Long-read genome sequencing enhances diagnostics of pediatric neurological disorders

Long-read genome sequencing enhances diagnostics of pediatric neurological disorders | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Long-read genome sequencing enhances diagnostics of pediatric neurological disorders Marlene Ek, Malin Kvarnung, Esmee Ten Berk Boer, Linnéa La Fleur, and 10 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6863124/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 14 You are reading this latest preprint version Abstract Background Singleton short-read genome sequencing (GS) is increasingly used as a first-line genetic test for childhood neurological disorders (such as intellectual disability, neurodevelopmental delay, motor delay, and hypotonia) with diagnostic yields from 26–35%, typically involving a mix of single nucleotide variants and small insertions/deletions (SNV/INDELs), structural variants (SVs), and short tandem repeats (STRs). Long-read GS is emerging as an attractive alternative, offering a more comprehensive assessment of the genome, but its utility still needs to be systematically evaluated in a clinical diagnostic setting. Methods We prospectively included 100 children and adolescents (≤ 20 years) with neurological disorders, newly referred for genetic testing. Routine DNA was used for standard clinical short-read GS in parallel with long-read GS (Oxford Nanopore Technologies). In addition to comprehensive variant calling, long-read GS data was also phased and underwent methylation analysis. Variant interpretation was restricted to in-silico gene panels targeting either intellectual disability (1,568 genes) or neuromuscular disorders (1,035 genes) depending on the clinical presentation. Results The long-read GS generated an average of 111 GB data per sample, with a median read-length of 5 kb and average N50 of 16 kb; resulting in an average coverage of 34X. Short-read and long-read GS identified the same 29% diagnostic yield, including SNV/INDELs (n = 18), SVs (n = 9), STRs (n = 1), and uniparental disomy (n = 1). Long-read GS provided additional diagnostic value in 13 cases involving 17 distinct variants, including phasing of SMN1 and biallelic SNVs/INDELs in autosomal recessive genes, accurate determination of STR length and sequence as well as detailed structural characterization of SVs. Of note, an unbalanced translocation, der(14)t(8;14)(p11.2;p23.1, required de novo assembly and T2T alignment resolve the breakpoint junctions. Furthermore, long-read GS detected disease-associated aberrant methylation patterns in the Prader-Willi region and across an FMR1 expansion. Conclusion In a clinical diagnostic setting, long-read GS proved to be a streamlined, first-line test, capturing the full spectrum of disease-causing variants, reducing the need for follow-up testing and enabled more precise interpretation. While the overall diagnostic yield may be comparable to that of short-read approaches, long-read GS offers significant added value across multiple variant types. Whole genome sequencing Long-read sequencing Short-read sequencing Rare diseases Clinical diagnostics Single nucleotide variants Chromosomal rearrangements Structural variants Short tandem repeat expansions Methylation analysis Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Background Neurological disorders encompass a broad spectrum of conditions that affect the central and/or peripheral nervous system. In children and adolescents, these disorders include neurodevelopmental disorders (NDD), such as developmental delay, intellectual disability (ID) and autism, as well as neuromuscular disorders (NMD). Intellectual disability affects approximately 1% of the population and involves limitations in intellectual functioning and adaptive behavior (e.g., language, social and practical skills) [ 1 , 2 ]. Intellectual disability can occur in isolation, together with other neurocognitive conditions (autism, attention deficit hyperactivity disorder), together with seizures or as part of a broader syndromic presentation [ 3 ]. Neuromuscular disorders (NMDs) are a heterogeneous group of disorders affecting the peripheral nerves, muscles, or muscular junction. Hypotonia and delayed motor development are common presentations in children with NMDs. However, there is often a phenotypic overlap in the presentation of NDDs and NMDs, particularly in young children [ 4 ]. The etiology of neurological disorders is heterogeneous and may include infections, or environmental factors, but a considerable proportion are genetic (i.e. monogenic or chromosomal) [ 5 , 6 ]. Using short-read genome sequencing (GS), we and others have demonstrated a diagnostic yield of approximately 25–35% depending on cohort characteristics [ 4 , 7 , 8 ]. Importantly, in these conditions a clinical test must capture the diverse landscape of disease-causing genetic variations, from single base pair alterations to chromosomal rearrangements. Small variants, including single nucleotide variants (SNVs) and insertions/deletions (INDELs) < 50bp in size, are reliably detected by short-read genome [ 9 , 10 ] and exome [ 8 ] sequencing. However, the detection of other variant types, such as short tandem repeat expansions (STRs), variants in paralogous regions and aberrant methylation, remains challenging. Furthermore, the complexity of structural variants (SVs) is often underestimated and balanced SVs are frequently missed by short read approaches [ 11 – 14 ]. Long-read GS is currently emerging as a promising method in clinical genetic diagnostics, with the potential to detect the full spectrum of variant types that are often missed or incompletely resolved by short-read technologies. Early applications have shown that long-read GS is able to: (i) detect and accurately characterize SVs, even when they are complex or involve breakpoints in highly repetitive regions [ 15 – 17 ], (ii) precisely genotype pathogenic STR expansions [ 18 ], and (iii) identify disease-associated methylation patterns, both in the context of imprinted gene disorders and more broadly to aid the interpretations of variants of uncertain significance (VUS ) [ 19 ]. In this study we evaluated the utilization of long-read GS as a first line clinical genetic test in children with neurological disorders. In a prospective, unselected cohort of 100 individuals referred to our laboratory for short-read GS, long-read GS achieved a comparable final diagnostic yield. Importantly, long-read GS provided a more comprehensive diagnostic workflow enabling direct phasing of compound heterozygous variants, detailed characterization of SVs and STRs and assessment of DNA methylation. Methods Clinical genetics and Genomics (Karolinska University Hospital, Stockholm, Sweden) is a tertiary center where short-read GS (with in silico gene panel analysis) is performed as the first line test for individuals with suspected rare genetic diseases, including NDDs, and malformation syndromes. Overall, roughly 5000 analyses are performed annually, the detailed analysis pipelines and overall workflow has previously been described [ 9 ]. In this study, we included 100 children and adolescents with neurological disorders referred to the Department of Clinical Genetics and Genomics (Karolinska University Hospital in Stockholm, Sweden), for diagnostic genetic testing with short-read GS between September 1, 2023, and April 15, 2024. The cohort consists of 55 males and 45 females with a median age of five years and a phenotype suggestive of either a NDD (n = 79) or an NMD (n = 21). Informed consent was obtained either from the affected individuals or their legal guardians. To obtain a clinically representative first-line testing cohort, individuals were contacted directly after the referral was received and before the short-read GS results were available. The first 100 individuals who gave consent and for whom DNA was available from both parents and the proband were included in the study; those without available parental or proband DNA were excluded. Although the study was conducted with singleton long-read GS, parental DNA was required for potential follow-up analyses. Clinical characteristics of the study cohort are summarized in Table 1 . Table 1 Cohort characteristics: Age, gender, and phenotypic presentation. COHORT CHARACTERISTICS % (N = 100) Median age, years (min-max) 5 (1 day − 20 years) Male 55 Consanguinity 5 Main phenotypic features (HPO terms) Autism / Autistic behavior 56 Intellectual disability 36 Global developmental delay 21 Delayed speech and language development / Language impairment 21 Attention deficit hyperactivity disorder 10 Motor delay 6 Hypotonia 6 Microcephaly 6 Genomic DNA was extracted from whole blood. For the majority (n = 79), extraction was performed using QIAsymphony (QIAGEN, Hilden, Germany) and the QIAsymphony DSP DNA Midi Kit (QIAGEN, Hilden, Germany) according to the standard protocol. The other samples were extracted either by using QIAGEN EZ1 (n = 14) (QIAGEN, Hilden, Germany) or by manual extraction with the QIAamp DNA blood midi kit (n = 7) (QIAGEN, Hilden, Germany) according to manufacturer’s protocol. Short-read genome sequencing Genomic DNA was sequenced on a NovaSeqX (Illumina, San Diego, CA, USA). The bioinformatic workflow calling SNV/INDELs, SVs, mobile element insertions, STR expansions as well as SMN1 / SMN2 copy number has been described previously [ 4 , 9 ]. Based on phenotypic presentation, in-silico gene panels were applied: (i) 1568 genes associated with NDD and/or (ii) 1035 genes associated with NMD (Additional file 1: Document S1). Detected variants were ranked and visualized as previously described [ 9 , 20 ] and classified from 1–5 according to the American College of Medical Genetics and Genomics (ACMG)/Association for Molecular Pathology (AMP) guidelines [ 21 ]. In addition, a temperature scale of detected VUS was applied, ranging from hot to ice cold, in order of estimated pathogenicity [ 22 ]. The “hot VUSs” were reported to the referring physician as they were considered more likely to be pathogenic. Long-read sequencing Genomic DNA was sheared with Covaris G-tube (17 samples; Covaris, Woburn, MA, USA) or Megaruptor (83 samples; Diagenode, Liège, Belgium) to obtain ~ 15–20 kb fragments followed by size selection using AMPure XP beads (Beckman Coulter, Brea, CA, USA) to remove fragments smaller than 2 kb. The libraries were prepared with the Ligation Sequencing Kit V14 and sequenced on a PromethIon (Oxford Nanopore Technologies, Oxford, UK), using one PromethIon flow cell per sample. Raw data was processed through the custom pipeline poorpipe [ 23 ]. In brief, the pipeline performs data alignment (Minimap2 [ 24 ]), analysis of SNVs [ 25 ], CNVs [ 26 , 27 ], SVs [ 28 ], methylation [ 29 ], STRs using our custom tool Abacus [ 30 ], and paralogous genes [ 31 ]. Abacus employs graph alignment and a Gaussian mixture model to quantify, phase and build consensus of STR expansions. The resulting data was phased using Whatshap [ 32 ], and annotated using Ensembl VEP [ 33 ]. The annotated variant lists were scored and ranked using Genmod [ 20 ]. All data was aligned to reference genome GRCh37 (hg19), and the classification of detected variants was performed in the same way as for clinical short-read GS. Quality assessment was performed using Picard tools, samtools, Nanostats and bcftools, and the resulting reports were aggregated using MultiQC. A local database containing variant calls from the 100 cases was built using SVDB [ 34 ] and used to filter calls unique to long-read GS. For variants called by both short-read and long-read GS, the clinical Karolinska short-read GS database was used for filtering [ 9 ]. SNVs, STR expansions and paralogous regions were assessed in our custom in-house interpretation software similar to the short-read GS data [ 9 ]. Imprinted regions were assessed using a custom script that compared the current case with a pool of controls (Additional file 1: Document S2). Finally, SVs were analyzed in two ways: large (> 10 kb) were assessed genome-wide and small (< 10 kb) were filtered based on gene panels. SVs localized in genes within the gene panel, as well as those consisting of more than two breakpoints were examined in the Integrative Genomics Viewer (IGV) [ 35 ] and the derivative chromosomes were resolved manually. Variants were interpreted clinically and classified in the same way as described for the short-read GS data. The long-read analysis pipeline is shown in Fig. 1 . Clinical verification Validation of the FMR1 expansion was performed according to the manufacturer’s protocol, using the AmplideX PCR/CE FMR1 kit (Asuragen, Austin, TX, USA) and an ABI 3500xL Genetic Analyzer (Applied Biosystems, Foster City, CA, USA). To confirm aberrant methylation pattern across the Prader Willi region, we performed methylation specific multiplex ligation-dependent probe amplification (MS-MLPA) using SALSA MLPA Probemix ME028 Prader-Willi/Angelman (MRC Holland, Amsterdam, Netherlands) according to manufacturer’s protocol. Results Long-read genome data quality and characteristics The long-read GS generated an average of 111 GB data per sample, with a median read-length of 5 kb and average N50 of 16 kb; resulting in an average coverage of 34X. The detailed quality and sequencing parameters are available in the Additional file 1: Document S3. Overall genetic findings In 100 unrelated individuals with neurodevelopmental (n = 79) or neuromuscular (n = 21) symptoms (Table 1 ), a likely genetic diagnosis was made in 34 individuals, 29 with pathogenic findings and five with hot VUSs. In total, those individuals harbored 36 variants that were further subdivided into SNV/INDELs (n = 24), SVs (n = 10), STR (n = 1) and uniparental disomy (n = 1) (Fig. 2 ; Additional file 3: Table S2 ). In addition, five rare variants of clinical interest (three SVs and two STRs) were further investigated but finally classified as benign. All those 41 variants were detectable with both short-read GS and long-read GS. The added value of long-read GS is showcased below for 13 cases from five different variant categories (SNV/INDELs, STRs, imprinted regions, paralogous regions and SVs). Table 2 Overview of 19 variants with added clinical value from long-read genome sequencing (GS). The table summarizes 17 variants identified in 13 individuals, grouped by variant type: Single nucleotide variant (SNV) and/or small insertion/deletion (INDEL), structural variant (SV), short tandem repeat (STR) expansion, and other. Identifying information has been removed for variants classified as likely benign (LB). Individual Age Sex Disease- causing gene Variant/s Added value of long-read GS Outcome Single nucleotide variants and/or small insertion/deletion RD_P694 6 y F SGCA (NM_000023.4) c.557A > T, p.Lys193Ter c.348_352dup, p.Gln118ProfsTer95 Phasing the two variants (SNV + INDEL) separated by 585 bp LP RD_P712 1 d F COL12A1 (NM_004370.6) c.8861G > A p.Gly2954Glu c.165C > G p.Tyr55Ter Phasing the two variants (SNV + SNV) separated by 105 kb VUS Structural variant (SV) RD_P623 3 y F SMN1 Homozygous deletion of SMN1 Phasing of paralogous regions, detecting no copy of SMN1 and phasing four copies of SMN2 P RD_P695 1 y M FGF14 Inversion of 13q disrupting FGF14 Inversion of chr 10 Mapping of SV breakpoints and identification of a 7.6 kb deletion Detection of a pericentric inversion of chromosome 10 P LB RD_P633 10 y M multiple Unbalanced translocation der(14)t(8;14) Detected telomeric sequence at one breakpoint Detected link to acrocentric p-arm at the other breakpoint P RD_P651 2 y F multiple Complex rearrangement of 9p Resolved CGR structure P RD_P655 9 m F multiple Mosaic ring chromosome 18/deletion of 18p Resolved CGR structure P NA - - none Complex SV involving 9p Complex SV involving 10p Calling of SV and mapping of breakpoints that don't disrupt disease-causing genes LB NA - - none Complex SV involving 1p Calling of SV and mapping of breakpoints that don't disrupt disease-causing genes LB Short tandem repeat (STR) expansion RD_P698 2 y M FMR1 Full expansion (654 CGG repeat) Characterization of STR expansion; size, loss of interrupting motifs, hypermethylation P NA - F FMR1 Expansion (60 CGG repeat) Characterization of STR and detection of interrupting motifs LB NA - - ATXN1 Expansion (37 CAG repeat) Characterization of STR and detection of interrupting motifs LB Other RD_P706 11 d M multiple Maternal uniparental isodisomy of chromosome 15 Methylation analysis confirms PWS diagnosis P NA, not available; y, year; m, month; d, day; F, female; M, Male; chr, chromosome; bp, base pair; CGR, complex genomic rearrangement; PWS, Prader-Willi syndrome; LP, likely pathogenic; P, pathogenic; VUS, variant of uncertain significance. Added value of long-read GS SNVs and INDELs Three individuals carried biallelic variants in two genes, SGCA (Limb-girdle muscular dystrophy, MIM# 608099) (RD_P694) and COL12A1 (Bethlem myopathy 2, MIM# 616471) (RD_P712). In both, phasing the long-read GS data showed that the two variants were in trans , which was later confirmed by segregation analysis of parental samples (Fig. 3 ). This increased the support for pathogenicity for SGCA that was upgraded to likely pathogenic, however the COL12A1 variants remained as a VUS. STR expansions A pathogenic FMR1 expansion (Fragile X syndrome, MIM# 300624) was detected in a two-year-old boy with global developmental delay (RD_P698). With long-read GS the CGG repeat size was 654, with short-read GS 76 and targeted FMR1 testing showed > 200. Methylation analysis further revealed that the FMR1 promotor was hypermethylated (Fig. 4 ; Additional file 4: Fig. S1 ). In comparison, in another patient, long-read GS also detected an FMR1 allele with 60 repeats and two interrupting AGG motifs that was not hypermethylated, but predominantly unmethylated (Fig. 4 ; Additional file 4: Fig. S1 ). A finding not interpreted as the cause of clinical symptoms. Finally, for an ATXN1 allele with 37 repeats, long-read GS allowed haplotype-resolved sizing (31 and 37 repeats) and revealed two interrupting CAT motifs in each allele (Fig. 4 ). Since interrupting CAT motifs in allele sizes spanning 36–44 repeats are considered normal, the variant could be dismissed. Imprinting disorders In a newborn boy (RD_P706) with neonatal hypotonia, long-read GS detected partial maternal uniparental isodisomy of chromosome 15, including the Prader Willi region. Methylation analysis revealed hypermethylated promotors of multiple genes in the Prader-Willi region, including NDN (z-score − 6.49), SNRPN (z-score − 3.35) and MAGEL2 (z-score − 13.5) that could also be visually inspected in IGV (Additional file 4: Fig. S2 ). Clinical validation using methylation specific multiplex ligation-dependent probe amplification (MS-MLPA) confirmed the presence of aberrant methylation patterns associated with Prader Willi syndrome (MIM#176270). Paralogous regions A three-year-old girl with progressive muscle weakness (RD_P623) had a homozygous deletion of SMN1 and four copies of SMN2. Phased long-read GS data could distinguish the four SMN2 copies (Additional file 4: Fig. S3 ). The presence of multiple copies of SMN2 is associated with a milder phenotype, and she was diagnosed with spinal muscular atrophy type 3 (MIM# 253400). Structural variants Including the above mentioned SMN1 deletion, a total of 14 rare potentially clinically significant SVs were detected (Additional file 2: Table S2 ) and the long-read GS data enabled a complete characterization in all of them; seven representative cases are highlighted below, including both simple SVs and complex events. Simple SVs In three simple SVs, identified in two individuals, long-read GS aided in the interpretation. First, in a 10-year-old male with intellectual disability (RD_P633), long-read GS was not only able to confirm the short-read call of a terminal duplication on chromosome 8, dup(8)(p23.3p23.1), but also identified telomeric sequence at one of the breakpoints and a link to the acrocentric p-arm of chromosome 14 at the other breakpoint indicating an unbalanced translocation. However, T2T alignment was required to fully resolve the translocation. Second, in a one-year-old boy (RD_P695) with delayed motor development and poor balance, two inversions were readily identified by long-read GS. One pathogenic event, an inversion on chromosome 13, inv(13)(q31.1q33.1), disrupts FGF14 resulting in the diagnosis of Spinocerebellar ataxia 27A, SCA27A, MIM#193003). The other event was a 23 Mb pericentric inversion on chromosome 10, inv(10)(p11.21;q21.1), was also detected. The pathogenic event was observed in the clinical short-read GS analysis, but not the inv(10). However, manual inspection of the VCF files showed that the inv(10) was also called in short-read GS data, but the event was not highly ranked. Of note, in a one-year-old girl with global developmental delay, a duplication of 22q11 was detected from read depth analysis, although characterization of the breakpoints was not possible. Complex SVs Five complex SVs in four individuals were resolved with long-read GS (Table 2 ). A DEL-INV-DUP rearrangement on chromosome 9 was found in a two-year-old girl (RD_P651) with syndromic global developmental delay. The identified structural rearrangement consisted of a terminal 1.8 Mb deletion at 9p24.3 and a 17.8 Mb duplicated segment at 9p24.3p22.1, separated by an 850-bp normal copy number segment. The duplicated segment had replaced the deleted segment, and was inserted in an inverted orientation, so that the normal copy number segment was flanked by two mirroring identical segments (Fig. 5 a). Long-read GS could fully resolve the derivative chromosome structure and although short-read GS detected the CNVs involved, no call (in the clinical interpretation tool) allowed us to resolve the structure of the rearrangement. In a 9-month-old girl RD_P655 with motor delay, long-read GS identified a heterozygous deletion of chromosome 18p and a mosaic deletion of the distal 18q. Six out of 28 long-read GS reads connected the q-arm to the p-arm indicating a mosaic ring chromosome in 42% of the cells (Fig. 5 b). The ring chromosome was also detected by karyotyping; 46,XX,del(18)(p11.1)[ 31 ]/46,XX,r(18)(p11.1q23)[ 8 ]. Re-evaluation of the ring breakpoint junction in IGV, found that it was not visible in the short-read data. In a one-year-old girl (RD_P649) with global developmental delay a complex rearrangement of chromosome 1 was resolved with long-read GS. The five duplicated segments were inserted into chromosome 1 in a seemingly unorganized manner. Although three genes overlap with the breakpoints, all retain a remaining functional copy (Additional file 4: Fig. S4 ). The complex SV was inherited from the unaffected mother and classified as benign, while a pathogenic de novo SNV/INDEL in ARID1A (c.3236-3239delinsG) led to a clinical diagnosis of Coffin-Siris syndrome 2 (MIM#614607). In a 13-year-old boy with intellectual disability (RD_P658), two separate complex SVs, a DUP-TRP/INV-DUP on chromosome 9 and a DEL-DUP on chromosome 10, were resolved by long-read GS (Additional file 4: Fig. S5). Parental analysis showed that the two events were inherited from the same healthy parent, and both were classified as benign. The affected CNVs were also identified with short-read GS but the rearrangements could not be phased. Discussion The diverse genetic background observed in neurological disorders such as NDDs and NMDs prompted us to evaluate the clinical value of singleton long-read GS as a first line genetic test in this patient group. Altogether, long-read GS provided clinically relevant information in regard to phasing, characterization of STR expansions, and resolution of SV structures, resulting in improved variant assessment. In addition, methylation analysis shows strong promise as a clinically valuable layer of information. Short-read GS has been a major success in rare disease diagnostics, enabling a streamlined workflow for individuals with suspected genetic conditions. Nevertheless, a substantial proportion of cases remain undiagnosed, with overall diagnostic yields typically below 50% [ 9 , 10 ]. While some of these cases may have non-genetic causes or involve as-yet undiscovered disease mechanisms, such as pathogenic non-coding variants or complex inheritance patterns including digenic or oligogenic transmission, the inherent limitations of the short-read technologies also contribute to the reduced detection rates. These limitations include missed or incorrectly resolved SV breakpoints in repetitive regions and poor genotyping accuracy of both SVs and STRs [ 14 , 36 ]. Furthermore, phasing is generally not possible, making it difficult to distinguish whether a variant is located in a gene of interest or its pseudogene. In addition, short reads cannot span across multiple variants in the same gene, especially when they are located in different exons. A clear and critical step likely to improve rare disease diagnostics is the implementation of long-read GS, which promises a more comprehensive variant characterization and a simpler interpretation process compared to short-read GS [ 37 – 39 ]. Recent studies have suggested a potential increase in diagnostic yield of up to 7.3% with long-read GS [ 39 ]. In this study, we focused on individuals with neurologic disorders with onset during childhood, a group known to have a genetically heterogeneous background. To simplify the clinical workflow, regular DNA was used instead of high-molecular weight DNA, which would have increased both cost and labor. This approach required no changes to sample collection, extraction methods, or storage protocols, thereby streamlining integration into existing diagnostic pipelines. We have previously shown that the quality of the regular DNA extraction results in acceptable long-read GS quality [ 11 ]. Here, the use of regular DNA is partly reflected in a relatively short average read length of approximately 5 kb; however, read lengths varied across the cohort and increased over time (Additional File 2: Table S1 ). As one of the aims of this study was to develop a clinically applicable standard workflow, the laboratory protocol was continuously optimized throughout the study, which contributed to variability in quality control metrics across samples. A notable improvement was observed with the introduction of Megaruptor-based DNA shearing, which increased the average read length from 3.3 kb to 5 kb (Additional File 2: Table S1 ). This approach streamlined the integration into existing diagnostic pipelines, and, in line with this, we also aligned all sequencing data to the same reference genome currently used in clinical practice. At Genomic Medicine Center Karolinska, we have an established and well-functioning clinical short-read GS workflow that captures a broad range of variant types [ 9 ]. However, this workflow currently relies on the hg19 reference genome. To enable direct comparison between short-read and long-read GS results within the same clinical framework, alignment and variant calling for long-read data were also performed against hg19. Although GRCh38 (hg38) provides a more complete representation of the genome, particularly in challenging regions such as pericentromeric and telomeric areas, using hg19 was necessary for consistency in this study. More recently, the telomere-to-telomere (T2T-CHM13) assembly has enabled more accurate mapping across repetitive regions (i.e. centromeric satellite arrays, segmental duplications and acrocentric p-arm sequences) [ 40 ]. Previous studies, including our own, have highlighted that T2T-CHM13 is critical for detecting and resolving some clinically relevant structural variants [ 11 , 12 , 16 , 17 , 38 ]. A limitation of our approach in this study is therefore that long-read GS was analyzed against hg19. This was necessary for an unbiased comparison with our current short-read workflow, but to fully utilize the long-read data alignment to GRCh38 as well as de novo assembly is needed. Importantly, the study cohort consisted of individuals referred for their first genomic analysis, allowing us to assess the performance of long-read GS as a true “first-line genetic test”. In this context, we show that long-read GS successfully identified all disease-causing variants detected by short-read GS. Although the overall diagnostic yield was the same (29%) and no additional diagnoses were made, long-read GS provided added clinical value across multiple variant types, reducing the need for follow up analyses after initial testing. One of the key clinical advantages observed was the ability of long-read GS to phase compound heterozygous SNV/INDELs causing autosomal recessive diseases, eliminating the need for parental segregation analysis. The possibility to phase depends on the read length as well as the presence of informative single nucleotide polymorphisms in the region. Not all genes with possible recessive variants could be phased due to the relatively short average read lengths discussed above, but this is likely to improve as more clinical-grade software and tools for optimizing phasing in such cases are developed. However, when phasing was possible, the clinical value was clear, exemplified by two cases (RD_P694 and RD_P712) where long-read GS detected and phased biallelic variants across genomic distances of 585 nt and 105 kb, respectively (Fig. 3 , Table 2 , Additional file 3: Table S2 ). None of these variants were phased with short-read GS, clearly showcasing a direct clinical application. Moreover, the clinical utility extends beyond confirming biallelic variants, as variants that fail to phase can also be downgraded in priority. By directly incorporating phasing information into the ranking of possible recessive variants, clinical interpretation process can be streamlined. In a typical workflow, one recessive heterozygous variant is first identified, followed by the search for a plausible second variant in the same gene. Phasing can then determine whether the two variants are located on opposite alleles (in trans) or on the same allele (in cis). In such a scenario, only true biallelic variants would require clinical interpretation, thereby reducing the number of variants needing evaluation and eliminating the need for segregation analysis in parental samples, likely reducing both time and cost. The ability to phase also facilitates the analysis of paralogous regions, as demonstrated by the identification of a homozygous deletion of SMN1 in RD_P623 (Table 2 , Additional file 4: Figure S3 ). The deletion was detected by both short-read GS and long-read GS, together with four copies of SMN2 . However, our analysis of the long-read GS data was able to phase the four different SMN2 copies and with improved bioinformatic processes it is possible to distinguish the full haplotypes across the two genes [ 41 ]. Today, with available treatments for spinal muscular atrophy, a test for the homozygous deletion of SMN1 exon 7 has been included in the newborn screening in Sweden. However, this accounts for approximately 95% of all SMA cases. The remaining 5% are caused by other pathogenic variants such as SNVs, INDELs, or atypical deletions in SMN1, highlighting the need for a robust clinical test to ensure comprehensive detection. In our cohort, long-read GS demonstrated utility in STR expansion analysis by accurately determined repeat lengths in a pathogenic FMR1 expansion (RD_P698; Fig. 4 , Table 2 ) and confidently ruling out two suspected STR expansions. The ability to detect interrupting motifs was highly relevant for clinical interpretation. Alternate motifs in STR expansions can be beneficial by reducing somatic and/or meiotic instability, but they can also contribute to pathogenesis when the alternative repeat sequence itself becomes pathogenic, when it is directly adjacent to or within the repeat [ 42 ]. The beneficial effect of interrupting alternate motifs has been shown in non-coding (e.g., FMR1 ) as well as coding STR expansions (e.g., ATXN1 and HTT ), where loss of those motifs leads to meiotic and/or mitotic instability [ 43 , 44 ]. SVs account for a considerable proportion of disease-causing variants, representing 31% (9/29) of diagnosed individuals in our cohort, including the aforementioned homozygous deletion of SMN1 . Long-read GS provided a more complete analysis of 14 SVs that were clinically relevant for 12 individuals. Although long-read GS detects more SVs per genome, the number of SVs requiring clinical evaluation is lower because they can be more accurately called and thus more easily filtered against databases [ 39 , 45 ]. This led to the discovery of SVs that were not included in the clinical evaluation of short-read GS, namely a complex SV on chromosome 1 and a pericentric inversion of chromosome 10 (inv(10)). The complex SV on chromosome 1 was determined to be a chromoanasynthesis event in which no known disease-causing genes were disrupted (Additional file 4: Fig S4 ). The inv(10) detected in an individual who also had an inversion disrupting FGF14 is likely the same founder inversion previously reported [ 46 ] and most likely a normal variant Additional file 4: Fig S5). Although these findings did not increase the diagnostic yield, they allowed for a more complete analysis of the included individuals. Using long-read GS, we were able to fully characterize all but one SV, the dup(22)(q11) in RD_P630, which was detected only by read depth analysis. Notably, complex SVs were common, observed both as disease-causing variants and as part of background variation. In our cohort, two of the nine disease-causing SVs detected were complex, and three additional complex SVs (Table 2 ) were ultimately classified as likely benign. In total, 4/100 individuals were carriers of complex SVs. Since complex rearrangements can cause monogenic disorders due to specific gene disruptions [ 47 , 48 ] and gene fusions [ 49 ], resolving the derivative structure is highly relevant for a correct classification. We were able to elucidate cytogenetic events that are typically only captured by karyotyping. Many of the SVs had breakpoints in difficult-to-map regions, including pericentromeric and telomeric sequences and acrocentric p-arms. The DEL-INV-DUP rearrangement on chromosome 9 (RD_P651) exhibited a classic SV structure in which the distal segment is deleted and replaced by an inverted copy of the duplicated segment (Fig. 5 b, Table 2 , Additional file 3: Table S2 ). This type of SV, which is thought to result from chromosome folding, was first described after laborious investigations and can now be characterized in a single experiment [ 50 ]. In this case, the long-read GS data directly resolves the complex genomic rearrangement structure, including extension far into telomeric sequences. Another example where long-read GS outperformed short-read was individual RD_P655 with a mosaic ring chromosome. This was first called as a heterozygous deletion of 18p and a mosaic deletion of the telomeric portion of 18q. Upon manual inspection of the breakpoint at 18q, we found reads extending from the q-arm to the p-arm (Fig. 5 a, Table 2 , Additional file 3: Table S2 ). To map the read sequence to the to the p-arm, it was necessary to manually align it to GRCh38, as it involved alpha satellites from the pericentric region that were better represented in that assembly. This highlights the need for GRCh38, or more complete assemblies, to become the standard reference in clinical genome sequencing to ensure optimal detection and characterization of structural variants. The reference was also a limiting factor in the analysis of the unbalanced translocation in RD_P633, der(14)t(8;14) (Table 2 , Additional file 3: Table S2 ), was identified through investigation of the breakpoints flanking the duplicated segment. In the initial analysis, we found that the duplication did not occur in tandem, with one end of the duplication breakpoint containing sequences that were not mappable in hg19. To investigate where the duplicated segment was located, we performed a de novo assembly and mapped the contig to the T2T assembly. We found that the other end of the segment was linked to the sequence of the acrocentric p-arm of chromosome 14. This enhanced detection capability of long-read GS has been demonstrated in both population-based studies and in rare disease cohorts [ 39 , 51 ]. It has been suggested that de novo assembly and alignment to a more complete reference genome would likely lead to further improvement of structural variant detection and characterization [ 38 ]. The recurrent copy number variants, i.e. genomic disorders, remain a challenge for sequencing-based analysis. This was highlighted by the dup(22)(q11) (RD_P630), where long-read GS was not able to pinpoint the breakpoints, although the aberration was detected through read depth analysis (Additional file 3: Table S2 ). The region at 22q11 which is recurrently involved in microdeletion- or microduplication syndromes, is flanked by segmental duplications. These segmental duplications promote rearrangement formation through non allelic homologous recombination [ 52 , 53 ] due to their sequence similarity, but also make the region difficult to sequence and map to the reference genome. This observation aligns with the results from the first 100 individuals sequenced by ONT in the 1000 Genomes Project, which identified recurrent assembly breaks associated with the flanking regions of known disease-causing CNVs [ 51 ], as well as with our own previous data showing that a suggested isodicentric chromosome 15 was only detected through read depth changes [ 11 ]. However, our analysis of the dup(22)(q11) was also limited by the relatively short DNA fragments in that sample (Additional file 2: Table S1 ). This sample was one of the initial samples where long-read GS was performed, it was sequenced twice and the estimated N50 of the sample was 4.73 kb and 6.14 kb, respectively. The fragment length was not enough to span across the flanking segmental duplications, highlighting the need for good quality input DNA. Methylation analysis of long-read GS shows great promise in a diagnostic setting and offers a clear advantage regarding imprinting disorders, since these are caused by defective methylation in genes with parent-of-origin specific expression. Such disorders can result from the loss of a maternal or paternal copy, disease-causing SNV/INDELs in the expressed allele, uniparental disomy (where both copies are inherited from the same parent) or variants affecting the imprinting center of the region. The advantage of long-read GS was showcased by an individual with maternal uniparental disomy of chromosome 15, where long-read GS enabled direct analysis of methylation patterns across genes in the imprinted Prader Willi region (15q11.2q13). We found that the promoters of genes normally expressed from the paternal allele (e.g., SNURF-SNRPN , NDN and MAGEL2 ) were hypermethylated. Thus, long-read GS detected both the genetic and epigenetic abnormalities, establishing a diagnosis of Prader Willi syndrome (MIM#176270) in a single experiment, whereas multiple tests are typically required in current practice. However, it is important to note that some imprinting regions are tissue-specific. For example, UBE3A (associated with Angelman syndrome, MIM#105830) is maternally imprinted in neurons but biallelically expressed in other tissues. This presents a challenge, as whole blood is often used for first-line analysis. Nonetheless, the ability to assess methylation across known imprinting regions offers hope for diagnosing more individuals with imprinting disorders, the incidence of which may currently be underappreciated. Analysis of global methylation patterns was not applied in our study, although there appears to be a strong potential for its diagnostic utility [ 54 ]. In one individual, we found a 263 kb duplication on chromosome 9 (9q34.3), inherited from a healthy parent and is still classified as a VUS. Duplications in this region have been linked to a syndrome with mild neurodevelopmental features, and previous studies have reported an association between such duplications and a distinct global DNA methylation profile. However, the most distal duplications included in the study, which align with our case, did not exhibit the same methylation pattern [ 55 ]. Further studies are needed to clarify the clinical relevance of implementing standard clinical methylation analysis in such cases. That would, however, require that large reference methylation databases are established based on long-read GS data. Conclusion This study shows that singleton long-read GS is a powerful first-line diagnostic tool for neurological disorders, enabling comprehensive variant detection, including SVs, STR expansions, and phasing, even from standard clinical DNA. Importantly, we show that this technology can resolve clinically relevant variants in repetitive and previously inaccessible regions of the genome, providing insights beyond the capabilities of short-read GS. The ability to assess epigenetic changes adds further clinical value, particularly for imprinting disorders. Despite a similar overall diagnostic yield to short-read GS in our cohort, long-read GS offers superior resolution and interpretive power, capable of capturing the full spectrum of disease-causing variants, reducing the need for follow-up testing and enabling more precise interpretation. As technologies and tools continue to evolve, long-read GS will likely offer even greater diagnostic and mechanistic insight, paving the way for improved precision medicine in rare diseases. Improved diagnostics, combined with the growing number of targeted therapies, represents a strong opportunity to advance precision medicine and improve outcomes for affected individuals and their families. Abbreviations CNV Copy number variant ID Intellectual disability INDEL Insertion/deletion GS Genome sequencing NDD Neurodevelopmental disorder NMD Neuromuscular disorder MS-MLPA Methylation specific multiplex ligation-dependent probe amplification SNV Single nucleotide variants STR Short tandem repeat SV Structural variant VUS Variant of uncertain significance Declarations Ethics approval and consent to participate The research involving human participants underwent review and approval by the Regional Ethical Review Authority in Stockholm, Sweden (ethics permit number 2019-04746). The research was conducted in accordance with the principles of the Helsinki Declaration. Written informed consent was obtained from the participants/their legal guardians/next of kin, in accordance with national legislation and institutional requirements. Consent for publication Written informed consent was obtained for publication. Availability of data and materials The datasets discussed in this article are not immediately accessible due to ethical and privacy constraints but are available from the corresponding author on reasonable request. Competing interests All authors declare that they have no competing interests. Funding This research was funded by Swedish Research Council, grant number 2019-02078, the Swedish Brain Foundation, grant number FO2022-0256, and the Stockholm Regional Council (ALF funding) and the Swedish Rare Diseases Research Foundation (Sällsyntafonden). Authors' contributions Conceptualization: ALi and VW. Collection of informed consent: LLj. Sample collection: ME. Library preparation and sequencing: LLa and ALy. Data analysis: JE and ET. Interpretation of data and formal analysis: ME, JE, HT. Clinical interpretation and phenotype: ALi, MJS and MK. Compilation of results: ME. Writing of first draft: ME. Reading, editing and approval of final manuscript: ALi, ALy, ET, JE, LLa, LLj, ME, MJS, MK, AN, VW. Figures: ME Acknowledgements We express our sincere gratitude to the participants and their families and acknowledge the Karolinska Institute’s membership in EURO-NMD and ERN-ITHACA. We also extend our appreciation to UPPMAX for providing computational infrastructure resources and to the Clinical Genomics Stockholm facility at Science for Life Laboratory and the Genomic Medicine Center Karolinska for their support in long-read genome sequencing. References Maulik PK, Mascarenhas MN, Mathers CD, Dua T, Saxena S: Prevalence of intellectual disability: a meta-analysis of population-based studies. Res Dev Disabil 2011, 32(2):419–436. 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In-silico gene lists used for short-read and long-read GS. Document S2. Differential methylation calling of ONT data. Additionalfile2TableS1.xlsx Additional file 2: Table S1: Quality and sequencing parameters. Additionalfile3TableS2.xlsx Additional file 3: Table S2. The clinically relevant findings from long-read GS. Additionalfile4.pdf Additional file 4: Fig. S1. Methylation pattern across FMR1 . Fig. S2. Methylation pattern across promoters of genes in the Prader-Willi region Fig. S3. Phasing of SMN1 and SMN2 . Fig. S4. Chromoanasynthesis event of chromosome 1p. Fig. S5.Complex SVs on chromosomes 9p and 10p. 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Lindstrand","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA60lEQVRIie3QvQrCMBDA8SsHTn6sKWJ9hUpBBF+mpUOXKoKLkwhCXNTZIvgMjo5KwEnMmsHNWQi4KCiYVhBcUkeH/KcE8uOOAJhMf1uPAGyzEzoEfyJuSrKn6P1K4EOCRd7jynK2k+C2umXO9/K+aUfJBFBKDSGnY0jUYn1bhJjMDlFniVCwtaNE7KYkWAtEKFHWWSmCRY2oi9i7ZYQztJ6URQ4CXh8a4oq4+Z6yDRHVFL+qvqGq26uhSMtXJBGhhzUaNZKxRe2phjhqMSEHw2DOd2frQtt1whmTN92YNP/7ao3ygMlkMplyegFuvkeO2A/gFQAAAABJRU5ErkJggg==","orcid":"","institution":"Karolinska Institutet","correspondingAuthor":true,"prefix":"","firstName":"Anna","middleName":"","lastName":"Lindstrand","suffix":""}],"badges":[],"createdAt":"2025-06-10 12:38:15","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6863124/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6863124/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":85346814,"identity":"17720adc-6ab0-49dd-b205-389a24210c9b","added_by":"auto","created_at":"2025-06-25 02:14:29","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":103052,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFlowchart of the long-read GS bioinformatic pipeline. \u003c/strong\u003eReads stored in binary alignment maps (BAMs) are aligned to the reference genome, followed by comprehensive variant calling, annotation and ranking. The varant callers include single nucleotide variants and small insertions/deletions (SNV/INDELs; blue), structural variants (SVs; purple), short tandem repeat (STR) expansions (pink), paralogous regions (green) as well as methylation calling (red).\u003c/p\u003e","description":"","filename":"OnlineFig1.png","url":"https://assets-eu.researchsquare.com/files/rs-6863124/v1/9dcd2d6dc5832861f90605b4.png"},{"id":85345821,"identity":"6d852e47-7bb2-4461-9cdc-1a6d41ce56ed","added_by":"auto","created_at":"2025-06-25 02:06:29","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":128356,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eDiagnostic yield and added value of long-read genome sequencing (GS).\u003c/strong\u003e Inner portion of pie chart illustrates the proportion of individuals with a pathogenic/likely pathogenic variant (dark green), individuals with a variant of uncertain significance (VUS) (light green), and undiagnosed individuals (grey). Outer portion depicts the types of variants identified: single nucleotide variants and/or small insertions/deletions (SNV/INDELs) (blue), structural variants (SVs) (purple), short tandem repeat (STR) expansion (pink) and uniparental disomy (UPD) (dark pink). The dark central circle represents the number of samples where long-read GS provided added clinical value (14%) (yellow).\u003c/p\u003e","description":"","filename":"OnlineFig2.png","url":"https://assets-eu.researchsquare.com/files/rs-6863124/v1/d717c2029ad497a5e3519181.png"},{"id":85345826,"identity":"25397ff9-ffcd-412e-ac20-cb1928e4bd81","added_by":"auto","created_at":"2025-06-25 02:06:29","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":175315,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ePhasing of compound heterozygous variants.\u003c/strong\u003e Top, schematic illustration of long reads phased into haplotype 1 (Hap1) and haplotype 2 (Hap2), aligning across two exons separated by an intron. Hap1 carries a variant in exon 1 (green) and Hap 2 carries a variant in exon 2 (red). Bottom, phasing results from two individuals diagnosed with compound heterozygous variants: Left, an insertion of five bases in exon 4 of \u003cem\u003eSGCA\u003c/em\u003e (Hap2) and a variant A\u0026gt;T in exon 5 (Hap1). Right, a variant G\u0026gt;A in exon 3 of \u003cem\u003eCOL12A1\u003c/em\u003e and a variant C\u0026gt;A in exon 63.\u003c/p\u003e","description":"","filename":"OnlineFig3.png","url":"https://assets-eu.researchsquare.com/files/rs-6863124/v1/7092ae2daa8bcc61a48c60d6.png"},{"id":85345816,"identity":"7ab3cbb8-395a-499a-a1ab-baf11528d6be","added_by":"auto","created_at":"2025-06-25 02:06:29","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":126886,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eIllustration of short tandem repeat (STR) expansions.\u003c/strong\u003e Top: STR analysis of a male (RD_P698) with full \u003cem\u003eFMR1\u003c/em\u003e expansion of 654 CGG repeats, a female with an premutation-range expansion of 60 repeats in \u003cem\u003eFMR1\u003c/em\u003e. The zoom-in highlights the presence of interrupting motifs in the female, and the loss of interrupting motifs in the boy. Bottom: STR analysis of \u003cem\u003eATXN1\u003c/em\u003e in an individual with one alleles in the intermediate range (37 repeats) with two interrupting CAT motifs.\u003c/p\u003e","description":"","filename":"OnlineFig4.png","url":"https://assets-eu.researchsquare.com/files/rs-6863124/v1/5bada603edd4443a2fb8ac18.png"},{"id":85345813,"identity":"f5a603f2-6089-4797-b7eb-8b2f35ff961f","added_by":"auto","created_at":"2025-06-25 02:06:29","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":203523,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eResolved structure of of complex structural variants (SVs). a.\u003c/strong\u003e DEL-INV-DUP rearrangement of chromosome 9. Top: Schematic showing the deleted (A) and duplicated (C) segments of chromosome 9p, with integrative genomics viewer (IGV) screenshots of the breakpoint regions. Below, an illustation of soft-clipped reads at the breakpoints. Bottom: An illustration of the reference chromosome 9, a subway plot of the rearrangement, and de resolved derivative structure. Below this, the alignment of soft-clipped reads on the derivative. \u003cstrong\u003eb.\u003c/strong\u003e Mosaic ring chromosome 18. Top: Schematic showing a deletion 18p (A) and mosaic deletion on 18q (C), with IGV screenshots of the breakpoint region at 18q. Below, an illustation of a soft-clipped read at the breakpoint. Bottom: Illustration of the reference chromosome 18, a subway plot of the ring chromosome, and the resolved derivative structure. Below this, alignment of the soft-clipped read on the derivative. \u003cstrong\u003ec.\u003c/strong\u003e Karyotyping images showing deletion of 18p (left) and ring chromosome 18 (right).\u003c/p\u003e","description":"","filename":"OnlineFig5.png","url":"https://assets-eu.researchsquare.com/files/rs-6863124/v1/7d2fd273edba18dd82ce0b8f.png"},{"id":85348913,"identity":"a34711e0-797c-4a50-916a-cbab0af31c55","added_by":"auto","created_at":"2025-06-25 02:30:36","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2444949,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6863124/v1/96f06d62-998b-4062-a14f-9ea9394f9042.pdf"},{"id":85345818,"identity":"8f0c3c2f-c6d6-40dd-ad87-58d93f6c76e4","added_by":"auto","created_at":"2025-06-25 02:06:29","extension":"pdf","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":62314,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eAdditional file 1:\u003c/strong\u003e \u003cstrong\u003eDocument S1.\u003c/strong\u003e In-silico gene lists used for short-read and long-read GS. \u003cstrong\u003eDocument S2.\u003c/strong\u003e Differential methylation calling of ONT data.\u003c/p\u003e","description":"","filename":"Additionalfile1.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6863124/v1/4a46c36f7e4a47feabf2c486.pdf"},{"id":85345815,"identity":"d0d9b9d2-273b-4972-80c8-ad0f1a37bb4b","added_by":"auto","created_at":"2025-06-25 02:06:29","extension":"xlsx","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":20863,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eAdditional file 2:\u003c/strong\u003e \u003cstrong\u003eTable S1: \u003c/strong\u003eQuality and sequencing parameters.\u003c/p\u003e","description":"","filename":"Additionalfile2TableS1.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-6863124/v1/80f34c0ea5e38707e9ff3e15.xlsx"},{"id":85346817,"identity":"ec998784-3cb4-4283-bff3-065e9b66354c","added_by":"auto","created_at":"2025-06-25 02:14:29","extension":"xlsx","order_by":5,"title":"","display":"","copyAsset":false,"role":"supplement","size":17737,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eAdditional file 3:\u003c/strong\u003e \u003cstrong\u003eTable S2.\u003c/strong\u003e The clinically relevant findings from long-read GS.\u003c/p\u003e","description":"","filename":"Additionalfile3TableS2.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-6863124/v1/903534a2a92efdf0ff1c16bf.xlsx"},{"id":85345827,"identity":"2c5df254-eb6a-43d1-ac82-e34680b7b3af","added_by":"auto","created_at":"2025-06-25 02:06:29","extension":"pdf","order_by":6,"title":"","display":"","copyAsset":false,"role":"supplement","size":908788,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eAdditional file 4: Fig. S1.\u003c/strong\u003e Methylation pattern across \u003cem\u003eFMR1\u003c/em\u003e. \u003cstrong\u003eFig. S2.\u003c/strong\u003e Methylation pattern across promoters of genes in the Prader-Willi region \u003cstrong\u003eFig. S3.\u003c/strong\u003e Phasing of \u003cem\u003eSMN1\u003c/em\u003e and \u003cem\u003eSMN2\u003c/em\u003e. \u003cstrong\u003eFig. S4.\u003c/strong\u003e Chromoanasynthesis event of chromosome 1p. \u003cstrong\u003eFig. S5.\u003c/strong\u003eComplex SVs on chromosomes 9p and 10p.\u003c/p\u003e","description":"","filename":"Additionalfile4.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6863124/v1/6c583ffedd61889b1cf8f65c.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Long-read genome sequencing enhances diagnostics of pediatric neurological disorders","fulltext":[{"header":"Background","content":"\u003cp\u003eNeurological disorders encompass a broad spectrum of conditions that affect the central and/or peripheral nervous system. In children and adolescents, these disorders include neurodevelopmental disorders (NDD), such as developmental delay, intellectual disability (ID) and autism, as well as neuromuscular disorders (NMD). Intellectual disability affects approximately 1% of the population and involves limitations in intellectual functioning and adaptive behavior (e.g., language, social and practical skills) [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Intellectual disability can occur in isolation, together with other neurocognitive conditions (autism, attention deficit hyperactivity disorder), together with seizures or as part of a broader syndromic presentation [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Neuromuscular disorders (NMDs) are a heterogeneous group of disorders affecting the peripheral nerves, muscles, or muscular junction. Hypotonia and delayed motor development are common presentations in children with NMDs. However, there is often a phenotypic overlap in the presentation of NDDs and NMDs, particularly in young children [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe etiology of neurological disorders is heterogeneous and may include infections, or environmental factors, but a considerable proportion are genetic (i.e. monogenic or chromosomal) [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Using short-read genome sequencing (GS), we and others have demonstrated a diagnostic yield of approximately 25\u0026ndash;35% depending on cohort characteristics [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Importantly, in these conditions a clinical test must capture the diverse landscape of disease-causing genetic variations, from single base pair alterations to chromosomal rearrangements. Small variants, including single nucleotide variants (SNVs) and insertions/deletions (INDELs)\u0026thinsp;\u0026lt;\u0026thinsp;50bp in size, are reliably detected by short-read genome [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e] and exome [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e] sequencing. However, the detection of other variant types, such as short tandem repeat expansions (STRs), variants in paralogous regions and aberrant methylation, remains challenging. Furthermore, the complexity of structural variants (SVs) is often underestimated and balanced SVs are frequently missed by short read approaches [\u003cspan additionalcitationids=\"CR12 CR13\" citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Long-read GS is currently emerging as a promising method in clinical genetic diagnostics, with the potential to detect the full spectrum of variant types that are often missed or incompletely resolved by short-read technologies. Early applications have shown that long-read GS is able to: (i) detect and accurately characterize SVs, even when they are complex or involve breakpoints in highly repetitive regions [\u003cspan additionalcitationids=\"CR16\" citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e], (ii) precisely genotype pathogenic STR expansions [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e], and (iii) identify disease-associated methylation patterns, both in the context of imprinted gene disorders and more broadly to aid the interpretations of variants of uncertain significance (VUS\u003cem\u003e)\u003c/em\u003e[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn this study we evaluated the utilization of long-read GS as a first line clinical genetic test in children with neurological disorders. In a prospective, unselected cohort of 100 individuals referred to our laboratory for short-read GS, long-read GS achieved a comparable final diagnostic yield. Importantly, long-read GS provided a more comprehensive diagnostic workflow enabling direct phasing of compound heterozygous variants, detailed characterization of SVs and STRs and assessment of DNA methylation.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003eClinical genetics and Genomics (Karolinska University Hospital, Stockholm, Sweden) is a tertiary center where short-read GS (with in silico gene panel analysis) is performed as the first line test for individuals with suspected rare genetic diseases, including NDDs, and malformation syndromes. Overall, roughly 5000 analyses are performed annually, the detailed analysis pipelines and overall workflow has previously been described [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn this study, we included 100 children and adolescents with neurological disorders referred to the Department of Clinical Genetics and Genomics (Karolinska University Hospital in Stockholm, Sweden), for diagnostic genetic testing with short-read GS between September 1, 2023, and April 15, 2024. The cohort consists of 55 males and 45 females with a median age of five years and a phenotype suggestive of either a NDD (n\u0026thinsp;=\u0026thinsp;79) or an NMD (n\u0026thinsp;=\u0026thinsp;21). Informed consent was obtained either from the affected individuals or their legal guardians. To obtain a clinically representative first-line testing cohort, individuals were contacted directly after the referral was received and before the short-read GS results were available. The first 100 individuals who gave consent and for whom DNA was available from both parents and the proband were included in the study; those without available parental or proband DNA were excluded. Although the study was conducted with singleton long-read GS, parental DNA was required for potential follow-up analyses. Clinical characteristics of the study cohort are summarized in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eCohort characteristics: Age, gender, and phenotypic presentation.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"2\"\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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCOHORT CHARACTERISTICS\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e% (N\u0026thinsp;=\u0026thinsp;100)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMedian age, years (min-max)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5 (1 day \u0026minus;\u0026thinsp;20 years)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e55\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eConsanguinity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMain phenotypic features (HPO terms)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAutism / Autistic behavior\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e56\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIntellectual disability\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e36\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGlobal developmental delay\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e21\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDelayed speech and language development / Language impairment\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e21\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAttention deficit hyperactivity disorder\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMotor delay\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHypotonia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMicrocephaly\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6\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\u003eGenomic DNA was extracted from whole blood. For the majority (n\u0026thinsp;=\u0026thinsp;79), extraction was performed using QIAsymphony (QIAGEN, Hilden, Germany) and the QIAsymphony DSP DNA Midi Kit (QIAGEN, Hilden, Germany) according to the standard protocol. The other samples were extracted either by using QIAGEN EZ1 (n\u0026thinsp;=\u0026thinsp;14) (QIAGEN, Hilden, Germany) or by manual extraction with the QIAamp DNA blood midi kit (n\u0026thinsp;=\u0026thinsp;7) (QIAGEN, Hilden, Germany) according to manufacturer\u0026rsquo;s protocol.\u003c/p\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eShort-read genome sequencing\u003c/h2\u003e \u003cp\u003eGenomic DNA was sequenced on a NovaSeqX (Illumina, San Diego, CA, USA). The bioinformatic workflow calling SNV/INDELs, SVs, mobile element insertions, STR expansions as well as \u003cem\u003eSMN1\u003c/em\u003e/\u003cem\u003eSMN2\u003c/em\u003e copy number has been described previously [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Based on phenotypic presentation, \u003cem\u003ein-silico\u003c/em\u003e gene panels were applied: (i) 1568 genes associated with NDD and/or (ii) 1035 genes associated with NMD (Additional file 1: Document S1). Detected variants were ranked and visualized as previously described [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e] and classified from 1\u0026ndash;5 according to the American College of Medical Genetics and Genomics (ACMG)/Association for Molecular Pathology (AMP) guidelines [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. In addition, a temperature scale of detected VUS was applied, ranging from hot to ice cold, in order of estimated pathogenicity [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. The \u0026ldquo;hot VUSs\u0026rdquo; were reported to the referring physician as they were considered more likely to be pathogenic.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eLong-read sequencing\u003c/h3\u003e\n\u003cp\u003eGenomic DNA was sheared with Covaris G-tube (17 samples; Covaris, Woburn, MA, USA) or Megaruptor (83 samples; Diagenode, Li\u0026egrave;ge, Belgium) to obtain\u0026thinsp;~\u0026thinsp;15\u0026ndash;20 kb fragments followed by size selection using AMPure XP beads (Beckman Coulter, Brea, CA, USA) to remove fragments smaller than 2 kb. The libraries were prepared with the Ligation Sequencing Kit V14 and sequenced on a PromethIon (Oxford Nanopore Technologies, Oxford, UK), using one PromethIon flow cell per sample. Raw data was processed through the custom pipeline poorpipe [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. In brief, the pipeline performs data alignment (Minimap2 [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]), analysis of SNVs [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e], CNVs [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e], SVs [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e], methylation [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e], STRs using our custom tool Abacus [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e], and paralogous genes [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. Abacus employs graph alignment and a Gaussian mixture model to quantify, phase and build consensus of STR expansions. The resulting data was phased using Whatshap [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e], and annotated using Ensembl VEP [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. The annotated variant lists were scored and ranked using Genmod [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. All data was aligned to reference genome GRCh37 (hg19), and the classification of detected variants was performed in the same way as for clinical short-read GS. Quality assessment was performed using Picard tools, samtools, Nanostats and bcftools, and the resulting reports were aggregated using MultiQC.\u003c/p\u003e \u003cp\u003eA local database containing variant calls from the 100 cases was built using SVDB [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e] and used to filter calls unique to long-read GS. For variants called by both short-read and long-read GS, the clinical Karolinska short-read GS database was used for filtering [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eSNVs, STR expansions and paralogous regions were assessed in our custom in-house interpretation software similar to the short-read GS data [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Imprinted regions were assessed using a custom script that compared the current case with a pool of controls (Additional file 1: Document S2). Finally, SVs were analyzed in two ways: large (\u0026gt;\u0026thinsp;10 kb) were assessed genome-wide and small (\u0026lt;\u0026thinsp;10 kb) were filtered based on gene panels. SVs localized in genes within the gene panel, as well as those consisting of more than two breakpoints were examined in the Integrative Genomics Viewer (IGV) [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e] and the derivative chromosomes were resolved manually. Variants were interpreted clinically and classified in the same way as described for the short-read GS data. The long-read analysis pipeline is shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e\n\u003ch3\u003eClinical verification\u003c/h3\u003e\n\u003cp\u003eValidation of the \u003cem\u003eFMR1\u003c/em\u003e expansion was performed according to the manufacturer\u0026rsquo;s protocol, using the AmplideX PCR/CE \u003cem\u003eFMR1\u003c/em\u003e kit (Asuragen, Austin, TX, USA) and an ABI 3500xL Genetic Analyzer (Applied Biosystems, Foster City, CA, USA).\u003c/p\u003e \u003cp\u003eTo confirm aberrant methylation pattern across the Prader Willi region, we performed methylation specific multiplex ligation-dependent probe amplification (MS-MLPA) using SALSA MLPA Probemix ME028 Prader-Willi/Angelman (MRC Holland, Amsterdam, Netherlands) according to manufacturer\u0026rsquo;s protocol.\u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eLong-read genome data quality and characteristics\u003c/h2\u003e \u003cp\u003eThe long-read GS generated an average of 111 GB data per sample, with a median read-length of 5 kb and average N50 of 16 kb; resulting in an average coverage of 34X. The detailed quality and sequencing parameters are available in the Additional file 1: Document S3.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eOverall genetic findings\u003c/h2\u003e \u003cp\u003eIn 100 unrelated individuals with neurodevelopmental (n\u0026thinsp;=\u0026thinsp;79) or neuromuscular (n\u0026thinsp;=\u0026thinsp;21) symptoms (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e), a likely genetic diagnosis was made in 34 individuals, 29 with pathogenic findings and five with hot VUSs. In total, those individuals harbored 36 variants that were further subdivided into SNV/INDELs (n\u0026thinsp;=\u0026thinsp;24), SVs (n\u0026thinsp;=\u0026thinsp;10), STR (n\u0026thinsp;=\u0026thinsp;1) and uniparental disomy (n\u0026thinsp;=\u0026thinsp;1) (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e; Additional file 3: Table \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e). In addition, five rare variants of clinical interest (three SVs and two STRs) were further investigated but finally classified as benign. All those 41 variants were detectable with both short-read GS and long-read GS. The added value of long-read GS is showcased below for 13 cases from five different variant categories (SNV/INDELs, STRs, imprinted regions, paralogous regions and SVs).\u003c/p\u003e \u003cp\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\u003e\u003cb\u003eOverview of 19 variants with added clinical value from long-read genome sequencing (GS).\u003c/b\u003e The table summarizes 17 variants identified in 13 individuals, grouped by variant type: Single nucleotide variant (SNV) and/or small insertion/deletion (INDEL), structural variant (SV), short tandem repeat (STR) expansion, and other. Identifying information has been removed for variants classified as likely benign (LB).\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\u003eIndividual\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSex\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDisease-\u003c/p\u003e \u003cp\u003ecausing gene\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eVariant/s\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eAdded value of long-read GS\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eOutcome\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"7\" nameend=\"c7\" namest=\"c1\"\u003e \u003cp\u003eSingle nucleotide variants and/or small insertion/deletion\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRD_P694\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6 y\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eSGCA\u003c/em\u003e\u003c/p\u003e \u003cp\u003e(NM_000023.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003ec.557A\u0026thinsp;\u0026gt;\u0026thinsp;T, p.Lys193Ter\u003c/p\u003e \u003cp\u003ec.348_352dup, p.Gln118ProfsTer95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003ePhasing the two variants (SNV\u0026thinsp;+\u0026thinsp;INDEL) separated by 585 bp\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eLP\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRD_P712\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1 d\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eCOL12A1\u003c/em\u003e\u003c/p\u003e \u003cp\u003e(NM_004370.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003ec.8861G\u0026thinsp;\u0026gt;\u0026thinsp;A p.Gly2954Glu\u003c/p\u003e \u003cp\u003ec.165C\u0026thinsp;\u0026gt;\u0026thinsp;G p.Tyr55Ter\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003ePhasing the two variants (SNV\u0026thinsp;+\u0026thinsp;SNV) separated by 105 kb\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eVUS\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"7\" nameend=\"c7\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eStructural variant (SV)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRD_P623\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3 y\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eSMN1\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eHomozygous deletion of \u003cem\u003eSMN1\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003ePhasing of paralogous regions, detecting no copy of \u003cem\u003eSMN1\u003c/em\u003e\u003c/p\u003e \u003cp\u003eand phasing four copies of \u003cem\u003eSMN2\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eP\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRD_P695\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1 y\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eFGF14\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eInversion of 13q disrupting \u003cem\u003eFGF14\u003c/em\u003e\u003c/p\u003e \u003cp\u003eInversion of chr 10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eMapping of SV breakpoints and identification of a 7.6 kb deletion\u003c/p\u003e \u003cp\u003eDetection of a pericentric inversion of chromosome 10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eP\u003c/p\u003e \u003cp\u003eLB\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRD_P633\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10 y\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003emultiple\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eUnbalanced translocation der(14)t(8;14)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eDetected telomeric sequence at one breakpoint\u003c/p\u003e \u003cp\u003eDetected link to acrocentric p-arm at the other breakpoint\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eP\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRD_P651\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2 y\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003emultiple\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eComplex rearrangement of 9p\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eResolved CGR structure\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eP\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRD_P655\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9 m\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003emultiple\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMosaic ring chromosome 18/deletion of 18p\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eResolved CGR structure\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eP\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\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\u003enone\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eComplex SV involving 9p \u003c/p\u003e \u003cp\u003eComplex SV involving 10p\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eCalling of SV and mapping of breakpoints\u003c/p\u003e \u003cp\u003ethat don't disrupt disease-causing genes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eLB\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\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\u003enone\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eComplex SV involving 1p\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eCalling of SV and mapping of breakpoints\u003c/p\u003e \u003cp\u003ethat don't disrupt disease-causing genes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eLB\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"7\" nameend=\"c7\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eShort tandem repeat (STR) expansion\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRD_P698\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2 y\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eFMR1\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eFull expansion (654 CGG repeat)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eCharacterization of STR expansion; size, loss of interrupting motifs,\u003c/p\u003e \u003cp\u003ehypermethylation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eP\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eFMR1\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eExpansion (60 CGG repeat)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eCharacterization of STR and detection of interrupting motifs\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eLB\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\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\u003e\u003cem\u003eATXN1\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eExpansion (37 CAG repeat)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eCharacterization of STR and detection of interrupting motifs\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eLB\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"7\" nameend=\"c7\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eOther\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRD_P706\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11 d\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003emultiple\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMaternal uniparental isodisomy of chromosome 15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eMethylation analysis confirms PWS diagnosis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eP\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\u003eNA, not available; y, year; m, month; d, day; F, female; M, Male; chr, chromosome; bp, base pair; CGR, complex genomic rearrangement; PWS, Prader-Willi syndrome; LP, likely pathogenic; P, pathogenic; VUS, variant of uncertain significance.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eAdded value of long-read GS\u003c/h3\u003e\n\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eSNVs and INDELs\u003c/h2\u003e \u003cp\u003eThree individuals carried biallelic variants in two genes, \u003cem\u003eSGCA\u003c/em\u003e (Limb-girdle muscular dystrophy, MIM# 608099) (RD_P694) and \u003cem\u003eCOL12A1\u003c/em\u003e (Bethlem myopathy 2, MIM# 616471) (RD_P712). In both, phasing the long-read GS data showed that the two variants were in \u003cem\u003etrans\u003c/em\u003e, which was later confirmed by segregation analysis of parental samples (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). This increased the support for pathogenicity for \u003cem\u003eSGCA\u003c/em\u003e that was upgraded to likely pathogenic, however the \u003cem\u003eCOL12A1\u003c/em\u003e variants remained as a VUS.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eSTR expansions\u003c/h2\u003e \u003cp\u003eA pathogenic \u003cem\u003eFMR1\u003c/em\u003e expansion (Fragile X syndrome, MIM# 300624) was detected in a two-year-old boy with global developmental delay (RD_P698). With long-read GS the CGG repeat size was 654, with short-read GS 76 and targeted \u003cem\u003eFMR1\u003c/em\u003e testing showed\u0026thinsp;\u0026gt;\u0026thinsp;200. Methylation analysis further revealed that the \u003cem\u003eFMR1\u003c/em\u003e promotor was hypermethylated (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e; Additional file 4: Fig. \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). In comparison, in another patient, long-read GS also detected an \u003cem\u003eFMR1\u003c/em\u003e allele with 60 repeats and two interrupting AGG motifs that was not hypermethylated, but predominantly unmethylated (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e; Additional file 4: Fig. \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). A finding not interpreted as the cause of clinical symptoms. Finally, for an \u003cem\u003eATXN1\u003c/em\u003e allele with 37 repeats, long-read GS allowed haplotype-resolved sizing (31 and 37 repeats) and revealed two interrupting CAT motifs in each allele (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). Since interrupting CAT motifs in allele sizes spanning 36\u0026ndash;44 repeats are considered normal, the variant could be dismissed.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eImprinting disorders\u003c/h2\u003e \u003cp\u003eIn a newborn boy (RD_P706) with neonatal hypotonia, long-read GS detected partial maternal uniparental isodisomy of chromosome 15, including the Prader Willi region. Methylation analysis revealed hypermethylated promotors of multiple genes in the Prader-Willi region, including \u003cem\u003eNDN\u003c/em\u003e (z-score \u0026minus;\u0026thinsp;6.49), \u003cem\u003eSNRPN\u003c/em\u003e (z-score \u0026minus;\u0026thinsp;3.35) and \u003cem\u003eMAGEL2\u003c/em\u003e (z-score \u0026minus;\u0026thinsp;13.5) that could also be visually inspected in IGV (Additional file 4: Fig. \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e). Clinical validation using methylation specific multiplex ligation-dependent probe amplification (MS-MLPA) confirmed the presence of aberrant methylation patterns associated with Prader Willi syndrome (MIM#176270).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eParalogous regions\u003c/h2\u003e \u003cp\u003eA three-year-old girl with progressive muscle weakness (RD_P623) had a homozygous deletion of \u003cem\u003eSMN1\u003c/em\u003e and four copies of \u003cem\u003eSMN2.\u003c/em\u003e Phased long-read GS data could distinguish the four \u003cem\u003eSMN2\u003c/em\u003e copies (Additional file 4: Fig. \u003cspan refid=\"MOESM3\" class=\"InternalRef\"\u003eS3\u003c/span\u003e). The presence of multiple copies of \u003cem\u003eSMN2\u003c/em\u003e is associated with a milder phenotype, and she was diagnosed with spinal muscular atrophy type 3 (MIM# 253400).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eStructural variants\u003c/h2\u003e \u003cp\u003eIncluding the above mentioned \u003cem\u003eSMN1\u003c/em\u003e deletion, a total of 14 rare potentially clinically significant SVs were detected (Additional file 2: Table \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e) and the long-read GS data enabled a complete characterization in all of them; seven representative cases are highlighted below, including both simple SVs and complex events.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eSimple SVs\u003c/h2\u003e \u003cp\u003eIn three simple SVs, identified in two individuals, long-read GS aided in the interpretation. First, in a 10-year-old male with intellectual disability (RD_P633), long-read GS was not only able to confirm the short-read call of a terminal duplication on chromosome 8, dup(8)(p23.3p23.1), but also identified telomeric sequence at one of the breakpoints and a link to the acrocentric p-arm of chromosome 14 at the other breakpoint indicating an unbalanced translocation. However, T2T alignment was required to fully resolve the translocation. Second, in a one-year-old boy (RD_P695) with delayed motor development and poor balance, two inversions were readily identified by long-read GS. One pathogenic event, an inversion on chromosome 13, inv(13)(q31.1q33.1), disrupts \u003cem\u003eFGF14\u003c/em\u003e resulting in the diagnosis of Spinocerebellar ataxia 27A, SCA27A, MIM#193003). The other event was a 23 Mb pericentric inversion on chromosome 10, inv(10)(p11.21;q21.1), was also detected. The pathogenic event was observed in the clinical short-read GS analysis, but not the inv(10). However, manual inspection of the VCF files showed that the inv(10) was also called in short-read GS data, but the event was not highly ranked. Of note, in a one-year-old girl with global developmental delay, a duplication of 22q11 was detected from read depth analysis, although characterization of the breakpoints was not possible.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eComplex SVs\u003c/h2\u003e \u003cp\u003eFive complex SVs in four individuals were resolved with long-read GS (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). A DEL-INV-DUP rearrangement on chromosome 9 was found in a two-year-old girl (RD_P651) with syndromic global developmental delay. The identified structural rearrangement consisted of a terminal 1.8 Mb deletion at 9p24.3 and a 17.8 Mb duplicated segment at 9p24.3p22.1, separated by an 850-bp normal copy number segment. The duplicated segment had replaced the deleted segment, and was inserted in an inverted orientation, so that the normal copy number segment was flanked by two mirroring identical segments (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ea). Long-read GS could fully resolve the derivative chromosome structure and although short-read GS detected the CNVs involved, no call (in the clinical interpretation tool) allowed us to resolve the structure of the rearrangement. In a 9-month-old girl RD_P655 with motor delay, long-read GS identified a heterozygous deletion of chromosome 18p and a mosaic deletion of the distal 18q. Six out of 28 long-read GS reads connected the q-arm to the p-arm indicating a mosaic ring chromosome in 42% of the cells (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eb). The ring chromosome was also detected by karyotyping; 46,XX,del(18)(p11.1)[\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]/46,XX,r(18)(p11.1q23)[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Re-evaluation of the ring breakpoint junction in IGV, found that it was not visible in the short-read data. In a one-year-old girl (RD_P649) with global developmental delay a complex rearrangement of chromosome 1 was resolved with long-read GS. The five duplicated segments were inserted into chromosome 1 in a seemingly unorganized manner. Although three genes overlap with the breakpoints, all retain a remaining functional copy (Additional file 4: Fig. \u003cspan refid=\"MOESM4\" class=\"InternalRef\"\u003eS4\u003c/span\u003e). The complex SV was inherited from the unaffected mother and classified as benign, while a pathogenic \u003cem\u003ede novo\u003c/em\u003e SNV/INDEL in \u003cem\u003eARID1A\u003c/em\u003e (c.3236-3239delinsG) led to a clinical diagnosis of Coffin-Siris syndrome 2 (MIM#614607). In a 13-year-old boy with intellectual disability (RD_P658), two separate complex SVs, a DUP-TRP/INV-DUP on chromosome 9 and a DEL-DUP on chromosome 10, were resolved by long-read GS (Additional file 4: Fig. S5). Parental analysis showed that the two events were inherited from the same healthy parent, and both were classified as benign. The affected CNVs were also identified with short-read GS but the rearrangements could not be phased.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe diverse genetic background observed in neurological disorders such as NDDs and NMDs prompted us to evaluate the clinical value of singleton long-read GS as a first line genetic test in this patient group. Altogether, long-read GS provided clinically relevant information in regard to phasing, characterization of STR expansions, and resolution of SV structures, resulting in improved variant assessment. In addition, methylation analysis shows strong promise as a clinically valuable layer of information.\u003c/p\u003e \u003cp\u003eShort-read GS has been a major success in rare disease diagnostics, enabling a streamlined workflow for individuals with suspected genetic conditions. Nevertheless, a substantial proportion of cases remain undiagnosed, with overall diagnostic yields typically below 50% [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. While some of these cases may have non-genetic causes or involve as-yet undiscovered disease mechanisms, such as pathogenic non-coding variants or complex inheritance patterns including digenic or oligogenic transmission, the inherent limitations of the short-read technologies also contribute to the reduced detection rates. These limitations include missed or incorrectly resolved SV breakpoints in repetitive regions and poor genotyping accuracy of both SVs and STRs [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. Furthermore, phasing is generally not possible, making it difficult to distinguish whether a variant is located in a gene of interest or its pseudogene. In addition, short reads cannot span across multiple variants in the same gene, especially when they are located in different exons. A clear and critical step likely to improve rare disease diagnostics is the implementation of long-read GS, which promises a more comprehensive variant characterization and a simpler interpretation process compared to short-read GS [\u003cspan additionalcitationids=\"CR38\" citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]. Recent studies have suggested a potential increase in diagnostic yield of up to 7.3% with long-read GS [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]. In this study, we focused on individuals with neurologic disorders with onset during childhood, a group known to have a genetically heterogeneous background.\u003c/p\u003e \u003cp\u003eTo simplify the clinical workflow, regular DNA was used instead of high-molecular weight DNA, which would have increased both cost and labor. This approach required no changes to sample collection, extraction methods, or storage protocols, thereby streamlining integration into existing diagnostic pipelines. We have previously shown that the quality of the regular DNA extraction results in acceptable long-read GS quality [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Here, the use of regular DNA is partly reflected in a relatively short average read length of approximately 5 kb; however, read lengths varied across the cohort and increased over time (Additional File 2: Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). As one of the aims of this study was to develop a clinically applicable standard workflow, the laboratory protocol was continuously optimized throughout the study, which contributed to variability in quality control metrics across samples. A notable improvement was observed with the introduction of Megaruptor-based DNA shearing, which increased the average read length from 3.3 kb to 5 kb (Additional File 2: Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). This approach streamlined the integration into existing diagnostic pipelines, and, in line with this, we also aligned all sequencing data to the same reference genome currently used in clinical practice.\u003c/p\u003e \u003cp\u003eAt Genomic Medicine Center Karolinska, we have an established and well-functioning clinical short-read GS workflow that captures a broad range of variant types [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. However, this workflow currently relies on the hg19 reference genome. To enable direct comparison between short-read and long-read GS results within the same clinical framework, alignment and variant calling for long-read data were also performed against hg19. Although GRCh38 (hg38) provides a more complete representation of the genome, particularly in challenging regions such as pericentromeric and telomeric areas, using hg19 was necessary for consistency in this study. More recently, the telomere-to-telomere (T2T-CHM13) assembly has enabled more accurate mapping across repetitive regions (i.e. centromeric satellite arrays, segmental duplications and acrocentric p-arm sequences) [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]. Previous studies, including our own, have highlighted that T2T-CHM13 is critical for detecting and resolving some clinically relevant structural variants [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. A limitation of our approach in this study is therefore that long-read GS was analyzed against hg19. This was necessary for an unbiased comparison with our current short-read workflow, but to fully utilize the long-read data alignment to GRCh38 as well as \u003cem\u003ede novo\u003c/em\u003e assembly is needed.\u003c/p\u003e \u003cp\u003eImportantly, the study cohort consisted of individuals referred for their first genomic analysis, allowing us to assess the performance of long-read GS as a true \u0026ldquo;first-line genetic test\u0026rdquo;. In this context, we show that long-read GS successfully identified all disease-causing variants detected by short-read GS. Although the overall diagnostic yield was the same (29%) and no additional diagnoses were made, long-read GS provided added clinical value across multiple variant types, reducing the need for follow up analyses after initial testing.\u003c/p\u003e \u003cp\u003eOne of the key clinical advantages observed was the ability of long-read GS to phase compound heterozygous SNV/INDELs causing autosomal recessive diseases, eliminating the need for parental segregation analysis. The possibility to phase depends on the read length as well as the presence of informative single nucleotide polymorphisms in the region. Not all genes with possible recessive variants could be phased due to the relatively short average read lengths discussed above, but this is likely to improve as more clinical-grade software and tools for optimizing phasing in such cases are developed. However, when phasing was possible, the clinical value was clear, exemplified by two cases (RD_P694 and RD_P712) where long-read GS detected and phased biallelic variants across genomic distances of 585 nt and 105 kb, respectively (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, Additional file 3: Table \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e). None of these variants were phased with short-read GS, clearly showcasing a direct clinical application. Moreover, the clinical utility extends beyond confirming biallelic variants, as variants that fail to phase can also be downgraded in priority. By directly incorporating phasing information into the ranking of possible recessive variants, clinical interpretation process can be streamlined. In a typical workflow, one recessive heterozygous variant is first identified, followed by the search for a plausible second variant in the same gene. Phasing can then determine whether the two variants are located on opposite alleles (in trans) or on the same allele (in cis). In such a scenario, only true biallelic variants would require clinical interpretation, thereby reducing the number of variants needing evaluation and eliminating the need for segregation analysis in parental samples, likely reducing both time and cost.\u003c/p\u003e \u003cp\u003eThe ability to phase also facilitates the analysis of paralogous regions, as demonstrated by the identification of a homozygous deletion of \u003cem\u003eSMN1\u003c/em\u003e in RD_P623 (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, Additional file 4: Figure \u003cspan refid=\"MOESM3\" class=\"InternalRef\"\u003eS3\u003c/span\u003e). The deletion was detected by both short-read GS and long-read GS, together with four copies of \u003cem\u003eSMN2\u003c/em\u003e. However, our analysis of the long-read GS data was able to phase the four different \u003cem\u003eSMN2\u003c/em\u003e copies and with improved bioinformatic processes it is possible to distinguish the full haplotypes across the two genes [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]. Today, with available treatments for spinal muscular atrophy, a test for the homozygous deletion of \u003cem\u003eSMN1\u003c/em\u003e exon 7 has been included in the newborn screening in Sweden. However, this accounts for approximately 95% of all SMA cases. The remaining 5% are caused by other pathogenic variants such as SNVs, INDELs, or atypical deletions in SMN1, highlighting the need for a robust clinical test to ensure comprehensive detection.\u003c/p\u003e \u003cp\u003eIn our cohort, long-read GS demonstrated utility in STR expansion analysis by accurately determined repeat lengths in a pathogenic \u003cem\u003eFMR1\u003c/em\u003e expansion (RD_P698; Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e, Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e) and confidently ruling out two suspected STR expansions. The ability to detect interrupting motifs was highly relevant for clinical interpretation. Alternate motifs in STR expansions can be beneficial by reducing somatic and/or meiotic instability, but they can also contribute to pathogenesis when the alternative repeat sequence itself becomes pathogenic, when it is directly adjacent to or within the repeat [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]. The beneficial effect of interrupting alternate motifs has been shown in non-coding (e.g., \u003cem\u003eFMR1\u003c/em\u003e) as well as coding STR expansions (e.g., \u003cem\u003eATXN1\u003c/em\u003e and \u003cem\u003eHTT\u003c/em\u003e), where loss of those motifs leads to meiotic and/or mitotic instability [\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e, \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eSVs account for a considerable proportion of disease-causing variants, representing 31% (9/29) of diagnosed individuals in our cohort, including the aforementioned homozygous deletion of \u003cem\u003eSMN1\u003c/em\u003e. Long-read GS provided a more complete analysis of 14 SVs that were clinically relevant for 12 individuals. Although long-read GS detects more SVs per genome, the number of SVs requiring clinical evaluation is lower because they can be more accurately called and thus more easily filtered against databases [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e, \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e]. This led to the discovery of SVs that were not included in the clinical evaluation of short-read GS, namely a complex SV on chromosome 1 and a pericentric inversion of chromosome 10 (inv(10)). The complex SV on chromosome 1 was determined to be a chromoanasynthesis event in which no known disease-causing genes were disrupted (Additional file 4: Fig \u003cspan refid=\"MOESM4\" class=\"InternalRef\"\u003eS4\u003c/span\u003e). The inv(10) detected in an individual who also had an inversion disrupting \u003cem\u003eFGF14\u003c/em\u003e is likely the same founder inversion previously reported [\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e] and most likely a normal variant Additional file 4: Fig S5). Although these findings did not increase the diagnostic yield, they allowed for a more complete analysis of the included individuals. Using long-read GS, we were able to fully characterize all but one SV, the dup(22)(q11) in RD_P630, which was detected only by read depth analysis. Notably, complex SVs were common, observed both as disease-causing variants and as part of background variation. In our cohort, two of the nine disease-causing SVs detected were complex, and three additional complex SVs (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e) were ultimately classified as likely benign. In total, 4/100 individuals were carriers of complex SVs. Since complex rearrangements can cause monogenic disorders due to specific gene disruptions [\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e, \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e] and gene fusions [\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e], resolving the derivative structure is highly relevant for a correct classification.\u003c/p\u003e \u003cp\u003eWe were able to elucidate cytogenetic events that are typically only captured by karyotyping. Many of the SVs had breakpoints in difficult-to-map regions, including pericentromeric and telomeric sequences and acrocentric p-arms. The DEL-INV-DUP rearrangement on chromosome 9 (RD_P651) exhibited a classic SV structure in which the distal segment is deleted and replaced by an inverted copy of the duplicated segment (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eb, Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, Additional file 3: Table \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e). This type of SV, which is thought to result from chromosome folding, was first described after laborious investigations and can now be characterized in a single experiment [\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e]. In this case, the long-read GS data directly resolves the complex genomic rearrangement structure, including extension far into telomeric sequences. Another example where long-read GS outperformed short-read was individual RD_P655 with a mosaic ring chromosome. This was first called as a heterozygous deletion of 18p and a mosaic deletion of the telomeric portion of 18q. Upon manual inspection of the breakpoint at 18q, we found reads extending from the q-arm to the p-arm (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ea, Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, Additional file 3: Table \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e). To map the read sequence to the to the p-arm, it was necessary to manually align it to GRCh38, as it involved alpha satellites from the pericentric region that were better represented in that assembly. This highlights the need for GRCh38, or more complete assemblies, to become the standard reference in clinical genome sequencing to ensure optimal detection and characterization of structural variants. The reference was also a limiting factor in the analysis of the unbalanced translocation in RD_P633, der(14)t(8;14) (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, Additional file 3: Table \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e), was identified through investigation of the breakpoints flanking the duplicated segment. In the initial analysis, we found that the duplication did not occur in tandem, with one end of the duplication breakpoint containing sequences that were not mappable in hg19. To investigate where the duplicated segment was located, we performed a de novo assembly and mapped the contig to the T2T assembly. We found that the other end of the segment was linked to the sequence of the acrocentric p-arm of chromosome 14. This enhanced detection capability of long-read GS has been demonstrated in both population-based studies and in rare disease cohorts [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e, \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e]. It has been suggested that de novo assembly and alignment to a more complete reference genome would likely lead to further improvement of structural variant detection and characterization [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe recurrent copy number variants, i.e. genomic disorders, remain a challenge for sequencing-based analysis. This was highlighted by the dup(22)(q11) (RD_P630), where long-read GS was not able to pinpoint the breakpoints, although the aberration was detected through read depth analysis (Additional file 3: Table \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e). The region at 22q11 which is recurrently involved in microdeletion- or microduplication syndromes, is flanked by segmental duplications. These segmental duplications promote rearrangement formation through non allelic homologous recombination [\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e, \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e] due to their sequence similarity, but also make the region difficult to sequence and map to the reference genome. This observation aligns with the results from the first 100 individuals sequenced by ONT in the 1000 Genomes Project, which identified recurrent assembly breaks associated with the flanking regions of known disease-causing CNVs [\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e], as well as with our own previous data showing that a suggested isodicentric chromosome 15 was only detected through read depth changes [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. However, our analysis of the dup(22)(q11) was also limited by the relatively short DNA fragments in that sample (Additional file 2: Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). This sample was one of the initial samples where long-read GS was performed, it was sequenced twice and the estimated N50 of the sample was 4.73 kb and 6.14 kb, respectively. The fragment length was not enough to span across the flanking segmental duplications, highlighting the need for good quality input DNA.\u003c/p\u003e \u003cp\u003eMethylation analysis of long-read GS shows great promise in a diagnostic setting and offers a clear advantage regarding imprinting disorders, since these are caused by defective methylation in genes with parent-of-origin specific expression. Such disorders can result from the loss of a maternal or paternal copy, disease-causing SNV/INDELs in the expressed allele, uniparental disomy (where both copies are inherited from the same parent) or variants affecting the imprinting center of the region. The advantage of long-read GS was showcased by an individual with maternal uniparental disomy of chromosome 15, where long-read GS enabled direct analysis of methylation patterns across genes in the imprinted Prader Willi region (15q11.2q13). We found that the promoters of genes normally expressed from the paternal allele (e.g., \u003cem\u003eSNURF-SNRPN\u003c/em\u003e, \u003cem\u003eNDN\u003c/em\u003e and \u003cem\u003eMAGEL2\u003c/em\u003e) were hypermethylated. Thus, long-read GS detected both the genetic and epigenetic abnormalities, establishing a diagnosis of Prader Willi syndrome (MIM#176270) in a single experiment, whereas multiple tests are typically required in current practice. However, it is important to note that some imprinting regions are tissue-specific. For example, \u003cem\u003eUBE3A\u003c/em\u003e (associated with Angelman syndrome, MIM#105830) is maternally imprinted in neurons but biallelically expressed in other tissues. This presents a challenge, as whole blood is often used for first-line analysis. Nonetheless, the ability to assess methylation across known imprinting regions offers hope for diagnosing more individuals with imprinting disorders, the incidence of which may currently be underappreciated.\u003c/p\u003e \u003cp\u003eAnalysis of global methylation patterns was not applied in our study, although there appears to be a strong potential for its diagnostic utility [\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e]. In one individual, we found a 263 kb duplication on chromosome 9 (9q34.3), inherited from a healthy parent and is still classified as a VUS. Duplications in this region have been linked to a syndrome with mild neurodevelopmental features, and previous studies have reported an association between such duplications and a distinct global DNA methylation profile. However, the most distal duplications included in the study, which align with our case, did not exhibit the same methylation pattern [\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e]. Further studies are needed to clarify the clinical relevance of implementing standard clinical methylation analysis in such cases. That would, however, require that large reference methylation databases are established based on long-read GS data.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis study shows that singleton long-read GS is a powerful first-line diagnostic tool for neurological disorders, enabling comprehensive variant detection, including SVs, STR expansions, and phasing, even from standard clinical DNA. Importantly, we show that this technology can resolve clinically relevant variants in repetitive and previously inaccessible regions of the genome, providing insights beyond the capabilities of short-read GS. The ability to assess epigenetic changes adds further clinical value, particularly for imprinting disorders. Despite a similar overall diagnostic yield to short-read GS in our cohort, long-read GS offers superior resolution and interpretive power, capable of capturing the full spectrum of disease-causing variants, reducing the need for follow-up testing and enabling more precise interpretation.\u003c/p\u003e \u003cp\u003eAs technologies and tools continue to evolve, long-read GS will likely offer even greater diagnostic and mechanistic insight, paving the way for improved precision medicine in rare diseases. Improved diagnostics, combined with the growing number of targeted therapies, represents a strong opportunity to advance precision medicine and improve outcomes for affected individuals and their families.\u003c/p\u003e"},{"header":"Abbreviations","content":" \u003cp\u003eCNV Copy number variant\u003c/p\u003e \u003cp\u003eID Intellectual disability\u003c/p\u003e \u003cp\u003eINDEL Insertion/deletion\u003c/p\u003e \u003cp\u003eGS Genome sequencing\u003c/p\u003e \u003cp\u003eNDD Neurodevelopmental disorder\u003c/p\u003e \u003cp\u003eNMD Neuromuscular disorder\u003c/p\u003e \u003cp\u003eMS-MLPA Methylation specific multiplex ligation-dependent probe amplification\u003c/p\u003e \u003cp\u003eSNV Single nucleotide variants\u003c/p\u003e \u003cp\u003eSTR Short tandem repeat\u003c/p\u003e \u003cp\u003eSV Structural variant\u003c/p\u003e \u003cp\u003eVUS Variant of uncertain significance\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cem\u003eEthics approval and consent to participate\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThe research involving human participants underwent review and approval by the Regional Ethical Review Authority in Stockholm, Sweden (ethics permit number 2019-04746). The research was conducted in accordance with the principles of the Helsinki Declaration. Written informed consent was obtained from the participants/their legal guardians/next of kin, in accordance with national legislation and institutional requirements.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eConsent for publication\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eWritten informed consent was obtained for publication.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eAvailability of data and materials\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets discussed in this article are not immediately accessible due to ethical and privacy constraints but are available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eCompeting interests\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eAll authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eFunding\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThis research was funded by Swedish Research Council, grant number 2019-02078, the Swedish Brain Foundation, grant number FO2022-0256, and the Stockholm Regional Council (ALF funding) and the Swedish Rare Diseases Research Foundation (S\u0026auml;llsyntafonden).\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eAuthors\u0026apos; contributions\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eConceptualization: ALi and VW. Collection of informed consent: LLj. Sample collection: ME. Library preparation and sequencing: LLa and ALy. Data analysis: JE and ET. Interpretation of data and formal analysis: ME, JE, HT. Clinical interpretation and phenotype: ALi, MJS and MK. Compilation of results: ME. Writing of first draft: ME. Reading, editing and approval of final manuscript: ALi, ALy, ET, JE, LLa, LLj, ME, MJS, MK, AN, VW. Figures: ME\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eAcknowledgements\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eWe express our sincere gratitude to the participants and their families and acknowledge the Karolinska Institute\u0026rsquo;s membership in EURO-NMD and ERN-ITHACA. We also extend our appreciation to UPPMAX for providing computational infrastructure resources and to the Clinical Genomics Stockholm facility at Science for Life Laboratory and the Genomic Medicine Center Karolinska for their support in long-read genome sequencing.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eMaulik PK, Mascarenhas MN, Mathers CD, Dua T, Saxena S: Prevalence of intellectual disability: a meta-analysis of population-based studies. \u003cem\u003eRes Dev Disabil\u003c/em\u003e 2011, 32(2):419\u0026ndash;436.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGlobal, regional, and national burden of 12 mental disorders in 204 countries and territories, 1990\u0026ndash;2019: a systematic analysis for the Global Burden of Disease Study 2019. \u003cem\u003eLancet Psychiatry\u003c/em\u003e 2022, 9(2):137\u0026ndash;150.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTuchman R: What is the Relationship Between Autism Spectrum Disorders and Epilepsy? \u003cem\u003eSemin Pediatr Neurol\u003c/em\u003e 2017, 24(4):292\u0026ndash;300.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eEk M, Nilsson D, Engvall M, Malmgren H, Thonberg H, Pettersson M, Anderlid B-M, Hammarsj\u0026ouml; A, Helgadottir HT, Arnardottir S \u003cem\u003eet al\u003c/em\u003e: Genome sequencing with comprehensive variant calling identifies structural variants and repeat expansions in a large fraction of individuals with ataxia and/or neuromuscular disorders. \u003cem\u003eFrontiers in Neurology\u003c/em\u003e 2023, 14.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNguengang Wakap S, Lambert DM, Olry A, Rodwell C, Gueydan C, Lanneau V, Murphy D, Le Cam Y, Rath A: Estimating cumulative point prevalence of rare diseases: analysis of the Orphanet database. \u003cem\u003eEur J Hum Genet\u003c/em\u003e 2020, 28(2):165\u0026ndash;173.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eVissers LE, Gilissen C, Veltman JA: Genetic studies in intellectual disability and related disorders. \u003cem\u003eNat Rev Genet\u003c/em\u003e 2016, 17(1):9\u0026ndash;18.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLindstrand A, Ek M, Kvarnung M, Anderlid BM, Bj\u0026ouml;rck E, Carlsten J, Eisfeldt J, Grigelioniene G, Gustavsson P, Hammarsj\u0026ouml; A \u003cem\u003eet al\u003c/em\u003e: Genome sequencing is a sensitive first-line test to diagnose individuals with intellectual disability. \u003cem\u003eGenet Med\u003c/em\u003e 2022, 24(11):2296\u0026ndash;2307.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003evan der Sanden B, Schobers G, Corominas Galbany J, Koolen DA, Sinnema M, van Reeuwijk J, Stumpel C, Kleefstra T, de Vries BBA, Ruiterkamp-Versteeg M \u003cem\u003eet al\u003c/em\u003e: The performance of genome sequencing as a first-tier test for neurodevelopmental disorders. \u003cem\u003eEur J Hum Genet\u003c/em\u003e 2023, 31(1):81\u0026ndash;88.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eStranneheim H, Lagerstedt-Robinson K, Magnusson M, Kvarnung M, Nilsson D, Lesko N, Engvall M, Anderlid BM, Arnell H, Johansson CB \u003cem\u003eet al\u003c/em\u003e: Integration of whole genome sequencing into a healthcare setting: high diagnostic rates across multiple clinical entities in 3219 rare disease patients. \u003cem\u003eGenome Med\u003c/em\u003e 2021, 13(1):40.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSmedley D, Smith KR, Martin A, Thomas EA, McDonagh EM, Cipriani V, Ellingford JM, Arno G, Tucci A, Vandrovcova J \u003cem\u003eet al\u003c/em\u003e: 100,000 Genomes Pilot on Rare-Disease Diagnosis in Health Care - 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Mutagen\u003c/em\u003e 2015, 56(5):419\u0026ndash;436.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGeysens M, Huremagic B, Souche E, Breckpot J, Devriendt K, Peeters H, Van Buggenhout G, Van Esch H, Van Den Bogaert K, Vermeesch JR: Clinical evaluation of long-read sequencing-based episignature detection in developmental disorders. \u003cem\u003eGenome Med\u003c/em\u003e 2025, 17(1):1.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRots D, Rooney K, Relator R, Kerkhof J, McConkey H, Pfundt R, Marcelis C, Willemsen MH, van Hagen JM, Zwijnenburg P \u003cem\u003eet al\u003c/em\u003e: Refining the 9q34.3 microduplication syndrome reveals mild neurodevelopmental features associated with a distinct global DNA methylation profile. \u003cem\u003eClin Genet\u003c/em\u003e 2024, 105(6):655\u0026ndash;660.\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":"genome-medicine","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [Genome Medicine](https://genomemedicine.biomedcentral.com/)","snPcode":"13073","submissionUrl":"https://submission.springernature.com/new-submission/13073/3","title":"Genome Medicine","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Whole genome sequencing, Long-read sequencing, Short-read sequencing, Rare diseases, Clinical diagnostics, Single nucleotide variants, Chromosomal rearrangements, Structural variants, Short tandem repeat expansions, Methylation analysis","lastPublishedDoi":"10.21203/rs.3.rs-6863124/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6863124/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eSingleton short-read genome sequencing (GS) is increasingly used as a first-line genetic test for childhood neurological disorders (such as intellectual disability, neurodevelopmental delay, motor delay, and hypotonia) with diagnostic yields from 26\u0026ndash;35%, typically involving a mix of single nucleotide variants and small insertions/deletions (SNV/INDELs), structural variants (SVs), and short tandem repeats (STRs). Long-read GS is emerging as an attractive alternative, offering a more comprehensive assessment of the genome, but its utility still needs to be systematically evaluated in a clinical diagnostic setting.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eWe prospectively included 100 children and adolescents (\u0026le;\u0026thinsp;20 years) with neurological disorders, newly referred for genetic testing. Routine DNA was used for standard clinical short-read GS in parallel with long-read GS (Oxford Nanopore Technologies). In addition to comprehensive variant calling, long-read GS data was also phased and underwent methylation analysis. Variant interpretation was restricted to \u003cem\u003ein-silico\u003c/em\u003e gene panels targeting either intellectual disability (1,568 genes) or neuromuscular disorders (1,035 genes) depending on the clinical presentation.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eThe long-read GS generated an average of 111 GB data per sample, with a median read-length of 5 kb and average N50 of 16 kb; resulting in an average coverage of 34X. Short-read and long-read GS identified the same 29% diagnostic yield, including SNV/INDELs (n\u0026thinsp;=\u0026thinsp;18), SVs (n\u0026thinsp;=\u0026thinsp;9), STRs (n\u0026thinsp;=\u0026thinsp;1), and uniparental disomy (n\u0026thinsp;=\u0026thinsp;1). Long-read GS provided additional diagnostic value in 13 cases involving 17 distinct variants, including phasing of \u003cem\u003eSMN1\u003c/em\u003e and biallelic SNVs/INDELs in autosomal recessive genes, accurate determination of STR length and sequence as well as detailed structural characterization of SVs. Of note, an unbalanced translocation, der(14)t(8;14)(p11.2;p23.1, required \u003cem\u003ede novo\u003c/em\u003e assembly and T2T alignment resolve the breakpoint junctions. Furthermore, long-read GS detected disease-associated aberrant methylation patterns in the Prader-Willi region and across an \u003cem\u003eFMR1\u003c/em\u003e expansion.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eIn a clinical diagnostic setting, long-read GS proved to be a streamlined, first-line test, capturing the full spectrum of disease-causing variants, reducing the need for follow-up testing and enabled more precise interpretation. While the overall diagnostic yield may be comparable to that of short-read approaches, long-read GS offers significant added value across multiple variant types.\u003c/p\u003e","manuscriptTitle":"Long-read genome sequencing enhances diagnostics of pediatric neurological disorders","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-06-25 02:06:23","doi":"10.21203/rs.3.rs-6863124/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-07-30T14:31:25+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-07-25T11:06:47+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-07-20T13:11:20+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"20618406768895484372411497838405509143","date":"2025-07-14T05:45:28+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"305989861565011560145376950324958284842","date":"2025-07-12T15:32:19+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-07-01T19:59:39+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-06-25T23:20:45+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-06-20T07:45:35+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"144109144632825429131623392919767014936","date":"2025-06-19T07:33:55+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"94586436050236740014025936856564645090","date":"2025-06-16T19:00:47+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-06-16T16:08:19+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-06-16T14:06:55+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-06-11T05:46:53+00:00","index":"","fulltext":""},{"type":"submitted","content":"Genome Medicine","date":"2025-06-10T12:25:46+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"genome-medicine","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [Genome Medicine](https://genomemedicine.biomedcentral.com/)","snPcode":"13073","submissionUrl":"https://submission.springernature.com/new-submission/13073/3","title":"Genome Medicine","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"a8d16671-51f1-41e6-930b-44c2497d288a","owner":[],"postedDate":"June 25th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2025-12-29T07:24:02+00:00","versionOfRecord":[],"versionCreatedAt":"2025-06-25 02:06:23","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6863124","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6863124","identity":"rs-6863124","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}