Functional Analysis of G6PD Variants Associated With Low G6PD Activity in the All of Us Research Program.

Glucose-6-phosphate dehydrogenase (G6PD) protects red blood cells against oxidative damage through regeneration of NADPH. Individuals with G6PD polymorphisms (variants) that produce an impaired G6PD enzyme are usually asymptomatic, but at risk of hemolytic anemia from oxidative stressors, including certain drugs and foods. Prevention of G6PD deficiency-related hemolytic anemia is achievable through G6PD genetic testing or whole-genome sequencing (WGS) to identify affected individuals who should avoid hemolytic triggers. However, accurately predicting the clinical consequence of G6PD variants is limited by over 800 G6PD variants which remain of uncertain significance. There also remains significant variability in which deficiency-causing variants are included in pharmacogenomic testing arrays across institutions: many panels only include c.202G>A, even though dozens of other variants can also cause G6PD deficiency. Here, we seek to improve G6PD genotype interpretation using data available in the All of Us Research Program and using a yeast functional assay. We confirm that G6PD coding variants are the main contributor to decreased G6PD activity, and that 13% of individuals in the All of Us data with deficiency-causing variants would be missed if only the c.202G>A variant were tested for. We expand clinical interpretation for G6PD variants of uncertain significance; reporting that c.595A>G, known as G6PD Dagua or G6PD Açores, and the newly identified variant c.430C>G, reduce activity sufficiently to lead to G6PD deficiency. We also provide evidence that five missense variants of uncertain significance are unlikely to lead to G6PD deficiency, since they were seen in hemi- or homozygous individuals without a reduction in G6PD activity. We also applied the new WHO guidelines and were able to classify two synonymous variants as WHO class C. We anticipate these results will improve the accuracy, and prompt increased use, of G6PD genetic tests through a more complete clinical interpretation of G6PD variants. As the All of Us data increases from 245,000 to 1 million participants, and additional functional assays are carried out, we expect this research to serve as a template to enable complete characterization of G6PD deficiency genotypes. With an increased number of interpreted variants, genetic testing of G6PD will be more informative for preemptively identifying individuals at risk for drug- or food-induced hemolytic anemia.

Mutations in Glycosyltransferases and Glycosidases: Implications for Associated Diseases.

Glycosylation, a crucial and the most common post-translational modification, coordinates a multitude of biological functions through the attachment of glycans to proteins and lipids. This process, predominantly governed by glycosyltransferases (GTs) and glycoside hydrolases (GHs), decides not only biomolecular functionality but also protein stability and solubility. Mutations in these enzymes have been implicated in a spectrum of diseases, prompting critical research into the structural and functional consequences of such genetic variations. This study compiles an extensive dataset from ClinVar and UniProt, providing a nuanced analysis of 2603 variants within 343 GT and GH genes. We conduct thorough MTR score analyses for the proteins with the most documented variants using MTR3D-AF2 via AlphaFold2 (AlphaFold v2.2.4) predicted protein structure, with the analyses indicating that pathogenic mutations frequently correlate with Beta Bridge secondary structures. Further, the calculation of the solvent accessibility score and variant visualisation show that pathogenic mutations exhibit reduced solvent accessibility, suggesting the mutated residues are likely buried and their localisation is within protein cores. We also find that pathogenic variants are often found proximal to active and binding sites, which may interfere with substrate interactions. We also incorporate computational predictions to assess the impact of these mutations on protein function, utilising tools such as mCSM to predict the destabilisation effect of variants. By identifying these critical regions that are prone to disease-associated mutations, our study opens avenues for designing small molecules or biologics that can modulate enzyme function or compensate for the loss of stability due to these mutations.

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.

Specifications of the ACMG/AMP Variant Classification Guidelines for Germline DICER1 Variant Curation.

Germline pathogenic variants in DICER1 predispose individuals to develop a variety of benign and malignant tumors. Accurate variant curation and classification is essential for reliable diagnosis of DICER1-related tumor predisposition and identification of individuals who may benefit from surveillance. Since 2015, most labs have followed the American College of Medical Genetics and Genomics and the Association for Molecular Pathology (ACMG/AMP) sequence variant classification guidelines for DICER1 germline variant curation. However, these general guidelines lack gene-specific nuances and leave room for subjectivity. Consequently, a group of DICER1 experts joined ClinGen to form the DICER1 and miRNA-Processing Genes Variant Curation Expert Panel (VCEP), to create DICER1- specific ACMG/AMP guidelines for germline variant curation. The VCEP followed the FDA-approved ClinGen protocol for adapting and piloting these guidelines. A diverse set of 40 DICER1 variants were selected for piloting, including 14 known Pathogenic/Likely Pathogenic (P/LP) variants, 12 known Benign/Likely Benign (B/LB) variants, and 14 variants classified as variants of uncertain significance (VUS) or with conflicting interpretations in ClinVar. Clinically meaningful classifications (i.e., P, LP, LB, or B) were achieved for 82.5% (33/40) of the pilot variants, with 100% concordance among the known P/LP and known B/LB variants. Half of the VUS or conflicting variants were resolved with four variants classified as LB and three as LP. These results demonstrate that the DICER1-specific guidelines for germline variant curation effectively classify known pathogenic and benign variants while reducing the frequency of uncertain classifications. Individuals and labs curating DICER1 variants should consider adopting this classification framework to encourage consistency and improve objectivity.

Analysis of Nucleotide Variations in Human G-Quadruplex Forming Regions Associated with Disease States.

While the role of G quadruplex (G4) structures has been identified in cancers and metabolic disorders, single nucleotide variations (SNVs) and their effect on G4s in disease contexts have not been extensively studied. The COSMIC and CLINVAR databases were used to detect SNVs present in G4s to identify sequence level changes and their effect on the alteration of the G4 secondary structure. A total of 37,515 G4 SNVs in the COSMIC database and 2378 in CLINVAR were identified. Of those, 7236 COSMIC (19.3%) and 457 (19%) of the CLINVAR variants result in G4 loss, while 2728 (COSMIC) and 129 (CLINVAR) SNVs gain a G4 structure. The remaining variants potentially affect the folding energy without affecting the presence of a G4. Analysis of mutational patterns in the G4 structure shows a higher selective pressure (3-fold) in the coding region on the template strand compared to the reverse strand. At the same time, an equal proportion of SNVs were observed among intronic, promoter, and enhancer regions across strands.

Pathogenic variants of human GABRA1 gene associated with epilepsy: A computational approach.

Critical for brain development, neurodevelopmental and network disorders, the GABRA1 gene encodes for the α1 subunit, an abundantly and developmentally expressed subunit of heteropentameric gamma-aminobutyric acid A receptors (GABAARs) mediating primary inhibition in the brain. Mutations of the GABAAR subunit genes including GABRA1 gene are associated with epilepsy, a group of syndromes, characterized by unprovoked seizures and diagnosed by integrative approach, that involves genetic testing. Despite the diagnostic use of genetic testing, a large fraction of the GABAAR subunit gene variants including the variants of GABRA1 gene is not known in terms of their molecular consequence, a challenge for precision and personalized medicine. Addressing this, one hundred thirty-seven GABRA1 gene variants of unknown clinical significance have been extracted from the ClinVar database and computationally analyzed for pathogenicity. Eight variants (L49H, P59L, W97R, D99G, G152S, V270G, T294R, P305L) are predicted as pathogenic and mapped to the α1 subunit's extracellular domain (ECD), transmembrane domains (TMDs) and extracellular linker. This is followed by the integration with relevant data for cellular pathology and severity of the epilepsy syndromes retrieved from the literature. Our results suggest that the pathogenic variants in the ECD of GABRA1 (L49H, P59L, W97R, D99G, G152S) will probably manifest decreased surface expression and reduced current with mild epilepsy phenotypes while V270G, T294R in the TMDs and P305L in the linker between the second and the third TMDs will likely cause reduced cell current with severe epilepsy phenotypes. The results presented in this study provides insights for clinical genetics and wet lab experimentation.

Evaluation of Liftover Tools for the Conversion of Genome Reference Consortium Human Build 37 to Build 38 Using ClinVar Variants.

Although Genome Reference Consortium Human Build 38 (GRCh38) was released with improvement over GRCh37, it has not been widely adopted. Several liftover tools have been developed as a convenient approach for GRCh38 implementation. This study aimed to investigate the accuracy of liftover tools for genome conversion. Two Variant Call Format (VCF) files aligned to GRCh37 and GRCh38 were downloaded from ClinVar (clinvar_20221217.vcf.gz). Liftover tools such as CrossMap, NCBI Remap, and UCSC liftOver were used to convert genome coordinates from GRCh37 to GRCh38. The accuracy of CrossMap, NCBI Remap, and UCSC liftOver were 99.81% (1,567,838/1,570,748), 99.69% (1,565,953/1,570,748), and 99.99% (1,570,550/1,570,748), respectively. Variants that failed conversion via all three liftover tools were all indels/duplications: a pathogenic/likely pathogenic variant (n = 1) and benign/likely benign variants (n = 7). The eight variants that failed conversion were identified in the ALMS, TTN, CFTR, SLCO, LDLR, PCNT, MID1, and GRIA3 genes, and all the variants were not in the VCF files aligned to GRCh37. This study demonstrated that three liftover tools could successfully convert reference genomes from GRCh37 to GRCh38 in more than 99% of ClinVar variants. This study takes the first step to clinically implement GRCh38 using liftover tools. Further clinical studies are warranted to compare the performance of liftover tools and to validate re-alignment approaches in routine clinical settings.

Variant Classification for Pompe disease; ACMG/AMP specifications from the ClinGen Lysosomal Diseases Variant Curation Expert Panel.

Accurate determination of the clinical significance of genetic variants is critical to the integration of genomics in medicine. To facilitate this process, the NIH-funded Clinical Genome Resource (ClinGen) has assembled Variant Curation Expert Panels (VCEPs), groups of experts and biocurators which provide gene- and disease- specifications to the American College of Medical Genetics & Genomics and Association for Molecular Pathology's (ACMG/AMP) variation classification guidelines. With the goal of classifying the clinical significance of GAA variants in Pompe disease (Glycogen storage disease, type II), the ClinGen Lysosomal Diseases (LD) VCEP has specified the ACMG/AMP criteria for GAA. Variant classification can play an important role in confirming the diagnosis of Pompe disease as well as in the identification of carriers. Furthermore, since the inclusion of Pompe disease on the Recommended Uniform Screening Panel (RUSP) for newborns in the USA in 2015, the addition of molecular genetic testing has become an important component in the interpretation of newborn screening results, particularly for asymptomatic individuals. To date, the LD VCEP has submitted classifications and supporting data on 243 GAA variants to public databases, specifically ClinVar and the ClinGen Evidence Repository. Here, we describe the ACMG/AMP criteria specification process for GAA, an update of the GAA-specific variant classification guidelines, and comparison of the ClinGen LD VCEP's GAA variant classifications with variant classifications submitted to ClinVar. The LD VCEP has added to the publicly available knowledge on the pathogenicity of variants in GAA by increasing the number of expert-curated GAA variants present in ClinVar, and aids in resolving conflicting classifications and variants of uncertain clinical significance.

ClinVar and HGMD genomic variant classification accuracy has improved over time, as measured by implied disease burden.

BACKGROUND: Curated databases of genetic variants assist clinicians and researchers in interpreting genetic variation. Yet, these databases contain some misclassified variants. It is unclear whether variant misclassification is abating as these databases rapidly grow and implement new guidelines. METHODS: Using archives of ClinVar and HGMD, we investigated how variant misclassification has changed over 6 years, across different ancestry groups. We considered inborn errors of metabolism (IEMs) screened in newborns as a model system because these disorders are often highly penetrant with neonatal phenotypes. We used samples from the 1000 Genomes Project (1KGP) to identify individuals with genotypes that were classified by the databases as pathogenic. Due to the rarity of IEMs, nearly all such classified pathogenic genotypes indicate likely variant misclassification in ClinVar or HGMD. RESULTS: While the false-positive rates of both ClinVar and HGMD have improved over time, HGMD variants currently imply two orders of magnitude more affected individuals in 1KGP than ClinVar variants. We observed that African ancestry individuals have a significantly increased chance of being incorrectly indicated to be affected by a screened IEM when HGMD variants are used. However, this bias affecting genomes of African ancestry was no longer significant once common variants were removed in accordance with recent variant classification guidelines. We discovered that ClinVar variants classified as Pathogenic or Likely Pathogenic are reclassified sixfold more often than DM or DM? variants in HGMD, which has likely resulted in ClinVar's lower false-positive rate. CONCLUSIONS: Considering misclassified variants that have since been reclassified reveals our increasing understanding of rare genetic variation. We found that variant classification guidelines and allele frequency databases comprising genetically diverse samples are important factors in reclassification. We also discovered that ClinVar variants common in European and South Asian individuals were more likely to be reclassified to a lower confidence category, perhaps due to an increased chance of these variants being classified by multiple submitters. We discuss features for variant classification databases that would support their continued improvement.

Heme Interactions as Regulators of the Alternative Pathway Complement Responses and Implications for Heme-Associated Pathologies.

Heme (Fe2+-protoporphyrin IX) is a pigment of life, and as a prosthetic group in several hemoproteins, it contributes to diverse critical cellular processes. While its intracellular levels are tightly regulated by networks of heme-binding proteins (HeBPs), labile heme can be hazardous through oxidative processes. In blood plasma, heme is scavenged by hemopexin (HPX), albumin and several other proteins, while it also interacts directly with complement components C1q, C3 and factor I. These direct interactions block the classical pathway (CP) and distort the alternative pathway (AP). Errors or flaws in heme metabolism, causing uncontrolled intracellular oxidative stress, can lead to several severe hematological disorders. Direct interactions of extracellular heme with alternative pathway complement components (APCCs) may be implicated molecularly in diverse conditions at sites of abnormal cell damage and vascular injury. In such disorders, a deregulated AP could be associated with the heme-mediated disruption of the physiological heparan sulphate-CFH coat of stressed cells and the induction of local hemostatic responses. Within this conceptual frame, a computational evaluation of HBMs (heme-binding motifs) aimed to determine how heme interacts with APCCs and whether these interactions are affected by genetic variation within putative HBMs. Combined computational analysis and database mining identified putative HBMs in all of the 16 APCCs examined, with 10 exhibiting disease-associated genetic (SNPs) and/or epigenetic variation (PTMs). Overall, this article indicates that among the pleiotropic roles of heme reviewed, the interactions of heme with APCCs could induce differential AP-mediated hemostasis-driven pathologies in certain individuals.

A Case Study on PPM1D and 9 Other Shared Germline Alterations in a Family.

BACKGROUND: The use of high-throughput genotyping techniques has enabled us to identify the rare germline genetic variants with different pathogenicity and penetrance, and understand their role in cancer predisposition. We report here a familial cancer case, a study from Western Indian. METHODS: NGS-WES was carried out in a lung cancer patient who has a family history of multiple cancers across generations, including tongue, lung, brain, cervical, urothelial, and esophageal cancer. The results were validated by data mining from available data bases. I-TASSER, RasMol and PyMol were used for protein structure modelling. RESULTS: The sequencing by NGS-WES revealed PPM1D c.1654C>T (p.Arg552Ter) mutation in hotspot region exon 6 leading to sudden protein truncation and loss of the C-terminal, due to the substitution of C>T. This mutation was classified as a variant of uncertain significance (VUS), due to limited data on lung cancer, The three unaffected siblings of proband did not show any pathogenic variants and comparative analysis of the four siblings indicate 9 shared genetic variants, classified as benign as per ClinVar. CONCLUSION: PPM1D constitutional genetic alterations are rare and uncommon in different ethnic populations. This gene encodes a phosphatase playing role in regulating the P53 tumor suppressor pathway and DNA damage response. Genetic alterations in the PPM1D gene maybe linked to history of gliomas, breast cancer, and ovarian cancer onset in the proband's family.
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Clinical implications of conflicting variant interpretations in the cancer genetics clinic.

PURPOSE: The aim of this study was to describe the clinical impact of commercial laboratories issuing conflicting classifications of genetic variants. METHODS: Results from 2000 patients undergoing a multigene hereditary cancer panel by a single laboratory were analyzed. Clinically significant discrepancies between the laboratory-provided test reports and other major commercial laboratories were identified, including differences between pathogenic/likely pathogenic and variant of uncertain significance (VUS) classifications, via review of ClinVar archives. For patients carrying a VUS, clinical documentation was assessed for evidence of provider awareness of the conflict. RESULTS: Fifty of 975 (5.1%) patients with non-negative results carried a variant with a clinically significant conflict, 19 with a pathogenic/likely pathogenic variant reported in APC or MUTYH, and 31 with a VUS reported in CDKN2A, CHEK2, MLH1, MSH2, MUTYH, RAD51C, or TP53. Only 10 of 28 (36%) patients with a VUS with a clinically significant conflict had a documented discussion by a provider about the conflict. Discrepant counseling strategies were used for different patients with the same variant. Among patients with a CDKN2A variant or a monoallelic MUTYH variant, providers were significantly more likely to make recommendations based on the laboratory-reported classification. CONCLUSION: Our findings highlight the frequency of variant interpretation discrepancies and importance of clinician awareness. Guidance is needed on managing patients with discrepant variants to support accurate risk assessment.

A Solve-RD ClinVar-based reanalysis of 1522 index cases from ERN-ITHACA reveals common pitfalls and misinterpretations in exome sequencing.

PURPOSE: Within the Solve-RD project (https://solve-rd.eu/), the European Reference Network for Intellectual disability, TeleHealth, Autism and Congenital Anomalies aimed to investigate whether a reanalysis of exomes from unsolved cases based on ClinVar annotations could establish additional diagnoses. We present the results of the "ClinVar low-hanging fruit" reanalysis, reasons for the failure of previous analyses, and lessons learned. METHODS: Data from the first 3576 exomes (1522 probands and 2054 relatives) collected from European Reference Network for Intellectual disability, TeleHealth, Autism and Congenital Anomalies was reanalyzed by the Solve-RD consortium by evaluating for the presence of single-nucleotide variant, and small insertions and deletions already reported as (likely) pathogenic in ClinVar. Variants were filtered according to frequency, genotype, and mode of inheritance and reinterpreted. RESULTS: We identified causal variants in 59 cases (3.9%), 50 of them also raised by other approaches and 9 leading to new diagnoses, highlighting interpretation challenges: variants in genes not known to be involved in human disease at the time of the first analysis, misleading genotypes, or variants undetected by local pipelines (variants in off-target regions, low quality filters, low allelic balance, or high frequency). CONCLUSION: The "ClinVar low-hanging fruit" analysis represents an effective, fast, and easy approach to recover causal variants from exome sequencing data, herewith contributing to the reduction of the diagnostic deadlock.

Vitamin B12 binding to mutated human transcobalamin, in-silico study of TCN2 alanine scanning and ClinVar missense mutations/SNPs.

Many missense mutations/SNPs of the TCN2 gene (which yield Transcobalamin (TC)) were reported in the literature but no study is available about their effect on binding to vitamin B12(B12) at the structural level experimentally nor computationally. Predict the effect of TC missense mutations/SNPs on binding affinity to B12 and characterize their contacts to B12 at the structural level. TC-B12 binding energy difference from the wildtype (ΔΔGmut) was calculated for 378 alanine scanning mutations and 76 ClinVar missense mutations, repeated on two distinct X-ray structures of holoTC namely 2BB5 and 4ZRP. Destabilizing mutations then went through 100 ns molecular dynamics simulation to study their effect on TC-B12 binding at the structural level employing 2BB5 structure. Out of the studied 454 mutations (378 alanine mutations + 76 ClinVar mutations), 19 were destabilizing representing 17 amino acid locations. Mutation energy results show a neutral effect on B12 binding of several missense SNPs reported in the literature including I23V, G94S, R215W, P259R, S348F, L376S, and R399Q. Compared to the wildtype, all the destabilizing mutations have higher average RMSD-Ligand in the last 25% of the MD simulation trajectories and lower average hydrogen bond count while the other parameters vary. Previously reported TCN2 SNPs with an unknown effect on TC-B12 binding were found to have a neutral effect in the current study based on mutation energy calculations. Also, we reported 17 possible amino acids that destabilize TC-B12 binding upon mutation (four listed in ClinVar) and studied their structural effect computationally.

Informing variant assessment using structured evidence from prior classifications (PS1, PM5, and PVS1 sequence variant interpretation criteria).

PURPOSE: This study aimed to explore whether evidence of pathogenicity from prior variant classifications in ClinVar could be used to inform variant interpretation using the American College of Medical Genetics and Genomics/Association for Molecular Pathology clinical guidelines. METHODS: We identified distinct single-nucleotide variants (SNVs) that are either similar in location or in functional consequence to pathogenic variants in ClinVar and analyzed evidence in support of pathogenicity using 3 interpretation criteria. RESULTS: Thousands of variants, including many in clinically actionable disease genes (American College of Medical Genetics and Genomics secondary findings v3.0), have evidence of pathogenicity from existing variant classifications, accounting for 2.5% of nonsynonymous SNVs within ClinVar. Notably, there are many variants with uncertain or conflicting classifications that cause the same amino acid substitution as other pathogenic variants (PS1, N = 323), variants that are predicted to cause different amino acid substitutions in the same codon as pathogenic variants (PM5, N = 7692), and loss-of-function variants that are present in genes in which many loss-of-function variants are classified as pathogenic (PVS1, N = 3635). Most of these variants have similar computational predictions of pathogenicity and splicing effect as their associated pathogenic variants. CONCLUSION: Broadly, for >1.4 million SNVs exome wide, information from previously classified variants could be used to provide evidence of pathogenicity. We have developed a pipeline to identify variants meeting these criteria that may inform interpretation efforts.

Evolutionary coupling analysis guides identification of mistrafficking-sensitive variants in cardiac K+ channels: Validation with hERG.

Loss of function (LOF) mutations of voltage sensitive K+ channel proteins hERG (Kv11.1) and KCNQ1 (Kv7.1) account for the majority of instances of congenital Long QT Syndrome (cLQTS) with the dominant molecular phenotype being a mistrafficking one resulting from protein misfolding. We explored the use of Evolutionary Coupling (EC) analysis, which identifies evolutionarily conserved pairwise amino acid interactions that may contribute to protein structural stability, to identify regions of the channels susceptible to misfolding mutations. Comparison with published experimental trafficking data for hERG and KCNQ1 showed that the method strongly predicts "scaffolding" regions of the channel membrane domains and has useful predictive power for trafficking phenotypes of individual variants. We identified a region in and around the cytoplasmic S2-S3 loop of the hERG Voltage Sensor Domain (VSD) as susceptible to destabilising mutation, and this was confirmed using a quantitative LI-COR ® based trafficking assay that showed severely attenuated trafficking in eight out of 10 natural hERG VSD variants selected using EC analysis. Our analysis highlights an equivalence in the scaffolding structures of the hERG and KCNQ1 membrane domains. Pathogenic variants of ion channels with an underlying mistrafficking phenotype are likely to be located within similar scaffolding structures that are identifiable by EC analysis.

Analysis of hereditary cancer gene variant classifications from ClinVar indicates a need for regular reassessment of clinical assertions.

The clinical classification of variants may change with new information, however, there is limited guidance on how often significant changes in variant classification occur. We used ClinVar to examine how variant classification changes over time. We developed a custom parser and accessed variant data from ClinVar between January 2015 and July 2021. The ClinVar-assigned "aggregate" classification of variants in 121 hereditary cancer genes was harmonized across releases to align to the American College of Medical Genetics and Genomics and the Association for Molecular Pathology terms. Aggregate classification categories were grouped as: benign/likely benign (B/LB); likely pathogenic/pathogenic (LP/P); variant of uncertain significance (VUS); conflicting interpretations of pathogenicity (Conflicting); or Other. We profiled changes in aggregate variant classification between consecutive semi-annual ClinVar releases. The proportion of variants that changed aggregate classification between semi-annual ClinVar releases ranged from 0.6% to 6.4%. The most frequent changes were "VUS to conflicting," "other to LP/P," and "B/LB to Conflicting." A limited number of variants changed aggregate classification from "LP/P to B/LB," or vice versa. Our analysis indicates need for regular reassessment of clinical variant interpretations. The parser developed for this project will facilitate extraction of relevant interpretation data from ClinVar.

A Comprehensive Evaluation of the Performance of Prediction Algorithms on Clinically Relevant Missense Variants.

The rapid integration of genomic technologies in clinical diagnostics has resulted in the detection of a multitude of missense variants whose clinical significance is often unknown. As a result, a plethora of computational tools have been developed to facilitate variant interpretation. However, choosing an appropriate software from such a broad range of tools can be challenging; therefore, systematic benchmarking with high-quality, independent datasets is critical. Using three independent benchmarking datasets compiled from the ClinVar database, we evaluated the performance of ten widely used prediction algorithms with missense variants from 21 clinically relevant genes, including BRCA1 and BRCA2. A fourth dataset consisting of 1053 missense variants was also used to investigate the impact of type 1 circularity on their performance. The performance of the prediction algorithms varied widely across datasets. Based on Matthews Correlation Coefficient and Area Under the Curve, SNPs&GO and PMut consistently displayed an overall above-average performance across the datasets. Most of the tools demonstrated greater sensitivity and negative predictive values at the expense of lower specificity and positive predictive values. We also demonstrated that type 1 circularity significantly impacts the performance of these tools and, if not accounted for, may confound the selection of the best performing algorithms.

Using Long-Term Follow-Up Data to Classify Genetic Variants in Newborn Screened Conditions.

With the rapid increase in publicly available sequencing data, healthcare professionals are tasked with understanding how genetic variation informs diagnosis and affects patient health outcomes. Understanding the impact of a genetic variant in disease could be used to predict susceptibility/protection and to help build a personalized medicine profile. In the United States, over 3.8 million newborns are screened for several rare genetic diseases each year, and the follow-up testing of screen-positive newborns often involves sequencing and the identification of variants. This presents the opportunity to use longitudinal health information from these newborns to inform the impact of variants identified in the course of diagnosis. To test this, we performed secondary analysis of a 10-year natural history study of individuals diagnosed with metabolic disorders included in newborn screening (NBS). We found 564 genetic variants with accompanying phenotypic data and identified that 161 of the 564 variants (29%) were not included in ClinVar. We were able to classify 139 of the 161 variants (86%) as pathogenic or likely pathogenic. This work demonstrates that secondary analysis of longitudinal data collected as part of NBS finds unreported genetic variants and the accompanying clinical information can inform the relationship between genotype and phenotype.

Carrier frequency of autosomal recessive genetic conditions in diverse populations: Lessons learned from the genome aggregation database.

An equitable approach by the American College of Medical Genetics and Genomics (ACMG) has recently recommended carrier screening for genes associated with moderate to severe autosomal recessive conditions with a carrier frequency of ≥1/200 in the Genome Aggregation Database exomes (gnomADv2.0.2). We analyzed carrier frequencies in gnomADv3.1.1 genomes representing diverse populations. ClinVar data on 35 996 pathogenic/likely pathogenic variants in 419 genes were used to estimate the gnomAD frequency of heterozygous carriers. We found that ninety-two genes had a carrier frequency of ≥1/200, of which 63 were shared between v3.1.1 and v2.0.2 and 29 were new in v3.1.1. Addition of new populations (Amish, Finnish and Middle Eastern) increased the number of new genes with a carrier frequency of ≥1/200 to 71. Changes in carrier frequencies were attributed to new gnomAD populations, different sample sizes, new ClinVar data, and technical differences between exomes and genomes. This study highlights the dynamic changes in carrier frequencies due to new datasets from diverse populations and provides updated carrier frequencies based on the combined data from 184 352 genomes and exomes in gnomAD. We recommend a periodic review for inclusion of new population data to update carrier screening panels in the future.