Gene-disease Pairs Research Articles

Comparative analysis of gene and disease selection in genomic newborn screening studies.

Genomic newborn screening (gNBS) is on the horizon given the decreasing costs of sequencing and the advanced understanding of the impact of genetic variants on health and diseases. Key to ongoing gNBS pilot studies is the selection of target diseases and associated genes to be included. In this study, we present a comprehensive analysis of seven published gene-disease lists from gNBS studies, evaluating gene-disease count, composition, group proportions, and ClinGen curations of individual disorders. Despite shared selection criteria, we observe substantial variation in total gene count (median 480, range 237-889) and disease group composition. An intersection was identified for 53 genes, primarily inherited metabolic diseases (83%, 44/53). Each study investigated a subset of exclusive gene-disease pairs, and the total number of exclusive gene-disease pairs was positively correlated with the total number of genes included per study. While most pairs receive "Definitive" or "Strong" ClinGen classifications, some are labeled as "Refuted" (n = 5) or "Disputed" (n = 28), particularly in genetic cardiac diseases. Importantly, 17%-48% of genes lack ClinGen curation. This study underscores the current absence of consensus recommendations for selection criteria for target diseases for gNBS resulting in diversity in proposed gene-disease pairs, their coupling with gene variations and the use of ClinGen curation. Our findings provide crucial insights into the selection of target diseases and accompanying gene variations for future gNBS program, emphasizing the necessity for ongoing collaboration and discussion about criteria harmonization for panel selection to ensure the screening's objectivity, integrity, and broad acceptance.

Gene selection for genomic newborn screening: Moving toward consensus?

PurposeGene 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. MethodsOur 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. ResultsWe 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. ConclusionWe have identified a consensus gene list for gNBS that can be used as a basis for systematic harmonization efforts internationally.

Rare copy-number variants as modulators of common disease susceptibility

BackgroundCopy-number variations (CNVs) have been associated with rare and debilitating genomic disorders (GDs) but their impact on health later in life in the general population remains poorly described.MethodsAssessing four modes of CNV action, we performed genome-wide association scans (GWASs) between the copy-number of CNV-proxy probes and 60 curated ICD-10 based clinical diagnoses in 331,522 unrelated white British UK Biobank (UKBB) participants with replication in the Estonian Biobank.ResultsWe identified 73 signals involving 40 diseases, all of which indicating that CNVs increased disease risk and caused earlier onset. We estimated that 16% of these associations are indirect, acting by increasing body mass index (BMI). Signals mapped to 45 unique, non-overlapping regions, nine of which being linked to known GDs. Number and identity of genes affected by CNVs modulated their pathogenicity, with many associations being supported by colocalization with both common and rare single-nucleotide variant association signals. Dissection of association signals provided insights into the epidemiology of known gene-disease pairs (e.g., deletions in BRCA1 and LDLR increased risk for ovarian cancer and ischemic heart disease, respectively), clarified dosage mechanisms of action (e.g., both increased and decreased dosage of 17q12 impacted renal health), and identified putative causal genes (e.g., ABCC6 for kidney stones). Characterization of the pleiotropic pathological consequences of recurrent CNVs at 15q13, 16p13.11, 16p12.2, and 22q11.2 in adulthood indicated variable expressivity of these regions and the involvement of multiple genes. Finally, we show that while the total burden of rare CNVs—and especially deletions—strongly associated with disease risk, it only accounted for ~ 0.02% of the UKBB disease burden. These associations are mainly driven by CNVs at known GD CNV regions, whose pleiotropic effect on common diseases was broader than anticipated by our CNV-GWAS.ConclusionsOur results shed light on the prominent role of rare CNVs in determining common disease susceptibility within the general population and provide actionable insights for anticipating later-onset comorbidities in carriers of recurrent CNVs.

Beyond gene-disease validity: capturing structured data on inheritance, allelic requirement, disease-relevant variant classes, and disease mechanism for inherited cardiac conditions

BackgroundAs the availability of genomic testing grows, variant interpretation will increasingly be performed by genomic generalists, rather than domain-specific experts. Demand is rising for laboratories to accurately classify variants in inherited cardiac condition (ICC) genes, including secondary findings.MethodsWe analyse evidence for inheritance patterns, allelic requirement, disease mechanism and disease-relevant variant classes for 65 ClinGen-curated ICC gene-disease pairs. We present this information for the first time in a structured dataset, CardiacG2P, and assess application in genomic variant filtering.ResultsFor 36/65 gene-disease pairs, loss of function is not an established disease mechanism, and protein truncating variants are not known to be pathogenic. Using the CardiacG2P dataset as an initial variant filter allows for efficient variant prioritisation whilst maintaining a high sensitivity for retaining pathogenic variants compared with two other variant filtering approaches.ConclusionsAccess to evidence-based structured data representing disease mechanism and allelic requirement aids variant filtering and analysis and is a pre-requisite for scalable genomic testing.

Perspectives of Rare Disease Experts on Newborn Genome Sequencing

Newborn genome sequencing (NBSeq) can detect infants at risk for treatable disorders currently undetected by conventional newborn screening. Despite broad stakeholder support for NBSeq, the perspectives of rare disease experts regarding which diseases should be screened have not been ascertained. To query rare disease experts about their perspectives on NBSeq and which gene-disease pairs they consider appropriate to evaluate in apparently healthy newborns. This survey study, designed between November 2, 2021, and February 11, 2022, assessed experts' perspectives on 6 statements related to NBSeq. Experts were also asked to indicate whether they would recommend including each of 649 gene-disease pairs associated with potentially treatable conditions in NBSeq. The survey was administered between February 11 and September 23, 2022, to 386 experts, including all 144 directors of accredited medical and laboratory genetics training programs in the US. Expert perspectives on newborn screening using genome sequencing. The proportion of experts indicating agreement or disagreement with each survey statement and those who selected inclusion of each gene-disease pair were tabulated. Exploratory analyses of responses by gender and age were conducted using t and χ2 tests. Of 386 experts invited, 238 (61.7%) responded (mean [SD] age, 52.6 [12.8] years [range 27-93 years]; 126 [52.9%] women and 112 [47.1%] men). Among the experts who responded, 161 (87.9%) agreed that NBSeq for monogenic treatable disorders should be made available to all newborns; 107 (58.5%) agreed that NBSeq should include genes associated with treatable disorders, even if those conditions were low penetrance; 68 (37.2%) agreed that actionable adult-onset conditions should be sequenced in newborns to facilitate cascade testing in parents, and 51 (27.9%) agreed that NBSeq should include screening for conditions with no established therapies or management guidelines. The following 25 genes were recommended by 85% or more of the experts: OTC, G6PC, SLC37A4, CYP11B1, ARSB, F8, F9, SLC2A1, CYP17A1, RB1, IDS, GUSB, DMD, GLUD1, CYP11A1, GALNS, CPS1, PLPBP, ALDH7A1, SLC26A3, SLC25A15, SMPD1, GATM, SLC7A7, and NAGS. Including these, 42 gene-disease pairs were endorsed by at least 80% of experts, and 432 genes were endorsed by at least 50% of experts. In this survey study, rare disease experts broadly supported NBSeq for treatable conditions and demonstrated substantial concordance regarding the inclusion of a specific subset of genes in NBSeq.

Predicting disease genes based on multi-head attention fusion

BackgroundThe identification of disease-related genes is of great significance for the diagnosis and treatment of human disease. Most studies have focused on developing efficient and accurate computational methods to predict disease-causing genes. Due to the sparsity and complexity of biomedical data, it is still a challenge to develop an effective multi-feature fusion model to identify disease genes.ResultsThis paper proposes an approach to predict the pathogenic gene based on multi-head attention fusion (MHAGP). Firstly, the heterogeneous biological information networks of disease genes are constructed by integrating multiple biomedical knowledge databases. Secondly, two graph representation learning algorithms are used to capture the feature vectors of gene-disease pairs from the network, and the features are fused by introducing multi-head attention. Finally, multi-layer perceptron model is used to predict the gene-disease association.ConclusionsThe MHAGP model outperforms all of other methods in comparative experiments. Case studies also show that MHAGP is able to predict genes potentially associated with diseases. In the future, more biological entity association data, such as gene-drug, disease phenotype-gene ontology and so on, can be added to expand the information in heterogeneous biological networks and achieve more accurate predictions. In addition, MHAGP with strong expansibility can be used for potential tasks such as gene-drug association and drug-disease association prediction.

Systematic gene-disease relationship (GDR) curation unveils 61 gene-disease associations and highlights the impact on genetic testing

PurposeWith this study, we aimed to explore the gene-disease relationship (GDR) evidence for 109 gene-disease pairs and the significance of a large Biodatabank for this classification. MethodsThe Clinical Genome Resource (ClinGen) Clinical Validity Framework for evaluation of GDR was applied. Most of the assessed genes were without a phenotype entry in the Online Mendelian Inheritance in Man (OMIM) database. Our Biodatabank with genetic data from over 670,000 previously tested individuals, in addition to data available in literature and public databases, were used for gene curation. ResultsWe confirmed 61 GDR (Definitive: 4 genes, Strong: 22 genes, Moderate: 35 genes). For 84 of 109 gene-disease pairs, a higher score was reached when using data from our Biodatabank in addition to externally obtainable data. This increased the final level of classification in 21 of the genes. Over 400 patients received a genetic report with clinically relevant variants in these 61 genes. ConclusionOur results demonstrate the importance of careful assessment of gene clinical validity data, along with the use of genetic data repositories. Implementation of the ClinGen Clinical Validity Framework for assessment of GDR is relatively straightforward. We encourage diagnostic laboratories to implement such a system and contribute to closing the knowledge gap in genetic research and diagnostics.

A Knowledge Graph-Enhanced Tensor Factorisation Model for Discovering Drug Targets.

The drug discovery and development process is a long and expensive one, costing over 1 billion USD on average per drug and taking 10-15 years. To reduce the high levels of attrition throughout the process, there has been a growing interest in applying machine learning methodologies to various stages of drug discovery and development in the recent decade, especially at the earliest stage - identification of druggable disease genes. In this paper, we have developed a new tensor factorisation model to predict potential drug targets (genes or proteins) for treating diseases. We created a three-dimensional data tensor consisting of 1,048 gene targets, 860 diseases and 230,011 evidence attributes and clinical outcomes connecting them, using data extracted from the Open Targets and PharmaProjects databases. We enriched the data with gene target representations learned from a drug discovery-oriented knowledge graph and applied our proposed method to predict the clinical outcomes for unseen gene target and disease pairs. We designed three evaluation strategies to measure the prediction performance and benchmarked several commonly used machine learning classifiers together with Bayesian matrix and tensor factorisation methods. The result shows that incorporating knowledge graph embeddings significantly improves the prediction accuracy and that training tensor factorisation alongside a dense neural network outperforms all other baselines. In summary, our framework combines two actively studied machine learning approaches to disease target identification, namely tensor factorisation and knowledge graph representation learning, which could be a promising avenue for further exploration in data-driven drug discovery.

Clinical validity assessment of genes frequently tested on intellectual disability/autism sequencing panels

Neurodevelopmental disorders (NDDs), such as intellectual disability (ID) and autism spectrum disorder (ASD), exhibit genetic and phenotypic heterogeneity, making them difficult to differentiate without a molecular diagnosis. The Clinical Genome Resource Intellectual Disability/Autism Gene Curation Expert Panel (GCEP) uses systematic curation to distinguish ID/ASD genes that are appropriate for clinical testing (ie, with substantial evidence supporting their relationship to disease) from those that are not. Using the Clinical Genome Resource gene-disease validity curation framework, the ID/Autism GCEP classified genes frequently included on clinical ID/ASD testing panels as Definitive, Strong, Moderate, Limited, Disputed, Refuted, or No Known Disease Relationship. As of September 2021, 156 gene-disease pairs have been evaluated. Although most (75%) were determined to have definitive roles in NDDs, 22 (14%) genes evaluated had either Limited or Disputed evidence. Such genes are currently not recommended for use in clinical testing owing to the limited ability to assess the effect of identified variants. Our understanding of gene-disease relationships evolves over time; new relationships are discovered and previously-held conclusions may be questioned. Without periodic re-examination, inaccurate gene-disease claims may be perpetuated. The ID/Autism GCEP will continue to evaluate these claims to improve diagnosis and clinical care for NDDs.

Data Integration Using Tensor Decomposition for the Prediction of miRNA-Disease Associations.

Dysfunction of miRNAs has an important relationship with diseases by impacting their target genes. Identifying disease-related miRNAs is of great significance to prevent and treat diseases. Integrating information of genes related miRNAs and/or diseases in calculational methods for miRNA-disease association studies is meaningful because of the complexity of biological mechanisms. Therefore, in this study, we propose a novel method based on tensor decomposition, termed TDMDA, to integrate multi-type data for identifying pathogenic miRNAs. First, we construct a three-order association tensor to express the associations of miRNA-disease pairs, the associations of miRNA-gene pairs, and the associations of gene-disease pairs simultaneously. Then, a tensor decomposition-based method with auxiliary information is applied to reconstruct the association tensor for predicting miRNA-disease associations, and the auxiliary information includes biological similarity information and adjacency information. The performance of TDMDA is compared with other advanced methods under 5-fold cross-validations. The experimental results indicate the TDMDA is a competitive method.

Utility of Whole Genome Sequencing for Population Screening of Deafness-Related Genetic Variants and Cytomegalovirus Infection in Newborns.

Background: Hearing loss affects approximately two out of every 1,000 newborns. Genetic factors and congenital cytomegalovirus (CMV) infections account for around 90% of the etiology. The purpose of this study was to develop and test a whole genome sequencing (WGS) approach to detect deafness-related genetic variants and CMV infections simultaneously in newborns. Method: Deafness-related genes causing congenital or childhood hearing loss were curated and selected for newborn screening. Nine dried blood spots from newborns with known genetic variants (n = 6) or CMV infections (n = 3) were employed to develop and validate the WGS testing and analytic pipeline. We then pilot tested the WGS analysis on 51 de-identified clinical samples. Results: 92 gene-disease pairs were selected for screening hearing loss in newborns. In the validation test, WGS accurately detected all types of genetic variants, including single nucleotide variations, insertions/deletions, and copy number variations in the nuclear or mitochondrial genome. Sequence reads mapping to the CMV reference genome were discovered in CMV infected samples. In the pilot test, WGS identified nine out of 51 (18%) newborns carrying pathogenic variants associated with deafness. Conclusion: WGS can simultaneously detect genetic variants and CMV infections in dried blood spot specimens from newborns. Our study provides proof of principle that genome sequencing can be a promising alternative for newborn screening of hearing loss.

TBGA: a large-scale Gene-Disease Association dataset for Biomedical Relation Extraction

BackgroundDatabases are fundamental to advance biomedical science. However, most of them are populated and updated with a great deal of human effort. Biomedical Relation Extraction (BioRE) aims to shift this burden to machines. Among its different applications, the discovery of Gene-Disease Associations (GDAs) is one of BioRE most relevant tasks. Nevertheless, few resources have been developed to train models for GDA extraction. Besides, these resources are all limited in size—preventing models from scaling effectively to large amounts of data.ResultsTo overcome this limitation, we have exploited the DisGeNET database to build a large-scale, semi-automatically annotated dataset for GDA extraction. DisGeNET stores one of the largest available collections of genes and variants involved in human diseases. Relying on DisGeNET, we developed TBGA: a GDA extraction dataset generated from more than 700K publications that consists of over 200K instances and 100K gene-disease pairs. Each instance consists of the sentence from which the GDA was extracted, the corresponding GDA, and the information about the gene-disease pair.ConclusionsTBGA is amongst the largest datasets for GDA extraction. We have evaluated state-of-the-art models for GDA extraction on TBGA, showing that it is a challenging and well-suited dataset for the task. We made the dataset publicly available to foster the development of state-of-the-art BioRE models for GDA extraction.

A systematic review and standardized clinical validity assessment of genes involved in female reproductive failure.

Genetic testing is becoming increasingly required at almost every stage of failed female reproduction/infertility. Nonetheless, clinical evidence for the majority of identified gene–disease relationships is ill-defined, thus leading to difficult gene variant interpretation and poor translation of existing knowledge into clinics. We aimed to identify the genes that have ever been implicated in monogenic female reproductive failure in humans and to classify the identified gene–disease relationship pairs using a standardized clinical validity assessment. A PubMed search following PRISMA guidelines was conducted on 20 September 2021 aiming to identify studies pertaining to genetic causes of phenotypes of female reproductive failure. The clinical validity of identified gene–disease pairs was assessed using standardized criteria, counting whether sufficient genetic and experimental evidence has been accumulated to consider a single gene ‘characterized’ for a single Mendelian disease. In total, 1256 articles were selected for the data extraction; 183 unique gene–disease pairs were classified spanning the following phenotypes: hypogonadotropic hypogonadism, ovarian dysgenesis, premature ovarian failure/insufficiency, ovarian hyperstimulation syndrome, empty follicle syndrome, oocyte maturation defect, fertilization failure, early embryonic arrest, recurrent hydatidiform mole, adrenal disfunction and Mullerian aplasia. Twenty-four gene–disease pairs showed definitive evidence, 36 – strong, 19 – moderate, 81 – limited and 23 – showed no evidence. Here, we provide comprehensive, systematic and timely information on the genetic causes of female infertility. Our classification of genetic causes of female reproductive failure will facilitate the composition of up-to-date guidelines on genetic testing in female reproduction, the development of diagnostic gene panels and the advancement of reproductive decision-making.

EP354: A systematic approach for applying disease-specific phenotype in clinical variant interpretation

The 2015 ACMG/AMP clinical variant interpretation guidelines incorporate an individual’s phenotype, including presenting features and family history consistent with a particular genetic cause of disease, across multiple pathogenicity criteria. However, they do not give detailed guidance on how to define or weight such disease-specific features, leading to uncertainty and discordant use in clinical variant interpretation. Here, we assessed the recommendations of existing ClinGen Variant Curation Expert Panels (VCEPs) for applying phenotypic data for their associated gene-disease pairs, and used this to create a broader framework to support more systematic use of phenotypic features in clinical variant interpretation.

Clinical validity and utility of preconception expanded carrier screening for the management of reproductive genetic risk in IVF and general population.

What is the clinical validity and utility of preconception Expanded Carrier Screening (ECS) application on the management of prospective parents? The high detection rate of at-risk couples (ARCs) and the high proportion opting for IVF/preimplantation genetic testing (PGT) treatment demonstrate the clinical utility of ECS in the preconception space in IVF and general population. About 2-4% of couples are at risk of conceiving a child with an autosomal recessive or X-linked genetic disorder. In recent years, the increasing cost-effectiveness of genetic diagnostic techniques has allowed the creation of ECS panels for the simultaneous detection of multiple recessive disorders. Comprehensive preconception genetic screening holds the potential to significantly improve couple's genetic risk assessment and reproductive planning to avoid detectable inheritable genetic offspring. A total of 3877 individuals without a family history of genetic conditions were analyzed between January 2017 and January 2020. Of the enrolled individuals, 1212 were gamete donors and 2665 were patients planning on conceiving from both the IVF and the natural conception group. From the non-donor cohort, 1133 were analyzed as individual patients, while the remaining ones were analyzed as couples, for a total of 766 couples. A focused ECS panel was developed following American College of Obstetrics and Gynecology ACOG-recommended criteria (prevalence, carrier rate, severity), including highly penetrant severe childhood conditions. Couples were defined at-risk when both partners carried an autosomal recessive pathogenic/likely pathogenic variant (PLP) on the same gene or when the woman was a carrier of an X-linked PLP variant. ARC detection rate defined the clinical validity of the ECS approach. Clinical utility was evaluated by monitoring ARCs reproductive decision making. A total of 402 individuals (10.4%) showed PLP for at least one of the genes tested. Among the 766 couples tested, 173 showed one carrier partner (22.6%), whereas 20 couples (2.6%) were found to be at increased risk. Interestingly, one ARC was identified as a result of cascade testing in the extended family of an individual carrying a pathogenic variant on the Survival Of Motor Neuron 1SMN1 gene. Of the identified ARCs, 5 (0.7%) were at risk for cystic fibrosis, 5 (0.7%) for fragile X syndrome, 4 (0.5%) for spinal muscular atrophy, 4 (0.5%) for Beta-Thalassemia/Sickle Cell Anemia, 1 (0.1%) for Smith-Lemli-Opitz Syndrome and 1 (0.1%) for Duchenne/Becker Dystrophy. Fifteen ARCs were successfully followed up from both the IVF and the natural conception groups. All of these (15/15) modified their reproductive planning by undergoing ART with Preimplantation Genetic Testing for Monogenic disease and Aneuploidies (PGT-M and PGT-A). To date, 6/15 (40%) couples completed their PGT cycle with euploid/unaffected embryos achieving a pregnancy after embryo transfer and three of them have already had an unaffected baby. The use of a limited panel of core gene-disease pairs represents a limitation on the research perspective as it can underestimate the rate of detectable carriers and ARCs in this cohort of prospective parents. Expanding the scope of ECS to a larger panel of conditions is becoming increasingly feasible, thanks to a persistent technological evolution and progressive cataloging of gene-disease associations. These results highlight the potential clinical validity and utility of ECS in reducing the risk of a pregnancy affected by a detectable inheritable genetic condition. The steady reduction in the costs of genetic analyses enables the expansion of monogenic testing/screening applications at the preimplantation stage, thus, providing valid decisional support and reproductive autonomy to patients, particularly in the context of IVF. No external funding was used for this study. A.C., M.F., S.C., M.P., L.G., and C.P. are employees of Igenomix Italy. C.S. is the head of the scientific board of Igenomix. N/A.

Expert interpretation of genes and variants in hereditary hearing loss

Abstract Background: Hearing loss (HL) is the most common sensory deficit from birth, with at least 50 % due to an underlying genetic etiology. A genetic evaluation is a recommended component to the medical workup for HL, and a genetic diagnosis can impact medical management and provide prognostic and recurrence risk information. The accuracy of a genetic diagnosis relies on the evidence supporting the gene–disease relationship, as well as the evidence supporting individual variant classifications. As such, the ClinGen Hearing Loss Working Group was formed and tasked with curating genes associated with genetic hearing loss and developing specifications of the ACMG/AMP variant interpretation guidelines with the goal of improving the genetic diagnosis of patients with HL. Objectives: To describe the prioritization and expert curation of genes and variants associated with HL performed under the purview of the ClinGen Hearing Loss Gene and Variant Expert Panels (HL GCEP and VCEP). Materials and methods: HL genes were taken from clinical testing panels in the Genetic Testing Registry and prioritized based on a nonsyndromic presentation. Variants were taken from ClinVar and those with diverse data types and medically significant conflicts were prioritized to test the specified variant interpretation guidelines and to resolve classification discrepancies, respectively. Conclusions: The ClinGen HL GCEP has curated 174 gene–disease pairs. The HL VCEP has submitted 77 variants, including the previously controversial p.Met34Thr and p.Val37Ile variants in GJB2, into ClinVar, as an FDA-recognized database. Collaboration across clinics and laboratories were crucial to these curations and highlight the impact that data sharing can have on patient care.

ClinGen expert clinical validity curation of 164 hearing loss gene–disease pairs

PurposeProper interpretation of genomic variants is critical to successful medical decision making based on genetic testing results. A fundamental prerequisite to accurate variant interpretation is the clear understanding of the clinical validity of gene–disease relationships. The Clinical Genome Resource (ClinGen) has developed a semiquantitative framework to assign clinical validity to gene–disease relationships. MethodsThe ClinGen Hearing Loss Gene Curation Expert Panel (HL GCEP) uses this framework to perform evidence-based curations of genes present on testing panels from 17 clinical laboratories in the Genetic Testing Registry. The HL GCEP curated and reviewed 142 genes and 164 gene–disease pairs, including 105 nonsyndromic and 59 syndromic forms of hearing loss. ResultsThe final outcome included 82 Definitive (50%), 12 Strong (7%), 25 Moderate (15%), 32 Limited (20%), 10 Disputed (6%), and 3 Refuted (2%) classifications. The summary of each curation is date stamped with the HL GCEP approval, is live, and will be kept up-to-date on the ClinGen website (https://search.clinicalgenome.org/kb/gene-validity). ConclusionThis gene curation approach serves to optimize the clinical sensitivity of genetic testing while reducing the rate of uncertain or ambiguous test results caused by the interrogation of genes with insufficient evidence oF.A. disease link.

Determining the clinical validity of hereditary colorectal cancer and polyposis susceptibility genes using the Clinical Genome Resource Clinical Validity Framework

PurposeGene–disease associations implicated in hereditary colorectal cancer and polyposis susceptibility were evaluated using the ClinGen Clinical Validity framework. MethodsForty-two gene–disease pairs were assessed for strength of evidence supporting an association with hereditary colorectal cancer and/or polyposis. Genetic and experimental evidence supporting each gene–disease relationship was curated independently by two trained biocurators. Evidence was reviewed with experts and assigned a final clinical validity classification. ResultsOf all gene–disease pairs evaluated, 14/42 (33.3%) were Definitive, 1/42 (2.4%) were Strong, 6/42 (14.3%) were Moderate, 18/42 (42.9%) were Limited, and 3/42 (7.1%) were either No Reported Evidence, Disputed, or Refuted. Of panels in the National Institutes of Health Genetic Testing Registry, 4/26 (~15.4%) contain genes with Limited clinical evidence. ConclusionClinicians and laboratory diagnosticians should note that <60% of the genes on clinically available panels have Strong or Definitive evidence of association with hereditary colon cancer or polyposis, and >40% have only Moderate, Limited, Disputed, or Refuted evidence. Continuing to expand the structured assessment of the clinical relevance of genes listed on hereditary cancer testing panels will help clinicians and diagnostic laboratories focus the communication of genetic testing results on clinically significant genes.

Clinical validity assessment of genes frequently tested on hereditary breast and ovarian cancer susceptibility sequencing panels

PurposeSeveral genes on hereditary breast and ovarian cancer susceptibility test panels have not been systematically examined for strength of association with disease. We employed the Clinical Genome Resource (ClinGen) clinical validity framework to assess the strength of evidence between selected genes and breast or ovarian cancer. MethodsThirty-one genes offered on cancer panel testing were selected for evaluation. The strength of gene–disease relationship was systematically evaluated and a clinical validity classification of either Definitive, Strong, Moderate, Limited, Refuted, Disputed, or No Reported Evidence was assigned. ResultsDefinitive clinical validity classifications were made for 10/31 and 10/32 gene–disease pairs for breast and ovarian cancer respectively. Two genes had a Moderate classification whereas, 6/31 and 6/32 genes had Limited classifications for breast and ovarian cancer respectively. Contradictory evidence resulted in Disputed or Refuted assertions for 9/31 genes for breast and 4/32 genes for ovarian cancer. No Reported Evidence of disease association was asserted for 5/31 genes for breast and 11/32 for ovarian cancer. ConclusionEvaluation of gene–disease association using the ClinGen clinical validity framework revealed a wide range of classifications. This information should aid laboratories in tailoring appropriate gene panels and assist health-care providers in interpreting results from panel testing.

An Age-Based Framework for Evaluating Genome-Scale Sequencing Results in Newborn Screening

To assess the performance of a standardized, age-based metric for scoring clinical actionability to evaluate conditions for inclusion in newborn screening and compare it with the results from other contemporary methods. The North Carolina Newborn Exome Sequencing for Universal Screening study developed an age-based, semiquantitative metric to assess the clinical actionability of gene-disease pairs and classify them with respect to age of onset or timing of interventions. This categorization was compared with the gold standard Recommended Uniform Screening Panel and other methods to evaluate gene-disease pairs for newborn genomic sequencing. We assessed 822 gene-disease pairs, enriched for pediatric onset of disease and suspected actionability. Of these, 466 were classified as having childhood onset and high actionability, analogous to conditions selected for the Recommended Uniform Screening Panel core panel. Another 245 were classified as having childhood onset and low to no actionability, 25 were classified as having adult onset and high actionability, 19 were classified as having adult onset and low to no actionability, and 67 were excluded due to controversial evidence and/or prenatal onset. This study describes a novel method to facilitate decisions about the potential use of genomic sequencing for newborn screening. These categories may assist parents and physicians in making informed decisions about the disclosure of results from voluntary genomic sequencing in children.