Recommendations for next generation sequencing data reanalysis of unsolved cases with suspected Mendelian disorders: A systematic review and meta-analysis

Genome sequencing reanalysis increases the diagnostic yield in dystonia

PURPOSE

We investigated the contribution of genomic data reanalysis to the diagnostic yield of dystonia patients who remained undiagnosed after prior genome sequencing.

METHODS

Probands with heterogeneous dystonia phenotypes who underwent initial genome sequencing (GS) analysis in 2019 were included in the reanalysis, which was performed through gene-specific discovery collaborations and systematic genomic data reanalysis.

RESULTS

Initial GS analysis in 2019 (n = 111) identified a molecular diagnosis in 11.7 % (13/111) of cases. Reanalysis between 2020 and 2023 increased the diagnostic yield by 7.2 % (8/111); 3.6 % (4/111) through focused gene-specific clinical correlation collaborative efforts [VPS16 (two probands), AOPEP and POLG], and 3.6 % (4/111) by systematic reanalysis completed in 2023 [NUS1 (two probands) and DDX3X variants, and a microdeletion encompassing VPS16]. Seven of these patients had a high phenotype-based dystonia score ≥3. Notable unverified findings in four additional cases included suspicious variants of uncertain significance in FBXL4 and EIF2AK2, and potential phenotypic expansion associated with SLC2A1 and TREX1 variants.

CONCLUSION

GS data reanalysis increased the diagnostic yield from 11.7 % to 18.9 %, with potential extension up to 22.5 %. While optimal timing for diagnostic reanalysis remains to be determined, this study demonstrates that periodic re-interrogation of dystonia GS datasets can provide additional genetic diagnoses, which may have significant implications for patients and their families.

It Is in Our DNA: Bringing Electronic Health Records and Genomic Data Together for Precision Medicine

Health care is at a turning point. We are shifting from protocolized medicine to precision medicine, and digital health systems are facilitating this shift. By providing clinicians with detailed information for each patient and analytic support for decision-making at the point of care, digital health technologies are enabling a new era of precision medicine. Genomic data also provide clinicians with information that can improve the accuracy and timeliness of diagnosis, optimize prescribing, and target risk reduction strategies, all of which are key elements for precision medicine. However, genomic data are predominantly seen as diagnostic information and are not routinely integrated into the clinical workflows of electronic medical records. The use of genomic data holds significant potential for precision medicine; however, as genomic data are fundamentally different from the information collected during routine practice, special considerations are needed to use this information in a digital health setting. This paper outlines the potential of genomic data integration with electronic records, and how these data can enable precision medicine.

Reanalysis of genomic data in rare disease: current practice and attitudes among Australian clinical and laboratory genetics services

Reanalyzing stored genomic data over time is highly effective in increasing diagnostic yield in rare disease. Automation holds the promise of delivering the benefits of reanalysis at scale. Our study aimed to understand current reanalysis practices among Australian clinical and laboratory genetics services and explore attitudes towards large-scale automated re-analysis. We collected audit data regarding testing and reanalysis volumes, policies and procedures from all Australian diagnostic laboratories providing rare disease genomic testing. A genetic health professionals' survey explored current practices, barriers to reanalysis, preferences and attitudes towards automation. Between 2018 and 2021, Australian diagnostic laboratories performed over 25,000 new genomic tests and 950 reanalyses, predominantly in response to clinician requests. Laboratory and clinical genetic health professionals (N = 134) identified workforce capacity as the principal barrier to reanalysis. No specific laboratory or clinical guidelines for genomic data reanalysis or policies were identified nationally. Perceptions of acceptability and feasibility of automating reanalysis were positive, with professionals emphasizing clinical and workflow benefits. In conclusion, there is a large and rapidly growing unmet need for reanalysis of existing genomic data. Beyond developing scalable automated reanalysis pipelines, leadership and policy are needed to successfully transform service delivery models and maximize clinical benefit.

Systematic reanalysis of genomic data by diagnostic laboratories: a scoping review of ethical, economic, legal and (psycho)social implications

With the introduction of Next Generation Sequencing (NGS) techniques increasing numbers of disease-associated variants are being identified. This ongoing progress might lead to diagnoses in formerly undiagnosed patients and novel insights in already solved cases. Therefore, many studies suggest introducing systematic reanalysis of NGS data in routine diagnostics. Introduction will, however, also have ethical, economic, legal and (psycho)social (ELSI) implications that Genetic Health Professionals (GHPs) from laboratories should consider before possible implementation of systematic reanalysis. To get a first impression we performed a scoping literature review. Our findings show that for the vast majority of included articles ELSI aspects were not mentioned as such. However, often these issues were raised implicitly. In total, we identified nine ELSI aspects, such as (perceived) professional responsibilities, implications for consent and cost-effectiveness. The identified ELSI aspects brought forward necessary trade-offs for GHPs to consciously take into account when considering responsible implementation of systematic reanalysis of NGS data in routine diagnostics, balancing the various strains on their laboratories and personnel while creating optimal results for new and former patients. Some important aspects are not well explored yet. For example, our study shows GHPs see the values of systematic reanalysis but also experience barriers, often mentioned as being practical or financial only, but in fact also being ethical or psychosocial. Engagement of these GHPs in further research on ELSI aspects is important for sustainable implementation.

Clinically significant changes in genes and variants associated with epilepsy over time: implications for re-analysis

Despite the significant advances in understanding the genetic architecture of epilepsy, many patients do not receive a molecular diagnosis after genomic testing. Re-analysing existing genomic data has emerged as a potent method to increase diagnostic yields-providing the benefits of genomic-enabled medicine to more individuals afflicted with a range of different conditions. The primary drivers for these new diagnoses are the discovery of novel gene-disease and variants-disease relationships; however, most decisions to trigger re-analysis are based on the passage of time rather than the accumulation of new knowledge. To explore how our understanding of a specific condition changes and how this impacts re-analysis of genomic data from epilepsy patients, we developed Vigelint. This approach combines the information from PanelApp and ClinVar to characterise how the clinically relevant genes and causative variants available to laboratories change over time, and this approach to five clinical-grade epilepsy panels. Applying the Vigelint pipeline to these panels revealed highly variable patterns in new, clinically relevant knowledge becoming publicly available. This variability indicates that a more dynamic approach to re-analysis may benefit the diagnosis and treatment of epilepsy patients. Moreover, this work suggests that Vigelint can provide empirical data to guide more nuanced, condition-specific approaches to re-analysis.

Next-generation sequencing and comprehensive data reassessment in 263 adult patients with neuromuscular disorders: insights into the gray zone of molecular diagnoses

BACKGROUND

Neuromuscular disorders (NMDs) are heterogeneous conditions with a considerable fraction attributed to monogenic defects. Despite the advancements in genomic medicine, many patients remain without a diagnosis. Here, we investigate whether a comprehensive reassessment strategy improves the diagnostic outcomes.

METHODS

We analyzed 263 patients with NMD phenotypes that underwent diagnostic exome or genome sequencing at our tertiary referral center between 2015 and 2023. We applied a comprehensive reassessment encompassing variant reclassification, re-phenotyping and NGS data reanalysis. Multivariable logistic regression was performed to identify predictive factors associated with a molecular diagnosis.

RESULTS

Initially, a molecular diagnosis was identified in 53 cases (20%), while an additional 23 (9%) had findings of uncertain significance. Following comprehensive reassessment, the diagnostic yield increased to 23%, revealing 44 distinct monogenic etiologies. Reasons for newly obtained molecular diagnoses were variant reclassifications in 7 and NGS data reanalysis in 3 cases including one recently described disease-gene association (DNAJB4). Male sex reduced the odds of receiving a molecular diagnosis (OR 0.42; 95%CI 0.21-0.82), while a positive family history (OR 5.46; 95%CI 2.60-11.76) and a myopathy phenotype (OR 2.72; 95%CI 1.11-7.14) increased the likelihood. 7% were resolved through targeted genetic testing or classified as acquired etiologies.

CONCLUSION

Our findings reinforce the use of NGS in NMDs of suspected monogenic origin. We show that a comprehensive reassessment enhances diagnostic accuracy. However, one needs to be aware that genetic diagnoses are often made with uncertainty and can even be downgraded based on new evidence.

Expanding the Genotype-Phenotype Correlations and Mutational Spectrum in Inherited Retinal Diseases: Novel and Recurrent Mutations

Background Inherited retinal diseases (IRD) represent a prominent etiology of visual impairment on a global scale. The lack of a clear definition of the etiology and genotypic spectrum of IRD is attributed to the significant genetic variability seen. Additionally, there is a scarcity of available data about the correlations between genotypes and phenotypes in this context. This study aimed to clarify the range of mutations and the associations between genotypes and phenotypes in IRD. Methods This cohort consists of 223 patients who have been diagnosed with a range of retinal illnesses, such as retinitis pigmentosa (RP), Stargardt (STGD)/STGD-like disease, Usher syndrome, and Leber congenital amaurosis (LCA). The validation of each mutation and its pathogenicity was conducted by bioinformatics analysis, Sanger sequencing-based co-segregation testing, and computational assessment. The link between genotype and phenotype was analyzed in all patients who possessed mutations as described in the recommendations established by the American College of Medical Genetics. Results A total of 223 cases, comprising Turkish and Syrian families, were examined, revealing the presence of 175 distinct mutations in the IRD gene. Among these mutations, 58 were identified as unique, indicating that they had not been previously reported. A total of 119 mutations were identified to be likely pathogenic, while 104 mutations were classified as pathogenic. The study identified patterns of heredity, namely autosomal recessive, dominant, and X-linked inheritance. Conclusions The findings of this study broaden the clinical and molecular aspects of IRD and further enhance our understanding of its complex nature. The discovery of previously unknown relationships between genetic variations and observable traits, as well as the wide range of genetic variants associated with IRD, significantly contributes to our existing understanding of the diverse phenotypic and genotypic characteristics of IRD. This new information will prove invaluable in facilitating accurate clinical diagnoses as well as personalized therapeutic interventions for individuals affected by IRD.

Ethics of artificial intelligence in prenatal and pediatric genomic medicine

This paper examines the ethics of introducing emerging forms of artificial intelligence (AI) into prenatal and pediatric genomic medicine. Application of genomic AI to these early life settings has not received much attention in the ethics literature. We focus on three contexts: (1) prenatal genomic sequencing for possible fetal abnormalities, (2) rapid genomic sequencing for critically ill children, and (3) reanalysis of genomic data obtained from children for diagnostic purposes. The paper identifies and discusses various ethical issues in the possible application of genomic AI in these settings, especially as they relate to concepts of beneficence, nonmaleficence, respect for autonomy, justice, transparency, accountability, privacy, and trust. The examination will inform the ethically sound introduction of genomic AI in early human life.

Combining a prioritization strategy and functional studies nominates 5'UTR variants underlying inherited retinal disease

BACKGROUND

5' untranslated regions (5'UTRs) are essential modulators of protein translation. Predicting the impact of 5'UTR variants is challenging and rarely performed in routine diagnostics. Here, we present a combined approach of a comprehensive prioritization strategy and functional assays to evaluate 5'UTR variation in two large cohorts of patients with inherited retinal diseases (IRDs).

METHODS

We performed an isoform-level re-analysis of retinal RNA-seq data to identify the protein-coding transcripts of 378 IRD genes with highest expression in retina. We evaluated the coverage of their 5'UTRs by different whole exome sequencing (WES) kits. The selected 5'UTRs were analyzed in whole genome sequencing (WGS) and WES data from IRD sub-cohorts from the 100,000 Genomes Project (n = 2397 WGS) and an in-house database (n = 1682 WES), respectively. Identified variants were annotated for 5'UTR-relevant features and classified into seven categories based on their predicted functional consequence. We developed a variant prioritization strategy by integrating population frequency, specific criteria for each category, and family and phenotypic data. A selection of candidate variants underwent functional validation using diverse approaches.

RESULTS

Isoform-level re-quantification of retinal gene expression revealed 76 IRD genes with a non-canonical retina-enriched isoform, of which 20 display a fully distinct 5'UTR compared to that of their canonical isoform. Depending on the probe design, 3-20% of IRD genes have 5'UTRs fully captured by WES. After analyzing these regions in both cohorts, we prioritized 11 (likely) pathogenic variants in 10 genes (ARL3, MERTK, NDP, NMNAT1, NPHP4, PAX6, PRPF31, PRPF4, RDH12, RD3), of which 7 were novel. Functional analyses further supported the pathogenicity of three variants. Mis-splicing was demonstrated for the PRPF31:c.-9+1G>T variant. The MERTK:c.-125G>A variant, overlapping a transcriptional start site, was shown to significantly reduce both luciferase mRNA levels and activity. The RDH12:c.-123C>T variant was found in cis with the hypomorphic RDH12:c.701G>A (p.Arg234His) variant in 11 patients. This 5'UTR variant, predicted to introduce an upstream open reading frame, was shown to result in reduced RDH12 protein but unaltered mRNA levels.

CONCLUSIONS

This study demonstrates the importance of 5'UTR variants implicated in IRDs and provides a systematic approach for 5'UTR annotation and validation that is applicable to other inherited diseases.

Reanalysis of whole-exome sequencing (WES) data of children with neurodevelopmental disorders in a standard patient care context

This study aims to inform future genetic reanalysis management by evaluating the yield of whole-exome sequencing (WES) reanalysis in standard patient care in the Netherlands. Single-center data of 159 patients with a neurodevelopmental disorder (NDD), in which WES analysis and reanalysis were performed between January 1, 2014, and December 31, 2021, was retrospectively collected. Patients were included if they were under the age of 18 years at initial analysis and if this initial analysis did not result in a diagnosis. Demographic, phenotypic, and genotypic characteristics of patients were collected and analyzed. The primary outcomes of our study were (i) diagnostic yield at reanalysis, (ii) reasons for detecting a new possibly causal variant at reanalysis, (iii) unsolicited findings, and (iv) factors associated with positive result of reanalysis. In addition, we conducted a questionnaire study amongst the 7 genetic department in the Netherlands creating an overview of used techniques, yield, and organization of WES reanalysis. The single-center data show that in most cases, WES reanalysis was initiated by the clinical geneticist (65%) or treating physician (30%). The mean time between initial WES analysis and reanalysis was 3.7 years. A new (likely) pathogenic variant or VUS with a clear link to the phenotype was found in 20 initially negative cases, resulting in a diagnostic yield of 12.6%. In 75% of these patients, the diagnosis had clinical consequences, as for example, a screening plan for associated signs and symptoms could be devised. Most (32%) of the (likely) causal variants identified at WES reanalysis were discovered due to a newly described gene-disease association. In addition to the 12.6% diagnostic yield based on new diagnoses, reclassification of a variant of uncertain significance found at initial analysis led to a definite diagnosis in three patients. Diagnostic yield was higher in patients with dysmorphic features compared to patients without clear dysmorphic features (yield 27% vs. 6%; p = 0.001).

CONCLUSIONS

Our results show that WES reanalysis in patients with NDD in standard patient care leads to a substantial increase in genetic diagnoses. In the majority of newly diagnosed patients, the diagnosis had clinical consequences. Knowledge about the clinical impact of WES reanalysis, clinical characteristics associated with higher yield, and the yield per year after a negative WES in larger clinical cohorts is warranted to inform guidelines for genetic reanalysis. These guidelines will be of great value for pediatricians, pediatric rehabilitation specialists, and pediatric neurologists in daily care of patients with NDD.

WHAT IS KNOWN

• Whole exome sequencing can cost-effectively identify a genetic cause of intellectual disability in about 30-40% of patients. • WES reanalysis in a research setting can lead to a definitive diagnosis in 10-20% of previously exome negative cases.

WHAT IS NEW

• WES reanalysis in standard patient care resulted in a diagnostic yield of 13% in previously exome negative children with NDD. • The presence of dysmorphic features is associated with an increased diagnostic yield of WES reanalysis.

Multiple molecular diagnoses in the field of intellectual disability and congenital anomalies: 3.5% of all positive cases

PURPOSE

Wide access to clinical exome/genome sequencing (ES/GS) enables the identification of multiple molecular diagnoses (MMDs), being a long-standing but underestimated concept, defined by two or more causal loci implicated in the phenotype of an individual with a rare disease. Only few series report MMDs rates (1.8% to 7.1%). This study highlights the increasing role of MMDs in a large cohort of individuals addressed for congenital anomalies/intellectual disability (CA/ID).

METHODS

From 2014 to 2021, our diagnostic laboratory rendered 880/2658 positive ES diagnoses for CA/ID aetiology. Exhaustive search on MMDs from ES data was performed prospectively (January 2019 to December 2021) and retrospectively (March 2014 to December 2018).

RESULTS

MMDs were identified in 31/880 individuals (3.5%), responsible for distinct (9/31) or overlapping (22/31) phenotypes, and potential MMDs in 39/880 additional individuals (4.4%).

CONCLUSION

MMDs are frequent in CA/ID and remain a strong challenge. Reanalysis of positive ES data appears essential when phenotypes are partially explained by the initial diagnosis or atypically enriched overtime. Up-to-date clinical data, clinical expertise from the referring physician, strong interactions between clinicians and biologists, and increasing gene discoveries and improved ES bioinformatics tools appear all the more fundamental to enhance chances of identifying MMDs. It is essential to provide appropriate patient care and genetic counselling.

A systematic review on machine learning approaches in the diagnosis and prognosis of rare genetic diseases

BACKGROUND

The diagnosis of rare genetic diseases is often challenging due to the complexity of the genetic underpinnings of these conditions and the limited availability of diagnostic tools. Machine learning (ML) algorithms have the potential to improve the accuracy and speed of diagnosis by analyzing large amounts of genomic data and identifying complex multiallelic patterns that may be associated with specific diseases. In this systematic review, we aimed to identify the methodological trends and the ML application areas in rare genetic diseases.

METHODS

We performed a systematic review of the literature following the PRISMA guidelines to search studies that used ML approaches to enhance the diagnosis of rare genetic diseases. Studies that used DNA-based sequencing data and a variety of ML algorithms were included, summarized, and analyzed using bibliometric methods, visualization tools, and a feature co-occurrence analysis.

FINDINGS

Our search identified 22 studies that met the inclusion criteria. We found that exome sequencing was the most frequently used sequencing technology (59%), and rare neoplastic diseases were the most prevalent disease scenario (59%). In rare neoplasms, the most frequent applications of ML models were the differential diagnosis or stratification of patients (38.5%) and the identification of somatic mutations (30.8%). In other rare diseases, the most frequent goals were the prioritization of rare variants or genes (55.5%) and the identification of biallelic or digenic inheritance (33.3%). The most employed method was the random forest algorithm (54.5%). In addition, the features of the datasets needed for training these algorithms were distinctive depending on the goal pursued, including the mutational load in each gene for the differential diagnosis of patients, or the combination of genotype features and sequence-derived features (such as GC-content) for the identification of somatic mutations.

CONCLUSIONS

ML algorithms based on sequencing data are mainly used for the diagnosis of rare neoplastic diseases, with random forest being the most common approach. We identified key features in the datasets used for training these ML models according to the objective pursued. These features can support the development of future ML models in the diagnosis of rare genetic diseases.

Clinical utility of periodic reinterpretation of CNVs of uncertain significance: an 8-year retrospective study

BACKGROUND

Array-CGH is the first-tier genetic test both in pre- and postnatal developmental disorders worldwide. Variants of uncertain significance (VUS) represent around 10~15% of reported copy number variants (CNVs). Even though VUS reanalysis has become usual in practice, no long-term study regarding CNV reinterpretation has been reported.

METHODS

This retrospective study examined 1641 CGH arrays performed over 8 years (2010-2017) to demonstrate the contribution of periodically re-analyzing CNVs of uncertain significance. CNVs were classified using AnnotSV on the one hand and manually curated on the other hand. The classification was based on the 2020 American College of Medical Genetics (ACMG) criteria.

RESULTS

Of the 1641 array-CGH analyzed, 259 (15.7%) showed at least one CNV initially reported as of uncertain significance. After reinterpretation, 106 of the 259 patients (40.9%) changed categories, and 12 of 259 (4.6%) had a VUS reclassified to likely pathogenic or pathogenic. Six were predisposing factors for neurodevelopmental disorder/autism spectrum disorder (ASD). CNV type (gain or loss) does not seem to impact the reclassification rate, unlike the length of the CNV: 75% of CNVs downgraded to benign or likely benign are less than 500 kb in size.

CONCLUSIONS

This study's high rate of reinterpretation suggests that CNV interpretation has rapidly evolved since 2010, thanks to the continuous enrichment of available databases. The reinterpreted CNV explained the phenotype for ten patients, leading to optimal genetic counseling. These findings suggest that CNVs should be reinterpreted at least every 2 years.

Reevaluation of ambiguous genetic variants in sudden unexplained deaths of a young cohort

Sudden death cases in the young population remain without a conclusive cause of decease in almost 40% of cases. In these situations, cardiac arrhythmia of genetic origin is suspected as the most plausible cause of death. Molecular autopsy may reveal a genetic defect in up to 20% of families. Most than 80% of rare variants remain classified with an ambiguous role, impeding a useful clinical translation. Our aim was to update rare variants originally classified as of unknown significance to clarify their role. Our cohort included fifty-one post-mortem samples of young cases who died suddenly and without a definite cause of death. Five years ago, molecular autopsy identified at least one rare genetic alteration classified then as ambiguous following the American College of Medical Genetics and Genomics' recommendations. We have reclassified the same rare variants including novel data. About 10% of ambiguous variants change to benign/likely benign mainly because of improved population frequencies. Excluding cases who died before one year of age, almost 21% of rare ambiguous variants change to benign/likely benign. This fact makes it important to discard these rare variants as a cause of sudden unexplained death, avoiding anxiety in relatives' carriers. Twenty-five percent of the remaining variants show a tendency to suspicious deleterious role, highlighting clinical follow-up of carriers. Periodical reclassification of rare variants originally classified as ambiguous is crucial, at least updating frequencies every 5 years. This action aids to increase accuracy to enable and conclude a cause of death as well as translation into the clinic.

Combining globally search for a regular expression and print matching lines with bibliographic monitoring of genomic database improves diagnosis

Introduction: Exome sequencing has a diagnostic yield ranging from 25% to 70% in rare diseases and regularly implicates genes in novel disorders. Retrospective data reanalysis has demonstrated strong efficacy in improving diagnosis, but poses organizational difficulties for clinical laboratories. Patients and methods: We applied a reanalysis strategy based on intensive prospective bibliographic monitoring along with direct application of the GREP command-line tool (to "globally search for a regular expression and print matching lines") in a large ES database. For 18 months, we submitted the same five keywords of interest [(intellectual disability, (neuro)developmental delay, and (neuro)developmental disorder)] to PubMed on a daily basis to identify recently published novel disease-gene associations or new phenotypes in genes already implicated in human pathology. We used the Linux GREP tool and an in-house script to collect all variants of these genes from our 5,459 exome database. Results: After GREP queries and variant filtration, we identified 128 genes of interest and collected 56 candidate variants from 53 individuals. We confirmed causal diagnosis for 19/128 genes (15%) in 21 individuals and identified variants of unknown significance for 19/128 genes (15%) in 23 individuals. Altogether, GREP queries for only 128 genes over a period of 18 months permitted a causal diagnosis to be established in 21/2875 undiagnosed affected probands (0.7%). Conclusion: The GREP query strategy is efficient and less tedious than complete periodic reanalysis. It is an interesting reanalysis strategy to improve diagnosis.

The Diagnostic Yield of Next Generation Sequencing in Inherited Retinal Diseases: A Systematic Review and Meta-analysis

PURPOSE

Accurate genotyping of individuals with inherited retinal diseases (IRD) is essential for patient management and identifying suitable candidates for gene therapies. This study evaluated the diagnostic yield of next generation sequencing (NGS) in IRDs.

DESIGN

Systematic review and meta-analysis.

METHODS

This systematic review was prospectively registered (CRD42021293619). Ovid MEDLINE and Ovid Embase were searched on 6 June 2022. Clinical studies evaluating the diagnostic yield of NGS in individuals with IRDs were eligible for inclusion. Risk of bias assessment was performed. Studies were pooled using a random...effects inverse variance model. Sources of heterogeneity were explored using stratified analysis, meta-regression, and sensitivity analysis.

RESULTS

This study included 105 publications from 28 countries. Most studies (90 studies) used targeted gene panels. The diagnostic yield of NGS was 61.3% (95% confidence interval: 57.8-64.7%; 51 studies) in mixed IRD phenotypes, 58.2% (51.6-64.6%; 41 studies) in rod-cone dystrophies, 57.7% (46.8-68.3%; eight studies) in macular and cone/cone-rod dystrophies, and 47.6% (95% CI: 41.0-54.3%; four studies) in familial exudative vitreoretinopathy. For mixed IRD phenotypes, a higher diagnostic yield was achieved pooling studies published between 2018-2022 (64.2% [59.5-68.7%]), studies using exome sequencing (73.5% [58.9-86.1%]), and studies using the American College of Medical Genetics variant interpretation standards (65.6% [60.8-70.4%]).

CONCLUSION

The current diagnostic yield of NGS in IRDs is between 52-74%. The certainty of the evidence was judged as low or very low. A key limitation of the current evidence is the significant heterogeneity between studies. Adoption of standardized reporting guidelines could improve confidence in future meta-analyses.

Approach to Cohort-Wide Re-Analysis of Exome Data in 1000 Individuals with Neurodevelopmental Disorders

The re-analysis of nondiagnostic exome sequencing (ES) has the potential to increase diagnostic yields in individuals with rare diseases, but its implementation in the daily routines of laboratories is limited due to restricted capacities. Here, we describe a systematic approach to re-analyse the ES data of a cohort consisting of 1040 diagnostic and nondiagnostic samples. We applied a strict filter cascade to reveal the most promising single-nucleotide variants (SNVs) of the whole cohort, which led to an average of 0.77 variants per individual that had to be manually evaluated. This variant set revealed seven novel diagnoses (0.8% of all nondiagnostic cases) and two secondary findings. Thirteen additional variants were identified by a scientific approach prior to this re-analysis and were also present in this variant set. This resulted in a total increase in the diagnostic yield of 2.3%. The filter cascade was optimised during the course of the study and finally resulted in sensitivity of 85%. After applying the filter cascade, our re-analysis took 20 h and enabled a workflow that can be used repeatedly. This work is intended to provide a practical recommendation for other laboratories wishing to introduce a resource-efficient re-analysis strategy into their clinical routine.