Alternative Splicing Analysis in a Spanish ASD (Autism Spectrum Disorders) Cohort: In silico Prediction and Validation

Alternative Splicing Analysis in a Spanish ASD (Autism Spectrum Disorders) Cohort: In silico Prediction and Validation | 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 Alternative Splicing Analysis in a Spanish ASD (Autism Spectrum Disorders) Cohort: In silico Prediction and Validation S Dominguez-Alonso, M Tubío-Fungueiriño, J González-Peñas, M Fernández-Prieto, and 4 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5136316/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 28 Mar, 2025 Read the published version in Scientific Reports → Version 1 posted 10 You are reading this latest preprint version Abstract Autism Spectrum Disorders (ASD) are complex and genetically heterogeneous neurodevelopmental conditions. Although alternative splicing (AS) has emerged as a potential contributor to ASD pathogenesis, its role in large-scale genomic studies has remained relatively unexplored. In this comprehensive study, we utilized computational tools to identify, predict, and validate splicing variants within a Spanish ASD cohort (360 trios), shedding light on their potential contributions to the disorder. We utilized SpliceAI, a newly developed machine-learning tool, to identify high-confidence splicing variants in the Spanish ASD cohort and applied a stringent threshold (Δ ≥ 0.8) to ensure robust confidence in the predictions. The in silico validation was then conducted using SpliceVault, which provided compelling evidence of the predicted splicing effects, using 335,663 reference RNA-sequencing (RNA-seq) datasets from GTEx v8 and the sequence read archive (SRA). Furthermore, ABSplice was employed for additional variant validation and to elucidate the tissue-specific impacts of the splicing variants. Notably, our analysis suggested the contribution of splicing variants within CACNA1I, CBLB, CLTB, DLGAP1, DVL3, KIAA0513, OFD1, PKD1, SLC13A3 , and SCN2A. Complementary datasets, including more than 42,000 ASD cases, were employed for gene validation and gene ontology (GO) analysis. These analyses revealed potential tissue-specific effects of the splicing variants, particularly in adipose tissue, testis, and the brain. These findings suggest the involvement of these tissues in ASD etiology, which opens up new avenues for further functional testing. Enrichments in molecular functions and biological processes imply the presence of separate pathways and mechanisms involved in the progression of the disorder, thereby distinguishing splicing genes from other ASD-related genes. Notably, splicing genes appear to be predominantly associated with synaptic organization and transmission, in contrast to non-splicing genes ( i.e. , genes harboring de novo and inherited coding variants not predicted to alter splicing), which have been mainly implicated in chromatin remodeling processes. In conclusion, this study advances our comprehension of the role of AS in ASD and calls for further investigations, including in vitro validation and integration with multi-omics data, to elucidate the functional roles of the highlighted genes and the intricate interplay of the splicing process with other regulatory mechanisms and tissues in ASD. Biological sciences/Genetics/Neurodevelopmental disorders/Autism spectrum disorders Biological sciences/Genetics/Genomics/Transcriptomics Health sciences/Neurology/Neurological disorders/Neurodevelopmental disorders Figures Figure 1 Figure 2 Figure 3 Introduction Autism Spectrum Disorders (ASD) encompass a group of phenotypically and genetically heterogeneous neurodevelopmental disorders (NDDs) characterized by difficulties in social interaction and communication, repetitive behavior, and restricted interests 1 . ASD have a strong heritability, estimated around 80% 2 , although their complex genetic etiology has limited progress towards understanding their molecular basis. Genomic approaches, including genome-wide association studies (GWAS) 3 – 5 , whole exome (WES) and whole-genome sequencing (WGS) of families 6 – 9 , as well as transcriptome analyses by RNA-sequencing (RNA-seq) and microarray techniques 10 – 12 , have yielded association with hundreds of genes over the last decades. However, a large portion of their genetic architecture remains uncharacterized, and ASD diagnosis continues to be a major challenge in both clinical and research settings. Emerging advances have implicated alternative splicing (AS) as yet another process influencing the development of the disease. AS is the mechanism by which introns are excised from the pre-mRNA primary transcript, and exons are selected and concatenated in different arrangements, generating multiple transcript isoforms and, consequently, protein products, from a single gene. More than 95% of multiexon human genes that encode proteins undergo AS, and most mRNA splice patterns exhibit tissue- and cell-type-specificity 13 . Consequently, AS is mainly accountable for generating the vast proteomic diversity observed in complex organisms and has been increasingly linked to functional intricacies within the central nervous system. It plays crucial roles in nervous system development, neuronal differentiation and maturation, as well as complex neuronal processes, such as the control of synaptic plasticity associated with cognition 14 , 15 . Some neuronal genes, like neurexins, possess the ability to produce hundreds of mRNA isoforms. This phenomenon stands as one of the most extensive cases of AS regulation observed to date and plays a pivotal role in optimal neuronal function 14 . Notably, aberrations in AS have been associated with several NDDs, including schizophrenia and bipolar disorders 16 , 17 , Rett syndrome 18 , 19 , fragile X syndrome 20 , 21 and ASD 16 , 22 – 29 . These aberrations may occur at two different levels: cis-acting motifs (exonic/intronic splicing enhancers and silencers) and trans-acting factors that regulate the assembly of the spliceosome by recognition of proximal cis-elements, such as RNA-binding proteins (RBPs) 14 , 30 , 31 . The majority of AS studies in ASD patients pertain to individual RBPs that govern the inclusion/exclusion of microexons (3–27 nt). Microexons constitute the most conserved component of the neural-regulated AS program. They generally reside on protein surfaces, specifically in domains with important roles in shaping protein-protein interactions. These sequences are significantly enriched in genes with neuronal function which have been genetically linked to ASD 25 , 32 , predominantly being neuronal-included 33 . RNA-Seq analysis of post-mortem brain samples in idiophatic ASD individuals shows misplicing in 30–40% of brain-specific microexons 25 . Analyses of larger cohorts of ASD individuals corroborated these observations and demonstrated that most of the differential splicing events involve the exclusion of these microexons 16 , 22 . Most microexons are controlled by the neural-specific Ser/Arg-related splicing factor, nSR100/SRRM4 26,27 , and its misregulation is linked to reduced expression levels of nSR100/SRRM4 in autistic brains. Employing heterozygous mutant mice expressing approximately 50% wild-type levels of nSR100, Quesnel-Vallières et al. , 2016 26 demonstrated that a single variant in a splicing regulator, and thus the disruption of its target splicing program, suffice to reproduce hallmark features of ASD, including altered social behaviour, synaptic transmission and neuronal excitability. Additional examples of RBPs implicated in the differential splicing changes observed in ASD brain are Rbfox1 34–36 and PTBP1 22,34,37 . Given the mounting evidence of splicing disruptions in ASD patients, we sought to investigate the possible effects of de novo mutations in AS within a Spanish cohort of 360 ASD trios. Here, we employed SpliceAI, a machine learning tool that robustly predicts splice sites and splice-disrupting variants, which has exhibited notable performance when compared to previously developed in silico detection tools 38 . Moreover, we utilized SpliceVault 39 and ABSsplice 40 , two newly developed tools that assess AS, in order to bolster robustness of the in silico splicing prediction and delve into the specific effects within human tissues. This paper provides initial insights into the potential funtional roles of genes harboring splicing variants, thereby highlighting molecular pathways and biological processes in which they are implicated. Methods Subjects The analysis described herein builds upon the complete sample set examined in Alonso Gonzalez et al . 2021 41 . DNA extraction from the Spanish ASD samples, consisting of 360 trios (unaffected parents and affected proband), was performed using the GentraPuregene blood kit (Qiagen Inc., Valencia, CA, USA) from peripheral blood. Participants from Santiago (n = 136) were recruited from the Complexo Hospitalario Universitario de Santiago de Compostela and Galician ASD organizations. Meanwhile, subjects from Madrid (n = 224) were enrolled through the AMITEA program at the Child and Adolescent Department of Psychiatry, Hospital General Universitario Gregorio Marañón. Inclusion criteria stipulated that only individuals aged 3 years or older were included in the study. Enrolled participants received a clinical diagnosis of ASD from trained pediatric neurologists or psychiatrists, following the criteria outlined in both the Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition Text Revision (DSM-IV-TR) and Fifth Edition (DSM-5). Additionally, when deemed necessary, the Autism Diagnostic Observation Schedule (ADOS) and the Autism Diagnostic Interview-Revised (ADI-R) were administered. All participants, along with their parents or legal representatives, provided written informed consent, and the study was conducted in accordance with the principles outlined in the Declaration of Helsinki. The Galician Comittee of Research Ethics (Xunta de Galicia) has approved this study under the Register 2020/400. Ninety samples from the Spanish cohort (360 trios) were already analyzed by Lim et al. , 2017 42 . The entire Spanish cohort was included in Satterstrom et al ., 2020 43 as part of the Autism Sequencing Consortium (ASC), a large-scale international genomic consortium integrating ASD cohorts and sequencing data from over one hundred investigators. All data generated as part of the ASC was transferred to dbGaP with Study Accession: phs000298.v4.p3. In silico prioritization of splicing variants To perform subsequent analyses, we leveraged previously reported biallelic de novo variants (DNVs) from our previous work. DNVs were defined as those present exclusively in probands and not in parents ( i.e. , genotypes 1/0 or 1/1 in probands and 0/0 in parents). The filtering steps carried out in Alonso-Gonzalez et al ., 2021 41 were not used with SpliceAI. Instead, appropriate quality filtering for splicing variants was applied to the raw de novo variants of the analysis, as detailed below. To perform in silico annotation and prioritization of splice variants, we used SpliceAI ( https://github.com/Illumina/SpliceAI ), a machine learning tool that robustly predicts splice sites and splice-disrupting variants. SpliceAI has been already utilized to assess the clinical impact of non-coding mutations that act through altered splicing in patients with ASD 38 . SpliceAI was executed with default parameters (-D: maximum distance between the variant and gained/lost splice site (default: 50); -M: mask scores representing annotated acceptor/donor gain and unannotated acceptor/donor loss (default: 0)). For the gene annotation file, we used he GENCODE V24 canonical annotation files included in the package. A SpliceAI annotation was available for 252,520 de novo variants (72.91% of 346,352 initial variants). Variants with delta scores Δ ≥ 0.8 (indicating confidently predicted splice-altering effects) were retained (n = 1,836). In addition, we considered only de novo variants with genome quality (GQ) ≥ 20 and alternate read depth (AD) ≥ 7 and removed any call if it had an allele frequency > 0.1% across the samples in our dataset or in the non-psychiatric subset of gnomAD (1,793 putative de novo variants excluded). In silico variant validation SpliceVault The tool SpliceVault 39 was employed to accurately predict the exact manner in which genetic variants affect the splicing process. It ranks the four most common unannotated splicing events across 335,663 reference RNA-seq samples (300K-RNA Top-4) from GTEx v8 (Genotype-Tissue Expression project) and SRA (Sequence Read Archive; an online archive of high-throughput RNA sequencing data). 300K-RNA is built in hg38 (GRCh38), so genome coordinates were converted between assemblies GRCh37 and GRCh38 using the UCSC web server's LiftOver tool ( https://genome.ucsc.edu/cgi-bin/hgLiftOver ). Splice variants were then interrogated against the SpliceVault web portal ( https://kidsneuro.shinyapps.io/splicevault/ ) using default settings recommended for clinical use: top 4 events, skipping of ≤ 2 exons and cryptic positions within +/- 600 nt. A variant was considered validated if it appeared in the top 4 events of SpliceVault. Tissue specificity: ABSplice Each variant was examined for its specific effect in any given human tissue. ABSplice 40 ( https://github.com/gagneurlab/absplice ), a model which maps acceptor and donor splice sites and quantifies their usage in 49 human tissues, was run with default parameters. For each variant, we recorded the tissue with the highest score for AS outputted by ABSplice. This score indicates the likelihood that a specific genetic variant causes abnormal splicing in a particular tissue. ABSplice thresholds are defined as 0.01 (low), 0.05 (intermediate), and 0.2 (high), which have approximately the same recalls as the high, medium, and low cutoffs of SpliceAI. A variant was considered validated if it appeared in the ABSplice dataset with a score higher than 0.2. Additionally, to enhance our understanding of the variant effects across various tissues, we annotated our variants by cross-referencing them with precomputed ABSplice-DNA scores for all tissues, not just the highest scoring one. The precomputed ABSplice-DNA scores for 49 human tissues and all possible SNVs genome-wide for hg38 were made available at Zenodo ( https://zenodo.org/records/7871809 ). Complementary datasets Comparing functional profiles can reveal functional consensus and differences among different experiments and helps in identifying differential functional modules in different datasets. For further validation of hereafter interrogated genes harboring splice variants, analysis of gene ontology (GO) enrichments and comparison of enriched terms in each dataset (see next section), we utilized previously reported ASD-related sets of genes (Table 1 ). After variant validation, genes harboring the validated variants were contrasted against different complementary gene datasets. These included genes associated with ASD and implicated in AS 25 , 28 , 29 , 35 , 38 , as well as the SFARI database. Genes that overlapped with these datasets were considered validated and were subsequently included in further GO analysis. Additionally, we utilized the same gene datasets (ASD-related genes implicated in AS) along with a study integrating de novo and inherited variants, not predicted to affect AS, in 42,607 ASD cases 44 . This information was employed for a detailed comparison of enriched terms among different categories of genetic variants, namely, de novo and inherited coding variants not predicted to alter splicing versus splicing variants. Table 1 Complementary datasets included in gene validation and GO analysis. Description Study type Samples Data availability Reference The Human Gene module of the SFARI database (up-to-date reference for all known human genes associated with autism ASD) was accessed (SFARI 07-17-2023 release) and queried against 1–2 scoring genes (high confidence and strong evidence of association with ASD, respectively). Mutation screening, family-based association, case-control, WES, WGS and CNV array - https://gene.sfari.org/database/human-gene/ ) - Highly conserved program of neural microexons primarily regulated by the neuronal-specific splicing factor nSR100/SRRM4. RNA-seq custom pipeline 22 autistic individuals, 20 controls Supplementary Table 2: Neural-regulated AS events in human 25 Unique patterns of AS and gene co-expression in ASD-affected dizygotic twins compared to their parents. AS and co-expression analyses Two pairs of DZ twins and their parents Supplementary table 1 : Differential AS events 28 Distinct AS patterns in the blood of patients with ASD compared to typically developing individuals. Whole genome exon arrays 30 ASD patient, 20 controls Additional file 2 29 Dysregulated splicing pattern of RBFOX1 -dependent alternative exons in the ASD brain. Post-mortem brain tissue 19 autism samples, 17 controls Supplementary Data: Differential Splicing Events 35 Detection of a 1.30-fold enrichment of de novo splicing mutations in ASD (p = 0.0203) compared to healthy controls when employing SpliceAI. High-depth mRNA sequencing 36 autism samples Supplementary Table 3 38 Analysis of de novo and inherited variants identifies 60 genes with exome-wide significance implicated in ASD, including five new risk genes ( NAV3 , ITSN1 , MARK2 , SCAF1 and HNRNPUL2 ). WES/WGS 42,607 autism cases Supplementary Table 1 44 CNV, copy number variant; DZ , dizygotic. The first 6 studies were used for gene validation, and the last 6 were used for comparison of enriched terms. Gene ontology (GO) analysis Gene network analysis HumanBase ( https://hb.flatironinstitute.org/ ) was used to build a gene network for the genes already associated, and thus, validated, in the aforementioned complementary datasets. HumanBase serves as a comprehensive resource for biological research and offers data-driven predictions related to gene expression, function, regulation, and interactions within the human domain, with a particular focus on specific cell types, tissues, and diseases. In order to capture tissue-specific gene function we used the “tissue specific gene networks: GIANT” analysis tool. The HumanBase GIANT analysis tool, constructs comprehensive genome-scale functional maps for various human tissues by integrating extensive datasets from over 14,000 distinct publications, covering thousands of experiments. The platform automatically evaluates the relevance of each dataset to 144 tissue- and cell lineage–specific functional contexts. The resulting functional gene maps offer detailed insights into protein function and interactions in specific human tissues and cell lineages. CACNA1I, CBLB, CLTB, DLGAP1, DVL3, KIAA0513, OFD1, PKD1, SLC13A3 and SCN2A ( i.e. , the validated genes) were selected as the input genes along with brain tissue in the 5 existing data types (co-expression, transcription factor binding, interaction, gene set enrichment analysis (GSEA) microRNA targets, and GSEA perturbations). The resultant network (henceforth designated as the splicing gene list, Supplementary Table 2, Supplementary Fig. 2) contains the subset of functionally related genes specific to brain tissue, capturing tissue-specific gene function, all of which were used to test for functional enrichment using genes annotated to GO biological process (BP), celular component (CC) and molecular function (MF) terms. Enrichment analysis ClusterProfiler ( https://github.com/YuLab-SMU/clusterProfiler ) 45 , an R package tailored for contrasting biological themes among gene groups, was harnessed to perform both GO over-representation test and to deduce enriched functional profiles on separate gene clusters ( i.e. , gene sets). GO enrichment analysis The package org.Hs.eg.db, provided by Bioconductor, was used as the genome wide annotation for Human. We employed the bitr tool (Biological Id TRanslator), already implemented in the clusterProfiler package (with parameters: fromType = "SYMBOL", toType="ENTREZID", OrgDb="org.Hs.eg.db"), to obtain Entrez Gene identifiers for the genes of interest. For genes failing conversion to an Entrez ID, we further employed the mygene module in Python ( https://pypi.org/project/mygene/ ), which obtains the gene annotation data from several public data resources (NCBI Entrez, Ensembl, Uniprot, NetAffx, PharmGKB, UCSC, and CPDB) and keep them up-to-date. GO enrichment analysis was performed with specific significance thresholds (p-valueCutoff = 0.01, q-valueCutoff = 0.05) adjusted by Benjamini-Hochberg procedure. Highly similar GO terms ( e.g ., > 0.25) were removed by applying the "simplify" function to retain the most representative terms ( i.e. , the most significant) with parameters: cutoff = 0.25, by = "p.adjust", and select_fun = min. Cluster comparer In order to perform a biological theme comparison between the aforementioned sets of ASD-related genes, we used the "compareCluster" function, which calculates enriched functional profiles of each gene dataset and aggregates the results into a single object. For visualization purposes, the “showCategory” parameter, indicating the display of the topmost significant categories, was set to 5. This tool was utilized to compare enrichements between: (i) the splicing gene list versus previously ASD-associated genes implicated in AS, and (ii) the splicing gene list versus genes harboring de novo /inherited variants with no predicted roles in AS. Gene expression analysis The GTEx Multi Gene Query tool of the GTEx Project version 8 ( https://www.gtexportal.org/home/ ) was employed to carry out the gene expression heatmaps. Those genes harboring in silico validated splice variants were used as an input. Expression values are represented as TPM (Transcripts Per Million), calculated as the number of reads for a gene and normalized by gene length. Additionally, different transcripts for each gene are collapsed during the normalization process. Heatmaps display the average expression per tissue. Darker blue means higher relative expression of that gene in each label (tissue type), compared to a yellow/light-green color in the same label. Genes and tissues are ordered by cluster. Results In silico variant prediction and validation Using previously reported de novo mutations in a Spanish cohort of 360 ASD trios 41 , several variant-level quality control filters were implemented. This process led to the identification of 43 high-confidence splicing variants. These variants demonstrated a SpliceAI Δ ≥ 0.8 in at least one of the interrogated splice sites, resulting in four predictions: acceptor gain (AG), acceptor loss (AL), donor gain (DG), and donor loss (DL) (Fig. 1 , Supplementary Table 1). It is worth noting that achieving ideal in vitro validation would necessitate access to the tissue of relevance (presumably developing brain), which was not feasible. Consequently, we have undertaken validation through the utilization of diverse methods and supplementary RNA-seq datasets from brain and other tissues. Several procedures were followed in order to ensure robustness in the in silico prediction of the splicing effects of these variants. Although the recommended threshold for splice-altering variants is Δ ≥ 0.5, we adopted a much more conservative threshold of Δ ≥ 0.8, which yields higher precision. This cutoff showed the highest validation rate and outperformed other popular classifiers that have been referenced in the literature for rare genetic disease diagnosis (GeneSplicer, MaxEntScan and NNSplice) 38 . In addition, we reassessed all variants using SpliceVault, which quantifies natural variation in splicing and potentially predicts variant-related splicing changes ( i.e. , exon-skipping events and cryptic splice sites). Our dataset included 8 variants exhibiting cryptic donor/acceptor sites scores of Δ ≥ 0.8 for site loss and Δ ≥ 0.5 for site gain. Among these, 87.5% of the cryptic activation variants were validated (n = 7 present in the Top-4 events ranked by SpliceVault, Table 2 ). Table 2 Variants with an AL/DL ∆ ≥ 0.8 and AG/DG ∆ ≥ 0.5. Variant (GRCh37) Gene AG AL DG DL SpliceVault check? chr1-155981618-G-A SSR2 0 (33) 0 (-47) 0.98 (-2) 0.92 (-17) Y chr7-1538341-A-C INTS1 0 (-26) 0 (39) 0.90 (34) 1.00 (2) Y chr9-114176268-T-C KIAA0368 0.66 (20) 0.98 (-2) 0 (21) 0 (-2) Y chr9-139407471-A-C NOTCH1 0 (28) 0 (34) 0.90 (34) 1.00 (2) Y chr12-3649770-A-C PRMT8 0.76 (10) 0.98 (2) 0 (8) 0 (1) Y chr16-85105388-G-T KIAA0513 0.92 (5) 1.00 (1) 0 (2) 0 (-32) Y chrX-47003870-A-C NDUFB11 0 (45) 0 (32) 0.71 (12) 1.00 (2) Y chr1-16895732-C-T NBPF1 0.74 (-2) 0.94 (-1) 0 (-2) 0 (-1) N (exon 23 skipping) Variants are in GRCh37. Columns AG, AL, DG and DL show SpliceAI ∆ scores. Delta position ( i.e. , the location where splicing changes in relation to the variant’s position) is shown between parenthesis (negative numbers refer to positions upstream of the variant while positive numbers refer to downstream positions). AG, acceptor gain; AL, acceptor loss; DG, donor gain; DL, donor loss. The remaining variant (chr1-16895732-C-T) resulted in exon 23 skipping in 51.9% of unannotated splice sites (Table 2 ). In order to detect single exon skipping events, we would need to observe: (i) one single variant with AL and DL Δ ≥ 0.8, or (ii) one individual harboring two different variants flanking the same exon with AL and DL Δ ≥ 0.8, respectively. Methodological limitations prevented us from validating this phenomenon. The use of exome sequencing data introduces the potential limitation that deep intronic variants, not detectable through this method, might be associated with exon exclusion. Furthermore, 35 variants yielded Δ ≥ 0.8 in only one out of four scored positions by SpliceAI (AG/AL/DG/DL). Excluding 5 variants with (i) no annotated splicing, (ii) gene not present in SpliceVault server ( FAM27B ), or (iii) no cryptic annotation (nonannotated splicing events); 30 variants were queried against the SpliceVault server (Table 3 ). Table 3 Variants with AG/AL/DG/DL ∆ ≥ 0.8. Variant (GRCh37) Gene AG AL DG DL SpliceVault check? Top 1 non-annotated event chr1-20650027-T-C VWA5B1 0 (-35) 0 (-41) 0 (-8) 0.94 (-2) Y NA chr1-67242087-G-A TCTEX1D1 0 (3) 0 (-50) 0 (3) 0.98 (-1) Y NA chr5-843723-C-A ZDHHC11 0 (37) 0 (-1) 0 (-8) 0.81 (0) Y NA chr16-5141894-G-C EEF2KMT 0.94 (1) 0 (45) 0 (-1) 0 (-32) Y NA chr2-95539855-T-G TEKT4 0 (-38) 0 (-40) 0.04 (-40) 0.98 (-2) N cryptic activation + 512 chr2-166170276-G-A SCN2A 0 (6) 0 (-30) 0 (10) 0.85 (-5) N cryptic activation + 213 chr3-122629685-A-C SEMA5B 0 (-32) 0 (49) 0.48 (24) 1 (2) N cryptic activation + 343 chr8-91033285-G-T DECR1 0 (-2) 0 (-48) 0 (-2) 0.98 (-1) N cryptic activation − 51 chr10-118620666-A-G ENO4 0 (-1) 0 (35) 0.90 (-1) 0.40 (35) N cryptic activation − 39 chr11-65784647-T-G CATSPER1 0.44 (-9) 0.96 (-2) 0 (43) 0 (-6) N cryptic activation + 31 chr13-114005162-A-C GRTP1 0 (1) 0 (2) 0 (-44) 0.88 (2) N cryptic activation − 52 chr16-711712-C-T WDR90 0 (-2) 0 (-38) 0.92 (-2) 0 (-29) N cryptic activation − 77 chr16-2163160-A-C PKD1 0 (7) 0 (25) 0.03 (-2) 0.86 (2) N cryptic activation + 5 chr16-20638576-A-T ACSM1 0 (47) 0 (2) 0 (-25) 0.98 (2) N cryptic activation − 67 chr16-29473043-G-A SULT1A4 0 (2) 0 (-43) 0.81 (1) 0.02 (16) N cryptic activation − 25 chr17-40835837-A-G CNTNAP1 0.02 (16) 1 (2) 0 (21) 0 (-49) N cryptic activation − 158 chr19-10572358-T-G PDE4A 0 (-14) 0 (-2) 0.18 (-14) 0.92 (-2) N cryptic activation + 35 chr3-105421304-C-A CBLB 0.17 (-21) 0.92 (-1) 0 (-21) 0 (-15) N exon skipping (12) chr4-110749291-T-G RRH 0 (32) 0 (-2) 0.37 (12) 0.90 (-2) N exon skipping (4–5) chr5-176958524-T-G FAM193B 0.17 (-22) 0.98 (-2) 0 (-22) 0 (-2) N exon skipping (5) chr5-179133258-G-A CANX 0 (2) 0.94 (1) 0 (-50) 0 (29) N exon skipping (3) chr9-78711019-G-A PCSK5 0 (17) 0 (-1) 0 (10) 0.96 (-1) N exon skipping (8) chr11-376072-A-C B4GALNT4 0 (37) 0.96 (2) 0 (-36) 0 (1) N exon skipping (12) chr15-42168847-T-G SPTBN5 0.15 (-8) 0.85 (-2) 0 (47) 0 (-2) N double exon skipping (19–20) chr16-22269096-G-T EEF2K 0 (31) 0 (-37) 0 (50) 0.81 (-5) N exon skipping (9) chr16-30910856-T-G CTF1 0 (14) 0 (-16) 0 (-30) 0.81 (-2) N exon skipping (2) chr16-56904007-G-A SLC12A3 0.11 (2) 1.00 (1) 0 (-22) 0 (0) N exon skipping (5) chr19-7686019-A-C XAB2 0 (0) 0 (35) 0.05 (13) 0.96 (2) N exon skipping (9) chr20-3641171-A-C GFRA4 0 (-30) 0 (15) 0 (23) 0.98 (2) N exon skipping (3) chrX-13767653-G-C OFD1 0 (3) 0 (-45) 0.46 (3) 1 (-1) N exon skipping (9) chr22-40060742-A-C CACNA1I 0.14 (6) 1 (2) 0 (36) 0 (1) * * chr11-118938598-C-G VPS11 0 (-1) 0 (-18) 0.96 (-1) 0.01 (-13) * * chr10-51130591-A-C PARG 0 (-38) 0 (30) 0 (-43) 0.98 (2) * * chr9-67793896-C-A FAM27B 0 (-47) 0 (-42) 0.08 (-46) 0.96 (1) ** ** chr18-3502489-A-G DLGAP1 0 (-17) 0 (2) 0.04 (-17) 0.98 (2) *** *** *No annotated splicing, **gene not present in the dataset, *** no cryptic annotation. Variants are in GRCh37. Columns AG, AL, DG and DL show SpliceAI ∆ scores. Delta position ( i.e. , the location where splicing changes in relation to the variant’s position) is shown between parenthesis (negative numbers refer to positions upstream of the variant while positive numbers refer to downstream positions). For exon skipping events, the skipped exon is shown in parenthesis. For cryptic activation events in SpliceVault, the cryptic position is depicted. AG, acceptor gain; AL, acceptor loss; DG, donor gain; DL, donor loss. One variant with AG Δ ≥ 0.8 and 3 with DL Δ ≥ 0.8 were confirmed by SpliceVault to be correctly predicted. For the rest of the variants (n = 26), 50% (n = 13) Top-1 event resulted in exon skipping (11 single exon skipping and 2 double-exon skipping), while 13 variants resulted in cryptic activation, not detected in our method. Further on, we sought to revalidate predicted variants against ABSplice 40 , with 60.46% (n = 26) of the variants yielding scores ≥ 0.2 (equivalent to the high precision cutoff Δ ≥ 0.8 in SpliceAI) (Table 4 ), and were thus, confirmed. Table 4 ABSplice prediction. Variant (GRCh37) Gene ABSplice score ABSplice tissue chr22-40060742-A-C CACNA1I 0.43 Brain Cerebellum chr12-3649770-A-C PRMT8 0.4 Brain Nucleus accumbens basal ganglia chr1-67242087-G-A TCTEX1D1 0.38 Brain Frontal Cortex BA9 chr8-91033285-G-T DECR1 0.36 Adipose Subcutaneous chr9-139407471-A-C NOTCH1 0.36 Adipose Subcutaneous chrX-13767653-G-C OFD1 0.36 Adipose Subcutaneous chr9-78711019-G-A PCSK5 0.35 Adipose Visceral Omentum chr16-2163160-A-C PKD1 0.34 Adipose Subcutaneous chr16-85105388-G-T KIAA0513 0.34 Adrenal Gland chr2-95539855-T-G TEKT4 0.34 Testis chr7-1538341-A-C INTS1 0.34 Adipose Subcutaneous chr16-22269096-G-T EEF2K 0.33 Adipose Subcutaneous chr3-122629685-A-C SEMA5B 0.31 Artery Coronary chr5-179133258-G-A CANX 0.29 Brain Amygdala chr13-114005162-A-C GRTP1 0.28 Adrenal Gland chr16-56904007-G-A SLC12A3 0.28 Kidney Cortex chr2-166170276-G-A SCN2A 0.28 Brain Cerebellar Hemisphere chr16-30910856-T-G CTF1 0.27 Adrenal Gland chr19-7686019-A-C XAB2 0.26 Adipose Subcutaneous chr9-114176268-T-C KIAA0368 0.26 Adipose Subcutaneous chr11-376072-A-C B4GALNT4 0.24 Brain Amygdala chr17-40835837-A-G CNTNAP1 0.23 Brain Anterior cingulate cortex BA24 chr3-105421304-C-A CBLB 0.23 Adipose Subcutaneous chr11-65784647-T-G CATSPER1 0.21 Testis chr18-3502489-A-G DLGAP1 0.21 Brain Anterior cingulate cortex BA24 chr15-42168847-T-G SPTBN5 0.2 Nerve Tibial chr1-20650027-T-C VWA5B1 0.18 Testis chr1-16895732-C-T NBPF1 0.18 Brain Cerebellar Hemisphere chr5-176958524-T-G FAM193B 0.17 Adipose Visceral Omentum chr16-29473043-G-A SULT1A4 0.09 Brain Cerebellum chr16-5141894-G-C EEF2KMT 0.07 Adipose Visceral Omentum chr16-20638576-A-T ACSM1 0.055 Testis chr5-843723-C-A ZDHHC11 0.045 Brain Cerebellar Hemisphere chr1-155981618-G-A SSR2 0.04 Adipose Subcutaneous chr20-3641171-A-C GFRA4 0.04 Brain Amygdala chr10-118620666-A-G ENO4 0.032 Testis chr16-711712-C-T WDR90 0.032 Adipose Subcutaneous chr19-10572358-T-G PDE4A 0.021 Testis chr4-110749291-T-G RRH < 0.01 NA chr11-118938598-C-G VPS11 < 0.01 NA chr9-67793896-C-A FAM27B * NA chr10-51130591-A-C PARG * NA chrX-47003870-A-C NDUFB11 * NA Variants are sorted by ABSplice scores. Variants with scores ≥ 0.2 (bold font) were confirmed. *Variant not present. After the validation process, 75.61% (n = 31) of the initially predicted splicing variants (excluding those variants not present in any of the complementary datasets (n = 2)) were confirmed (Supplementary Table 1). Tissue specificity of predicted splice variants Following cross-referencing with SpliceVault and ABSplice, we further evaluated tissue-specific effects of the in silico validated variants (n = 31), albeit the score provided by ABSplice. This approach allowed us to globally assess tissue-specific effects of all validated variants, acknowledging that some may not be high confidence in ABSplice. Thus, variants that did not reach the 0.2 impact score threshold in ABSplice but were validated in SpliceVault were also included in this analysis (Supplementary Table 1). After removing one variant not present in the dataset, we evaluated tissue-specific effects in 26 variants with score ≥ 0.2 (high impact), 2 variants with score ≥ 0.05 (medium impact) and 2 with score ≥ 0.01 (low impact). Notably, adipose tissue yielded the highest scores for 38.7% of the variants (n = 12), followed by brain with 32.1% (n = 9), testis and adrenal gland with 9.6% each (n = 3), and the remaining 3 variants (each comprising 3.6% of the total) were distributed among nerve tibial, kidney cortex, and artery coronary. Then, genes harboring the in silico validated splice altering variants were queried against the GTEx portal to assess whether the predicted tissue-specific effects were attributable to gene expression restricted to that particular tissue. Overall, genes like CACNA1l , SCN2A , DLGAP1 and PRMT8 , which harbor variants predicted to have their highest impact in brain tissue, do show higher expression values restricted to brain tissues (Fig. 2 , gene name in green). Only one of these genes (namely, CANX ), with a validated variant predicted to have the highest impact in amygdala, did not show a high expression limited to the brain. However, genes that host splicing variants with the highest impact in adipose tissue (Fig. 2 , gene name in purple) do not exhibit expression limited to any specific tissue. This is in contrast to the expectation, as a specific expression limited to adipose tissue would provide a logical rationale for the increased burden of variants yielding the highest scores in adipose tissue Nonetheless, variants were checked against the whole set of tissues, and it was observed that most of the variants with predicted highest scores in brain, and all the variants with predicted highest effect in adipose tissue and adrenal gland, yielded the same high scores in other tissues ( i.e. , ABSplice scores were not exclusive for that tissue) (data not shown). In contrast, 3 variants with the highest score identified in testis, exhibited a clear tissue-specificity ( i.e. , the ABSplice scores retrieved for the remaining GTEx tissues of tissues were notably lower) (Supplementary Fig. 1). However, genes harboring these variants ( CATSPER1 , TEKT4 , VWA5B1 ) had tissue specific expression (Fig. 2 , gene name in orange) restricted to testis. Cluster enrichment Genes harboring high-confidence splice variants predicted by SpliceAI and validated with SpliceVault and ABSplice (n = 31 variants, one variant per gene) were cross-referenced with: (i) previously reported genes associated with ASD AS 25 , 28 , 29 , 35 , 38 , to check for similarities in splicing relevant pathways and perform gene validation, (ii) ASD-associated genes in the SFARI Gene (category 1: high confidence, category 2: strong candidate), for gene validation only, and (iii) genes harboring de novo and inherited coding mutations 44 , to see if different types of mutations act through distinct mechanisms. CACNA1I, CBLB, CLTB, DLGAP1, DVL3, KIAA0513, OFD1, PKD1, SLC13A3 and SCN2A were present in at least one of the above-mentioned datasets and were thus used to construct a brain-specific network of functionally-related genes (Supplementary Fig. 2, Supplementary Table 2, n = 60). The resultant gene network was interrogated for enrichment in the GO categories of BP, MF, and CC. The analysis revealed that these genes were significantly enriched for biological processes related to proper neuronal functioning (modulation of chemical synaptic transmission (gene ratio 12/60, q-value = 1.68 − 5 ), trans-synaptic signaling (gene ratio 12/60, q-value = 1.68 − 5 ), synaptic plasticity (gene ratio 8/60, q-value = 1.07 − 4 ), cognition (gene ratio 8/60, q-value = 1.07 − 4 ), and memory (gene ratio 9/60, q-value = 1.68 − 4 ). Enriched CC terms were all related to the synapse, with top significant findings including postsynaptic specialization (gene ratio 12/60, q-value = 1.12 x 10 − 7 ), synaptic membrane (gene ratio 10/60, q-value = 7.84 x 10 − 6 ), glutamatergic synapse (gene ratio 10/60, q-value = 1.03 x 10 − 5 ), and presynaptic synapse (gene ratio 12/60, q-value = 3.34 x 10 − 4 ), among others. Top enriched MF terms were associated with calmodulin binding channels (gene ratio 7/60, q-value = 3.25 x 10 − 4 ), transmembrane receptor protein kinase activities (gene ratio 5/60, q-value = 3.25 x 10 − 4 ), calcium ion channels (gene ratio 6/60, q-value = 3.25 x 10 − 4 ), and tyrosine activities (gene ratio 4/60, q-value = 1.67 x 10 − 4 ), among others. Furthermore, we compared functional profiles amongst the different datasets and calculated enriched functional profiles of each gene clusters. In analyzing datasets for genes implicated in AS in ASD, we found that our gene network clusters together in terms of BP, CC and MF (Supplementary Figs. 3–5). Examples of common significantly enriched terms included: (i) protein autophosphorylation and modulation of chemical synaptic transmission, for BP, (ii) postsynaptic specialization and cell leading edge, for CC, and (iii) acting/calmodulin binding, for MF. However, when contrasting these findings with genes harboring coding variants (henceforth designated as the non-splicing gene list), the overlap between enriched categories is notably dissipated (Fig. 3 , Supplementary Fig. 6). CC terms did not exhibit a clear separation between datasets, with all common enriched terms relating to synapse components or postsynaptic density (Supplementary Fig. 6). While some BP ( e.g. , cognition, learning and memory) were significantly enriched in both datasets, others ( e.g. , histone modification (gene ratio 17/72, q-value = 3.77 x 10 − 9 ) and chromatin remodeling (gene ratio 15/72, q-value = 2.45 x 10 − 8 )) were specifically enriched in the non-splicing gene list, absent in our 60-gene list (Table 5 ). On the other hand, genes from both datasets were incorporated into categories associated with the proper function and organization of the synapse. However, the majority of significant enrichments in the splicing gene list (48 out of 62 enriched BP terms) were exclusively identified within that particular dataset. Some examples of the top enrichments are provided in Table 5 . Table 5 Common/unique enriched biological processes in splicing-related gene lists and the non-splicing gene list. Gene list Description Gene ratio q-value Splicing gene list regulation of signaling receptor activity 6/60 2.79 x 10 − 3 regulation of neurotransmitter receptor activity 4/60 6.00 x 10 − 3 protein autophosphorylation 6/60 8.17 x 10 − 3 regulation of JNK cascade 5/60 8.17 x 10 − 3 dendrite development 6/60 8.17 x 10 − 3 regulation of protein catabolic process 7/60 1.02 x 10 − 2 positive regulation of MAPK cascade 8/60 1.08 x 10 − 2 JNK cascade 5/60 1.34 x 10 − 2 regulation of phosphatidylinositol 3-kinase signaling 4/60 2.15 x 10 − 2 regulation of synapse assembly 4/60 2.15 x 10 − 2 calcium ion transport 7/60 2.25 x 10 − 2 excitatory postsynaptic potential 4/60 2.46 x 10 − 2 Non-splicing gene list histone modification 17/72 3.77 x 10 − 9 chromatin remodeling 15/72 2.45 x 10 − 8 histone lysine methylation 7/72 4.11 x 10 − 5 histone methylation 7/72 1.20 x 10 − 4 histone H3-K4 methylation 5/72 3.40 x 10 − 4 regulation of histone methylation 3/72 2.07 x 10 − 2 regulation of histone modification 4/72 2.57 x 10 − 2 positive regulation of histone H3-K4 methylation 2/72 3.05 x 10 − 2 histone lysine demethylation 2/72 3.50 x 10 − 2 histone demethylation 2/72 3.67 x 10 − 2 Common learning or memory (splicing gene list) learning or memory (non-splicing gene list) 8/60 2/72 4.34 x 10 − 4 2.52 x 10 − 7 cognition (splicing gene list) cognition (non-splicing gene list) 9/60 13/72 1.68 x 10 − 4 1.24 x 10 − 7 The most pronounced difference emerged when analyzing enriched terms in the MF category: genes with predicted splice variants were significantly enriched in terms such as calmodulin binding (gene ratio 7/60, q-value = 3.25 x 10 − 4 ), calcium ion channel/transporter activity (gene ratio 5/60, q-value = 3.25 x 10 − 4 ), and transmembrane receptor protein kinase/tyrosine activity (gene ratio 6/60, q-value = 3.25 x 10 − 4 ), while genes in the non-splicing list were specific to histone lysine N-methyltransferase activity (gene ratio 6/72, q-value = 3.85 x 10 − 6 ) and beta-catenin binding (gene ratio 7/72, q-value = 3.85 x 10 − 6 ) (Fig. 3 ). Discussion The multifaceted genetic etiology of ASD, characterized by substantial phenotypic and genetic heterogeneity, has long been a challenge in unraveling its underlying molecular basis. The identification of splicing variants has not been included in the major WGS or WES genetic studies involving large ASD cohorts. However, AS, an intricate mechanism that diversifies protein isoforms from a single gene, has recently garnered attention as a potential contributor to ASD pathogenesis. The present study delved into the intricate landscape of AS in ASD through in silico prediction and validation of splicing variants. However, the conservative threshold (SpliceAI Δ ≥ 0.8) chosen for splice-altering variant prediction 38 may, in turn, result in elevated numbers of false negatives. While this approach was necessary, in vitro confirmation of the predicted variants ( e.g. , by Sanger sequencing), validation of the predicted alterations on AS (by reverse transcription polymerase chain reaction (RT-PCR)) and functional analysis of their molecular impacts (RNA-Seq and/or minigene reporter systems 46 ), would prove much more adequate and sensitive. Still, in vitro validation was unfeasible due to the lack of sample availability and the difficulty in contacting participants for resampling. Thus, our validation strategy using SpliceVault and ABSplice, at least partially, provided further evidence on the robustness of the predicted splicing effects. On the one hand, SpliceVault exhibited superior sensitivity and positive predictive value than SpliceAI when it comes to exon- and double-exon skipping predictions or cryptic splice site activation, and represents the first evidence-based method for predicting the nature of variant-associated mis-splicing 39 . On the other hand, another study demonstrated that applying SpliceAI on the tissue-specific splice sites defined by SpliceMap (integrated into ABSplice) increased the precision of SpliceAI to 22% at 20% recall, with a significantly higher auPRC consistently across tissues 40 . Notably, our validation approach revealed that the majority of cryptic activation events were successfully corroborated when leveraging evidence-based data (Supplementary Table 1). This further demonstrates the role of these predicted cryptic sites in ASD-associated splicing perturbations. Nevertheless, splicing branchpoints present an additional source of potentially damaging non-coding variants which are amenable to systematic analysis in WGS data 47 , but remain undetectable in WES data. This represents another methodological constraint in our approach. Of note, the availability of WGS data could enhance our understanding of the splicing landscape in ASD by enabling the detection of intron retention, a splicing aberration already associated with ASD and other NDDs 48 , 49 . Furthermore, 35% of cryptic splice variants with weak and intermediate predicted scores (Δ 0.35–0.8) exhibit significant differences in the fraction of normal and aberrant transcripts produced across tissues. Variants with high predicted scores are significantly less likely to produce tissue-specific effects 38 . Therefore, being able to choose a less conservative threshold and perform in vitro validation, would be tremendously helpful in gaining insight into tissue-specific effects. However, the tissue-specific effects of splicing variants gained prominence through our assessment using ABSplice. Tissue-specificity in alternative splicing (AS) Research in ASD has primarily focused on neurological aspects, looking at factors such as brain structure and function, and neurotransmitter systems. However, it is worth noting that there is ongoing research in the field of neuroimmunology and the gut-brain axis, which explores the connections between the gut and the brain. Recently, GI dysfunction has been described in various neurodevelopmental and psychiatric disorders including ASD 50 , 51 . Moreover, some studies have suggested a possible link between GI tissue, adipose tissue and brain, with accumulating evidence suggesting that the communication pathways linking them might be promising intervention points for metabolic disorders 52 . However, since adipose tissue can produce certain signaling molecules, such as adipokines, it is possible that there could also be indirect connections between adipose tissue and neurodevelopmental conditions like ASD 53 , but this area of research is still emerging and not yet well-understood. There are some studies regarding the role of adipokines in neurogenesis, neuroprotection, synaptogenesis, synaptic plasticity, and even neurodegenerative diseases such as Alzheimer’s disease 54 – 57 . However, we urge caution in interpreting our results, as they are based on in silico validation with RNA-seq data from GTEX and not from ASD patients. Functional in vivo analyses ( e.g. , iPScs, organoids) are necessary to confirm the potential connections between adipose tissue and NDDs. Importantly, preliminary findings of this study indicated a role of adipose tissue in ASD (Table 4 ). Yet, upon examining the expression of genes containing variants with adipose tissue-specific effects we noted a relatively uniform expression across all tissues (Fig. 2 ). Notably, these genes exhibited elevated expression levels in the brain, and the associated splicing variants also yielded high scores in brain tissues. Consequently, the function of adipose tissue remains unclear. However, considering this in silico evidence alongside previous studies, further research is necessary. Further studies could test this hypothesis by interrogating whether these genes are driving pleiotropic effects in both sets of tissues, or by performing overall comparisons between splice site usage in neuronal versus adipose tissue. Moreover, three variants in the final set of splicing validated variants, show unique values of tissue-specificity in testis (Supplementary Fig. 1). Nonetheless, when comparing transcriptomes, it has been observed that the brain and testis significantly surpass other tissues in terms of the diversity of expressed splice variants 58 . Consequently, our findings may lack sufficient power to attribute specific significance to testis in the context of ASD risk. Biological underpinnings of AS On another note, the convergence of genes harboring validated splicing variants with previously reported ASD-associated genes from various datasets substantiates the potential significance of AS in ASD. Our creation of a brain-specific network encompassing functionally related genes (Supplementary Fig. 2, Supplementary Table 2) demonstrated enrichment in BP intricately tied to neuronal functioning, synaptic transmission, synaptic plasticity, cognition, and memory (Fig. 3 ). Although we identified a relatively small number of genes with in silico validated splice variants, these findings align with previous studies showcasing aberrant splicing patterns in genes critical to neural development, which may collectively contribute to the complex ASD phenotype. Additional support for this evidence includes: (i) analyzing larger ASD cohorts under the same criteria, both to augment our splicing gene-list (and thus provide more statistical support for enrichments in GO categories) and to perform a burden test analysis of numbers of mutations (are the numbers of splicing variants per gene consistent with gene length, conservation, and the number of different isoforms?), (ii) testing whether the splicing genes carry an excess of non-splice DNVs in autism probands to further correct this measure, and (iii) using multiplex family/case-control cohorts to check if splice DNVs are enriched in affected individuals when compared with healthy siblings. In addition, we cross-referenced our gene list with genes that have exome-wide significance when combining evidence from both coding DNVs and rare inherited variants (non-splicing gene list), to encompass a broader spectrum of the disorder. Interestingly, our gene list shows significant enrichment in MF different from those enriched in the non-splicing gene list (Fig. 3 A). Moreover, none of these non-splicing enriched terms were found in any other splicing complementary dataset analyzed in this study. Similar results were observed for the category of BP (Fig. 3 B), where cognition and learning or memory terms are common amongst both lists (splicing versus non-splicing), but chromatin remodeling and histone modification are specific to the non-splicing gene list, and most synaptic-related terms are specific to the splicing gene list (Table 5 ). The fact that GO enrichment analysis points out to different MF and BP in genes harboring splicing variants and in the non-splicing gene list, might suggest a divergence of affected pathways and mechanisms, thus pointing to different mechanisms in which they participate in the development of the disease. In fact, a previous study on the full-length isoform transcriptome of the developing human brain 15 has shown that differentially expressed isoforms (DEIs) reveal distinct signals relative to differentially expressed genes (DEGs). The GO enrichment analyses demonstrated stronger enrichment of DEI in neurodevelopment-relevant processes compared with DEGs. In contrast, DEGs were enriched in basic biological function-related processes, such as mitotic cell cycle, metabolic processes, protein targeting, and localization. Also, molecular functions such as kinase activity (one out of three most significant molecular functions strictly associated in our gene list) were solely linked to DEIs. In vitro studies using human cortical neurons treated with the anticonvulsant valproic acid (VPA) have shown that differentially expressed genes (DEGs) exhibit enrichment in distinct molecular functions compared to genes with differential transcript isoform usage (DTU) 59 . These findings align with those presented in this study, suggesting that the full-length isoform transcriptome provides better biological insights into brain development than the gene transcriptome. However, when comparing these results at the level of molecular functions specific to DEGs or DTUs, caution should be exercised, since these in vitro experiments involve specific lines of neurons from ASD patients, unlike our in silico experiments, using GTEX RNA-seq data from brain and other tissues. Therefore, further studies utilizing transcriptomic methods in brain samples from ASD patients and controls, combined with WGS data, are needed to address this question. In general, this situation mirrors the still unresolved question of whether de novo and inherited variations affect the same biological pathways. Ruzzo et al. illustrated that inherited variations cluster in specific biological pathways, introducing novel pathways linked to ion transport, the cell cycle, and the microtubule cytoskeleton 60 distinct from those enriched for de novo variants 6 . In a separate study, Wilfert and colleagues stated that DNVs and transmitted LGD variants converge on the same pathway but may be targeting distinct sets of genes 61 . Future perspectives and study limitations In essence, this study exemplifies the intricate genetic landscape of the disorder and aims to raise new questions regarding the involvement of AS in ASD, proposing novel avenues for future research. The comprehensive in silico validation pipeline employed here showcases the potential of such methods in deciphering splicing perturbations. However, as with any computational approach, in vitro validation is paramount to fully comprehend the functional consequences of these predicted splicing changes. Further studies should expand on the identified splicing events’ downstream effects on protein function, signaling pathways, and cellular processes. Integration of our findings with multi-omics data, such as full-length isoform transcriptomics, may provide a more holistic view of the intricate ASD molecular network. Moreover, examining the potential interplay between splicing and other regulatory mechanisms, such as epigenetics, could elucidate additional layers of complexity in ASD etiology. While numerous questions remain unanswered, and the functional validation of most predicted splice-disrupting variants is still necessary to affirm a molecular diagnosis, in silico tools’ predictions can serve as supportive evidence in variant classification. Commonly used variant annotation tools are not designed to assess the deleterious impact of splicing variants and their predictions are largely restricted to canonical splice sites. However, if multiple computational sources, such as the framework presented here, indicate that a variant has a deleterious effect, these predictions can be employed. Furthermore, they can aid in prioritizing splice-disrupting variants for subsequent functional testing or experimental validation. This study underscores the utility of computational predictions in identifying splicing variants. However, to comprehensively address the involvement of AS processes in ASD etiology, several functional and in vitro studies are needed in the near future. These include: (i) the validation of splicing variants (by RT-PCR or RNA-seq), (ii) functional characterization, performing functional assays to elucidate how splicing variants influence protein function and pathway activity ( e.g. , iPSCs and/or animal models), (iii) integrative omics approaches, integrating splicing variant data with other omics data ( e.g. , genomic, epigenomic, proteomic) to gain a comprehensive understanding of the molecular mechanisms underlying ASD, and (iv) to deeper explore the clinical implications of splicing variants in ASD, including potential biomarker discovery and therapeutic targets. These future studies will aim, together with the in silico workflow using AI tools as presented in this study, to advance our understanding of splicing perturbations in ASD and their broader implications. Data availbility WES (Whole Exome Sequencing) data from the Spanish cohort were generated as part of the ASC and are transferred to dbGaP with Study Accession: phs000298.v4.p3. Previously published in Satterstrom FK, Kosmicki JA, Wang J, Breen MS, De Rubeis S, An JY, Peng M, Collins R, Grove J, Klei L, Stevens C, Reichert J, Mulhern MS, Artomov M, Gerges S, Sheppard B, Xu X, Bhaduri A, Norman U, Brand H, Schwartz G, Nguyen R, Guerrero EE, Dias C; Autism Sequencing Consortium; iPSYCH-Broad Consortium; Betancur C, Cook EH, Gallagher L, Gill M, Sutcliffe JS, Thurm A, Zwick ME, Børglum AD, State MW, Cicek AE, Talkowski ME, Cutler DJ, Devlin B, Sanders SJ, Roeder K, Daly MJ, Buxbaum JD. Large-Scale Exome Sequencing Study Implicates Both Developmental and Functional Changes in the Neurobiology of Autism. Cell. 2020 Feb 6;180(3):568–584.e23. doi: 10.1016/j.cell.2019.12.036 . Epub 2020 Jan 23. PMID: 31981491; PMCID: PMC7250485. Splicing variants identified in this research are provided within the manuscript and the supplementary information. Declarations Author Contribution M Tubio-Fungueiriño., M. Fernandez-Prieto., J Gonzalez-Peñas, A. Carracedo, C. Arango and M.Parellada participated in the recruitment of samples. S.Dominguez-Alonso has carried out the analyses and wrote the paper. A.Carrecedo., C.Rodriguez.-Fontenla, ,M.Parellada and C.Arango participated in the design and coordination of this study. A.Carracedo. and C.Rodriguez.-Fontenla. critically revised the work and approved the final content. Acknowledgement We would like to warmly thank the ASC (Autism Sequencing Consortium) (https://genome.emory.edu/ASC/) that has sequenced the Spanish trios. Data Availability WES (Whole Exome Sequencing) data from the Spanish cohort were generated as part of the ASC and are transferred to dbGaP with Study Accession: phs000298.v4.p3 . Previously published in Satterstrom FK, Kosmicki JA, Wang J, Breen MS, De Rubeis S, An JY, Peng M, Collins R, Grove J, Klei L, Stevens C, Reichert J, Mulhern MS, Artomov M, Gerges S, Sheppard B, Xu X, Bhaduri A, Norman U, Brand H, Schwartz G, Nguyen R, Guerrero EE, Dias C; Autism Sequencing Consortium; iPSYCH-Broad Consortium; Betancur C, Cook EH, Gallagher L, Gill M, Sutcliffe JS, Thurm A, Zwick ME, Børglum AD, State MW, Cicek AE, Talkowski ME, Cutler DJ, Devlin B, Sanders SJ, Roeder K, Daly MJ, Buxbaum JD. Large-Scale Exome Sequencing Study Implicates Both Developmental and Functional Changes in the Neurobiology of Autism. Cell. 2020 Feb 6;180(3):568-584.e23. doi: 10.1016/j.cell.2019.12.036. Epub 2020 Jan 23. PMID: 31981491; PMCID: PMC7250485.Splicing variants identified in this research are provided within the manuscript and the supplementary information. References American Psychiatric Association, American Psychiatric Association, eds. 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Bull. 37 (8), 1091–1106. 10.1007/s12264-021-00685-w (2021). Rodriguez-Fontenla, C. & Carracedo, A. UTMOST, a single and cross-tissue TWAS (Transcriptome Wide Association Study), reveals new ASD (Autism Spectrum Disorder) associated genes. Transl Psychiatry . 11 (1), 1–11. 10.1038/s41398-021-01378-8 (2021). Niesler, B. & Rappold, G. A. Emerging evidence for gene mutations driving both brain and gut dysfunction in autism spectrum disorder. Mol. Psychiatry . 26 (5), 1442–1444. 10.1038/s41380-020-0778-5 (2021). Yi, C. X. & Tschöp, M. H. Brain–gut–adipose-tissue communication pathways at a glance. Dis. Model. Mech. 5 (5), 583–587. 10.1242/dmm.009902 (2012). Puente-Ruiz, S. C. & Jais, A. Reciprocal signaling between adipose tissue depots and the central nervous system. Front. Cell. Dev. Biol. 10 , 979251. 10.3389/fcell.2022.979251 (2022). Ge, T., Fan, J., Yang, W., Cui, R. & Li, B. Leptin in depression: a potential therapeutic target. Cell. Death Dis. 9 (11), 1–10. 10.1038/s41419-018-1129-1 (2018). Bouret, S. G. Neurodevelopmental actions of leptin. Brain Res. 1350 , 2–9. 10.1016/j.brainres.2010.04.011 (2010). Beccano-Kelly, D., Harvey, J. & Leptin A Novel Therapeutic Target in Alzheimer’s Disease? Int. J. Alzheimer’s Dis. 2012 , e594137. 10.1155/2012/594137 (2012). McGregor, G. & Harvey, J. Leptin Regulation of Synaptic Function at Hippocampal TA-CA1 and SC-CA1 Synapses: Implications for Health and Disease. Neurochem Res. 44 (3), 650–660. 10.1007/s11064-017-2362-1 (2019). Naro, C., Cesari, E. & Sette, C. Splicing regulation in brain and testis: common themes for highly specialized organs. Cell. Cycle . 20 (5–6), 480–489. 10.1080/15384101.2021.1889187 (2021). Leung, C. S. et al. Dysregulation of the chromatin environment leads to differential alternative splicing as a mechanism of disease in a human model of autism spectrum disorder. Hum. Mol. Genet. 32 (10), 1634–1646. 10.1093/hmg/ddad002 (2023). Ruzzo, E. K. et al. Inherited and De Novo Genetic Risk for Autism Impacts Shared Networks. Cell . 178 (4), 850–866e26. 10.1016/j.cell.2019.07.015 (2019). Wilfert, A. B. et al. Recent ultra-rare inherited variants implicate new autism candidate risk genes. Nat. Genet. 53 (8), 1125–1134. 10.1038/s41588-021-00899-8 (2021). Additional Declarations No competing interests reported. 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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-5136316","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":366309647,"identity":"4d4b6959-e08e-4d74-91f7-be20bb83143a","order_by":0,"name":"S Dominguez-Alonso","email":"","orcid":"","institution":"Universidad de Santiago de Compostela","correspondingAuthor":false,"prefix":"","firstName":"S","middleName":"","lastName":"Dominguez-Alonso","suffix":""},{"id":366309648,"identity":"f0612526-0488-4d31-9bd9-f4d11cec157a","order_by":1,"name":"M Tubío-Fungueiriño","email":"","orcid":"","institution":"Universidad de Santiago de Compostela","correspondingAuthor":false,"prefix":"","firstName":"M","middleName":"","lastName":"Tubío-Fungueiriño","suffix":""},{"id":366309649,"identity":"74d1a79f-cbeb-414c-98bc-d0880f0edd0c","order_by":2,"name":"J González-Peñas","email":"","orcid":"","institution":"Hospital General Universitario Gregorio Marañón, Instituto de Investigación Sanitaria Gregorio Marañón, Universidad Complutense","correspondingAuthor":false,"prefix":"","firstName":"J","middleName":"","lastName":"González-Peñas","suffix":""},{"id":366309650,"identity":"6bc34fd5-5659-4d9f-8380-61f4802f2d95","order_by":3,"name":"M Fernández-Prieto","email":"","orcid":"","institution":"Universidad de Santiago de Compostela","correspondingAuthor":false,"prefix":"","firstName":"M","middleName":"","lastName":"Fernández-Prieto","suffix":""},{"id":366309651,"identity":"8c45de60-1634-499e-b6a2-577310a935db","order_by":4,"name":"M Parellada","email":"","orcid":"","institution":"Hospital General Universitario Gregorio Marañón, Instituto de Investigación Sanitaria Gregorio Marañón, Universidad Complutense","correspondingAuthor":false,"prefix":"","firstName":"M","middleName":"","lastName":"Parellada","suffix":""},{"id":366309652,"identity":"568b327e-f929-4b6e-9f49-5ce0b30678e0","order_by":5,"name":"C Arango","email":"","orcid":"","institution":"Hospital General Universitario Gregorio Marañón, Instituto de Investigación Sanitaria Gregorio Marañón, Universidad Complutense","correspondingAuthor":false,"prefix":"","firstName":"C","middleName":"","lastName":"Arango","suffix":""},{"id":366309653,"identity":"72ce2500-9d95-4772-acb4-21da8102214f","order_by":6,"name":"A Carracedo","email":"","orcid":"","institution":"Universidad de Santiago de Compostela","correspondingAuthor":false,"prefix":"","firstName":"A","middleName":"","lastName":"Carracedo","suffix":""},{"id":366309655,"identity":"630e8819-feb0-4b97-8e2d-5b5526fb1d60","order_by":7,"name":"C Rodriguez-Fontenla","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABb0lEQVRIie2RMWvCQBiGv0NIlotdLyjmFxQuBKTQFv9KQkEXNxeHICmBc1G6KhT6F4RC6RgJxCXVVSiIImRphrhZqNLTxFAFOxeaB44kl3vu/e4+gIyMP4rDBwZAVvKZvIjx4wouzinCqYKd/S4EZOtMlvBzhyOFOkcLL9tvC2dtzorKk83ma5iV8u+u/YGbLcBidzRfvRJFm9YWEfCZmLJfo8OO18DUQ221Aw1NHhvsGvsCYDw27vs+UV+mdY2A7x4UpwqOZOmYCogRDLox8BHTJIahQuqqLTGCuCIAYofyypMAhhuuKAwx+StVtgTwTtkwUnnu1QJA27SwaRXcXQp4iBWSFHspWTRWeLQxIHqZ30QuVQJwix4vzDNYoUh1TeYp6JHPYOyr/S4jdz0/1IjupWeZVHOr0NQrij0K5LCpl/K+uIxCs1XCYodGn+zm9qHNbywyD4WdQOMWkX1zj9DPtDMhF/3+PyMjI+Of8Q1m2okNqYGu1AAAAABJRU5ErkJggg==","orcid":"","institution":"Universidad de Santiago de Compostela","correspondingAuthor":true,"prefix":"","firstName":"C","middleName":"","lastName":"Rodriguez-Fontenla","suffix":""}],"badges":[],"createdAt":"2024-09-23 08:17:49","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5136316/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5136316/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1038/s41598-025-95456-2","type":"published","date":"2025-03-28T15:56:56+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":67283321,"identity":"512588e3-5ce7-47e7-8761-d8d091620f28","added_by":"auto","created_at":"2024-10-23 09:12:06","extension":"jpeg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":558397,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eWorkflow for \u003c/strong\u003e\u003cem\u003e\u003cstrong\u003ein silico\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003e variant prediction, variant validation, and gene validation. \u003c/strong\u003eRed font color is used to indicate variants that have not been confirmed or genes harboring unconfirmed variants\u003cem\u003e. AD, alternate read depth, AG, acceptor gain; AL, acceptor loss; DG, donor gain; DL, donor loss; GQ, genome quality.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"floatimage1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-5136316/v1/e0bbfce2dd9c8b3ece0a7faa.jpeg"},{"id":67281342,"identity":"0e4f89b0-2397-4473-9019-bae700590bdd","added_by":"auto","created_at":"2024-10-23 09:04:06","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":648410,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eTissue specific expression of genes harboring 31 \u003c/strong\u003e\u003cem\u003e\u003cstrong\u003ein silico\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003e validated variants. \u003c/strong\u003eGene expression heatmap for genes harboring splice variants with predicted tissue-specific impact. Genes and tissues are ordered by cluster. Genes are highlighted based on the tissue where their splicing variant yields the highest score: green for the brain, purple for adipose tissue, orange for testis, and gray for other tissues.\u003c/p\u003e","description":"","filename":"floatimage243.png","url":"https://assets-eu.researchsquare.com/files/rs-5136316/v1/a0610e4445ac9933be2fc5aa.png"},{"id":67281344,"identity":"9045f50b-f3a7-46ff-8e4c-1346ee997b7a","added_by":"auto","created_at":"2024-10-23 09:04:06","extension":"jpeg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":718834,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eCluster enrichment analysis for splicing vs. inherited/de novo datasets.\u003c/strong\u003e Graphs depicting the number of genes of each list included in a) molecular functions, b) biological processes. The circle size is proportional to the number of genes included in each category, and not significance as depicted in the legend (both panels have different size ratios).\u003c/p\u003e","description":"","filename":"floatimage4.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-5136316/v1/369e1f1c65f5d8fedc81fd80.jpeg"},{"id":79604733,"identity":"36f52fb4-b98d-4065-92b2-370fcdcf5e03","added_by":"auto","created_at":"2025-03-31 16:01:57","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3480331,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5136316/v1/984f7b5a-3cf9-4351-a20c-244c201b2a38.pdf"},{"id":67281340,"identity":"699d0b21-c6d3-47de-8337-d8b32fbf2036","added_by":"auto","created_at":"2024-10-23 09:04:06","extension":"xlsx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":61154,"visible":true,"origin":"","legend":"","description":"","filename":"splicingsupptablesPAPER.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-5136316/v1/bd647b81a67ab6fba42cdd92.xlsx"},{"id":67281346,"identity":"49d0b5a5-5790-446b-a106-50360674f8db","added_by":"auto","created_at":"2024-10-23 09:04:06","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":30844704,"visible":true,"origin":"","legend":"","description":"","filename":"supplementaryfiguressplicingBP.docx","url":"https://assets-eu.researchsquare.com/files/rs-5136316/v1/522fb4e354b7906fa336e7fb.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Alternative Splicing Analysis in a Spanish ASD (Autism Spectrum Disorders) Cohort: In silico Prediction and Validation","fulltext":[{"header":"Introduction","content":"\u003cp\u003eAutism Spectrum Disorders (ASD) encompass a group of phenotypically and genetically heterogeneous neurodevelopmental disorders (NDDs) characterized by difficulties in social interaction and communication, repetitive behavior, and restricted interests\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e. ASD have a strong heritability, estimated around 80%\u003csup\u003e2\u003c/sup\u003e, although their complex genetic etiology has limited progress towards understanding their molecular basis. Genomic approaches, including genome-wide association studies (GWAS)\u003csup\u003e\u003cspan additionalcitationids=\"CR4\" citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e–\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e, whole exome (WES) and whole-genome sequencing (WGS) of families\u003csup\u003e\u003cspan additionalcitationids=\"CR7 CR8\" citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e–\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e, as well as transcriptome analyses by RNA-sequencing (RNA-seq) and microarray techniques\u003csup\u003e\u003cspan additionalcitationids=\"CR11\" citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e–\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e, have yielded association with hundreds of genes over the last decades. However, a large portion of their genetic architecture remains uncharacterized, and ASD diagnosis continues to be a major challenge in both clinical and research settings.\u003c/p\u003e \u003cp\u003eEmerging advances have implicated alternative splicing (AS) as yet another process influencing the development of the disease. AS is the mechanism by which introns are excised from the pre-mRNA primary transcript, and exons are selected and concatenated in different arrangements, generating multiple transcript isoforms and, consequently, protein products, from a single gene. More than 95% of multiexon human genes that encode proteins undergo AS, and most mRNA splice patterns exhibit tissue- and cell-type-specificity\u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e. Consequently, AS is mainly accountable for generating the vast proteomic diversity observed in complex organisms and has been increasingly linked to functional intricacies within the central nervous system. It plays crucial roles in nervous system development, neuronal differentiation and maturation, as well as complex neuronal processes, such as the control of synaptic plasticity associated with cognition\u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e,\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e. Some neuronal genes, like neurexins, possess the ability to produce hundreds of mRNA isoforms. This phenomenon stands as one of the most extensive cases of AS regulation observed to date and plays a pivotal role in optimal neuronal function\u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eNotably, aberrations in AS have been associated with several NDDs, including schizophrenia and bipolar disorders\u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e,\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e, Rett syndrome\u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e,\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e, fragile X syndrome\u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e,\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e and ASD\u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e,\u003cspan additionalcitationids=\"CR23 CR24 CR25 CR26 CR27 CR28\" citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e–\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u003c/sup\u003e. These aberrations may occur at two different levels: cis-acting motifs (exonic/intronic splicing enhancers and silencers) and trans-acting factors that regulate the assembly of the spliceosome by recognition of proximal cis-elements, such as RNA-binding proteins (RBPs)\u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e,\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e,\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eThe majority of AS studies in ASD patients pertain to individual RBPs that govern the inclusion/exclusion of microexons (3–27 nt). Microexons constitute the most conserved component of the neural-regulated AS program. They generally reside on protein surfaces, specifically in domains with important roles in shaping protein-protein interactions. These sequences are significantly enriched in genes with neuronal function which have been genetically linked to ASD\u003csup\u003e\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e,\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u003c/sup\u003e, predominantly being neuronal-included\u003csup\u003e\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u003c/sup\u003e. RNA-Seq analysis of post-mortem brain samples in idiophatic ASD individuals shows misplicing in 30–40% of brain-specific microexons\u003csup\u003e\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e. Analyses of larger cohorts of ASD individuals corroborated these observations and demonstrated that most of the differential splicing events involve the exclusion of these microexons\u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e,\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e. Most microexons are controlled by the neural-specific Ser/Arg-related splicing factor, nSR100/SRRM4\u003csup\u003e26,27\u003c/sup\u003e, and its misregulation is linked to reduced expression levels of nSR100/SRRM4 in autistic brains. Employing heterozygous mutant mice expressing approximately 50% wild-type levels of nSR100, Quesnel-Vallières \u003cem\u003eet al.\u003c/em\u003e, 2016\u003csup\u003e26\u003c/sup\u003e demonstrated that a single variant in a splicing regulator, and thus the disruption of its target splicing program, suffice to reproduce hallmark features of ASD, including altered social behaviour, synaptic transmission and neuronal excitability. Additional examples of RBPs implicated in the differential splicing changes observed in ASD brain are Rbfox1\u003csup\u003e34–36\u003c/sup\u003e and PTBP1\u003csup\u003e22,34,37\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eGiven the mounting evidence of splicing disruptions in ASD patients, we sought to investigate the possible effects of \u003cem\u003ede novo\u003c/em\u003e mutations in AS within a Spanish cohort of 360 ASD trios. Here, we employed SpliceAI, a machine learning tool that robustly predicts splice sites and splice-disrupting variants, which has exhibited notable performance when compared to previously developed \u003cem\u003ein silico\u003c/em\u003e detection tools\u003csup\u003e\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e\u003c/sup\u003e. Moreover, we utilized SpliceVault\u003csup\u003e\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e\u003c/sup\u003e and ABSsplice\u003csup\u003e\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e\u003c/sup\u003e, two newly developed tools that assess AS, in order to bolster robustness of the \u003cem\u003ein silico\u003c/em\u003e splicing prediction and delve into the specific effects within human tissues. This paper provides initial insights into the potential funtional roles of genes harboring splicing variants, thereby highlighting molecular pathways and biological processes in which they are implicated.\u003c/p\u003e \u003cp\u003e\u003c/p\u003e \n\n "},{"header":"Methods","content":"\u003cp\u003eSubjects\u003c/p\u003e\u003cp\u003eThe analysis described herein builds upon the complete sample set examined in Alonso Gonzalez \u003cem\u003eet al\u003c/em\u003e. 2021\u003csup\u003e41\u003c/sup\u003e. DNA extraction from the Spanish ASD samples, consisting of 360 trios (unaffected parents and affected proband), was performed using the GentraPuregene blood kit (Qiagen Inc., Valencia, CA, USA) from peripheral blood.\u003c/p\u003e\u003cp\u003e Participants from Santiago (n = 136) were recruited from the Complexo Hospitalario Universitario de Santiago de Compostela and Galician ASD organizations. Meanwhile, subjects from Madrid (n = 224) were enrolled through the AMITEA program at the Child and Adolescent Department of Psychiatry, Hospital General Universitario Gregorio Marañón. Inclusion criteria stipulated that only individuals aged 3 years or older were included in the study.\u003c/p\u003e\u003cp\u003eEnrolled participants received a clinical diagnosis of ASD from trained pediatric neurologists or psychiatrists, following the criteria outlined in both the Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition Text Revision (DSM-IV-TR) and Fifth Edition (DSM-5). Additionally, when deemed necessary, the Autism Diagnostic Observation Schedule (ADOS) and the Autism Diagnostic Interview-Revised (ADI-R) were administered.\u003c/p\u003e\u003cp\u003e All participants, along with their parents or legal representatives, provided written informed consent, and the study was conducted in accordance with the principles outlined in the Declaration of Helsinki.\u003c/p\u003e\u003cp\u003e The Galician Comittee of Research Ethics (Xunta de Galicia) has approved this study under the Register 2020/400.\u003c/p\u003e\u003cp\u003eNinety samples from the Spanish cohort (360 trios) were already analyzed by Lim \u003cem\u003eet al.\u003c/em\u003e, 2017\u003csup\u003e42\u003c/sup\u003e. The entire Spanish cohort was included in Satterstrom \u003cem\u003eet al\u003c/em\u003e., 2020\u003csup\u003e43\u003c/sup\u003e as part of the Autism Sequencing Consortium (ASC), a large-scale international genomic consortium integrating ASD cohorts and sequencing data from over one hundred investigators. All data generated as part of the ASC was transferred to dbGaP with Study Accession: phs000298.v4.p3.\u003c/p\u003e\u003cp\u003e \u003cem\u003eIn silico\u003c/em\u003e prioritization of splicing variants\u003c/p\u003e\u003cp\u003eTo perform subsequent analyses, we leveraged previously reported biallelic \u003cem\u003ede novo\u003c/em\u003e variants (DNVs) from our previous work. DNVs were defined as those present exclusively in probands and not in parents (\u003cem\u003ei.e.\u003c/em\u003e, genotypes 1/0 or 1/1 in probands and 0/0 in parents). The filtering steps carried out in Alonso-Gonzalez \u003cem\u003eet al\u003c/em\u003e., 2021\u003csup\u003e41\u003c/sup\u003e were not used with SpliceAI. Instead, appropriate quality filtering for splicing variants was applied to the raw \u003cem\u003ede novo\u003c/em\u003e variants of the analysis, as detailed below.\u003c/p\u003e\u003cp\u003eTo perform \u003cem\u003ein silico\u003c/em\u003e annotation and prioritization of splice variants, we used SpliceAI (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://github.com/Illumina/SpliceAI\u003c/span\u003e\u003cspan address=\"https://github.com/Illumina/SpliceAI\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), a machine learning tool that robustly predicts splice sites and splice-disrupting variants. SpliceAI has been already utilized to assess the clinical impact of non-coding mutations that act through altered splicing in patients with ASD\u003csup\u003e\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eSpliceAI was executed with default parameters (-D: maximum distance between the variant and gained/lost splice site (default: 50); -M: mask scores representing annotated acceptor/donor gain and unannotated acceptor/donor loss (default: 0)). For the gene annotation file, we used he GENCODE V24 canonical annotation files included in the package.\u003c/p\u003e\u003cp\u003eA SpliceAI annotation was available for 252,520 \u003cem\u003ede novo\u003c/em\u003e variants (72.91% of 346,352 initial variants). Variants with delta scores Δ ≥ 0.8 (indicating confidently predicted splice-altering effects) were retained (n = 1,836). In addition, we considered only \u003cem\u003ede novo\u003c/em\u003e variants with genome quality (GQ) ≥ 20 and alternate read depth (AD) ≥ 7 and removed any call if it had an allele frequency \u0026gt; 0.1% across the samples in our dataset or in the non-psychiatric subset of gnomAD (1,793 putative \u003cem\u003ede novo\u003c/em\u003e variants excluded).\u003c/p\u003e\u003cp\u003e \u003cem\u003eIn silico\u003c/em\u003e variant validation\u003c/p\u003e\u003cp\u003eSpliceVault\u003c/p\u003e\u003cp\u003eThe tool SpliceVault\u003csup\u003e\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e\u003c/sup\u003e was employed to accurately predict the exact manner in which genetic variants affect the splicing process. It ranks the four most common unannotated splicing events across 335,663 reference RNA-seq samples (300K-RNA Top-4) from GTEx v8 (Genotype-Tissue Expression project) and SRA (Sequence Read Archive; an online archive of high-throughput RNA sequencing data). 300K-RNA is built in hg38 (GRCh38), so genome coordinates were converted between assemblies GRCh37 and GRCh38 using the UCSC web server's LiftOver tool (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://genome.ucsc.edu/cgi-bin/hgLiftOver\u003c/span\u003e\u003cspan address=\"https://genome.ucsc.edu/cgi-bin/hgLiftOver\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eSplice variants were then interrogated against the SpliceVault web portal (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://kidsneuro.shinyapps.io/splicevault/\u003c/span\u003e\u003cspan address=\"https://kidsneuro.shinyapps.io/splicevault/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) using default settings recommended for clinical use: top 4 events, skipping of ≤ 2 exons and cryptic positions within +/- 600 nt. A variant was considered validated if it appeared in the top 4 events of SpliceVault.\u003c/p\u003e\u003cp\u003eTissue specificity: ABSplice\u003c/p\u003e\u003cp\u003eEach variant was examined for its specific effect in any given human tissue. ABSplice\u003csup\u003e\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e\u003c/sup\u003e (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://github.com/gagneurlab/absplice\u003c/span\u003e\u003cspan address=\"https://github.com/gagneurlab/absplice\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), a model which maps acceptor and donor splice sites and quantifies their usage in 49 human tissues, was run with default parameters. For each variant, we recorded the tissue with the highest score for AS outputted by ABSplice. This score indicates the likelihood that a specific genetic variant causes abnormal splicing in a particular tissue. ABSplice thresholds are defined as 0.01 (low), 0.05 (intermediate), and 0.2 (high), which have approximately the same recalls as the high, medium, and low cutoffs of SpliceAI. A variant was considered validated if it appeared in the ABSplice dataset with a score higher than 0.2.\u003c/p\u003e\u003cp\u003eAdditionally, to enhance our understanding of the variant effects across various tissues, we annotated our variants by cross-referencing them with precomputed ABSplice-DNA scores for all tissues, not just the highest scoring one. The precomputed ABSplice-DNA scores for 49 human tissues and all possible SNVs genome-wide for hg38 were made available at Zenodo (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://zenodo.org/records/7871809\u003c/span\u003e\u003cspan address=\"https://zenodo.org/records/7871809\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eComplementary datasets\u003c/p\u003e\u003cp\u003eComparing functional profiles can reveal functional consensus and differences among different experiments and helps in identifying differential functional modules in different datasets.\u003c/p\u003e\u003cp\u003eFor further validation of hereafter interrogated genes harboring splice variants, analysis of gene ontology (GO) enrichments and comparison of enriched terms in each dataset (see next section), we utilized previously reported ASD-related sets of genes (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eAfter variant validation, genes harboring the validated variants were contrasted against different complementary gene datasets. These included genes associated with ASD and implicated in AS\u003csup\u003e\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e,\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e,\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e,\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e,\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e\u003c/sup\u003e, as well as the SFARI database. Genes that overlapped with these datasets were considered validated and were subsequently included in further GO analysis.\u003c/p\u003e\u003cp\u003eAdditionally, we utilized the same gene datasets (ASD-related genes implicated in AS) along with a study integrating \u003cem\u003ede novo\u003c/em\u003e and inherited variants, not predicted to affect AS, in 42,607 ASD cases\u003csup\u003e\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e\u003c/sup\u003e. This information was employed for a detailed comparison of enriched terms among different categories of genetic variants, namely, de \u003cem\u003enovo\u003c/em\u003e and inherited coding variants not predicted to alter splicing \u003cem\u003eversus\u003c/em\u003e splicing variants.\u003c/p\u003e\u003cp\u003e \u003c/p\u003e\u003cdiv class=\"gridtable\"\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=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\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\u003eComplementary datasets included in gene validation and GO analysis.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003c/colgroup\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDescription\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eStudy type\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSamples\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eData availability\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eThe Human Gene module of the SFARI database (up-to-date reference for all known human genes associated with autism ASD) was accessed (SFARI 07-17-2023 release) and queried against 1–2 scoring genes (high confidence and strong evidence of association with ASD, respectively).\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMutation screening, family-based association, case-control, WES, WGS and CNV array\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://gene.sfari.org/database/human-gene/\u003c/span\u003e\u003cspan address=\"https://gene.sfari.org/database/human-gene/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHighly conserved program of neural microexons primarily regulated by the neuronal-specific splicing factor nSR100/SRRM4.\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRNA-seq custom pipeline\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e22 autistic individuals, 20 controls\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSupplementary Table\u0026nbsp;2: Neural-regulated AS events in human\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e25\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUnique patterns of AS and gene co-expression in ASD-affected dizygotic twins compared to their parents.\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAS and co-expression analyses\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTwo pairs of DZ twins and their parents\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSupplementary table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003e1\u003c/span\u003e: Differential AS events\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e28\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDistinct AS patterns in the blood of patients with ASD compared to typically developing individuals.\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWhole genome exon arrays\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e30 ASD patient, 20 controls\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAdditional file 2\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e29\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDysregulated splicing pattern of \u003cem\u003eRBFOX1\u003c/em\u003e-dependent alternative exons in the ASD brain.\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePost-mortem brain tissue\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e19 autism samples, 17 controls\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSupplementary Data: Differential Splicing Events\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e35\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDetection of a 1.30-fold enrichment of \u003cem\u003ede\u003c/em\u003e novo splicing mutations in ASD (p = 0.0203) compared to healthy controls when employing SpliceAI.\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHigh-depth mRNA sequencing\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e36 autism samples\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSupplementary Table\u0026nbsp;3\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e38\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAnalysis of \u003cem\u003ede novo\u003c/em\u003e and inherited variants identifies 60 genes with exome-wide significance implicated in ASD, including five new risk genes (\u003cem\u003eNAV3\u003c/em\u003e, \u003cem\u003eITSN1\u003c/em\u003e, \u003cem\u003eMARK2\u003c/em\u003e, \u003cem\u003eSCAF1\u003c/em\u003e and \u003cem\u003eHNRNPUL2\u003c/em\u003e).\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWES/WGS\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e42,607 autism cases\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSupplementary Table\u0026nbsp;1\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e44\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/table\u003e\u003c/div\u003e\u003cp\u003e \u003cem\u003eCNV, copy number variant; DZ\u003c/em\u003e, dizygotic. \u003cem\u003eThe first 6 studies were used for gene validation, and the last 6 were used for comparison of enriched terms.\u003c/em\u003e\u003c/p\u003e\u003cp\u003eGene ontology (GO) analysis\u003c/p\u003e\u003cp\u003eGene network analysis\u003c/p\u003e\u003cp\u003eHumanBase (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://hb.flatironinstitute.org/\u003c/span\u003e\u003cspan address=\"https://hb.flatironinstitute.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) was used to build a gene network for the genes already associated, and thus, validated, in the aforementioned complementary datasets. HumanBase serves as a comprehensive resource for biological research and offers data-driven predictions related to gene expression, function, regulation, and interactions within the human domain, with a particular focus on specific cell types, tissues, and diseases.\u003c/p\u003e\u003cp\u003eIn order to capture tissue-specific gene function we used the “tissue specific gene networks: GIANT” analysis tool. The HumanBase GIANT analysis tool, constructs comprehensive genome-scale functional maps for various human tissues by integrating extensive datasets from over 14,000 distinct publications, covering thousands of experiments. The platform automatically evaluates the relevance of each dataset to 144 tissue- and cell lineage–specific functional contexts. The resulting functional gene maps offer detailed insights into protein function and interactions in specific human tissues and cell lineages.\u003c/p\u003e\u003cp\u003e \u003cem\u003eCACNA1I, CBLB, CLTB, DLGAP1, DVL3, KIAA0513, OFD1, PKD1, SLC13A3\u003c/em\u003e and \u003cem\u003eSCN2A\u003c/em\u003e (\u003cem\u003ei.e.\u003c/em\u003e, the validated genes) were selected as the input genes along with brain tissue in the 5 existing data types (co-expression, transcription factor binding, interaction, gene set enrichment analysis (GSEA) microRNA targets, and GSEA perturbations). The resultant network (henceforth designated as the splicing gene list, Supplementary Table\u0026nbsp;2, Supplementary Fig.\u0026nbsp;2) contains the subset of functionally related genes specific to brain tissue, capturing tissue-specific gene function, all of which were used to test for functional enrichment using genes annotated to GO biological process (BP), celular component (CC) and molecular function (MF) terms.\u003c/p\u003e\u003cp\u003eEnrichment analysis\u003c/p\u003e\u003cp\u003eClusterProfiler (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://github.com/YuLab-SMU/clusterProfiler\u003c/span\u003e\u003cspan address=\"https://github.com/YuLab-SMU/clusterProfiler\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e)\u003csup\u003e45\u003c/sup\u003e, an R package tailored for contrasting biological themes among gene groups, was harnessed to perform both GO over-representation test and to deduce enriched functional profiles on separate gene clusters (\u003cem\u003ei.e.\u003c/em\u003e, gene sets).\u003c/p\u003e\u003ch3\u003eGO enrichment analysis\u003c/h3\u003e\u003cp\u003eThe package org.Hs.eg.db, provided by Bioconductor, was used as the genome wide annotation for Human. We employed the bitr tool (Biological Id TRanslator), already implemented in the clusterProfiler package (with parameters: fromType = \"SYMBOL\", toType=\"ENTREZID\", OrgDb=\"org.Hs.eg.db\"), to obtain Entrez Gene identifiers for the genes of interest. For genes failing conversion to an Entrez ID, we further employed the mygene module in Python (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://pypi.org/project/mygene/\u003c/span\u003e\u003cspan address=\"https://pypi.org/project/mygene/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), which obtains the gene annotation data from several public data resources (NCBI Entrez, Ensembl, Uniprot, NetAffx, PharmGKB, UCSC, and CPDB) and keep them up-to-date.\u003c/p\u003e\u003cp\u003eGO enrichment analysis was performed with specific significance thresholds (p-valueCutoff = 0.01, q-valueCutoff = 0.05) adjusted by Benjamini-Hochberg procedure. Highly similar GO terms (\u003cem\u003ee.g\u003c/em\u003e., \u0026gt; 0.25) were removed by applying the \"simplify\" function to retain the most representative terms (\u003cem\u003ei.e.\u003c/em\u003e, the most significant) with parameters: cutoff = 0.25, by = \"p.adjust\", and select_fun = min.\u003c/p\u003e\u003ch2\u003eCluster comparer\u003c/h2\u003e\u003cp\u003eIn order to perform a biological theme comparison between the aforementioned sets of ASD-related genes, we used the \"compareCluster\" function, which calculates enriched functional profiles of each gene dataset and aggregates the results into a single object. For visualization purposes, the “showCategory” parameter, indicating the display of the topmost significant categories, was set to 5.\u003c/p\u003e\u003cp\u003eThis tool was utilized to compare enrichements between: (i) the splicing gene list \u003cem\u003eversus\u003c/em\u003e previously ASD-associated genes implicated in AS, and (ii) the splicing gene list versus genes harboring \u003cem\u003ede novo\u003c/em\u003e/inherited variants with no predicted roles in AS.\u003c/p\u003e\u003cp\u003eGene expression analysis\u003c/p\u003e\u003cp\u003eThe GTEx Multi Gene Query tool of the GTEx Project version 8 (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.gtexportal.org/home/\u003c/span\u003e\u003cspan address=\"https://www.gtexportal.org/home/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) was employed to carry out the gene expression heatmaps. Those genes harboring \u003cem\u003ein silico\u003c/em\u003e validated splice variants were used as an input.\u003c/p\u003e\u003cp\u003eExpression values are represented as TPM (Transcripts Per Million), calculated as the number of reads for a gene and normalized by gene length. Additionally, different transcripts for each gene are collapsed during the normalization process.\u003c/p\u003e\u003cp\u003eHeatmaps display the average expression per tissue. Darker blue means higher relative expression of that gene in each label (tissue type), compared to a yellow/light-green color in the same label. Genes and tissues are ordered by cluster.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e \u003cem\u003eIn silico\u003c/em\u003e variant prediction and validation\u003c/p\u003e \u003cp\u003eUsing previously reported \u003cem\u003ede novo\u003c/em\u003e mutations in a Spanish cohort of 360 ASD trios\u003csup\u003e\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e\u003c/sup\u003e, several variant-level quality control filters were implemented. This process led to the identification of 43 high-confidence splicing variants. These variants demonstrated a SpliceAI Δ\u0026thinsp;\u0026ge;\u0026thinsp;0.8 in at least one of the interrogated splice sites, resulting in four predictions: acceptor gain (AG), acceptor loss (AL), donor gain (DG), and donor loss (DL) (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e1\u003c/span\u003e, Supplementary Table\u0026nbsp;1).\u003c/p\u003e \u003cp\u003eIt is worth noting that achieving ideal \u003cem\u003ein vitro\u003c/em\u003e validation would necessitate access to the tissue of relevance (presumably developing brain), which was not feasible. Consequently, we have undertaken validation through the utilization of diverse methods and supplementary RNA-seq datasets from brain and other tissues.\u003c/p\u003e \u003cp\u003eSeveral procedures were followed in order to ensure robustness in the \u003cem\u003ein silico\u003c/em\u003e prediction of the splicing effects of these variants. Although the recommended threshold for splice-altering variants is Δ \u0026ge; 0.5, we adopted a much more conservative threshold of Δ \u0026ge; 0.8, which yields higher precision. This cutoff showed the highest validation rate and outperformed other popular classifiers that have been referenced in the literature for rare genetic disease diagnosis (GeneSplicer, MaxEntScan and NNSplice)\u003csup\u003e\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eIn addition, we reassessed all variants using SpliceVault, which quantifies natural variation in splicing and potentially predicts variant-related splicing changes (\u003cem\u003ei.e.\u003c/em\u003e, exon-skipping events and cryptic splice sites). Our dataset included 8 variants exhibiting cryptic donor/acceptor sites scores of Δ \u0026ge; 0.8 for site loss and Δ \u0026ge; 0.5 for site gain. Among these, 87.5% of the cryptic activation variants were validated (n\u0026thinsp;=\u0026thinsp;7 present in the Top-4 events ranked by SpliceVault, Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\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\u003eVariants with an AL/DL ∆ \u0026ge; 0.8 and AG/DG ∆ \u0026ge; 0.5.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\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=\"left\" 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=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariant (GRCh37)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGene\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAG\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAL\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eDG\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eDL\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eSpliceVault check?\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003echr1-155981618-G-A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eSSR2\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0 (33)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0 (-47)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.98 (-2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.92 (-17)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eY\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003echr7-1538341-A-C\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eINTS1\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0 (-26)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0 (39)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.90 (34)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.00 (2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eY\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003echr9-114176268-T-C\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eKIAA0368\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.66 (20)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.98 (-2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0 (21)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0 (-2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eY\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003echr9-139407471-A-C\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eNOTCH1\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0 (28)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0 (34)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.90 (34)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.00 (2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eY\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003echr12-3649770-A-C\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003ePRMT8\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.76 (10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.98 (2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0 (8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0 (1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eY\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003echr16-85105388-G-T\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eKIAA0513\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.92 (5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.00 (1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0 (2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0 (-32)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eY\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003echrX-47003870-A-C\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eNDUFB11\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0 (45)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0 (32)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.71 (12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.00 (2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eY\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003echr1-16895732-C-T\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eNBPF1\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.74 (-2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.94 (-1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0 (-2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0 (-1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eN (exon 23 skipping)\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\u003eVariants are in GRCh37. Columns AG, AL, DG and DL show SpliceAI \u003cb\u003e∆\u003c/b\u003e scores. Delta position (\u003cem\u003ei.e.\u003c/em\u003e, the location where splicing changes in relation to the variant\u0026rsquo;s position) is shown between parenthesis (negative numbers refer to positions upstream of the variant while positive numbers refer to downstream positions). \u003cem\u003eAG, acceptor gain; AL, acceptor loss; DG, donor gain; DL, donor loss.\u003c/em\u003e\u003c/p\u003e \u003cp\u003eThe remaining variant (chr1-16895732-C-T) resulted in exon 23 skipping in 51.9% of unannotated splice sites (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). In order to detect single exon skipping events, we would need to observe: (i) one single variant with AL and DL Δ \u0026ge; 0.8, or (ii) one individual harboring two different variants flanking the same exon with AL and DL Δ \u0026ge; 0.8, respectively. Methodological limitations prevented us from validating this phenomenon. The use of exome sequencing data introduces the potential limitation that deep intronic variants, not detectable through this method, might be associated with exon exclusion.\u003c/p\u003e \u003cp\u003eFurthermore, 35 variants yielded Δ \u0026ge; 0.8 in only one out of four scored positions by SpliceAI (AG/AL/DG/DL). Excluding 5 variants with (i) no annotated splicing, (ii) gene not present in SpliceVault server (\u003cem\u003eFAM27B\u003c/em\u003e), or (iii) no cryptic annotation (nonannotated splicing events); 30 variants were queried against the SpliceVault server (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eVariants with AG/AL/DG/DL ∆ \u0026ge; 0.8.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\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=\"left\" 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=\"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=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariant (GRCh37)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGene\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAG\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAL\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eDG\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eDL\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eSpliceVault check?\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eTop 1 non-annotated event\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003echr1-20650027-T-C\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eVWA5B1\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0 (-35)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0 (-41)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0 (-8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.94 (-2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eY\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003echr1-67242087-G-A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eTCTEX1D1\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0 (3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0 (-50)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0 (3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.98 (-1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eY\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003echr5-843723-C-A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eZDHHC11\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0 (37)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0 (-1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0 (-8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.81 (0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eY\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003echr16-5141894-G-C\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eEEF2KMT\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.94 (1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0 (45)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0 (-1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0 (-32)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eY\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003echr2-95539855-T-G\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eTEKT4\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0 (-38)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0 (-40)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.04 (-40)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.98 (-2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003ecryptic activation\u0026thinsp;+\u0026thinsp;512\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003echr2-166170276-G-A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eSCN2A\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0 (6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0 (-30)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0 (10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.85 (-5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003ecryptic activation\u0026thinsp;+\u0026thinsp;213\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003echr3-122629685-A-C\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eSEMA5B\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0 (-32)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0 (49)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.48 (24)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1 (2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003ecryptic activation\u0026thinsp;+\u0026thinsp;343\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003echr8-91033285-G-T\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eDECR1\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0 (-2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0 (-48)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0 (-2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.98 (-1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003ecryptic activation \u0026minus;\u0026thinsp;51\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003echr10-118620666-A-G\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eENO4\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0 (-1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0 (35)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.90 (-1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.40 (35)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003ecryptic activation \u0026minus;\u0026thinsp;39\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003echr11-65784647-T-G\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eCATSPER1\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.44 (-9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.96 (-2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0 (43)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0 (-6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003ecryptic activation\u0026thinsp;+\u0026thinsp;31\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003echr13-114005162-A-C\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eGRTP1\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0 (1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0 (2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0 (-44)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.88 (2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003ecryptic activation \u0026minus;\u0026thinsp;52\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003echr16-711712-C-T\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eWDR90\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0 (-2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0 (-38)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.92 (-2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0 (-29)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003ecryptic activation \u0026minus;\u0026thinsp;77\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003echr16-2163160-A-C\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003ePKD1\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0 (7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0 (25)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.03 (-2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.86 (2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003ecryptic activation\u0026thinsp;+\u0026thinsp;5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003echr16-20638576-A-T\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eACSM1\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0 (47)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0 (2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0 (-25)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.98 (2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003ecryptic activation \u0026minus;\u0026thinsp;67\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003echr16-29473043-G-A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eSULT1A4\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0 (2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0 (-43)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.81 (1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.02 (16)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003ecryptic activation \u0026minus;\u0026thinsp;25\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003echr17-40835837-A-G\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eCNTNAP1\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.02 (16)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1 (2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0 (21)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0 (-49)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003ecryptic activation \u0026minus;\u0026thinsp;158\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003echr19-10572358-T-G\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003ePDE4A\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0 (-14)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0 (-2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.18 (-14)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.92 (-2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003ecryptic activation\u0026thinsp;+\u0026thinsp;35\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003echr3-105421304-C-A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eCBLB\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.17 (-21)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.92 (-1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0 (-21)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0 (-15)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eexon skipping (12)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003echr4-110749291-T-G\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eRRH\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0 (32)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0 (-2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.37 (12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.90 (-2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eexon skipping (4\u0026ndash;5)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003echr5-176958524-T-G\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eFAM193B\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.17 (-22)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.98 (-2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0 (-22)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0 (-2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eexon skipping (5)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003echr5-179133258-G-A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eCANX\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0 (2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.94 (1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0 (-50)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0 (29)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eexon skipping (3)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003echr9-78711019-G-A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003ePCSK5\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0 (17)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0 (-1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0 (10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.96 (-1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eexon skipping (8)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003echr11-376072-A-C\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eB4GALNT4\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0 (37)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.96 (2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0 (-36)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0 (1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eexon skipping (12)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003echr15-42168847-T-G\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eSPTBN5\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.15 (-8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.85 (-2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0 (47)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0 (-2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003edouble exon skipping (19\u0026ndash;20)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003echr16-22269096-G-T\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eEEF2K\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0 (31)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0 (-37)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0 (50)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.81 (-5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eexon skipping (9)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003echr16-30910856-T-G\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eCTF1\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0 (14)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0 (-16)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0 (-30)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.81 (-2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eexon skipping (2)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003echr16-56904007-G-A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eSLC12A3\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.11 (2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.00 (1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0 (-22)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0 (0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eexon skipping (5)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003echr19-7686019-A-C\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eXAB2\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0 (0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0 (35)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.05 (13)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.96 (2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eexon skipping (9)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003echr20-3641171-A-C\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eGFRA4\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0 (-30)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0 (15)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0 (23)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.98 (2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eexon skipping (3)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003echrX-13767653-G-C\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eOFD1\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0 (3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0 (-45)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.46 (3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1 (-1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eexon skipping (9)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003echr22-40060742-A-C\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eCACNA1I\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.14 (6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1 (2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0 (36)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0 (1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003echr11-118938598-C-G\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eVPS11\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0 (-1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0 (-18)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.96 (-1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.01 (-13)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003echr10-51130591-A-C\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003ePARG\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0 (-38)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0 (30)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0 (-43)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.98 (2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003echr9-67793896-C-A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eFAM27B\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0 (-47)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0 (-42)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.08 (-46)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.96 (1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003echr18-3502489-A-G\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eDLGAP1\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0 (-17)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0 (2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.04 (-17)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.98 (2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e***\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\u003e*No annotated splicing, **gene not present in the dataset, *** no cryptic annotation. Variants are in GRCh37. Columns AG, AL, DG and DL show SpliceAI \u003cb\u003e∆\u003c/b\u003e scores. Delta position (\u003cem\u003ei.e.\u003c/em\u003e, the location where splicing changes in relation to the variant\u0026rsquo;s position) is shown between parenthesis (negative numbers refer to positions upstream of the variant while positive numbers refer to downstream positions). For exon skipping events, the skipped exon is shown in parenthesis. For cryptic activation events in SpliceVault, the cryptic position is depicted. \u003cem\u003eAG, acceptor gain; AL, acceptor loss; DG, donor gain; DL, donor loss.\u003c/em\u003e\u003c/p\u003e \u003cp\u003eOne variant with AG Δ \u0026ge; 0.8 and 3 with DL Δ \u0026ge; 0.8 were confirmed by SpliceVault to be correctly predicted. For the rest of the variants (n\u0026thinsp;=\u0026thinsp;26), 50% (n\u0026thinsp;=\u0026thinsp;13) Top-1 event resulted in exon skipping (11 single exon skipping and 2 double-exon skipping), while 13 variants resulted in cryptic activation, not detected in our method.\u003c/p\u003e \u003cp\u003eFurther on, we sought to revalidate predicted variants against ABSplice\u003csup\u003e\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e\u003c/sup\u003e, with 60.46% (n\u0026thinsp;=\u0026thinsp;26) of the variants yielding scores \u0026ge; 0.2 (equivalent to the high precision cutoff Δ \u0026ge; 0.8 in SpliceAI) (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e), and were thus, confirmed.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eABSplice prediction.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\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=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariant (GRCh37)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGene\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eABSplice score\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eABSplice tissue\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003echr22-40060742-A-C\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eCACNA1I\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.43\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eBrain Cerebellum\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003echr12-3649770-A-C\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003ePRMT8\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.4\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eBrain Nucleus accumbens basal ganglia\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003echr1-67242087-G-A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eTCTEX1D1\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.38\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eBrain Frontal Cortex BA9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003echr8-91033285-G-T\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eDECR1\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.36\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAdipose Subcutaneous\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003echr9-139407471-A-C\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eNOTCH1\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.36\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAdipose Subcutaneous\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003echrX-13767653-G-C\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eOFD1\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.36\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAdipose Subcutaneous\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003echr9-78711019-G-A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003ePCSK5\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.35\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAdipose Visceral Omentum\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003echr16-2163160-A-C\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003ePKD1\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.34\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAdipose Subcutaneous\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003echr16-85105388-G-T\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eKIAA0513\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.34\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAdrenal Gland\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003echr2-95539855-T-G\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eTEKT4\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.34\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eTestis\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003echr7-1538341-A-C\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eINTS1\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.34\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAdipose Subcutaneous\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003echr16-22269096-G-T\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eEEF2K\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.33\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAdipose Subcutaneous\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003echr3-122629685-A-C\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eSEMA5B\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.31\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eArtery Coronary\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003echr5-179133258-G-A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eCANX\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.29\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eBrain Amygdala\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003echr13-114005162-A-C\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eGRTP1\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.28\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAdrenal Gland\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003echr16-56904007-G-A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eSLC12A3\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.28\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eKidney Cortex\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003echr2-166170276-G-A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eSCN2A\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.28\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eBrain Cerebellar Hemisphere\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003echr16-30910856-T-G\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eCTF1\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.27\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAdrenal Gland\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003echr19-7686019-A-C\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eXAB2\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.26\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAdipose Subcutaneous\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003echr9-114176268-T-C\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eKIAA0368\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.26\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAdipose Subcutaneous\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003echr11-376072-A-C\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eB4GALNT4\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.24\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eBrain Amygdala\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003echr17-40835837-A-G\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eCNTNAP1\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.23\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eBrain Anterior cingulate cortex BA24\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003echr3-105421304-C-A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eCBLB\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.23\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAdipose Subcutaneous\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003echr11-65784647-T-G\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eCATSPER1\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.21\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eTestis\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003echr18-3502489-A-G\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eDLGAP1\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.21\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eBrain Anterior cingulate cortex BA24\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003echr15-42168847-T-G\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eSPTBN5\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.2\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNerve Tibial\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003echr1-20650027-T-C\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eVWA5B1\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eTestis\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003echr1-16895732-C-T\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eNBPF1\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eBrain Cerebellar Hemisphere\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003echr5-176958524-T-G\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eFAM193B\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAdipose Visceral Omentum\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003echr16-29473043-G-A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eSULT1A4\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eBrain Cerebellum\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003echr16-5141894-G-C\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eEEF2KMT\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAdipose Visceral Omentum\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003echr16-20638576-A-T\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eACSM1\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.055\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eTestis\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003echr5-843723-C-A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eZDHHC11\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.045\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eBrain Cerebellar Hemisphere\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003echr1-155981618-G-A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eSSR2\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAdipose Subcutaneous\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003echr20-3641171-A-C\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eGFRA4\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eBrain Amygdala\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003echr10-118620666-A-G\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eENO4\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.032\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eTestis\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003echr16-711712-C-T\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eWDR90\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.032\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAdipose Subcutaneous\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003echr19-10572358-T-G\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003ePDE4A\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.021\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eTestis\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003echr4-110749291-T-G\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eRRH\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003echr11-118938598-C-G\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eVPS11\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003echr9-67793896-C-A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eFAM27B\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003echr10-51130591-A-C\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003ePARG\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003echrX-47003870-A-C\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eNDUFB11\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNA\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\u003eVariants are sorted by ABSplice scores. Variants with scores \u0026ge; 0.2 (bold font) were confirmed. *Variant not present.\u003c/p\u003e \u003cp\u003eAfter the validation process, 75.61% (n\u0026thinsp;=\u0026thinsp;31) of the initially predicted splicing variants (excluding those variants not present in any of the complementary datasets (n\u0026thinsp;=\u0026thinsp;2)) were confirmed (Supplementary Table\u0026nbsp;1).\u003c/p\u003e \u003cp\u003eTissue specificity of predicted splice variants\u003c/p\u003e \u003cp\u003eFollowing cross-referencing with SpliceVault and ABSplice, we further evaluated tissue-specific effects of the \u003cem\u003ein silico\u003c/em\u003e validated variants (n\u0026thinsp;=\u0026thinsp;31), albeit the score provided by ABSplice. This approach allowed us to globally assess tissue-specific effects of all validated variants, acknowledging that some may not be high confidence in ABSplice. Thus, variants that did not reach the 0.2 impact score threshold in ABSplice but were validated in SpliceVault were also included in this analysis (Supplementary Table\u0026nbsp;1).\u003c/p\u003e \u003cp\u003eAfter removing one variant not present in the dataset, we evaluated tissue-specific effects in 26 variants with score \u0026ge; 0.2 (high impact), 2 variants with score \u0026ge; 0.05 (medium impact) and 2 with score \u0026ge; 0.01 (low impact). Notably, adipose tissue yielded the highest scores for 38.7% of the variants (n\u0026thinsp;=\u0026thinsp;12), followed by brain with 32.1% (n\u0026thinsp;=\u0026thinsp;9), testis and adrenal gland with 9.6% each (n\u0026thinsp;=\u0026thinsp;3), and the remaining 3 variants (each comprising 3.6% of the total) were distributed among nerve tibial, kidney cortex, and artery coronary.\u003c/p\u003e \u003cp\u003eThen, genes harboring the \u003cem\u003ein silico\u003c/em\u003e validated splice altering variants were queried against the GTEx portal to assess whether the predicted tissue-specific effects were attributable to gene expression restricted to that particular tissue.\u003c/p\u003e \u003cp\u003eOverall, genes like \u003cem\u003eCACNA1l\u003c/em\u003e, \u003cem\u003eSCN2A\u003c/em\u003e, \u003cem\u003eDLGAP1\u003c/em\u003e and \u003cem\u003ePRMT8\u003c/em\u003e, which harbor variants predicted to have their highest impact in brain tissue, do show higher expression values restricted to brain tissues (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e2\u003c/span\u003e, gene name in green). Only one of these genes (namely, \u003cem\u003eCANX\u003c/em\u003e), with a validated variant predicted to have the highest impact in amygdala, did not show a high expression limited to the brain.\u003c/p\u003e \u003cp\u003eHowever, genes that host splicing variants with the highest impact in adipose tissue (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e2\u003c/span\u003e, gene name in purple) do not exhibit expression limited to any specific tissue. This is in contrast to the expectation, as a specific expression limited to adipose tissue would provide a logical rationale for the increased burden of variants yielding the highest scores in adipose tissue\u003c/p\u003e \u003cp\u003eNonetheless, variants were checked against the whole set of tissues, and it was observed that most of the variants with predicted highest scores in brain, and all the variants with predicted highest effect in adipose tissue and adrenal gland, yielded the same high scores in other tissues (\u003cem\u003ei.e.\u003c/em\u003e, ABSplice scores were not exclusive for that tissue) (data not shown).\u003c/p\u003e \u003cp\u003eIn contrast, 3 variants with the highest score identified in testis, exhibited a clear tissue-specificity (\u003cem\u003ei.e.\u003c/em\u003e, the ABSplice scores retrieved for the remaining GTEx tissues of tissues were notably lower) (Supplementary Fig.\u0026nbsp;1). However, genes harboring these variants (\u003cem\u003eCATSPER1\u003c/em\u003e, \u003cem\u003eTEKT4\u003c/em\u003e, \u003cem\u003eVWA5B1\u003c/em\u003e) had tissue specific expression (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e2\u003c/span\u003e, gene name in orange) restricted to testis.\u003c/p\u003e \u003cp\u003eCluster enrichment\u003c/p\u003e \u003cp\u003eGenes harboring high-confidence splice variants predicted by SpliceAI and validated with SpliceVault and ABSplice (n\u0026thinsp;=\u0026thinsp;31 variants, one variant per gene) were cross-referenced with: (i) previously reported genes associated with ASD AS\u003csup\u003e\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e,\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e,\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e,\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e,\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e\u003c/sup\u003e, to check for similarities in splicing relevant pathways and perform gene validation, (ii) ASD-associated genes in the SFARI Gene (category 1: high confidence, category 2: strong candidate), for gene validation only, and (iii) genes harboring \u003cem\u003ede novo\u003c/em\u003e and inherited coding mutations\u003csup\u003e\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e\u003c/sup\u003e, to see if different types of mutations act through distinct mechanisms.\u003c/p\u003e \u003cp\u003e \u003cem\u003eCACNA1I, CBLB, CLTB, DLGAP1, DVL3, KIAA0513, OFD1, PKD1, SLC13A3\u003c/em\u003e and \u003cem\u003eSCN2A\u003c/em\u003e were present in at least one of the above-mentioned datasets and were thus used to construct a brain-specific network of functionally-related genes (Supplementary Fig.\u0026nbsp;2, Supplementary Table\u0026nbsp;2, n\u0026thinsp;=\u0026thinsp;60). The resultant gene network was interrogated for enrichment in the GO categories of BP, MF, and CC.\u003c/p\u003e \u003cp\u003eThe analysis revealed that these genes were significantly enriched for biological processes related to proper neuronal functioning (modulation of chemical synaptic transmission (gene ratio 12/60, q-value\u0026thinsp;=\u0026thinsp;1.68\u003csup\u003e\u0026minus;\u0026thinsp;5\u003c/sup\u003e), trans-synaptic signaling (gene ratio 12/60, q-value\u0026thinsp;=\u0026thinsp;1.68\u003csup\u003e\u0026minus;\u0026thinsp;5\u003c/sup\u003e), synaptic plasticity (gene ratio 8/60, q-value\u0026thinsp;=\u0026thinsp;1.07\u003csup\u003e\u0026minus;\u0026thinsp;4\u003c/sup\u003e), cognition (gene ratio 8/60, q-value\u0026thinsp;=\u0026thinsp;1.07\u003csup\u003e\u0026minus;\u0026thinsp;4\u003c/sup\u003e), and memory (gene ratio 9/60, q-value\u0026thinsp;=\u0026thinsp;1.68\u003csup\u003e\u0026minus;\u0026thinsp;4\u003c/sup\u003e).\u003c/p\u003e \u003cp\u003eEnriched CC terms were all related to the synapse, with top significant findings including postsynaptic specialization (gene ratio 12/60, q-value\u0026thinsp;=\u0026thinsp;1.12 x 10\u003csup\u003e\u0026minus;\u0026thinsp;7\u003c/sup\u003e), synaptic membrane (gene ratio 10/60, q-value\u0026thinsp;=\u0026thinsp;7.84 x 10\u003csup\u003e\u0026minus;\u0026thinsp;6\u003c/sup\u003e), glutamatergic synapse (gene ratio 10/60, q-value\u0026thinsp;=\u0026thinsp;1.03 x 10\u003csup\u003e\u0026minus;\u0026thinsp;5\u003c/sup\u003e), and presynaptic synapse (gene ratio 12/60, q-value\u0026thinsp;=\u0026thinsp;3.34 x 10\u003csup\u003e\u0026minus;\u0026thinsp;4\u003c/sup\u003e), among others.\u003c/p\u003e \u003cp\u003eTop enriched MF terms were associated with calmodulin binding channels (gene ratio 7/60, q-value\u0026thinsp;=\u0026thinsp;3.25 x 10\u003csup\u003e\u0026minus;\u0026thinsp;4\u003c/sup\u003e), transmembrane receptor protein kinase activities (gene ratio 5/60, q-value\u0026thinsp;=\u0026thinsp;3.25 x 10\u003csup\u003e\u0026minus;\u0026thinsp;4\u003c/sup\u003e), calcium ion channels (gene ratio 6/60, q-value\u0026thinsp;=\u0026thinsp;3.25 x 10\u003csup\u003e\u0026minus;\u0026thinsp;4\u003c/sup\u003e), and tyrosine activities (gene ratio 4/60, q-value\u0026thinsp;=\u0026thinsp;1.67 x 10\u003csup\u003e\u0026minus;\u0026thinsp;4\u003c/sup\u003e), among others.\u003c/p\u003e \u003cp\u003eFurthermore, we compared functional profiles amongst the different datasets and calculated enriched functional profiles of each gene clusters. In analyzing datasets for genes implicated in AS in ASD, we found that our gene network clusters together in terms of BP, CC and MF (Supplementary Figs.\u0026nbsp;3\u0026ndash;5). Examples of common significantly enriched terms included: (i) protein autophosphorylation and modulation of chemical synaptic transmission, for BP, (ii) postsynaptic specialization and cell leading edge, for CC, and (iii) acting/calmodulin binding, for MF.\u003c/p\u003e \u003cp\u003eHowever, when contrasting these findings with genes harboring coding variants (henceforth designated as the non-splicing gene list), the overlap between enriched categories is notably dissipated (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, Supplementary Fig.\u0026nbsp;6). CC terms did not exhibit a clear separation between datasets, with all common enriched terms relating to synapse components or postsynaptic density (Supplementary Fig.\u0026nbsp;6). While some BP (\u003cem\u003ee.g.\u003c/em\u003e, cognition, learning and memory) were significantly enriched in both datasets, others (\u003cem\u003ee.g.\u003c/em\u003e, histone modification (gene ratio 17/72, q-value\u0026thinsp;=\u0026thinsp;3.77 x 10\u003csup\u003e\u0026minus;\u0026thinsp;9\u003c/sup\u003e) and chromatin remodeling (gene ratio 15/72, q-value\u0026thinsp;=\u0026thinsp;2.45 x 10\u003csup\u003e\u0026minus;\u0026thinsp;8\u003c/sup\u003e)) were specifically enriched in the non-splicing gene list, absent in our 60-gene list (Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). On the other hand, genes from both datasets were incorporated into categories associated with the proper function and organization of the synapse. However, the majority of significant enrichments in the splicing gene list (48 out of 62 enriched BP terms) were exclusively identified within that particular dataset. Some examples of the top enrichments are provided in Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eCommon/unique enriched biological processes in splicing-related gene lists and the non-splicing gene list.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\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=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGene list\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDescription\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGene ratio\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eq-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"11\" rowspan=\"12\"\u003e \u003cp\u003eSplicing gene list\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eregulation of signaling receptor activity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6/60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.79 x 10\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eregulation of neurotransmitter receptor activity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4/60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6.00 x 10\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eprotein autophosphorylation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6/60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8.17 x 10\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eregulation of JNK cascade\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5/60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8.17 x 10\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003edendrite development\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6/60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8.17 x 10\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eregulation of protein catabolic process\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7/60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.02 x 10\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003epositive regulation of MAPK cascade\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8/60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.08 x 10\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eJNK cascade\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5/60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.34 x 10\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eregulation of phosphatidylinositol 3-kinase signaling\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4/60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.15 x 10\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eregulation of synapse assembly\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4/60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.15 x 10\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ecalcium ion transport\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7/60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.25 x 10\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eexcitatory postsynaptic potential\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4/60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.46 x 10\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"9\" rowspan=\"10\"\u003e \u003cp\u003eNon-splicing gene list\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ehistone modification\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e17/72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.77 x 10\u003csup\u003e\u0026minus;\u0026thinsp;9\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003echromatin remodeling\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e15/72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.45 x 10\u003csup\u003e\u0026minus;\u0026thinsp;8\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ehistone lysine methylation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7/72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.11 x 10\u003csup\u003e\u0026minus;\u0026thinsp;5\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ehistone methylation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7/72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.20 x 10\u003csup\u003e\u0026minus;\u0026thinsp;4\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ehistone H3-K4 methylation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5/72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.40 x 10\u003csup\u003e\u0026minus;\u0026thinsp;4\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eregulation of histone methylation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3/72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.07 x 10\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eregulation of histone modification\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4/72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.57 x 10\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003epositive regulation of histone H3-K4 methylation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2/72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.05 x 10\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ehistone lysine demethylation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2/72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.50 x 10\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ehistone demethylation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2/72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.67 x 10\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eCommon\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003elearning or memory (splicing gene list)\u003c/p\u003e \u003cp\u003elearning or memory (non-splicing gene list)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8/60\u003c/p\u003e \u003cp\u003e2/72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.34 x 10\u003csup\u003e\u0026minus;\u0026thinsp;4\u003c/sup\u003e\u003c/p\u003e \u003cp\u003e2.52 x 10\u003csup\u003e\u0026minus;\u0026thinsp;7\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ecognition (splicing gene list)\u003c/p\u003e \u003cp\u003ecognition (non-splicing gene list)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9/60\u003c/p\u003e \u003cp\u003e13/72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.68 x 10\u003csup\u003e\u0026minus;\u0026thinsp;4\u003c/sup\u003e\u003c/p\u003e \u003cp\u003e1.24 x 10\u003csup\u003e\u0026minus;\u0026thinsp;7\u003c/sup\u003e\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\u003eThe most pronounced difference emerged when analyzing enriched terms in the MF category: genes with predicted splice variants were significantly enriched in terms such as calmodulin binding (gene ratio 7/60, q-value\u0026thinsp;=\u0026thinsp;3.25 x 10\u003csup\u003e\u0026minus;\u0026thinsp;4\u003c/sup\u003e), calcium ion channel/transporter activity (gene ratio 5/60, q-value\u0026thinsp;=\u0026thinsp;3.25 x 10\u003csup\u003e\u0026minus;\u0026thinsp;4\u003c/sup\u003e), and transmembrane receptor protein kinase/tyrosine activity (gene ratio 6/60, q-value\u0026thinsp;=\u0026thinsp;3.25 x 10\u003csup\u003e\u0026minus;\u0026thinsp;4\u003c/sup\u003e), while genes in the non-splicing list were specific to histone lysine N-methyltransferase activity (gene ratio 6/72, q-value\u0026thinsp;=\u0026thinsp;3.85 x 10\u003csup\u003e\u0026minus;\u0026thinsp;6\u003c/sup\u003e) and beta-catenin binding (gene ratio 7/72, q-value\u0026thinsp;=\u0026thinsp;3.85 x 10\u003csup\u003e\u0026minus;\u0026thinsp;6\u003c/sup\u003e) (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe multifaceted genetic etiology of ASD, characterized by substantial phenotypic and genetic heterogeneity, has long been a challenge in unraveling its underlying molecular basis. The identification of splicing variants has not been included in the major WGS or WES genetic studies involving large ASD cohorts. However, AS, an intricate mechanism that diversifies protein isoforms from a single gene, has recently garnered attention as a potential contributor to ASD pathogenesis.\u003c/p\u003e \u003cp\u003eThe present study delved into the intricate landscape of AS in ASD through \u003cem\u003ein silico\u003c/em\u003e prediction and validation of splicing variants. However, the conservative threshold (SpliceAI Δ\u0026thinsp;\u0026ge;\u0026thinsp;0.8) chosen for splice-altering variant prediction\u003csup\u003e\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e\u003c/sup\u003e may, in turn, result in elevated numbers of false negatives. While this approach was necessary, \u003cem\u003ein vitro\u003c/em\u003e confirmation of the predicted variants (\u003cem\u003ee.g.\u003c/em\u003e, by Sanger sequencing), validation of the predicted alterations on AS (by reverse transcription polymerase chain reaction (RT-PCR)) and functional analysis of their molecular impacts (RNA-Seq and/or minigene reporter systems\u003csup\u003e\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e\u003c/sup\u003e), would prove much more adequate and sensitive.\u003c/p\u003e \u003cp\u003eStill, \u003cem\u003ein vitro\u003c/em\u003e validation was unfeasible due to the lack of sample availability and the difficulty in contacting participants for resampling. Thus, our validation strategy using SpliceVault and ABSplice, at least partially, provided further evidence on the robustness of the predicted splicing effects. On the one hand, SpliceVault exhibited superior sensitivity and positive predictive value than SpliceAI when it comes to exon- and double-exon skipping predictions or cryptic splice site activation, and represents the first evidence-based method for predicting the nature of variant-associated mis-splicing\u003csup\u003e\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e\u003c/sup\u003e. On the other hand, another study demonstrated that applying SpliceAI on the tissue-specific splice sites defined by SpliceMap (integrated into ABSplice) increased the precision of SpliceAI to 22% at 20% recall, with a significantly higher auPRC consistently across tissues\u003csup\u003e\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e\u003c/sup\u003e. Notably, our validation approach revealed that the majority of cryptic activation events were successfully corroborated when leveraging evidence-based data (Supplementary Table\u0026nbsp;1). This further demonstrates the role of these predicted cryptic sites in ASD-associated splicing perturbations.\u003c/p\u003e \u003cp\u003eNevertheless, splicing branchpoints present an additional source of potentially damaging non-coding variants which are amenable to systematic analysis in WGS data\u003csup\u003e\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e\u003c/sup\u003e, but remain undetectable in WES data. This represents another methodological constraint in our approach. Of note, the availability of WGS data could enhance our understanding of the splicing landscape in ASD by enabling the detection of intron retention, a splicing aberration already associated with ASD and other NDDs\u003csup\u003e\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e,\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e\u003c/sup\u003e .\u003c/p\u003e \u003cp\u003eFurthermore, 35% of cryptic splice variants with weak and intermediate predicted scores (Δ 0.35\u0026ndash;0.8) exhibit significant differences in the fraction of normal and aberrant transcripts produced across tissues. Variants with high predicted scores are significantly less likely to produce tissue-specific effects\u003csup\u003e\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e\u003c/sup\u003e. Therefore, being able to choose a less conservative threshold and perform \u003cem\u003ein vitro\u003c/em\u003e validation, would be tremendously helpful in gaining insight into tissue-specific effects. However, the tissue-specific effects of splicing variants gained prominence through our assessment using ABSplice.\u003c/p\u003e \u003cp\u003eTissue-specificity in alternative splicing (AS)\u003c/p\u003e\u003cp\u003eResearch in ASD has primarily focused on neurological aspects, looking at factors such as brain structure and function, and neurotransmitter systems. However, it is worth noting that there is ongoing research in the field of neuroimmunology and the gut-brain axis, which explores the connections between the gut and the brain. Recently, GI dysfunction has been described in various neurodevelopmental and psychiatric disorders including ASD\u003csup\u003e\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e,\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e\u003c/sup\u003e. Moreover, some studies have suggested a possible link between GI tissue, adipose tissue and brain, with accumulating evidence suggesting that the communication pathways linking them might be promising intervention points for metabolic disorders\u003csup\u003e\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e\u003c/sup\u003e. However, since adipose tissue can produce certain signaling molecules, such as adipokines, it is possible that there could also be indirect connections between adipose tissue and neurodevelopmental conditions like ASD\u003csup\u003e\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e\u003c/sup\u003e, but this area of research is still emerging and not yet well-understood. There are some studies regarding the role of adipokines in neurogenesis, neuroprotection, synaptogenesis, synaptic plasticity, and even neurodegenerative diseases such as Alzheimer\u0026rsquo;s disease\u003csup\u003e\u003cspan additionalcitationids=\"CR55 CR56\" citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e\u003c/sup\u003e. However, we urge caution in interpreting our results, as they are based on \u003cem\u003ein silico\u003c/em\u003e validation with RNA-seq data from GTEX and not from ASD patients. Functional \u003cem\u003ein vivo\u003c/em\u003e analyses (\u003cem\u003ee.g.\u003c/em\u003e, iPScs, organoids) are necessary to confirm the potential connections between adipose tissue and NDDs.\u003c/p\u003e \u003cp\u003eImportantly, preliminary findings of this study indicated a role of adipose tissue in ASD (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). Yet, upon examining the expression of genes containing variants with adipose tissue-specific effects we noted a relatively uniform expression across all tissues (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Notably, these genes exhibited elevated expression levels in the brain, and the associated splicing variants also yielded high scores in brain tissues. Consequently, the function of adipose tissue remains unclear. However, considering this \u003cem\u003ein silico\u003c/em\u003e evidence alongside previous studies, further research is necessary. Further studies could test this hypothesis by interrogating whether these genes are driving pleiotropic effects in both sets of tissues, or by performing overall comparisons between splice site usage in neuronal \u003cem\u003eversus\u003c/em\u003e adipose tissue.\u003c/p\u003e \u003cp\u003eMoreover, three variants in the final set of splicing validated variants, show unique values of tissue-specificity in testis (Supplementary Fig.\u0026nbsp;1). Nonetheless, when comparing transcriptomes, it has been observed that the brain and testis significantly surpass other tissues in terms of the diversity of expressed splice variants\u003csup\u003e\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e\u003c/sup\u003e. Consequently, our findings may lack sufficient power to attribute specific significance to testis in the context of ASD risk.\u003c/p\u003e \u003cp\u003eBiological underpinnings of AS\u003c/p\u003e \u003cp\u003eOn another note, the convergence of genes harboring validated splicing variants with previously reported ASD-associated genes from various datasets substantiates the potential significance of AS in ASD. Our creation of a brain-specific network encompassing functionally related genes (Supplementary Fig.\u0026nbsp;2, Supplementary Table\u0026nbsp;2) demonstrated enrichment in BP intricately tied to neuronal functioning, synaptic transmission, synaptic plasticity, cognition, and memory (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Although we identified a relatively small number of genes with \u003cem\u003ein silico\u003c/em\u003e validated splice variants, these findings align with previous studies showcasing aberrant splicing patterns in genes critical to neural development, which may collectively contribute to the complex ASD phenotype.\u003c/p\u003e \u003cp\u003eAdditional support for this evidence includes: (i) analyzing larger ASD cohorts under the same criteria, both to augment our splicing gene-list (and thus provide more statistical support for enrichments in GO categories) and to perform a burden test analysis of numbers of mutations (are the numbers of splicing variants per gene consistent with gene length, conservation, and the number of different isoforms?), (ii) testing whether the splicing genes carry an excess of non-splice DNVs in autism probands to further correct this measure, and (iii) using multiplex family/case-control cohorts to check if splice DNVs are enriched in affected individuals when compared with healthy siblings.\u003c/p\u003e \u003cp\u003eIn addition, we cross-referenced our gene list with genes that have exome-wide significance when combining evidence from both coding DNVs and rare inherited variants (non-splicing gene list), to encompass a broader spectrum of the disorder. Interestingly, our gene list shows significant enrichment in MF different from those enriched in the non-splicing gene list (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA). Moreover, none of these non-splicing enriched terms were found in any other splicing complementary dataset analyzed in this study. Similar results were observed for the category of BP (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB), where cognition and learning or memory terms are common amongst both lists (splicing \u003cem\u003eversus\u003c/em\u003e non-splicing), but chromatin remodeling and histone modification are specific to the non-splicing gene list, and most synaptic-related terms are specific to the splicing gene list (Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe fact that GO enrichment analysis points out to different MF and BP in genes harboring splicing variants and in the non-splicing gene list, might suggest a divergence of affected pathways and mechanisms, thus pointing to different mechanisms in which they participate in the development of the disease.\u003c/p\u003e \u003cp\u003eIn fact, a previous study on the full-length isoform transcriptome of the developing human brain\u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e has shown that differentially expressed isoforms (DEIs) reveal distinct signals relative to differentially expressed genes (DEGs). The GO enrichment analyses demonstrated stronger enrichment of DEI in neurodevelopment-relevant processes compared with DEGs. In contrast, DEGs were enriched in basic biological function-related processes, such as mitotic cell cycle, metabolic processes, protein targeting, and localization. Also, molecular functions such as kinase activity (one out of three most significant molecular functions strictly associated in our gene list) were solely linked to DEIs. \u003cem\u003eIn vitro\u003c/em\u003e studies using human cortical neurons treated with the anticonvulsant valproic acid (VPA) have shown that differentially expressed genes (DEGs) exhibit enrichment in distinct molecular functions compared to genes with differential transcript isoform usage (DTU)\u003csup\u003e\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e\u003c/sup\u003e. These findings align with those presented in this study, suggesting that the full-length isoform transcriptome provides better biological insights into brain development than the gene transcriptome. However, when comparing these results at the level of molecular functions specific to DEGs or DTUs, caution should be exercised, since these \u003cem\u003ein vitro\u003c/em\u003e experiments involve specific lines of neurons from ASD patients, unlike our \u003cem\u003ein silico\u003c/em\u003e experiments, using GTEX RNA-seq data from brain and other tissues. Therefore, further studies utilizing transcriptomic methods in brain samples from ASD patients and controls, combined with WGS data, are needed to address this question.\u003c/p\u003e \u003cp\u003eIn general, this situation mirrors the still unresolved question of whether \u003cem\u003ede novo\u003c/em\u003e and inherited variations affect the same biological pathways. Ruzzo \u003cem\u003eet al.\u003c/em\u003e illustrated that inherited variations cluster in specific biological pathways, introducing novel pathways linked to ion transport, the cell cycle, and the microtubule cytoskeleton\u003csup\u003e\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e\u003c/sup\u003e distinct from those enriched for \u003cem\u003ede novo\u003c/em\u003e variants\u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e. In a separate study, Wilfert and colleagues stated that DNVs and transmitted LGD variants converge on the same pathway but may be targeting distinct sets of genes\u003csup\u003e\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eFuture perspectives and study limitations\u003c/p\u003e \u003cp\u003eIn essence, this study exemplifies the intricate genetic landscape of the disorder and aims to raise new questions regarding the involvement of AS in ASD, proposing novel avenues for future research. The comprehensive \u003cem\u003ein silico\u003c/em\u003e validation pipeline employed here showcases the potential of such methods in deciphering splicing perturbations.\u003c/p\u003e \u003cp\u003eHowever, as with any computational approach, \u003cem\u003ein vitro\u003c/em\u003e validation is paramount to fully comprehend the functional consequences of these predicted splicing changes. Further studies should expand on the identified splicing events\u0026rsquo; downstream effects on protein function, signaling pathways, and cellular processes. Integration of our findings with multi-omics data, such as full-length isoform transcriptomics, may provide a more holistic view of the intricate ASD molecular network. Moreover, examining the potential interplay between splicing and other regulatory mechanisms, such as epigenetics, could elucidate additional layers of complexity in ASD etiology.\u003c/p\u003e \u003cp\u003eWhile numerous questions remain unanswered, and the functional validation of most predicted splice-disrupting variants is still necessary to affirm a molecular diagnosis, \u003cem\u003ein silico\u003c/em\u003e tools\u0026rsquo; predictions can serve as supportive evidence in variant classification. Commonly used variant annotation tools are not designed to assess the deleterious impact of splicing variants and their predictions are largely restricted to canonical splice sites. However, if multiple computational sources, such as the framework presented here, indicate that a variant has a deleterious effect, these predictions can be employed. Furthermore, they can aid in prioritizing splice-disrupting variants for subsequent functional testing or experimental validation.\u003c/p\u003e \u003cp\u003eThis study underscores the utility of computational predictions in identifying splicing variants. However, to comprehensively address the involvement of AS processes in ASD etiology, several functional and \u003cem\u003ein vitro\u003c/em\u003e studies are needed in the near future. These include: (i) the validation of splicing variants (by RT-PCR or RNA-seq), (ii) functional characterization, performing functional assays to elucidate how splicing variants influence protein function and pathway activity (\u003cem\u003ee.g.\u003c/em\u003e, iPSCs and/or animal models), (iii) integrative omics approaches, integrating splicing variant data with other omics data (\u003cem\u003ee.g.\u003c/em\u003e, genomic, epigenomic, proteomic) to gain a comprehensive understanding of the molecular mechanisms underlying ASD, and (iv) to deeper explore the clinical implications of splicing variants in ASD, including potential biomarker discovery and therapeutic targets.\u003c/p\u003e \u003cp\u003eThese future studies will aim, together with the \u003cem\u003ein silico\u003c/em\u003e workflow using AI tools as presented in this study, to advance our understanding of splicing perturbations in ASD and their broader implications.\u003c/p\u003e \u003cp\u003eData availbility\u003c/p\u003e \u003cp\u003eWES (Whole Exome Sequencing) data from the Spanish cohort were generated as part of the ASC and are transferred to dbGaP with Study Accession: phs000298.v4.p3. Previously published in Satterstrom FK, Kosmicki JA, Wang J, Breen MS, De Rubeis S, An JY, Peng M, Collins R, Grove J, Klei L, Stevens C, Reichert J, Mulhern MS, Artomov M, Gerges S, Sheppard B, Xu X, Bhaduri A, Norman U, Brand H, Schwartz G, Nguyen R, Guerrero EE, Dias C; Autism Sequencing Consortium; iPSYCH-Broad Consortium; Betancur C, Cook EH, Gallagher L, Gill M, Sutcliffe JS, Thurm A, Zwick ME, B\u0026oslash;rglum AD, State MW, Cicek AE, Talkowski ME, Cutler DJ, Devlin B, Sanders SJ, Roeder K, Daly MJ, Buxbaum JD. Large-Scale Exome Sequencing Study Implicates Both Developmental and Functional Changes in the Neurobiology of Autism. Cell. 2020 Feb 6;180(3):568\u0026ndash;584.e23. doi: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.cell.2019.12.036\u003c/span\u003e\u003cspan address=\"10.1016/j.cell.2019.12.036\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. Epub 2020 Jan 23. PMID: 31981491; PMCID: PMC7250485.\u003c/p\u003e \u003cp\u003eSplicing variants identified in this research are provided within the manuscript and the supplementary information.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eM Tubio-Fungueiri\u0026ntilde;o., M. Fernandez-Prieto., J Gonzalez-Pe\u0026ntilde;as, A. Carracedo, C. Arango and M.Parellada participated in the recruitment of samples. S.Dominguez-Alonso has carried out the analyses and wrote the paper. A.Carrecedo., C.Rodriguez.-Fontenla, ,M.Parellada and C.Arango participated in the design and coordination of this study. A.Carracedo. and C.Rodriguez.-Fontenla. critically revised the work and approved the final content.\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e\u003cp\u003eWe would like to warmly thank the ASC (Autism Sequencing Consortium) (https://genome.emory.edu/ASC/) that has sequenced the Spanish trios.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eWES (Whole Exome Sequencing) data from the Spanish cohort were generated as part of the ASC and are transferred to dbGaP with Study Accession: phs000298.v4.p3 . Previously published in Satterstrom FK, Kosmicki JA, Wang J, Breen MS, De Rubeis S, An JY, Peng M, Collins R, Grove J, Klei L, Stevens C, Reichert J, Mulhern MS, Artomov M, Gerges S, Sheppard B, Xu X, Bhaduri A, Norman U, Brand H, Schwartz G, Nguyen R, Guerrero EE, Dias C; Autism Sequencing Consortium; iPSYCH-Broad Consortium; Betancur C, Cook EH, Gallagher L, Gill M, Sutcliffe JS, Thurm A, Zwick ME, B\u0026oslash;rglum AD, State MW, Cicek AE, Talkowski ME, Cutler DJ, Devlin B, Sanders SJ, Roeder K, Daly MJ, Buxbaum JD. Large-Scale Exome Sequencing Study Implicates Both Developmental and Functional Changes in the Neurobiology of Autism. Cell. 2020 Feb 6;180(3):568-584.e23. doi: 10.1016/j.cell.2019.12.036. Epub 2020 Jan 23. PMID: 31981491; PMCID: PMC7250485.Splicing variants identified in this research are provided within the manuscript and the supplementary information.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAmerican Psychiatric Association, American Psychiatric Association, eds. \u003cem\u003eDiagnostic and Statistical Manual of Mental Disorders: DSM-5\u003c/em\u003e 5th edn (American Psychiatric Association, 2013).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSandin, S. et al. The Heritability of Autism Spectrum Disorder. \u003cem\u003eJAMA\u003c/em\u003e. \u003cb\u003e318\u003c/b\u003e (12), 1182. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1001/jama.2017.12141\u003c/span\u003e\u003cspan address=\"10.1001/jama.2017.12141\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2017).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGrove, J. et al. 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Recent ultra-rare inherited variants implicate new autism candidate risk genes. \u003cem\u003eNat. Genet.\u003c/em\u003e \u003cb\u003e53\u003c/b\u003e (8), 1125\u0026ndash;1134. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1038/s41588-021-00899-8\u003c/span\u003e\u003cspan address=\"10.1038/s41588-021-00899-8\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2021).\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-5136316/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5136316/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eAutism Spectrum Disorders (ASD) are complex and genetically heterogeneous neurodevelopmental conditions. Although alternative splicing (AS) has emerged as a potential contributor to ASD pathogenesis, its role in large-scale genomic studies has remained relatively unexplored. In this comprehensive study, we utilized computational tools to identify, predict, and validate splicing variants within a Spanish ASD cohort (360 trios), shedding light on their potential contributions to the disorder.\u003c/p\u003e \u003cp\u003eWe utilized SpliceAI, a newly developed machine-learning tool, to identify high-confidence splicing variants in the Spanish ASD cohort and applied a stringent threshold (Δ\u0026thinsp;\u0026ge;\u0026thinsp;0.8) to ensure robust confidence in the predictions. The \u003cem\u003ein silico\u003c/em\u003e validation was then conducted using SpliceVault, which provided compelling evidence of the predicted splicing effects, using 335,663 reference RNA-sequencing (RNA-seq) datasets from GTEx v8 and the sequence read archive (SRA). Furthermore, ABSplice was employed for additional variant validation and to elucidate the tissue-specific impacts of the splicing variants. Notably, our analysis suggested the contribution of splicing variants within \u003cem\u003eCACNA1I, CBLB, CLTB, DLGAP1, DVL3, KIAA0513, OFD1, PKD1, SLC13A3\u003c/em\u003e, and \u003cem\u003eSCN2A.\u003c/em\u003e\u003c/p\u003e \u003cp\u003eComplementary datasets, including more than 42,000 ASD cases, were employed for gene validation and gene ontology (GO) analysis. These analyses revealed potential tissue-specific effects of the splicing variants, particularly in adipose tissue, testis, and the brain. These findings suggest the involvement of these tissues in ASD etiology, which opens up new avenues for further functional testing. Enrichments in molecular functions and biological processes imply the presence of separate pathways and mechanisms involved in the progression of the disorder, thereby distinguishing splicing genes from other ASD-related genes. Notably, splicing genes appear to be predominantly associated with synaptic organization and transmission, in contrast to non-splicing genes (\u003cem\u003ei.e.\u003c/em\u003e, genes harboring \u003cem\u003ede novo\u003c/em\u003e and inherited coding variants not predicted to alter splicing), which have been mainly implicated in chromatin remodeling processes.\u003c/p\u003e \u003cp\u003eIn conclusion, this study advances our comprehension of the role of AS in ASD and calls for further investigations, including \u003cem\u003ein vitro\u003c/em\u003e validation and integration with multi-omics data, to elucidate the functional roles of the highlighted genes and the intricate interplay of the splicing process with other regulatory mechanisms and tissues in ASD.\u003c/p\u003e","manuscriptTitle":"Alternative Splicing Analysis in a Spanish ASD (Autism Spectrum Disorders) Cohort: In silico Prediction and Validation","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-10-23 09:04:01","doi":"10.21203/rs.3.rs-5136316/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-02-14T08:37:05+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-02-02T20:47:31+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-01-28T22:32:03+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"29697067759670622535958250104060062468","date":"2025-01-28T01:26:58+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"279098436694208932011360694084697971206","date":"2025-01-23T14:23:18+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-01-21T11:19:15+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-01-13T09:37:52+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2024-10-21T11:15:10+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-10-15T12:50:50+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2024-09-23T08:16:26+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"d99f35f9-2b09-4a84-a497-b0375c479ce8","owner":[],"postedDate":"October 23rd, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":38971758,"name":"Biological sciences/Genetics/Neurodevelopmental disorders/Autism spectrum disorders"},{"id":38971759,"name":"Biological sciences/Genetics/Genomics/Transcriptomics"},{"id":38971760,"name":"Health sciences/Neurology/Neurological disorders/Neurodevelopmental disorders"}],"tags":[],"updatedAt":"2025-03-31T15:58:19+00:00","versionOfRecord":{"articleIdentity":"rs-5136316","link":"https://doi.org/10.1038/s41598-025-95456-2","journal":{"identity":"scientific-reports","isVorOnly":false,"title":"Scientific Reports"},"publishedOn":"2025-03-28 15:56:56","publishedOnDateReadable":"March 28th, 2025"},"versionCreatedAt":"2024-10-23 09:04:01","video":"","vorDoi":"10.1038/s41598-025-95456-2","vorDoiUrl":"https://doi.org/10.1038/s41598-025-95456-2","workflowStages":[]},"version":"v1","identity":"rs-5136316","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5136316","identity":"rs-5136316","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}