Short-Read Genome Sequencing at Population Scale: Diagnostic Insights From 2,317 Patients

Short-Read Genome Sequencing at Population Scale: Diagnostic Insights From 2,317 Patients | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Short-Read Genome Sequencing at Population Scale: Diagnostic Insights From 2,317 Patients Søren Faergeman, Lotte Andreasen, Naja Becher, Mette Christiansen, and 8 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7432106/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 31 Mar, 2026 Read the published version in European Journal of Human Genetics → Version 1 posted 9 You are reading this latest preprint version Abstract Introduction : As part of the Danish National Genome Centre (DNGC) initiative, the Central Denmark Region has implemented short-read whole genome sequencing (srWGS) as a first-tier diagnostic tool for suspected monogenetic disorders. Despite increasing adoption of genome sequencing, evidence from large-scale implementation across clinical specialties remains limited. Here, we evaluate the implementation from patient inclusion and srWGS scaling to diagnostic performance. Methods: From 2021-2024, we sequenced 2,317 patients with suspected genetic diseases across a wide range of medical specialities. Following clinical evaluation and informed consent, Illumina srWGS was performed. Patients were categorised into clinical subgroups based on phenotype and age to support targeted variant filtration and germline variant reporting. The primary outcome was diagnostic yield across all disease groups/categories. The project is a public/privat partnership co-funded by the Novo Nordisk Foundation. Results: Diagnostic yield ranged from 3% in patients with liver disease to 60% in those with skin disorders, with an overall yield of 20%. We observed a substantial variation in clinical usage of srWGS as a first-tier diagnostic tool across patient categories. Conclusion: Regional implementation of srWGS within the DNGC framework demonstrates its scalability as a first-tier diagnostic tool for monogenic disorders. Importantly, the combination of expert-guided inclusion criteria for srWGS and mixed public/private funding has ensured equitable access to genetic diagnostics. We identify patient groups with high diagnostic returns well suited for srWGS, as well as groups where alternative strategies could also be applied. Health sciences/Health care/Diagnosis/Genetic testing Health sciences/Medical research/Genetics research whole genome sequencing rare disease genetic testing Figures Figure 1 Figure 2 Figure 4 Figure 5 Introduction Short-read Whole Genome Sequencing (srWGS) has transformed and shortened the diagnostic odyssey of patients with hereditary diseases ( 1 ). The benefits are manifold: srWGS renders exome enrichment and amplification steps redundant, which reduces hands-on costs and turnaround time in the laboratory, and importantly results in more uniform coverage of the analysed genomes. Expanding sequencing to the entire genome, improves detection of copy number variants, inversions, translocations, and repeat expansions, in addition to single nucleotide variants (SNVs) and insertions/deletions (indel) variant calling ( 2 ). Additional genes are easily included in the diagnostic workflow and new genes may be re-analysed in silico post initial reporting. Consequently, srWGS is becoming a first-tier clinical test that can help rule out genetic causes of disease, deliver a definitive molecular diagnosis to direct patient care, or support further analyses such as transcriptome, methylome, long-read WGS or functional analyses that may be required for variant classification( 3 ). However, continuous evaluation of diagnostic yields of srWGS across rare diseases is imperative to guide clinical and laboratory decision making. Specifically, delineating which patient groups benefit from genetic diagnostics based on srWGS is essential to enable comparison across diagnostic centres and for identifying those in whom causative genetic variants are likely to be missed by srWGS. This study is based on the establishment of the Danish National Genome Center (DNGC), a nationally coordinated, top-down initiative aimed at ensuring equitable access to srWGS-based personalised medicine in Denmark. In 2018, the Novo Nordisk Foundation granted DKK 990 million (€133 million) over a five-year period to develop and operate the DNGC's infrastructure, to enable genome sequencing facilities in Aarhus and Copenhagen, with the aim to analyse 60,000 genomes( 4 ). The DNGC was officially founded in 2019 under the Danish Ministry of Health( 5 ). Patient group selection for srWGS involved reviewing 72 proposals and was subsequently consolidated into 18 patient categories( 6 ). National specialist networks of clinicians and clinical laboratory experts were formed to guide srWGS implementation, including defining patient inclusion criteria, counselling, and assessing clinical impact( 7 ). A Steering Committee with representatives from key health and academic institutions oversaw the process, supported by advisory groups addressing clinical and technical aspects( 7 ). This structured process was intended to ensure that patient group selection was clinically relevant, feasible, and beneficial for personalised medicine( 7 ). The implementation of srWGS largely replaced previous methods from single gene analysis (e.g. by Sanger sequencing, MLPA or repeat-primed PCR) to targeted gene panels including exomes and array-based methods. This study presents the implementation of srWGS in the Central Denmark Region, framed by the infrastructure and legal foundation of the DNGC. The study outlines the diagnostic yield across 15 patient categories and provides a framework for scaling genomic medicine and inform the design of future diagnostic strategies. Materials and methods Sequencing infrastructure within DNGC DNA was extracted from EDTA-stabilized peripheral blood using QIAsymphony DSP DNA Midi Kit (Qiagen, Germany) according to the manufacture’s protocol. Libraries were prepared using 300 ng input DNA with Illumina DNA PCR-Free Tagmentation protocol. A control sample was analyzed for 45 common SNPs to verify sample identity. Sequencing was conducted on Illumina NovaSeq 6000 or NovaSeqX+ (Illumina, USA) (from September 2023 and onwards). The bioinformatic pipeline was created and maintained by DNGC. Briefly, reads were mapped to the hg38 reference genome(8), variants (SNVs and small indels) were called using GATK HaplotyperCaller(9), whereas structural variant calling developed over time and included different callers(10-13). Repeat expansions were called using ExpansionHunter(14). Copy number for SMN1 and SMN2 were called using SMNCopyNumberCaller(15). Variants were filtered using VarSeq (Golden Helix, USA) with filtering strategies tailored to each patient group. Reported variants were classified (Classes C1–C5) according to guidelines of the American College of Medical Genetics and Genomics(16). All laboratory work was done in ISO15189 accredited laboratories. Patient inclusion During the inclusion period from January 1, 2021, to September 30, 2024, a total of 2,317 patients were included and sequenced by srWGS in the Central Denmark Region through the DNGC. During the study period, clinical reports were issued by one of three departments in the Central Denmark Region: Department of Clinical Immunology, Department of Clinical Genetics, or Department of Molecular Medicine. This study focus’ on all inherited diseases to allow for comparison of diagnostic yield across presumed monogenetic disorders. Somatic cancer is not included in this study. An overview of patient groups/subgroups and inclusion time in DNGC is given in supplementary Table S1. Data collection Information about age at testing, sex, referral reason, and reported genetic findings was collected from the laboratory information systems of the reporting laboratories. Categorization of reported findings Explanatory findings : C4 or C5 variant(s) in a gene known to cause a phenotype matching the clinical presentation of the patient, consistent with the expected inheritance pattern (biallelic or hemizygous for recessive; monoallelic for dominant). Partial findings : Heterozygous C4 or C5 variant in a gene associated with a matching recessive phenotype, but without a second variant identified. Uncertain findings : C3 variants were reported based on clinical context. For patients referred for hereditary heart disease, inborn errors of immunity, hereditary cancer, haematological, or endocrinological conditions, C3 variants were routinely reported upon clinician request. For other indications, C3 variants were reported when located in a gene known to cause a phenotype matching the clinical presentation of the patient, and reclassification to either C2 or C4, based on functional or family studies, was deemed likely. Secondary findings : C4 or C5 variants in genes not associated with the patient’s phenotype were reported in accordance with national guidelines from the Danish Society of Medical Genetics(17) Ethics All patients had provided written consent for srWGS in accordance with Danish law, which also accommodates the patient’s choice on reporting of secondary findings. Briefly, patients undergoing comprehensive genome sequencing can choose to 1) receive information on all identified secondary findings, 2) receive information only on secondary findings if there is a possibility of prevention or treatment, or 3) not to be informed about secondary findings(18). The Scientific Ethics Committees of the Central Denmark Region evaluated the study and determined that it did not require ethics approval (case 1-10-72-103-24). The study was approved as a quality project by the hospital management at Aarhus University Hospital, Aarhus, Denmark. Statistical analysis Statistical calculations and figure preparation for this publication were done using GraphPad Prims 10 (Dotmatics, USA). Results Implementation of srWGS During the 45-month inclusion period, we completed srWGS and clinical reports for 2,317 index patients with median (range) age and sex distribution per patient category as listed in Table 1 . The initial implementation progressed slowly, with only a limited number of sequencing runs conducted per month (see supplementary Fig. 1). This led to extended turnaround times (TAT), making it unfeasible to include patient groups such as those in fetal medicine and acutely ill children where rapid analysis is essential. As a result, the inclusion of these groups was not possible until the fourth quarter of 2022. We observed approximately equal distribution of sexes across patient categories, except in the Haematology category, which was dominated by women examined for genetic coagulopathies following excessive bleeding during labour. Despite the ambition to initiate srWGS-based diagnostics simultaneously across patient categories, we observed marked differences in implementation (Fig. 1 a and 1 b). For example, hereditary heart disease and rare diseases among children < 18 years of age were the most frequently represented clinical indications among the patients very early on, whereas other diseases such as psychiatric disease and kidney disease were less commonly present among sequenced patients. During the study period, the sequencing costs were reduced significantly, decreasing from approximately 1,150 USD per sample (based on 8 samples per run on NovaSeq 6000 S2 flow cells) to 475 USD per sample (based on 40 samples per run on NovaSeq X Plus 25B flow cells). In contrast, the cost of targeted sequencing was reduced by only around 10%, as this requires more manual handing and sequencing expenses account for a smaller proportion of the overall price (Internal data). Table 1 Overview of number of patients mean/median age and sex distribution in each patient category. *For fetal medicine, gestational age and fetal sex are not stated in genetic reports and hence not available in the dataset used in this study. Patient group number (%) Age Median(range) males (%) Hereditary heart disease 556 ( 24 ) 52 (0–86) 60 Rare diseases, children < 18 520 ( 22 ) 5 (0–21) 40 Neurogenetic patients 248 ( 11 ) 42 (0–85) 49 Inborn errors of immunity 195 ( 8 ) 27 (0–71) 44 Childhood cancer 137 ( 6 ) 10 (0–29) 50 Endocrinological patients 135 ( 6 ) 33 (0–83) 45 Fetal medicine* 107 ( 5 ) NA NA Hereditary skin disorders 84 ( 4 ) 31 (0–74) 39 Hematology 79 ( 3 ) 41 (13–76) 20 Audiogenetics 74 ( 3 ) 5 (0–81) 50 Rare diseases, adults 74 ( 3 ) 31 (2–77) 46 Cancer in young adults and hereditary cancer in adults 45 ( 2 ) 39 (21–86) 31 Hereditary cholestatic and fibrotic liver diseases 36 ( 2 ) 32 (0–88) 47 Kidney failure 19 ( 1 ) 44 (5–61) 53 Psychiatry, children and adolescents 8 (< 1) 11 ( 3 – 13 ) 56 Diagnostic yield We calculated the diagnostic yield as the percentage of test reports containing explanatory findings (i.e., C4 or C5 variants consistent with both the inheritance pattern and the patient’s phenotype). The percentage of reports with partial findings i.e ., single heterozygous C4 or C5 variants in recessive genes relevant to the phenotype, were also calculated (Fig. 2 ). We observed a weighted average of 20% diagnostic yield across all patient groups with considerable variation across patient categories. For example, the diagnostic yield of hereditary skin disorders was 60% and only 3% in patients suspected of hereditary cholestatic and fibrotic liver disease (Fig. 2 ). Reporting of uncertain and secondary findings Reporting practices for uncertain findings (C3 variants) differed across patient groups, as detailed in the Methods section. Among patients with hereditary heart disease, inborn errors of immunity, hereditary cancer, haematological, and endocrine conditions, 20–50% had uncertain findings reported (Fig. 3 A). In contrast, such findings were reported in only 3–14% of patients in the remaining groups. Secondary findings (Fig. 3 B) were reported with a low degree of variation (0–4%) across all patient categories. Dissecting diagnostic yields within selected patient categories Within each patient group, the DNGC or the analysing departments defined patient/diagnostic subcategories. Sequencing strategies included either trio sequencing (mother, father, proband) or singleton sequencing (proband only). We focused our analysis on selected patient groups (rare diseases in children and adults, neurogenetic disorders, and endocrinological patients), to evaluate variations in diagnostic yield as well as frequency of reported C3 variants and secondary findings (Fig. 4 ). Rare disease cohorts All children suspected of having a rare disease, including malformations, neurodevelopmental or metabolic disorders in addition to monogenetic causes of acute illness, were analysed using a trio-based approach. This strategy delivered an overall diagnostic yield of 29% (Fig. 4 A). In patients with phenotypically well-defined rare disorders, we obtained a diagnostic yield of 23% by using a custom singleton approach, whereby analysed genes were defined by the referring clinician or the Department of Clinical Genetics. Within specific subgroups, we observed higher diagnostic yields: 38% for children with skeletal dysplasia and 50%for adults with suspected monogenic skeletal dysplasia (though in a small cohort, n = 8). Diagnostic yields from both custom gene panels and trio-based analyses in adults were comparable (28% and 22%, respectively; Fig. 4 B). Neurogenetic disorders Explanatory findings were identified in 20% of patients overall. Subgroup analyses uncovered diagnostic yields of 23–28% in patients with epilepsy, neuropathy, or basal ganglia disease. In contrast, lower yields were found for myopathy (12%), ataxia (16%), and hereditary spastic paraplegia (19%). Among patients in the subcategory with dementia, ALS/FTD or small vessel disease, the overall yield was 7% (Fig. 4 C). Endocrinological patients The overall diagnostic yield was 12%. Within the 8 endocrine subgroups, yields ranged from 0% in patients with organic hypoglycemia (5 patients) to almost 18% in patients disorders of sex development, monogenic diabetes (N = 35) and rare thyroid disorders (Fig. 4 D). Discussion The implementation of srWGS for a broad spectrum of diseases in Central Region Denmark has transformed and streamlined genetic testing towards srWGS. On average, the program has delivered a genetically verified diagnosis for 1 in 5 of all analysed patients. These findings have guided clinical decision-making and facilitated genetic counselling for patients and their families. In parallel, the significant reduction in cost during the study period, has made srWGS an economically viable method in clinical genetic diagnostics, further substantiating srWGS as a first-tier test in personalised medicine. The execution of the genomic program has required considerable efforts by national expert panels to standardise patient inclusion criteria. Similarly, laboratory organisation, analytical workflows and pipeline design were also standardised across the country. Whilst this process reduced local flexibility, it was also a clinical support in everyday practice to have national expert panel to substantiate national consensus on clinical decisions, for example on the reporting secondary findings and C3 variants( 19 ). Accordingly, the standardisation has overall been successful and led to a much tighter collaboration across the country with exchange of clinical and bioinformatic information in multidisciplinary teams. The new legal framework adopted into Danish law in 2019 demanding written consent informing the patient about the submission of their WGS data to the DNGC and specifying the patient’s will regarding reporting of secondary findings. This task necessitated significant clinical resources to ensure an informed consent procedure( 20 ). On the one hand, this was initially viewed as cumbersome, it may partly contribute to the differences in adaptation of srWGS between clinical specialties and the inclusion of patients. On the other hand, patients have become more empowered to take active part in the decision-making on which secondary findings are reported for the benefit of themselves and their families. In accordance with the Danish Society of Medical Genetics guideline, secondary findings were not actively searched for in the genomic analysis( 17 ), which contrasts the recommendation of the American College of Medical Genetics( 21 ). However, we identified secondary findings in 3% of analysed index cases with limited variation across patient groups, which is comparable to similar studies ( 22 ). The differential approach to the reporting of C3 variants was in our experience operational given that options for further analysis vary considerably between patient groups. For example, C3 variants identified in patients with Inborn errors of immunity (20%) may be further evaluated functionally, as leukocytes, the primary tissue of interest, are readily accessible for downstream testing, unlike in many other disorders. In contrast, in the prenatal setting, C3 variants are rarely reported (3%) which is supported by both national and international guidelines( 23 ) ( 24 ). Delineating the diagnostic yield of each patient group has direct implications for pre-test genetic counselling, highlighting the importance of continuous monitoring of genetic testing strategies. However, patient selection and prior genetic analysis constitute likely sources of bias diagnostic yield estimates. In the first year, arrayCGH was the first-tier test in patients with rare diseases and consequently patients diagnosed by arrayCGH were not included in the study, thus introducing an underestimation of the overall diagnostic yield. Insights from comparable national genomic programs help contextualize such diagnostic yield data. For example, the UK´s 100,000 Genomes project´s Pilot on Rare-Disease in 2,183 probands, reported an overall diagnostic yield of 25% ( 25 ). Their higher diagnostic rate compared to ours may partly be explained by the inclusion of a relatively large sub cohort with ophthalmological disorders (16% of the entire cohort), which demonstrated a markedly higher diagnostic yield (40%). Likewise, a recent study of 12,737 patients from the 2025 French Genomic Medicine Initiative generated an overall diagnostic yield of 30.6%, which was largely achieved, according to the authors, through inclusion of Malformation and Neurodevelopmental disorders, representing 62.2% of the French cohort with a diagnostic yield of 30.8%( 26 ). In our study, rare disease in children constituted 24% of the cohort and here we found a diagnostic yield of 28%, which is comparable to diagnostic yields in rare disease cohorts in other studies ( 27 ). Also, the diagnostic yields of skeletal dysplasia in our cohort of paediatric and adult patients were comparable to a recent study demonstrating an overall diagnostic yield of 37%-42% among children in this patient group ( 28 ). We were surprised to find that only 19% of patients within the Ataxia and spastic paraplegia patient subgroup received a genetic diagnosis. Comparable srWGS based studies have reported higher diagnostic yields 38.9% for hereditary spastic paraplegia ( 29 ) and 43% for patients with hereditary ataxia( 30 ). Interestingly, the authors behind this systematic review ( 30 ) note that higher diagnostic yields were associated with specific phenotype selection, suggesting a more narrow patient selection in our study might increase the diagnostic yield. In contrast, there are no studies of broadly included endocrine patients to compare with, however, we anticipate that the diagnostic yield will increase in the future with the experience gained from the current project, especially the identification of patients for genetic testing has improved during the project period. There is currently ongoing work within the European Reference Network on rare endocrine conditions to define the optimal approach to testing of endocrine patients, and a preliminary report on testing has been published( 31 ). As such, many factors affect the diagnostic yield of a genomic analysis, most importantly patient selection, pipeline design and variant interpretation including options for functional variant studies. Though these differentiating factors complicates direct comparison between genomic programs, the data provides an important current baseline for the diagnostic yield of short-read sequencing-based genomic analysis. Except for hereditary skin diseases, it is important to recognise that most of the analysed patients suspected of monogenetically driven disease remain genetically undiagnosed. Increases in the diagnostic yield may likely be achieved through better genomic data sharing infrastructure facilitated through important initiatives such as the European Genomic Data Infrastructure funded by the European Commission ( 32 ) and the Federated European Genome-Phenome Archive launched in 2022 ( 33 ). A more stringent selection of patients with phenotypes highly suggestive of monogenetic disease will also increase the diagnostic yield albeit at the potential cost of reduced diagnostic yield in patients with less severe or atypical phenotypes. Looking ahead, long-read sequencing is expected to provide higher diagnostic yields compared to short read sequencing primarily due to its improved ability to detect repeat expansions, variants in pseudogenes, methylation differences, and structural variants ( 34 , 35 ). It is, however, important to emphasise that also with long-read sequencing it is equally essential to harmonise national and international legal frameworks on genomic data infrastructure projects to allow for cross-border data sharing to fully harness the benefits of this technology. Conclusion In conclusion, the Central Denmark Region’s implementation of srWGS demonstrates the feasibility and equity of large-scale genome sequencing as a first-tier test in routine care. Diagnostic yields across the investigated patient groups mirror comparable national genomic programs in the UK and France, and the expert-guided harmonisation of the clinical and analytical approaches has set new standards for diagnosing monogenic disorders. Abbreviations ALS: Amyotrophic Lateral Sclerosis C4: Class 4; likely pathogenic variant C5: Class 5; pathogenic variant DNGC: Danish National Genome Center FTD: FrontoTemporal Dementia NGS: Next Generation Sequencing SF: Secondary Findings srWGS: short read Whole Genome Sequencing TAT: turn-around time VUS: Variant of Unknown Significance Declarations Competing Interest No conflicts of interest DL, SLF, JMBJ, NB, MC, KSS, LA, SM, UBJ, SV. Conflict of interest CHG (has received honorariums for scientific talks from Novo Nordisk, Merck and Astra-Zeneca) OHL (has received honorariums for scientific talks from Illumina) Financial support for this study None: DL, SLF, JMBJ, NB, MC, OHL, LA, SM, UBJ, SV. CHG: International Fund of Congenital Adrenal Hyperplasia, Health Research Fund of Central Denmark Region, Aase and Einar Danielsen Fund, and “Fonden til Lægevidenskabens Fremme” Acknowledgements We sincerely acknowledge the computational, organisational and laboratory support behind this work. In particular, thank you to Kenneth Lyneborg Hvam and Louise Paludan for data management and to all involved laboratory technicians headed by Lone Hedegaard. Data Availability Statement The data collected for this study is not publicly available as it contains information that could compromise patient privacy and safety. Access to the data requires that the Danish National Committee on Health Research Ethics approve the requestors’ intended use of the data, and that the legal entity of the data requestor enters into a data protection agreement with the Danish data controller, the Central Denmark Region. Author contributions SLF: Study concept and design, acquisition of data, analysis and interpretation of data, drafting the manuscript, final approval LA: Analysis and interpretation of data, reviewing the manuscript and final approval NB: Analysis and interpretation of data, reviewing the manuscript and final approval MC: Analysis and interpretation of data, reviewing the manuscript and final approval CHG: Analysis and interpretation of data, drafting the manuscript, final approval UBJ: Analysis and interpretation of data, drafting the manuscript, final approval JMBJ: Analysis and interpretation of data, drafting the manuscript, final approval OHL: Study concept and design, interpretation of data, reviewing the manuscript and final approval SM: Analysis and interpretation of data, reviewing the manuscript and final approval KSS: Acquisition of data, reviewing the manuscript and final approval. SV: Study concept and design, Acquisition of data, Analysis of data, reviewing the manuscript, Final approval of published version. Accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved. DL: study concept and design, acquisition of data, analysis and interpretation of data, drafting the manuscript, final approval. References Riess O, Sturm M, Menden B, Liebmann A, Demidov G, Witt D, et al. Genomes in clinical care. NPJ Genom Med. 2024;9(1):20. Ewans LJ, Minoche AE, Schofield D, Shrestha R, Puttick C, Zhu Y, et al. Whole exome and genome sequencing in mendelian disorders: a diagnostic and health economic analysis. Eur J Hum Genet. 2022;30(10):1121–31. Wojcik MH, Lemire G, Berger E, Zaki MS, Wissmann M, Win W, et al. Genome Sequencing for Diagnosing Rare Diseases. 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Cite Share Download PDF Status: Published Journal Publication published 31 Mar, 2026 Read the published version in European Journal of Human Genetics → Version 1 posted Editorial decision: revise 07 Nov, 2025 Review # 1 received at journal 19 Oct, 2025 Review # 2 received at journal 18 Sep, 2025 Reviewer # 2 agreed at journal 09 Sep, 2025 Reviewer # 1 agreed at journal 08 Sep, 2025 Reviewers invited by journal 08 Sep, 2025 Submission checks completed at journal 28 Aug, 2025 Editor assigned by journal 22 Aug, 2025 First submitted to journal 22 Aug, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7432106","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":511912281,"identity":"77587efe-77ae-476d-a5d5-4069c941bcb7","order_by":0,"name":"Søren Faergeman","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAm0lEQVRIiWNgGAWjYJCCDwwMNiDamGgdjDMYGNJI13KYBC3y7c0HG35UnM/XbWDebECUFoMzxxIbe87cttx2gK04gTgtEjnmD3jbbhuYHeAxPkCcw2bkf2z8++8cCVoYbuQwNvM2HABrIdJhZ44ZNsscSzYwO8xWTJz3gSH2sPFNjZ2B2fHmzRLEOQwOmElUPwpGwSgYBaMADwAAwNkwJb+35dEAAAAASUVORK5CYII=","orcid":"https://orcid.org/0000-0002-4482-1578","institution":"Aarhus University Hospital /Institute of Clinical Medicine; Aarhus University","correspondingAuthor":true,"prefix":"","firstName":"Søren","middleName":"","lastName":"Faergeman","suffix":""},{"id":511912282,"identity":"d57b12df-bd86-4489-a144-5698e88e47cc","order_by":1,"name":"Lotte Andreasen","email":"","orcid":"","institution":"Aarhus University Hospital","correspondingAuthor":false,"prefix":"","firstName":"Lotte","middleName":"","lastName":"Andreasen","suffix":""},{"id":511912283,"identity":"cff53f0c-4f91-4d88-ba4a-54fb92612cea","order_by":2,"name":"Naja Becher","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Naja","middleName":"","lastName":"Becher","suffix":""},{"id":511912284,"identity":"285b414e-3b83-40a7-9427-1bd7fa6cde11","order_by":3,"name":"Mette Christiansen","email":"","orcid":"","institution":"Aarhus University Hospital/Institute of Clinical Medicine; Aarhus University","correspondingAuthor":false,"prefix":"","firstName":"Mette","middleName":"","lastName":"Christiansen","suffix":""},{"id":511912285,"identity":"ccfbfb9d-b4ab-423e-9e36-acdc33dd4308","order_by":4,"name":"Claus Gravholt","email":"","orcid":"","institution":"Aarhus University Hospital","correspondingAuthor":false,"prefix":"","firstName":"Claus","middleName":"","lastName":"Gravholt","suffix":""},{"id":511912286,"identity":"ab5109cc-640b-42e9-afac-12c1b33c2260","order_by":5,"name":"Uffe Jensen","email":"","orcid":"https://orcid.org/0000-0002-6205-6355","institution":"Aarhus University","correspondingAuthor":false,"prefix":"","firstName":"Uffe","middleName":"","lastName":"Jensen","suffix":""},{"id":511912287,"identity":"c7c28d4c-d289-4812-a73f-bac0bd6e0a0c","order_by":6,"name":"Jens Magnus Bernth Jensen","email":"","orcid":"https://orcid.org/0000-0002-5004-7822","institution":"Aarhus University Hospital/Institute of Clinical Medicine; Aarhus University","correspondingAuthor":false,"prefix":"","firstName":"Jens","middleName":"Magnus Bernth","lastName":"Jensen","suffix":""},{"id":511912288,"identity":"fec0dcbf-1885-4f1e-a6a1-05b235a84a55","order_by":7,"name":"Ole Larsen","email":"","orcid":"","institution":"Aarhus University Hospital/Institute of Clinical Medicine; Aarhus University","correspondingAuthor":false,"prefix":"","firstName":"Ole","middleName":"","lastName":"Larsen","suffix":""},{"id":511912289,"identity":"d2f0edac-011d-4bd0-b3a4-b9f07a64656b","order_by":8,"name":"Sara Markholt","email":"","orcid":"","institution":"Aarhus University Hospital","correspondingAuthor":false,"prefix":"","firstName":"Sara","middleName":"","lastName":"Markholt","suffix":""},{"id":511912290,"identity":"b9b8f942-33b7-4332-a798-127d7e4a1544","order_by":9,"name":"Katrine Sandgaard","email":"","orcid":"","institution":"Aarhus University Hospital/Institute of Clinical Medicine; Aarhus University","correspondingAuthor":false,"prefix":"","firstName":"Katrine","middleName":"","lastName":"Sandgaard","suffix":""},{"id":511912291,"identity":"78dd8e44-6eb9-4a9b-ba22-8ad6c5a59d0b","order_by":10,"name":"Søren Vang","email":"","orcid":"","institution":"Aarhus University Hospital/Institute of Clinical Medicine; Aarhus University","correspondingAuthor":false,"prefix":"","firstName":"Søren","middleName":"","lastName":"Vang","suffix":""},{"id":511912292,"identity":"8978638d-62b0-4ddd-9d7c-e739575bfec4","order_by":11,"name":"Dorte Lildballe","email":"","orcid":"https://orcid.org/0000-0003-0223-1673","institution":"Aarhus University Hospital","correspondingAuthor":false,"prefix":"","firstName":"Dorte","middleName":"","lastName":"Lildballe","suffix":""}],"badges":[],"createdAt":"2025-08-22 08:05:59","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7432106/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7432106/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1038/s41431-026-02089-8","type":"published","date":"2026-03-31T04:00:00+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":91817038,"identity":"b60126d6-97dd-457b-9848-ee1bcce3f22a","added_by":"auto","created_at":"2025-09-22 06:53:22","extension":"jpg","order_by":2,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":2301436,"visible":true,"origin":"","legend":"","description":"","filename":"Figure2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7432106/v1/ef10c29d29f6856482999b27.jpg"},{"id":91817149,"identity":"7cbf3178-aa6a-4b2c-a380-bb9003c1d4ee","added_by":"auto","created_at":"2025-09-22 06:53:43","extension":"jpg","order_by":3,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":2974889,"visible":true,"origin":"","legend":"","description":"","filename":"Figure3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7432106/v1/df99dc98ec17839d7073e98d.jpg"},{"id":91817073,"identity":"8b4a165b-b1d8-4cfb-b9ee-0f78c11620ec","added_by":"auto","created_at":"2025-09-22 06:53:31","extension":"jpg","order_by":4,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":2199163,"visible":true,"origin":"","legend":"","description":"","filename":"Figure4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7432106/v1/015b7b404772ad8cd28bac73.jpg"},{"id":91816723,"identity":"94db1e48-ef8d-4456-b3a8-e1ead6bd40a8","added_by":"auto","created_at":"2025-09-22 06:52:43","extension":"jpg","order_by":5,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":1135107,"visible":true,"origin":"","legend":"","description":"","filename":"supplementaryFig1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7432106/v1/c4d11484c7eca957a5910d7c.jpg"},{"id":91816817,"identity":"75640eee-dbb4-48e7-8405-e68e62c69552","added_by":"auto","created_at":"2025-09-22 06:52:46","extension":"xml","order_by":7,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":97802,"visible":true,"origin":"","legend":"","description":"","filename":"100525EJHG0enriched.xml","url":"https://assets-eu.researchsquare.com/files/rs-7432106/v1/0b3d74ebd44f5b178c41972e.xml"},{"id":91816596,"identity":"0549c40c-6c56-4ece-bc47-f6ec92556779","added_by":"auto","created_at":"2025-09-22 06:52:05","extension":"jpg","order_by":12,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":1135107,"visible":true,"origin":"","legend":"","description":"","filename":"supplementaryFig1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7432106/v1/2487ebc2b0dd6b7d64488865.jpg"},{"id":91816950,"identity":"9c5b7d0e-af64-4a78-b35e-34e3c8c1d0e1","added_by":"auto","created_at":"2025-09-22 06:53:05","extension":"png","order_by":15,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":2926387,"visible":true,"origin":"","legend":"","description":"","filename":"OnlineFigure3.png","url":"https://assets-eu.researchsquare.com/files/rs-7432106/v1/3ffd83dc0f6502008e818909.png"},{"id":91817083,"identity":"5ff28a5d-523d-42eb-991f-9f5822926806","added_by":"auto","created_at":"2025-09-22 06:53:35","extension":"png","order_by":16,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":610995,"visible":true,"origin":"","legend":"","description":"","filename":"OnlineFigure4.png","url":"https://assets-eu.researchsquare.com/files/rs-7432106/v1/62cd2b887f3d711e1c5ae15a.png"},{"id":91488146,"identity":"c21ffbad-f2ef-4847-9ff4-c11b3480cf89","added_by":"auto","created_at":"2025-09-17 05:07:37","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":2481213,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eCumulated number of patients by patient group. \u003c/strong\u003eA) Patient groups containing more than 100 patients in total. B) Patient groups containing less than 100 patients in total. The patient groups were included in DNGC at different time points, see supplementary table S1. Month no. 1: January 2021.\u003c/p\u003e","description":"","filename":"Figure1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7432106/v1/a2295466280535721ea977de.jpg"},{"id":91488156,"identity":"f9112a8a-a031-4128-ad81-4e5321f3d43d","added_by":"auto","created_at":"2025-09-17 05:07:37","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":2301436,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ePercentage of clinical reports with explanatory findings or partial findings across patient categories\u003c/strong\u003e.\u003cbr\u003e\n Stacked histograms displaying the proportion of test reports classified as having explanatory findings or partial findings within each patient category. \u003cem\u003eExplanatory findings\u003c/em\u003e were defined as C4 or C5 variant(s) in a gene known to cause a phenotype matching that of the patient, with a genotype consistent with the expected inheritance pattern (biallelic or hemizygous for recessive; monoallelic for dominant). Partial findings refer to single heterozygous C4 or C5 variants in genes associated with a matching recessive phenotype, but without a second variant identified.\u003c/p\u003e","description":"","filename":"Figure2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7432106/v1/2f7d16b8007fa2c45f5227ec.jpg"},{"id":91816637,"identity":"fd2d4489-d304-4bae-83ac-f5601856c7f1","added_by":"auto","created_at":"2025-09-22 06:52:22","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":2974889,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFrequency of reported uncertain and secondary findings across patient categories. \u003c/strong\u003eHistograms depict the percentage of clinical reports with uncertain findings (i.e., reported C3 variants) (\u003cstrong\u003eA\u003c/strong\u003e) and secondary findings (i.e., C4 or C5 variants not associated with the patient’s phenotype) (\u003cstrong\u003eB\u003c/strong\u003e).\u003c/p\u003e","description":"","filename":"Figure3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7432106/v1/149255255c7d5d80f5bb7906.jpg"},{"id":91488144,"identity":"49581bfc-c266-4434-8159-890f710f2886","added_by":"auto","created_at":"2025-09-17 05:07:37","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":2199163,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eDiagnostic yield in selected patient subgroups.\u003c/strong\u003e Diagnostic yield was defined as the number of cases with explanatory findings (C4 or C5 variant(s) in a gene relevant to the phenotype, with a genotype consistent with the expected inheritance pattern) relative to all sequenced cases within each patient category.A) rare diseases in children, B) rare diseases in adults, C) neurogenetics, and D) endocrinological patients.\u003c/p\u003e","description":"","filename":"Figure4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7432106/v1/bed62eeeee279940f1f97025.jpg"},{"id":105886550,"identity":"429ebe75-7531-46cb-a125-79bd2fb39d33","added_by":"auto","created_at":"2026-04-01 07:29:58","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":10737855,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7432106/v1/fb32da5a-5be0-4087-95c2-07176a05aefb.pdf"},{"id":91488143,"identity":"cfbc4f68-8076-4d0c-81a2-7d29336cd18e","added_by":"auto","created_at":"2025-09-17 05:07:37","extension":"xlsx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":14828,"visible":true,"origin":"","legend":"S1","description":"","filename":"SupplementaryTableS1.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-7432106/v1/c0e5ed6fae5ce524377b8b6a.xlsx"},{"id":91817266,"identity":"e7c71196-098a-42f3-a027-d6b2424f95e3","added_by":"auto","created_at":"2025-09-22 06:54:31","extension":"jpg","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":1135107,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSupplementary figure 1. Turnaround times (TAT) in days per whole genome sequencing (WGS) equivalent from October 2021 to September 2024\u003c/strong\u003e. The median TAT remained around 20 days until June 2022, after which it was reduced to approximately 9 days. From January to October 2021, the sample volume was too low to support weekly sequencing runs; therefore, sequencing was carried out approximately every second week to ensure economic viability.\u003c/p\u003e","description":"","filename":"supplementaryFig1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7432106/v1/51a6cf3a8383787817794d97.jpg"}],"financialInterests":"There is no duality of interest","formattedTitle":"Short-Read Genome Sequencing at Population Scale: Diagnostic Insights From 2,317 Patients","fulltext":[{"header":"Introduction","content":"\u003cp\u003eShort-read Whole Genome Sequencing (srWGS) has transformed and shortened the diagnostic odyssey of patients with hereditary diseases (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e). The benefits are manifold: srWGS renders exome enrichment and amplification steps redundant, which reduces hands-on costs and turnaround time in the laboratory, and importantly results in more uniform coverage of the analysed genomes. Expanding sequencing to the entire genome, improves detection of copy number variants, inversions, translocations, and repeat expansions, in addition to single nucleotide variants (SNVs) and insertions/deletions (indel) variant calling (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e). Additional genes are easily included in the diagnostic workflow and new genes may be re-analysed \u003cem\u003ein silico\u003c/em\u003e post initial reporting. Consequently, srWGS is becoming a first-tier clinical test that can help rule out genetic causes of disease, deliver a definitive molecular diagnosis to direct patient care, or support further analyses such as transcriptome, methylome, long-read WGS or functional analyses that may be required for variant classification(\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e). However, continuous evaluation of diagnostic yields of srWGS across rare diseases is imperative to guide clinical and laboratory decision making. Specifically, delineating which patient groups benefit from genetic diagnostics based on srWGS is essential to enable comparison across diagnostic centres and for identifying those in whom causative genetic variants are likely to be missed by srWGS.\u003c/p\u003e\u003cp\u003eThis study is based on the establishment of the Danish National Genome Center (DNGC), a nationally coordinated, top-down initiative aimed at ensuring equitable access to srWGS-based personalised medicine in Denmark. In 2018, the Novo Nordisk Foundation granted DKK 990\u0026nbsp;million (\u0026euro;133\u0026nbsp;million) over a five-year period to develop and operate the DNGC's infrastructure, to enable genome sequencing facilities in Aarhus and Copenhagen, with the aim to analyse 60,000 genomes(\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e). The DNGC was officially founded in 2019 under the Danish Ministry of Health(\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e). Patient group selection for srWGS involved reviewing 72 proposals and was subsequently consolidated into 18 patient categories(\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e). National specialist networks of clinicians and clinical laboratory experts were formed to guide srWGS implementation, including defining patient inclusion criteria, counselling, and assessing clinical impact(\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e). A Steering Committee with representatives from key health and academic institutions oversaw the process, supported by advisory groups addressing clinical and technical aspects(\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e). This structured process was intended to ensure that patient group selection was clinically relevant, feasible, and beneficial for personalised medicine(\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThe implementation of srWGS largely replaced previous methods from single gene analysis (e.g. by Sanger sequencing, MLPA or repeat-primed PCR) to targeted gene panels including exomes and array-based methods. This study presents the implementation of srWGS in the Central Denmark Region, framed by the infrastructure and legal foundation of the DNGC. The study outlines the diagnostic yield across 15 patient categories and provides a framework for scaling genomic medicine and inform the design of future diagnostic strategies.\u003c/p\u003e"},{"header":"Materials and methods","content":"\u003ch2\u003eSequencing infrastructure within DNGC\u003c/h2\u003e\n\u003cp\u003eDNA was extracted from EDTA-stabilized peripheral blood using QIAsymphony DSP DNA Midi Kit (Qiagen, Germany) according to the manufacture’s protocol. Libraries were prepared using 300 ng input DNA with Illumina DNA PCR-Free Tagmentation protocol. A control sample was analyzed for 45 common SNPs to verify sample identity. Sequencing was conducted on Illumina NovaSeq 6000 or NovaSeqX+ (Illumina, USA) (from September 2023 and onwards). The bioinformatic pipeline was created and maintained by DNGC. Briefly, reads were mapped to the hg38 reference genome(8), variants (SNVs and small indels) were called using GATK HaplotyperCaller(9), whereas structural variant calling developed over time and included different callers(10-13). Repeat expansions were called using ExpansionHunter(14). Copy number for \u003cem\u003eSMN1\u003c/em\u003e and \u003cem\u003eSMN2\u003c/em\u003e were called using SMNCopyNumberCaller(15). Variants were filtered using VarSeq (Golden Helix,\u0026nbsp;USA) with filtering strategies tailored to each patient group. Reported variants were classified (Classes C1–C5) according to guidelines of the American College of Medical Genetics and Genomics(16). All laboratory work was done in ISO15189 accredited laboratories.\u0026nbsp;\u003c/p\u003e\n\u003ch2\u003ePatient inclusion\u003c/h2\u003e\n\u003cp\u003eDuring the inclusion period from January 1, 2021, to September 30, 2024, a total of 2,317 patients were included and sequenced by srWGS in the Central Denmark Region through the DNGC. During the study period, clinical reports were issued by one of three departments in the Central Denmark Region: Department of Clinical Immunology, Department of Clinical Genetics, or Department of Molecular Medicine. This study focus’ on all inherited diseases to allow for comparison of diagnostic yield across presumed monogenetic disorders. Somatic cancer is not included in this study. An overview of patient groups/subgroups and inclusion time in DNGC is given in supplementary Table S1.\u003c/p\u003e\n\u003ch2\u003eData collection\u003c/h2\u003e\n\u003cp\u003eInformation about age at testing, sex, referral reason, and reported genetic findings was collected from the laboratory information systems of the reporting laboratories.\u003c/p\u003e\n\u003ch2\u003eCategorization of reported findings\u003c/h2\u003e\n\u003cp\u003e\u003cem\u003eExplanatory findings\u003c/em\u003e: C4 or C5 variant(s) in a gene known to cause a phenotype matching the clinical presentation of the patient, consistent with the expected inheritance pattern (biallelic or hemizygous for recessive; monoallelic for dominant).\u003c/p\u003e\n\u003cp\u003e\u003cem\u003ePartial findings\u003c/em\u003e: Heterozygous C4 or C5 variant in a gene associated with a matching recessive phenotype, but without a second variant identified.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eUncertain findings\u003c/em\u003e: C3 variants were reported based on clinical context. For patients referred for hereditary heart disease, inborn errors of immunity, hereditary cancer, haematological, or endocrinological conditions, C3 variants were routinely reported upon clinician request. For other indications, C3 variants were reported when located in a gene known to cause a phenotype matching the clinical presentation of the patient, and reclassification to either C2 or C4, based on functional or family studies, was deemed likely.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eSecondary findings\u003c/em\u003e: C4 or C5 variants in genes not associated with the patient’s phenotype were reported in accordance with national guidelines from the Danish Society of Medical Genetics(17)\u0026nbsp;\u003c/p\u003e\n\u003ch2\u003eEthics\u003c/h2\u003e\n\u003cp\u003eAll patients had provided written consent for srWGS in accordance with Danish law, which also accommodates the patient’s choice on reporting of secondary findings. Briefly, patients undergoing comprehensive genome sequencing can choose to 1) receive information on all identified secondary findings, 2) receive information only on secondary findings if there is a possibility of prevention or treatment, or 3) not to be informed about secondary findings(18). The Scientific Ethics Committees of the Central Denmark Region evaluated the study and determined that it did not require ethics approval (case 1-10-72-103-24). The study was approved as a quality project by the hospital management at Aarhus University Hospital, Aarhus, Denmark.\u003c/p\u003e\n\u003ch2\u003eStatistical analysis\u003c/h2\u003e\n\u003cp\u003eStatistical calculations and figure preparation for this publication were done using GraphPad Prims 10 (Dotmatics, USA).\u0026nbsp;\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eImplementation of srWGS\u003c/p\u003e\u003cp\u003eDuring the 45-month inclusion period, we completed srWGS and clinical reports for 2,317 index patients with median (range) age and sex distribution per patient category as listed in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. The initial implementation progressed slowly, with only a limited number of sequencing runs conducted per month (see supplementary Fig.\u0026nbsp;1). This led to extended turnaround times (TAT), making it unfeasible to include patient groups such as those in fetal medicine and acutely ill children where rapid analysis is essential. As a result, the inclusion of these groups was not possible until the fourth quarter of 2022. We observed approximately equal distribution of sexes across patient categories, except in the Haematology category, which was dominated by women examined for genetic coagulopathies following excessive bleeding during labour.\u003c/p\u003e\u003cp\u003eDespite the ambition to initiate srWGS-based diagnostics simultaneously across patient categories, we observed marked differences in implementation (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ea and \u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eb). For example, hereditary heart disease and rare diseases among children\u0026thinsp;\u0026lt;\u0026thinsp;18 years of age were the most frequently represented clinical indications among the patients very early on, whereas other diseases such as psychiatric disease and kidney disease were less commonly present among sequenced patients.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eDuring the study period, the sequencing costs were reduced significantly, decreasing from approximately 1,150 USD per sample (based on 8 samples per run on NovaSeq 6000 S2 flow cells) to 475 USD per sample (based on 40 samples per run on NovaSeq X Plus 25B flow cells). In contrast, the cost of targeted sequencing was reduced by only around 10%, as this requires more manual handing and sequencing expenses account for a smaller proportion of the overall price (Internal data).\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\u003eOverview of number of patients mean/median age and sex distribution in each patient category. *For fetal medicine, gestational age and fetal sex are not stated in genetic reports and hence not available in the dataset used in this study.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePatient group\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003enumber\u003c/p\u003e\u003cp\u003e(%)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eAge\u003c/p\u003e\u003cp\u003eMedian(range)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003emales (%)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHereditary heart disease\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e556 (\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e52 (0\u0026ndash;86)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e60\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRare diseases, children\u0026thinsp;\u0026lt;\u0026thinsp;18\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e520 (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e5 (0\u0026ndash;21)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e40\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNeurogenetic patients\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e248 (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e42 (0\u0026ndash;85)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e49\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eInborn errors of immunity\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e195 (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e27 (0\u0026ndash;71)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e44\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eChildhood\u0026nbsp;cancer\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e137 (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e10 (0\u0026ndash;29)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e50\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEndocrinological patients\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e135 (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e33 (0\u0026ndash;83)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e45\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFetal medicine*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e107 (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eNA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eNA\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHereditary skin disorders\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e84 (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e31 (0\u0026ndash;74)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e39\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHematology\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e79 (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e41 (13\u0026ndash;76)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e20\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAudiogenetics\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e74 (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e5 (0\u0026ndash;81)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e50\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRare diseases, adults\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e74 (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e31 (2\u0026ndash;77)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e46\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCancer\u0026nbsp;in\u0026nbsp;young\u0026nbsp;adults\u0026nbsp;and\u0026nbsp;hereditary\u0026nbsp;cancer\u0026nbsp;in\u0026nbsp;adults\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e45 (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e39 (21\u0026ndash;86)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e31\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHereditary cholestatic and fibrotic liver diseases\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e36 (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e32 (0\u0026ndash;88)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e47\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eKidney failure\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e19 (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e44 (5\u0026ndash;61)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e53\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePsychiatry, children and adolescents\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e8 (\u0026lt;\u0026thinsp;1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e11 (\u003cspan additionalcitationids=\"CR4 CR5 CR6 CR7 CR8 CR9 CR10 CR11 CR12\" citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e56\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\u003eDiagnostic yield\u003c/p\u003e\u003cp\u003eWe calculated the diagnostic yield as the percentage of test reports containing explanatory findings (i.e., C4 or C5 variants consistent with both the inheritance pattern and the patient\u0026rsquo;s phenotype). The percentage of reports with partial findings \u003cem\u003ei.e\u003c/em\u003e., single heterozygous C4 or C5 variants in recessive genes relevant to the phenotype, were also calculated (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). We observed a weighted average of 20% diagnostic yield across all patient groups with considerable variation across patient categories. For example, the diagnostic yield of hereditary skin disorders was 60% and only 3% in patients suspected of hereditary cholestatic and fibrotic liver disease (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eReporting of uncertain and secondary findings\u003c/p\u003e\u003cp\u003eReporting practices for uncertain findings (C3 variants) differed across patient groups, as detailed in the Methods section. Among patients with hereditary heart disease, inborn errors of immunity, hereditary cancer, haematological, and endocrine conditions, 20\u0026ndash;50% had uncertain findings reported (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA). In contrast, such findings were reported in only 3\u0026ndash;14% of patients in the remaining groups. Secondary findings (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB) were reported with a low degree of variation (0\u0026ndash;4%) across all patient categories.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eDissecting diagnostic yields within selected patient categories\u003c/p\u003e\u003cp\u003eWithin each patient group, the DNGC or the analysing departments defined patient/diagnostic subcategories. Sequencing strategies included either trio sequencing (mother, father, proband) or singleton sequencing (proband only).\u003c/p\u003e\u003cp\u003eWe focused our analysis on selected patient groups (rare diseases in children and adults, neurogenetic disorders, and endocrinological patients), to evaluate variations in diagnostic yield as well as frequency of reported C3 variants and secondary findings (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\n\u003ch3\u003eRare disease cohorts\u003c/h3\u003e\n\u003cp\u003eAll children suspected of having a rare disease, including malformations, neurodevelopmental or metabolic disorders in addition to monogenetic causes of acute illness, were analysed using a trio-based approach. This strategy delivered an overall diagnostic yield of 29% (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA). In patients with phenotypically well-defined rare disorders, we obtained a diagnostic yield of 23% by using a custom singleton approach, whereby analysed genes were defined by the referring clinician or the Department of Clinical Genetics. Within specific subgroups, we observed higher diagnostic yields: 38% for children with skeletal dysplasia and 50%for adults with suspected monogenic skeletal dysplasia (though in a small cohort, n\u0026thinsp;=\u0026thinsp;8). Diagnostic yields from both custom gene panels and trio-based analyses in adults were comparable (28% and 22%, respectively; Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eB).\u003c/p\u003e\n\u003ch3\u003eNeurogenetic disorders\u003c/h3\u003e\n\u003cp\u003eExplanatory findings were identified in 20% of patients overall. Subgroup analyses uncovered diagnostic yields of 23\u0026ndash;28% in patients with epilepsy, neuropathy, or basal ganglia disease. In contrast, lower yields were found for myopathy (12%), ataxia (16%), and hereditary spastic paraplegia (19%). Among patients in the subcategory with dementia, ALS/FTD or small vessel disease, the overall yield was 7% (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eC).\u003c/p\u003e\n\u003ch3\u003eEndocrinological patients\u003c/h3\u003e\n\u003cp\u003eThe overall diagnostic yield was 12%. Within the 8 endocrine subgroups, yields ranged from 0% in patients with organic hypoglycemia (5 patients) to almost 18% in patients disorders of sex development, monogenic diabetes (N\u0026thinsp;=\u0026thinsp;35) and rare thyroid disorders (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eD).\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe implementation of srWGS for a broad spectrum of diseases in Central Region Denmark has transformed and streamlined genetic testing towards srWGS. On average, the program has delivered a genetically verified diagnosis for 1 in 5 of all analysed patients. These findings have guided clinical decision-making and facilitated genetic counselling for patients and their families. In parallel, the significant reduction in cost during the study period, has made srWGS an economically viable method in clinical genetic diagnostics, further substantiating srWGS as a first-tier test in personalised medicine.\u003c/p\u003e\u003cp\u003eThe execution of the genomic program has required considerable efforts by national expert panels to standardise patient inclusion criteria. Similarly, laboratory organisation, analytical workflows and pipeline design were also standardised across the country. Whilst this process reduced local flexibility, it was also a clinical support in everyday practice to have national expert panel to substantiate national consensus on clinical decisions, for example on the reporting secondary findings and C3 variants(\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e). Accordingly, the standardisation has overall been successful and led to a much tighter collaboration across the country with exchange of clinical and bioinformatic information in multidisciplinary teams.\u003c/p\u003e\u003cp\u003eThe new legal framework adopted into Danish law in 2019 demanding written consent informing the patient about the submission of their WGS data to the DNGC and specifying the patient\u0026rsquo;s will regarding reporting of secondary findings. This task necessitated significant clinical resources to ensure an informed consent procedure(\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e). On the one hand, this was initially viewed as cumbersome, it may partly contribute to the differences in adaptation of srWGS between clinical specialties and the inclusion of patients. On the other hand, patients have become more empowered to take active part in the decision-making on which secondary findings are reported for the benefit of themselves and their families. In accordance with the Danish Society of Medical Genetics guideline, secondary findings were not actively searched for in the genomic analysis(\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e), which contrasts the recommendation of the American College of Medical Genetics(\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e). However, we identified secondary findings in 3% of analysed index cases with limited variation across patient groups, which is comparable to similar studies (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThe differential approach to the reporting of C3 variants was in our experience operational given that options for further analysis vary considerably between patient groups. For example, C3 variants identified in patients with Inborn errors of immunity (20%) may be further evaluated functionally, as leukocytes, the primary tissue of interest, are readily accessible for downstream testing, unlike in many other disorders. In contrast, in the prenatal setting, C3 variants are rarely reported (3%) which is supported by both national and international guidelines(\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e) (\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eDelineating the diagnostic yield of each patient group has direct implications for pre-test genetic counselling, highlighting the importance of continuous monitoring of genetic testing strategies. However, patient selection and prior genetic analysis constitute likely sources of bias diagnostic yield estimates. In the first year, arrayCGH was the first-tier test in patients with rare diseases and consequently patients diagnosed by arrayCGH were not included in the study, thus introducing an underestimation of the overall diagnostic yield. Insights from comparable national genomic programs help contextualize such diagnostic yield data. For example, the UK\u0026acute;s 100,000 Genomes project\u0026acute;s Pilot on Rare-Disease in 2,183 probands, reported an overall diagnostic yield of 25% (\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e). Their higher diagnostic rate compared to ours may partly be explained by the inclusion of a relatively large sub cohort with ophthalmological disorders (16% of the entire cohort), which demonstrated a markedly higher diagnostic yield (40%). Likewise, a recent study of 12,737 patients from the 2025 French Genomic Medicine Initiative generated an overall diagnostic yield of 30.6%, which was largely achieved, according to the authors, through inclusion of Malformation and Neurodevelopmental disorders, representing 62.2% of the French cohort with a diagnostic yield of 30.8%(\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e). In our study, rare disease in children constituted 24% of the cohort and here we found a diagnostic yield of 28%, which is comparable to diagnostic yields in rare disease cohorts in other studies (\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e). Also, the diagnostic yields of skeletal dysplasia in our cohort of paediatric and adult patients were comparable to a recent study demonstrating an overall diagnostic yield of 37%-42% among children in this patient group (\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e). We were surprised to find that only 19% of patients within the Ataxia and spastic paraplegia patient subgroup received a genetic diagnosis. Comparable srWGS based studies have reported higher diagnostic yields 38.9% for hereditary spastic paraplegia (\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e) and 43% for patients with hereditary ataxia(\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e). Interestingly, the authors behind this systematic review (\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e) note that higher diagnostic yields were associated with specific phenotype selection, suggesting a more narrow patient selection in our study might increase the diagnostic yield. In contrast, there are no studies of broadly included endocrine patients to compare with, however, we anticipate that the diagnostic yield will increase in the future with the experience gained from the current project, especially the identification of patients for genetic testing has improved during the project period. There is currently ongoing work within the European Reference Network on rare endocrine conditions to define the optimal approach to testing of endocrine patients, and a preliminary report on testing has been published(\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eAs such, many factors affect the diagnostic yield of a genomic analysis, most importantly patient selection, pipeline design and variant interpretation including options for functional variant studies. Though these differentiating factors complicates direct comparison between genomic programs, the data provides an important current baseline for the diagnostic yield of short-read sequencing-based genomic analysis.\u003c/p\u003e\u003cp\u003eExcept for hereditary skin diseases, it is important to recognise that most of the analysed patients suspected of monogenetically driven disease remain genetically undiagnosed. Increases in the diagnostic yield may likely be achieved through better genomic data sharing infrastructure facilitated through important initiatives such as the European Genomic Data Infrastructure funded by the European Commission (\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e) and the Federated European Genome-Phenome Archive launched in 2022 (\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e). A more stringent selection of patients with phenotypes highly suggestive of monogenetic disease will also increase the diagnostic yield albeit at the potential cost of reduced diagnostic yield in patients with less severe or atypical phenotypes.\u003c/p\u003e\u003cp\u003eLooking ahead, long-read sequencing is expected to provide higher diagnostic yields compared to short read sequencing primarily due to its improved ability to detect repeat expansions, variants in pseudogenes, methylation differences, and structural variants (\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e). It is, however, important to emphasise that also with long-read sequencing it is equally essential to harmonise national and international legal frameworks on genomic data infrastructure projects to allow for cross-border data sharing to fully harness the benefits of this technology.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eIn conclusion, the Central Denmark Region\u0026rsquo;s implementation of srWGS demonstrates the feasibility and equity of large-scale genome sequencing as a first-tier test in routine care. Diagnostic yields across the investigated patient groups mirror comparable national genomic programs in the UK and France, and the expert-guided harmonisation of the clinical and analytical approaches has set new standards for diagnosing monogenic disorders.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eALS: Amyotrophic Lateral Sclerosis\u003c/p\u003e\n\u003cp\u003eC4: Class 4; likely pathogenic variant\u003c/p\u003e\n\u003cp\u003eC5: Class 5; pathogenic variant\u003c/p\u003e\n\u003cp\u003eDNGC: Danish National Genome Center\u003c/p\u003e\n\u003cp\u003eFTD: FrontoTemporal Dementia\u003c/p\u003e\n\u003cp\u003eNGS: Next Generation Sequencing\u003c/p\u003e\n\u003cp\u003eSF: Secondary Findings\u003c/p\u003e\n\u003cp\u003esrWGS: short read Whole Genome Sequencing\u003c/p\u003e\n\u003cp\u003eTAT: turn-around time\u003c/p\u003e\n\u003cp\u003eVUS: Variant of Unknown Significance\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eCompeting Interest\u003c/h2\u003e\n\u003cp\u003eNo conflicts of interest\u003c/p\u003e\n\u003cp\u003eDL, SLF, JMBJ, NB, MC, KSS, LA, SM, UBJ, SV.\u003c/p\u003e\n\u003cp\u003eConflict of interest\u003c/p\u003e\n\u003cp\u003eCHG (has received honorariums for scientific talks from Novo Nordisk, Merck and Astra-Zeneca)\u003c/p\u003e\n\u003cp\u003eOHL (has received honorariums for scientific talks from Illumina)\u003c/p\u003e\n\u003ch2\u003eFinancial support for this study\u0026nbsp;\u003c/h2\u003e\n\u003cp\u003eNone: DL, SLF, JMBJ, NB, MC, OHL, LA, SM, UBJ, SV.\u003c/p\u003e\n\u003cp\u003eCHG: International Fund of Congenital Adrenal Hyperplasia, Health Research Fund of Central Denmark Region, Aase and Einar Danielsen Fund, and \u0026ldquo;Fonden til L\u0026aelig;gevidenskabens Fremme\u0026rdquo;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe sincerely acknowledge the computational, organisational and laboratory support behind this work. In particular, thank you to Kenneth Lyneborg Hvam and Louise Paludan for data management and to all involved laboratory technicians headed by Lone Hedegaard.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability Statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data collected for this study is not publicly available as it contains information that could compromise patient privacy and safety. Access to the data requires that the Danish National Committee on Health Research Ethics approve the requestors\u0026rsquo; intended use of the data, and that the legal entity of the data requestor enters into a data protection agreement with the Danish data controller, the Central Denmark Region.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSLF: Study concept and design, acquisition of data, analysis and interpretation of data, drafting the manuscript, final approval\u003c/p\u003e\n\u003cp\u003eLA: Analysis and interpretation of data, reviewing the manuscript and final approval\u003c/p\u003e\n\u003cp\u003eNB: Analysis and interpretation of data,\u0026nbsp;reviewing the manuscript and final approval\u003c/p\u003e\n\u003cp\u003eMC: Analysis and interpretation of data,\u0026nbsp;reviewing the manuscript and final approval\u003c/p\u003e\n\u003cp\u003eCHG: Analysis and interpretation of data, drafting the manuscript, final approval\u003c/p\u003e\n\u003cp\u003eUBJ: Analysis and interpretation of data, drafting the manuscript, final approval\u003c/p\u003e\n\u003cp\u003eJMBJ: Analysis and interpretation of data, drafting the manuscript, final approval\u003c/p\u003e\n\u003cp\u003eOHL: Study concept and design, interpretation of data, reviewing the manuscript and final approval\u003c/p\u003e\n\u003cp\u003eSM: Analysis and interpretation of data, reviewing the manuscript and final approval\u003c/p\u003e\n\u003cp\u003eKSS: Acquisition of data,\u0026nbsp;reviewing the manuscript and final approval.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eSV: Study concept and design, Acquisition of data, Analysis of data,\u0026nbsp;reviewing the manuscript,\u0026nbsp;Final approval of published version. Accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved.\u003c/p\u003e\n\u003cp\u003eDL: study concept and design, acquisition of data, analysis and interpretation of data, drafting the manuscript, final approval.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eRiess O, Sturm M, Menden B, Liebmann A, Demidov G, Witt D, et al. Genomes in clinical care. NPJ Genom Med. 2024;9(1):20.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eEwans LJ, Minoche AE, Schofield D, Shrestha R, Puttick C, Zhu Y, et al. Whole exome and genome sequencing in mendelian disorders: a diagnostic and health economic analysis. Eur J Hum Genet. 2022;30(10):1121\u0026ndash;31.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWojcik MH, Lemire G, Berger E, Zaki MS, Wissmann M, Win W, et al. 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Orphanet J Rare Dis. 2020;15(1):144.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003ePascucci E, Causio FA, Pesole G, Chiara M, Morelli M, Tonon G, et al. Progressing towards personalised medicine: the Genomic Data Infrastructure (GDI) project. Eur J Public Health. 2024;34(Supplement_3).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eD'Altri T, Freeberg MA, Curwin AJ, Alonso A, Freitas AT, Capella-Gutierrez S, et al. The Federated European Genome-Phenome Archive as a global network for sharing human genomics data. Nat Genet. 2025;57(3):481\u0026ndash;5.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eDel Gobbo GF, Boycott KM. The additional diagnostic yield of long-read sequencing in undiagnosed rare diseases. Genome Res. 2025;35(4):559\u0026ndash;71.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHiatt SM, Lawlor JMJ, Handley LH, Latner DR, Bonnstetter ZT, Finnila CR, et al. Long-read genome sequencing and variant reanalysis increase diagnostic yield in neurodevelopmental disorders. medRxiv. 2024.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"european-journal-of-human-genetics","isNatureJournal":false,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"ejhg","sideBox":"Learn more about [European Journal of Human Genetics](http://www.nature.com/ejhg/)","snPcode":"41431","submissionUrl":"https://mts-ejhg.nature.com/cgi-bin/main.plex","title":"European Journal of Human Genetics","twitterHandle":"@ejhg_journal","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"ejp","reportingPortfolio":"Nature AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"whole genome sequencing, rare disease, genetic testing","lastPublishedDoi":"10.21203/rs.3.rs-7432106/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7432106/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eIntroduction\u003c/strong\u003e: As part of the Danish National Genome Centre (DNGC) initiative, the Central Denmark Region has implemented short-read whole genome sequencing (srWGS) as a first-tier diagnostic tool for suspected monogenetic disorders. Despite increasing adoption of genome sequencing, evidence from large-scale implementation across clinical specialties remains limited. Here, we evaluate the implementation from patient inclusion and srWGS scaling to diagnostic performance.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods:\u003c/strong\u003e From 2021-2024, we sequenced 2,317 patients with suspected genetic diseases across a wide range of medical specialities. Following clinical evaluation and informed consent, Illumina srWGS was performed. Patients were categorised into clinical subgroups based on phenotype and age to support targeted variant filtration and germline variant reporting. The primary outcome was diagnostic yield across all disease groups/categories. The project is a public/privat partnership co-funded by the Novo Nordisk Foundation.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults: \u003c/strong\u003eDiagnostic yield ranged from 3% in patients with liver disease to 60% in those with skin disorders, with an overall yield of 20%. We observed a substantial variation in clinical usage of srWGS as a first-tier diagnostic tool across patient categories.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion: \u003c/strong\u003eRegional implementation of srWGS within the DNGC framework demonstrates its scalability as a first-tier diagnostic tool for monogenic disorders. Importantly, the combination of expert-guided inclusion criteria for srWGS and mixed public/private funding has ensured equitable access to genetic diagnostics. We identify patient groups with high diagnostic returns well suited for srWGS, as well as groups where alternative strategies could also be applied.\u003c/p\u003e","manuscriptTitle":"Short-Read Genome Sequencing at Population Scale: Diagnostic Insights From 2,317 Patients","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-09-17 05:07:32","doi":"10.21203/rs.3.rs-7432106/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"revise","date":"2025-11-07T10:38:16+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"This content is not available.","date":"2025-10-19T04:26:13+00:00","index":1,"fulltext":"This content is not available."},{"type":"editorInvitedReview","content":"This content is not available.","date":"2025-09-18T04:54:52+00:00","index":2,"fulltext":"This content is not available."},{"type":"reviewerAgreed","content":"This content is not available.","date":"2025-09-09T04:34:20+00:00","index":2,"fulltext":"This content is not available."},{"type":"reviewerAgreed","content":"This content is not available.","date":"2025-09-08T20:15:11+00:00","index":1,"fulltext":"This content is not available."},{"type":"reviewersInvited","content":"","date":"2025-09-08T12:39:19+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-08-28T15:31:33+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-08-22T08:03:08+00:00","index":"","fulltext":""},{"type":"submitted","content":"European Journal of Human Genetics","date":"2025-08-22T08:03:07+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"european-journal-of-human-genetics","isNatureJournal":false,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"ejhg","sideBox":"Learn more about [European Journal of Human Genetics](http://www.nature.com/ejhg/)","snPcode":"41431","submissionUrl":"https://mts-ejhg.nature.com/cgi-bin/main.plex","title":"European Journal of Human Genetics","twitterHandle":"@ejhg_journal","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"ejp","reportingPortfolio":"Nature AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"03d29af8-072b-4398-8327-9ce5a8df0c52","owner":[],"postedDate":"September 17th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":54368668,"name":"Health sciences/Health care/Diagnosis/Genetic testing"},{"id":54368669,"name":"Health sciences/Medical research/Genetics research"}],"tags":[],"updatedAt":"2026-04-01T07:29:27+00:00","versionOfRecord":{"articleIdentity":"rs-7432106","link":"https://doi.org/10.1038/s41431-026-02089-8","journal":{"identity":"european-journal-of-human-genetics","isVorOnly":false,"title":"European Journal of Human Genetics"},"publishedOn":"2026-03-31 04:00:00","publishedOnDateReadable":"March 31st, 2026"},"versionCreatedAt":"2025-09-17 05:07:32","video":"","vorDoi":"10.1038/s41431-026-02089-8","vorDoiUrl":"https://doi.org/10.1038/s41431-026-02089-8","workflowStages":[]},"version":"v1","identity":"rs-7432106","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7432106","identity":"rs-7432106","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}