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.

Prospective cohort study of genomic newborn screening: BabyScreen+ pilot study protocol.

INTRODUCTION: Newborn bloodspot screening (NBS) is a highly successful public health programme that uses biochemical and other assays to screen for severe but treatable childhood-onset conditions. Introducing genomic sequencing into NBS programmes increases the range of detectable conditions but raises practical and ethical issues. Evidence from prospectively ascertained cohorts is required to guide policy and future implementation. This study aims to develop, implement and evaluate a genomic NBS (gNBS) pilot programme. METHODS AND ANALYSIS: The BabyScreen+ study will pilot gNBS in three phases. In the preimplementation phase, study materials, including education resources, decision support and data collection tools, will be designed. Focus groups and key informant interviews will also be undertaken to inform delivery of the study and future gNBS programmes. During the implementation phase, we will prospectively recruit birth parents in Victoria, Australia, to screen 1000 newborns for over 600 severe, treatable, childhood-onset conditions. Clinically accredited whole genome sequencing will be performed following standard NBS using the same sample. High chance results will be returned by genetic healthcare professionals, with follow-on genetic and other confirmatory testing and referral to specialist services as required. The postimplementation phase will evaluate the feasibility of gNBS as the primary aim, and assess ethical, implementation, psychosocial and health economic factors to inform future service delivery. ETHICS AND DISSEMINATION: This project received ethics approval from the Royal Children's Hospital Melbourne Research Ethics Committee: HREC/91500/RCHM-2023, HREC/90929/RCHM-2022 and HREC/91392/RCHM-2022. Findings will be disseminated to policy-makers, and through peer-reviewed journals and conferences.

Consensus reporting guidelines to address gaps in descriptions of ultra-rare genetic conditions.

Genome-wide sequencing and genetic matchmaker services are propelling a new era of genotype-driven ascertainment of novel genetic conditions. The degree to which reported phenotype data in discovery-focused studies address informational priorities for clinicians and families is unclear. We identified reports published from 2017 to 2021 in 10 genetics journals of novel Mendelian disorders. We adjudicated the quality and detail of the phenotype data via 46 questions pertaining to six priority domains: (I) Development, cognition, and mental health; (II) Feeding and growth; (III) Medication use and treatment history; (IV) Pain, sleep, and quality of life; (V) Adulthood; and (VI) Epilepsy. For a subset of articles, all subsequent published follow-up case descriptions were identified and assessed in a similar manner. A modified Delphi approach was used to develop consensus reporting guidelines, with input from content experts across four countries. In total, 200 of 3243 screened publications met inclusion criteria. Relevant phenotypic details across each of the 6 domains were rated superficial or deficient in >87% of papers. For example, less than 10% of publications provided details regarding neuropsychiatric diagnoses and "behavioural issues", or about the type/nature of feeding problems. Follow-up reports (n = 95) rarely contributed this additional phenotype data. In summary, phenotype information relevant to clinical management, genetic counselling, and the stated priorities of patients and families is lacking for many newly described genetic diseases. The PHELIX (PHEnotype LIsting fiX) reporting guideline checklists were developed to improve phenotype reporting in the genomic era.

Exome copy number variant detection, analysis, and classification in a large cohort of families with undiagnosed rare genetic disease.

Copy number variants (CNVs) are significant contributors to the pathogenicity of rare genetic diseases and, with new innovative methods, can now reliably be identified from exome sequencing. Challenges still remain in accurate classification of CNV pathogenicity. CNV calling using GATK-gCNV was performed on exomes from a cohort of 6,633 families (15,759 individuals) with heterogeneous phenotypes and variable prior genetic testing collected at the Broad Institute Center for Mendelian Genomics of the Genomics Research to Elucidate the Genetics of Rare Diseases consortium and analyzed using the seqr platform. The addition of CNV detection to exome analysis identified causal CNVs for 171 families (2.6%). The estimated sizes of CNVs ranged from 293 bp to 80 Mb. The causal CNVs consisted of 140 deletions, 15 duplications, 3 suspected complex structural variants (SVs), 3 insertions, and 10 complex SVs, the latter two groups being identified by orthogonal confirmation methods. To classify CNV variant pathogenicity, we used the 2020 American College of Medical Genetics and Genomics/ClinGen CNV interpretation standards and developed additional criteria to evaluate allelic and functional data as well as variants on the X chromosome to further advance the framework. We interpreted 151 CNVs as likely pathogenic/pathogenic and 20 CNVs as high-interest variants of uncertain significance. Calling CNVs from existing exome data increases the diagnostic yield for individuals undiagnosed after standard testing approaches, providing a higher-resolution alternative to arrays at a fraction of the cost of genome sequencing. Our improvements to the classification approach advances the systematic framework to assess the pathogenicity of CNVs.

Determining priority indicators of utility for genomic testing in rare disease: A Delphi study.

PURPOSE: Determining the value of genomic tests in rare disease necessitates a broader conceptualization of genomic utility beyond diagnostic yield. Despite widespread discussion, consensus toward which aspects of value to consider is lacking. This study aimed to use expert opinion to identify and refine priority indicators of utility in rare disease genomic testing. METHODS: We used 2 survey rounds following Delphi methodology to obtain consensus on indicators of utility among experts involved in policy, clinical, research, and consumer advocacy leadership in Australia. We analyzed quantitative and qualitative data to identify, define, and determine priority indicators. RESULTS: Twenty-five experts completed round 1 and 18 completed both rounds. Twenty indicators reached consensus as a priority in value assessment, including those relating to prognostic information, timeliness of results, practical and health care outcomes, clinical accreditation, and diagnostic yield. Whereas indicators pertaining to discovery research, disutility, and factors secondary to primary reason for testing were considered less of a priority and were removed. CONCLUSION: This study obtained expert consensus on different utility indicators that are considered a priority in determining the value of genomic testing in rare disease in Australia. Indicators may inform a standardized approach to evidence generation and assessment to guide future research, decision making, and implementation efforts.

Eliciting parental preferences and values for the return of additional findings from genomic sequencing.

Health economic evidence is needed to inform the design of high-value and cost-effective processes for returning genomic results from analyses for additional findings (AF). This study reports the results of a discrete-choice experiment designed to elicit preferences for the process of returning AF results from the perspective of parents of children with rare conditions and to estimate the value placed on AF analysis. Overall, 94 parents recruited within the Australian Genomics and Melbourne Genomics programmes participated in the survey, providing preferences in a total of 1128 choice scenarios. Statistically significant preferences were identified for the opportunity to change the choices made about AF; receiving positive AF in person from a genetic counsellor; timely access to a medical specialist and high-quality online resources; receiving automatic updates through a secure online portal if new information becomes available; and lower costs. For AF uptake rates ranging between 50-95%, the mean per person value from AF analysis was estimated at AU$450-$1700 (US$300-$1140). The findings enable the design of a value-maximising process of analysis for AF in rare-disease genomic sequencing.

Narrowing the diagnostic gap: Genomes, episignatures, long-read sequencing, and health economic analyses in an exome-negative intellectual disability cohort.

PURPOSE: Genome sequencing (GS)-specific diagnostic rates in prospective tightly ascertained exome sequencing (ES)-negative intellectual disability (ID) cohorts have not been reported extensively. METHODS: ES, GS, epigenetic signatures, and long-read sequencing diagnoses were assessed in 74 trios with at least moderate ID. RESULTS: The ES diagnostic yield was 42 of 74 (57%). GS diagnoses were made in 9 of 32 (28%) ES-unresolved families. Repeated ES with a contemporary pipeline on the GS-diagnosed families identified 8 of 9 single-nucleotide variations/copy-number variations undetected in older ES, confirming a GS-unique diagnostic rate of 1 in 32 (3%). Episignatures contributed diagnostic information in 9% with GS corroboration in 1 of 32 (3%) and diagnostic clues in 2 of 32 (6%). A genetic etiology for ID was detected in 51 of 74 (69%) families. Twelve candidate disease genes were identified. Contemporary ES followed by GS cost US$4976 (95% CI: $3704; $6969) per diagnosis and first-line GS at a cost of $7062 (95% CI: $6210; $8475) per diagnosis. CONCLUSION: Performing GS only in ID trios would be cost equivalent to ES if GS were available at $2435, about a 60% reduction from current prices. This study demonstrates that first-line GS achieves higher diagnostic rate than contemporary ES but at a higher cost.

Gene selection for genomic newborn screening: Moving toward consensus?

PURPOSE: Gene selection for genomic newborn screening (gNBS) underpins the validity, acceptability, and ethical application of this technology. Existing gNBS gene lists are highly variable despite being based on shared principles of gene-disease validity, treatability, and age of onset. This study aimed to curate a gNBS gene list that builds upon existing efforts and provide a core consensus list of gene-disease pairs assessed by multiple expert groups worldwide. METHODS: Our multidisciplinary expert team curated a gene list using an open platform and multiple existing curated resources. We included severe treatable disorders with age of disease onset <5 years with established gene-disease associations and reliable variant detection. We compared the final list with published lists from 5 other gNBS projects to determine consensus genes and to identify areas of discrepancy. RESULTS: We reviewed 1279 genes and 604 met our inclusion criteria. Metabolic conditions comprised the largest group (25%), followed by immunodeficiencies (21%) and endocrine disorders (15%). We identified 55 consensus genes included by all 6 gNBS research projects. Common reasons for discrepancy included variable definitions of treatability and strength of gene-disease association. CONCLUSION: We have identified a consensus gene list for gNBS that can be used as a basis for systematic harmonization efforts internationally.

Australian Public Perspectives on Genomic Newborn Screening: Risks, Benefits, and Preferences for Implementation.

Recent dramatic reductions in the timeframe in which genomic sequencing can deliver results means its application in time-sensitive screening programs such as newborn screening (NBS) is becoming a reality. As genomic NBS (gNBS) programs are developed around the world, there is an increasing need to address the ethical and social issues that such initiatives raise. This study therefore aimed to explore the Australian public's perspectives and values regarding key gNBS characteristics and preferences for service delivery. We recruited English-speaking members of the Australian public over 18 years of age via social media; 75 people aged 23-72 participated in 1 of 15 focus groups. Participants were generally supportive of introducing genomic sequencing into newborn screening, with several stating that the adoption of such revolutionary and beneficial technology was a moral obligation. Participants consistently highlighted receiving an early diagnosis as the leading benefit, which was frequently linked to the potential for early treatment and intervention, or access to other forms of assistance, such as peer support. Informing parents about the test during pregnancy was considered important. This study provides insights into the Australian public's views and preferences to inform the delivery of a gNBS program in the Australian context.

The cost of proband and trio exome and genome analysis in rare disease: A micro-costing study.

PURPOSE: Rare disease genomic testing is a complex process involving various resources. Accurate resource estimation is required for informed prioritization and reimbursement decisions. This study aims to analyze the costs and cost drivers of clinical genomic testing. METHODS: Based on genomic sequencing workflows we microcosted limited virtual panel analysis on exome sequencing backbone, proband and trio exome, and genome testing for proband and trio analysis in 2023 Australian Dollars ($). Deterministic and probabilistic sensitivity analyses were undertaken. RESULTS: Panel testing costs AUD $2373 ($733-$6166), and exome sequencing costs $2823 ($802-$7206) and $5670 ($2006-$11,539) for proband and trio analysis, respectively. Genome sequencing costs $4840 ($2153-$9890) and $11,589 ($5842-$16,562) for proband and trio analysis. The most expensive cost component of genomic testing was sequencing (36.9%-69.4% of total cost), with labor accounting for 27.1%-63.2% of total cost. CONCLUSION: We provide a comprehensive analysis of rare disease genomic testing costs, for a range of clinical testing types and contexts. This information will accurately inform economic evaluations of rare disease genomic testing and decision making on policy settings that assist with implementation, such as genomic testing reimbursement.

Role of CAMK2D in neurodevelopment and associated conditions.

The calcium/calmodulin-dependent protein kinase type 2 (CAMK2) family consists of four different isozymes, encoded by four different genes-CAMK2A, CAMK2B, CAMK2G, and CAMK2D-of which the first three have been associated recently with neurodevelopmental disorders. CAMK2D is one of the major CAMK2 proteins expressed in the heart and has been associated with cardiac anomalies. Although this CAMK2 isoform is also known to be one of the major CAMK2 subtypes expressed during early brain development, it has never been linked with neurodevelopmental disorders until now. Here we show that CAMK2D plays an important role in neurodevelopment not only in mice but also in humans. We identified eight individuals harboring heterozygous variants in CAMK2D who display symptoms of intellectual disability, delayed speech, behavioral problems, and dilated cardiomyopathy. The majority of the variants tested lead to a gain of function (GoF), which appears to cause both neurological problems and dilated cardiomyopathy. In contrast, loss-of-function (LoF) variants appear to induce only neurological symptoms. Together, we describe a cohort of individuals with neurodevelopmental disorders and cardiac anomalies, harboring pathogenic variants in CAMK2D, confirming an important role for the CAMK2D isozyme in both heart and brain function.

Reanalysis of genomic data, how do we do it now and what if we automate it? A qualitative study.

Automating reanalysis of genomic data for undiagnosed rare disease patients presents a paradigm shift in how clinical genomics is delivered. We aimed to map the current manual and proposed automated approach to reanalysis and identify possible implementation strategies to address clinical and laboratory staff's perceived challenges to automation. Fourteen semi-structured interviews guided by a simplified process map were conducted with clinical and laboratory staff across Australia. Individual process maps were integrated into an overview of the current process, noting variation in service delivery. Participants then mapped an automated approach and were invited to discuss perceived challenges and possible supports to automation. Responses were analysed using the Consolidated Framework for Implementation Research, linking to the Expert Recommendations for Implementing Change framework to identify theory-informed implementation strategies. Process mapping demonstrates how automation streamlines processes with eleven steps reduced to seven. Although participants welcomed automation, challenges were raised at six of the steps. Strategies to overcome challenges include embedding project champions, developing education materials, facilitating clinical innovation and quality monitoring tools, and altering reimbursement structures. Future work can build on these findings to develop context specific implementation strategies to guide translation of an automated approach to reanalysis to improve clinical care and patient outcomes.

Toward robust clinical genome interpretation: Developing a consistent terminology to characterize Mendelian disease-gene relationships-allelic requirement, inheritance modes, and disease mechanisms.

PURPOSE: The terminology used for gene-disease curation and variant annotation to describe inheritance, allelic requirement, and both sequence and functional consequences of a variant is currently not standardized. There is considerable discrepancy in the literature and across clinical variant reporting in the derivation and application of terms. Here, we standardize the terminology for the characterization of disease-gene relationships to facilitate harmonized global curation and to support variant classification within the ACMG/AMP framework. METHODS: Terminology for inheritance, allelic requirement, and both structural and functional consequences of a variant used by Gene Curation Coalition members and partner organizations was collated and reviewed. Harmonized terminology with definitions and use examples was created, reviewed, and validated. RESULTS: We present a standardized terminology to describe gene-disease relationships, and to support variant annotation. We demonstrate application of the terminology for classification of variation in the ACMG SF 2.0 genes recommended for reporting of secondary findings. Consensus terms were agreed and formalized in both Sequence Ontology (SO) and Human Phenotype Ontology (HPO) ontologies. Gene Curation Coalition member groups intend to use or map to these terms in their respective resources. CONCLUSION: The terminology standardization presented here will improve harmonization, facilitate the pooling of curation datasets across international curation efforts and, in turn, improve consistency in variant classification and genetic test interpretation.

Development of a microcosting protocol to determine the economic cost of diagnostic genomic testing for rare diseases in Australia.

INTRODUCTION: Genomic testing is a relatively new, disruptive and complex health technology with multiple clinical applications in rare diseases, cancer and infection control. Genomic testing is increasingly being implemented into clinical practice, following regulatory approval, funding and adoption in models of care, particularly in the area of rare disease diagnosis. A significant barrier to the adoption and implementation of genomic testing is funding. What remains unclear is what the cost of genomic testing is, what the underlying drivers of cost are and whether policy differences contribute to cost variance in different jurisdictions, such as the requirement to have staff with a medical license involved in testing. This costing study will be useful in future economic evaluations and health technology assessments to inform optimal levels of reimbursement and to support comprehensive and comparable assessment of healthcare resource utilisation in the delivery of genomic testing globally. METHODS: A framework is presented that focuses on uncovering the process of genomic testing for any given laboratory, evaluating its utilisation and unit costs, and modelling the cost drivers and overall expenses associated with delivering genomic testing. The goal is to aid in refining and implementing policies regarding both the regulation and funding of genomic testing. A process-focused (activity-based) methodology is outlined, which encompasses resources, assesses individual cost components through a combination of bottom-up and top-down microcosting techniques and allows disaggregation of resource type and process step. ETHICS AND DISSEMINATION: The outputs of the study will be reported at relevant regional genetics and health economics conferences, as well as submitted to a peer-reviewed journal focusing on genomics. Human research ethics committee approval is not required for this microcosting study. This study does not involve research on human subjects, and all data used in the analysis are either publicly available.

Exome copy number variant detection, analysis and classification in a large cohort of families with undiagnosed rare genetic disease.

Copy number variants (CNVs) are significant contributors to the pathogenicity of rare genetic diseases and with new innovative methods can now reliably be identified from exome sequencing. Challenges still remain in accurate classification of CNV pathogenicity. CNV calling using GATK-gCNV was performed on exomes from a cohort of 6,633 families (15,759 individuals) with heterogeneous phenotypes and variable prior genetic testing collected at the Broad Institute Center for Mendelian Genomics of the GREGoR consortium. Each family's CNV data was analyzed using the seqr platform and candidate CNVs classified using the 2020 ACMG/ClinGen CNV interpretation standards. We developed additional evidence criteria to address situations not covered by the current standards. The addition of CNV calling to exome analysis identified causal CNVs for 173 families (2.6%). The estimated sizes of CNVs ranged from 293 bp to 80 Mb with estimates that 44% would not have been detected by standard chromosomal microarrays. The causal CNVs consisted of 141 deletions, 15 duplications, 4 suspected complex structural variants (SVs), 3 insertions and 10 complex SVs, the latter two groups being identified by orthogonal validation methods. We interpreted 153 CNVs as likely pathogenic/pathogenic and 20 CNVs as high interest variants of uncertain significance. Calling CNVs from existing exome data increases the diagnostic yield for individuals undiagnosed after standard testing approaches, providing a higher resolution alternative to arrays at a fraction of the cost of genome sequencing. Our improvements to the classification approach advances the systematic framework to assess the pathogenicity of CNVs.

Determining the utility of diagnostic genomics: a conceptual framework.

BACKGROUND: Diagnostic efficacy is now well established for diagnostic genomic testing in rare disease. Assessment of overall utility is emerging as a key next step, however ambiguity in the conceptualisation and measurement of utility has impeded its assessment in a comprehensive manner. We propose a conceptual framework to approach determining the broader utility of diagnostic genomics encompassing patients, families, clinicians, health services and health systems to assist future evidence generation and funding decisions. BODY: Building upon previous work, our framework posits that utility of diagnostic genomics consists of three dimensions: the domain or type and extent of utility (what), the relationship and perspective of utility (who), and the time horizon of utility (when). Across the description, assessment, and summation of these three proposed dimensions of utility, one could potentially triangulate a singular point of utility axes of type, relationship, and time. Collectively, the multiple different points of individual utility might be inferred to relate to a concept of aggregate utility. CONCLUSION: This ontological framework requires retrospective and prospective application to enable refinement and validation. Moving forward our framework, and others which have preceded it, promote a better characterisation and description of genomic utility to inform decision-making and optimise the benefits of genomic diagnostic testing.