Personalised genomic strategies improve diagnostic yield in inherited retinal dystrophies: a stepwise, patient-centred approach

Personalised genomic strategies improve diagnostic yield in inherited retinal dystrophies: a stepwise, patient-centred approach | 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 Personalised genomic strategies improve diagnostic yield in inherited retinal dystrophies: a stepwise, patient-centred approach Cinthia Aguilera, Anna Esteve-Garcia, Ariadna Padró-Miquel, Jaume Català-Mora, and 8 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6196723/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 09 Sep, 2025 Read the published version in Eye → Version 1 posted 7 You are reading this latest preprint version Abstract Background : Inherited retinal dystrophies (IRDs) are genetically heterogeneous group of conditions, with approximately 40% of cases remaining unresolved after initial genetic testing. This study aimed to assess the impact of a personalised genomic approach integrating whole-exome sequencing (WES) reanalysis, whole-genome sequencing (WGS), customised gene panels and functional assays to improve diagnostic yield. Subjects/Methods : A cohort of 597 individuals with IRDs, including 525 probands and 72 affected relatives, underwent a stepwise genetic assessment. Re-evaluation included WES reanalysis, WGS and customised gene panels for unresolved cases. Variant interpretation was refined using updated classification guidelines, functional assays such as mRNA and minigene/midigene assays and segregation studies. Results : Initial genetic testing yielded a diagnostic rate of 59.6% (313/525) in probands. Re-evaluation of 101 unresolved cases resulted in 42 additional proband diagnoses and resolution of 7 familial cases, increasing the total number of new diagnoses to 49 (48.5% of re-evaluated cases). This raised the overall diagnostic yield in probands to 67.6% (355/525). Functional assays confirmed pathogenicity of variants in ABCA4 , ATF6 , REEP6 and TULP1 , while WGS enabled the identification of large structural and deep intronic variants, further enhancing molecular diagnostic accuracy. Conclusions : A patient-centred, multi-tiered genomic strategy significantly improved the diagnostic yield for IRDs, refining genotype-phenotype correlations and enabling personalised genetic counselling. Periodic re-evaluation incorporating advanced sequencing and functional assays is essential to improve IRD molecular diagnostics. Biological sciences/Genetics/Clinical genetics/Disease genetics Health sciences/Diseases/Eye diseases/Hereditary eye disease inherited retinal dystrophies genetic diagnosis whole-exome sequencing whole-genome sequencing functional analysis and variant reclassification Figures Figure 1 Figure 2 Figure 3 What was known before Whole-exome sequencing (WES) is the primary diagnostic tool for inherited retinal dystrophies (IRDs), yet approximately 40% of cases remain unresolved. Whole-genome sequencing (WGS) and functional assays have demonstrated potential to improve the diagnostic yield. Variants of uncertain significance (VUS) complicate clinical interpretation and limit patient access to gene therapies. What this study adds: A personalised genomic approach integrating WES reanalysis, WGS and customised gene panels improves IRD diagnosis. Deep intronic, non-coding and structural variants were identified, broadening the spectrum of IRD-related variants. Functional assays and systematic variant reclassification resolved previously undiagnosed cases. Introduction Inherited retinal dystrophies (IRDs) are a leading cause of blindness worldwide, characterised by extensive genetic heterogeneity that complicates molecular diagnosis ( 1 – 3 ). To date, pathogenic variants in over 300 genes have been implicated in IRDs (RetNet, https://web.sph.uth.edu/RetNet/ ; accessed on 3 February 2025), affecting both coding and non-coding regions. These include deep intronic variants (e.g., ABCA4 , CEP290 and USH2A ), GC-rich regions ( RPGR -ORF15) and structural variants (SVs) such as large deletions and complex rearrangements ( 4 – 10 ). Next-generation sequencing (NGS), particularly whole-exome sequencing (WES) and gene panels, has transformed IRD diagnostics by enabling simultaneous analysis of multiple genes ( 11 – 13 ). However, despite these advancements, a significant proportion of cases remain unresolved, with diagnostic yields ranging from 49–75%, leaving nearly 40% of patients without a molecular diagnosis ( 3 , 12 – 14 ). This highlights key limitations of current NGS approaches: gene panels fail to capture non-targeted regions and WES has limited sensitivity for deep intronic variants ( 15 ). Additionally, both methods struggle with SVs detection and pathogenic variants in repetitive or homologous regions ( 15 ). Whole-genome sequencing (WGS) provides a more comprehensive analysis, covering both coding and non-coding regions and allowing the identification of complex genomic rearrangements often missed by WES ( 16 – 20 ). Beyond sequencing, variant interpretation remains a challenge, with many variants classified as variants of uncertain significance (VUS), complicating clinical decision-making ( 2 , 21 ). Emerging research continues to refine variant classification, revealing that synonymous and hypomorphic variants, previously considered benign, can contribute to IRD pathogenesis ( 22 , 23 ). This highlights the need for periodic WES reanalysis and refinements to American College of Medical Genetics and Genomics and the - Association for Molecular Pathology (ACMG-AMP) guidelines to improve diagnostic accuracy ( 24 – 26 ). Functional validation is critical for confirming variant pathogenicity, particularly in non-coding regions. However, the inaccessibility of retinal tissue remains a key challenge ( 27 ). In vitro assays, such as mRNA analysis and minigene/midigene assays, have emerged as powerful tools to elucidate IRD mechanisms and improve variant interpretation ( 28 , 29 ). This study aimed to enhance the diagnostic yield for IRDs through a personalised, case-by-case strategy integrating WES reanalysis, customised gene panels, WGS, functional assays and updated classification frameworks. By refining molecular diagnosis, this approach not only deepens our understanding of IRD pathogenesis but also facilitates access to gene therapies and enables more precise genetic counselling. Subjects and Methods Cohort description We analysed a cohort of 597 adult patients with a clinical diagnosis of IRD who had previously undergone genetic testing. This included 525 probands from unrelated families and 72 affected relatives (familial cases), all monitored at the Hereditary Retinal Dystrophies Unit of Bellvitge University Hospital. Clinical diagnoses were based on comprehensive ophthalmological assessments, including fundus examination, optical coherence tomography, autofluorescence imaging and electrophysiology when indicated. Genetic testing results, which included previous analyses using gene panels or WES, were systematically reviewed to evaluate genotype-phenotype correlations and/or variant classifications. Genetic testing workflow A stepwise, case-by-case genetic testing strategy was applied. Initially, cases were classified as resolved or unresolved based on the review of prior genetic results. Unresolved cases underwent variant re-evaluation and reclassification, WES reanalysis, WGS, customised gene panels or functional studies, depending on the specific characteristics of each case. Priority was determined according to clinical and genetic factors, including family history, genotype-phenotype correlation and the presence of a single pathogenic variant in recessive genes. Exclusion criteria included patient mortality, lack of clinical follow-up or reclassification of the phenotype as non-IRD. WES reanalysis Unresolved cases underwent WES reanalysis using an updated IRD gene panel for non-syndromic cases (Supplementary Data) and a phenotype-driven approach with Human Phenotype Ontology-guided analysis for syndromic IRDs ( 30 ). Updated annotation tools were applied in both analyses, with bioinformatics performed using Datagenomics software (versions 19.1 and 22.4.0) and copy number variant detection was carried out via the Varseq platform (Golden Helix). Whole-genome sequencing analysis and customised gene panel sequencing WGS was performed using the KAPA HyperPrep Kit (Roche) and the xGen DNA Library Prep EZ Kit (Integrated DNA Technologies), with sequencing conducted on the Illumina NovaSeq 6000 platform. Bioinformatics analysis was carried out using the CNAG (Centro Nacional de Análisis Genómico) GPAP (Genome-Phenome Analysis Platform) (hg19) and Emedgene (Illumina, hg37) platforms. Variants were filtered using an expanded IRD panel that also included candidate IRD genes (Supplementary Data) ( 17 , 31 ). A customised gene panel targeting ABCA4 deep intronic regions and RPGR -ORF15 repetitive region was processed using the Agilent SureSelect XT HS2 and the Magnis NGS Prep system (Agilent Technologies, CA, USA) and sequenced on the Illumina MiSeq platform. Variant filtering and classification Variants with read depth > 20x and an allele frequency ≥ 20% were considered, except for RPGR -ORF15, where all variants were retained. A minor allele frequency threshold of 0.05 in gnomAD v2.1.1 ( 32 ) was applied, prioritising deleterious variants, including nonsense, frameshift, splice site and missense variants. Pathogenicity was assessed using REVEL ( 33 ) for missense variants and SpliceAI ( 34 ) for splicing impact. Variants were classified according to the ACMG-AMP classifications standards ( 24 ), the latest recommendations from the Sequence Variant Interpretation Working Group (SVI-WG) ( 35 ) and gene-specific adaptations, such as those from Cornelis et. al. (2023) for ABCA4 gene ( 36 ). Validation of variants and splice site assays Variants were validated using Sanger sequencing, digital PCR, array CGH or MLPA, depending on variant type, following standard protocols. Splicing impact was assessed through mRNA analysis and minigene/midigene assays. To evaluate the impact of REEP6 c.349-4G > T and c.349-1G > A variants, as well as the ATF6 c.160-8A > G variant, RNA was extracted from nasal ciliary cells ( REEP6 ) and whole blood ( ATF6 ) using the RNeasy Mini Kit (Qiagen) and Maxwell® RSC SimplyRNA Blood Kit (Promega), respectively. cDNA synthesis was performed using the PrimeScript RT Reagent Kit (TaKaRa), followed by PCR amplification with primers listed in Supplementary Table S1 . PCR products were purified (ExoSAP-IT, Applied Biosystems) and analysed by Sanger sequencing (BigDye Terminator v3.1, Applied Biosystems). Electropherograms were analysed using Mutation Surveyor v5.1.2 ( ATF6 ) and FinchTV ( REEP6 ). The potential protein impact of these variants was assessed using Expasy ( 37 ). The splicing effect of ABCA4 c.859-442C > T variant was investigated using an in vitro splice assay based on a previously established wild-type midigene (BA7) containing ABCA4 exons 7 to 11 ( 38 ). The variant was introduced via site-directed mutagenesis using oligonucleotides listed in Supplementary Table S4. Wild-type and mutant constructs were transfected into HEK293T cells, followed by RNA extraction (Nucleospin RNA, Machery-Nagel) and cDNA synthesis (iScript, Bio-Rad). RT-PCR was performed using ACTB and RHO exon 5 as controls. Splicing defects were analysed via electrophoresis, Sanger sequencing and semi-quantitative mRNA analysis using Fiji. Further details on the midigene assay are provided in the Supplementary Material. Additionally, a minigene splice assay for TULP1 c.822G > T was conducted as previously described ( 39 ). Ethical considerations This study was approved by the Research Ethics Committee of Bellvitge University Hospital (reference number PR014/22) and conducted in accordance with the Declaration of Helsinki ( 40 ). Informed consent was obtained from all participants and biological samples were sourced from the Biobank HUB-ICO-IDIBELL, part of the ISCIII Biobanks and Biomodels Platform when needed. Results Cohort characterisation Of the 597 cases, 376 were classified as genetically resolved (P1-P376 in Supplementary Table S2 ), comprising 313 probands and 63 familial cases. This cohort exhibited a near-equal sex distribution (189 females, 187 males). The mean age of symptom onset was 23.26 years (range: 1 to 75 years), with 51.1% (192/376) of patients reporting a family history of IRD. Pathogenic variants were identified in 70 genes across 24 IRD subtypes (Fig. 1 ). Among the 525 probands tested, first-tier genetic testing achieved a diagnostic yield of 59.6% (313/525). Diagnostic improvement Of the 221 unresolved cases, 101 were prioritised for further analysis based on clinical and genetic criteria. Variant re-evaluation and reclassification were conducted for 41 cases with VUS that matched the clinical phenotype (P377 - P417, Table 1), resolving 18 cases through VUS reclassification to likely pathogenic or pathogenic (Fig. 2 ). WES reanalysis identified 16 additional diagnoses, while WGS and customised gene panels provided molecular diagnoses for 15 more cases from a subset of 60 patients (P418 to P477, Table 2). In total, 49 new molecular diagnoses were established, comprising 42 probands and 7 familial cases. This personalised approach achieved a diagnostic rate of 48.5% (49/101) in reassessed cases and increased the overall diagnostic rate for probands to 67.6% (355/525), reflecting a 13.4% improvement in diagnostic yield. Reclassification of candidate variants Family co-segregation and functional studies played crucial roles in reclassifying VUS. In patient P392, the REEP6 c.349-4G > T variant, initially classified as a VUS, was upgraded to likely pathogenic following co-segregation and mRNA analysis, which revealed a 32 nt deletion in exon 4, resulting in a frameshift in both alleles (Supplementary Fig. S1 ). Computational predictions (SpliceAI) predicted minimal splicing impact (acceptor loss: 0.07; cryptic acceptor activation: 0.16) (Supplementary Table S3), but cDNA sequencing demonstrated loss-of-function, supporting pathogenicity. Conversely, the AIPL1 c.767T > G variant remained classified as a VUS due to insufficient functional evidence, despite a strong genotype-phenotype correlation. In addition to the 18 resolved cases, the reclassification of variants also contributed to the partial resolution of 23 additional cases, for which future evidence may provide further insights leading to conclusive classification. Non-coding variants Pathogenic non-coding variants were identified in ABCA4 , ATF6 , NPHP4 , RPGRIP1 and USH2A genes through WES reanalysis, a customised ABCA4 panel and WGS (Table 2). Seven deep intronic variants in ABCA4 were detected, including six previously reported ( 36 ) (c.4539 + 2064C > T in P420, P429 and P432; c.5196 + 1137G > A in P423 and P430; and c.4253 + 43G > A in P428) and a novel c.859-442C > T variant in patient P434. Segregation analysis clarified cases initially classified as resolved. For example, in patients P423 and P430, it confirmed that ABCA4 variants were in cis , leading to the identification of deep intronic variants in trans , which resolved both cases. WGS identified the novel ABCA4 c.859-442C > T variant in P434. SpliceAI predictions indicated acceptor and donor gain (acceptor gain: 0.28; donor gain: 0.23) (Supplementary Table S3). In vitro splice assays revealed three splicing alterations: inclusion of a 238 nt pseudoexon (37%), exons 8–10 skipping (35%) and exon 8 skipping (7%) (Supplementary Table S5), which resulted in frameshifts and premature stop codons, likely disrupting ABCA4 function (Fig. 3 ). Consequently, the c.859-442C > T variant was classified as moderately-severe in line with previous severity ABCA4 variants classifications ( 28 , 38 , 41 ). In patient P448, diagnosed with non-progressive cone-rod dystrophy at age 10, WES reanalysis identified a homozygous ATF6 c.160-8A > G variant with a strong genotype-phenotype correlation. SpliceAI predicted a highly impactful acceptor gain (score: 0.99) and a minor acceptor loss (score 0.11) (Supplementary Table S3). mRNA analysis confirmed the inclusion of 7 nt of intron 2 into de coding sequence, leading to a frameshift and introducing a premature stop codon (Supplementary Fig. S2 ). This led to the reclassification of the variant as likely pathogenic. In siblings P435 and P436, biallelic splice-site NPHP4 variants (c.2485 + 2T > C and c.2611 + 1G > A) were identified, which had been missed due to the absence of NPHP4 from the original virtual panel. This finding confirmed the diagnosis of Senior-Løken syndrome and revealed previously unrecognised kidney involvement in one sibling. WES reanalysis also identified a second RPGRIP1 c.2367 + 23del variant in trans with a previously detected c.1111C > T pathogenic variant in patient P445, confirming the molecular diagnosis. Additionally, WGS identified a novel deep intronic USH2A c.11048-1055A > G variant in siblings P446 and P447, reinforcing their clinical diagnosis. Structural and copy number variants WGS detected previously overlooked structural variants (Table 2), including a partial exon 6 deletion in ABCA4 in patient P433 and a homozygous deletion affecting three genes, including NPHP1 , in patient P440, who presented with retinitis pigmentosa and renal disease. WES reanalysis identified a homozygous intragenic PDE6B deletion in P443 and a likely pathogenic deletion involving ARSG exon 2 in P418, which had been missed due to limitations in the original analysis pipeline for detecting copy number variants. Coding variants and emerging gene associations WES reanalysis identified previously overlooked coding variants in ABCA4 , CERKL , HK1 , RPGR and TULP1 (Table 2). Emerging functional evidence, newly reported gene-disease associations, and advances in bioinformatics facilitated the detection and reclassification of variants. For example, biallelic ABCA4 variants, including the c.5603A > T hypomorphic variant, were identified in several cases (P421, P422, P424, P425, P426, P427 and P431). Additionally, a pathogenic homozygous CERKL c.769C > T variant was detected in patient P441, previously missed due to outdated transcript annotation. In patient P444, a de novo pathogenic HK1 c.1334C > G variant was identified through WES reanalysis, guided by Human Phenotype Ontology terms. In patients P438, P439 and P442, variants in RPGR -ORF15 were detected using a customised RPGR panel, which facilitated the detection of variants in low-coverage regions. Finally, a novel splice-site TULP1 c.822G > T variant was identified in patient P437, with its pathogenicity validated through a minigene assay previously reported by our group ( 39 ). Discussion This study demonstrates a significant improvement in the molecular diagnostic yield for IRDs through a patient-centred, multi-step genomic approach. By integrating variant reclassification, WES reanalysis, WGS, customised gene panels and functional assays, we resolved previously undiagnosed cases, providing deeper insights into IRD pathogenesis. These findings highlight the diagnostic challenges posed by the genetic heterogeneity of IRDs, particularly the presence of SVs and variants in GC-rich and non-coding regions, which are often missed by conventional methods ( 42 ). The initial WES diagnostic yield in our cohort was 59.6%, aligning with previously reported rates for IRDs ( 3 , 12 – 14 ). However, incorporating variant reclassification, additional sequencing and functional validation increased the yield to 48.5% among the prioritised cases that were re-evaluated. This surpasses diagnostic rates reported in large cohort studies where WGS alone was used as a second-tier method (33.3% ( 19 ), 24% ( 27 ) and 13% ( 18 )), emphasising the value of a personalised approach. While WES and WGS significantly contributed to variant identification, the interpretation of VUS remains challenging. Cases P392 and P448 exemplify how segregation analysis and functional assays can refine variant classification and resolve previously inconclusive cases. In contrast, patient P395 remained unresolved despite a strong genotype-phenotype correlation, underscoring the need for periodic reassessment and functional validation beyond in silico predictions. The ABCA4 c.5603A > T hypomorphic variant, now recognised as pathogenic ( 22 ), was initially not reported due to limited evidence of pathogenicity. This variant is estimated to account for approximately 50% of unresolved cases in individuals carrying only one ABCA4 pathogenic variant ( 43 ), reinforcing the need for WES reanalysis as new evidence emerges ( 44 ). However, its interpretation requires caution, as it is only considered pathogenic when in trans with severe variants. For instance, in cases P449, P459 and P472, c.5603A > T was found in trans with non-loss-of-function variants, limiting resolution of these cases. Additionally, updates to transcript annotation were crucial, as demonstrated by case P441, where a CERKL variant was initially undetected. Similarly, in other cases, the detection of causative variants was hindered by the absence of certain genes in the applied virtual panels, highlighting the need for continuous updates to gene lists, transcript-aware analysis and periodic WES reanalysis using updated bioinformatics pipelines ( 44 , 45 ). One case was resolved through Human Phenotype Ontology-driven reanalysis, demonstrating its utility in syndromic cases, though its impact on non-syndromic IRDs remains limited ( 46 ). Furthermore, the use of a customised RPGR panel enriched for low-coverage regions proved particularly effective in detecting variants within the ORF15 region, providing a cost-effective alternative to WGS and long-read sequencing technologies for sequencing this hotspot ( 47 , 48 ). Our findings reinforce the role of non-coding variants in IRD pathogenesis ( 18 ). The identification of pathogenic intronic variants in ABCA4 , ATF6 , NPHP4 , RPGRIP1 and USH2A further validate their significance in disease development ( 4 , 25 , 49 ). Notable examples include the novel deep intronic variants ABCA4 :c.859-442C > T and USH2A :c.11048-1055A > G, both classified as pathogenic following segregation and/or functional analyses. The acceptor gain position at ABCA4 :c.859 − 685 (243 nt upstream of -442) appears to be recurrently activated, as shown in Khan et al. (2020) and Corradi et al. (2022) ( 50 , 51 ), where it coincided with pseudo-exon inclusion and the largest exon elongation for the − 25A > G variant of intron 7. Another variant using this splice acceptor site has also been reported ( 22 ), reinforcing its functional relevance. These findings highlight the need for routine non-coding region screening, particularly in ABCA4 and USH2A . Cases P423 and P430 further emphasise the importance of segregation analysis to prevent misinterpretation when pathogenic variants are inherited in cis . The identification of structural variants−including a previously overlooked homozygous intragenic PDE6B deletion, a partial exon 6 deletion in ABCA4 , which represents the second most frequently reported SV in ABCA4 , particularly prevalent in the Spanish population ( 52 ) and a large deletion involving NPHP1 −reinforces the need to integrate WGS as a second-tier test in unresolved cases. These findings highlight the limitations of WES in detecting complex genomic rearrangements and emphasise the need for complementary approaches to improve diagnostic accuracy ( 18 , 19 ). Beyond diagnostics, these findings have direct clinical implications. Establishing a molecular diagnosis enables tailored genetic counselling, informed clinical decision-making and eligibility for emerging gene-specific therapies ( 53 ). In addition to the 49 new diagnoses, segregation analysis in asymptomatic individuals identified carriers, facilitating reproductive counselling in at-risk couples, some of whom opted for preimplantation genetic diagnosis, directly impacting the next generation. Moreover, the identification of previously undetected variants enhances our understanding of disease mechanisms, which is crucial for developing more precise molecular diagnostic protocols for IRDs ( 41 , 54 ). In conclusion, our case-by-case genomic approach significantly improved the diagnostic yield for IRDs. These findings support the routine integration of advanced sequencing methodologies, variant reclassification and functional validation in IRD diagnostics to optimise patient outcomes and expand the role of precision medicine in ophthalmic genetics. Additionally, dynamic case discussions and regular genetic re-evaluation in light of emerging evidence are essential to refine diagnoses, reassess variant pathogenicity, and ensure patients receive the most up-to-date care. Future efforts should prioritise refining diagnostic workflows, identifying novel candidate genes, improving variant classification systems and incorporating emerging technologies such as long-read sequencing to further enhance diagnostic accuracy and patient care ( 47 , 55 ). Declarations Acknowledgments We thank the patients and their families for their participation in this study. This work was supported by Bellvitge University Hospital (Grant No. PUB22015) and the Macula Society with the International Retinal Research Foundation Award (DON23020). We also acknowledge the valuable contributions of Health in Code and the Biobank HUB-ICO-IDIBELL (PT20/00171), which is integrated in the ISCIII Biobanks and Biomodels Platform. Additionally, we thank CERCA Programme/Generalitat de Catalunya for their institutional support. Funding This research was funded by the Macula Society (grant number: DON23020). E.C. received funding from Bellvitge University Hospital (grant number: PUB22015). Z.C. was supported by the Stichting tot Verbetering van het Lot der Blinden, Stichting voor Ooglijders and the Stichting Blindenhulp. G.G.-G. acknowledges two grants from Instituto de Salud Carlos III (ISCIII), CP22/00028 and PI22/01371, co-funded by the European Union. P.B.-M. had a grant from Ministerio de Universidades FPU20/04736. J.M. has a grant from Instituto de Salud Carlos III (ISCIII), PI22/00213, co-funded by the European Union. Conflict of interest The authors declare no competing financial interests. Author contribution statement A.E.-G. designed the study, performed the genetic analyses and drafted the manuscript. C.A. contributed to data analysis and interpretation and conducted mRNA analysis. 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Corradi Z, Khan M, Hitti-Malin R, Mishra K, Whelan L, Cornelis SS, et al. Targeted sequencing and in vitro splice assays shed light on ABCA4-associated retinopathies missing heritability. Human Genetics and Genomics Advances. 2023 Oct;4(4):100237. Rodriguez-Muñoz A, Liquori A, García-Bohorquez B, Jaijo T, Aller E, Millán JM, et al. Functional assays of non-canonical splice-site variants in inherited retinal dystrophies genes. Sci Rep. 2022 Jan 7;12(1):68. Robinson PN, Köhler S, Bauer S, Seelow D, Horn D, Mundlos S. The Human Phenotype Ontology: A Tool for Annotating and Analyzing Human Hereditary Disease. The American Journal of Human Genetics. 2008 Nov;83(5):610–5. Carss KJ, Arno G, Erwood M, Stephens J, Sanchis-Juan A, Hull S, et al. Comprehensive Rare Variant Analysis via Whole-Genome Sequencing to Determine the Molecular Pathology of Inherited Retinal Disease. The American Journal of Human Genetics. 2017 Jan;100(1):75–90. Karczewski KJ, Francioli LC, Tiao G, Cummings BB, Alföldi J, Wang Q, et al. The mutational constraint spectrum quantified from variation in 141,456 humans. Nature. 2020 May 28;581(7809):434–43. Ioannidis NM, Rothstein JH, Pejaver V, Middha S, McDonnell SK, Baheti S, et al. REVEL: An Ensemble Method for Predicting the Pathogenicity of Rare Missense Variants. The American Journal of Human Genetics. 2016 Oct;99(4):877–85. Jaganathan K, Kyriazopoulou Panagiotopoulou S, McRae JF, Darbandi SF, Knowles D, Li YI, et al. Predicting Splicing from Primary Sequence with Deep Learning. Cell. 2019 Jan;176(3):535-548.e24. Walker LC, Hoya M de la, Wiggins GAR, Lindy A, Vincent LM, Parsons MT, et al. Using the ACMG/AMP framework to capture evidence related to predicted and observed impact on splicing: Recommendations from the ClinGen SVI Splicing Subgroup. The American Journal of Human Genetics. 2023 Jul;110(7):1046–67. Cornelis SS, Bauwens M, Haer-Wigman L, De Bruyne M, Pantrangi M, De Baere E, et al. Compendium of Clinical Variant Classification for 2,246 Unique ABCA4 Variants to Clarify Variant Pathogenicity in Stargardt Disease Using a Modified ACMG/AMP Framework. Hum Mutat. 2023 Dec 26;2023:1–12. Gasteiger E, Gattiker A, Hoogland C, Ivanyi I, Appel RD, Bairoch A. ExPASy: The proteomics server for in-depth protein knowledge and analysis. Nucleic Acids Res. 2003 Jul 1;31(13):3784–8. Sangermano R, Khan M, Cornelis SS, Richelle V, Albert S, Garanto A, et al. ABCA4 midigenes reveal the full splice spectrum of all reported noncanonical splice site variants in Stargardt disease. Genome Res. 2018 Jan;28(1):100–10. Esteve-Garcia A, Cobos E, Sau C, Padró-Miquel A, Català-Mora J, Barberán-Martínez P, et al. Deciphering complexity: TULP1 variants linked to an atypical retinal dystrophy phenotype. Front Genet. 2024 Feb 21;15. World Medical Association Declaration of Helsinki. JAMA. 2013 Nov 27;310(20):2191. Cremers FPM, Lee W, Collin RWJ, Allikmets R. Clinical spectrum, genetic complexity and therapeutic approaches for retinal disease caused by ABCA4 mutations. Prog Retin Eye Res. 2020 Nov;79:100861. Wen S, Wang M, Qian X, Li Y, Wang K, Choi J, et al. Systematic assessment of the contribution of structural variants to inherited retinal diseases. bioRxiv. 2023 Jan 3; Zernant J, Lee W, Collison FT, Fishman GA, Sergeev Y V, Schuerch K, et al. Frequent hypomorphic alleles account for a significant fraction of ABCA4 disease and distinguish it from age-related macular degeneration. J Med Genet. 2017 Jun;54(6):404–12. Surl D, Won D, Lee ST, Lee CS, Lee J, Lim HT, et al. Clinician-Driven Reanalysis of Exome Sequencing Data From Patients With Inherited Retinal Diseases. JAMA Netw Open. 2024 May 31;7(5):e2414198. Romero R, de la Fuente L, Del Pozo-Valero M, Riveiro-Álvarez R, Trujillo-Tiebas MJ, Martín-Mérida I, et al. An evaluation of pipelines for DNA variant detection can guide a reanalysis protocol to increase the diagnostic ratio of genetic diseases. NPJ Genom Med. 2022 Jan 27;7(1):7. Perea-Romero I, Blanco-Kelly F, Sanchez-Navarro I, Lorda-Sanchez I, Tahsin-Swafiri S, Avila-Fernandez A, et al. NGS and phenotypic ontology-based approaches increase the diagnostic yield in syndromic retinal diseases. Hum Genet. 2021 Dec 26;140(12):1665–78. Bonetti G, Cozza W, Bernini A, Kaftalli J, Mareso C, Cristofoli F, et al. Towards a Long-Read Sequencing Approach for the Molecular Diagnosis of RPGRORF15 Genetic Variants. Int J Mol Sci. 2023 Nov 28;24(23):16881. Li J, Tang J, Feng Y, Xu M, Chen R, Zou X, et al. Improved Diagnosis of Inherited Retinal Dystrophies by High-Fidelity PCR of ORF15 followed by Next-Generation Sequencing. J Mol Diagn. 2016 Nov;18(6):817–24. Nassisi M, Mohand-Saïd S, Andrieu C, Antonio A, Condroyer C, Méjécase C, et al. Prevalence of ABCA4 Deep-Intronic Variants and Related Phenotype in An Unsolved “One-Hit” Cohort with Stargardt Disease. Int J Mol Sci. 2019 Oct 11;20(20):5053. Khan M, Cornelis SS, De Pozo-Valero M l, Whelan L, Runhart EH, Mishra K, et al. Resolving the dark matter of ABCA4 for 1054 Stargardt disease probands through integrated genomics and transcriptomics. Genetics in Medicine. 2020 Jul;22(7):1235–46. Corradi Z, Salameh M, Khan M, Héon E, Mishra K, Hitti-Malin RJ, et al. ABCA4 c.859-25A>G, a Frequent Palestinian Founder Mutation Affecting the Intron 7 Branchpoint, Is Associated With Early-Onset Stargardt Disease. Investigative Opthalmology & Visual Science. 2022 Apr 27;63(4):20. Corradi Z, Dhaenens CM, Grunewald O, Kocabaş IS, Meunier I, Banfi S, et al. Novel and Recurrent Copy Number Variants in ABCA4-Associated Retinopathy. Int J Mol Sci. 2024 May 29;25(11):5940. Georgiou M, Fujinami K, Michaelides M. Inherited retinal diseases: Therapeutics, clinical trials and end points-A review. Clin Exp Ophthalmol. 2021 Apr;49(3):270–88. Brar AS, Parameswarappa DC, Takkar B, Narayanan R, Jalali S, Mandal S, et al. Gene Therapy for Inherited Retinal Diseases: From Laboratory Bench to Patient Bedside and Beyond. Ophthalmol Ther. 2024 Jan;13(1):21–50. Gupta P, Nakamichi K, Bonnell AC, Yanagihara R, Radulovich N, Hisama FM, et al. Familial co-segregation and the emerging role of long-read sequencing to re-classify variants of uncertain significance in inherited retinal diseases. NPJ Genom Med. 2023 Aug 10;8(1):20. Tables Tables 1 to 2 are available in the Supplementary Files section Additional Declarations There is no conflict of interest Supplementary Files SupplementaryMaterial.docx Table1.xlsx Table2.xlsx Cite Share Download PDF Status: Published Journal Publication published 09 Sep, 2025 Read the published version in Eye → Version 1 posted Editorial decision: revise 01 Jul, 2025 Review # 1 received at journal 05 Apr, 2025 Reviewer # 1 agreed at journal 25 Mar, 2025 Reviewers invited by journal 25 Mar, 2025 Editor assigned by journal 19 Mar, 2025 Submission checks completed at journal 10 Mar, 2025 First submitted to journal 10 Mar, 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. 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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-6196723","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":433569831,"identity":"db5223a1-7f14-4b48-a549-9bb694784516","order_by":0,"name":"Cinthia Aguilera","email":"data:image/png;base64,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","orcid":"","institution":"Bellvitge University Hospital, Institut d’Investigació Biomèdica de Bellvitge (IDIBELL)","correspondingAuthor":true,"prefix":"","firstName":"Cinthia","middleName":"","lastName":"Aguilera","suffix":""},{"id":433569832,"identity":"09053e89-240f-46cc-b64d-ca696e769a2e","order_by":1,"name":"Anna Esteve-Garcia","email":"","orcid":"https://orcid.org/0000-0002-6005-2756","institution":"Bellvitge University Hospital","correspondingAuthor":false,"prefix":"","firstName":"Anna","middleName":"","lastName":"Esteve-Garcia","suffix":""},{"id":433569833,"identity":"ea08e0d3-aff8-452b-96b5-ba18cf885ef5","order_by":2,"name":"Ariadna Padró-Miquel","email":"","orcid":"https://orcid.org/0000-0002-6488-447X","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Ariadna","middleName":"","lastName":"Padró-Miquel","suffix":""},{"id":433569834,"identity":"c0069adf-111c-4f67-97f8-737bf1a1f1fa","order_by":3,"name":"Jaume Català-Mora","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Jaume","middleName":"","lastName":"Català-Mora","suffix":""},{"id":433569835,"identity":"80bf5da2-c767-46e0-a881-cd6331ed330e","order_by":4,"name":"Cristina 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Barberán-Martínez","email":"","orcid":"https://orcid.org/0000-0002-3604-7068","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Pilar","middleName":"","lastName":"Barberán-Martínez","suffix":""},{"id":433569840,"identity":"7ddc2b13-858d-435b-9e48-f0888f1dcc54","order_by":9,"name":"José Millán","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"José","middleName":"","lastName":"Millán","suffix":""},{"id":433569841,"identity":"2766e39e-f644-4f61-9bbf-61b0c15c7c61","order_by":10,"name":"Gema García-García","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Gema","middleName":"","lastName":"García-García","suffix":""},{"id":433569842,"identity":"887be4bb-1992-4bc7-aa9c-dadaef3f290c","order_by":11,"name":"Estefania Cobos","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Estefania","middleName":"","lastName":"Cobos","suffix":""}],"badges":[],"createdAt":"2025-03-10 14:55:34","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6196723/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6196723/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1038/s41433-025-03981-1","type":"published","date":"2025-09-09T04:00:00+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":79835140,"identity":"a80b2ac2-7c66-4f77-aaf6-ffba693927c6","added_by":"auto","created_at":"2025-04-03 11:15:26","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":25818806,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eClinical and genetic distribution in resolved cases. (A) \u003c/strong\u003eThe most common clinical diagnoses in the genetically resolved cohort (n=376) were non-syndromic retinitis pigmentosa (nsRP, 40.4%, 152/376), Stargardt disease (STGD, 11.4%, 43/376) and Usher syndrome (USH, 6.3%, 24/376). \u003cstrong\u003e(B)\u003c/strong\u003e Pathogenic variants were identified in 70 genes across 24 IRD subtypes. Only genes implicated in ≥5 cases are shown; the remaining 49 genes not shown were found in fewer than 5 cases. The most frequently affected genes were \u003cem\u003eABCA4\u003c/em\u003e(18.1%, 68/376), \u003cem\u003eUSH2A\u003c/em\u003e (8.5%, 32/376), and \u003cem\u003eRPGR\u003c/em\u003e (6.7%, 25/376). Abbreviations: CD, cone dystrophy; BMD, Best macular dystrophy; LCA, Leber congenital amaurosis; sRP, syndromic retinitis pigmentosa; CRD, cone-rod dystrophy; BBS, Bardet-Biedl syndrome; CHM, choroideremia; CSNB, congenital stationary night blindness; ACHM, achromatopsia; AOVMD, adult-onset vitelliform macular dystrophy; PXE, pseudoxanthoma elasticum.\u003c/p\u003e","description":"","filename":"Fig.1.png","url":"https://assets-eu.researchsquare.com/files/rs-6196723/v1/70e45b76ec0283858eef356b.png"},{"id":79835133,"identity":"e150ce83-511c-4953-968d-6e88e0b0257b","added_by":"auto","created_at":"2025-04-03 11:15:26","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":1825214,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eDiagnostic yield improvement through stepwise testing\u003c/strong\u003e. Among the 101 unresolved cases prioritised for further assessment, 41 cases (37 probands and 4 familial) with phenotype-associated VUS underwent reclassification, leading to 18 new diagnoses (15 probands and 3 familial). The remaining 23 cases were considered partially resolved due to phenotype-matching VUS. WES reanalysis identified 16 additional diagnoses (14 probands and 2 familial), while targeted \u003cem\u003eABCA4\u003c/em\u003e/\u003cem\u003eRPGR\u003c/em\u003e panel testing and WGS contributed to 5 and 10 new diagnoses, respectively. However, 29 cases (28 probands and 1 familial) remained inconclusive due to insufficient evidence of pathogenicity or genotype-phenotype discordance.\u003c/p\u003e","description":"","filename":"Fig.2.png","url":"https://assets-eu.researchsquare.com/files/rs-6196723/v1/c9117f332cf3f7694a4f9f81.png"},{"id":79835136,"identity":"3a6a46d8-79df-45f6-bf6b-d9353ead9a9a","added_by":"auto","created_at":"2025-04-03 11:15:26","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":3008071,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eOverview of splice defects caused by \u003c/strong\u003e\u003cem\u003e\u003cstrong\u003eABCA4\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003ec.859-442C\u0026gt;T variant in HEK293T cells. (A)\u003c/strong\u003e Wild-type and mutant midigenes assay results. \u003cem\u003eRhodopsin\u003c/em\u003e exon 5 (\u003cem\u003eRHO\u003c/em\u003eex5) RT-PCR was used as a control for transfection efficiency. To the right, schematic representation of WT midigene (BA7_WT), in which the position of the variant is indicated with an arrow and the forward (fwd) and reverse (rev) primers used for PCR amplification are depicted as triangles. Beneath, schematic representation of the four RT-PCR products identified in panel, heteroduplex bands are labelled with an asterisk. \u003cem\u003eABCA4\u003c/em\u003e c.859-442C\u0026gt;T variant leads to the inclusion of a 238 nt long pseudoexon (PE) in intron 7 (Fragment 1), exon 8 skipping (Fragment 3), exon 8 to 10 skipping (Fragment 4) and WT product (Fragment 2). \u003cstrong\u003e(B)\u003c/strong\u003e The chromatograms show the exact exonic and intronic breakpoints in the four fragments as confirmed by Sanger sequencing.\u003c/p\u003e","description":"","filename":"Fig.3.png","url":"https://assets-eu.researchsquare.com/files/rs-6196723/v1/5b17aa12ff7181cb6dcbfd1a.png"},{"id":91151364,"identity":"87b7e10c-706a-483a-810d-ce024de83a5b","added_by":"auto","created_at":"2025-09-12 07:12:10","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":31859429,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6196723/v1/59d390e7-055a-48ed-9aea-92565a023e33.pdf"},{"id":79836342,"identity":"793b8d6c-58fc-4a19-ac72-bb210992e5a3","added_by":"auto","created_at":"2025-04-03 11:23:26","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":318122,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryMaterial.docx","url":"https://assets-eu.researchsquare.com/files/rs-6196723/v1/1661c34f5b8e18f0b174fc0a.docx"},{"id":79836341,"identity":"ff3fb230-95cd-40d5-b8a7-cb7a9797fa07","added_by":"auto","created_at":"2025-04-03 11:23:26","extension":"xlsx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":1172671,"visible":true,"origin":"","legend":"","description":"","filename":"Table1.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-6196723/v1/0a97bc7390cc5b703f1aac8c.xlsx"},{"id":79835139,"identity":"e7ddb241-53ee-4ec6-8ae7-e04d13decc52","added_by":"auto","created_at":"2025-04-03 11:15:26","extension":"xlsx","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":1751921,"visible":true,"origin":"","legend":"","description":"","filename":"Table2.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-6196723/v1/9a2bd2f1ad86f8df7bcc8a3b.xlsx"}],"financialInterests":"There is no conflict of interest","formattedTitle":"Personalised genomic strategies improve diagnostic yield in inherited retinal dystrophies: a stepwise, patient-centred approach","fulltext":[{"header":"What was known before","content":"\u003cul type=\"disc\"\u003e\n \u003cli\u003eWhole-exome sequencing (WES) is the primary diagnostic tool for inherited retinal dystrophies (IRDs), yet approximately 40% of cases remain unresolved.\u003c/li\u003e\n \u003cli\u003eWhole-genome sequencing (WGS) and functional assays have demonstrated potential to improve the diagnostic yield.\u003c/li\u003e\n \u003cli\u003eVariants of uncertain significance (VUS) complicate clinical interpretation and limit patient access to gene therapies.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eWhat this study adds:\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cul type=\"disc\"\u003e\n \u003cli\u003eA personalised genomic approach integrating WES reanalysis, WGS and customised gene panels improves IRD diagnosis.\u003c/li\u003e\n \u003cli\u003eDeep intronic, non-coding and structural variants were identified, broadening the spectrum of IRD-related variants.\u003c/li\u003e\n \u003cli\u003eFunctional assays and systematic variant reclassification resolved previously undiagnosed cases.\u003cstrong\u003e\u003cbr\u003e\u0026nbsp;\u003c/strong\u003e\u003c/li\u003e\n\u003c/ul\u003e"},{"header":"Introduction","content":"\u003cp\u003eInherited retinal dystrophies (IRDs) are a leading cause of blindness worldwide, characterised by extensive genetic heterogeneity that complicates molecular diagnosis (\u003cspan additionalcitationids=\"CR2\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e). To date, pathogenic variants in over 300 genes have been implicated in IRDs (RetNet, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://web.sph.uth.edu/RetNet/\u003c/span\u003e\u003cspan address=\"https://web.sph.uth.edu/RetNet/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e; accessed on 3 February 2025), affecting both coding and non-coding regions. These include deep intronic variants (e.g., \u003cem\u003eABCA4\u003c/em\u003e, \u003cem\u003eCEP290\u003c/em\u003e and \u003cem\u003eUSH2A\u003c/em\u003e), GC-rich regions (\u003cem\u003eRPGR\u003c/em\u003e-ORF15) and structural variants (SVs) such as large deletions and complex rearrangements (\u003cspan additionalcitationids=\"CR5 CR6 CR7 CR8 CR9\" citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eNext-generation sequencing (NGS), particularly whole-exome sequencing (WES) and gene panels, has transformed IRD diagnostics by enabling simultaneous analysis of multiple genes (\u003cspan additionalcitationids=\"CR12\" citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e). However, despite these advancements, a significant proportion of cases remain unresolved, with diagnostic yields ranging from 49\u0026ndash;75%, leaving nearly 40% of patients without a molecular diagnosis (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan additionalcitationids=\"CR13\" citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e). This highlights key limitations of current NGS approaches: gene panels fail to capture non-targeted regions and WES has limited sensitivity for deep intronic variants (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e). Additionally, both methods struggle with SVs detection and pathogenic variants in repetitive or homologous regions (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e). Whole-genome sequencing (WGS) provides a more comprehensive analysis, covering both coding and non-coding regions and allowing the identification of complex genomic rearrangements often missed by WES (\u003cspan additionalcitationids=\"CR17 CR18 CR19\" citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eBeyond sequencing, variant interpretation remains a challenge, with many variants classified as variants of uncertain significance (VUS), complicating clinical decision-making (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e). Emerging research continues to refine variant classification, revealing that synonymous and hypomorphic variants, previously considered benign, can contribute to IRD pathogenesis (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e). This highlights the need for periodic WES reanalysis and refinements to American College of Medical Genetics and Genomics and the - Association for Molecular Pathology (ACMG-AMP) guidelines to improve diagnostic accuracy (\u003cspan additionalcitationids=\"CR25\" citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eFunctional validation is critical for confirming variant pathogenicity, particularly in non-coding regions. However, the inaccessibility of retinal tissue remains a key challenge (\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e). \u003cem\u003eIn vitro\u003c/em\u003e assays, such as mRNA analysis and minigene/midigene assays, have emerged as powerful tools to elucidate IRD mechanisms and improve variant interpretation (\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThis study aimed to enhance the diagnostic yield for IRDs through a personalised, case-by-case strategy integrating WES reanalysis, customised gene panels, WGS, functional assays and updated classification frameworks. By refining molecular diagnosis, this approach not only deepens our understanding of IRD pathogenesis but also facilitates access to gene therapies and enables more precise genetic counselling.\u003c/p\u003e"},{"header":"Subjects and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eCohort description\u003c/h2\u003e \u003cp\u003eWe analysed a cohort of 597 adult patients with a clinical diagnosis of IRD who had previously undergone genetic testing. This included 525 probands from unrelated families and 72 affected relatives (familial cases), all monitored at the Hereditary Retinal Dystrophies Unit of Bellvitge University Hospital. Clinical diagnoses were based on comprehensive ophthalmological assessments, including fundus examination, optical coherence tomography, autofluorescence imaging and electrophysiology when indicated. Genetic testing results, which included previous analyses using gene panels or WES, were systematically reviewed to evaluate genotype-phenotype correlations and/or variant classifications.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eGenetic testing workflow\u003c/h3\u003e\n\u003cp\u003eA stepwise, case-by-case genetic testing strategy was applied. Initially, cases were classified as resolved or unresolved based on the review of prior genetic results. Unresolved cases underwent variant re-evaluation and reclassification, WES reanalysis, WGS, customised gene panels or functional studies, depending on the specific characteristics of each case. Priority was determined according to clinical and genetic factors, including family history, genotype-phenotype correlation and the presence of a single pathogenic variant in recessive genes. Exclusion criteria included patient mortality, lack of clinical follow-up or reclassification of the phenotype as non-IRD.\u003c/p\u003e\n\u003ch3\u003eWES reanalysis\u003c/h3\u003e\n\u003cp\u003eUnresolved cases underwent WES reanalysis using an updated IRD gene panel for non-syndromic cases (Supplementary Data) and a phenotype-driven approach with Human Phenotype Ontology-guided analysis for syndromic IRDs (\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e). Updated annotation tools were applied in both analyses, with bioinformatics performed using Datagenomics software (versions 19.1 and 22.4.0) and copy number variant detection was carried out via the Varseq platform (Golden Helix).\u003c/p\u003e\n\u003ch3\u003eWhole-genome sequencing analysis and customised gene panel sequencing\u003c/h3\u003e\n\u003cp\u003eWGS was performed using the KAPA HyperPrep Kit (Roche) and the xGen DNA Library Prep EZ Kit (Integrated DNA Technologies), with sequencing conducted on the Illumina NovaSeq 6000 platform. Bioinformatics analysis was carried out using the CNAG (Centro Nacional de An\u0026aacute;lisis Gen\u0026oacute;mico) GPAP (Genome-Phenome Analysis Platform) (hg19) and Emedgene (Illumina, hg37) platforms. Variants were filtered using an expanded IRD panel that also included candidate IRD genes (Supplementary Data) (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e). A customised gene panel targeting \u003cem\u003eABCA4\u003c/em\u003e deep intronic regions and \u003cem\u003eRPGR\u003c/em\u003e-ORF15 repetitive region was processed using the Agilent SureSelect XT HS2 and the Magnis NGS Prep system (Agilent Technologies, CA, USA) and sequenced on the Illumina MiSeq platform.\u003c/p\u003e\n\u003ch3\u003eVariant filtering and classification\u003c/h3\u003e\n\u003cp\u003eVariants with read depth\u0026thinsp;\u0026gt;\u0026thinsp;20x and an allele frequency\u0026thinsp;\u0026ge;\u0026thinsp;20% were considered, except for \u003cem\u003eRPGR\u003c/em\u003e-ORF15, where all variants were retained. A minor allele frequency threshold of 0.05 in gnomAD v2.1.1 (\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e) was applied, prioritising deleterious variants, including nonsense, frameshift, splice site and missense variants. Pathogenicity was assessed using REVEL (\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e) for missense variants and SpliceAI (\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e) for splicing impact. Variants were classified according to the ACMG-AMP classifications standards (\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e), the latest recommendations from the Sequence Variant Interpretation Working Group (SVI-WG) (\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e) and gene-specific adaptations, such as those from Cornelis et. al. (2023) for \u003cem\u003eABCA4\u003c/em\u003e gene (\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e).\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eValidation of variants and splice site assays\u003c/h2\u003e \u003cp\u003eVariants were validated using Sanger sequencing, digital PCR, array CGH or MLPA, depending on variant type, following standard protocols. Splicing impact was assessed through mRNA analysis and minigene/midigene assays.\u003c/p\u003e \u003cp\u003eTo evaluate the impact of \u003cem\u003eREEP6\u003c/em\u003e c.349-4G\u0026thinsp;\u0026gt;\u0026thinsp;T and c.349-1G\u0026thinsp;\u0026gt;\u0026thinsp;A variants, as well as the \u003cem\u003eATF6\u003c/em\u003e c.160-8A\u0026thinsp;\u0026gt;\u0026thinsp;G variant, RNA was extracted from nasal ciliary cells (\u003cem\u003eREEP6\u003c/em\u003e) and whole blood (\u003cem\u003eATF6\u003c/em\u003e) using the RNeasy Mini Kit (Qiagen) and Maxwell\u0026reg; RSC SimplyRNA Blood Kit (Promega), respectively. cDNA synthesis was performed using the PrimeScript RT Reagent Kit (TaKaRa), followed by PCR amplification with primers listed in Supplementary Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e. PCR products were purified (ExoSAP-IT, Applied Biosystems) and analysed by Sanger sequencing (BigDye Terminator v3.1, Applied Biosystems). Electropherograms were analysed using Mutation Surveyor v5.1.2 (\u003cem\u003eATF6\u003c/em\u003e) and FinchTV (\u003cem\u003eREEP6\u003c/em\u003e). The potential protein impact of these variants was assessed using Expasy (\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe splicing effect of \u003cem\u003eABCA4\u003c/em\u003e c.859-442C\u0026thinsp;\u0026gt;\u0026thinsp;T variant was investigated using an \u003cem\u003ein vitro\u003c/em\u003e splice assay based on a previously established wild-type midigene (BA7) containing \u003cem\u003eABCA4\u003c/em\u003e exons 7 to 11 (\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e). The variant was introduced via site-directed mutagenesis using oligonucleotides listed in Supplementary Table S4. Wild-type and mutant constructs were transfected into HEK293T cells, followed by RNA extraction (Nucleospin RNA, Machery-Nagel) and cDNA synthesis (iScript, Bio-Rad). RT-PCR was performed using \u003cem\u003eACTB\u003c/em\u003e and \u003cem\u003eRHO\u003c/em\u003e exon 5 as controls. Splicing defects were analysed via electrophoresis, Sanger sequencing and semi-quantitative mRNA analysis using Fiji. Further details on the midigene assay are provided in the Supplementary Material.\u003c/p\u003e \u003cp\u003eAdditionally, a minigene splice assay for \u003cem\u003eTULP1\u003c/em\u003e c.822G\u0026thinsp;\u0026gt;\u0026thinsp;T was conducted as previously described (\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eEthical considerations\u003c/h3\u003e\n\u003cp\u003eThis study was approved by the Research Ethics Committee of Bellvitge University Hospital (reference number PR014/22) and conducted in accordance with the Declaration of Helsinki (\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e). Informed consent was obtained from all participants and biological samples were sourced from the Biobank HUB-ICO-IDIBELL, part of the ISCIII Biobanks and Biomodels Platform when needed.\u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eCohort characterisation\u003c/h2\u003e \u003cp\u003eOf the 597 cases, 376 were classified as genetically resolved (P1-P376 in Supplementary Table \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e), comprising 313 probands and 63 familial cases. This cohort exhibited a near-equal sex distribution (189 females, 187 males). The mean age of symptom onset was 23.26 years (range: 1 to 75 years), with 51.1% (192/376) of patients reporting a family history of IRD. Pathogenic variants were identified in 70 genes across 24 IRD subtypes (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Among the 525 probands tested, first-tier genetic testing achieved a diagnostic yield of 59.6% (313/525).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eDiagnostic improvement\u003c/h2\u003e \u003cp\u003eOf the 221 unresolved cases, 101 were prioritised for further analysis based on clinical and genetic criteria. Variant re-evaluation and reclassification were conducted for 41 cases with VUS that matched the clinical phenotype (P377 - P417, Table\u0026nbsp;1), resolving 18 cases through VUS reclassification to likely pathogenic or pathogenic (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). WES reanalysis identified 16 additional diagnoses, while WGS and customised gene panels provided molecular diagnoses for 15 more cases from a subset of 60 patients (P418 to P477, Table\u0026nbsp;2).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eIn total, 49 new molecular diagnoses were established, comprising 42 probands and 7 familial cases. This personalised approach achieved a diagnostic rate of 48.5% (49/101) in reassessed cases and increased the overall diagnostic rate for probands to 67.6% (355/525), reflecting a 13.4% improvement in diagnostic yield.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eReclassification of candidate variants\u003c/h2\u003e \u003cp\u003eFamily co-segregation and functional studies played crucial roles in reclassifying VUS. In patient P392, the \u003cem\u003eREEP6\u003c/em\u003e c.349-4G\u0026thinsp;\u0026gt;\u0026thinsp;T variant, initially classified as a VUS, was upgraded to likely pathogenic following co-segregation and mRNA analysis, which revealed a 32 nt deletion in exon 4, resulting in a frameshift in both alleles (Supplementary Fig. \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). Computational predictions (SpliceAI) predicted minimal splicing impact (acceptor loss: 0.07; cryptic acceptor activation: 0.16) (Supplementary Table S3), but cDNA sequencing demonstrated loss-of-function, supporting pathogenicity. Conversely, the \u003cem\u003eAIPL1\u003c/em\u003e c.767T\u0026thinsp;\u0026gt;\u0026thinsp;G variant remained classified as a VUS due to insufficient functional evidence, despite a strong genotype-phenotype correlation.\u003c/p\u003e \u003cp\u003eIn addition to the 18 resolved cases, the reclassification of variants also contributed to the partial resolution of 23 additional cases, for which future evidence may provide further insights leading to conclusive classification.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eNon-coding variants\u003c/h2\u003e \u003cp\u003ePathogenic non-coding variants were identified in \u003cem\u003eABCA4\u003c/em\u003e, \u003cem\u003eATF6\u003c/em\u003e, \u003cem\u003eNPHP4\u003c/em\u003e, \u003cem\u003eRPGRIP1\u003c/em\u003e and \u003cem\u003eUSH2A\u003c/em\u003e genes through WES reanalysis, a customised \u003cem\u003eABCA4\u003c/em\u003e panel and WGS (Table\u0026nbsp;2). Seven deep intronic variants in \u003cem\u003eABCA4\u003c/em\u003e were detected, including six previously reported (\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e) (c.4539\u0026thinsp;+\u0026thinsp;2064C\u0026thinsp;\u0026gt;\u0026thinsp;T in P420, P429 and P432; c.5196\u0026thinsp;+\u0026thinsp;1137G\u0026thinsp;\u0026gt;\u0026thinsp;A in P423 and P430; and c.4253\u0026thinsp;+\u0026thinsp;43G\u0026thinsp;\u0026gt;\u0026thinsp;A in P428) and a novel c.859-442C\u0026thinsp;\u0026gt;\u0026thinsp;T variant in patient P434. Segregation analysis clarified cases initially classified as resolved. For example, in patients P423 and P430, it confirmed that \u003cem\u003eABCA4\u003c/em\u003e variants were in \u003cem\u003ecis\u003c/em\u003e, leading to the identification of deep intronic variants in \u003cem\u003etrans\u003c/em\u003e, which resolved both cases.\u003c/p\u003e \u003cp\u003eWGS identified the novel \u003cem\u003eABCA4\u003c/em\u003e c.859-442C\u0026thinsp;\u0026gt;\u0026thinsp;T variant in P434. SpliceAI predictions indicated acceptor and donor gain (acceptor gain: 0.28; donor gain: 0.23) (Supplementary Table S3). \u003cem\u003eIn vitro\u003c/em\u003e splice assays revealed three splicing alterations: inclusion of a 238 nt pseudoexon (37%), exons 8\u0026ndash;10 skipping (35%) and exon 8 skipping (7%) (Supplementary Table S5), which resulted in frameshifts and premature stop codons, likely disrupting \u003cem\u003eABCA4\u003c/em\u003e function (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Consequently, the c.859-442C\u0026thinsp;\u0026gt;\u0026thinsp;T variant was classified as moderately-severe in line with previous severity \u003cem\u003eABCA4\u003c/em\u003e variants classifications (\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eIn patient P448, diagnosed with non-progressive cone-rod dystrophy at age 10, WES reanalysis identified a homozygous \u003cem\u003eATF6\u003c/em\u003e c.160-8A\u0026thinsp;\u0026gt;\u0026thinsp;G variant with a strong genotype-phenotype correlation. SpliceAI predicted a highly impactful acceptor gain (score: 0.99) and a minor acceptor loss (score 0.11) (Supplementary Table S3). mRNA analysis confirmed the inclusion of 7 nt of intron 2 into de coding sequence, leading to a frameshift and introducing a premature stop codon (Supplementary Fig. \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e). This led to the reclassification of the variant as likely pathogenic.\u003c/p\u003e \u003cp\u003eIn siblings P435 and P436, biallelic splice-site \u003cem\u003eNPHP4\u003c/em\u003e variants (c.2485\u0026thinsp;+\u0026thinsp;2T\u0026thinsp;\u0026gt;\u0026thinsp;C and c.2611\u0026thinsp;+\u0026thinsp;1G\u0026thinsp;\u0026gt;\u0026thinsp;A) were identified, which had been missed due to the absence of \u003cem\u003eNPHP4\u003c/em\u003e from the original virtual panel. This finding confirmed the diagnosis of Senior-L\u0026oslash;ken syndrome and revealed previously unrecognised kidney involvement in one sibling.\u003c/p\u003e \u003cp\u003eWES reanalysis also identified a second \u003cem\u003eRPGRIP1\u003c/em\u003e c.2367\u0026thinsp;+\u0026thinsp;23del variant in \u003cem\u003etrans\u003c/em\u003e with a previously detected c.1111C\u0026thinsp;\u0026gt;\u0026thinsp;T pathogenic variant in patient P445, confirming the molecular diagnosis. Additionally, WGS identified a novel deep intronic \u003cem\u003eUSH2A\u003c/em\u003e c.11048-1055A\u0026thinsp;\u0026gt;\u0026thinsp;G variant in siblings P446 and P447, reinforcing their clinical diagnosis.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eStructural and copy number variants\u003c/h2\u003e \u003cp\u003eWGS detected previously overlooked structural variants (Table\u0026nbsp;2), including a partial exon 6 deletion in \u003cem\u003eABCA4\u003c/em\u003e in patient P433 and a homozygous deletion affecting three genes, including \u003cem\u003eNPHP1\u003c/em\u003e, in patient P440, who presented with retinitis pigmentosa and renal disease. WES reanalysis identified a homozygous intragenic \u003cem\u003ePDE6B\u003c/em\u003e deletion in P443 and a likely pathogenic deletion involving \u003cem\u003eARSG\u003c/em\u003e exon 2 in P418, which had been missed due to limitations in the original analysis pipeline for detecting copy number variants.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eCoding variants and emerging gene associations\u003c/h2\u003e \u003cp\u003eWES reanalysis identified previously overlooked coding variants in \u003cem\u003eABCA4\u003c/em\u003e, \u003cem\u003eCERKL\u003c/em\u003e, \u003cem\u003eHK1\u003c/em\u003e, \u003cem\u003eRPGR\u003c/em\u003e and \u003cem\u003eTULP1\u003c/em\u003e (Table\u0026nbsp;2). Emerging functional evidence, newly reported gene-disease associations, and advances in bioinformatics facilitated the detection and reclassification of variants. For example, biallelic \u003cem\u003eABCA4\u003c/em\u003e variants, including the c.5603A\u0026thinsp;\u0026gt;\u0026thinsp;T hypomorphic variant, were identified in several cases (P421, P422, P424, P425, P426, P427 and P431). Additionally, a pathogenic homozygous \u003cem\u003eCERKL\u003c/em\u003e c.769C\u0026thinsp;\u0026gt;\u0026thinsp;T variant was detected in patient P441, previously missed due to outdated transcript annotation.\u003c/p\u003e \u003cp\u003eIn patient P444, a \u003cem\u003ede novo\u003c/em\u003e pathogenic \u003cem\u003eHK1\u003c/em\u003e c.1334C\u0026thinsp;\u0026gt;\u0026thinsp;G variant was identified through WES reanalysis, guided by Human Phenotype Ontology terms. In patients P438, P439 and P442, variants in \u003cem\u003eRPGR\u003c/em\u003e-ORF15 were detected using a customised \u003cem\u003eRPGR\u003c/em\u003e panel, which facilitated the detection of variants in low-coverage regions. Finally, a novel splice-site \u003cem\u003eTULP1\u003c/em\u003e c.822G\u0026thinsp;\u0026gt;\u0026thinsp;T variant was identified in patient P437, with its pathogenicity validated through a minigene assay previously reported by our group (\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study demonstrates a significant improvement in the molecular diagnostic yield for IRDs through a patient-centred, multi-step genomic approach. By integrating variant reclassification, WES reanalysis, WGS, customised gene panels and functional assays, we resolved previously undiagnosed cases, providing deeper insights into IRD pathogenesis. These findings highlight the diagnostic challenges posed by the genetic heterogeneity of IRDs, particularly the presence of SVs and variants in GC-rich and non-coding regions, which are often missed by conventional methods (\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe initial WES diagnostic yield in our cohort was 59.6%, aligning with previously reported rates for IRDs (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan additionalcitationids=\"CR13\" citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e). However, incorporating variant reclassification, additional sequencing and functional validation increased the yield to 48.5% among the prioritised cases that were re-evaluated. This surpasses diagnostic rates reported in large cohort studies where WGS alone was used as a second-tier method (33.3% (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e), 24% (\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e) and 13% (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e)), emphasising the value of a personalised approach.\u003c/p\u003e \u003cp\u003eWhile WES and WGS significantly contributed to variant identification, the interpretation of VUS remains challenging. Cases P392 and P448 exemplify how segregation analysis and functional assays can refine variant classification and resolve previously inconclusive cases. In contrast, patient P395 remained unresolved despite a strong genotype-phenotype correlation, underscoring the need for periodic reassessment and functional validation beyond in silico predictions.\u003c/p\u003e \u003cp\u003eThe \u003cem\u003eABCA4\u003c/em\u003e c.5603A\u0026thinsp;\u0026gt;\u0026thinsp;T hypomorphic variant, now recognised as pathogenic (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e), was initially not reported due to limited evidence of pathogenicity. This variant is estimated to account for approximately 50% of unresolved cases in individuals carrying only one \u003cem\u003eABCA4\u003c/em\u003e pathogenic variant (\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e), reinforcing the need for WES reanalysis as new evidence emerges (\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e). However, its interpretation requires caution, as it is only considered pathogenic when in \u003cem\u003etrans\u003c/em\u003e with severe variants. For instance, in cases P449, P459 and P472, c.5603A\u0026thinsp;\u0026gt;\u0026thinsp;T was found in \u003cem\u003etrans\u003c/em\u003e with non-loss-of-function variants, limiting resolution of these cases.\u003c/p\u003e \u003cp\u003eAdditionally, updates to transcript annotation were crucial, as demonstrated by case P441, where a \u003cem\u003eCERKL\u003c/em\u003e variant was initially undetected. Similarly, in other cases, the detection of causative variants was hindered by the absence of certain genes in the applied virtual panels, highlighting the need for continuous updates to gene lists, transcript-aware analysis and periodic WES reanalysis using updated bioinformatics pipelines (\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e, \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eOne case was resolved through Human Phenotype Ontology-driven reanalysis, demonstrating its utility in syndromic cases, though its impact on non-syndromic IRDs remains limited (\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e). Furthermore, the use of a customised \u003cem\u003eRPGR\u003c/em\u003e panel enriched for low-coverage regions proved particularly effective in detecting variants within the ORF15 region, providing a cost-effective alternative to WGS and long-read sequencing technologies for sequencing this hotspot (\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e, \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eOur findings reinforce the role of non-coding variants in IRD pathogenesis (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e). The identification of pathogenic intronic variants in \u003cem\u003eABCA4\u003c/em\u003e, \u003cem\u003eATF6\u003c/em\u003e, \u003cem\u003eNPHP4\u003c/em\u003e, \u003cem\u003eRPGRIP1\u003c/em\u003e and \u003cem\u003eUSH2A\u003c/em\u003e further validate their significance in disease development (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e, \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e). Notable examples include the novel deep intronic variants \u003cem\u003eABCA4\u003c/em\u003e:c.859-442C\u0026thinsp;\u0026gt;\u0026thinsp;T and \u003cem\u003eUSH2A\u003c/em\u003e:c.11048-1055A\u0026thinsp;\u0026gt;\u0026thinsp;G, both classified as pathogenic following segregation and/or functional analyses. The acceptor gain position at \u003cem\u003eABCA4\u003c/em\u003e:c.859\u0026thinsp;\u0026minus;\u0026thinsp;685 (243 nt upstream of -442) appears to be recurrently activated, as shown in Khan et al. (2020) and Corradi et al. (2022) (\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e, \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e), where it coincided with pseudo-exon inclusion and the largest exon elongation for the \u0026minus;\u0026thinsp;25A\u0026thinsp;\u0026gt;\u0026thinsp;G variant of intron 7. Another variant using this splice acceptor site has also been reported (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e), reinforcing its functional relevance. These findings highlight the need for routine non-coding region screening, particularly in \u003cem\u003eABCA4\u003c/em\u003e and \u003cem\u003eUSH2A\u003c/em\u003e. Cases P423 and P430 further emphasise the importance of segregation analysis to prevent misinterpretation when pathogenic variants are inherited in \u003cem\u003ecis\u003c/em\u003e.\u003c/p\u003e \u003cp\u003eThe identification of structural variants\u0026minus;including a previously overlooked homozygous intragenic \u003cem\u003ePDE6B\u003c/em\u003e deletion, a partial exon 6 deletion in \u003cem\u003eABCA4\u003c/em\u003e, which represents the second most frequently reported SV in \u003cem\u003eABCA4\u003c/em\u003e, particularly prevalent in the Spanish population (\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e) and a large deletion involving \u003cem\u003eNPHP1\u003c/em\u003e\u0026minus;reinforces the need to integrate WGS as a second-tier test in unresolved cases. These findings highlight the limitations of WES in detecting complex genomic rearrangements and emphasise the need for complementary approaches to improve diagnostic accuracy (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eBeyond diagnostics, these findings have direct clinical implications. Establishing a molecular diagnosis enables tailored genetic counselling, informed clinical decision-making and eligibility for emerging gene-specific therapies (\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e). In addition to the 49 new diagnoses, segregation analysis in asymptomatic individuals identified carriers, facilitating reproductive counselling in at-risk couples, some of whom opted for preimplantation genetic diagnosis, directly impacting the next generation. Moreover, the identification of previously undetected variants enhances our understanding of disease mechanisms, which is crucial for developing more precise molecular diagnostic protocols for IRDs (\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e, \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn conclusion, our case-by-case genomic approach significantly improved the diagnostic yield for IRDs. These findings support the routine integration of advanced sequencing methodologies, variant reclassification and functional validation in IRD diagnostics to optimise patient outcomes and expand the role of precision medicine in ophthalmic genetics. Additionally, dynamic case discussions and regular genetic re-evaluation in light of emerging evidence are essential to refine diagnoses, reassess variant pathogenicity, and ensure patients receive the most up-to-date care. Future efforts should prioritise refining diagnostic workflows, identifying novel candidate genes, improving variant classification systems and incorporating emerging technologies such as long-read sequencing to further enhance diagnostic accuracy and patient care (\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e, \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e).\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe thank the patients and their families for their participation in this study. This work was supported by\u0026nbsp;Bellvitge University Hospital\u0026nbsp;(Grant No. PUB22015) and the Macula Society with the International Retinal Research Foundation Award (DON23020). We also acknowledge the valuable contributions of Health in Code and the Biobank HUB-ICO-IDIBELL (PT20/00171), which is integrated in the ISCIII Biobanks and Biomodels Platform. Additionally, we thank CERCA Programme/Generalitat de Catalunya for their institutional support.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research was funded by the Macula Society (grant number: DON23020). E.C. received funding from\u0026nbsp;Bellvitge University Hospital\u0026nbsp;(grant number: PUB22015). Z.C. was supported by the Stichting tot Verbetering van het Lot der Blinden, Stichting voor Ooglijders and the Stichting Blindenhulp. G.G.-G. acknowledges two grants from Instituto de Salud Carlos III (ISCIII), CP22/00028 and PI22/01371, co-funded by the European Union. P.B.-M. had a grant from Ministerio de Universidades FPU20/04736. J.M. has a grant from Instituto de Salud Carlos III (ISCIII), PI22/00213, co-funded by the European Union.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing financial interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contribution statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA.E.-G. designed the study, performed the genetic analyses and drafted the manuscript. C.A. contributed to data analysis and interpretation and conducted mRNA analysis. J.C.-M. and E.C. reviewed the clinical diagnoses. D.Y. reanalysed WES data. C.S. and A.P.-M. provided critical feedback on the manuscript. Z.C. and F.C. designed the \u003cem\u003eABCA4\u003c/em\u003e midigene assay. P.B.-M., J.M. and G.G.-G. performed the \u003cem\u003eREEP6\u003c/em\u003e mRNA analysis. E.C. and C.A. supervised the study. All authors reviewed and approved the final manuscript.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eGarc\u0026iacute;a Boh\u0026oacute;rquez B, Aller E, Rodr\u0026iacute;guez Mu\u0026ntilde;oz A, Jaijo T, Garc\u0026iacute;a Garc\u0026iacute;a G, Mill\u0026aacute;n JM. Updating the Genetic Landscape of Inherited Retinal Dystrophies. Front Cell Dev Biol. 2021 Jul 13;9. \u003c/li\u003e\n\u003cli\u003eMurro V, Banfi S, Testa F, Iarossi G, Falsini B, Sodi A, et al. A multidisciplinary approach to inherited retinal dystrophies from diagnosis to initial care: a narrative review with inputs from clinical practice. Orphanet J Rare Dis. 2023 Jul 31;18(1):223. \u003c/li\u003e\n\u003cli\u003ePerea-Romero I, Gordo G, Iancu IF, Del Pozo-Valero M, Almoguera B, Blanco-Kelly F, et al. Genetic landscape of 6089 inherited retinal dystrophies affected cases in Spain and their therapeutic and extended epidemiological implications. 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An evaluation of pipelines for DNA variant detection can guide a reanalysis protocol to increase the diagnostic ratio of genetic diseases. NPJ Genom Med. 2022 Jan 27;7(1):7. \u003c/li\u003e\n\u003cli\u003ePerea-Romero I, Blanco-Kelly F, Sanchez-Navarro I, Lorda-Sanchez I, Tahsin-Swafiri S, Avila-Fernandez A, et al. NGS and phenotypic ontology-based approaches increase the diagnostic yield in syndromic retinal diseases. Hum Genet. 2021 Dec 26;140(12):1665\u0026ndash;78. \u003c/li\u003e\n\u003cli\u003eBonetti G, Cozza W, Bernini A, Kaftalli J, Mareso C, Cristofoli F, et al. Towards a Long-Read Sequencing Approach for the Molecular Diagnosis of RPGRORF15 Genetic Variants. Int J Mol Sci. 2023 Nov 28;24(23):16881. \u003c/li\u003e\n\u003cli\u003eLi J, Tang J, Feng Y, Xu M, Chen R, Zou X, et al. Improved Diagnosis of Inherited Retinal Dystrophies by High-Fidelity PCR of ORF15 followed by Next-Generation Sequencing. J Mol Diagn. 2016 Nov;18(6):817\u0026ndash;24. \u003c/li\u003e\n\u003cli\u003eNassisi M, Mohand-Sa\u0026iuml;d S, Andrieu C, Antonio A, Condroyer C, M\u0026eacute;j\u0026eacute;case C, et al. Prevalence of ABCA4 Deep-Intronic Variants and Related Phenotype in An Unsolved \u0026ldquo;One-Hit\u0026rdquo; Cohort with Stargardt Disease. Int J Mol Sci. 2019 Oct 11;20(20):5053. \u003c/li\u003e\n\u003cli\u003eKhan M, Cornelis SS, De Pozo-Valero M l, Whelan L, Runhart EH, Mishra K, et al. Resolving the dark matter of ABCA4 for 1054 Stargardt disease probands through integrated genomics and transcriptomics. Genetics in Medicine. 2020 Jul;22(7):1235\u0026ndash;46. \u003c/li\u003e\n\u003cli\u003eCorradi Z, Salameh M, Khan M, H\u0026eacute;on E, Mishra K, Hitti-Malin RJ, et al. ABCA4 c.859-25A\u0026gt;G, a Frequent Palestinian Founder Mutation Affecting the Intron 7 Branchpoint, Is Associated With Early-Onset Stargardt Disease. Investigative Opthalmology \u0026amp; Visual Science. 2022 Apr 27;63(4):20. \u003c/li\u003e\n\u003cli\u003eCorradi Z, Dhaenens CM, Grunewald O, Kocabaş IS, Meunier I, Banfi S, et al. Novel and Recurrent Copy Number Variants in ABCA4-Associated Retinopathy. Int J Mol Sci. 2024 May 29;25(11):5940. \u003c/li\u003e\n\u003cli\u003eGeorgiou M, Fujinami K, Michaelides M. Inherited retinal diseases: Therapeutics, clinical trials and end points-A review. Clin Exp Ophthalmol. 2021 Apr;49(3):270\u0026ndash;88. \u003c/li\u003e\n\u003cli\u003eBrar AS, Parameswarappa DC, Takkar B, Narayanan R, Jalali S, Mandal S, et al. Gene Therapy for Inherited Retinal Diseases: From Laboratory Bench to Patient Bedside and Beyond. Ophthalmol Ther. 2024 Jan;13(1):21\u0026ndash;50. \u003c/li\u003e\n\u003cli\u003eGupta P, Nakamichi K, Bonnell AC, Yanagihara R, Radulovich N, Hisama FM, et al. Familial co-segregation and the emerging role of long-read sequencing to re-classify variants of uncertain significance in inherited retinal diseases. NPJ Genom Med. 2023 Aug 10;8(1):20. \u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003eTables 1 to 2 are available in the Supplementary Files section\u003c/p\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":"eye","isNatureJournal":false,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"eye","sideBox":"Learn more about [Eye](http://www.nature.com/eye/)","snPcode":"41433","submissionUrl":"https://mts-eye.nature.com/cgi-bin/main.plex","title":"Eye","twitterHandle":"@eye_journal","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"ejp","reportingPortfolio":"Nature AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"inherited retinal dystrophies, genetic diagnosis, whole-exome sequencing, whole-genome sequencing, functional analysis and variant reclassification","lastPublishedDoi":"10.21203/rs.3.rs-6196723/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6196723/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground\u003c/strong\u003e: Inherited retinal dystrophies (IRDs) are genetically heterogeneous group of conditions, with approximately 40% of cases remaining unresolved after initial genetic testing. This study aimed to assess the impact of a personalised genomic approach integrating whole-exome sequencing (WES) reanalysis, whole-genome sequencing (WGS), customised gene panels and functional assays to improve diagnostic yield.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSubjects/Methods\u003c/strong\u003e: A cohort of 597 individuals with IRDs, including 525 probands and 72 affected relatives, underwent a stepwise genetic assessment. Re-evaluation included WES reanalysis, WGS and customised gene panels for unresolved cases. Variant interpretation was refined using updated classification guidelines, functional assays such as mRNA and minigene/midigene assays and segregation studies.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults\u003c/strong\u003e: Initial genetic testing yielded a diagnostic rate of 59.6% (313/525) in probands. Re-evaluation of 101 unresolved cases resulted in 42 additional proband diagnoses and resolution of 7 familial cases, increasing the total number of new diagnoses to 49 (48.5% of re-evaluated cases). This raised the overall diagnostic yield in probands to 67.6% (355/525). Functional assays confirmed pathogenicity of variants in \u003cem\u003eABCA4\u003c/em\u003e, \u003cem\u003eATF6\u003c/em\u003e, \u003cem\u003eREEP6 \u003c/em\u003eand \u003cem\u003eTULP1\u003c/em\u003e, while WGS enabled the identification of large structural and deep intronic variants, further enhancing molecular diagnostic accuracy.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusions\u003c/strong\u003e: A patient-centred, multi-tiered genomic strategy significantly improved the diagnostic yield for IRDs, refining genotype-phenotype correlations and enabling personalised genetic counselling. Periodic re-evaluation incorporating advanced sequencing and functional assays is essential to improve IRD molecular diagnostics.\u003c/p\u003e","manuscriptTitle":"Personalised genomic strategies improve diagnostic yield in inherited retinal dystrophies: a stepwise, patient-centred approach","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-04-03 11:15:21","doi":"10.21203/rs.3.rs-6196723/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"revise","date":"2025-07-01T07:31:57+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"This content is not available.","date":"2025-04-05T20:19:15+00:00","index":1,"fulltext":"This content is not available."},{"type":"reviewerAgreed","content":"This content is not available.","date":"2025-03-25T06:56:18+00:00","index":1,"fulltext":"This content is not available."},{"type":"reviewersInvited","content":"","date":"2025-03-25T06:51:41+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-03-19T14:43:16+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-03-10T15:38:36+00:00","index":"","fulltext":""},{"type":"submitted","content":"Eye","date":"2025-03-10T14:50:46+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"eye","isNatureJournal":false,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"eye","sideBox":"Learn more about [Eye](http://www.nature.com/eye/)","snPcode":"41433","submissionUrl":"https://mts-eye.nature.com/cgi-bin/main.plex","title":"Eye","twitterHandle":"@eye_journal","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"ejp","reportingPortfolio":"Nature AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"38a1f890-563a-449e-9944-78fd4fb8209b","owner":[],"postedDate":"April 3rd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":46164855,"name":"Biological sciences/Genetics/Clinical genetics/Disease genetics"},{"id":46164856,"name":"Health sciences/Diseases/Eye diseases/Hereditary eye disease"}],"tags":[],"updatedAt":"2025-09-12T06:55:28+00:00","versionOfRecord":{"articleIdentity":"rs-6196723","link":"https://doi.org/10.1038/s41433-025-03981-1","journal":{"identity":"eye","isVorOnly":false,"title":"Eye"},"publishedOn":"2025-09-09 04:00:00","publishedOnDateReadable":"September 9th, 2025"},"versionCreatedAt":"2025-04-03 11:15:21","video":"","vorDoi":"10.1038/s41433-025-03981-1","vorDoiUrl":"https://doi.org/10.1038/s41433-025-03981-1","workflowStages":[]},"version":"v1","identity":"rs-6196723","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6196723","identity":"rs-6196723","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}