The Genomic Medicine Center Karolinska 10-year report on genome sequencing for rare diseases and a strategy for stepwise clinical implementation

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This transformation has significantly impacted rare disease diagnostics, increasingly done through gene panels, whole exome and whole genome sequencing. To advance beyond genomics into precision medicine and encompass the breadth of relevant clinical scenarios, a true systems shift is required that challenges conventional barriers and enables the formation of cross-disciplinary, integrated environments. Methods The Genomic Medicine Center Karolinska Rare Diseases (GMCK-RD) has, for the past 10 years, brought together healthcare and academia to enable large-scale genome sequencing in a clinical diagnostics context. Within GMCK-RD, experts from various medical disciplines collaborate closely with clinical geneticists, bioinformaticians, and researchers to integrate genome sequencing into healthcare. Results In total, 15 644 individuals with suspected rare diseases were analyzed using clinical genome sequencing, including pediatric (48%), adult (48%) and fetal (4%) samples. The overall diagnostic yield was 22.6% providing a diagnosis for 3 538 individuals with variants in 1 570 genes. Moreover, a rare disease analysis tool suite developed and validated in house includes a bioinformatic pipeline allowing for comprehensive data analysis covering a wide range of genetic variants including SNVs, INDELs, repeat expansions, uniparental disomies, balanced and unbalanced structural variants as well as insertions of mobile elements. Results are visualized and interpreted in custom-developed decision support systems functioning as an interpretation portal as well as a knowledge-base to capture the interpretation efforts made in a structured format allowing future secondary use. Conclusions Altogether, GMCK-RD has shifted healthcare in our region towards precision diagnostics. We emphasize the need to transition from traditional clinical genetic diagnostics to a broader clinical genomics approach. Beyond this shift, we advocate integrating genomics with specialized clinical and laboratory medicine, a concept pioneered for inborn errors of metabolism (IEM) with stepwise spread to additional disease groups. In this model, a multidisciplinary unit combines screening, targeted diagnostics, individualized treatment, and long-term patient follow-up. Here we provide a road map and guide for inspiration for centers aiming to implement genome sequencing in rare disease diagnostics. genome sequencing rare diseases clinical diagnostics single nucleotide variants chromosomal rearrangements structural variants precision medicine Figures Figure 1 Figure 2 Figure 3 Figure 4 BACKGROUND Healthcare is currently undergoing a transition towards precision medicine and a crucial step in enabling personalized therapies and interventions is a well-functioning workflow for precision diagnostics. This is especially relevant in the field of rare diseases where genomics analysis has revolutionized diagnostics, resulting in higher diagnostic yields with shorter turn-around times. The two main genomic approaches used in genetic testing for rare diseases are whole exome sequencing (WES) and whole genome sequencing (WGS). Although both methods yield similar diagnostic rates of approximately 20–50% (reviewed in ( 1 )) and perform similarly in detecting coding single nucleotide variants (SNVs) and large copy number variants (CNVs), WGS provides additional benefits ( 2 , 3 ). In particular, WGS enables the detection of non-coding variation, a broader spectrum of structural variants (SVs), short tandem repeats (STRs), and, with specialized software, some paralogous regions ( 4 – 6 ). Many initiatives from across the globe are prioritizing rare diseases as one of the focus areas in their genomic medicine programs. Today, thousands of rare disease patients are assessed and analyzed by WES in a clinical setting each week. However, as more centers transition to WGS, there is considerable variability in the pipelines and workflows used, leading to differences between centers in the types of variants detected, how they are prioritized, and which findings are reported. This article delves into the journey of Genomic Medicine Center Karolinska Rare Diseases (GMCK-RD) in the Stockholm healthcare region, highlighting its collaborative approach, methodologies, and the transformative impact on diagnosing and managing patients with rare diseases. We outline the key steps necessary for a successful integration of WGS into rare disease diagnostics – ranging from sequence generation, variant calling and prioritization to clinical interpretation and reporting, while also addressing other critical factors like data structuring, storage and reanalysis. Furthermore, we discuss the criteria for selecting patients for genome sequencing, providing specific guidelines for different disease groups. Lastly, we present a strategy for the transition from genomics into precision medicine, a long-term systems shift that necessitates deep clinical integration across multiple specialties, challenging the current organization of healthcare. METHODS The Genomic Medicine Center Karolinska – Rare Diseases GMCK-RD was initiated formally in 2017, as a node in the national Genomic Medicine Sweden initiative ( 7 , 8 ), building on a collaboration that started years earlier. In brief, the initiative has four partners; the Clinical Genomics research infrastructure at SciLifeLab (Stockholm), which is responsible for sequencing, bioinformatics and data management, and three hospital-based clinical units at the Karolinska University Hospital (Clinical Genetics and Genomics (KGG), the Centre for Inherited Metabolic Diseases (CMMS), and Clinical Immunology and Transfusion Medicine (KITM)), which are responsible for setting inclusion criteria, interpreting results and clinical reporting. In addition to genomic diagnostics, the initiative provides genetic counselling, family testing, and referrals for clinical surveillance, treatment, and long-term follow-up when appropriate. GMCK-RD currently offers three distinct clinical genome pathways; two integrated precision medicine workflows, available through CMMS and KITM, and a general genetics service provided through KGG. A detailed description of the partnership is given in Stranneheim et al. ( 2 ). Since then, the concept has been further developed by supporting multidisciplinary workflows in order to implement genomics all the way into acute clinical medicine. Material All patients were referred for diagnostic WGS testing from January 1st 2015 until December 31st 2023. The referrals were sent primarily from various subspecialized clinics within the Stockholm healthcare region as well as, for some disease groups, from the whole of Sweden. Altogether 11,274 cases were analyzed through the general genetics (KGG) arm and 3,571 and 799 cases through CMMS and Clinical Immunology, respectively. Genome Sequencing The overall workflow for clinical WGS has been custom built by Clinical Genomics and has been described in detail previously ( 2 ). In brief, since 2015, samples have been sequenced using PCR-free whole-genome sequencing protocols on various generations of high-throughput short-read sequencing platforms, including HiSeq X (2015–2018), NovaSeq 6000 (2018–2023) and NovaSeq X Plus (2023-) to approximately 30x median coverage. Today library preparation and associated sequencing is carried out four times per week, followed by automated start of the bioinformatic analysis. Bioinformatic analysis The bioinformatic analysis includes calling of single nucleotide variants (SNVs), insertions and deletions (INDELs), short tandem repeats (STRs), uniparental disomies and structural variants (SVs) including deletions, duplications, inversions as well as insertions of mobile elements (MEI) ( 2 , 9 ). Also, variants in the mitochondrial genome are analyzed. Finally, the number of SMN1 and SMN2 copies is estimated ( 4 ). The bioinformatic pipelines have during the recent years been transitioned from the previously reported MIP pipeline to the current nextflow-based pipeline, which we have made publicly available on the nf-core pipeline repository ( https://github.com/nf-core/raredisease ) (Additional File 1: Figure S1 ). The current pipeline is summarized in Fig. 1 . Data structures and storage infrastructure Upon completion of the bioinformatic analysis, a data structuring process is initiated consisting of storage of selected key files; this includes raw sequence data, alignment files, raw variant files (vcf) as well as annotated variant files. The latter provides a snapshot of the information that was available upon clinical interpretation of the variants. Results of the clinical interpretation carried out in Scout ( 2 ), a custom-developed decision support system, is also captured in static reports, including dismissed variants, variant-level comments and variant classifications according to ACMG guidelines ( 10 ). Metadata associated with the sequencing and bioinformatic analysis is structured using HL7 FHIR resource GenomicStudy ( https://build.fhir.org/genomicstudy.html ) to facilitate interoperability and data exchange. Computational prioritization of called variants Called variants are annotated and prioritized using an in-house developed pathogenicity scoring system Genmod ( 2 , 11 ) (Additional File 1: Figure S2 ). This system assigns a rank score to each variant based on multiple parameters, including functional impact, inheritance model, allele frequency, and presence of a second allele for compound heterozygosity (Fig. 2 A). Of note, variants classified as Likely Pathogenic or Pathogenic in ClinVar, with a gold star review status, are always retained regardless of frequency or consequence. To evaluate the performance of this prioritization approach, we retrospectively analyzed 3,042 previously reported pathogenic variants. For each case, the relevant version of the rank model was applied, followed by filtering using the appropriate gene panel and the Scout clinical filter. This filter retains variants with moderate to high predicted impact (based on VEP ( 12 )), located in exonic or splice-site regions, and below strict population allele frequency thresholds (gnomAD ( 25 ) < 1% for SNVs and INDELs; <1% in the local count database for SVs). Rank model versions with minor updates, such as changes in label nomenclature or local database expansion, were grouped for consistency, and structural variants were evaluated in parallel using dedicated SV-specific rank groups. Interpretation of called variants Following bioinformatic analysis and variant ranking, the data from individual cases are interpreted at the clinical unit responsible for the patient case. To limit the number of variants that warrant manual assessment, the above-mentioned Scout clinical filter is applied. The ranked variants are then further filtered based on phenotype and inheritance (Fig. 2 ). Phenotype-Based Filtering The phenotype-based strategy relies on in silico gene panels (referred to as panels) and restricts the clinical interpretation to variants in genes associated with the patient's phenotype and disease. GMCK-RD panels are regularly updated with new disease genes according to PanelApp ( 13 ), OMIM ( https://omim.org/ ), targeted PubMed searches, personal communication with experts (such as European Reference Networks and other national and international expert groups) as well as presentations at conferences. Personalized gene panels may also be created upon request for analysis of specific genes based on clinical indication or when no established panel is suitable. In such cases, patient-specific Human Phenotype Ontology (HPO) ( 14 ) terms help build custom panels, or suitable PanelApp panels can be adapted. Inheritance-Based Filtering The inheritance-based strategy is used when family members have been included in the analysis and employs a genotype-driven approach, filtering for variants that are heterozygous, bi-allelic, or X-linked. In family-based analyses, it is possible to filter based on inheritance patterns, for example: compound heterozygous recessive, de novo dominant, and multigenerational dominant. Of note, variant ranking is also influenced by inheritance (Fig. 2 A). Variant Interpretation Variant interpretation is conducted in Scout ( 2 ), a purpose-built decision support system. The platform presents each case with an overview of chromosome coverage using ideograms and separate tabs for querying SNVs/INDELs, structural variants, repeat expansions, and mobile element insertions. Custom filters can be applied to each category, and variants can be annotated, ACMG-classified, and pinned for follow-up. The system integrates data from publicly available population frequency databases (gnomAD ( 15 ), SweFreq (16)), our in house frequency database (LocusDB ( 17 )), ClinVar ( 18 ) and in silico pathogenicity tools (SIFT ( 19 ), Polyphen 2 ( 20 ), CADD ( 21 ), REVEL ( 22 )) and provides various other annotations (Fig. 2 ). Variants previously reported as pathogenic are flagged and known founder variants are also highlighted. When warranted, in addition to the Scout evaluation, a digital chromosome analysis is performed using vcf2cytosure ( 6 ), which enables a genome-wide analysis that simulates chromosomal microarray analysis (CMA). This method allows data to be processed within the same system as clinical arrays, enabling detected variants to be annotated and compared to the in-house database from ~ 10 000 cases previously analyzed by CMA. The case assessment starts with a specially trained clinical laboratory geneticist who reviews the data and flags variants of interest. Variants are evaluated based on scout rank scores and additional information from updated population databases (gnomAD ( 15 )) and ClinVar ( 18 ). Of note, if a variant is reported as pathogenic in ClinVar, it is highlighted in the interpretation system. Different allele frequency thresholds are applied to discard variants depending on the suspected inheritance pattern (dominant or recessive), the expected penetrance and age of onset. For dominant pediatric diseases with an early onset and high penetrance, variants are discarded if they are present more than five times in gnomAD v2. In contrast, the most common variant reported, a risk allele for inherited breast cancer in CHEK2 , is present in 1/240 individuals in gnomAD v2. In the case of recessive disorders, variants are discarded if more than five individuals are homozygous for the variant in gnomAD v2. This is likely too high and has been complemented over time with panel specific criteria based on age of onset and penetrance. In addition to canonical splice variants, we also investigate potential splice-altering variants within the intronic regions (+/- 20 bases). Deep intronic variants already reported in ClinVar are also considered, as discussed above. For gene-specific phenotypes involving a limited number of genes (1–2 genes), we additionally assess all rare (gnomAD AF > 0.001) intronic variants. Possible effects on splicing are further evaluated using bioinformatics tools such as splice prediction software in Scout (SPIDEX ( 23 ), SpliceAI ( 24 )) as well as software incorporated in Alamut VisualPlus (Sophia Genetics). Follow up evaluation is done with RNA analysis using cDNA sequencing or whole transcriptome sequencing. This genetic assessment, based on a joint evaluation of all these factors results in a short list of variants that undergo medical evaluation. The medical doctor(s) evaluates the selected variants in concordance with the detailed patient symptoms and variants deemed as potentially disease causing are reported. At this step, the three clinical workflows in GMCK-RD diverge and the specific multidisciplinary teams have created tailored criteria for inclusion as well as recommendations for WGS data interpretation, ensuring that reported variants are directly relevant and beneficial for the needs of the patients and their family members. Importantly, data can be shared between teams, enabling broad as well as targeted analyses in patients with unclear, atypical and evolving clinical phenotypes. General Genetics WGS workflow at Clinical Genetics and Genomics In the general genetics workflow for clinical WGS at the Department of Clinical Genetics and Genomics, all referrals are reviewed by a medical doctor (either a clinical genetics specialist or a supervised resident). Based on the information provided in the referral, and when needed, additional information from the patient’s electronic health record, the physician determines which gene panel should be applied. Within this framework, 11 274 WGS analyses were performed over a nine-year period, from 2015 to 2023. Of these, 1 126 (10%) were analyzed using a family-based approach, while 6 853 (61%) underwent singleton analysis with one of the seven most frequently used phenotype-based panels. The remaining 3 295 analyses (29%) were assessed using either smaller curated panels or custom-made panels tailored to the individual’s clinical presentation. For individuals with highly specific symptoms, curated panels are available for conditions such as disorders of sex development, retinopathies, hearing loss, and ciliopathies. In many of these cases, patient-specific gene panels are generated based on phenotype data using Human Phenotype Ontology (HPO) terms ( 14 ) or tools like PanelApp ( 13 ). Below, we describe the clinical indications for the seven most commonly used panels, including associated symptoms, diagnostic criteria, and relevant coexisting conditions that support the use of WGS and guide panel selection. We also provide an overview of the family-based approach and prenatal testing using genome sequencing. Intellectual Disability (ID panel) The ID panel is used for a heterogeneous group of individuals with neurodevelopmental disorders (NDDs) including intellectual disability (ID), autism spectrum disorder, developmental delay, speech and language disorders and related phenotypes. In many cases, a specific subdiagnosis within the NDD spectrum had not yet been established at the time of genetic testing. Additional clinical features are sometimes present alongside NDD. The vast majority of referred cases (90%) were children (average 9 years; range 0–58). The gender distribution was 63% male and 37% female. The ID panel includes 1,567 genes and STRs are assessed at seven loci (Additional File 2: Table S1 ). The genetic analysis also incorporates genome-wide detection of structural variants using a pipeline that replaces the need for CMA, referred to as WGS-CMA. Due to the large number of genes included, the analysis primarily focuses on clearly pathogenic variants (ACMG class 4 and 5). Variants of uncertain clinical significance (VUS) are generally not reported; however, VUS deemed clinically relevant, such as those with strong gene-disease association and phenotype overlap, may be included in the clinical report after multidisciplinary review (Fig. 3 , Table 2 ). In cases where an uncertain variant warrants further investigation, the result is classified as inconclusive and parental samples are requested for segregation analysis. Neuromuscular, ataxia and spastic paraplegia disorders (NMD panel) The NMD panel is used for a clinically heterogeneous group of individuals with suspected neuromuscular disorders (NMDs), including myopathies, neuropathies, spastic paraplegia, and ataxia. The gender distribution was 54% male and 46% female. The age of the referred individuals spanned from infancy (0–1 years, 11%), childhood (2–17 years, 23%), and adulthood (18–69 years, 56%) to more than 70 years of age (10%). Regardless of age, all samples in the NMD panel were analyzed as singletons. Most individuals have a clinical diagnosis of myopathy or neuropathy prior to genetic testing. However, cases with nonspecific neuromuscular symptoms, such as hypotonia or arthrogryposis, are also tested using the NMD panel as part of a broader diagnostic evaluation. The panel also includes individuals with overlapping phenotypes, such as ataxia, that may fall within the neuromuscular spectrum. The NMD panel includes 1,035 genes (Additional File 2: Table S2 ), covering established NMD genes as well as those associated with spastic paraparesis, ataxia, and other movement disorders. The analysis includes STR-analysis for 29 loci (Additional File 2: Table S2 ) and a specific assessment of the SMN1 -gene copy number. As with other large panels, the reporting of VUS is minimized and restricted to variants with potential clinical significance (Fig. 3 , Table 2 ). When available, clinical findings and results from neurophysiological investigations, muscle biopsy analyses, biochemical analyses are used to support the interpretation. Parental samples are often difficult to obtain for adult individuals, but in pediatric cases, parental testing may aid in the variant interpretation. Inherited cancer (IC panel) The IC panel is used for individuals with a suspected hereditary cancer predisposition and included both children (37%) and adults (63%) under different inclusion criteria. For children, all newly diagnosed cancer cases in Sweden have been part of a national study using paired genome sequencing (tumor/normal) since 2021 ( 23 ) and are sequenced as part of clinical routine since 2024. Although initially conducted as research, these samples are processed through the clinical laboratory, and cancer predisposition variants are reported. Adult individuals are referred for testing based on family or personal medical history suggestive of a genetic predisposition, such as early-onset cancer, multiple primary tumors, or suspected hereditary cancer syndromes. The IC panel consists of 165 genes (Additional File 2: Table S3), covering a broad range of hereditary cancer conditions, from genetically heterogeneous conditions like hereditary paraganglioma to single-gene disorders such as retinoblastoma. Analysis is conducted on a singleton basis. Variants identified in the IC panel are reported following gene-specific ACMG criteria for pathogenicity (such as TP53 ( 25 ) and CDH1 ( 26 )). Risk factors for cancer predisposition are reported only when national care guidelines exist. Examples of cases where a risk variant would lead to special surveillance include families with hereditary breast cancer where truncating variants in ATM, BARD1, CHEK2, RAD51C and RAD51D result in annual mammograms instead of every two years and families with hereditary ovarian cancer where carriers of truncating variants in BRIP1, RAD51C and RAD51D are offered an option of post-menopausal salpingo-oophorectomy. VUS are rarely reported. If the variant is a possible de novo variant in a child with cancer, the analysis is reported as inconclusive and parental samples are requested before a final report is issued. Of note, we do not report out carriership of heterozygous missense variants in genes that cause autosomal recessive conditions, such as MUTYH or RAD51C , as carriership does not lead to an increased risk of cancer that would lead to additional surveillance of the patient. Cancer predisposition variants may also be incidentally detected during WGS analysis for other indications, such as truncating variants in BRCA1/2 or PALB2 which are present in 0.2% of the gnomAD population ( 15 ). Even though the targeted analysis approach minimizes the risk of incidental findings, some cancer genes are included in other gene panels as they can also cause autosomal recessive disease. For instance, bi-allelic ATM variants can cause autosomal recessive ataxia-telangiectasia and bi-allelic BRCA2 can cause autosomal recessive Fanconi anemia and can thus be detected by chance. If this happens, a comprehensive review of the patient’s medical- and family history is conducted, and an individualized decision on whether to report the variant as an incidental finding or not is made for each patient based on the ethical principles that guide health care as well as what is known about the variant, the expected penetrance, the availability of prevention programs and the family history Connective Tissue Disease (CTD panel) The CTD panel is used for individuals with suspected heritable connective tissue disorders (HDCTs) and heritable thoracic aortic disease, including conditions such as Marfan syndrome, Loeys-Dietz syndrome and Ehlers-Danlos syndrome. The majority of the referrals were adults (81%) with children accounting for 19%. All samples were analyzed as singletons. The CTD panel includes in total 154 genes (Additional File 2: Table S4). In addition to HDCT-related genes, it also covers genes relevant for differential diagnoses due to overlapping clinical features with other inherited conditions, such as mild skeletal dysplasias, collagen-related myopathies, and Birt-Hogg-Dubé syndrome. Typically, variants classified as disease causing (ACMG Class 4 and 5) are reported. However, VUS are sometimes included in reports. Neurodegenerative disorders (NeuroDeg panel) The NeuroDeg panel is used for individuals with suspected adult-onset neurodegenerative conditions. The majority of referrals (> 80%) involved individuals with clinical suspicion of dementia or other neurodegenerative disease. All tested individuals were adults, with 88% over the age of 50 years. All samples were analyzed as singletons. The NeuroDeg panel consists of 138 genes and includes STR analysis at 17 loci (Additional File 2: Table S5). It targets disorders associated with cognitive impairment and dementia, such as Alzheimer disease, frontotemporal dementia, and dementia with Lewy bodies, as well as causative genes for Creutzfeldt-Jakob disease, ALS, and Parkinson’s disease. The known mutation spectrum across these genes is highly diverse encompassing a wide range of variants, from large duplications (e.g., APP ( 27 , 28 )) to SNVs, STRs, and small deletions (e.g., GRN ). Many neurodegenerative diseases exhibit overlapping clinical presentations, making diagnostics challenging. However, identifying a causative genetic variant can establish a definitive diagnosis, eliminating the need for a postmortem neuropathological examination. Typically, variants classified as disease causing (ACMG Class 4 and 5) are reported. However, VUS are sometimes included in reports. Skeletal dysplasia disorders (SKD panel) The SKD panel is used for a heterogenous group of patients with suspected genetic skeletal disorders, based on clinical and radiographic findings of abnormal skeletal morphology or abnormal bone density. The majority of referrals were children (59%), with adults accounting for 30% and prenatal samples for 11%. All samples were analyzed as singletons. Before genetic testing is initiated, basic clinical information is required including radiographic findings, as well as symptoms from internal organs. The SKD panel includes 681 genes (Additional File 2: Table S6). VUS are selectively reported only when the radiological phenotype is specific and aligns with the disorder linked to the gene in question. In cases where a VUS requires further investigation, the result is reported as inconclusive and parental samples are requested. Inherited cardiac conditions (ICC panel) The ICC panel is used for individuals with suspected inherited arrhythmias and cardiomyopathies, with a focus on early diagnosis to prevent severe complications, including sudden cardiac death. The majority of referrals were adults (88%). All samples were analyzed as singletons. When genetic testing is initiated, detailed clinical information is required to classify individuals into defined phenotype groups, including hypertrophic cardiomyopathy, dilated cardiomyopathy, arrhythmogenic right ventricular cardiomyopathy, left ventricular non-compaction cardiomyopathy, long QT syndrome, catecholaminergic polymorphic ventricular tachycardia and Brugada syndrome. The clinical workflow is carried out in close collaboration with cardiologists. The panel includes 94 genes (Additional File 2: Table S7) associated with both channelopathies and cardiomyopathies, including key genes for hypertrophic and dilated cardiomyopathy, long QT syndrome, and related conditions. Variants classified as disease causing (ACMG Class 4 and 5) are reported and selected VUS are included when they warrant follow-up through re-evaluation or segregation analysis. We follow the 2023 ESC Guidelines for the management of cardiomyopathies and other cardiac conditions ( 29 , 30 ) and apply an evidence-based assessment of gene-disease relationships, including recent expert curation efforts in dilated cardiomyopathy ( 31 ) and hypertrophic cardiomyopathy ( 32 ) to guide gene inclusion and variant interpretation. Family-based (TRIO) analysis For highly heterogeneous disorders, a family-centered approach, typically trio analysis involving the affected individual and both parents, was often employed. This strategy is particularly valuable in pediatric cases with suspected congenital syndromes and may be used either as a first-tier test or as a follow-up after singleton panel testing, most commonly following the ID, NMD, or SKD panels. The majority of cases were children, with 13% under 1 year and 76% under 18 years of age. The analysis includes all genes with a known association to disease based on the morbid OMIM gene list ( 33 ) and the NHS Genomic Medicine Service Signed Off panel. These gene lists are updated quarterly to reflect the most current set of genes known to cause monogenic disease. Pre-test counseling is provided by clinical geneticists or physicians with specialized training and includes discussion of the potential for incidental findings with both the affected individual and their parents. Variant interpretation includes all variant types and considers different monogenic inheritance patterns, including de novo variants, X-linked inheritance, and autosomal recessive inheritance. The analysis also incorporates genome-wide structural variant detection using WGS-CMA, provided that the analysis had not been previously performed. Fetal samples WGS was offered prenatally in cases of suspected fetal malformations ( 34 , 35 ), either as trio analysis (n = 148) or as singleton analysis in specific scenarios such as non-immune hydrops fetalis (n = 67) or suspected skeletal dysplasia (n = 57). The clinical workflow is carried out in close collaboration with ultrasound and fetal medicine specialists. Referral forms must clearly specify the malformations detected and indicate whether a termination is planned or if the parents are awaiting genetic results to support decision-making. For non-immune hydrops fetalis, a targeted gene panel of 343 genes is analyzed (Additional File 2: Table S8). In fetuses with malformations, the analysis includes all morbid OMIM genes, all variant types, and genome-wide structural variant detection using WGS-CMA. Only pathogenic and likely pathogenic variants (ACMG class 4 and 5) expected to cause the condition are reported. Integrated WGS workflow at the Centre for Inherited Metabolic Diseases (CMMS) The CMMS has adopted a multidisciplinary, patient-centered organizational structure combining clinical and laboratory medicine. In this unit, experienced specialists in pediatric and adult neurology, metabolic medicine, endocrinology, clinical genetics and clinical chemistry work closely together with experts in molecular genetics, analytical chemistry and bioinformatics. In all, this enables a targeted analysis of relevant genes/gene panels, integration of genome data with biochemical and clinical investigations, functional validation of unclear genetic variants and rapid translation into individualized treatment. During the study period, 3,571 individuals underwent WGS analysis at CMMS. Indications could be broadly divided into five clinical groups where the two most common indications were suspected inborn errors of metabolism (1,859 cases; 52%) and epilepsy (774 cases; 22%). All investigations are jointly reviewed by both a clinical laboratory geneticist and an experienced senior consultant with expertise in a relevant clinical specialty. The genomic analysis includes evaluation of small-scale variants (SNVs, INDELs, SVs) as well as large-scale SVs. At CMMS, all cases are presented at multidisciplinary rounds where variants of potential interest are selected for in-depth discussion. Inborn errors of metabolism including mitochondrial disorders (IEM panel) The IEM panel is used for individuals with suspected inborn errors of metabolism (IEM), a diverse group of disorders affecting various parts of intermediary metabolism including dysfunction in the cellular organelles such as mitochondria, lysosomes and peroxisomes. Biochemical analysis of plasma and/or urine can often detect the accumulation of molecules from intermediary metabolism and is therefore used in the diagnostic workup. Individuals with IEMs often have symptoms already in the neonatal period or later in childhood. However, patients with milder forms of these disorders may present in adulthood. Until the end of 2023, 1 091 children and 768 adults were analyzed. Samples are usually analyzed as singletons. The IEM panel includes 1 099 genes (Additional File 2: Table S9), with most conditions inherited in an autosomal recessive manner. Genetic findings are interpreted in combination with biochemical data, which often helps confirm or exclude the relevance of uncertain variants. Some of the mitochondrial disorders are caused by variants in the mitochondrial DNA (mtDNA), and if such a disorder is clinically or biochemically suspected mtDNA is analyzed. The pool of mtDNA consists of inherited polymorphisms and, in some cases, disease-causing variants present in varying ratios across different tissues, a phenomenon referred to as heteroplasmy. In suspected mitochondrial diseases, a muscle biopsy is performed for biochemical evaluation of the respiratory chain. As heteroplasmy levels are typically lower in blood-derived DNA, muscle-derived DNA is used for WGS in these cases. Variants classified as pathogenic or likely pathogenic (ACMG class 4 and 5) are reported. Class 3 variants (VUS) may also be reported when supported by relevant biochemical evidence or a strong clinical phenotype. Diagnostic validation in newborn screening for inherited metabolic diseases (NBS-M panel) The national newborn screening, primarily based on tandem mass spectrometry (MS/MS) analysis of metabolites extracted from dried filter papers or enzyme assay, is centralized to CMMS for all infants born in Sweden (about 100 000 children/year). CMMS is also responsible for the biochemical and genetic confirmation of the metabolic diagnoses for over half of the newborns. If the biochemical diagnostic analysis confirms the screening result, a genetic diagnosis can be established in nearly 100% of cases. If a single gene is responsible for the disorder Sanger-based sequencing is used for time and cost efficacy. This applies to phenylketonuria (PKU), medium-chain acyl-coenzyme A dehydrogenase deficiency (MCADD), or very long-chain acyl-coenzyme A dehydrogenase deficiency (VLCADD). For other disorders where multiple potential causative genes exist, a singleton WGS-based strategy is used. Examples of such disorders include maple syrup urine disease (MSUD) or multiple acyl-coenzyme A dehydrogenase deficiency (MADD). In cases where follow-up biochemical analysis is essentially normal, NBS-M panel analysis may be performed to exclude the suspected disease with greater certainty, thereby eliminating the need for further clinical follow-up. The panel contains 51 genes (Additional File 2: Table S10). Since the NBS-M panel started in 2021 the total number of individuals analyzed is 23. Monogenic diabetes (DIAB panel) The DIAB panel is used for individuals with suspected monogenic diabetes mellitus, a group of conditions that account for approximately 2–5% of diabetes cases diagnosed before the age of 35. The DIAB panel is typically used in the diagnostic work-up of patients whose clinical presentation is not typical of classical type 1 or type 2 diabetes, typically relatively young, non-obese individuals without pancreatic islet antibodies, often with first-degree relatives exhibiting a similar phenotype. The panel includes 54 genes (Additional File 2: Table S11) and also assesses mtDNA, with a particular focus on the m.3243A > G variant associated with maternally inherited diabetes and deafness. A total of 154 individuals have been analyzed of which 29% are pediatric. All cases are discussed in a multidisciplinary conference, to which diabetologists from all hospitals in Stockholm are invited. Based on the specific gene identified, a tailored treatment regimen can often be implemented. The majority of monogenic diabetes conditions follow an autosomal dominant inheritance pattern. When a pathogenic variant is identified, genetic testing is recommended for affected relatives, and a gene-specific treatment strategy can often be implemented. Acute liver failure and cholestasis in children (PEDHEP panel) The PEDHEP panel is used primarily in the evaluation of children with various types of liver disease, ranging from acute liver failure to cholestatic diseases, suspected bile acid synthesis disorders, and ductal plate malformations. The panel includes 172 genes (Additional File 2: Table S12), and the samples are analyzed as singletons. Many inborn errors of metabolism (e.g., glycogen storage disorders, tyrosinemia, and congenital disorders of glycosylation) present with a pronounced hepatic phenotype. Depending on the clinical history and biochemical work-up results, the IEM panel may be added to the genetic evaluation. Up to 2024, 233 individuals were analyzed, of whom 82% are pediatric. All cases are reviewed in a multidisciplinary conference including experts in pediatric hepatology. Epilepsy (EP panel) The EP panel is used in the evaluation of individuals with suspected genetic epilepsy, particularly those with treatment-refractory epilepsy. It is used in both adult (27%) and pediatric (73%) individuals, but the diagnostic yield is higher in pediatric cases ( 36 ). This is likely due to the more polygenic nature of adult epilepsy and the lower rate of testing in that population. The EP panel includes 565 genes (Additional File 2: Table S13), and for young children it is often combined with IEM, as the clinical picture may be less clear in infants. The analysis is preferably performed as a trio due to the high prevalence of de novo variants. Variant interpretation integrates detailed phenotypic characterization with paraclinical data such as electroencephalogram (EEG) and brain magnetic resonance imaging (MRI). A deep understanding of epileptology is critical for accurate interpretation. Extra precaution is warranted, as many epilepsy-related genes display reduced penetrance. Additionally, VUS are often reported, since they may have clinical implications in the choice of anti-seizure medication. Likewise, a clinical response to a specific treatment may strengthen a genetic finding. Integrated WGS workflow at Clinical Immunology and Transfusion Medicine (KITM) The KITM unit applies a multidisciplinary, patient-centered approach that integrates clinical and laboratory expertise. Referrals are assessed by medical doctors or certified clinical laboratory geneticists who determine the most appropriate gene panel based on the information in the referral. When necessary, general electronic health records are reviewed and referring physicians are contacted for additional clinical information. Variant interpretation is performed in close collaboration between clinical immunologists and clinical laboratory geneticists, and is supported by immunological phenotyping, functional assays and other relevant laboratory data. All cases are discussed at weekly multidisciplinary rounds before results are reported back to the clinicians. Additional laboratory investigations to functionally assess variants are conducted either within the Clinical Immunology unit or in collaboration with specialized research laboratories. National multidisciplinary patient conferences are held regularly together with immunodeficiency and hematology specialists to discuss the genetic findings, guide further diagnostics testing and inform patient treatment. Three gene panels are used in this setting: Primary Immunodeficiency (PID), Autoinflammation (AID; introduced in 2023) and Inherited Bone Marrow Failure Syndromes (IBFMS) and rare hematologic conditions. To date, 799 individuals have been analyzed, the majority of which (621 cases; 78%) were assessed using the PID panel. Primary Immunodeficiency (PID panel) The PID panel is used in the evaluation of individuals with a wide spectrum of suspected immunological disorders, ranging from those with specific, well described immunodeficiencies such as severe combined immunodeficiency (SCID) to less well-defined diagnosis, such as immune dysregulation or recurrent infections. The panel is usually performed as a singleton and currently includes 482 genes (Additional File 2: Table S14). Individuals tested with this panel range from infants and children with severe early-onset phenotypes to children and adults with milder, late-onset presentations. Of the individuals analyzed, 64% were children (< 18 years) and 3% were referred through the national newborn SCID screening program, which has been active since August 2019. Autoinflammation (AID panel) The AID panel, introduced in 2023 as a smaller version of the PID panel, focusing on 73 genes (Additional File 2: Table S15) associated with the innate immune system disorders that lead to autoinflammation. This includes genes such as MEFV , for familial mediterranean fever. The narrower scope of the AID panel allows for more time-efficient analysis and reduces the risk of incidental findings. It is particularly suited for individuals presenting with isolated autoinflammatory symptoms without additional signs of immunodeficiency. Inherited Bone Marrow Failure (IBMFS) and rare hematologic condition panel The panel is used for individuals with suspected bone marrow failure or rare hematologic conditions resulting in peripheral blood cytopenias, including but not limited to Fanconi anemia, telomere biology disorders, congenital neutropenia, Diamond Blackfan anemia and macrothrombocytopenia. The panel is usually performed as a singleton and currently includes 236 genes (Additional File 2: Table S16). Most individuals tested are children (82%), and commonly the analysis is conducted in parallel to chromosomal breakage and telomere length analysis. RESULTS Turnaround time The median turnaround time (TAT) for sequence generation and bioinformatic analysis has steadily decreased over the years and in 2023 it was 11 days for regular priority samples and 9 days for priority samples (Table 1 ). Express samples are rarely handled but the current TAT is about 4 days. Table 1 Average Sequencing Turnaround Times 2017 2018 2019 2020 2021 2022 2023 Standard 14.4 13.3 14.9 15.0 12.6 12.4 11.1 Priority 10.6 10.6 13.9 12.5 10.1 11.6 9.4 Overall diagnostic findings The overall diagnostic yield was 22.6%, resulting in a genetic diagnosis for 3,538 individuals. In total, 4,460 variants were reported across 1,570 unique genes, including 130 short tandem repeat (STR) expansions in 15 different genes (Additional File 2: Table S17). Regarding structural variants: 105 SVs were identified using the analyzed gene panels and 57 additional SVs were detected through genome-wide analysis using WGS-CMA (Additional File 2: Table S18). An overview of results across 20 different gene panels is shown in Table 2 . Table 2 Overview of findings from clinical genome sequencing. Team Panels Cases Male Pediatric Pathogenic variant VUS Clinical Genetics ID 1495 63% 90% 22% 15% NMD 1428 55% 33% 24% 15% IC 1350 46% 37% 13% 3% CTD 993 45% 19% 9% 11% NeuroDeg 609 48% 0% 12% 5% SKD 520 44% 59% 43% 11% ICC 458 59% 0% 24% 6% General genetics HPO 3295 50% 53% 25% 10% Trio analysis 1126 50% 76% 24% 12% CMMS IEM 1859 54% 59% 26% 0% EP 774 56% 73% 26% 1% NBS-M 23 48% 96% 65% 0% DIAB 154 47% 29% 23% 2% PEDHEP 233 53% 82% 25% 1% mtDNA 390 53% 50% 7% 0% CMMS HPO 138 46% 59% 26% 4% KITM PID 621 52% 59% 30%* 0% IBMFS 144 51% 83% 29%* 0% AID 18 56% 50% 11%* 0% PID + IBMFS 16 53% 47% 19%* 0% VUS, Variant of Unknown Significance; CMMS, Centre for Inherited Metabolic Diseases; KITM, Clinical Immunology and Transfusion Medicine; ID, Intellectual Disability; NMD, Neuromuscular Disorder; IC, Inherited Cancer; CTD, Connective Tissue Disease; Neurodeg, Neurodegenerative Disorder; SKD, Skeletal Dysplasia; ICC, Inherited Cardiac Condition; HPO, Human Phenotype Ontology; IEM, Inherited Metabolic Disorders; EP, Epilepsy; NBS-M, Newborn Screening Metabolic; DIAB, Monogenic Diabetes, PEDHEP, Pediatric Liver Disease; Mtdna, Mitochondrial DNA; PID, Primary Immunodeficiency; AID, Autoinflammation; IBFMS, Inherited Bone Marrow Failure Syndrome; *Pathogenic and VUS The majority (54%) of the diagnosed individuals had a pathogenic variant in a gene that was responsible for disease in only 1–3 individuals (21% (750 genes), 18% (316 genes) and 15% (180 genes) detected in one, two and three individuals respectively). A total of 31% (n = 1,097) of cases involved more than 10 individuals sharing the same genetic diagnosis, affecting 57 different genes. The most common findings were SNVs/INDELs in NF1 (n = 59), PTPN11 (n = 40), RYR1 (n = 39), and TTN (n = 39). Among STRs, C9orf72 expansion causing FTD/ALS (n = 39) and among SVs, the 22q11 recurrent deletion (n = 8) were the most common. Some of the disorders were identified by multiple teams, such as pathogenic variants in CFTR , G6PD , NOTCH1 , PTPN11 and SBDS reported by all three clinics. In addition, 225 genes were reported by two clinical teams (3 genes by CMMS and KITM, 45 genes by KITM and Clinical Genetics, and 177 genes by CMMS and Clinical Genetics). For the most commonly used panels, the ten most frequently reported genes are listed in Table 3 , while Fig. 3 illustrates the age distribution, diagnostic yield, and the increase in the number of analyzed cases over the years. As expected, the diagnostic yield is generally higher for panels with a large proportion of pediatric cases. Table 3 The top ten most commonly identified genes for ten selected panels. The gene name is followed by the number of patients in which variant(s) in this gene were identified as causative. IF NMD NeuroDeg IEM EP CTD SKD ICC PID Trio ANKRD11 11 RFC1 (STR) 17 C9orf72 (STR) 38 PMM2 8 SCN1A 18 FBN1 32 COL1A1 23 TTN 26 TNFRSF13B 16 ARID1B 6 MECP2 9 DMD 16 SOD1 7 OPA1 7 KCNQ2 13 COL5A1 15 FGFR3 18 MYH7 22 MEFV 10 MECP2 5 NF1 7 RYR1 11 SORL1 5 PHKA2 7 STXBP1 12 COL3A1 13 COL2A1 18 MYBPC3 16 CYBB 6 NSD1 4 POGZ 7 PMP22 11 TBK1 4 ATP7B 6 PRRT2 11 MYH11 10 COL1A2 11 KCNQ1 13 NFKB1 5 FRAS1 4 DDX3X 6 TTN 11 GRN 4 ETFDH 6 CACNA1A 7 TGFBR2 7 DYNC2H1 9 TNNI3 6 STAT3 5 HUWE1 4 DNMT3A 6 COL6A3 9 PSEN1 4 PDHA1 6 SLC2A1 7 TGFB3 6 EXT1 8 DSG2 5 BTK 4 NUS1 3 KMT2A 6 CAPN3 8 VCP 3 TPO 6 MECP2 6 COL1A1 6 GNAS 7 FLNC 5 ADA2 4 L1CAM 3 TCF4 6 SH3TC2 6 CHMP2B 2 ABCC8 5 SCN8A 6 COL2A1 5 COMP 6 PKP2 5 FAS 4 SETD5 3 ARID1B 5 MPZ 6 TARDBP 2 GALC 5 CDKL5 5 ACTA2 5 EXT2 5 TNNT2 4 ADA 4 BRAT1 3 FMR1 (STR) 5 COL6A1 5 ATXN8OS (STR) 2 GLDC 5 PCDH19 5 COL5A2 5 RPL13 5 SCN5A 4 FOXN1 4 MYH3 3 MED13L 5 MAPT 2 ALPK3 4 PTPN11 5 CHCHD10 2 SHANK3 5 ID, Intellectual Disability; NMD, Neuromuscular Disorder; Neurodeg, Neurodegenerative Disorder; IEM, Inherited Metabolic Disorders; EP, Epilepsy; CTD, Connective Tissue Disease; SKD, Skeletal Dysplasia; ICC, Inherited Cardiac Condition; PID, Primary Immunodeficiency High Sensitivity of Variant Ranking for Known Diagnoses The benchmarking demonstrated that the vast majority of previously reported pathogenic variants were indeed ranked highly in our system. Out of 3,042 variants, 1,063 (35%) were ranked as the top candidate (rank 1), with a median rank position of 2 across the entire cohort of 3,042 variants. The mean rank was 5.3 ± 0.3 (95% CI, SD 8.3). Only 20 variants (0.7%) ranked below position 50 (the approximate cutoff for routine manual assessment). The maximum observed rank was 129 (Additional file 1: Figure S3). DISCUSSION Our findings show that genome sequencing is a valuable clinical test across an expanding range of disorders and age groups. By building a format with pre- and post-test procedures customized to different clinical scenarios, we ensure that the right patients are tested for the established genes associated with their phenotype, and that test results are truly helpful in providing individualized care to the individual patient. Obvious challenges include continuously refining the inclusion criteria and updating gene panels to accommodate the ever-growing body of genetic knowledge. As new disease groups, such as eye and hearing disorders or blood diseases (anemia, coagulation defects and erythrocyte membrane defects), start to use genomic analysis as a baseline test, extensive training and education efforts are needed. Furthermore, technological advancements like long-read sequencing ( 37 ) and RNA sequencing have shown great potential to further enhance diagnostic capability ( 38 , 39 ). Integrating these emerging technologies into clinical workflows will help refine variant interpretation and expand the range of detectable genetic alterations, which in turn will increase the diagnostic outcome. In addition to technological advances, robust variant prioritization pipelines have been critical for enabling efficient WGS based diagnostics. In our evaluation, the majority of known causative variants ranked among the top candidates, allowing them to be rapidly identified during clinical interpretation. This high-ranking performance streamlines manual review, supports diagnostic consistency, and enhances patient safety. However, variants that received low prioritization scores frequently lacked a second allele required for compound scoring or were incorrectly penalized due to erroneous assumptions about inheritance patterns, particularly in known dominant conditions. These issues reflect known limitations of the Genmod ( 11 ) scoring model, which is currently undergoing revision. Ongoing improvements include refinement of compound scoring and integration of Bayesian models and machine learning to further optimize accuracy. Alongside accuracy, TAT is increasingly important, particularly for acutely ill patients. The median TAT for sequencing and bioinformatic analysis has steadily decreased, reaching 11 days for routine samples and 9 days for priority cases in 2023. To offer more robust and shorter TATs, sequencing instruments are now run more frequently, aiming for 4–5 times per week in 2025, allowing for greater flexibility and faster processing. There is growing demand for genome sequencing in medically urgent scenarios, and we are developing differentiated clinical tracks targeting TATs of 2–3 days (ultra-urgent), 7–10 days (priority), and 14 days (routine) by 2025, calculated from reception of sample to clinical report being issued. Of note, the total time from patient sampling to final clinical report includes not only sequencing, but also sample transport, DNA extraction and QC, bioinformatic processing, variant interpretation, and multidisciplinary review. Achieving shorter TATs across these tracks requires ongoing overall optimization of logistics, lab workflows, automation, data processing, and clinical interpretation pipelines. Implementation of genome sequencing into the management of rare diseases represents a first step in the ongoing transformation towards precision medicine. We have adopted a strategy for this gradual, long-term transition that contains two main axes (Fig. 4 ) In parallel to the general genetics service transitioning from traditional tests to genome-based analysis, targeted, multidisciplinary patient flows are being developed for different disease groups using the IEM concept as a model. This is particularly important for disease groups where specialized functional investigations are essential for diagnostics and rapid treatment is critical for patient outcome. The patient flow for primary immunodeficiencies is being organized according to the principles developed for IEM, including targeted diagnostics after newborn screening for SCID and development towards a national coordination. An important challenge for the future is to establish a digital infrastructure that enables integration of data from genome analyses with other laboratory, clinical and imaging investigations to facilitate multimodal diagnostics. In parallel to the targeted workflows and equally important, the Department of Clinical Genetics and Genomics plays a pivotal role in cultivating clinical genetic expertise, serving as a hub for many teams and providing guidance in prenatal diagnostics, family investigations, and interpretation of complex genomic rearrangements. This department trains clinical geneticists and clinical laboratory geneticists who, in addition to working in the genetic service laboratory, support and/or join established and newly formed integrated teams to ensure that their expertise enhances the precision and quality of genome sequencing services. Furthermore, it guarantees that new bioinformatics pipeline modules are gradually made accessible to these integrated teams, so they can leverage the latest advancements in variant interpretation and genomic analysis. This ensures that the multidisciplinary teams remain well-informed and continue delivering high-quality, personalized care. CONCLUSIONS In conclusion, we have adopted a strategy for moving from genomics into precision medicine, first by implementing genome sequencing into the clinical laboratories through GMCK-RD and followed by further implementation into healthcare through integrated multidisciplinary teams. This model is gradually consolidated and expanded as part of a long-term systems shift in rare disease diagnostics and management at Karolinska, with the aim to play a strong part in the emerging national precision medicine landscape in Sweden. Abbreviations ACMG, American College of Medical Genetics; AID, Autoinflammation; CMA, Clinical Microarray Analysis; CMMS, Centre for Inherited Metabolic Diseases; CNV, Copy Number Variant; CTD, Connective Tissue Disease, DIAB, Monogenic Diabetes; EEG, Electroencephalogram; EP, Epilepsy; GMCK-RD, The Genomic Medicine Center Karolinska Rare Diseases; HDCT, Heritable Connective Tissue Disorders; IBFMS, Inherited Bone Marrow Failure Syndrome; IC, Inherited cancer; ICC, Inherited Cardiac Conditions; ID, Intellectual Disability; IEM, Inborn Errors of Metabolism; INDEL, Insertion and Deletion; KITM, Clinical Immunology and Transfusion Medicine; MADD, Multiple acyl-coenzyme A dehydrogenase deficiency; MCADD, Medium-chain acyl-coenzyme A dehydrogenase deficiency; MIT, Mitochondria; mtDNA, MRI, Magnetic Resonance Imaging; MS/MS, Tandem Mass Spectrometry; Mitochondrial DNA; MSUD, Maple syrup urine disease; NBS-M, Newborn Screening Metabolic; NDD, Neurodevelopmental Disorders; NeuroDeg, Neurodegenerative Disorders; NHV, National Highly Specialized Care; NMD, Neuromuscular Disorders; PEDHEP, Pediatric Liver Disease; PID, Primary Immunodeficiency; PKU, Phenylketonuria; SCID, Severe Combined Immunodeficiency; SKD, Skeletal Dysplasia Disorder; SNV, Single Nucleotide Variant; STR, Short Tandem Repeats; SV, Structural Variant; TAT, Turnaround time; VUS, Variant of Uncertain Clinical Significance; VLCADD, Very long-chain acyl-coenzyme A dehydrogenase deficiency; WES, Whole Exome Sequencing; WGS, Whole Genome Sequencing Declarations Acknowledgements We are very grateful to the participating families. The authors would like to acknowledge the expertise and support with NGS services provided by Clinical Genomics Stockholm facility at the Science for Life Laboratory (jointly hosted by Department of Microbiology, Tumor and Cell biology at Karolinska Institutet and Department of Gene Technology at School of Engineering Sciences in Chemistry, Biotechnology and Health at KTH Royal Institute of Technology) and the Genomic Medicine Center Karolinska at the Karolinska University Hospital. Mattias Karlen helped with the design of illustrations. Funding AL was supported by grants from the Swedish Research Council (2019-02078), Region Stockholm (FoUI-1000468 and FoUI-978581), the Rare Diseases Research Foundation (Sällsyntafonden), the Swedish Brain Foundation (FO2024-0128-HK-44) and the Swedish Cancer Society (24 3504 Pj). AW was supported by the Swedish Research Council (2023-02388), Karolinska Institutet, Region Stockholm (FoUI-955096), Knut & Alice Wallenberg Foundation (Wallenberg Clinical Scholar, KAW2020.0228) and ET by Region Stockholm (FoUI-973659). Availability of data and materials The ethical approval did not permit sharing of WGS data, and the in-house databases used in this article are not publicly available. The nf-core pipeline is open source and available at (https://github.com/nf-core/raredisease). All other softwares used, have been previously published and are cited in the text. Authors’ contributions AL, VW, and AW conceptualized the study and wrote the first draft. KLR, ASM, NL, SV, and DN analyzed and interpreted the data. KLR, MK, SY, GG, MO, MW, SV, PM, MHP, HT, ET, MM, AJ, and DN contributed specific text sections. AJ, CR, DN, JE, RJ, KNy, LPP, PP, AR, RN, and KS developed bioinformatics software and pipelines. KLR, ET, MHP, KDG, BT, AN, BMA, MK, SY, SV, MO, GG, NL, HB, BT and TS curated gene panels. AL, ME, AJ, DN, and AW prepared the figures. All other authors participated in data generation, clinical evaluation, variant interpretation, and manuscript review. All authors read and approved the final manuscript. Ethics approval and consent to participate. Ethics approval was given by the Regional Ethical Review Board in Stockholm, Sweden (ethics permit numbers 2008-351-31, 2012/2106-31/4, 2012/222-31/3, 2014/995-32, 2015/416-31 and 2024-05341-01). These ethics permits allow for use of clinical samples for analysis of scientific importance as part of clinical development, and for lifting clinical filters to interrogate the whole genome in selected cases. Our IRB approval does not require us to get written consent for clinical testing. The research conformed to the principles of the Helsinki Declaration. 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Supplementary Files AdditionalFile1.docx Additional File 1: Figure S1. Steps performed in the nf-core rare disease pipeline; Figure S2. Variant scoring and prioritization with Genmod; Figure S3: Rank score performance over time AdditionalFile2.xlsx Additional file 2: Table S1-16: Panel gene lists; Table S17: Reported genes with small variants; Table S18 Reported structural variants. 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Hospital","correspondingAuthor":false,"prefix":"","firstName":"Anna","middleName":"","lastName":"Wredenberg","suffix":""},{"id":470455537,"identity":"8e16817d-b4bb-410b-a30a-d305c25c5c3e","order_by":80,"name":"Anna Zetterlund","email":"","orcid":"","institution":"Karolinska Institutet","correspondingAuthor":false,"prefix":"","firstName":"Anna","middleName":"","lastName":"Zetterlund","suffix":""},{"id":470455538,"identity":"9e1c0912-ae7d-4444-8cfa-d40abf939421","order_by":81,"name":"Rolf Zetterström","email":"","orcid":"","institution":"Karolinska University Hospital","correspondingAuthor":false,"prefix":"","firstName":"Rolf","middleName":"","lastName":"Zetterström","suffix":""},{"id":470455539,"identity":"6a7defe0-f5ba-4ea3-b978-c0a886b03f0e","order_by":82,"name":"Ingegerd Öfverholm","email":"","orcid":"","institution":"Karolinska University Hospital","correspondingAuthor":false,"prefix":"","firstName":"Ingegerd","middleName":"","lastName":"Öfverholm","suffix":""},{"id":470455540,"identity":"c9a762ae-56e8-4cd9-963b-c5e4f12a4e05","order_by":83,"name":"Ann Nordgren","email":"","orcid":"","institution":"Karolinska University Hospital","correspondingAuthor":false,"prefix":"","firstName":"Ann","middleName":"","lastName":"Nordgren","suffix":""},{"id":470455541,"identity":"45a75cf3-78a2-4431-912c-e1e020dc4d43","order_by":84,"name":"Henrik Stranneheim","email":"","orcid":"","institution":"Karolinska University Hospital","correspondingAuthor":false,"prefix":"","firstName":"Henrik","middleName":"","lastName":"Stranneheim","suffix":""},{"id":470455542,"identity":"eb0b5830-f473-4f84-b626-aff3d4674f38","order_by":85,"name":"Valtteri Wirta","email":"","orcid":"","institution":"Karolinska University Hospital","correspondingAuthor":false,"prefix":"","firstName":"Valtteri","middleName":"","lastName":"Wirta","suffix":""},{"id":470455543,"identity":"074277c5-8391-4cf2-b4d7-1903c61943ca","order_by":86,"name":"Anna Wedell","email":"","orcid":"","institution":"Karolinska University Hospital","correspondingAuthor":false,"prefix":"","firstName":"Anna","middleName":"","lastName":"Wedell","suffix":""}],"badges":[],"createdAt":"2025-05-31 09:53:21","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6790162/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6790162/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":84780719,"identity":"2e10e37b-8a9a-46ad-9b93-b4038c573299","added_by":"auto","created_at":"2025-06-17 09:27:21","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":115274,"visible":true,"origin":"","legend":"\u003cp\u003eA) Schematic description of the bioinformatic WGS analysis pipeline for rare diseases at GMCK-RD. Different callers and annotations steps are shown as black circles. B) Timeline over when new variant types were added to the pipeline. SNV/INDEL= single nucleotide variants / insertions and deletions, SV, Structural Variants; STR, Short Tandem Repeats; MEI, Mobile Element Insertion; MT, Mitochondrial Variants; SMA, Spinal Muscular Atrophy (\u003cem\u003eSMN1\u003c/em\u003e copy number analysis).\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-6790162/v1/92c9f6836b2c70d12c9a34c8.png"},{"id":84781335,"identity":"775cb3ff-584e-417a-966e-caa5760030c8","added_by":"auto","created_at":"2025-06-17 09:35:21","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":232992,"visible":true,"origin":"","legend":"\u003cp\u003eA) Schematic of the different variant annotation steps that start with genomic features, inheritance model, scoring based on multiple parameters and finally scoring of compound pairs. To the right the specific scored paraments that are shown with the example variant reaching a rank score of 29. B) Top-ranked variants are further assessed and scored according to the ACMG criteria with Class 4 and 5 reported as pathogenic. Green = Class 1 - Benign, Yellow = Class 3 – Uncertain significance, Red = Class 5 – Pathogenic.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-6790162/v1/d587dbf0008731bff6096187.png"},{"id":84780721,"identity":"b712ea34-e5e5-4cac-bb85-691db9001f67","added_by":"auto","created_at":"2025-06-17 09:27:21","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":411101,"visible":true,"origin":"","legend":"\u003cp\u003eA. Results from twelve selected gene panels including number of individuals analyzed per year, age distribution and yield (red indicates pathogenic variants, yellow prioritized variants of uncertain significance and grey unsolved cases).\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-6790162/v1/39af5b73311a971e6c090ffe.png"},{"id":84778720,"identity":"caeae9fa-0065-4ddd-9206-8400a8383744","added_by":"auto","created_at":"2025-06-17 09:19:21","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":491685,"visible":true,"origin":"","legend":"\u003cp\u003eSchematic strategy for step-by-step clinical implementation of genome sequencing across multiple rare disease groups. The strategy includes both integrated patient-centered workflows and the general genetic investigation service at Clinical genetics and genomics. When fully developed, the integrated workflow concept comprises several medical specialities and organizes targeted genomics in combination with e.g., specialized functional investigations, and actively promotes national coordination. The concept is established for inborn errors of metabolism and followed by the primary immunodeficiency team, as indicated by the horizontal lines. Additional rare disease workflows, where supporting investigations are available but not organized together, are in various stages of development. National highly specialized care (NHV) in Sweden refers to the centralization of complex, rare, or resource-intensive healthcare services to a few expert hospitals to ensure high-quality and efficient care. IEM = inherited metabolic disorders, ID = intellectual disability, SKD = skeletal dysplasia, IC = inherited cancer, PID = primary immunodeficiency, EP = epilepsy, NMD = neuromuscular disorder\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-6790162/v1/318703b44f906ec373064428.png"},{"id":84782302,"identity":"9beb4c23-5d7b-47c0-b228-e711f95dcfc1","added_by":"auto","created_at":"2025-06-17 09:43:23","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3224474,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6790162/v1/b696afc3-2733-414f-bb26-99f51bec2401.pdf"},{"id":84778716,"identity":"a3ecdad7-7bac-4a70-ad30-bcf5e04fd828","added_by":"auto","created_at":"2025-06-17 09:19:21","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":465191,"visible":true,"origin":"","legend":"\u003cp\u003eAdditional File 1: Figure S1. Steps performed in the nf-core rare disease pipeline; Figure S2. Variant scoring and prioritization with Genmod; Figure S3: Rank score performance over time\u003c/p\u003e","description":"","filename":"AdditionalFile1.docx","url":"https://assets-eu.researchsquare.com/files/rs-6790162/v1/9b63f353e0264d4233a6f54b.docx"},{"id":84780723,"identity":"a619a38f-b0ee-437d-975a-cff619706fd0","added_by":"auto","created_at":"2025-06-17 09:27:21","extension":"xlsx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":144763,"visible":true,"origin":"","legend":"\u003cp\u003eAdditional file 2: Table S1-16: Panel gene lists; Table S17: Reported genes with small variants; Table S18 Reported structural variants.\u003c/p\u003e","description":"","filename":"AdditionalFile2.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-6790162/v1/54ea845f284969c0985fd2dd.xlsx"}],"financialInterests":"Competing interest reported. AL has received speakers’ honoraria from Pacific Biosciences and Illumina. VW has received speaker’s honoraria from Illumina. \nAll other authors declare no competing interests.","formattedTitle":"The Genomic Medicine Center Karolinska 10-year report on genome sequencing for rare diseases and a strategy for stepwise clinical implementation","fulltext":[{"header":"BACKGROUND","content":"\u003cp\u003eHealthcare is currently undergoing a transition towards precision medicine and a crucial step in enabling personalized therapies and interventions is a well-functioning workflow for precision diagnostics. This is especially relevant in the field of rare diseases where genomics analysis has revolutionized diagnostics, resulting in higher diagnostic yields with shorter turn-around times.\u003c/p\u003e \u003cp\u003eThe two main genomic approaches used in genetic testing for rare diseases are whole exome sequencing (WES) and whole genome sequencing (WGS). Although both methods yield similar diagnostic rates of approximately 20\u0026ndash;50% (reviewed in (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e)) and perform similarly in detecting coding single nucleotide variants (SNVs) and large copy number variants (CNVs), WGS provides additional benefits (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e). In particular, WGS enables the detection of non-coding variation, a broader spectrum of structural variants (SVs), short tandem repeats (STRs), and, with specialized software, some paralogous regions (\u003cspan additionalcitationids=\"CR5\" citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eMany initiatives from across the globe are prioritizing rare diseases as one of the focus areas in their genomic medicine programs. Today, thousands of rare disease patients are assessed and analyzed by WES in a clinical setting each week. However, as more centers transition to WGS, there is considerable variability in the pipelines and workflows used, leading to differences between centers in the types of variants detected, how they are prioritized, and which findings are reported.\u003c/p\u003e \u003cp\u003eThis article delves into the journey of Genomic Medicine Center Karolinska Rare Diseases (GMCK-RD) in the Stockholm healthcare region, highlighting its collaborative approach, methodologies, and the transformative impact on diagnosing and managing patients with rare diseases. We outline the key steps necessary for a successful integration of WGS into rare disease diagnostics \u0026ndash; ranging from sequence generation, variant calling and prioritization to clinical interpretation and reporting, while also addressing other critical factors like data structuring, storage and reanalysis. Furthermore, we discuss the criteria for selecting patients for genome sequencing, providing specific guidelines for different disease groups. Lastly, we present a strategy for the transition from genomics into precision medicine, a long-term systems shift that necessitates deep clinical integration across multiple specialties, challenging the current organization of healthcare.\u003c/p\u003e"},{"header":"METHODS","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eThe Genomic Medicine Center Karolinska \u0026ndash; Rare Diseases\u003c/h2\u003e \u003cp\u003eGMCK-RD was initiated formally in 2017, as a node in the national Genomic Medicine Sweden initiative (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e), building on a collaboration that started years earlier. In brief, the initiative has four partners; the Clinical Genomics research infrastructure at SciLifeLab (Stockholm), which is responsible for sequencing, bioinformatics and data management, and three hospital-based clinical units at the Karolinska University Hospital (Clinical Genetics and Genomics (KGG), the Centre for Inherited Metabolic Diseases (CMMS), and Clinical Immunology and Transfusion Medicine (KITM)), which are responsible for setting inclusion criteria, interpreting results and clinical reporting. In addition to genomic diagnostics, the initiative provides genetic counselling, family testing, and referrals for clinical surveillance, treatment, and long-term follow-up when appropriate. GMCK-RD currently offers three distinct clinical genome pathways; two integrated precision medicine workflows, available through CMMS and KITM, and a general genetics service provided through KGG. A detailed description of the partnership is given in Stranneheim et al. (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e). Since then, the concept has been further developed by supporting multidisciplinary workflows in order to implement genomics all the way into acute clinical medicine.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eMaterial\u003c/h3\u003e\n\u003cp\u003eAll patients were referred for diagnostic WGS testing from January 1st 2015 until December 31st 2023. The referrals were sent primarily from various subspecialized clinics within the Stockholm healthcare region as well as, for some disease groups, from the whole of Sweden. Altogether 11,274 cases were analyzed through the general genetics (KGG) arm and 3,571 and 799 cases through CMMS and Clinical Immunology, respectively.\u003c/p\u003e\n\u003ch3\u003eGenome Sequencing\u003c/h3\u003e\n\u003cp\u003eThe overall workflow for clinical WGS has been custom built by Clinical Genomics and has been described in detail previously (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e). In brief, since 2015, samples have been sequenced using PCR-free whole-genome sequencing protocols on various generations of high-throughput short-read sequencing platforms, including HiSeq X (2015\u0026ndash;2018), NovaSeq 6000 (2018\u0026ndash;2023) and NovaSeq X Plus (2023-) to approximately 30x median coverage. Today library preparation and associated sequencing is carried out four times per week, followed by automated start of the bioinformatic analysis.\u003c/p\u003e\n\u003ch3\u003eBioinformatic analysis\u003c/h3\u003e\n\u003cp\u003eThe bioinformatic analysis includes calling of single nucleotide variants (SNVs), insertions and deletions (INDELs), short tandem repeats (STRs), uniparental disomies and structural variants (SVs) including deletions, duplications, inversions as well as insertions of mobile elements (MEI) (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e). Also, variants in the mitochondrial genome are analyzed. Finally, the number of \u003cem\u003eSMN1\u003c/em\u003e and \u003cem\u003eSMN2\u003c/em\u003e copies is estimated (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e). The bioinformatic pipelines have during the recent years been transitioned from the previously reported MIP pipeline to the current nextflow-based pipeline, which we have made publicly available on the nf-core pipeline repository (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://github.com/nf-core/raredisease\u003c/span\u003e\u003cspan address=\"https://github.com/nf-core/raredisease\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e)\u003c/span\u003e (Additional File 1: Figure \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). The current pipeline is summarized in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e\n\u003ch3\u003eData structures and storage infrastructure\u003c/h3\u003e\n\u003cp\u003eUpon completion of the bioinformatic analysis, a data structuring process is initiated consisting of storage of selected key files; this includes raw sequence data, alignment files, raw variant files (vcf) as well as annotated variant files. The latter provides a snapshot of the information that was available upon clinical interpretation of the variants. Results of the clinical interpretation carried out in Scout (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e), a custom-developed decision support system, is also captured in static reports, including dismissed variants, variant-level comments and variant classifications according to ACMG guidelines (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e). Metadata associated with the sequencing and bioinformatic analysis is structured using HL7 FHIR resource GenomicStudy (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://build.fhir.org/genomicstudy.html\u003c/span\u003e\u003cspan address=\"https://build.fhir.org/genomicstudy.html\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e)\u003c/span\u003e to facilitate interoperability and data exchange.\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e\u003cb\u003eComputational prioritization of called variants\u003c/b\u003e\u003c/h2\u003e \u003cp\u003eCalled variants are annotated and prioritized using an in-house developed pathogenicity scoring system Genmod (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e) (Additional File 1: Figure \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e). This system assigns a rank score to each variant based on multiple parameters, including functional impact, inheritance model, allele frequency, and presence of a second allele for compound heterozygosity (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA). Of note, variants classified as Likely Pathogenic or Pathogenic in ClinVar, with a gold star review status, are always retained regardless of frequency or consequence.\u003c/p\u003e \u003cp\u003eTo evaluate the performance of this prioritization approach, we retrospectively analyzed 3,042 previously reported pathogenic variants. For each case, the relevant version of the rank model was applied, followed by filtering using the appropriate gene panel and the Scout clinical filter. This filter retains variants with moderate to high predicted impact (based on VEP (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e)), located in exonic or splice-site regions, and below strict population allele frequency thresholds (gnomAD (\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e)\u0026thinsp;\u0026lt;\u0026thinsp;1% for SNVs and INDELs; \u0026lt;1% in the local count database for SVs).\u003c/p\u003e \u003cp\u003eRank model versions with minor updates, such as changes in label nomenclature or local database expansion, were grouped for consistency, and structural variants were evaluated in parallel using dedicated SV-specific rank groups.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eInterpretation of called variants\u003c/h3\u003e\n\u003cp\u003eFollowing bioinformatic analysis and variant ranking, the data from individual cases are interpreted at the clinical unit responsible for the patient case. To limit the number of variants that warrant manual assessment, the above-mentioned Scout clinical filter is applied. The ranked variants are then further filtered based on phenotype and inheritance (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e\n\u003ch3\u003ePhenotype-Based Filtering\u003c/h3\u003e\n\u003cp\u003eThe phenotype-based strategy relies on \u003cem\u003ein silico\u003c/em\u003e gene panels (referred to as panels) and restricts the clinical interpretation to variants in genes associated with the patient's phenotype and disease. GMCK-RD panels are regularly updated with new disease genes according to PanelApp (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e), OMIM (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://omim.org/\u003c/span\u003e\u003cspan address=\"https://omim.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), targeted PubMed searches, personal communication with experts (such as European Reference Networks and other national and international expert groups) as well as presentations at conferences. Personalized gene panels may also be created upon request for analysis of specific genes based on clinical indication or when no established panel is suitable. In such cases, patient-specific Human Phenotype Ontology (HPO) (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e) terms help build custom panels, or suitable PanelApp panels can be adapted.\u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eInheritance-Based Filtering\u003c/h2\u003e \u003cp\u003eThe inheritance-based strategy is used when family members have been included in the analysis and employs a genotype-driven approach, filtering for variants that are heterozygous, bi-allelic, or X-linked. In family-based analyses, it is possible to filter based on inheritance patterns, for example: compound heterozygous recessive, \u003cem\u003ede novo\u003c/em\u003e dominant, and multigenerational dominant. Of note, variant ranking is also influenced by inheritance (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eVariant Interpretation\u003c/h2\u003e \u003cp\u003eVariant interpretation is conducted in Scout (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e), a purpose-built decision support system. The platform presents each case with an overview of chromosome coverage using ideograms and separate tabs for querying SNVs/INDELs, structural variants, repeat expansions, and mobile element insertions. Custom filters can be applied to each category, and variants can be annotated, ACMG-classified, and pinned for follow-up. The system integrates data from publicly available population frequency databases (gnomAD (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e), SweFreq (16)), our in house frequency database (LocusDB (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e)), ClinVar (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e) and \u003cem\u003ein silico\u003c/em\u003e pathogenicity tools (SIFT (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e), Polyphen 2 (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e), CADD (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e), REVEL (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e)) and provides various other annotations (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Variants previously reported as pathogenic are flagged and known founder variants are also highlighted. When warranted, in addition to the Scout evaluation, a digital chromosome analysis is performed using vcf2cytosure (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e), which enables a genome-wide analysis that simulates chromosomal microarray analysis (CMA). This method allows data to be processed within the same system as clinical arrays, enabling detected variants to be annotated and compared to the in-house database from ~\u0026thinsp;10 000 cases previously analyzed by CMA.\u003c/p\u003e \u003cp\u003eThe case assessment starts with a specially trained clinical laboratory geneticist who reviews the data and flags variants of interest. Variants are evaluated based on scout rank scores and additional information from updated population databases (gnomAD (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e)) and ClinVar (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e). Of note, if a variant is reported as pathogenic in ClinVar, it is highlighted in the interpretation system.\u003c/p\u003e \u003cp\u003eDifferent allele frequency thresholds are applied to discard variants depending on the suspected inheritance pattern (dominant or recessive), the expected penetrance and age of onset. For dominant pediatric diseases with an early onset and high penetrance, variants are discarded if they are present more than five times in gnomAD v2. In contrast, the most common variant reported, a risk allele for inherited breast cancer in \u003cem\u003eCHEK2\u003c/em\u003e, is present in 1/240 individuals in gnomAD v2. In the case of recessive disorders, variants are discarded if more than five individuals are homozygous for the variant in gnomAD v2. This is likely too high and has been complemented over time with panel specific criteria based on age of onset and penetrance.\u003c/p\u003e \u003cp\u003eIn addition to canonical splice variants, we also investigate potential splice-altering variants within the intronic regions (+/- 20 bases). Deep intronic variants already reported in ClinVar are also considered, as discussed above. For gene-specific phenotypes involving a limited number of genes (1\u0026ndash;2 genes), we additionally assess all rare (gnomAD AF\u0026thinsp;\u0026gt;\u0026thinsp;0.001) intronic variants. Possible effects on splicing are further evaluated using bioinformatics tools such as splice prediction software in Scout (SPIDEX (\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e), SpliceAI (\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e)) as well as software incorporated in Alamut VisualPlus (Sophia Genetics). Follow up evaluation is done with RNA analysis using cDNA sequencing or whole transcriptome sequencing.\u003c/p\u003e \u003cp\u003eThis genetic assessment, based on a joint evaluation of all these factors results in a short list of variants that undergo medical evaluation. The medical doctor(s) evaluates the selected variants in concordance with the detailed patient symptoms and variants deemed as potentially disease causing are reported. At this step, the three clinical workflows in GMCK-RD diverge and the specific multidisciplinary teams have created tailored criteria for inclusion as well as recommendations for WGS data interpretation, ensuring that reported variants are directly relevant and beneficial for the needs of the patients and their family members. Importantly, data can be shared between teams, enabling broad as well as targeted analyses in patients with unclear, atypical and evolving clinical phenotypes.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eGeneral Genetics WGS workflow at Clinical Genetics and Genomics\u003c/h2\u003e \u003cp\u003eIn the general genetics workflow for clinical WGS at the Department of Clinical Genetics and Genomics, all referrals are reviewed by a medical doctor (either a clinical genetics specialist or a supervised resident). Based on the information provided in the referral, and when needed, additional information from the patient\u0026rsquo;s electronic health record, the physician determines which gene panel should be applied.\u003c/p\u003e \u003cp\u003eWithin this framework, 11 274 WGS analyses were performed over a nine-year period, from 2015 to 2023. Of these, 1 126 (10%) were analyzed using a family-based approach, while 6 853 (61%) underwent singleton analysis with one of the seven most frequently used phenotype-based panels. The remaining 3 295 analyses (29%) were assessed using either smaller curated panels or custom-made panels tailored to the individual\u0026rsquo;s clinical presentation.\u003c/p\u003e \u003cp\u003eFor individuals with highly specific symptoms, curated panels are available for conditions such as disorders of sex development, retinopathies, hearing loss, and ciliopathies. In many of these cases, patient-specific gene panels are generated based on phenotype data using Human Phenotype Ontology (HPO) terms (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e) or tools like PanelApp (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eBelow, we describe the clinical indications for the seven most commonly used panels, including associated symptoms, diagnostic criteria, and relevant coexisting conditions that support the use of WGS and guide panel selection. We also provide an overview of the family-based approach and prenatal testing using genome sequencing.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eIntellectual Disability (ID panel)\u003c/h2\u003e \u003cp\u003eThe ID panel is used for a heterogeneous group of individuals with neurodevelopmental disorders (NDDs) including intellectual disability (ID), autism spectrum disorder, developmental delay, speech and language disorders and related phenotypes. In many cases, a specific subdiagnosis within the NDD spectrum had not yet been established at the time of genetic testing. Additional clinical features are sometimes present alongside NDD. The vast majority of referred cases (90%) were children (average 9 years; range 0\u0026ndash;58). The gender distribution was 63% male and 37% female.\u003c/p\u003e \u003cp\u003eThe ID panel includes 1,567 genes and STRs are assessed at seven loci (Additional File 2: Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). The genetic analysis also incorporates genome-wide detection of structural variants using a pipeline that replaces the need for CMA, referred to as WGS-CMA. Due to the large number of genes included, the analysis primarily focuses on clearly pathogenic variants (ACMG class 4 and 5). Variants of uncertain clinical significance (VUS) are generally not reported; however, VUS deemed clinically relevant, such as those with strong gene-disease association and phenotype overlap, may be included in the clinical report after multidisciplinary review (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). In cases where an uncertain variant warrants further investigation, the result is classified as inconclusive and parental samples are requested for segregation analysis.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eNeuromuscular, ataxia and spastic paraplegia disorders (NMD panel)\u003c/h2\u003e \u003cp\u003eThe NMD panel is used for a clinically heterogeneous group of individuals with suspected neuromuscular disorders (NMDs), including myopathies, neuropathies, spastic paraplegia, and ataxia. The gender distribution was 54% male and 46% female. The age of the referred individuals spanned from infancy (0\u0026ndash;1 years, 11%), childhood (2\u0026ndash;17 years, 23%), and adulthood (18\u0026ndash;69 years, 56%) to more than 70 years of age (10%). Regardless of age, all samples in the NMD panel were analyzed as singletons.\u003c/p\u003e \u003cp\u003eMost individuals have a clinical diagnosis of myopathy or neuropathy prior to genetic testing. However, cases with nonspecific neuromuscular symptoms, such as hypotonia or arthrogryposis, are also tested using the NMD panel as part of a broader diagnostic evaluation. The panel also includes individuals with overlapping phenotypes, such as ataxia, that may fall within the neuromuscular spectrum.\u003c/p\u003e \u003cp\u003eThe NMD panel includes 1,035 genes (Additional File 2: Table \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e), covering established NMD genes as well as those associated with spastic paraparesis, ataxia, and other movement disorders. The analysis includes STR-analysis for 29 loci (Additional File 2: Table \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e) and a specific assessment of the \u003cem\u003eSMN1\u003c/em\u003e-gene copy number. As with other large panels, the reporting of VUS is minimized and restricted to variants with potential clinical significance (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). When available, clinical findings and results from neurophysiological investigations, muscle biopsy analyses, biochemical analyses are used to support the interpretation. Parental samples are often difficult to obtain for adult individuals, but in pediatric cases, parental testing may aid in the variant interpretation.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eInherited cancer (IC panel)\u003c/h2\u003e \u003cp\u003eThe IC panel is used for individuals with a suspected hereditary cancer predisposition and included both children (37%) and adults (63%) under different inclusion criteria. For children, all newly diagnosed cancer cases in Sweden have been part of a national study using paired genome sequencing (tumor/normal) since 2021 (\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e) and are sequenced as part of clinical routine since 2024. Although initially conducted as research, these samples are processed through the clinical laboratory, and cancer predisposition variants are reported. Adult individuals are referred for testing based on family or personal medical history suggestive of a genetic predisposition, such as early-onset cancer, multiple primary tumors, or suspected hereditary cancer syndromes.\u003c/p\u003e \u003cp\u003eThe IC panel consists of 165 genes (Additional File 2: Table S3), covering a broad range of hereditary cancer conditions, from genetically heterogeneous conditions like hereditary paraganglioma to single-gene disorders such as retinoblastoma. Analysis is conducted on a singleton basis. Variants identified in the IC panel are reported following gene-specific ACMG criteria for pathogenicity (such as \u003cem\u003eTP53\u003c/em\u003e (\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e) and \u003cem\u003eCDH1\u003c/em\u003e (\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e)). Risk factors for cancer predisposition are reported only when national care guidelines exist. Examples of cases where a risk variant would lead to special surveillance include families with hereditary breast cancer where truncating variants in \u003cem\u003eATM, BARD1, CHEK2, RAD51C\u003c/em\u003e and \u003cem\u003eRAD51D\u003c/em\u003e result in annual mammograms instead of every two years and families with hereditary ovarian cancer where carriers of truncating variants in \u003cem\u003eBRIP1, RAD51C\u003c/em\u003e and \u003cem\u003eRAD51D\u003c/em\u003e are offered an option of post-menopausal salpingo-oophorectomy. VUS are rarely reported. If the variant is a possible \u003cem\u003ede novo\u003c/em\u003e variant in a child with cancer, the analysis is reported as inconclusive and parental samples are requested before a final report is issued. Of note, we do not report out carriership of heterozygous missense variants in genes that cause autosomal recessive conditions, such as \u003cem\u003eMUTYH or RAD51C\u003c/em\u003e, as carriership does not lead to an increased risk of cancer that would lead to additional surveillance of the patient.\u003c/p\u003e \u003cp\u003eCancer predisposition variants may also be incidentally detected during WGS analysis for other indications, such as truncating variants in \u003cem\u003eBRCA1/2\u003c/em\u003e or \u003cem\u003ePALB2\u003c/em\u003e which are present in 0.2% of the gnomAD population (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e). Even though the targeted analysis approach minimizes the risk of incidental findings, some cancer genes are included in other gene panels as they can also cause autosomal recessive disease. For instance, bi-allelic \u003cem\u003eATM\u003c/em\u003e variants can cause autosomal recessive ataxia-telangiectasia and bi-allelic \u003cem\u003eBRCA2\u003c/em\u003e can cause autosomal recessive Fanconi anemia and can thus be detected by chance. If this happens, a comprehensive review of the patient\u0026rsquo;s medical- and family history is conducted, and an individualized decision on whether to report the variant as an incidental finding or not is made for each patient based on the ethical principles that guide health care as well as what is known about the variant, the expected penetrance, the availability of prevention programs and the family history\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003eConnective Tissue Disease (CTD panel)\u003c/h2\u003e \u003cp\u003eThe CTD panel is used for individuals with suspected heritable connective tissue disorders (HDCTs) and heritable thoracic aortic disease, including conditions such as Marfan syndrome, Loeys-Dietz syndrome and Ehlers-Danlos syndrome. The majority of the referrals were adults (81%) with children accounting for 19%. All samples were analyzed as singletons.\u003c/p\u003e \u003cp\u003eThe CTD panel includes in total 154 genes (Additional File 2: Table S4). In addition to HDCT-related genes, it also covers genes relevant for differential diagnoses due to overlapping clinical features with other inherited conditions, such as mild skeletal dysplasias, collagen-related myopathies, and Birt-Hogg-Dub\u0026eacute; syndrome.\u003c/p\u003e \u003cp\u003eTypically, variants classified as disease causing (ACMG Class 4 and 5) are reported. However, VUS are sometimes included in reports.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003eNeurodegenerative disorders (NeuroDeg panel)\u003c/h2\u003e \u003cp\u003eThe NeuroDeg panel is used for individuals with suspected adult-onset neurodegenerative conditions. The majority of referrals (\u0026gt;\u0026thinsp;80%) involved individuals with clinical suspicion of dementia or other neurodegenerative disease. All tested individuals were adults, with 88% over the age of 50 years. All samples were analyzed as singletons.\u003c/p\u003e \u003cp\u003eThe NeuroDeg panel consists of 138 genes and includes STR analysis at 17 loci (Additional File 2: Table S5). It targets disorders associated with cognitive impairment and dementia, such as Alzheimer disease, frontotemporal dementia, and dementia with Lewy bodies, as well as causative genes for Creutzfeldt-Jakob disease, ALS, and Parkinson\u0026rsquo;s disease. The known mutation spectrum across these genes is highly diverse encompassing a wide range of variants, from large duplications (e.g., \u003cem\u003eAPP\u003c/em\u003e (\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e)) to SNVs, STRs, and small deletions (e.g., \u003cem\u003eGRN\u003c/em\u003e). Many neurodegenerative diseases exhibit overlapping clinical presentations, making diagnostics challenging. However, identifying a causative genetic variant can establish a definitive diagnosis, eliminating the need for a postmortem neuropathological examination.\u003c/p\u003e \u003cp\u003eTypically, variants classified as disease causing (ACMG Class 4 and 5) are reported. However, VUS are sometimes included in reports.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003eSkeletal dysplasia disorders (SKD panel)\u003c/h2\u003e \u003cp\u003eThe SKD panel is used for a heterogenous group of patients with suspected genetic skeletal disorders, based on clinical and radiographic findings of abnormal skeletal morphology or abnormal bone density. The majority of referrals were children (59%), with adults accounting for 30% and prenatal samples for 11%. All samples were analyzed as singletons. Before genetic testing is initiated, basic clinical information is required including radiographic findings, as well as symptoms from internal organs.\u003c/p\u003e \u003cp\u003eThe SKD panel includes 681 genes (Additional File 2: Table S6). VUS are selectively reported only when the radiological phenotype is specific and aligns with the disorder linked to the gene in question. In cases where a VUS requires further investigation, the result is reported as inconclusive and parental samples are requested.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003eInherited cardiac conditions (ICC panel)\u003c/h2\u003e \u003cp\u003eThe ICC panel is used for individuals with suspected inherited arrhythmias and cardiomyopathies, with a focus on early diagnosis to prevent severe complications, including sudden cardiac death. The majority of referrals were adults (88%). All samples were analyzed as singletons.\u003c/p\u003e \u003cp\u003eWhen genetic testing is initiated, detailed clinical information is required to classify individuals into defined phenotype groups, including hypertrophic cardiomyopathy, dilated cardiomyopathy, arrhythmogenic right ventricular cardiomyopathy, left ventricular non-compaction cardiomyopathy, long QT syndrome, catecholaminergic polymorphic ventricular tachycardia and Brugada syndrome. The clinical workflow is carried out in close collaboration with cardiologists.\u003c/p\u003e \u003cp\u003eThe panel includes 94 genes (Additional File 2: Table S7) associated with both channelopathies and cardiomyopathies, including key genes for hypertrophic and dilated cardiomyopathy, long QT syndrome, and related conditions. Variants classified as disease causing (ACMG Class 4 and 5) are reported and selected VUS are included when they warrant follow-up through re-evaluation or segregation analysis. We follow the 2023 ESC Guidelines for the management of cardiomyopathies and other cardiac conditions (\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e) and apply an evidence-based assessment of gene-disease relationships, including recent expert curation efforts in dilated cardiomyopathy (\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e) and hypertrophic cardiomyopathy (\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e) to guide gene inclusion and variant interpretation.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec21\" class=\"Section2\"\u003e \u003ch2\u003eFamily-based (TRIO) analysis\u003c/h2\u003e \u003cp\u003eFor highly heterogeneous disorders, a family-centered approach, typically trio analysis involving the affected individual and both parents, was often employed. This strategy is particularly valuable in pediatric cases with suspected congenital syndromes and may be used either as a first-tier test or as a follow-up after singleton panel testing, most commonly following the ID, NMD, or SKD panels. The majority of cases were children, with 13% under 1 year and 76% under 18 years of age.\u003c/p\u003e \u003cp\u003eThe analysis includes all genes with a known association to disease based on the morbid OMIM gene list (\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e) and the NHS Genomic Medicine Service Signed Off panel. These gene lists are updated quarterly to reflect the most current set of genes known to cause monogenic disease. Pre-test counseling is provided by clinical geneticists or physicians with specialized training and includes discussion of the potential for incidental findings with both the affected individual and their parents.\u003c/p\u003e \u003cp\u003eVariant interpretation includes all variant types and considers different monogenic inheritance patterns, including \u003cem\u003ede novo\u003c/em\u003e variants, X-linked inheritance, and autosomal recessive inheritance. The analysis also incorporates genome-wide structural variant detection using WGS-CMA, provided that the analysis had not been previously performed.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec22\" class=\"Section2\"\u003e \u003ch2\u003eFetal samples\u003c/h2\u003e \u003cp\u003eWGS was offered prenatally in cases of suspected fetal malformations (\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e), either as trio analysis (n\u0026thinsp;=\u0026thinsp;148) or as singleton analysis in specific scenarios such as non-immune hydrops fetalis (n\u0026thinsp;=\u0026thinsp;67) or suspected skeletal dysplasia (n\u0026thinsp;=\u0026thinsp;57). The clinical workflow is carried out in close collaboration with ultrasound and fetal medicine specialists. Referral forms must clearly specify the malformations detected and indicate whether a termination is planned or if the parents are awaiting genetic results to support decision-making.\u003c/p\u003e \u003cp\u003eFor non-immune hydrops fetalis, a targeted gene panel of 343 genes is analyzed (Additional File 2: Table S8). In fetuses with malformations, the analysis includes all morbid OMIM genes, all variant types, and genome-wide structural variant detection using WGS-CMA. Only pathogenic and likely pathogenic variants (ACMG class 4 and 5) expected to cause the condition are reported.\u003c/p\u003e \u003cdiv id=\"Sec23\" class=\"Section3\"\u003e \u003ch2\u003eIntegrated WGS workflow at the Centre for Inherited Metabolic Diseases (CMMS)\u003c/h2\u003e \u003cp\u003eThe CMMS has adopted a multidisciplinary, patient-centered organizational structure combining clinical and laboratory medicine. In this unit, experienced specialists in pediatric and adult neurology, metabolic medicine, endocrinology, clinical genetics and clinical chemistry work closely together with experts in molecular genetics, analytical chemistry and bioinformatics. In all, this enables a targeted analysis of relevant genes/gene panels, integration of genome data with biochemical and clinical investigations, functional validation of unclear genetic variants and rapid translation into individualized treatment.\u003c/p\u003e \u003cp\u003eDuring the study period, 3,571 individuals underwent WGS analysis at CMMS. Indications could be broadly divided into five clinical groups where the two most common indications were suspected inborn errors of metabolism (1,859 cases; 52%) and epilepsy (774 cases; 22%). All investigations are jointly reviewed by both a clinical laboratory geneticist and an experienced senior consultant with expertise in a relevant clinical specialty. The genomic analysis includes evaluation of small-scale variants (SNVs, INDELs, SVs) as well as large-scale SVs. At CMMS, all cases are presented at multidisciplinary rounds where variants of potential interest are selected for in-depth discussion.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec24\" class=\"Section2\"\u003e \u003ch2\u003eInborn errors of metabolism including mitochondrial disorders (IEM panel)\u003c/h2\u003e \u003cp\u003eThe IEM panel is used for individuals with suspected inborn errors of metabolism (IEM), a diverse group of disorders affecting various parts of intermediary metabolism including dysfunction in the cellular organelles such as mitochondria, lysosomes and peroxisomes.\u003c/p\u003e \u003cp\u003eBiochemical analysis of plasma and/or urine can often detect the accumulation of molecules from intermediary metabolism and is therefore used in the diagnostic workup. Individuals with IEMs often have symptoms already in the neonatal period or later in childhood. However, patients with milder forms of these disorders may present in adulthood. Until the end of 2023, 1 091 children and 768 adults were analyzed. Samples are usually analyzed as singletons.\u003c/p\u003e \u003cp\u003eThe IEM panel includes 1 099 genes (Additional File 2: Table S9), with most conditions inherited in an autosomal recessive manner. Genetic findings are interpreted in combination with biochemical data, which often helps confirm or exclude the relevance of uncertain variants. Some of the mitochondrial disorders are caused by variants in the mitochondrial DNA (mtDNA), and if such a disorder is clinically or biochemically suspected mtDNA is analyzed. The pool of mtDNA consists of inherited polymorphisms and, in some cases, disease-causing variants present in varying ratios across different tissues, a phenomenon referred to as heteroplasmy. In suspected mitochondrial diseases, a muscle biopsy is performed for biochemical evaluation of the respiratory chain. As heteroplasmy levels are typically lower in blood-derived DNA, muscle-derived DNA is used for WGS in these cases.\u003c/p\u003e \u003cp\u003eVariants classified as pathogenic or likely pathogenic (ACMG class 4 and 5) are reported. Class 3 variants (VUS) may also be reported when supported by relevant biochemical evidence or a strong clinical phenotype.\u003c/p\u003e \u003cdiv id=\"Sec25\" class=\"Section3\"\u003e \u003ch2\u003eDiagnostic validation in newborn screening for inherited metabolic diseases (NBS-M panel)\u003c/h2\u003e \u003cp\u003eThe national newborn screening, primarily based on tandem mass spectrometry (MS/MS) analysis of metabolites extracted from dried filter papers or enzyme assay, is centralized to CMMS for all infants born in Sweden (about 100 000 children/year). CMMS is also responsible for the biochemical and genetic confirmation of the metabolic diagnoses for over half of the newborns. If the biochemical diagnostic analysis confirms the screening result, a genetic diagnosis can be established in nearly 100% of cases. If a single gene is responsible for the disorder Sanger-based sequencing is used for time and cost efficacy. This applies to phenylketonuria (PKU), medium-chain acyl-coenzyme A dehydrogenase deficiency (MCADD), or very long-chain acyl-coenzyme A dehydrogenase deficiency (VLCADD). For other disorders where multiple potential causative genes exist, a singleton WGS-based strategy is used. Examples of such disorders include maple syrup urine disease (MSUD) or multiple acyl-coenzyme A dehydrogenase deficiency (MADD). In cases where follow-up biochemical analysis is essentially normal, NBS-M panel analysis may be performed to exclude the suspected disease with greater certainty, thereby eliminating the need for further clinical follow-up. The panel contains 51 genes (Additional File 2: Table S10). Since the NBS-M panel started in 2021 the total number of individuals analyzed is 23.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec26\" class=\"Section3\"\u003e \u003ch2\u003eMonogenic diabetes (DIAB panel)\u003c/h2\u003e \u003cp\u003eThe DIAB panel is used for individuals with suspected monogenic diabetes mellitus, a group of conditions that account for approximately 2\u0026ndash;5% of diabetes cases diagnosed before the age of 35. The DIAB panel is typically used in the diagnostic work-up of patients whose clinical presentation is not typical of classical type 1 or type 2 diabetes, typically relatively young, non-obese individuals without pancreatic islet antibodies, often with first-degree relatives exhibiting a similar phenotype. The panel includes 54 genes (Additional File 2: Table S11) and also assesses mtDNA, with a particular focus on the \u003cem\u003em.3243A\u0026thinsp;\u0026gt;\u0026thinsp;G\u003c/em\u003e variant associated with maternally inherited diabetes and deafness. A total of 154 individuals have been analyzed of which 29% are pediatric. All cases are discussed in a multidisciplinary conference, to which diabetologists from all hospitals in Stockholm are invited. Based on the specific gene identified, a tailored treatment regimen can often be implemented.\u003c/p\u003e \u003cp\u003eThe majority of monogenic diabetes conditions follow an autosomal dominant inheritance pattern. When a pathogenic variant is identified, genetic testing is recommended for affected relatives, and a gene-specific treatment strategy can often be implemented.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec27\" class=\"Section3\"\u003e \u003ch2\u003eAcute liver failure and cholestasis in children (PEDHEP panel)\u003c/h2\u003e \u003cp\u003eThe PEDHEP panel is used primarily in the evaluation of children with various types of liver disease, ranging from acute liver failure to cholestatic diseases, suspected bile acid synthesis disorders, and ductal plate malformations. The panel includes 172 genes (Additional File 2: Table S12), and the samples are analyzed as singletons. Many inborn errors of metabolism (e.g., glycogen storage disorders, tyrosinemia, and congenital disorders of glycosylation) present with a pronounced hepatic phenotype. Depending on the clinical history and biochemical work-up results, the IEM panel may be added to the genetic evaluation. Up to 2024, 233 individuals were analyzed, of whom 82% are pediatric. All cases are reviewed in a multidisciplinary conference including experts in pediatric hepatology.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec28\" class=\"Section2\"\u003e \u003ch2\u003eEpilepsy (EP panel)\u003c/h2\u003e \u003cp\u003eThe EP panel is used in the evaluation of individuals with suspected genetic epilepsy, particularly those with treatment-refractory epilepsy. It is used in both adult (27%) and pediatric (73%) individuals, but the diagnostic yield is higher in pediatric cases (\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e). This is likely due to the more polygenic nature of adult epilepsy and the lower rate of testing in that population. The EP panel includes 565 genes (Additional File 2: Table S13), and for young children it is often combined with IEM, as the clinical picture may be less clear in infants. The analysis is preferably performed as a trio due to the high prevalence of \u003cem\u003ede novo\u003c/em\u003e variants.\u003c/p\u003e \u003cp\u003eVariant interpretation integrates detailed phenotypic characterization with paraclinical data such as electroencephalogram (EEG) and brain magnetic resonance imaging (MRI). A deep understanding of epileptology is critical for accurate interpretation. Extra precaution is warranted, as many epilepsy-related genes display reduced penetrance. Additionally, VUS are often reported, since they may have clinical implications in the choice of anti-seizure medication. Likewise, a clinical response to a specific treatment may strengthen a genetic finding.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec29\" class=\"Section2\"\u003e \u003ch2\u003eIntegrated WGS workflow at Clinical Immunology and Transfusion Medicine (KITM)\u003c/h2\u003e \u003cp\u003eThe KITM unit applies a multidisciplinary, patient-centered approach that integrates clinical and laboratory expertise. Referrals are assessed by medical doctors or certified clinical laboratory geneticists who determine the most appropriate gene panel based on the information in the referral. When necessary, general electronic health records are reviewed and referring physicians are contacted for additional clinical information.\u003c/p\u003e \u003cp\u003eVariant interpretation is performed in close collaboration between clinical immunologists and clinical laboratory geneticists, and is supported by immunological phenotyping, functional assays and other relevant laboratory data. All cases are discussed at weekly multidisciplinary rounds before results are reported back to the clinicians. Additional laboratory investigations to functionally assess variants are conducted either within the Clinical Immunology unit or in collaboration with specialized research laboratories. National multidisciplinary patient conferences are held regularly together with immunodeficiency and hematology specialists to discuss the genetic findings, guide further diagnostics testing and inform patient treatment. Three gene panels are used in this setting: Primary Immunodeficiency (PID), Autoinflammation (AID; introduced in 2023) and Inherited Bone Marrow Failure Syndromes (IBFMS) and rare hematologic conditions.\u003c/p\u003e \u003cp\u003eTo date, 799 individuals have been analyzed, the majority of which (621 cases; 78%) were assessed using the PID panel.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003ePrimary Immunodeficiency (PID panel)\u003c/h3\u003e\n\u003cp\u003eThe PID panel is used in the evaluation of individuals with a wide spectrum of suspected immunological disorders, ranging from those with specific, well described immunodeficiencies such as severe combined immunodeficiency (SCID) to less well-defined diagnosis, such as immune dysregulation or recurrent infections. The panel is usually performed as a singleton and currently includes 482 genes (Additional File 2: Table S14). Individuals tested with this panel range from infants and children with severe early-onset phenotypes to children and adults with milder, late-onset presentations. Of the individuals analyzed, 64% were children (\u0026lt;\u0026thinsp;18 years) and 3% were referred through the national newborn SCID screening program, which has been active since August 2019.\u003c/p\u003e \u003cdiv id=\"Sec31\" class=\"Section2\"\u003e \u003ch2\u003eAutoinflammation (AID panel)\u003c/h2\u003e \u003cp\u003eThe AID panel, introduced in 2023 as a smaller version of the PID panel, focusing on 73 genes (Additional File 2: Table S15) associated with the innate immune system disorders that lead to autoinflammation. This includes genes such as \u003cem\u003eMEFV\u003c/em\u003e, for familial mediterranean fever. The narrower scope of the AID panel allows for more time-efficient analysis and reduces the risk of incidental findings. It is particularly suited for individuals presenting with isolated autoinflammatory symptoms without additional signs of immunodeficiency.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec32\" class=\"Section2\"\u003e \u003ch2\u003eInherited Bone Marrow Failure (IBMFS) and rare hematologic condition panel\u003c/h2\u003e \u003cp\u003eThe panel is used for individuals with suspected bone marrow failure or rare hematologic conditions resulting in peripheral blood cytopenias, including but not limited to Fanconi anemia, telomere biology disorders, congenital neutropenia, Diamond Blackfan anemia and macrothrombocytopenia. The panel is usually performed as a singleton and currently includes 236 genes (Additional File 2: Table S16). Most individuals tested are children (82%), and commonly the analysis is conducted in parallel to chromosomal breakage and telomere length analysis.\u003c/p\u003e \u003c/div\u003e"},{"header":"RESULTS","content":"\u003cdiv id=\"Sec34\" class=\"Section2\"\u003e \u003ch2\u003eTurnaround time\u003c/h2\u003e \u003cp\u003eThe median turnaround time (TAT) for sequence generation and bioinformatic analysis has steadily decreased over the years and in 2023 it was 11 days for regular priority samples and 9 days for priority samples (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Express samples are rarely handled but the current TAT is about 4 days.\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\u003eAverage Sequencing Turnaround Times\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2017\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2018\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2019\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2020\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2021\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2022\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003e2023\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eStandard\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e14.4\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e13.3\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e14.9\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e15.0\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e12.6\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e12.4\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e11.1\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePriority\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e10.6\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e10.6\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e13.9\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e12.5\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e10.1\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e11.6\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e9.4\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eOverall diagnostic findings\u003c/h3\u003e\n\u003cp\u003eThe overall diagnostic yield was 22.6%, resulting in a genetic diagnosis for 3,538 individuals. In total, 4,460 variants were reported across 1,570 unique genes, including 130 short tandem repeat (STR) expansions in 15 different genes (Additional File 2: Table S17). Regarding structural variants: 105 SVs were identified using the analyzed gene panels and 57 additional SVs were detected through genome-wide analysis using WGS-CMA (Additional File 2: Table S18). An overview of results across 20 different gene panels is shown in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eOverview of findings from clinical genome sequencing.\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=\"char\" char=\".\" 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\u003eTeam\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePanels\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCases\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003ePediatric\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003ePathogenic variant\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eVUS\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"8\" rowspan=\"9\"\u003e \u003cp\u003eClinical\u003c/p\u003e \u003cp\u003eGenetics\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eID\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1495\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e63%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e90%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e22%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e15%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNMD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1428\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e55%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e33%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e24%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e15%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1350\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e46%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e37%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e13%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e3%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCTD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e993\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e45%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e19%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e9%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e11%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNeuroDeg\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e609\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e48%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e12%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e5%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSKD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e520\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e44%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e59%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e43%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e11%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eICC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e458\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e59%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e24%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e6%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGeneral genetics HPO\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3295\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e50%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e53%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e25%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e10%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTrio analysis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1126\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e50%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e76%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e24%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e12%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"6\" rowspan=\"7\"\u003e \u003cp\u003eCMMS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIEM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1859\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e54%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e59%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e26%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e774\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e56%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e73%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e26%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNBS-M\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e48%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e96%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e65%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDIAB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e154\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e47%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e29%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e23%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePEDHEP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e233\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e53%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e82%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e25%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003emtDNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e390\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e53%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e50%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e7%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCMMS HPO\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e138\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e46%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e59%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e26%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e4%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003eKITM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePID\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e621\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e52%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e59%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e30%*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIBMFS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e144\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e51%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e83%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e29%*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAID\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e56%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e50%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e11%*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePID\u0026thinsp;+\u0026thinsp;IBMFS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e53%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e47%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e19%*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0%\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\u003eVUS, Variant of Unknown Significance; CMMS, Centre for Inherited Metabolic Diseases; KITM, Clinical Immunology and Transfusion Medicine; ID, Intellectual Disability; NMD, Neuromuscular Disorder; IC, Inherited Cancer; CTD, Connective Tissue Disease; Neurodeg, Neurodegenerative Disorder; SKD, Skeletal Dysplasia; ICC, Inherited Cardiac Condition; HPO, Human Phenotype Ontology; IEM, Inherited Metabolic Disorders; EP, Epilepsy; NBS-M, Newborn Screening Metabolic; DIAB, Monogenic Diabetes, PEDHEP, Pediatric Liver Disease; Mtdna, Mitochondrial DNA; PID, Primary Immunodeficiency; AID, Autoinflammation; IBFMS, Inherited Bone Marrow Failure Syndrome; *Pathogenic and VUS\u003c/p\u003e \u003cp\u003eThe majority (54%) of the diagnosed individuals had a pathogenic variant in a gene that was responsible for disease in only 1\u0026ndash;3 individuals (21% (750 genes), 18% (316 genes) and 15% (180 genes) detected in one, two and three individuals respectively). A total of 31% (n\u0026thinsp;=\u0026thinsp;1,097) of cases involved more than 10 individuals sharing the same genetic diagnosis, affecting 57 different genes. The most common findings were SNVs/INDELs in \u003cem\u003eNF1\u003c/em\u003e (n\u0026thinsp;=\u0026thinsp;59), \u003cem\u003ePTPN11\u003c/em\u003e (n\u0026thinsp;=\u0026thinsp;40), \u003cem\u003eRYR1\u003c/em\u003e (n\u0026thinsp;=\u0026thinsp;39), and \u003cem\u003eTTN\u003c/em\u003e (n\u0026thinsp;=\u0026thinsp;39). Among STRs, \u003cem\u003eC9orf72\u003c/em\u003e expansion causing FTD/ALS (n\u0026thinsp;=\u0026thinsp;39) and among SVs, the 22q11 recurrent deletion (n\u0026thinsp;=\u0026thinsp;8) were the most common. Some of the disorders were identified by multiple teams, such as pathogenic variants in \u003cem\u003eCFTR\u003c/em\u003e, \u003cem\u003eG6PD\u003c/em\u003e, \u003cem\u003eNOTCH1\u003c/em\u003e, \u003cem\u003ePTPN11\u003c/em\u003e and \u003cem\u003eSBDS\u003c/em\u003e reported by all three clinics. In addition, 225 genes were reported by two clinical teams (3 genes by CMMS and KITM, 45 genes by KITM and Clinical Genetics, and 177 genes by CMMS and Clinical Genetics). For the most commonly used panels, the ten most frequently reported genes are listed in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, while Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e illustrates the age distribution, diagnostic yield, and the increase in the number of analyzed cases over the years. As expected, the diagnostic yield is generally higher for panels with a large proportion of pediatric cases.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eThe top ten most commonly identified genes for ten selected panels. The gene name is followed by the number of patients in which variant(s) in this gene were identified as causative.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"20\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c12\" colnum=\"12\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c13\" colnum=\"13\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c14\" colnum=\"14\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c15\" colnum=\"15\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c16\" colnum=\"16\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c17\" colnum=\"17\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c18\" colnum=\"18\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c19\" colnum=\"19\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c20\" colnum=\"20\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eIF\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003eNMD\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003eNeuroDeg\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003eIEM\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e \u003cp\u003eEP\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c12\" namest=\"c11\"\u003e \u003cp\u003eCTD\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c14\" namest=\"c13\"\u003e \u003cp\u003eSKD\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c16\" namest=\"c15\"\u003e \u003cp\u003eICC\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c18\" namest=\"c17\"\u003e \u003cp\u003ePID\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c20\" namest=\"c19\"\u003e \u003cp\u003eTrio\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eANKRD11\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eRFC1 (STR)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eC9orf72 (STR)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003ePMM2\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cem\u003eSCN1A\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u003cem\u003eFBN1\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e\u003cem\u003eCOL1A1\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c14\"\u003e \u003cp\u003e23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e\u003cem\u003eTTN\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c16\"\u003e \u003cp\u003e26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c17\"\u003e \u003cp\u003e\u003cem\u003eTNFRSF13B\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c18\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c19\"\u003e \u003cp\u003e\u003cem\u003eARID1B\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c20\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eMECP2\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eDMD\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eSOD1\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003eOPA1\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cem\u003eKCNQ2\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u003cem\u003eCOL5A1\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e\u003cem\u003eFGFR3\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c14\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e\u003cem\u003eMYH7\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c16\"\u003e \u003cp\u003e22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c17\"\u003e \u003cp\u003e\u003cem\u003eMEFV\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c18\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c19\"\u003e \u003cp\u003e\u003cem\u003eMECP2\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c20\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eNF1\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eRYR1\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eSORL1\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003ePHKA2\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cem\u003eSTXBP1\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u003cem\u003eCOL3A1\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e\u003cem\u003eCOL2A1\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c14\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e\u003cem\u003eMYBPC3\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c16\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c17\"\u003e \u003cp\u003e\u003cem\u003eCYBB\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c18\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c19\"\u003e \u003cp\u003e\u003cem\u003eNSD1\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c20\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003ePOGZ\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003ePMP22\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eTBK1\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003eATP7B\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cem\u003ePRRT2\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u003cem\u003eMYH11\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e\u003cem\u003eCOL1A2\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c14\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e\u003cem\u003eKCNQ1\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c16\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c17\"\u003e \u003cp\u003e\u003cem\u003eNFKB1\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c18\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c19\"\u003e \u003cp\u003e\u003cem\u003eFRAS1\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c20\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eDDX3X\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eTTN\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eGRN\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003eETFDH\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cem\u003eCACNA1A\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u003cem\u003eTGFBR2\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e\u003cem\u003eDYNC2H1\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c14\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e\u003cem\u003eTNNI3\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c16\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c17\"\u003e \u003cp\u003e\u003cem\u003eSTAT3\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c18\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c19\"\u003e \u003cp\u003e\u003cem\u003eHUWE1\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c20\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eDNMT3A\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eCOL6A3\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003ePSEN1\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003ePDHA1\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cem\u003eSLC2A1\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u003cem\u003eTGFB3\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e\u003cem\u003eEXT1\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c14\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e\u003cem\u003eDSG2\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c16\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c17\"\u003e \u003cp\u003e\u003cem\u003eBTK\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c18\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c19\"\u003e \u003cp\u003e\u003cem\u003eNUS1\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c20\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eKMT2A\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eCAPN3\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eVCP\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003eTPO\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cem\u003eMECP2\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u003cem\u003eCOL1A1\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e\u003cem\u003eGNAS\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c14\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e\u003cem\u003eFLNC\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c16\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c17\"\u003e \u003cp\u003e\u003cem\u003eADA2\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c18\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c19\"\u003e \u003cp\u003e\u003cem\u003eL1CAM\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c20\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eTCF4\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eSH3TC2\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eCHMP2B\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003eABCC8\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cem\u003eSCN8A\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u003cem\u003eCOL2A1\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e\u003cem\u003eCOMP\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c14\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e\u003cem\u003ePKP2\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c16\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c17\"\u003e \u003cp\u003e\u003cem\u003eFAS\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c18\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c19\"\u003e \u003cp\u003e\u003cem\u003eSETD5\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c20\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eARID1B\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eMPZ\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eTARDBP\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003eGALC\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cem\u003eCDKL5\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u003cem\u003eACTA2\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e\u003cem\u003eEXT2\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c14\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e\u003cem\u003eTNNT2\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c16\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c17\"\u003e \u003cp\u003e\u003cem\u003eADA\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c18\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c19\"\u003e \u003cp\u003e\u003cem\u003eBRAT1\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c20\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eFMR1 (STR)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eCOL6A1\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eATXN8OS (STR)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003eGLDC\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cem\u003ePCDH19\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u003cem\u003eCOL5A2\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e\u003cem\u003eRPL13\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c14\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e\u003cem\u003eSCN5A\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c16\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c17\"\u003e \u003cp\u003e\u003cem\u003eFOXN1\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c18\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c19\"\u003e \u003cp\u003e\u003cem\u003eMYH3\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c20\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eMED13L\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eMAPT\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e\u003cem\u003eALPK3\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c16\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c17\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c18\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c19\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c20\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003ePTPN11\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eCHCHD10\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c17\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c18\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c19\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c20\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eSHANK3\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c17\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c18\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c19\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c20\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eID, Intellectual Disability; NMD, Neuromuscular Disorder; Neurodeg, Neurodegenerative Disorder; IEM, Inherited Metabolic Disorders; EP, Epilepsy; CTD, Connective Tissue Disease; SKD, Skeletal Dysplasia; ICC, Inherited Cardiac Condition; PID, Primary Immunodeficiency\u003c/p\u003e \u003cp\u003e \u003c/p\u003e\n\u003ch3\u003eHigh Sensitivity of Variant Ranking for Known Diagnoses\u003c/h3\u003e\n\u003cp\u003eThe benchmarking demonstrated that the vast majority of previously reported pathogenic variants were indeed ranked highly in our system. Out of 3,042 variants, 1,063 (35%) were ranked as the top candidate (rank 1), with a median rank position of 2 across the entire cohort of 3,042 variants. The mean rank was 5.3\u0026thinsp;\u0026plusmn;\u0026thinsp;0.3 (95% CI, SD 8.3). Only 20 variants (0.7%) ranked below position 50 (the approximate cutoff for routine manual assessment). The maximum observed rank was 129 (Additional file 1: Figure S3).\u003c/p\u003e"},{"header":"DISCUSSION","content":"\u003cp\u003eOur findings show that genome sequencing is a valuable clinical test across an expanding range of disorders and age groups. By building a format with pre- and post-test procedures customized to different clinical scenarios, we ensure that the right patients are tested for the established genes associated with their phenotype, and that test results are truly helpful in providing individualized care to the individual patient.\u003c/p\u003e \u003cp\u003eObvious challenges include continuously refining the inclusion criteria and updating gene panels to accommodate the ever-growing body of genetic knowledge. As new disease groups, such as eye and hearing disorders or blood diseases (anemia, coagulation defects and erythrocyte membrane defects), start to use genomic analysis as a baseline test, extensive training and education efforts are needed. Furthermore, technological advancements like long-read sequencing (\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e) and RNA sequencing have shown great potential to further enhance diagnostic capability (\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e). Integrating these emerging technologies into clinical workflows will help refine variant interpretation and expand the range of detectable genetic alterations, which in turn will increase the diagnostic outcome.\u003c/p\u003e \u003cp\u003eIn addition to technological advances, robust variant prioritization pipelines have been critical for enabling efficient WGS based diagnostics. In our evaluation, the majority of known causative variants ranked among the top candidates, allowing them to be rapidly identified during clinical interpretation. This high-ranking performance streamlines manual review, supports diagnostic consistency, and enhances patient safety. However, variants that received low prioritization scores frequently lacked a second allele required for compound scoring or were incorrectly penalized due to erroneous assumptions about inheritance patterns, particularly in known dominant conditions. These issues reflect known limitations of the Genmod (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e) scoring model, which is currently undergoing revision. Ongoing improvements include refinement of compound scoring and integration of Bayesian models and machine learning to further optimize accuracy.\u003c/p\u003e \u003cp\u003eAlongside accuracy, TAT is increasingly important, particularly for acutely ill patients. The median TAT for sequencing and bioinformatic analysis has steadily decreased, reaching 11 days for routine samples and 9 days for priority cases in 2023. To offer more robust and shorter TATs, sequencing instruments are now run more frequently, aiming for 4\u0026ndash;5 times per week in 2025, allowing for greater flexibility and faster processing. There is growing demand for genome sequencing in medically urgent scenarios, and we are developing differentiated clinical tracks targeting TATs of 2\u0026ndash;3 days (ultra-urgent), 7\u0026ndash;10 days (priority), and 14 days (routine) by 2025, calculated from reception of sample to clinical report being issued.\u003c/p\u003e \u003cp\u003eOf note, the total time from patient sampling to final clinical report includes not only sequencing, but also sample transport, DNA extraction and QC, bioinformatic processing, variant interpretation, and multidisciplinary review. Achieving shorter TATs across these tracks requires ongoing overall optimization of logistics, lab workflows, automation, data processing, and clinical interpretation pipelines.\u003c/p\u003e \u003cp\u003eImplementation of genome sequencing into the management of rare diseases represents a first step in the ongoing transformation towards precision medicine. We have adopted a strategy for this gradual, long-term transition that contains two main axes (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e)\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eIn parallel to the general genetics service transitioning from traditional tests to genome-based analysis, targeted, multidisciplinary patient flows are being developed for different disease groups using the IEM concept as a model. This is particularly important for disease groups where specialized functional investigations are essential for diagnostics and rapid treatment is critical for patient outcome. The patient flow for primary immunodeficiencies is being organized according to the principles developed for IEM, including targeted diagnostics after newborn screening for SCID and development towards a national coordination. An important challenge for the future is to establish a digital infrastructure that enables integration of data from genome analyses with other laboratory, clinical and imaging investigations to facilitate multimodal diagnostics. In parallel to the targeted workflows and equally important, the Department of Clinical Genetics and Genomics plays a pivotal role in cultivating clinical genetic expertise, serving as a hub for many teams and providing guidance in prenatal diagnostics, family investigations, and interpretation of complex genomic rearrangements. This department trains clinical geneticists and clinical laboratory geneticists who, in addition to working in the genetic service laboratory, support and/or join established and newly formed integrated teams to ensure that their expertise enhances the precision and quality of genome sequencing services. Furthermore, it guarantees that new bioinformatics pipeline modules are gradually made accessible to these integrated teams, so they can leverage the latest advancements in variant interpretation and genomic analysis. This ensures that the multidisciplinary teams remain well-informed and continue delivering high-quality, personalized care.\u003c/p\u003e"},{"header":"CONCLUSIONS","content":"\u003cp\u003eIn conclusion, we have adopted a strategy for moving from genomics into precision medicine, first by implementing genome sequencing into the clinical laboratories through GMCK-RD and followed by further implementation into healthcare through integrated multidisciplinary teams. This model is gradually consolidated and expanded as part of a long-term systems shift in rare disease diagnostics and management at Karolinska, with the aim to play a strong part in the emerging national precision medicine landscape in Sweden.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eACMG, American College of Medical Genetics; AID, Autoinflammation; CMA, Clinical Microarray Analysis; CMMS, Centre for Inherited Metabolic Diseases; CNV, Copy Number Variant; CTD, Connective Tissue Disease, DIAB, Monogenic Diabetes; EEG, Electroencephalogram; EP, Epilepsy; GMCK-RD, The Genomic Medicine Center Karolinska Rare Diseases; HDCT, Heritable Connective Tissue Disorders; IBFMS, Inherited Bone Marrow Failure Syndrome; IC, Inherited cancer; ICC, Inherited Cardiac Conditions; ID, Intellectual Disability; IEM, Inborn Errors of Metabolism; INDEL, Insertion and Deletion; KITM, Clinical Immunology and Transfusion Medicine; MADD, Multiple acyl-coenzyme A dehydrogenase deficiency; MCADD, Medium-chain acyl-coenzyme A dehydrogenase deficiency; MIT, Mitochondria; mtDNA, MRI, Magnetic Resonance Imaging; MS/MS, Tandem Mass Spectrometry; Mitochondrial DNA; MSUD, Maple syrup urine disease; NBS-M, Newborn Screening Metabolic; NDD, Neurodevelopmental Disorders; NeuroDeg, Neurodegenerative Disorders; NHV, National Highly Specialized Care; NMD, Neuromuscular Disorders; PEDHEP, Pediatric Liver Disease; PID, Primary Immunodeficiency; PKU, Phenylketonuria; SCID, Severe Combined Immunodeficiency; SKD, Skeletal Dysplasia Disorder; SNV, Single Nucleotide Variant; STR, Short Tandem Repeats; SV, Structural Variant; TAT, Turnaround time; VUS, Variant of Uncertain Clinical Significance; VLCADD, Very long-chain acyl-coenzyme A dehydrogenase deficiency; WES, Whole Exome Sequencing; WGS, Whole Genome Sequencing\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe are very grateful to the participating families. The authors would like to acknowledge the expertise and support with NGS services provided by Clinical Genomics Stockholm facility at the Science for Life Laboratory (jointly hosted by Department of Microbiology, Tumor and Cell biology at Karolinska Institutet and Department of Gene Technology at School of Engineering Sciences in Chemistry, Biotechnology and Health at KTH Royal Institute of Technology) and the Genomic Medicine Center Karolinska at the Karolinska University Hospital. Mattias Karlen helped with the design of illustrations.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAL was supported by grants from the Swedish Research Council (2019-02078), Region Stockholm (FoUI-1000468 and FoUI-978581), the Rare Diseases Research Foundation (S\u0026auml;llsyntafonden), the Swedish Brain Foundation (FO2024-0128-HK-44) and the Swedish Cancer Society (24 3504 Pj). AW was supported by the Swedish Research Council (2023-02388), Karolinska Institutet, Region Stockholm (FoUI-955096), Knut \u0026amp; Alice Wallenberg Foundation (Wallenberg Clinical Scholar, KAW2020.0228) and ET by Region Stockholm (FoUI-973659).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe ethical approval did not permit sharing of WGS data, and the in-house databases used in this article are not publicly available. The nf-core pipeline is open source and available at (https://github.com/nf-core/raredisease). All other softwares used, have been previously published and are cited in the text.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eAuthors\u0026rsquo; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAL, VW, and AW conceptualized the study and wrote the first draft. KLR, ASM, NL, SV, and DN analyzed and interpreted the data. KLR, MK, SY, GG, MO, MW, SV, PM, MHP, HT, ET, MM, AJ, and DN contributed specific text sections. AJ, CR, DN, JE, RJ, KNy, LPP, PP, AR, RN, and KS developed bioinformatics software and pipelines. KLR, ET, MHP, KDG, BT, AN, BMA, MK, SY, SV, MO, GG, NL, HB, BT and TS curated gene panels. AL, ME, AJ, DN, and AW prepared the figures. All other authors participated in data generation, clinical evaluation, variant interpretation, and manuscript review. All authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003cstrong\u003eEthics approval and consent to participate.\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eEthics approval was given by the Regional Ethical Review Board in Stockholm, Sweden (ethics permit numbers 2008-351-31, 2012/2106-31/4, 2012/222-31/3, 2014/995-32, 2015/416-31 and 2024-05341-01). These ethics permits allow for use of clinical samples for analysis of scientific importance as part of clinical development, and for lifting clinical filters to interrogate the whole genome in selected cases. Our IRB approval does not require us to get written consent for clinical testing. The research conformed to the principles of the Helsinki Declaration. \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAL has received speakers\u0026rsquo; honoraria from Pacific Biosciences and Illumina. VW has received speaker\u0026rsquo;s honoraria from Illumina.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAll other authors declare no competing interests.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003cstrong\u003eSupporting information\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAdditional File 1: Figure S1. Steps performed in the nf-core rare disease pipeline; Figure S2. Variant scoring and prioritization with Genmod; Figure S3: Rank score performance over time\u003c/p\u003e\n\u003cp\u003eAdditional file 2: Table S1-16: Panel gene lists; Table S17: Reported genes with small variants; Table S18 Reported structural variants.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eTesi B, Boileau C, Boycott KM, Canaud G, Caulfield M, Choukair D, et al. Precision medicine in rare diseases: What is next? J Intern Med. 2023;294(4):397-412.\u003c/li\u003e\n\u003cli\u003eStranneheim H, Lagerstedt-Robinson K, Magnusson M, Kvarnung M, Nilsson D, Lesko N, et al. 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The human splicing code reveals new insights into the genetic determinants of disease. Science. 2015;347(6218):1254806.\u003c/li\u003e\n\u003cli\u003eJaganathan K, Kyriazopoulou Panagiotopoulou S, McRae JF, Darbandi SF, Knowles D, Li YI, et al. Predicting Splicing from Primary Sequence with Deep Learning. Cell. 2019;176(3):535-48 e24.\u003c/li\u003e\n\u003cli\u003eFortuno C, Lee K, Olivier M, Pesaran T, Mai PL, de Andrade KC, et al. Specifications of the ACMG/AMP variant interpretation guidelines for germline TP53 variants. Hum Mutat. 2021;42(3):223-36.\u003c/li\u003e\n\u003cli\u003eLee K, Krempely K, Roberts ME, Anderson MJ, Carneiro F, Chao E, et al. Specifications of the ACMG/AMP variant curation guidelines for the analysis of germline CDH1 sequence variants. Hum Mutat. 2018;39(11):1553-68.\u003c/li\u003e\n\u003cli\u003eSleegers K, Brouwers N, Gijselinck I, Theuns J, Goossens D, Wauters J, et al. APP duplication is sufficient to cause early onset Alzheimer\u0026apos;s dementia with cerebral amyloid angiopathy. Brain. 2006;129(Pt 11):2977-83.\u003c/li\u003e\n\u003cli\u003eRovelet-Lecrux A, Hannequin D, Raux G, Le Meur N, Laquerriere A, Vital A, et al. APP locus duplication causes autosomal dominant early-onset Alzheimer disease with cerebral amyloid angiopathy. Nat Genet. 2006;38(1):24-6.\u003c/li\u003e\n\u003cli\u003eArbelo E, Protonotarios A, Gimeno JR, Arbustini E, Barriales-Villa R, Basso C, et al. 2023 ESC Guidelines for the management of cardiomyopathies. Eur Heart J. 2023;44(37):3503-626.\u003c/li\u003e\n\u003cli\u003eWilde AAM, Semsarian C, Marquez MF, Shamloo AS, Ackerman MJ, Ashley EA, et al. European Heart Rhythm Association (EHRA)/Heart Rhythm Society (HRS)/Asia Pacific Heart Rhythm Society (APHRS)/Latin American Heart Rhythm Society (LAHRS) Expert Consensus Statement on the state of genetic testing for cardiac diseases. 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Ultrasound Obstet Gynecol. 2022;60(4):487-93.\u003c/li\u003e\n\u003cli\u003eWestenius E, Conner P, Pettersson M, Sahlin E, Papadogiannakis N, Lindstrand A, et al. Whole-genome sequencing in prenatally detected congenital malformations: prospective cohort study in clinical setting. Ultrasound Obstet Gynecol. 2024;63(5):658-63.\u003c/li\u003e\n\u003cli\u003eHenry OJ, Ygberg S, Barbaro M, Lesko N, Karlsson L, Pena-Perez L, et al. Clinical whole genome sequencing in pediatric epilepsy: Genetic and phenotypic spectrum of 733 individuals. Epilepsia. 2025.\u003c/li\u003e\n\u003cli\u003eEisfeldt J, Ek M, Nordenskjold M, Lindstrand A. Toward clinical long-read genome sequencing for rare diseases. Nat Genet. 2025.\u003c/li\u003e\n\u003cli\u003eFresard L, Smail C, Ferraro NM, Teran NA, Li X, Smith KS, et al. Identification of rare-disease genes using blood transcriptome sequencing and large control cohorts. Nat Med. 2019;25(6):911-9.\u003c/li\u003e\n\u003cli\u003eGonorazky HD, Naumenko S, Ramani AK, Nelakuditi V, Mashouri P, Wang P, et al. Expanding the Boundaries of RNA Sequencing as a Diagnostic Tool for Rare Mendelian Disease. Am J Hum Genet. 2019;104(3):466-83.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"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":"genome sequencing, rare diseases, clinical diagnostics, single nucleotide variants, chromosomal rearrangements, structural variants, precision medicine","lastPublishedDoi":"10.21203/rs.3.rs-6790162/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6790162/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eAs clinical genetics evolves towards the broader field of clinical genomics, the diagnostic approach to rare diseases is undergoing a paradigm shift. This transformation has significantly impacted rare disease diagnostics, increasingly done through gene panels, whole exome and whole genome sequencing. To advance beyond genomics into precision medicine and encompass the breadth of relevant clinical scenarios, a true systems shift is required that challenges conventional barriers and enables the formation of cross-disciplinary, integrated environments.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eThe Genomic Medicine Center Karolinska Rare Diseases (GMCK-RD) has, for the past 10 years, brought together healthcare and academia to enable large-scale genome sequencing in a clinical diagnostics context. Within GMCK-RD, experts from various medical disciplines collaborate closely with clinical geneticists, bioinformaticians, and researchers to integrate genome sequencing into healthcare.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eIn total, 15 644 individuals with suspected rare diseases were analyzed using clinical genome sequencing, including pediatric (48%), adult (48%) and fetal (4%) samples. The overall diagnostic yield was 22.6% providing a diagnosis for 3 538 individuals with variants in 1 570 genes. Moreover, a rare disease analysis tool suite developed and validated \u003cem\u003ein house\u003c/em\u003e includes a bioinformatic pipeline allowing for comprehensive data analysis covering a wide range of genetic variants including SNVs, INDELs, repeat expansions, uniparental disomies, balanced and unbalanced structural variants as well as insertions of mobile elements. Results are visualized and interpreted in custom-developed decision support systems functioning as an interpretation portal as well as a knowledge-base to capture the interpretation efforts made in a structured format allowing future secondary use.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eAltogether, GMCK-RD has shifted healthcare in our region towards precision diagnostics. We emphasize the need to transition from traditional clinical genetic diagnostics to a broader clinical genomics approach. Beyond this shift, we advocate integrating genomics with specialized clinical and laboratory medicine, a concept pioneered for inborn errors of metabolism (IEM) with stepwise spread to additional disease groups. In this model, a multidisciplinary unit combines screening, targeted diagnostics, individualized treatment, and long-term patient follow-up. Here we provide a road map and guide for inspiration for centers aiming to implement genome sequencing in rare disease diagnostics.\u003c/p\u003e","manuscriptTitle":"The Genomic Medicine Center Karolinska 10-year report on genome sequencing for rare diseases and a strategy for stepwise clinical implementation","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-06-17 09:19:16","doi":"10.21203/rs.3.rs-6790162/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-11-21T15:58:04+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-09-19T11:27:05+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"255649991935596537712769043024178541478","date":"2025-08-31T19:25:16+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-08-01T04:32:41+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"126252808114398653555581368139611776244","date":"2025-07-19T07:32:26+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-06-12T14:40:17+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-06-04T16:04:39+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-06-02T07:09:21+00:00","index":"","fulltext":""},{"type":"submitted","content":"Genome Medicine","date":"2025-05-31T09:51:52+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 17th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-02-10T12:08:53+00:00","versionOfRecord":[],"versionCreatedAt":"2025-06-17 09:19:16","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6790162","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6790162","identity":"rs-6790162","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}