been shown to produce gangliosides and polysialic acid in neural cells, which play crucial roles in synaptogenesis and subsequently neurotransmission and cognition ( 12 , 14 ). St3gal3 -deficient mice exhibit significant reduction in myelin thickness and major myelin proteins ( 15 ). In humans, ST3GAL3 is therefore believed to play a vital role in brain development. Mutations of the ST3GAL3 gene have been liked with a range of neurological manifestations including intellectual developmental disorder ( 16 ), developmental and epileptic encephalopathy ( 17 ), West syndrome ( 18 ), and autosomal recessive intellectual disability ( 19 ). Recent epigenetic research into ADHD also implicates ST3GAL3 . A methylome-wide analysis on children at birth and at age 7, using cord blood at birth and whole blood at age 7 ( 6 ), found DNA methylation of a probe annotated to ST3GAL3 (Illumina Infinium array probe cg09989037) at birth to be significantly associated with ADHD diagnosis at age 7. This probe was found to be hypomethylated in comparison to neurotypical controls. However, this association was not maintained at age 7. The variability of findings in the literature highlights a need for further examination of ST3GAL3 in paediatric ADHD cohorts. This study therefore aimed to investigate the relationship between DNA methylation of ST3GAL3 and ADHD, both categorically (diagnosis) and dimensionally (symptom severity). In addition to ST3GAL3 , probes annotated to all other genes involved in sialic acid metabolism (SAM) were examined. It was hypothesised that variability in DNA methylation of ST3GAL3 , and broader SAM genes, will be associated with ADHD diagnosis and symptom severity. Materials and methods Participants Data from the Children’s Attention Project (CAP) study ( 20 ) was used in this study. The recruitment protocol and procedures are documented elsewhere ( 21 ). Briefly, children were initially recruited at age 6–8 years across Melbourne, Australia, from 43 socio-economically diverse primary schools. Screening was performed using the Conners 3 ADHD Index ( 22 ) and a parent face-to-face structured diagnostic interview (NIMH Diagnostic Interview Schedule for Children IV – DISC-IV; ( 23 )) was administered for case-confirmation. At a 36-month follow-up (aged 9–11 years), diagnostic status was re-assessed, and a subset of participants were invited to provide a saliva sample. Participants provided 3 ml of saliva by passive drool into a 50 ml centrifuge tube (Eppendorf South Pacific, NSW, Australia), and stored in fridge at -80°C. Clinical, behavioural, and epigenetic assessments were conducted by researchers blinded to the diagnostic status of the participants. This study was approved by The Royal Children’s Hospital Human Research Ethics Committee (HREC #34071), and parents or guardians gave informed consent for participating children. Exclusion criteria included intellectual disability, serious medical conditions, genetic disorders, moderate-severe sensory impairment, neurological problems, and parents with insufficient English to complete interviews/questionnaires. Following quality control, one individual was removed due to poor sample performance, the final sample consisted of 90 children with ADHD [m age = 10.40 (0.49); 66% male] and 50 non-ADHD controls [m age = 10.40 (0.45); 48% male]. Of the ADHD group, 24% of children were on medication at the time of sample collection. See supplementary material for more information on medication status. Measures Demographics and clinical assessment ADHD diagnostic status was ascertained using the Diagnostic Interview Schedule for Children (DISC-IV) ( 23 ). Assessment was conducted upon recruitment (6–8 years) and repeated at the current sampling (9–11 years). Children were categorized in the ADHD group if they had a childhood history of ADHD (meeting criteria at recruitment and/or sampling wave). ADHD symptom severity was assessed using the Conners-3 ADHD Index (parent report) ( 22 ). At recruitment, parents/guardians completed a self-report questionnaire that included retrospective questions relating to fetal gestation period (weeks), birth weight (grams), post-natal intensive care stay (y/n) and trimester 1, 2 & 3 maternal alcohol and smoking consumption. Between group differences in these were assessed using either between-groups t -tests or chi-squared tests. DNA methylation analysis Probe selection Rather than examining a single candidate gene site, this study adopted a pathways approach (Fig. 1 ) whereby the broader biochemical pathway was accounted for during probe selection. In this case, ST3GAL3 is implicated in sialic acid metabolism (SAM). Definition of the SAM pathway can be found on the Reactome website ( https://reactome.org/content/detail/R-HSA-4085001 ) ( 24 ). The following protocol outlines how a set of genomic regions of interest was defined for SAM. This region set of interest included probes annotated to all involved genes, as well as probes within associated promoter and enhancer regions. [INSERT FIGURE 1 HERE] Figure 1 . Steps taken to identify probes within a genomic region of interest. Step 1. Microarray pre-processing and quality control For comprehensive overview of microarray pre-processing and quality control see supplementary material. Briefly, genomic DNA was extracted from saliva samples and pre-processed. Extracted genomic DNA samples were bisulphite treated and hybridised to Infinium MethylationEPIC arrays (EPIC; Illumina, San Diego, CA, USA) at NTX-Dx (Diagenode, Ghent, Belgium). These arrays generate data from over 850,000 CpGs throughout the human genome. Raw intensity data was imported into R (3.6.3, http://cran.r-project.org/ ). Data underwent subset-quantile normalisation (SQN) ( 25 ), followed by data quality assessment using the minfi (v1.34.0) Bioconductor package ( 26 ). EPIC probes ( n = 865,859) were filtered by removing those with a poor signal to noise ratio (mean detection p -value of > 0.01, n = 113,011), containing a single nucleotide polymorphism at the CpG site ( n = 26,455), mapping to sex chromosomes (n = 16,776), or cross-reactivity to multiple genomic locations ( n = 34,471) ( 27 ). DNA methylation levels were reported as beta (β) values (proportion of methylated intensity over total intensity values). Beta values were converted to M -values (log 2 ratio of the methylated intensity divided unmethylated intensity) for statistical regression analyses ( 28 ). Step 2. Define the Reactome pathway of interest The Reactome library links genes to different biochemical pathways throughout the body. Searching of the Reactome library ( https://reactome.org/ ) confirmed ST3GAL3 to play a role in SAM. The Reactome library used in this study was downloaded as a GMT file ( https://reactome.org/download/current/ReactomePathways.gmt.zip ) in Aug 2023 and was stored and accessed locally. Step 3. Define SAM gene set The search resulted in 33 genes associated with the SAM pathway. A summary of these genes can be seen in Table 1 . Table 1 Summary of Sialic Acid Metabolism (SAM) genes and probes Gene Probes Gene Probes Gene Probes CMAS 18 SCL17A5 25 ST6GALNAC2 27 CTSA 30 SLC35A1 24 ST6GALNAC3 68 GLB1 52 ST3GAL1 94 ST6GALNAC4 33 GNE 31 ST3GAL2 60 ST6GALNAC5 46 NANP 23 ST3GAL4 93 ST6GALNAC6 33 NANS 23 ST3GAL5 72 ST8SIA1 39 NEU1 102 ST3GAL5 44 ST8SIA2 41 NEU2 12 ST3GAL6 44 ST8SIA3 18 NEU3 21 ST6GAL1 74 ST8SIA4 47 NEU4 43 ST6GAL2 50 ST8SIA5 28 NPL 35 ST6GALNAC1 21 ST8SIA6 48 Table 1 . Summary of Sialic Acid Metabolism (SAM) genes and probes [INSERT Table 1 HERE] Step 4. Extract probes from annotation file (gene names) Following the definition of SAM genes, a list of probes ( n = 1331) was created. Using R, the Illumina annotation file for the EPIC array ( https://sapac.support.illumina.com/array/array_kits/infinium-methylationepic-beadchip-kit/downloads.html ) was subset to only include probes annotate to SAM genes. Step 5. Extract probes from annotation file (gene names) In addition to the list created in Step 4, a secondary probe list was created based on gene location ( n = 696). Again, the Illumina annotation file for the EPIC array was subset to only include probes that were located within 3 kb of the first and last exon of each SAM gene, as well as within the defined gene coding region. Step 6. Extract associated enhancer and promotor probes Finally, probes identified as SAM gene associated promoters/enhancers, with a gene association score greater than 50 were also included in the probe list ( n = 653). These probes were identified through downloading gene information via the GeneCards database ( https://genealacart.genecards.org/ ) ( 29 ) on 02/06/2023. Probes that existed within these defined promotor/enhancer regions were then extracted from the Illumina annotation file for the EPIC array. Step 7. Generate list of all pathway probes export file for use in differential methylation analysis Following extraction of gene-associated probes (name and location), as well as associated enhancers/promoters, lists were combined, and duplicates removed to include only unique probes. This resulted in 1419 probes identified across the 33 genes involved the SAM pathway, including ST3GAL3 . A summary of the number of probes per gene can be seen in Table 1 . Step 8. Calculation of sample cell heterogeneity As previously recommended ( 30 ), cell-type heterogeneity of samples were controlled for during analysis. Epithelial cell count was estimated using the Epigenetic Dissection of Intra-Sample Heterogeneity (EpiDISH) ( 31 – 34 ) and included as a covariate in all analysis. Step 9. Conduct differential methylation analysis The identification of differentially methylated probes was performed using the Bioconductor limma package ( 35 ). Following analysis, p- values were adjusted for multiple testing using a false discovery rate (FDR) method (Benjamini and Hochberg, 1995). Differentially methylated CpG probes (DMPs) were considered significant if they were p FDR < 0.05, however, top ranked probes significant at an uncorrected level of significance were also explored. Linear regression analysis was conducted for both ADHD diagnostic status (Model A) and ADHD symptom severity (Model B) as separate dependent variables. M -values for 1188 probes within the region of interest were independent variables. Covariates ( cov ) were epithelial cell count, array batch, age and sex. Sensitivity testing run in GPower ( 36 , 37 ) with a = 0.05, revealed the current study ( n = 140) has 90% power to detect a minimum effect of 6%, with a critical t -score of 1.66. $$\varvec{M}\varvec{o}\varvec{d}\varvec{e}\varvec{l} \varvec{A}: ADHD Diagnostic Model: Y= \beta 0+ cov+\beta Group$$ A. $$\varvec{M}\varvec{o}\varvec{d}\varvec{e}\varvec{l} \varvec{B}: ADHD Symptom Model: Y= \beta 0+ cov+\beta Symptom Severity$$ B. For both models A and B, differentially methylated regions (DMRs) were identified using the DMRcate package ( 38 , 39 ). Significance testing for DMRs was conducted on M- values using the Bioconductor limma pipeline ( 35 ). Similarly to the DMP analysis, p FDR < 0.05 was established as the cut-off. Where appropriate, DMRs were ranked using Fisher’s multiple comparison statistic. Results Cohort characteristics Independent sample t -tests revealed that the groups did not differ significantly on age ( t = 2.013; p = 0.786) or gestation period (weeks) ( m ADHD = 38.578, m Control = 39.120, t = -1.497, p = 0.136). The groups did differ significantly on birth weight (grams) ( m ADHD = 3278.780, m Control = 3529.211, t = -2.481, p = 0.014); however, chi-squared tests showed no significant differences in intensive care (y/n) ( X 2 = 0.009, p = 0.925), trimester 1–3 maternal alcohol consumption ( X 2 = 1.175, p = 0.759; X 2 = 2.368, p = 0.450; X 2 = 1.681, p = 0.641) or trimester 1–3 maternal smoking ( X 2 = 2.266, p = 0.519; X 2 = 3.269, p = 0.352; X 2 = 4.094, p = 0.252). DNA methylation of SAM genes by ADHD diagnosis Initially we investigated associations between SAM genes and diagnostic classification of ADHD. At an uncorrected level, 38 probes reached statistical significance at a = 0.05 and three of these were annotated to ST3GAL3 (cg25630069, cg05180596, cg19326856). A summary of these probes can be found in Table 2 . Of the 33 total SAM genes, 20 were represented in the 38 significant probes with 8 genes each contributing one probe ( GLB, NANP, NLP, SLC17A5, ST3GAL2, ST6GALNAC5, ST8SIA5 ), 5 genes each contributing 2 probes ( NANS, ST3GAL4, ST6GALNAC1, ST6GALNAC4, ST8SIA1 ) and 7 genes contributing 3 probes each ( NEU4, ST3GAL1, ST3GAL3, ST6GAL1, ST6GALNAC1, ST8SIA6 ) (Fig. 2 ). A summary of delta beta values ( Δβ ; average difference of beta values between groups) for these probes can be seen in Fig. 2 . The ST3GAL3 probe previously reported by Walton et al (2017) (cg09989037) was ranked 275 of 1188 probes ( Δβ = -4.58 x 10 − 5 ; t = 0.75; p = 0.45; p FDR = 0.98). No individual probes survived correction for multiple comparisons. Table 2 Summary Rank of Differentially Methylated Probes from Group Regression Modelling (uncorrected p < 0.05) Probe Gene Chr Position Rel 1 Rel 2 t p p FDR Dβ 1 cg02637438 ST3GAL1 8 134584246 Tss200 Island 3.394 0.001 0.884 -0.010 2 cg06575763 NANP 20 25604643 1stExon;5'utr Island 3.014 0.003 0.998 -0.009 3 cg04684105 ST6GALNAC4 9 130677075 Body;5'utr N Shelf -2.844 0.005 0.998 0.010 4 cg15832710 NANS 9 100818990 5'utr;1stExon Island 2.686 0.008 0.998 -0.009 5 cg16970851 ST8SIA1 12 22487658 Tss200 Island 2.672 0.008 0.998 -0.008 6 cg27169166* SLC35A1 6 88182413 Tss1500 Island 2.643 0.009 0.998 0.006 7 cg02472348 ST8SIA1 12 22487781 Tss200 Island -2.575 0.011 0.998 -0.019 8 cg12841113 NANS 9 100818782 Tss200 Island 2.564 0.011 0.998 -0.007 9 cg01477546 ST3GAL5 2 86096098 Tss1500;Body Open Sea 2.530 0.012 0.998 0.006 10 cg25630069* ST3GAL3 1 44385621 3'utr Open Sea 2.522 0.013 0.998 0.004 11 cg12715330 ST6GAL1 3 186698055 5'utr Open Sea -2.471 0.014 0.998 0.003 12 cg05180596 ST3GAL3 1 44226543 Body Open Sea 2.452 0.015 0.998 0.013 13 cg17702024 ST3GAL5 2 86116326 Tss200 Island 2.361 0.019 0.998 0.006 14 cg09810707 ST3GAL4 11 126274745 5'utr;Tss1500 Open Sea 2.283 0.024 0.998 0.013 15 cg23290912 NEU4 2 242750944 5'utr; Tss1500 N Shelf -2.235 0.027 0.998 0.006 16 cg27558802 NPL 1 182797087 Body;3'utr Open Sea 2.219 0.028 0.998 -0.016 17 cg13215049 GLB1 3 33089948 Body Open Sea 2.163 0.032 0.998 0.011 18 cg13191925 ST8SIA5 18 44292608 Body Open Sea 2.145 0.033 0.998 -0.010 19 cg02923228 ST3GAL1 8 134480356 Body Open Sea -2.142 0.034 0.998 0.011 20 cg14497130 ST6GALNAC3 1 76717350 Body Open Sea -2.121 0.035 0.998 -0.020 21 cg13153065 ST6GALNAC4 9 130674860 Body Island 2.119 0.036 0.998 0.005 22 cg25638604* ST6GALNAC1 17 74623627 Body Open Sea 2.116 0.036 0.998 0.006 23 cg13460167 SLC17A5 6 74308079 Body Open Sea -2.094 0.038 0.998 0.021 24 cg21604970 ST6GALNAC5 1 77505574 Body Open Sea -2.084 0.039 0.998 -0.008 25 cg23680447 ST3GAL4 11 126238371 5'UTR Open Sea -2.081 0.039 0.998 0.006 26 cg15026574 ST6GAL1 3 186683429 5'UTR Open Sea 2.070 0.040 0.998 0.008 27 cg05816879* ST8SIA6 10 17440929 Body Open Sea 2.067 0.040 0.998 0.008 28 cg14207785 ST8SIA6 10 17440426 Body Open Sea 2.067 0.040 0.998 0.003 29 cg00579505 ST3GAL1 8 134477188 ExonBnd;Body Open Sea 2.055 0.042 0.998 0.005 30 cg18485872* NEU4 2 242750355 5'utr;1stExon N Shelf 2.052 0.042 0.998 0.010 31 cg00163462 ST6GALNAC3 1 76723043 Body Open Sea 2.047 0.042 0.998 0.007 32 cg04490113 ST6GALNAC3 1 76704467 Body Open Sea 2.041 0.043 0.998 0.012 33 cg05065226 ST6GALNAC1 17 74636203 Body;5'utr Open Sea 2.011 0.046 0.998 0.010 34 cg15211453* NEU4 2 242749533 Tss1500 Open Sea 2.004 0.047 0.998 0.005 35 cg22189991 ST3GAL2 16 70468543 5'utr Open Sea -2.001 0.047 0.998 0.007 36 cg05650740 ST3GAL5 2 86113142 Body N Shelf -1.991 0.048 0.998 0.007 37 cg09023643 ST6GAL1 3 186646931 Tss1500 N Shore -1.988 0.048 0.998 -0.006 38 cg19326856* ST3GAL3 1 44170220 3'utr;Body N Shelf 1.986 0.049 0.998 -0.007 Note: Chr = Chromosome; Position = Chromosomal coordinate of the CpG (Build 37); Rel 1 = Gene region feature category ( ; Rel 2 = Location of CpG relative to CpG Island; t = t-statistic, p = uncorrected p -value; p FDR = FDR corrected p -value, Coeff = Standardised Regression Coefficient; D β = Delta Beta; *Probes also associated with symptom severity at an uncorrected level. Table 2 . Summary Rank of Differentially Methylated Probes from Group Regression Modelling (uncorrected p < 0.05) [INSERT Table 2 HERE] [INSERT FIGURE 2 HERE] Figure 2 . Waterfall plot of delta beta values of significant differentially methylated probes (uncorrected p < .05) for ADHD compared to controls from group regression modelling. Enrichment analysis To explore the possibility of SAM pathway and/or regional enrichment between those with and without ADHD, a series of t -tests of the resultant regression t -scores were conducted. In summary, significant differences in t -score distribution were found for the entire SAM pathway ( m = 0.093, t = 3.414, p = 6.623 x 10 − 4 ), as well as CpG island shelves (regions 2–4 kb upstream (5’) of CpG islands; m = 0.325, t = 2.474, p = 0.016); probes located in open sea (regions of the genome that are located far from any known CpG islands; m = 0.139, t = 3.426, p = 6.562 x 10 − 4 ), probes within the 5’ untranslated region between transcriptional start site and ATG start site ( m = 0.208, t = 3.894, p = 1.202 x 10 − 4 ) and probes located in the body of the gene between ATG start site and stop codon ( m = 0.077, t = 2.013, p = 0.045). This indicates that for the SAM pathway, and probes that lie in these regions, children with ADHD show a subtle pattern of increased DNA methylation. For histograms of t -distributions see Figure S2. ADHD symptom severity Secondly, we investigated associations between SAM genes and ADHD symptom severity as estimated by the Conners-3 ADHD Index. At an uncorrected level, 63 probes reached statistical significance at a = 0.05 and 4 of these were annotated to ST3GAL3 (cg25630069, cg10874168, cg06176087, cg22397365). A summary of these probes can be found in Table S1 . These probes were distributed across 27 genes. Of the 33 total SAM genes, 27 were represented in the 63 significant probes with 9 genes contributing one probe ( NANP, NANS NEU4, SLC35A1, ST3GAL2, ST6GALNAC1, ST6GALNAC6, ST8SIA4, ST8SIA5 ), 5 genes contributing 2 probes ( NPL, ST3GAL1, ST3GAL5, ST6GALNAC4, ST8SIA6 ), 3 genes contributing 3 probes each (G LB1, NEU1, ST6GALNAC5 ), 4 genes contributing 4 probes ( NEU3, ST3GAL3, ST6GALNAC3, ST8SIA2 ) 1 gene contributing 5 probes ( ST6GAL2 ) and 2 genes contributing 6 probes ( ST3GAL4, ST6GAL1 ). The ST3GAL3 probe previously reported by Walton et al (2017) (cg09989037) was ranked 539 of 1188 probes ( Δβ = -4.584 x 10 − 5 ; t = 1.201; p = 0.232; p FDR = 0.943). No individual probes survived correction for multiple comparisons. When compared to the diagnostic regression, there was an overlap of 7 probes indicating that DNA methylation of these probes was significantly associated with both ADHD diagnostic status and symptom severity at an uncorrected level. These probes are marked with a star in the relevant tables. Discussion Despite the understanding that ADHD is heritable with a neurophysiological basis ( 40 , 41 ), understanding of the biological mechanisms and drivers is still lacking. Both genetic and epigenetic research have highlighted the potential role of ST3GAL3 in ADHD ( 42 ). Given the role this gene plays in brain formation and function ( 14 ), and the broader neuroimaging evidence indicating structural and functional brain differences between children with and without ADHD ( 1 , 2 ), this work set out to investigate the epigenetic contribution of ST3GAL3 to ADHD, along with other genes apart of the sialic acid metabolism (SAM) pathway. Adopting an alternative approach to candidate-gene studies, this work employed a pathway-approach, whereby genes implicated in the broader SAM pathway were also examined. Once correcting for multiple comparisons across 1188 probes, no group-level differences in DNA methylation were found for ST3GAL3 or SAM probes, at an uncorrected level, there were 38 significant (unadjusted p < 0.05) probes, 3 of which were annotated to ST3GAL3 (see Table 2 ). These lay within the body of the gene and the 3’ untranslated region (cg05180596, cg25630069, cg19326856). Post-hoc analysis also suggested an overall subtly increased pattern of DNA methylation across the entire SAM pathway for the ADHD group. In addition to this, overall increases were seen in the ADHD group compared to the controls in SAM probes located in shelves 2–4 kb upstream (5’) of CpG islands, open sea, within 5’ untranslated region between transcriptional start site and ATG start site, and probes located in the body of the gene between ATG start site and stop codon. These results indicate that nuanced aberration in sialic acid metabolism may play a role in ADHD. Enrichment analysis of the SAM pathway showed an overall increase in DNA methylation for the ADHD group. However, caution must be taken with this interpretation as the relationship between DNA methylation and gene expression can vary at different sites across the genome ( 43 – 45 ). At the pathway level, this may indicate an overall reduced expression of genes associated with sialic acid metabolism. Examination of probes significant at an uncorrected level showed that 84% of these probes ( n = 32) were annotated to genes involved in the biosynthesis of sialic acid rather than its catabolism. Of these 32 probes, 28 were annotated to sialyltransferases (ST6GAL1, ST3GAL1-5, ST6GALNAC1,3–5, and ST8SIA1,5,6). Though the specific role of each sialyltransferase varies, their primary role is to catalyse the addition of sialic acid from CMP-Sia (a nucleotide sugar donor) to the terminus of the oligosaccharide chain of a glycoprotein or glycolipid, ultimately resulting in the glycoconjugate structure transported to the bilipid membrane layer ( 46 ). In the brain, the glycome is dominated by gangliosides (sialylated glycosphingolipids) which carry roughly 75% of the brain’s sialic acid, functioning as both intra- and extra-cellular recognition and regulation molecules( 12 ). Knock-out mouse models have shown that mutations to sialyltransferase genes involved in ganglioside biosynthesis impact axon-myelin interactions, with mice experiencing extensive motor deficits accompanied by significant ( 47 , 48 ). Interestingly, human brain imaging studies of ADHD cohorts show disruptions to wide-spread white matter networks ( 2 , 49 – 51 ). It is difficult to ascertain from these studies, however, whether disruptions to the axon-myelin relationship lay at the heart of these differences. Diffusion weighted imaging is the dominant form of white matter neuroimaging in ADHD, yet differences in white matter microstructure metrics are not specific to myelin and could also represent other elements of white matter microstructure such as fibre architecture, axon diameter and cell swelling ( 52 ). This highlights the need for future work to specifically focus on brain white matter myelin in ADHD and the potential epigenetic contribution of SAM Due to its dynamic nature, epigenetic state is generally temporal. Similarly, brain development, starting in utero and continuing into early adulthood, presents a consistently varying landscape ( 53 ). Although ADHD is believed to stem from a disruption to brain development, the underlying timing of behavioural, brain and epigenetic changes are not well understood, highlighting a need for longitudinal work. One such study adopted a methylome-wide prospective investigation with ADHD symptom trajectories (7–15 years). Of the 13 probes found to be differentially methylated at birth (cord blood) between high and low ADHD symptom trajectories, one was annotated to ST3GAL3 – a SAM sialyltransferase ( 6 ). Interestingly, concurrent DNA methylation of this probe at age 7 (whole blood) was not associated with either high or low ADHD symptom trajectory, aligning with the current work. This could indicate potential epigenetic staging effects whereby differences in DNA methylation of ST3GAL3 may precede neurological and behavioural manifestations of ADHD ( 42 ). Although our study failed to replicate effects of the ST3GAL3 probe highlighted in Walton et al. , at birth, results do align with analysis conducted at age 7 whereby no association was found with the probe and ADHD presentation. Interestingly however, here the SAM pathway as a whole, was seen to be hypermethylated in the ADHD cohort. Together, this information suggests disruption to SAM in ADHD. Sialic acid (specifically polysia – multiple sialic acids) plays a fundamental role in neurogenesis ( 54 – 59 ) and appears to both positively and negatively regulate synaptogenesis postnatally ( 60 ). In rat brains, different stages of synaptic outgrowth are marked by distinct polysia profiles whereby initially polysia labels the entire synapse yet is progressively reduced to pre- and post-synaptic membranes and ultimately lost altogether as synapses are formed ( 60 ). The differential expression profiles of sialic acid during neurogenesis and synaptogenesis highlights the presence of epigenetic regulation of SAM genes throughout pre- and post- natal development and underscores the potential widespread yet subtle consequences of disruption to this pathway. Future work is encouraged to not only adopt longitudinal study designs, but to also examine the relationship between DNA methylation of SAM genes at birth and ADHD behavioural manifestations where possible. The pathway approach adopted here proved a strong and viable alternative to the long-adopted candidate-gene study (CGS) approach. Similarly to CGS, the study research questions, and design were informed by the broader literature; however, the inclusion of genes involved in the broader biochemical pathways offers an opportunity to not only examine a singular gene (or genes) but also those involved in the same biological processes. This means that relationships can be examined at a greater degree, compared to targeted approaches. For example, instead of focusing on a few CpG sites in the promoter, we can investigate CpG sites throughout the whole gene (or pathway) and associated regulatory elements. From a financial perspective, genome-wide assaying, as conducted here with the Illumina EPIC array, may prove more fruitful compared to targeted assays. Although the overarching cost of genome-wide assay is more at face value, they continue to become relatively cheaper compared to targeted assays as the utility gained could be considered worthwhile. Associated time and labour are less, and the resultant data allows for more comprehensive, nuanced research questions to be answered. The results of this study should be interpreted considering a number of limitations. Firstly, the sample size ( n = 140) is considered small for an epigenetic study, though was constrained by the existing cohort size. Secondly, peripheral tissues samples were collected in the form of saliva. Although this reduces the sensitivity of conclusions drawn relating to brain function, studies indicate that the correlation between DNA methylation of brain tissues and saliva samples is strong (r = 0.90) ( 61 ), however it is yet to be established whether this relationship holds true within clinical cohorts. Lastly, the categorical approach to ADHD assessment in both epigenetic and genetic studies potentially masks the specificity of results. A more dimensional approach, such as we adopted here with symptom severity, may help with more targeted outcomes, thus paving the way for more individualised aid, in both diagnosis and intervention. In addition, adoption of more functionally relevant behavioural and cognitive measures may be useful in adequately capturing the true multi-dimensionality of ADHD and further explore the role of epigenetics. In conclusion, our study is the first to adopt a pathways approach to explore the epigenetic role of ST3GAL3 and other sialic acid metabolism genes in ADHD. While effects of less than 1% were seen for individual probes, a broader pattern of hypermethylation was found for the entire SAM pathway. This indicates that ST3GAL3 , as well as other sialyltransferase genes and the broader SAM pathway, could contribute to disrupted epigenetic regulation in ADHD. Future longitudinal pre- and post-natal research across broad developmental age ranges is necessary to further explore these findings. Declarations Conflict of Interest All authors report no conflict of interest, financial or otherwise. Acknowledgements The NICAP cohort is supported by the National Health and Medical Research Council of Australia (NHMRC #1065895 and #2029361) and a grant from the Waterloo Foundation. This research was supported by use of the Nectar Research Cloud, a collaborative Australian research platform supported by the NCRIS-funded Australian Research Data Commons (ARDC). Authors gratefully acknowledge the contribution to this work of the Victorian Operational Infrastructure Support Program received by the Burnet Institute. References Parlatini V, Itahashi T, Lee Y, Liu S, Nguyen TT, Aoki YY, et al. White matter alterations in Attention-Deficit/Hyperactivity Disorder (ADHD): a systematic review of 129 diffusion imaging studies with meta-analysis. Mol Psychiatry. 2023. Yu M, Gao X, Niu X, Zhang M, Yang Z, Han S, et al. Meta-analysis of structural and functional alterations of brain in patients with attention-deficit/hyperactivity disorder. Frontiers in Psychiatry. 2023;13. Faraone SV, Larsson H. Genetics of attention deficit hyperactivity disorder. Mol Psychiatry. 2019;24(4):562–75. Demontis D, Walters GB, Athanasiadis G, Walters R, Therrien K, Nielsen TT, et al. Genome-wide analyses of ADHD identify 27 risk loci, refine the genetic architecture and implicate several cognitive domains. Nat Genet. 2023;55(2):198–208. Demontis D, Walters RK, Martin J, Mattheisen M, Als TD, Agerbo E, et al. Discovery of the first genome-wide significant risk loci for attention deficit/hyperactivity disorder. Nat Genet. 2019;51(1):63–75. Walton E, Pingault JB, Cecil CAM, Gaunt TR, Relton CL, Mill J, et al. Epigenetic profiling of ADHD symptoms trajectories: a prospective, methylome-wide study. Mol Psychiatry. 2017;22(2):250–6. Faraone SV. Understanding Environmental Exposures and ADHD: a Pathway Forward. Prevention Science. 2024;25(Suppl 2):337–42. Van Ewijk H, Heslenfeld DJ, Zwiers MP, Buitelaar JK, Oosterlaan J. Diffusion Tensor Imaging in Attention Deficit/Hyperactivity Disorder: A Systematic Review and Meta-Analysis. Neurosci Biobehav Rev. 2012. Leenders AEM, Damatac CG, Soheili-Nezhad S, Chauvin RJ, Mennes M, Zwiers MP, et al. Associations Between Attention-deficit Hyperactivity Disorder (ADHD) Symptom Remission and White Matter Microstructure: A Longitudinal Analysis. Journal of Child Psychology and Psychiatry Advances. 2021. Nagel BJ, Bathula D, Herting M, Schmitt C, Kroenke CD, Fair D, et al. Altered white matter microstructure in children with attention-deficit/hyperactivity disorder. J Am Acad Child Adolesc Psychiatry. 2011;50(3):283–92. Onnink AMH, Zwiers MP, Hoogman M, Mostert JC, Dammers J, Kan CC, et al. Deviant white matter structure in adults with attention-deficit/hyperactivity disorder points to aberrant myelination and affects neuropsychological performance. Prog Neuropsychopharmacol Biol Psychiatry. 2015;63:14–22. Schnaar RL, Gerardy-Schahn R, Hildebrandt H. Sialic Acids in the Brain: Gangliosides and Polysialic Acid in Nervous System Development, Stability, Disease, and Regeneration. Physiol Rev. 2014;94(2):461–518. Schauer R. Sialic acids: chemistry, metabolism, and function: Springer Science & Business Media; 2012. Schnaar RL. ST3 Beta-Galactoside Alpha-2,3-Sialyltransferase 3 (ST3GAL3). In: Taniguchi N, Honke K, Fukuda M, Narimatsu H, Yamaguchi Y, Angata T, editors. Handbook of Glycosyltransferases and Related Genes. Tokyo: Springer Japan; 2014. p. 657–65. Yoo SW, Motari MG, Susuki K, Prendergast J, Mountney A, Hurtado A, et al. Sialylation regulates brain structure and function. FASEB J. 2015;29(7):3040–53. Hu H, Eggers K, Chen W, Garshasbi M, Motazacker MM, Wrogemann K, et al. ST3GAL3 Mutations Impair the Development of Higher Cognitive Functions. Am J Hum Genet. 2011. Indellicato R, Domenighini R, Malagolini N, Cereda A, Mamoli D, Pezzani L, et al. A novel nonsense and inactivating variant of ST3GAL3 in two infant siblings suffering severe epilepsy and expressing circulating CA19.9. Glycobiology. 2020;30(2):95–104. Edvardson S, Baumann AM, Mühlenhoff M, Stephan O, Kuss AW, Shaag A, et al. West syndrome caused by ST3Gal-III deficiency. Epilepsia. 2013;54(2):e24-e7. Farajollahi Z, Razmara E, Heidari E, Jafarinia E, Garshasbi M. A novel variant of ST3GAL3 causes non-syndromic autosomal recessive intellectual disability in Iranian patients. The Journal of Gene Medicine. 2020;22(11):e3253. Silk TJ, Genc S, Anderson V, Efron D, Hazell P, Nicholson JM, et al. Developmental brain trajectories in children with ADHD and controls: a longitudinal neuroimaging study. BMC Psychiatry. 2016;16(1):59. Sciberras E, Efron D, Schilpzand EJ, Anderson V, Jongeling B, Hazell P, et al. The Children’s Attention Project: a community-based longitudinal study of children with ADHD and non-ADHD controls. BMC Psychiatry. 2013;13(1):18. Conners C. Conners third edition (Conners 3): Los Angel CA West Psychol Serv; 2008. Shaffer D, Fisher P, Lucas CP, Dulcan MK, Schwab-Stone ME. NIMH Diagnostic Interview Schedule for Children Version IV (NIMH DISC-IV): description, differences from previous versions, and reliability of some common diagnoses. J Am Acad Child Adolesc Psychiatry. 2000;39(1):28–38. Milacic M, Beavers D, Conley P, Gong C, Gillespie M, Griss J, et al. The Reactome Pathway Knowledgebase 2024. Nucleic Acids Res. 2024;52(D1):D672-d8. Touleimat N, Tost J. Complete pipeline for Infinium(®) Human Methylation 450K BeadChip data processing using subset quantile normalization for accurate DNA methylation estimation. Epigenomics. 2012;4(3):325–41. Aryee MJ, Jaffe AE, Corrada-Bravo H, Ladd-Acosta C, Feinberg AP, Hansen KD, et al. Minfi: a flexible and comprehensive Bioconductor package for the analysis of Infinium DNA methylation microarrays. Bioinformatics. 2014;30(10):1363–9. Chen YA, Lemire M, Choufani S, Butcher DT, Grafodatskaya D, Zanke BW, et al. Discovery of cross-reactive probes and polymorphic CpGs in the Illumina Infinium HumanMethylation450 microarray. Epigenetics. 2013;8(2):203–9. Maksimovic J, Phipson B, Oshlack A. A cross-package Bioconductor workflow for analysing methylation array data. F1000Research. 2016;5:1281. Fishilevich S, Nudel R, Rappaport N, Hadar R, Plaschkes I, Iny Stein T, et al. GeneHancer: genome-wide integration of enhancers and target genes in GeneCards. Database. 2017;2017. Teschendorff AE, Zheng SC. Cell-type deconvolution in epigenome-wide association studies: a review and recommendations. Epigenomics. 2017;9(5):757–68. Zheng SC, Webster AP, Dong D, Feber A, Graham DG, Sullivan R, et al. A novel cell-type deconvolution algorithm reveals substantial contamination by immune cells in saliva, buccal and cervix. Epigenomics. 2018;10(7):925–40. Newman AM, Liu CL, Green MR, Gentles AJ, Feng W, Xu Y, et al. Robust enumeration of cell subsets from tissue expression profiles. Nature Methods. 2015;12(5):453–7. Houseman EA, Accomando WP, Koestler DC, Christensen BC, Marsit CJ, Nelson HH, et al. DNA methylation arrays as surrogate measures of cell mixture distribution. BMC Bioinformatics. 2012;13(1):86. Teschendorff AE, Breeze CE, Zheng SC, Beck S. A comparison of reference-based algorithms for correcting cell-type heterogeneity in Epigenome-Wide Association Studies. BMC Bioinformatics. 2017;18(1):105. Ritchie ME, Phipson B, Wu D, Hu Y, Law CW, Shi W, et al. limma powers differential expression analyses for RNA-sequencing and microarray studies. Nucleic Acids Res. 2015;43(7):e47-e. Faul F, Erdfelder E, Buchner A, Lang A-G. Statistical power analyses using G*Power 3.1: Tests for correlation and regression analyses. Behavior Research Methods. 2009;41(4):1149–60. Faul F, Erdfelder E, Lang AG, Buchner A. G*Power 3: a flexible statistical power analysis program for the social, behavioral, and biomedical sciences. Behaviour Research Methods. 2007;39(2):175–91. Peters Timothy J, Buckley Michael J, Chen Y, Smyth Gordon K, Goodnow Christopher C, Clark Susan J. Calling differentially methylated regions from whole genome bisulphite sequencing with DMRcate. Nucleic Acids Res. 2021;49(19):e109-e. Peters TJ, Buckley MJ, Statham AL, Pidsley R, Samaras K, V Lord R, et al. De novo identification of differentially methylated regions in the human genome. Epigenetics & Chromatin. 2015;8(1):6. Nigg JT, Sibley MH, Thapar A, Karalunas SL. Development of ADHD: Etiology, Heterogeneity, and Early Life Course. Annual Review of Developmental Psychology. 2020;2(Volume 2, 2020):559 – 83. Michelini G, Norman LJ, Shaw P, Loo SK. Treatment biomarkers for ADHD: Taking stock and moving forward. Translational Psychiatry. 2022;12(1):444. Lesch KP. Editorial: Can dysregulated myelination be linked to ADHD pathogenesis and persistence? J Child Psychol Psychiatry. 2019;60(3):229–31. Brenet F, Moh M, Funk P, Feierstein E, Viale AJ, Socci ND, et al. DNA methylation of the first exon is tightly linked to transcriptional silencing. PLoS One. 2011;6(1):e14524. Hellman A, Chess A. Gene body-specific methylation on the active X chromosome. Science. 2007;315(5815):1141–3. Ball MP, Li JB, Gao Y, Lee JH, LeProust EM, Park IH, et al. Targeted and genome-scale strategies reveal gene-body methylation signatures in human cells. Nat Biotechnol. 2009;27(4):361–8. Audry M, Jeanneau C, Imberty A, Harduin-Lepers A, Delannoy P, Breton C. Current trends in the structure–activity relationships of sialyltransferases. Glycobiology. 2010;21(6):716–26. Pan B, Fromholt SE, Hess EJ, Crawford TO, Griffin JW, Sheikh KA, et al. Myelin-associated glycoprotein and complementary axonal ligands, gangliosides, mediate axon stability in the CNS and PNS: neuropathology and behavioral deficits in single- and double-null mice. Exp Neurol. 2005;195(1):208–17. Chiavegatto S, Sun J, Nelson RJ, Schnaar RL. A functional role for complex gangliosides: motor deficits in GM2/GD2 synthase knockout mice. Exp Neurol. 2000;166(2):227–34. Aoki Y, Abe O, Nippashi Y, Yamasue H. Comparison of white matter integrity between autism spectrum disorder subjects and typically developing individuals: a meta-analysis of diffusion tensor imaging tractography studies. Molecular Autism. 2013;4(1):25. Chen L, Hu X, Ouyang L, He N, Liao Y, Liu Q, et al. A Systematic Review and Meta-Analysis of Tract-Based Spatial Statistics Studies Regarding Attention-Deficit/Hyperactivity Disorder. Neurosci Biobehav Rev. 2016. Zhao Y, Yang L, Gong G, Cao Q, Liu J. Identify aberrant white matter microstructure in ASD, ADHD and other neurodevelopmental disorders: A meta-analysis of diffusion tensor imaging studies. Prog Neuropsychopharmacol Biol Psychiatry. 2022;113:110477. Grey matter correlates of autistic traits in women with anorexia nervosa. J Psychiatry Neurosci. 2018;43(2):79–86. Silbereis John C, Pochareddy S, Zhu Y, Li M, Sestan N. The Cellular and Molecular Landscapes of the Developing Human Central Nervous System. Neuron. 2016;89(2):248–68. Bernier PJ, Vinet J, Cossette M, Parent A. Characterization of the subventricular zone of the adult human brain: evidence for the involvement of Bcl-2. Neurosci Res. 2000;37(1):67–78. Curtis MA, Kam M, Nannmark U, Anderson MF, Axell MZ, Wikkelso C, et al. Human neuroblasts migrate to the olfactory bulb via a lateral ventricular extension. Science. 2007;315(5816):1243–9. Quiñones-Hinojosa A, Sanai N, Soriano-Navarro M, Gonzalez-Perez O, Mirzadeh Z, Gil-Perotin S, et al. Cellular composition and cytoarchitecture of the adult human subventricular zone: a niche of neural stem cells. J Comp Neurol. 2006;494(3):415–34. Sanai N, Nguyen T, Ihrie RA, Mirzadeh Z, Tsai HH, Wong M, et al. Corridors of migrating neurons in the human brain and their decline during infancy. Nature. 2011;478(7369):382–6. Sanai N, Tramontin AD, Quiñones-Hinojosa A, Barbaro NM, Gupta N, Kunwar S, et al. Unique astrocyte ribbon in adult human brain contains neural stem cells but lacks chain migration. Nature. 2004;427(6976):740–4. Weickert CS, Webster MJ, Colvin SM, Herman MM, Hyde TM, Weinberger DR, et al. Localization of epidermal growth factor receptors and putative neuroblasts in human subependymal zone. J Comp Neurol. 2000;423(3):359–72. Uryu K, Butler AK, Chesselet MF. Synaptogenesis and ultrastructural localization of the polysialylated neural cell adhesion molecule in the developing striatum. J Comp Neurol. 1999;405(2):216–32. Nishitani S, Isozaki M, Yao A, Higashino Y, Yamauchi T, Kidoguchi M, et al. Cross-tissue correlations of genome-wide DNA methylation in Japanese live human brain and blood, saliva, and buccal epithelial tissues. Translational Psychiatry. 2023;13(1):72. Additional Declarations The authors have declared there is NO conflict of interest to disclose Supplementary Files SupplementaryMaterial.docx 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-4519315","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":339382523,"identity":"47d55e70-0e52-427a-b275-b192a4e8b3c6","order_by":0,"name":"Lillian Dipnall","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABD0lEQVRIie3QMUsDMRTA8RcDdgneGlDrV4h0KHKDXyXBIcsNHW8QzCGki+DqcOhXsItzjkCnFFfHTJ06dHQS350IKr3WsUP+0wvcj7wcQCq1hx1RWjkAynAmLpYUgBGDB9pLDikx3wScDP8h8EW6g1O2Jd28hQyI8Qzyk/HxbXTqMR9md95GKHNlBkH0LYZEs4t6Lpx60SO+qKYCglaGFduIZ+JNtsQr80osJxYH2En02qna3zx15ANJttpFCrzFeCkWFRKDhPff0tQC3/JQTJyc6/NZaCzHYWT5crKJZJlv4qrML8dcz+L6Oj8bBr3kOJzeZ1fPm38zwAHDBX7vINsF+r7HyDv8JalUKpX60SfCAmJyKNra6wAAAABJRU5ErkJggg==","orcid":"https://orcid.org/0000-0002-1202-8030","institution":"Deakin University","correspondingAuthor":true,"prefix":"","firstName":"Lillian","middleName":"","lastName":"Dipnall","suffix":""},{"id":339382524,"identity":"9284b917-f554-420f-bc59-34930faf79ed","order_by":1,"name":"Mark Ziemann","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Mark","middleName":"","lastName":"Ziemann","suffix":""},{"id":339382525,"identity":"ab1ce955-a55d-4c72-a83e-97a0fd4e29ca","order_by":2,"name":"Peter Fransquet","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Peter","middleName":"","lastName":"Fransquet","suffix":""},{"id":339382526,"identity":"c1b37dbf-47db-4b49-a2b5-ef52773d6d1f","order_by":3,"name":"Jo Wrigglesworth","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Jo","middleName":"","lastName":"Wrigglesworth","suffix":""},{"id":339382527,"identity":"82255b4d-6bda-464d-b6ff-7df520659a07","order_by":4,"name":"Ian Fuelscher","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Ian","middleName":"","lastName":"Fuelscher","suffix":""},{"id":339382528,"identity":"a1618dc0-8846-44a4-96b2-e911e644c3d7","order_by":5,"name":"Jeffrey Craig","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Jeffrey","middleName":"","lastName":"Craig","suffix":""},{"id":339382529,"identity":"42e972e7-116a-4701-91dc-ff3e6bee2a8e","order_by":6,"name":"Tim Silk","email":"","orcid":"https://orcid.org/0000-0002-7290-512X","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Tim","middleName":"","lastName":"Silk","suffix":""}],"badges":[],"createdAt":"2024-06-03 05:05:06","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4519315/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4519315/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":66329207,"identity":"2ec4614d-98be-468b-a3da-70f1b909e82d","added_by":"auto","created_at":"2024-10-10 13:11:09","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":150910,"visible":true,"origin":"","legend":"\u003cp\u003eSteps taken to identify probes within a genomic region of interest.\u003c/p\u003e","description":"","filename":"fig1.png","url":"https://assets-eu.researchsquare.com/files/rs-4519315/v1/c7948cd1a6fd455de949aa1a.png"},{"id":66329208,"identity":"b3782395-6378-4bbe-911d-ee0029e19b94","added_by":"auto","created_at":"2024-10-10 13:11:09","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":408132,"visible":true,"origin":"","legend":"\u003cp\u003eWaterfall plot of delta beta values of significant differentially methylated probes (uncorrected \u003cem\u003ep\u003c/em\u003e\u0026lt;.05) for ADHD compared to controls from group regression modelling\u003c/p\u003e","description":"","filename":"fig2.png","url":"https://assets-eu.researchsquare.com/files/rs-4519315/v1/7861941df2d98641edf7844c.png"},{"id":76839985,"identity":"69146f0e-bdb1-46be-a68b-c1f0f934a846","added_by":"auto","created_at":"2025-02-21 10:04:17","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1530001,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4519315/v1/584a790a-f301-49ff-aeb6-c96a99b1cb02.pdf"},{"id":66329209,"identity":"f4bbcd68-0f5a-41de-aab0-2cf28e851f48","added_by":"auto","created_at":"2024-10-10 13:11:09","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":217130,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cbr\u003e\u003c/p\u003e","description":"","filename":"SupplementaryMaterial.docx","url":"https://assets-eu.researchsquare.com/files/rs-4519315/v1/577412d01ec83afb39aa7db2.docx"}],"financialInterests":"The authors have declared there is \u003cb\u003eNO\u003c/b\u003e conflict of interest to disclose","formattedTitle":"Epigenetic Analysis of ST3GAL3 and other Sialic Acid Metabolism Genes in ADHD","fulltext":[{"header":"Introduction","content":"\u003cp\u003eAttention Deficit Hyperactivity Disorder (ADHD) is a neurodevelopmental disorder primarily characterized by difficulties with inattention and/or impulsivity and hyperactivity with neuroimaging research highlighting broad structural and functional brain differences between those with ADHD and neurotypical individuals (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e). Although genetic studies suggest substantial heritability (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e), understanding of ADHD pathophysiology is still lacking. Genome-wide association studies (GWAS) indicate complex underlying polygenic architecture (\u003cspan additionalcitationids=\"CR5\" citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e) potentially contributing to aberrant brain structure and function in ADHD, while neuroimaging research of brain white matter microstructure have reported differences in ADHD, with suggestion that this could be attributed to differences in myelination (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan additionalcitationids=\"CR7 CR8\" citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e). In addition to this, broader work suggests potential contribution of environmental factors (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e). Examination of epigenetic modifications to gene expression, such as DNA methylation, could aid in unravelling the complex underpinnings of ADHD as it presents a window through which to examine interactions between gene and environment. This highlights a need for epigenetic exploration of ADHD-implicated genes.\u003c/p\u003e \u003cp\u003eRecently, the largest genome-wide association study (GWAS) to date was conducted, with 20,183 diagnosed ADHD cases and 35,191 controls (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e). The highest-ranked of twelve loci associated with ADHD was annotated to \u003cem\u003eST3GAL3\u003c/em\u003e, whose function is implicated in myelination (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e), a key structural component of brain tissue. Aligning with this gene\u0026rsquo;s role, neuroimaging research of brain white matter microstructure have reported differences in ADHD, with suggestion that this could be attributed to differences in myelination (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan additionalcitationids=\"CR9 CR10\" citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e). \u003cem\u003eST3GAL3\u003c/em\u003e, located on chromosome 1p34.1 encodes β-galactoside-α2,3-sialyltransferase-III (ST3Gal-III), which is actively involved in sialylation, an integral part of the glycocalyx of glycoproteins and glycolipids (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e). With glycolipids forming a fundamental component of myelin, any disruption to intercellular communication through differential glycocalyx formation could ultimately disrupt the formation and function of neural white matter. Human neural sialic acid concentrations appear two to four times higher than other mammals, and in comparison to non-neural cellular membranes, sialic acid is up to twenty times higher in neural cellular membranes (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e). In addition to mediating sialylation, in the mouse brain, \u003cem\u003eSt3gal3\u003c/em\u003e has also been shown to produce gangliosides and polysialic acid in neural cells, which play crucial roles in synaptogenesis and subsequently neurotransmission and cognition (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e). \u003cem\u003eSt3gal3\u003c/em\u003e-deficient mice exhibit significant reduction in myelin thickness and major myelin proteins (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e). In humans, \u003cem\u003eST3GAL3\u003c/em\u003e is therefore believed to play a vital role in brain development. Mutations of the \u003cem\u003eST3GAL3\u003c/em\u003e gene have been liked with a range of neurological manifestations including intellectual developmental disorder (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e), developmental and epileptic encephalopathy (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e), West syndrome (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e), and autosomal recessive intellectual disability (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eRecent epigenetic research into ADHD also implicates \u003cem\u003eST3GAL3\u003c/em\u003e. A methylome-wide analysis on children at birth and at age 7, using cord blood at birth and whole blood at age 7 (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e), found DNA methylation of a probe annotated to \u003cem\u003eST3GAL3\u003c/em\u003e (Illumina Infinium array probe cg09989037) at birth to be significantly associated with ADHD diagnosis at age 7. This probe was found to be hypomethylated in comparison to neurotypical controls. However, this association was not maintained at age 7. The variability of findings in the literature highlights a need for further examination of \u003cem\u003eST3GAL3\u003c/em\u003e in paediatric ADHD cohorts.\u003c/p\u003e \u003cp\u003eThis study therefore aimed to investigate the relationship between DNA methylation of \u003cem\u003eST3GAL3\u003c/em\u003e and ADHD, both categorically (diagnosis) and dimensionally (symptom severity). In addition to \u003cem\u003eST3GAL3\u003c/em\u003e, probes annotated to all other genes involved in sialic acid metabolism (SAM) were examined. It was hypothesised that variability in DNA methylation of \u003cem\u003eST3GAL3\u003c/em\u003e, and broader SAM genes, will be associated with ADHD diagnosis and symptom severity.\u003c/p\u003e"},{"header":"Materials and methods","content":"\u003cp\u003eParticipants\u003c/p\u003e \u003cp\u003eData from the Children\u0026rsquo;s Attention Project (CAP) study (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e) was used in this study. The recruitment protocol and procedures are documented elsewhere (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e). Briefly, children were initially recruited at age 6\u0026ndash;8 years across Melbourne, Australia, from 43 socio-economically diverse primary schools. Screening was performed using the Conners 3 ADHD Index (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e) and a parent face-to-face structured diagnostic interview (NIMH Diagnostic Interview Schedule for Children IV \u0026ndash; DISC-IV; (\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e)) was administered for case-confirmation. At a 36-month follow-up (aged 9\u0026ndash;11 years), diagnostic status was re-assessed, and a subset of participants were invited to provide a saliva sample. Participants provided 3 ml of saliva by passive drool into a 50 ml centrifuge tube (Eppendorf South Pacific, NSW, Australia), and stored in fridge at -80\u0026deg;C. Clinical, behavioural, and epigenetic assessments were conducted by researchers blinded to the diagnostic status of the participants. This study was approved by The Royal Children\u0026rsquo;s Hospital Human Research Ethics Committee (HREC #34071), and parents or guardians gave informed consent for participating children.\u003c/p\u003e \u003cp\u003eExclusion criteria included intellectual disability, serious medical conditions, genetic disorders, moderate-severe sensory impairment, neurological problems, and parents with insufficient English to complete interviews/questionnaires. Following quality control, one individual was removed due to poor sample performance, the final sample consisted of 90 children with ADHD [m\u003csub\u003eage\u003c/sub\u003e= 10.40 (0.49); 66% male] and 50 non-ADHD controls [m\u003csub\u003eage\u003c/sub\u003e= 10.40 (0.45); 48% male]. Of the ADHD group, 24% of children were on medication at the time of sample collection. See supplementary material for more information on medication status.\u003c/p\u003e \u003cp\u003eMeasures\u003c/p\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eDemographics and clinical assessment\u003c/h2\u003e \u003cp\u003eADHD diagnostic status was ascertained using the Diagnostic Interview Schedule for Children (DISC-IV) (\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e). Assessment was conducted upon recruitment (6\u0026ndash;8 years) and repeated at the current sampling (9\u0026ndash;11 years). Children were categorized in the ADHD group if they had a childhood history of ADHD (meeting criteria at recruitment and/or sampling wave). ADHD symptom severity was assessed using the Conners-3 ADHD Index (parent report) (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e). At recruitment, parents/guardians completed a self-report questionnaire that included retrospective questions relating to fetal gestation period (weeks), birth weight (grams), post-natal intensive care stay (y/n) and trimester 1, 2 \u0026amp; 3 maternal alcohol and smoking consumption. Between group differences in these were assessed using either between-groups \u003cem\u003et\u003c/em\u003e-tests or chi-squared tests.\u003c/p\u003e \u003cp\u003eDNA methylation analysis\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eProbe selection\u003c/h2\u003e \u003cp\u003eRather than examining a single candidate gene site, this study adopted a pathways approach (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e) whereby the broader biochemical pathway was accounted for during probe selection. In this case, \u003cem\u003eST3GAL3\u003c/em\u003e is implicated in sialic acid metabolism (SAM). Definition of the SAM pathway can be found on the Reactome website (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://reactome.org/content/detail/R-HSA-4085001\u003c/span\u003e\u003cspan address=\"https://reactome.org/content/detail/R-HSA-4085001\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) (\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e). The following protocol outlines how a set of genomic regions of interest was defined for SAM. This region set of interest included probes annotated to all involved genes, as well as probes within associated promoter and enhancer regions.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e[INSERT FIGURE \u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e HERE]\u003c/p\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003eSteps taken to identify probes within a genomic region of interest.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eStep 1. Microarray pre-processing and quality control\u003c/h2\u003e \u003cp\u003eFor comprehensive overview of microarray pre-processing and quality control see supplementary material. Briefly, genomic DNA was extracted from saliva samples and pre-processed. Extracted genomic DNA samples were bisulphite treated and hybridised to Infinium MethylationEPIC arrays (EPIC; Illumina, San Diego, CA, USA) at NTX-Dx (Diagenode, Ghent, Belgium). These arrays generate data from over 850,000 CpGs throughout the human genome. Raw intensity data was imported into R (3.6.3, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://cran.r-project.org/\u003c/span\u003e\u003cspan address=\"http://cran.r-project.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). Data underwent subset-quantile normalisation (SQN) (\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e), followed by data quality assessment using the \u003cem\u003eminfi\u003c/em\u003e (v1.34.0) Bioconductor package (\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e). EPIC probes (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;865,859) were filtered by removing those with a poor signal to noise ratio (mean detection \u003cem\u003ep\u003c/em\u003e-value of \u0026gt;\u0026thinsp;0.01, \u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;113,011), containing a single nucleotide polymorphism at the CpG site (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;26,455), mapping to sex chromosomes (n\u0026thinsp;=\u0026thinsp;16,776), or cross-reactivity to multiple genomic locations (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;34,471) (\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eDNA methylation levels were reported as beta (β) values (proportion of methylated intensity over total intensity values). Beta values were converted to \u003cem\u003eM\u003c/em\u003e-values (log\u003csub\u003e2\u003c/sub\u003e ratio of the methylated intensity divided unmethylated intensity) for statistical regression analyses (\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eStep 2. Define the Reactome pathway of interest\u003c/h2\u003e \u003cp\u003eThe Reactome library links genes to different biochemical pathways throughout the body. Searching of the Reactome library (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://reactome.org/\u003c/span\u003e\u003cspan address=\"https://reactome.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) confirmed \u003cem\u003eST3GAL3\u003c/em\u003e to play a role in SAM. The Reactome library used in this study was downloaded as a GMT file (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://reactome.org/download/current/ReactomePathways.gmt.zip\u003c/span\u003e\u003cspan address=\"https://reactome.org/download/current/ReactomePathways.gmt.zip\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) in Aug 2023 and was stored and accessed locally.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eStep 3. Define SAM gene set\u003c/h2\u003e \u003cp\u003eThe search resulted in 33 genes associated with the SAM pathway. A summary of these genes can be seen in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSummary of Sialic Acid Metabolism (SAM) genes and probes\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGene\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eProbes\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGene\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eProbes\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eGene\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eProbes\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eCMAS\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eSCL17A5\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eST6GALNAC2\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e27\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eCTSA\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eSLC35A1\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eST6GALNAC3\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e68\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eGLB1\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eST3GAL1\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eST6GALNAC4\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e33\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eGNE\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eST3GAL2\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eST6GALNAC5\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e46\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eNANP\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eST3GAL4\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eST6GALNAC6\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e33\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eNANS\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eST3GAL5\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eST8SIA1\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e39\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eNEU1\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e102\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eST3GAL5\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eST8SIA2\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e41\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eNEU2\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eST3GAL6\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eST8SIA3\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eNEU3\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eST6GAL1\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eST8SIA4\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e47\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eNEU4\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eST6GAL2\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eST8SIA5\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e28\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eNPL\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eST6GALNAC1\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eST8SIA6\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e48\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. Summary of Sialic Acid Metabolism (SAM) genes and probes\u003c/p\u003e \u003cp\u003e[INSERT Table \u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e HERE]\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eStep 4. Extract probes from annotation file (gene names)\u003c/h2\u003e \u003cp\u003eFollowing the definition of SAM genes, a list of probes (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1331) was created. Using R, the Illumina annotation file for the EPIC array (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://sapac.support.illumina.com/array/array_kits/infinium-methylationepic-beadchip-kit/downloads.html\u003c/span\u003e\u003cspan address=\"https://sapac.support.illumina.com/array/array_kits/infinium-methylationepic-beadchip-kit/downloads.html\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) was subset to only include probes annotate to SAM genes.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eStep 5. Extract probes from annotation file (gene names)\u003c/h2\u003e \u003cp\u003eIn addition to the list created in Step 4, a secondary probe list was created based on gene location (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;696). Again, the Illumina annotation file for the EPIC array was subset to only include probes that were located within 3 kb of the first and last exon of each SAM gene, as well as within the defined gene coding region.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eStep 6. Extract associated enhancer and promotor probes\u003c/h2\u003e \u003cp\u003eFinally, probes identified as SAM gene associated promoters/enhancers, with a gene association score greater than 50 were also included in the probe list (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;653). These probes were identified through downloading gene information via the GeneCards database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://genealacart.genecards.org/\u003c/span\u003e\u003cspan address=\"https://genealacart.genecards.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) (\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e) on 02/06/2023. Probes that existed within these defined promotor/enhancer regions were then extracted from the Illumina annotation file for the EPIC array.\u003c/p\u003e \u003cp\u003e \u003cem\u003eStep 7. Generate list of all pathway probes export file for use in differential methylation analysis\u003c/em\u003e \u003c/p\u003e \u003cp\u003eFollowing extraction of gene-associated probes (name and location), as well as associated enhancers/promoters, lists were combined, and duplicates removed to include only unique probes. This resulted in 1419 probes identified across the 33 genes involved the SAM pathway, including \u003cem\u003eST3GAL3\u003c/em\u003e. A summary of the number of probes per gene can be seen in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eStep 8. Calculation of sample cell heterogeneity\u003c/h2\u003e \u003cp\u003eAs previously recommended (\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e), cell-type heterogeneity of samples were controlled for during analysis. Epithelial cell count was estimated using the Epigenetic Dissection of Intra-Sample Heterogeneity (EpiDISH) (\u003cspan additionalcitationids=\"CR32 CR33\" citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e) and included as a covariate in all analysis.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eStep 9. Conduct differential methylation analysis\u003c/h2\u003e \u003cp\u003eThe identification of differentially methylated probes was performed using the Bioconductor \u003cem\u003elimma\u003c/em\u003e package (\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e). Following analysis, \u003cem\u003ep-\u003c/em\u003evalues were adjusted for multiple testing using a false discovery rate (FDR) method (Benjamini and Hochberg, 1995). Differentially methylated CpG probes (DMPs) were considered significant if they were \u003cem\u003ep\u003c/em\u003e\u003csub\u003e\u003cem\u003eFDR\u003c/em\u003e\u003c/sub\u003e \u0026lt; 0.05, however, top ranked probes significant at an uncorrected level of significance were also explored.\u003c/p\u003e \u003cp\u003eLinear regression analysis was conducted for both ADHD diagnostic status (Model A) and ADHD symptom severity (Model B) as separate dependent variables. \u003cem\u003eM\u003c/em\u003e-values for 1188 probes within the region of interest were independent variables. Covariates (\u003cem\u003ecov\u003c/em\u003e) were epithelial cell count, array batch, age and sex. Sensitivity testing run in GPower (\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e) with a\u0026thinsp;=\u0026thinsp;0.05, revealed the current study (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;140) has 90% power to detect a minimum effect of 6%, with a critical \u003cem\u003et\u003c/em\u003e-score of 1.66.\u003cdiv id=\"Equa\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equa\" name=\"EquationSource\"\u003e\n$$\\varvec{M}\\varvec{o}\\varvec{d}\\varvec{e}\\varvec{l} \\varvec{A}: ADHD Diagnostic Model: Y= \\beta 0+ cov+\\beta Group$$\u003c/div\u003e\u003c/div\u003eA. \u003cdiv id=\"Equb\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equb\" name=\"EquationSource\"\u003e\n$$\\varvec{M}\\varvec{o}\\varvec{d}\\varvec{e}\\varvec{l} \\varvec{B}: ADHD Symptom Model: Y= \\beta 0+ cov+\\beta Symptom Severity$$\u003c/div\u003e\u003c/div\u003eB. \u003c/p\u003e \u003cp\u003eFor both models A and B, differentially methylated regions (DMRs) were identified using the \u003cem\u003eDMRcate\u003c/em\u003e package (\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e). Significance testing for DMRs was conducted on \u003cem\u003eM-\u003c/em\u003evalues using the Bioconductor \u003cem\u003elimma\u003c/em\u003e pipeline (\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e). Similarly to the DMP analysis, \u003cem\u003ep\u003c/em\u003e\u003csub\u003e\u003cem\u003eFDR\u003c/em\u003e\u003c/sub\u003e \u0026lt; 0.05 was established as the cut-off. Where appropriate, DMRs were ranked using Fisher\u0026rsquo;s multiple comparison statistic.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003eCohort characteristics\u003c/p\u003e \u003cp\u003eIndependent sample \u003cem\u003et\u003c/em\u003e-tests revealed that the groups did not differ significantly on age (\u003cem\u003et\u003c/em\u003e\u0026thinsp;=\u0026thinsp;2.013; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.786) or gestation period (weeks) (\u003cem\u003em\u003c/em\u003e\u003csub\u003eADHD\u003c/sub\u003e = 38.578, \u003cem\u003em\u003c/em\u003e\u003csub\u003eControl\u003c/sub\u003e = 39.120, \u003cem\u003et\u003c/em\u003e = -1.497, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.136). The groups did differ significantly on birth weight (grams) (\u003cem\u003em\u003c/em\u003e\u003csub\u003eADHD\u003c/sub\u003e = 3278.780, \u003cem\u003em\u003c/em\u003e\u003csub\u003eControl\u003c/sub\u003e = 3529.211, \u003cem\u003et\u003c/em\u003e = -2.481, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.014); however, chi-squared tests showed no significant differences in intensive care (y/n) (\u003cem\u003eX\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.009, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.925), trimester 1\u0026ndash;3 maternal alcohol consumption (\u003cem\u003eX\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;1.175, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.759; \u003cem\u003eX\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;2.368, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.450; \u003cem\u003eX\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;1.681, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.641) or trimester 1\u0026ndash;3 maternal smoking (\u003cem\u003eX\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;2.266, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.519; \u003cem\u003eX\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;3.269, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.352; \u003cem\u003eX\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;4.094, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.252).\u003c/p\u003e \u003cp\u003eDNA methylation of SAM genes by ADHD diagnosis\u003c/p\u003e \u003cp\u003eInitially we investigated associations between SAM genes and diagnostic classification of ADHD. At an uncorrected level, 38 probes reached statistical significance at \u003cem\u003ea\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.05 and three of these were annotated to \u003cem\u003eST3GAL3\u003c/em\u003e (cg25630069, cg05180596, cg19326856). A summary of these probes can be found in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. Of the 33 total SAM genes, 20 were represented in the 38 significant probes with 8 genes each contributing one probe (\u003cem\u003eGLB, NANP, NLP, SLC17A5, ST3GAL2, ST6GALNAC5, ST8SIA5\u003c/em\u003e), 5 genes each contributing 2 probes (\u003cem\u003eNANS, ST3GAL4, ST6GALNAC1, ST6GALNAC4, ST8SIA1\u003c/em\u003e) and 7 genes contributing 3 probes each (\u003cem\u003eNEU4, ST3GAL1, ST3GAL3, ST6GAL1, ST6GALNAC1, ST8SIA6\u003c/em\u003e) (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). A summary of delta beta values (\u003cem\u003eΔβ\u003c/em\u003e; average difference of beta values between groups) for these probes can be seen in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. The \u003cem\u003eST3GAL3\u003c/em\u003e probe previously reported by Walton et al (2017) (cg09989037) was ranked 275 of 1188 probes (\u003cem\u003eΔβ\u003c/em\u003e = -4.58 x 10\u003csup\u003e\u0026minus;\u0026thinsp;5\u003c/sup\u003e; \u003cem\u003et\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.75; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.45; \u003cem\u003ep\u003c/em\u003e\u003csub\u003e\u003cem\u003eFDR\u003c/em\u003e\u003c/sub\u003e = 0.98). No individual probes survived correction for multiple comparisons.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSummary Rank of Differentially Methylated Probes from Group Regression Modelling (uncorrected \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"11\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eProbe\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGene\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eChr\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003ePosition\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eRel 1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eRel 2\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cem\u003et\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u003csub\u003e\u003cem\u003eFDR\u003c/em\u003e\u003c/sub\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u003cem\u003eDβ\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ecg02637438\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eST3GAL1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e134584246\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eTss200\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eIsland\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e3.394\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.884\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e-0.010\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ecg06575763\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNANP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e25604643\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1stExon;5'utr\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eIsland\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e3.014\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.998\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e-0.009\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ecg04684105\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eST6GALNAC4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e130677075\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eBody;5'utr\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eN Shelf\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e-2.844\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.998\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.010\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ecg15832710\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNANS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e100818990\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e5'utr;1stExon\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eIsland\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e2.686\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.008\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.998\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e-0.009\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ecg16970851\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eST8SIA1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e22487658\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eTss200\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eIsland\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e2.672\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.008\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.998\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e-0.008\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ecg27169166*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSLC35A1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e88182413\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eTss1500\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eIsland\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e2.643\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.009\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.998\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.006\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ecg02472348\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eST8SIA1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e22487781\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eTss200\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eIsland\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e-2.575\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.011\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.998\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e-0.019\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ecg12841113\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNANS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e100818782\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eTss200\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eIsland\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e2.564\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.011\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.998\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e-0.007\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ecg01477546\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eST3GAL5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e86096098\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eTss1500;Body\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eOpen Sea\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e2.530\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.012\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.998\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.006\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ecg25630069*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eST3GAL3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e44385621\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3'utr\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eOpen Sea\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e2.522\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.013\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.998\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.004\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ecg12715330\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eST6GAL1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e186698055\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e5'utr\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eOpen Sea\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e-2.471\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.014\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.998\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ecg05180596\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eST3GAL3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e44226543\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eBody\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eOpen Sea\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e2.452\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.015\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.998\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.013\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ecg17702024\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eST3GAL5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e86116326\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eTss200\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eIsland\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e2.361\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.019\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.998\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.006\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ecg09810707\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eST3GAL4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e126274745\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e5'utr;Tss1500\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eOpen Sea\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e2.283\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.024\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.998\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.013\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ecg23290912\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNEU4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e242750944\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e5'utr; Tss1500\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eN Shelf\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e-2.235\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.027\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.998\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.006\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ecg27558802\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNPL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e182797087\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eBody;3'utr\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eOpen Sea\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e2.219\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.028\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.998\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e-0.016\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ecg13215049\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGLB1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e33089948\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eBody\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eOpen Sea\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e2.163\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.032\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.998\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.011\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ecg13191925\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eST8SIA5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e44292608\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eBody\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eOpen Sea\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e2.145\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.033\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.998\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e-0.010\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ecg02923228\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eST3GAL1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e134480356\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eBody\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eOpen Sea\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e-2.142\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.034\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.998\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.011\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ecg14497130\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eST6GALNAC3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e76717350\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eBody\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eOpen Sea\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e-2.121\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.035\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.998\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e-0.020\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ecg13153065\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eST6GALNAC4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e130674860\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eBody\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eIsland\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e2.119\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.036\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.998\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.005\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ecg25638604*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eST6GALNAC1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e74623627\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eBody\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eOpen Sea\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e2.116\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.036\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.998\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.006\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ecg13460167\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSLC17A5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e74308079\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eBody\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eOpen Sea\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e-2.094\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.038\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.998\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.021\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ecg21604970\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eST6GALNAC5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e77505574\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eBody\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eOpen Sea\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e-2.084\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.039\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.998\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e-0.008\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ecg23680447\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eST3GAL4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e126238371\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e5'UTR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eOpen Sea\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e-2.081\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.039\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.998\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.006\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ecg15026574\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eST6GAL1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e186683429\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e5'UTR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eOpen Sea\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e2.070\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.040\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.998\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.008\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ecg05816879*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eST8SIA6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e17440929\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eBody\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eOpen Sea\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e2.067\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.040\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.998\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.008\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ecg14207785\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eST8SIA6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e17440426\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eBody\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eOpen Sea\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e2.067\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.040\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.998\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ecg00579505\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eST3GAL1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e134477188\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eExonBnd;Body\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eOpen Sea\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e2.055\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.042\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.998\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.005\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ecg18485872*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNEU4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e242750355\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e5'utr;1stExon\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eN Shelf\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e2.052\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.042\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.998\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.010\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ecg00163462\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eST6GALNAC3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e76723043\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eBody\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eOpen Sea\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e2.047\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.042\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.998\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.007\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ecg04490113\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eST6GALNAC3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e76704467\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eBody\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eOpen Sea\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e2.041\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.043\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.998\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.012\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ecg05065226\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eST6GALNAC1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e74636203\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eBody;5'utr\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eOpen Sea\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e2.011\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.046\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.998\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.010\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ecg15211453*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNEU4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e242749533\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eTss1500\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eOpen Sea\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e2.004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.047\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.998\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.005\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ecg22189991\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eST3GAL2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e70468543\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e5'utr\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eOpen Sea\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e-2.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.047\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.998\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.007\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ecg05650740\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eST3GAL5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e86113142\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eBody\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eN Shelf\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e-1.991\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.048\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.998\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.007\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ecg09023643\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eST6GAL1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e186646931\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eTss1500\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eN Shore\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e-1.988\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.048\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.998\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e-0.006\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ecg19326856*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eST3GAL3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e44170220\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3'utr;Body\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eN Shelf\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1.986\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.049\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.998\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e-0.007\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"11\"\u003eNote: Chr\u0026thinsp;=\u0026thinsp;Chromosome; Position\u0026thinsp;=\u0026thinsp;Chromosomal coordinate of the CpG (Build 37); Rel 1\u0026thinsp;=\u0026thinsp;Gene region feature category ( ; Rel 2\u0026thinsp;=\u0026thinsp;Location of CpG relative to CpG Island; \u003cem\u003et\u003c/em\u003e\u0026thinsp;=\u0026thinsp;t-statistic, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;uncorrected \u003cem\u003ep\u003c/em\u003e-value; \u003cem\u003ep\u003c/em\u003e\u003csub\u003eFDR\u003c/sub\u003e = FDR corrected \u003cem\u003ep\u003c/em\u003e-value, \u003cem\u003eCoeff\u003c/em\u003e\u0026thinsp;=\u0026thinsp;Standardised Regression Coefficient; D\u003cem\u003eβ\u003c/em\u003e\u0026thinsp;=\u0026thinsp;Delta Beta; *Probes also associated with symptom severity at an uncorrected level.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. Summary Rank of Differentially Methylated Probes from Group Regression Modelling (uncorrected \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05)\u003c/p\u003e \u003cp\u003e[INSERT Table \u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e HERE]\u003c/p\u003e \u003cp\u003e[INSERT FIGURE \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e HERE]\u003c/p\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e \u003cp\u003eWaterfall plot of delta beta values of significant differentially methylated probes (uncorrected \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.05) for ADHD compared to controls from group regression modelling.\u003c/p\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eEnrichment analysis\u003c/h2\u003e \u003cp\u003eTo explore the possibility of SAM pathway and/or regional enrichment between those with and without ADHD, a series of \u003cem\u003et\u003c/em\u003e-tests of the resultant regression \u003cem\u003et\u003c/em\u003e-scores were conducted. In summary, significant differences in \u003cem\u003et\u003c/em\u003e-score distribution were found for the entire SAM pathway (\u003cem\u003em\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.093, \u003cem\u003et\u003c/em\u003e\u0026thinsp;=\u0026thinsp;3.414, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;6.623 x 10\u003csup\u003e\u0026minus;\u0026thinsp;4\u003c/sup\u003e), as well as CpG island shelves (regions 2\u0026ndash;4 kb upstream (5\u0026rsquo;) of CpG islands; \u003cem\u003em\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.325, \u003cem\u003et\u003c/em\u003e\u0026thinsp;=\u0026thinsp;2.474, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.016); probes located in open sea (regions of the genome that are located far from any known CpG islands; \u003cem\u003em\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.139, \u003cem\u003et\u003c/em\u003e\u0026thinsp;=\u0026thinsp;3.426, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;6.562 x 10\u003csup\u003e\u0026minus;\u0026thinsp;4\u003c/sup\u003e), probes within the 5\u0026rsquo; untranslated region between transcriptional start site and ATG start site (\u003cem\u003em\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.208, \u003cem\u003et\u003c/em\u003e\u0026thinsp;=\u0026thinsp;3.894, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1.202 x 10\u003csup\u003e\u0026minus;\u0026thinsp;4\u003c/sup\u003e) and probes located in the body of the gene between ATG start site and stop codon (\u003cem\u003em\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.077, \u003cem\u003et\u003c/em\u003e\u0026thinsp;=\u0026thinsp;2.013, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.045). This indicates that for the SAM pathway, and probes that lie in these regions, children with ADHD show a subtle pattern of increased DNA methylation. For histograms of \u003cem\u003et\u003c/em\u003e-distributions see Figure S2.\u003c/p\u003e \u003cp\u003eADHD symptom severity\u003c/p\u003e \u003cp\u003eSecondly, we investigated associations between SAM genes and ADHD symptom severity as estimated by the Conners-3 ADHD Index. At an uncorrected level, 63 probes reached statistical significance at \u003cem\u003ea\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.05 and 4 of these were annotated to \u003cem\u003eST3GAL3\u003c/em\u003e (cg25630069, cg10874168, cg06176087, cg22397365). A summary of these probes can be found in Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e. These probes were distributed across 27 genes. Of the 33 total SAM genes, 27 were represented in the 63 significant probes with 9 genes contributing one probe (\u003cem\u003eNANP, NANS NEU4, SLC35A1, ST3GAL2, ST6GALNAC1, ST6GALNAC6, ST8SIA4, ST8SIA5\u003c/em\u003e), 5 genes contributing 2 probes (\u003cem\u003eNPL, ST3GAL1, ST3GAL5, ST6GALNAC4, ST8SIA6\u003c/em\u003e), 3 genes contributing 3 probes each (G\u003cem\u003eLB1, NEU1, ST6GALNAC5\u003c/em\u003e), 4 genes contributing 4 probes (\u003cem\u003eNEU3, ST3GAL3, ST6GALNAC3, ST8SIA2\u003c/em\u003e) 1 gene contributing 5 probes (\u003cem\u003eST6GAL2\u003c/em\u003e) and 2 genes contributing 6 probes (\u003cem\u003eST3GAL4, ST6GAL1\u003c/em\u003e). The \u003cem\u003eST3GAL3\u003c/em\u003e probe previously reported by Walton et al (2017) (cg09989037) was ranked 539 of 1188 probes (\u003cem\u003eΔβ\u003c/em\u003e = -4.584 x 10\u003csup\u003e\u0026minus;\u0026thinsp;5\u003c/sup\u003e; \u003cem\u003et\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1.201; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.232; \u003cem\u003ep\u003c/em\u003e\u003csub\u003e\u003cem\u003eFDR\u003c/em\u003e\u003c/sub\u003e = 0.943). No individual probes survived correction for multiple comparisons.\u003c/p\u003e \u003cp\u003eWhen compared to the diagnostic regression, there was an overlap of 7 probes indicating that DNA methylation of these probes was significantly associated with both ADHD diagnostic status and symptom severity at an uncorrected level. These probes are marked with a star in the relevant tables.\u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eDespite the understanding that ADHD is heritable with a neurophysiological basis (\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e), understanding of the biological mechanisms and drivers is still lacking. Both genetic and epigenetic research have highlighted the potential role of \u003cem\u003eST3GAL3\u003c/em\u003e in ADHD (\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e). Given the role this gene plays in brain formation and function (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e), and the broader neuroimaging evidence indicating structural and functional brain differences between children with and without ADHD (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e), this work set out to investigate the epigenetic contribution of \u003cem\u003eST3GAL3\u003c/em\u003e to ADHD, along with other genes apart of the sialic acid metabolism (SAM) pathway.\u003c/p\u003e \u003cp\u003eAdopting an alternative approach to candidate-gene studies, this work employed a pathway-approach, whereby genes implicated in the broader SAM pathway were also examined. Once correcting for multiple comparisons across 1188 probes, no group-level differences in DNA methylation were found for \u003cem\u003eST3GAL3\u003c/em\u003e or SAM probes, at an uncorrected level, there were 38 significant (unadjusted \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05) probes, 3 of which were annotated to \u003cem\u003eST3GAL3\u003c/em\u003e (see Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). These lay within the body of the gene and the 3\u0026rsquo; untranslated region (cg05180596, cg25630069, cg19326856). Post-hoc analysis also suggested an overall subtly increased pattern of DNA methylation across the entire SAM pathway for the ADHD group. In addition to this, overall increases were seen in the ADHD group compared to the controls in SAM probes located in shelves 2\u0026ndash;4 kb upstream (5\u0026rsquo;) of CpG islands, open sea, within 5\u0026rsquo; untranslated region between transcriptional start site and ATG start site, and probes located in the body of the gene between ATG start site and stop codon. These results indicate that nuanced aberration in sialic acid metabolism may play a role in ADHD.\u003c/p\u003e \u003cp\u003eEnrichment analysis of the SAM pathway showed an overall increase in DNA methylation for the ADHD group. However, caution must be taken with this interpretation as the relationship between DNA methylation and gene expression can vary at different sites across the genome (\u003cspan additionalcitationids=\"CR44\" citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e). At the pathway level, this may indicate an overall reduced expression of genes associated with sialic acid metabolism. Examination of probes significant at an uncorrected level showed that 84% of these probes (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;32) were annotated to genes involved in the biosynthesis of sialic acid rather than its catabolism. Of these 32 probes, 28 were annotated to sialyltransferases (ST6GAL1, ST3GAL1-5, ST6GALNAC1,3\u0026ndash;5, and ST8SIA1,5,6). Though the specific role of each sialyltransferase varies, their primary role is to catalyse the addition of sialic acid from CMP-Sia (a nucleotide sugar donor) to the terminus of the oligosaccharide chain of a glycoprotein or glycolipid, ultimately resulting in the glycoconjugate structure transported to the bilipid membrane layer (\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e). In the brain, the glycome is dominated by gangliosides (sialylated glycosphingolipids) which carry roughly 75% of the brain\u0026rsquo;s sialic acid, functioning as both intra- and extra-cellular recognition and regulation molecules(\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e). Knock-out mouse models have shown that mutations to sialyltransferase genes involved in ganglioside biosynthesis impact axon-myelin interactions, with mice experiencing extensive motor deficits accompanied by significant (\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e, \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e). Interestingly, human brain imaging studies of ADHD cohorts show disruptions to wide-spread white matter networks (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan additionalcitationids=\"CR50\" citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e). It is difficult to ascertain from these studies, however, whether disruptions to the axon-myelin relationship lay at the heart of these differences. Diffusion weighted imaging is the dominant form of white matter neuroimaging in ADHD, yet differences in white matter microstructure metrics are not specific to myelin and could also represent other elements of white matter microstructure such as fibre architecture, axon diameter and cell swelling (\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e). This highlights the need for future work to specifically focus on brain white matter myelin in ADHD and the potential epigenetic contribution of SAM\u003c/p\u003e \u003cp\u003eDue to its dynamic nature, epigenetic state is generally temporal. Similarly, brain development, starting \u003cem\u003ein utero\u003c/em\u003e and continuing into early adulthood, presents a consistently varying landscape (\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e). Although ADHD is believed to stem from a disruption to brain development, the underlying timing of behavioural, brain and epigenetic changes are not well understood, highlighting a need for longitudinal work. One such study adopted a methylome-wide prospective investigation with ADHD symptom trajectories (7\u0026ndash;15 years). Of the 13 probes found to be differentially methylated at birth (cord blood) between high and low ADHD symptom trajectories, one was annotated to \u003cem\u003eST3GAL3\u003c/em\u003e \u0026ndash; a SAM sialyltransferase (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e). Interestingly, concurrent DNA methylation of this probe at age 7 (whole blood) was not associated with either high or low ADHD symptom trajectory, aligning with the current work. This could indicate potential epigenetic staging effects whereby differences in DNA methylation of \u003cem\u003eST3GAL3\u003c/em\u003e may precede neurological and behavioural manifestations of ADHD (\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e). Although our study failed to replicate effects of the \u003cem\u003eST3GAL3\u003c/em\u003e probe highlighted in Walton \u003cem\u003eet al.\u003c/em\u003e, at birth, results do align with analysis conducted at age 7 whereby no association was found with the probe and ADHD presentation. Interestingly however, here the SAM pathway as a whole, was seen to be hypermethylated in the ADHD cohort. Together, this information suggests disruption to SAM in ADHD. Sialic acid (specifically polysia \u0026ndash; multiple sialic acids) plays a fundamental role in neurogenesis (\u003cspan additionalcitationids=\"CR55 CR56 CR57 CR58\" citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e) and appears to both positively and negatively regulate synaptogenesis postnatally (\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e). In rat brains, different stages of synaptic outgrowth are marked by distinct polysia profiles whereby initially polysia labels the entire synapse yet is progressively reduced to pre- and post-synaptic membranes and ultimately lost altogether as synapses are formed (\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e). The differential expression profiles of sialic acid during neurogenesis and synaptogenesis highlights the presence of epigenetic regulation of SAM genes throughout pre- and post- natal development and underscores the potential widespread yet subtle consequences of disruption to this pathway. Future work is encouraged to not only adopt longitudinal study designs, but to also examine the relationship between DNA methylation of SAM genes at birth and ADHD behavioural manifestations where possible.\u003c/p\u003e \u003cp\u003eThe pathway approach adopted here proved a strong and viable alternative to the long-adopted candidate-gene study (CGS) approach. Similarly to CGS, the study research questions, and design were informed by the broader literature; however, the inclusion of genes involved in the broader biochemical pathways offers an opportunity to not only examine a singular gene (or genes) but also those involved in the same biological processes. This means that relationships can be examined at a greater degree, compared to targeted approaches. For example, instead of focusing on a few CpG sites in the promoter, we can investigate CpG sites throughout the whole gene (or pathway) and associated regulatory elements. From a financial perspective, genome-wide assaying, as conducted here with the Illumina EPIC array, may prove more fruitful compared to targeted assays. Although the overarching cost of genome-wide assay is more at face value, they continue to become relatively cheaper compared to targeted assays as the utility gained could be considered worthwhile. Associated time and labour are less, and the resultant data allows for more comprehensive, nuanced research questions to be answered.\u003c/p\u003e \u003cp\u003eThe results of this study should be interpreted considering a number of limitations. Firstly, the sample size (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;140) is considered small for an epigenetic study, though was constrained by the existing cohort size. Secondly, peripheral tissues samples were collected in the form of saliva. Although this reduces the sensitivity of conclusions drawn relating to brain function, studies indicate that the correlation between DNA methylation of brain tissues and saliva samples is strong (r\u0026thinsp;=\u0026thinsp;0.90) (\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e), however it is yet to be established whether this relationship holds true within clinical cohorts. Lastly, the categorical approach to ADHD assessment in both epigenetic and genetic studies potentially masks the specificity of results. A more dimensional approach, such as we adopted here with symptom severity, may help with more targeted outcomes, thus paving the way for more individualised aid, in both diagnosis and intervention. In addition, adoption of more functionally relevant behavioural and cognitive measures may be useful in adequately capturing the true multi-dimensionality of ADHD and further explore the role of epigenetics.\u003c/p\u003e \u003cp\u003eIn conclusion, our study is the first to adopt a pathways approach to explore the epigenetic role of \u003cem\u003eST3GAL3\u003c/em\u003e and other sialic acid metabolism genes in ADHD. While effects of less than 1% were seen for individual probes, a broader pattern of hypermethylation was found for the entire SAM pathway. This indicates that \u003cem\u003eST3GAL3\u003c/em\u003e, as well as other sialyltransferase genes and the broader SAM pathway, could contribute to disrupted epigenetic regulation in ADHD. Future longitudinal pre- and post-natal research across broad developmental age ranges is necessary to further explore these findings.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e \u003ch2\u003eConflict of Interest\u003c/h2\u003e \u003cp\u003eAll authors report no conflict of interest, financial or otherwise.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eAcknowledgements\u003c/h2\u003e \u003cp\u003eThe NICAP cohort is supported by the National Health and Medical Research Council of Australia (NHMRC #1065895 and #2029361) and a grant from the Waterloo Foundation. This research was supported by use of the Nectar Research Cloud, a collaborative Australian research platform supported by the NCRIS-funded Australian Research Data Commons (ARDC). Authors gratefully acknowledge the contribution to this work of the Victorian Operational Infrastructure Support Program received by the Burnet Institute.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eParlatini V, Itahashi T, Lee Y, Liu S, Nguyen TT, Aoki YY, et al. White matter alterations in Attention-Deficit/Hyperactivity Disorder (ADHD): a systematic review of 129 diffusion imaging studies with meta-analysis. Mol Psychiatry. 2023.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYu M, Gao X, Niu X, Zhang M, Yang Z, Han S, et al. Meta-analysis of structural and functional alterations of brain in patients with attention-deficit/hyperactivity disorder. Frontiers in Psychiatry. 2023;13.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFaraone SV, Larsson H. Genetics of attention deficit hyperactivity disorder. Mol Psychiatry. 2019;24(4):562\u0026ndash;75.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDemontis D, Walters GB, Athanasiadis G, Walters R, Therrien K, Nielsen TT, et al. Genome-wide analyses of ADHD identify 27 risk loci, refine the genetic architecture and implicate several cognitive domains. Nat Genet. 2023;55(2):198\u0026ndash;208.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDemontis D, Walters RK, Martin J, Mattheisen M, Als TD, Agerbo E, et al. Discovery of the first genome-wide significant risk loci for attention deficit/hyperactivity disorder. Nat Genet. 2019;51(1):63\u0026ndash;75.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWalton E, Pingault JB, Cecil CAM, Gaunt TR, Relton CL, Mill J, et al. Epigenetic profiling of ADHD symptoms trajectories: a prospective, methylome-wide study. Mol Psychiatry. 2017;22(2):250\u0026ndash;6.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFaraone SV. Understanding Environmental Exposures and ADHD: a Pathway Forward. Prevention Science. 2024;25(Suppl 2):337\u0026ndash;42.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eVan Ewijk H, Heslenfeld DJ, Zwiers MP, Buitelaar JK, Oosterlaan J. Diffusion Tensor Imaging in Attention Deficit/Hyperactivity Disorder: A Systematic Review and Meta-Analysis. Neurosci Biobehav Rev. 2012.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLeenders AEM, Damatac CG, Soheili-Nezhad S, Chauvin RJ, Mennes M, Zwiers MP, et al. Associations Between Attention-deficit Hyperactivity Disorder (ADHD) Symptom Remission and White Matter Microstructure: A Longitudinal Analysis. Journal of Child Psychology and Psychiatry Advances. 2021.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNagel BJ, Bathula D, Herting M, Schmitt C, Kroenke CD, Fair D, et al. Altered white matter microstructure in children with attention-deficit/hyperactivity disorder. J Am Acad Child Adolesc Psychiatry. 2011;50(3):283\u0026ndash;92.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eOnnink AMH, Zwiers MP, Hoogman M, Mostert JC, Dammers J, Kan CC, et al. Deviant white matter structure in adults with attention-deficit/hyperactivity disorder points to aberrant myelination and affects neuropsychological performance. Prog Neuropsychopharmacol Biol Psychiatry. 2015;63:14\u0026ndash;22.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSchnaar RL, Gerardy-Schahn R, Hildebrandt H. Sialic Acids in the Brain: Gangliosides and Polysialic Acid in Nervous System Development, Stability, Disease, and Regeneration. Physiol Rev. 2014;94(2):461\u0026ndash;518.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSchauer R. Sialic acids: chemistry, metabolism, and function: Springer Science \u0026amp; Business Media; 2012.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSchnaar RL. ST3 Beta-Galactoside Alpha-2,3-Sialyltransferase 3 (ST3GAL3). In: Taniguchi N, Honke K, Fukuda M, Narimatsu H, Yamaguchi Y, Angata T, editors. Handbook of Glycosyltransferases and Related Genes. Tokyo: Springer Japan; 2014. p. 657\u0026ndash;65.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYoo SW, Motari MG, Susuki K, Prendergast J, Mountney A, Hurtado A, et al. Sialylation regulates brain structure and function. FASEB J. 2015;29(7):3040\u0026ndash;53.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHu H, Eggers K, Chen W, Garshasbi M, Motazacker MM, Wrogemann K, et al. ST3GAL3 Mutations Impair the Development of Higher Cognitive Functions. Am J Hum Genet. 2011.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eIndellicato R, Domenighini R, Malagolini N, Cereda A, Mamoli D, Pezzani L, et al. A novel nonsense and inactivating variant of ST3GAL3 in two infant siblings suffering severe epilepsy and expressing circulating CA19.9. Glycobiology. 2020;30(2):95\u0026ndash;104.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eEdvardson S, Baumann AM, M\u0026uuml;hlenhoff M, Stephan O, Kuss AW, Shaag A, et al. West syndrome caused by ST3Gal-III deficiency. Epilepsia. 2013;54(2):e24-e7.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFarajollahi Z, Razmara E, Heidari E, Jafarinia E, Garshasbi M. A novel variant of ST3GAL3 causes non-syndromic autosomal recessive intellectual disability in Iranian patients. The Journal of Gene Medicine. 2020;22(11):e3253.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSilk TJ, Genc S, Anderson V, Efron D, Hazell P, Nicholson JM, et al. Developmental brain trajectories in children with ADHD and controls: a longitudinal neuroimaging study. BMC Psychiatry. 2016;16(1):59.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSciberras E, Efron D, Schilpzand EJ, Anderson V, Jongeling B, Hazell P, et al. The Children\u0026rsquo;s Attention Project: a community-based longitudinal study of children with ADHD and non-ADHD controls. BMC Psychiatry. 2013;13(1):18.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eConners C. Conners third edition (Conners 3): Los Angel CA West Psychol Serv; 2008.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eShaffer D, Fisher P, Lucas CP, Dulcan MK, Schwab-Stone ME. NIMH Diagnostic Interview Schedule for Children Version IV (NIMH DISC-IV): description, differences from previous versions, and reliability of some common diagnoses. J Am Acad Child Adolesc Psychiatry. 2000;39(1):28\u0026ndash;38.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMilacic M, Beavers D, Conley P, Gong C, Gillespie M, Griss J, et al. The Reactome Pathway Knowledgebase 2024. Nucleic Acids Res. 2024;52(D1):D672-d8.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTouleimat N, Tost J. Complete pipeline for Infinium(\u0026reg;) Human Methylation 450K BeadChip data processing using subset quantile normalization for accurate DNA methylation estimation. Epigenomics. 2012;4(3):325\u0026ndash;41.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAryee MJ, Jaffe AE, Corrada-Bravo H, Ladd-Acosta C, Feinberg AP, Hansen KD, et al. Minfi: a flexible and comprehensive Bioconductor package for the analysis of Infinium DNA methylation microarrays. Bioinformatics. 2014;30(10):1363\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChen YA, Lemire M, Choufani S, Butcher DT, Grafodatskaya D, Zanke BW, et al. Discovery of cross-reactive probes and polymorphic CpGs in the Illumina Infinium HumanMethylation450 microarray. Epigenetics. 2013;8(2):203\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMaksimovic J, Phipson B, Oshlack A. A cross-package Bioconductor workflow for analysing methylation array data. F1000Research. 2016;5:1281.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFishilevich S, Nudel R, Rappaport N, Hadar R, Plaschkes I, Iny Stein T, et al. GeneHancer: genome-wide integration of enhancers and target genes in GeneCards. Database. 2017;2017.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTeschendorff AE, Zheng SC. Cell-type deconvolution in epigenome-wide association studies: a review and recommendations. Epigenomics. 2017;9(5):757\u0026ndash;68.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZheng SC, Webster AP, Dong D, Feber A, Graham DG, Sullivan R, et al. A novel cell-type deconvolution algorithm reveals substantial contamination by immune cells in saliva, buccal and cervix. Epigenomics. 2018;10(7):925\u0026ndash;40.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNewman AM, Liu CL, Green MR, Gentles AJ, Feng W, Xu Y, et al. Robust enumeration of cell subsets from tissue expression profiles. Nature Methods. 2015;12(5):453\u0026ndash;7.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHouseman EA, Accomando WP, Koestler DC, Christensen BC, Marsit CJ, Nelson HH, et al. DNA methylation arrays as surrogate measures of cell mixture distribution. BMC Bioinformatics. 2012;13(1):86.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTeschendorff AE, Breeze CE, Zheng SC, Beck S. A comparison of reference-based algorithms for correcting cell-type heterogeneity in Epigenome-Wide Association Studies. BMC Bioinformatics. 2017;18(1):105.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRitchie ME, Phipson B, Wu D, Hu Y, Law CW, Shi W, et al. limma powers differential expression analyses for RNA-sequencing and microarray studies. Nucleic Acids Res. 2015;43(7):e47-e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFaul F, Erdfelder E, Buchner A, Lang A-G. Statistical power analyses using G*Power 3.1: Tests for correlation and regression analyses. Behavior Research Methods. 2009;41(4):1149\u0026ndash;60.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFaul F, Erdfelder E, Lang AG, Buchner A. G*Power 3: a flexible statistical power analysis program for the social, behavioral, and biomedical sciences. Behaviour Research Methods. 2007;39(2):175\u0026ndash;91.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePeters Timothy J, Buckley Michael J, Chen Y, Smyth Gordon K, Goodnow Christopher C, Clark Susan J. Calling differentially methylated regions from whole genome bisulphite sequencing with DMRcate. Nucleic Acids Res. 2021;49(19):e109-e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePeters TJ, Buckley MJ, Statham AL, Pidsley R, Samaras K, V Lord R, et al. De novo identification of differentially methylated regions in the human genome. Epigenetics \u0026amp; Chromatin. 2015;8(1):6.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNigg JT, Sibley MH, Thapar A, Karalunas SL. Development of ADHD: Etiology, Heterogeneity, and Early Life Course. Annual Review of Developmental Psychology. 2020;2(Volume 2, 2020):559\u0026thinsp;\u0026ndash;\u0026thinsp;83.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMichelini G, Norman LJ, Shaw P, Loo SK. Treatment biomarkers for ADHD: Taking stock and moving forward. Translational Psychiatry. 2022;12(1):444.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLesch KP. Editorial: Can dysregulated myelination be linked to ADHD pathogenesis and persistence? J Child Psychol Psychiatry. 2019;60(3):229\u0026ndash;31.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBrenet F, Moh M, Funk P, Feierstein E, Viale AJ, Socci ND, et al. DNA methylation of the first exon is tightly linked to transcriptional silencing. PLoS One. 2011;6(1):e14524.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHellman A, Chess A. Gene body-specific methylation on the active X chromosome. Science. 2007;315(5815):1141\u0026ndash;3.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBall MP, Li JB, Gao Y, Lee JH, LeProust EM, Park IH, et al. Targeted and genome-scale strategies reveal gene-body methylation signatures in human cells. Nat Biotechnol. 2009;27(4):361\u0026ndash;8.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAudry M, Jeanneau C, Imberty A, Harduin-Lepers A, Delannoy P, Breton C. Current trends in the structure\u0026ndash;activity relationships of sialyltransferases. Glycobiology. 2010;21(6):716\u0026ndash;26.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePan B, Fromholt SE, Hess EJ, Crawford TO, Griffin JW, Sheikh KA, et al. Myelin-associated glycoprotein and complementary axonal ligands, gangliosides, mediate axon stability in the CNS and PNS: neuropathology and behavioral deficits in single- and double-null mice. Exp Neurol. 2005;195(1):208\u0026ndash;17.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChiavegatto S, Sun J, Nelson RJ, Schnaar RL. A functional role for complex gangliosides: motor deficits in GM2/GD2 synthase knockout mice. Exp Neurol. 2000;166(2):227\u0026ndash;34.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAoki Y, Abe O, Nippashi Y, Yamasue H. Comparison of white matter integrity between autism spectrum disorder subjects and typically developing individuals: a meta-analysis of diffusion tensor imaging tractography studies. Molecular Autism. 2013;4(1):25.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChen L, Hu X, Ouyang L, He N, Liao Y, Liu Q, et al. A Systematic Review and Meta-Analysis of Tract-Based Spatial Statistics Studies Regarding Attention-Deficit/Hyperactivity Disorder. Neurosci Biobehav Rev. 2016.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhao Y, Yang L, Gong G, Cao Q, Liu J. Identify aberrant white matter microstructure in ASD, ADHD and other neurodevelopmental disorders: A meta-analysis of diffusion tensor imaging studies. Prog Neuropsychopharmacol Biol Psychiatry. 2022;113:110477.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGrey matter correlates of autistic traits in women with anorexia nervosa. J Psychiatry Neurosci. 2018;43(2):79\u0026ndash;86.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSilbereis John C, Pochareddy S, Zhu Y, Li M, Sestan N. The Cellular and Molecular Landscapes of the Developing Human Central Nervous System. Neuron. 2016;89(2):248\u0026ndash;68.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBernier PJ, Vinet J, Cossette M, Parent A. Characterization of the subventricular zone of the adult human brain: evidence for the involvement of Bcl-2. Neurosci Res. 2000;37(1):67\u0026ndash;78.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCurtis MA, Kam M, Nannmark U, Anderson MF, Axell MZ, Wikkelso C, et al. Human neuroblasts migrate to the olfactory bulb via a lateral ventricular extension. Science. 2007;315(5816):1243\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eQui\u0026ntilde;ones-Hinojosa A, Sanai N, Soriano-Navarro M, Gonzalez-Perez O, Mirzadeh Z, Gil-Perotin S, et al. Cellular composition and cytoarchitecture of the adult human subventricular zone: a niche of neural stem cells. J Comp Neurol. 2006;494(3):415\u0026ndash;34.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSanai N, Nguyen T, Ihrie RA, Mirzadeh Z, Tsai HH, Wong M, et al. Corridors of migrating neurons in the human brain and their decline during infancy. Nature. 2011;478(7369):382\u0026ndash;6.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSanai N, Tramontin AD, Qui\u0026ntilde;ones-Hinojosa A, Barbaro NM, Gupta N, Kunwar S, et al. Unique astrocyte ribbon in adult human brain contains neural stem cells but lacks chain migration. Nature. 2004;427(6976):740\u0026ndash;4.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWeickert CS, Webster MJ, Colvin SM, Herman MM, Hyde TM, Weinberger DR, et al. Localization of epidermal growth factor receptors and putative neuroblasts in human subependymal zone. J Comp Neurol. 2000;423(3):359\u0026ndash;72.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eUryu K, Butler AK, Chesselet MF. Synaptogenesis and ultrastructural localization of the polysialylated neural cell adhesion molecule in the developing striatum. J Comp Neurol. 1999;405(2):216\u0026ndash;32.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNishitani S, Isozaki M, Yao A, Higashino Y, Yamauchi T, Kidoguchi M, et al. Cross-tissue correlations of genome-wide DNA methylation in Japanese live human brain and blood, saliva, and buccal epithelial tissues. Translational Psychiatry. 2023;13(1):72.\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":"","lastPublishedDoi":"10.21203/rs.3.rs-4519315/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4519315/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eResearch indicates that the underlying neurobiology of Attention Deficit/Hyperactivity Disorder (ADHD) may stem from a combination of genetic and environmental contributions. Genetic and epigenetic research have highlighted the potential role of the sialtransferase gene \u003cem\u003eST3GAL3\u003c/em\u003e in this process. Adopting a pathways approach, this study sought to examine the role that \u003cem\u003eST3GAL3\u003c/em\u003e and other sialic acid metabolism (SAM) genes play in ADHD. Peripheral measures of DNA methylation (Illumina 850k EPIC; saliva samples) and clinical data were collected as part of a community-based pediatric cohort consisting of 90 children with ADHD [\u003cem\u003em\u003c/em\u003e\u003csub\u003eage\u003c/sub\u003e= 10.40 (0.49); 66% male] and 50 non-ADHD controls [\u003cem\u003em\u003c/em\u003e\u003csub\u003eage\u003c/sub\u003e= 10.40 (0.45); 48% male]. Using Reactome, 33 SAM genes were defined and resulted in a total of 1419 probes which included associated promotor/enhancer regions. Linear regression analysis was undertaken to explore differences in SAM probe DNA methylation between children with and without ADHD. The relationship with ADHD symptom severity was also examined. Analysis found 38 probes in the group-regression, and 64 probes in the symptom severity regression reached significance at an uncorrected level (a\u0026thinsp;=\u0026thinsp;0.05). No probes survived correction for multiple comparisons. Enrichment analysis revealed an overall pattern of hypermethylation across the SAM pathway for the ADHD group, with 84% of nominally significant probes being annotated to sialyltransferase genes. These results suggest that \u003cem\u003eST3GAL3\u003c/em\u003e and the broader SAM pathway could contribute to subtly disrupted epigenetic regulation in ADHD. However, extensive longitudinal research, across broad developmental age ranges, is necessary to further explore these findings.\u003c/p\u003e","manuscriptTitle":"Epigenetic Analysis of ST3GAL3 and other Sialic Acid Metabolism Genes in ADHD","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-10-10 13:11:04","doi":"10.21203/rs.3.rs-4519315/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":"9b6ff799-186e-47a0-adb2-4f503e4ff32c","owner":[],"postedDate":"October 10th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":35954348,"name":"Biological sciences/Neuroscience/Epigenetics in the nervous system"},{"id":35954349,"name":"Health sciences/Pathogenesis"}],"tags":[],"updatedAt":"2025-02-21T09:56:09+00:00","versionOfRecord":[],"versionCreatedAt":"2024-10-10 13:11:04","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4519315","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4519315","identity":"rs-4519315","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}