Beyond the exome: What's next in diagnostic testing for Mendelian conditions.

Despite advances in clinical genetic testing, including the introduction of exome sequencing (ES), more than 50% of individuals with a suspected Mendelian condition lack a precise molecular diagnosis. Clinical evaluation is increasingly undertaken by specialists outside of clinical genetics, often occurring in a tiered fashion and typically ending after ES. The current diagnostic rate reflects multiple factors, including technical limitations, incomplete understanding of variant pathogenicity, missing genotype-phenotype associations, complex gene-environment interactions, and reporting differences between clinical labs. Maintaining a clear understanding of the rapidly evolving landscape of diagnostic tests beyond ES, and their limitations, presents a challenge for non-genetics professionals. Newer tests, such as short-read genome or RNA sequencing, can be challenging to order, and emerging technologies, such as optical genome mapping and long-read DNA sequencing, are not available clinically. Furthermore, there is no clear guidance on the next best steps after inconclusive evaluation. Here, we review why a clinical genetic evaluation may be negative, discuss questions to be asked in this setting, and provide a framework for further investigation, including the advantages and disadvantages of new approaches that are nascent in the clinical sphere. We present a guide for the next best steps after inconclusive molecular testing based upon phenotype and prior evaluation, including when to consider referral to research consortia focused on elucidating the underlying cause of rare unsolved genetic disorders.

Understanding bacterial pathogen diversity: A proteogenomic analysis and use of an array of genome assemblies to identify novel virulence factors of the honey bee bacterial pathogen Paenibacillus larvae.

Mass spectrometry proteomics data are typically evaluated against publicly available annotated sequences, but the proteogenomics approach is a useful alternative. A single genome is commonly utilized in custom proteomic and proteogenomic data analysis. We pose the question of whether utilizing numerous different genome assemblies in a search database would be beneficial. We reanalyzed raw data from the exoprotein fraction of four reference Enterobacterial Repetitive Intergenic Consensus (ERIC) I-IV genotypes of the honey bee bacterial pathogen Paenibacillus larvae and evaluated them against three reference databases (from NCBI-protein, RefSeq, and UniProt) together with an array of protein sequences generated by six-frame direct translation of 15 genome assemblies from GenBank. The wide search yielded 453 protein hits/groups, which UpSet analysis categorized into 50 groups based on the success of protein identification by the 18 database components. Nine hits that were not identified by a unique peptide were not considered for marker selection, which discarded the only protein that was not identified by the reference databases. We propose that the variability in successful identifications between genome assemblies is useful for marker mining. The results suggest that various strains of P. larvae can exhibit specific traits that set them apart from the established genotypes ERIC I-V.

High prevalence of multilocus pathogenic variation in neurodevelopmental disorders in the Turkish population.

Neurodevelopmental disorders (NDDs) are clinically and genetically heterogenous; many such disorders are secondary to perturbation in brain development and/or function. The prevalence of NDDs is > 3%, resulting in significant sociocultural and economic challenges to society. With recent advances in family-based genomics, rare-variant analyses, and further exploration of the Clan Genomics hypothesis, there has been a logarithmic explosion in neurogenetic "disease-associated genes" molecular etiology and biology of NDDs; however, the majority of NDDs remain molecularly undiagnosed. We applied genome-wide screening technologies, including exome sequencing (ES) and whole-genome sequencing (WGS), to identify the molecular etiology of 234 newly enrolled subjects and 20 previously unsolved Turkish NDD families. In 176 of the 234 studied families (75.2%), a plausible and genetically parsimonious molecular etiology was identified. Out of 176 solved families, deleterious variants were identified in 218 distinct genes, further documenting the enormous genetic heterogeneity and diverse perturbations in human biology underlying NDDs. We propose 86 candidate disease-trait-associated genes for an NDD phenotype. Importantly, on the basis of objective and internally established variant prioritization criteria, we identified 51 families (51/176 = 28.9%) with multilocus pathogenic variation (MPV), mostly driven by runs of homozygosity (ROHs) - reflecting genomic segments/haplotypes that are identical-by-descent. Furthermore, with the use of additional bioinformatic tools and expansion of ES to additional family members, we established a molecular diagnosis in 5 out of 20 families (25%) who remained undiagnosed in our previously studied NDD cohort emanating from Turkey.

High number of candidate gene variants are identified as disease-causing in a period of 4 years.

Advances in bioinformatic tools paired with the ongoing accumulation of genetic knowledge and periodic reanalysis of genomic sequencing data have led to an improvement in genetic diagnostic rates. Candidate gene variants (CGVs) identified during sequencing or on reanalysis but not yet implicated in human disease or associated with a phenotypically distinct condition are often not revisited, leading to missed diagnostic opportunities. Here, we revisited 33 such CGVs from our previously published study and determined that 16 of them are indeed disease-causing (novel or phenotype expansion) since their identification. These results emphasize the need to focus on previously identified CGVs during sequencing or reanalysis and the importance of sharing that information with researchers around the world, including relevant functional analysis to establish disease causality.

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

This study aims to inform future genetic reanalysis management by evaluating the yield of whole-exome sequencing (WES) reanalysis in standard patient care in the Netherlands. Single-center data of 159 patients with a neurodevelopmental disorder (NDD), in which WES analysis and reanalysis were performed between January 1, 2014, and December 31, 2021, was retrospectively collected. Patients were included if they were under the age of 18 years at initial analysis and if this initial analysis did not result in a diagnosis. Demographic, phenotypic, and genotypic characteristics of patients were collected and analyzed. The primary outcomes of our study were (i) diagnostic yield at reanalysis, (ii) reasons for detecting a new possibly causal variant at reanalysis, (iii) unsolicited findings, and (iv) factors associated with positive result of reanalysis. In addition, we conducted a questionnaire study amongst the 7 genetic department in the Netherlands creating an overview of used techniques, yield, and organization of WES reanalysis. The single-center data show that in most cases, WES reanalysis was initiated by the clinical geneticist (65%) or treating physician (30%). The mean time between initial WES analysis and reanalysis was 3.7 years. A new (likely) pathogenic variant or VUS with a clear link to the phenotype was found in 20 initially negative cases, resulting in a diagnostic yield of 12.6%. In 75% of these patients, the diagnosis had clinical consequences, as for example, a screening plan for associated signs and symptoms could be devised. Most (32%) of the (likely) causal variants identified at WES reanalysis were discovered due to a newly described gene-disease association. In addition to the 12.6% diagnostic yield based on new diagnoses, reclassification of a variant of uncertain significance found at initial analysis led to a definite diagnosis in three patients. Diagnostic yield was higher in patients with dysmorphic features compared to patients without clear dysmorphic features (yield 27% vs. 6%; p = 0.001). CONCLUSIONS: Our results show that WES reanalysis in patients with NDD in standard patient care leads to a substantial increase in genetic diagnoses. In the majority of newly diagnosed patients, the diagnosis had clinical consequences. Knowledge about the clinical impact of WES reanalysis, clinical characteristics associated with higher yield, and the yield per year after a negative WES in larger clinical cohorts is warranted to inform guidelines for genetic reanalysis. These guidelines will be of great value for pediatricians, pediatric rehabilitation specialists, and pediatric neurologists in daily care of patients with NDD. WHAT IS KNOWN: • Whole exome sequencing can cost-effectively identify a genetic cause of intellectual disability in about 30-40% of patients. • WES reanalysis in a research setting can lead to a definitive diagnosis in 10-20% of previously exome negative cases. WHAT IS NEW: • WES reanalysis in standard patient care resulted in a diagnostic yield of 13% in previously exome negative children with NDD. • The presence of dysmorphic features is associated with an increased diagnostic yield of WES reanalysis.

The novel molecular mechanism of pulmonary fibrosis: insight into lipid metabolism from reanalysis of single-cell RNA-seq databases.

Pulmonary fibrosis (PF) is a severe pulmonary disease with limited available therapeutic choices. Recent evidence increasingly points to abnormal lipid metabolism as a critical factor in PF pathogenesis. Our latest research identifies the dysregulation of low-density lipoprotein (LDL) is a new risk factor for PF, contributing to alveolar epithelial and endothelial cell damage, and fibroblast activation. In this study, we first integrative summarize the published literature about lipid metabolite changes found in PF, including phospholipids, glycolipids, steroids, fatty acids, triglycerides, and lipoproteins. We then reanalyze two single-cell RNA-sequencing (scRNA-seq) datasets of PF, and the corresponding lipid metabolomic genes responsible for these lipids' biosynthesis, catabolism, transport, and modification processes are uncovered. Intriguingly, we found that macrophage is the most active cell type in lipid metabolism, with almost all lipid metabolic genes being altered in macrophages of PF. In type 2 alveolar epithelial cells, lipid metabolic differentially expressed genes (DEGs) are primarily associated with the cytidine diphosphate diacylglycerol pathway, cholesterol metabolism, and triglyceride synthesis. Endothelial cells are partly responsible for sphingomyelin, phosphatidylcholine, and phosphatidylethanolamines reprogramming as their metabolic genes are dysregulated in PF. Fibroblasts may contribute to abnormal cholesterol, phosphatidylcholine, and phosphatidylethanolamine metabolism in PF. Therefore, the reprogrammed lipid profiles in PF may be attributed to the aberrant expression of lipid metabolic genes in different cell types. Taken together, these insights underscore the potential of targeting lipid metabolism in developing innovative therapeutic strategies, potentially leading to extended overall survival in individuals affected by PF.

The current landscape of clinical exome and genome reanalysis in the U.S.

The majority of patients undergoing exome or genome sequencing receive a nondiagnostic result. Periodic reanalysis is known to increase diagnostic yield from exome sequencing, yet laboratory reanalysis practices are obscure. We sought to define the landscape of exome and genome reanalysis across clinical laboratories. Genetic testing registries were queried to identify eligible clinical genetic laboratories offering exome and/or genome sequencing in the United States. A survey administered to lab representatives investigated reanalysis offerings, policies, perceived uptake, bioinformatic steps, and billing options. The analysis consisted of descriptive statistics. Survey data were collected from 30 of 32 eligible laboratories (93%), comprising 28 exome products and 13 genome products. Reanalysis was widely available for both exomes (n = 27/28, 96%) and genomes (n = 12/13, 92%). Most participating laboratories required ordering providers to initiate reanalysis (n = 24/28, 86%). Most respondents estimated providers initiated reanalysis in less than 10% of all exomes (n = 12/22) or genomes (n = 6/9) sequenced. The approach to reanalysis varied greatly by laboratory. Laboratory approaches to exome and genome reanalysis are highly variable and typically require provider initiation. This could contribute to low reanalysis uptake and increased administrative burden on providers. Further work should emphasize development of clinical exome and genome reanalysis standards.

Considerations and perspectives on the practice of incurred sample reanalysis assessment for chiral drugs.

Given the inherent complexities of bioanalysis, the role of incurred sample reanalysis (ISR) is increasingly appreciated in regulatory bioanalysis. Incurred sample reanalysis has evolved as an integral part of an assay to ensure method reproducibility. The current regulatory ISR guidelines do not provide clarity regarding ISR assessment for chiral drugs comprising enantiomers. Because chiral assays evaluate two enantiomers, there are additional complexities associated with the ISR data generation and interpretation. Based on the current literature, the practices for conducting ISR in chiral methods were reviewed and assessed. While ISR was conducted in chiral methods for both enantiomers using the acceptance criteria prescribed for non-chiral methods, there may be a need to streamline the nuances of ISR data interpretation and define the ISR requirements for chiral methods. The article provides perspectives on the ISR of enantiomeric drugs, including strategy development, by providing various hypothetical scenarios and possible considerations for defining ISR evaluation for chiral assays.

ZPD Retrieval Performances of the First Operational Ship-Based Network of GNSS Receivers over the North-West Mediterranean Sea.

This work presents the design and implementation of an operational infrastructure for the monitoring of atmospheric parameters at sea through GNSS meteorology sensors installed on liners operating in the north-west Mediterranean Sea. A measurement system, capable of operationally and continuously providing the values of surface parameters, is implemented together with software procedures based on a float-PPP approach for estimating zenith path delay (ZPD) values. The values continuously registered over a three year period (2020-2022) from this infrastructure are compared with the data from a numerical meteorological reanalysis model (MERRA-2). The results clearly prove the ability of the system to estimate the ZPD from ship-based GNSS-meteo equipment, with the accuracy evaluated in terms of correlation and root mean square error reaching values between 0.94 and 0.65 and between 18.4 and 42.9 mm, these extreme values being from the best and worst performing installations, respectively. This offers a new perspective on the operational exploitation of GNSS signals over sea areas in climate and operational meteorological applications.

Correlation of powers of Hüsler-Reiss vectors and Brown-Resnick fields, and application to insured wind losses.

Hüsler-Reiss vectors and Brown-Resnick fields are popular models in multivariate and spatial extreme-value theory, respectively, and are widely used in applications. We provide analytical formulas for the correlation between powers of the components of the bivariate Hüsler-Reiss vector, extend these to the case of the Brown-Resnick field, and thoroughly study the properties of the resulting dependence measure. The use of correlation is justified by spatial risk theory, while power transforms are insightful when taking correlation as dependence measure, and are moreover very suited damage functions for weather events such as wind extremes or floods. This makes our theoretical results worthwhile for, e.g., actuarial applications. We finally perform a case study involving insured losses from extreme wind speeds in Germany, and obtain valuable conclusions for the insurance industry.

Tissue-based absolute quantification using large-scale TMT and LFQ experiments.

Relative and absolute intensity-based protein quantification across cell lines, tissue atlases and tumour datasets is increasingly available in public datasets. These atlases enable researchers to explore fundamental biological questions, such as protein existence, expression location, quantity and correlation with RNA expression. Most studies provide MS1 feature-based label-free quantitative (LFQ) datasets; however, growing numbers of isobaric tandem mass tags (TMT) datasets remain unexplored. Here, we compare traditional intensity-based absolute quantification (iBAQ) proteome abundance ranking to an analogous method using reporter ion proteome abundance ranking with data from an experiment where LFQ and TMT were measured on the same samples. This new TMT method substitutes reporter ion intensities for MS1 feature intensities in the iBAQ framework. Additionally, we compared LFQ-iBAQ values to TMT-iBAQ values from two independent large-scale tissue atlas datasets (one LFQ and one TMT) using robust bottom-up proteomic identification, normalisation and quantitation workflows.

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.

Bridging clinical care and research in Ontario, Canada: Maximizing diagnoses from reanalysis of clinical exome sequencing data.

We examined the utility of clinical and research processes in the reanalysis of publicly-funded clinical exome sequencing data in Ontario, Canada. In partnership with eight sites, we recruited 287 families with suspected rare genetic diseases tested between 2014 and 2020. Data from seven laboratories was reanalyzed with the referring clinicians. Reanalysis of clinically relevant genes identified diagnoses in 4% (13/287); four were missed by clinical testing. Translational research methods, including analysis of novel candidate genes, identified candidates in 21% (61/287). Of these, 24 families have additional evidence through data sharing to support likely diagnoses (8% of cohort). This study indicates few diagnoses are missed by clinical laboratories, the incremental gain from reanalysis of clinically-relevant genes is modest, and the highest yield comes from validation of novel disease-gene associations. Future implementation of translational research methods, including continued reporting of compelling genes of uncertain significance by clinical laboratories, should be considered to maximize diagnoses.

Retrospective identification of patients with SRRM2-related neurodevelopmental disorder in a single tertiary children's hospital.

SRRM2-related neurodevelopmental disorder is a recently described genetic diagnosis caused by loss-of-function variants in SRRM2. In order to understand the clinical spectrum of SRRM2-related neurodevelopmental disorder, we performed a retrospective exome data and clinical chart review at a single tertiary children's hospital, Children's Hospital of Philadelphia (CHOP). Among approximately 3100 clinical exome sequencing cases performed at CHOP, we identified three patients with SRRM2 loss-of-function pathogenic variants, in addition to one patient previously described in the literature. Common clinical features include developmental delay, attention deficit hyperactivity disorder, macrocephaly, hypotonia, gastroesophageal reflux, overweight/obesity, and autism. While developmental disabilities are commonly seen in all individuals with SRRM2 variants, the degree of developmental delay and intellectual disability is variable. Our data suggest that SRRM2-related neurodevelopmental disorder can be identified in 0.3% of individuals with developmental disabilities receiving exome sequencing.

Solving the unsolved genetic epilepsies: Current and future perspectives.

Many patients with epilepsy undergo exome or genome sequencing as part of a diagnostic workup; however, many remain genetically unsolved. There are various factors that account for negative results in exome/genome sequencing for patients with epilepsy: (1) the underlying cause is not genetic; (2) there is a complex polygenic explanation; (3) the illness is monogenic but the causative gene remains to be linked to a human disorder; (4) family segregation with reduced penetrance; (5) somatic mosaicism or the complexity of, for example, a structural rearrangement; or (6) limited knowledge or diagnostic tools that hinder the proper classification of a variant, resulting in its designation as a variant of unknown significance. The objective of this review is to outline some of the diagnostic options that lie beyond the exome/genome, and that might become clinically relevant within the foreseeable future. These options include: (1) re-analysis of older exome/genome data as knowledge increases or symptoms change; (2) looking for somatic mosaicism or long-read sequencing to detect low-complexity repeat variants or specific structural variants missed by traditional exome/genome sequencing; (3) exploration of the non-coding genome including disruption of topologically associated domains, long range non-coding RNA, or other regulatory elements; and finally (4) transcriptomics, DNA methylation signatures, and metabolomics as complementary diagnostic methods that may be used in the assessment of variants of unknown significance. Some of these tools are currently not integrated into standard diagnostic workup. However, it is reasonable to expect that they will become increasingly available and improve current diagnostic capabilities, thereby enabling precision diagnosis in patients who are currently undiagnosed.

Reanalysis of exome sequencing data reveals a treatable neurometabolic origin in two previously undiagnosed siblings with neurodevelopmental disorder.

Neurodevelopmental disorders (NDDs) have broad heterogeneity both clinically and genetically. Inborn errors of metabolism can be one of the reasons of neurodevelopmental disruption causing specific NDDs. Although there is tremendous advance in molecular identification via next-generation sequencing (NGS), there are still many unsolved patients with NDD. Reanalysis of NGS data with different pipelines can at least partially accomplish this challenge. Herein, we report clinic and genetic components of an adult sib-pair with an undiagnosed NDD condition, which has been solved through reanalysis of whole-exome sequencing (WES). Parallel analysis of SNP-based genotyping and WES was performed to focus on variants only in loci with positive logarithm of the odds scores. WES data was analyzed through three different pipelines with two distinct bed files. Reanalysis of WES data led us to detect a homozygous FOLR1 variant (ENST00000393676.5:c.610C > T, p.(Arg204Ter), rs952165627) in the affected sib-pair. Surprisingly, the variant could not be detected in the first analysis as the variant region is not included in the first bed file which may frequently be used. Biochemical tests of CSF have confirmed the genetic analysis, CSF folic acid levels were detected low in sib-pair, and intravenous folinic acid treatment improved the disease course for the first 6 months of follow-up even at late diagnosis age. Although combined analysis of SNP-based genotyping and WES is a powerful tool to reveal the genetic components of heterogeneous diseases, reanalysis of genome data still should be considered in unsolved patients. Also, biochemical screening helps us to decipher undiagnosed NDD that may be a treatable neurometabolic condition.

Re-evaluation of Genetic Variants in Parkinson's Disease Using Targeted Panel and Next-Generation Sequencing.

Parkinson's disease (PD) is a complex disorder with a significant genetic component. Genetic variations associated with PD play a crucial role in the disease's inheritance and prognosis. Currently, 31 genes have been linked to PD in the OMIM database, and the number of genes and genetic variations identified is steadily increasing. To establish a robust correlation between phenotype and genotype, it is essential to compare research findings with existing literature. In this study, we aimed to identify genetic variants associated with PD using a targeted gene panel with next-generation sequencing (NGS) technology. Our objective was also to explore the idea of re-analyzing genetic variants of unknown significance (VUS). We screened 18 genes known to be related to PD using NGS in 43 patients who visited our outpatient clinic between 2018-2019. After 12-24 months, we re-evaluated the detected variants. We found 14 different heterozygous variants classified as pathogenic, likely pathogenic, or VUS in 14 individuals from nonconsanguineous families. We re-evaluated 15 variants and found changes in their interpretation. Targeted gene panel analysis with NGS can help identify genetic variants associated with PD with confidence. Re-analyzing certain variants at specific time intervals can be especially beneficial in selected situations. Our study aims to expand the clinical and genetic understanding of PD and emphasizes the importance of re-analysis.

Estimation of Reference Evapotranspiration in a Semi-Arid Region of Mexico.

Reference evapotranspiration (ET0) is the first step in calculating crop irrigation demand, and numerous methods have been proposed to estimate this parameter. FAO-56 Penman-Monteith (PM) is the only standard method for defining and calculating ET0. However, it requires radiation, air temperature, atmospheric humidity, and wind speed data, limiting its application in regions where these data are unavailable; therefore, new alternatives are required. This study compared the accuracy of ET0 calculated with the Blaney-Criddle (BC) and Hargreaves-Samani (HS) methods versus PM using information from an automated weather station (AWS) and the NASA-POWER platform (NP) for different periods. The information collected corresponds to Module XII of the Lagunera Region Irrigation District 017, a semi-arid region in the North of Mexico. The HS method underestimated the reference evapotranspiration (ET0) by 5.5% compared to the PM method considering the total ET0 of the study period (26 February to 9 August 2021) and yielded the best fit in the different evaluation periods (daily, 5-day mean, and 5-day cumulative); the latter showed the best values of inferential parameters. The information about maximum and minimum temperatures from the NP platform was suitable for estimating ET0 using the HS equation. This data source is a suitable alternative, particularly in semi-arid regions with limited climatological data from weather stations.

Evaluation of InSAR Tropospheric Delay Correction Methods in the Plateau Monsoon Climate Region Considering Spatial-Temporal Variability.

The tropospheric delay caused by the temporal and spatial variation of meteorological parameters is the main error source in interferometric synthetic aperture radar (InSAR) applications for geodesy. To minimize the impact of tropospheric delay errors, it is necessary to select the appropriate tropospheric delay correction method for different regions. In this study, the interferogram results of the InSAR, corrected for tropospheric delay using the Linear, Generic Atmospheric Correction Online Service for InSAR (GACOS) and ERA-5 atmospheric reanalysis dataset (ERA5) methods, are presented for the study area of the junction of the Hengduan Mountains and the Yunnan-Kweichow Plateau, which is significantly influenced by the plateau monsoon climate. Four representative regions, Eryuan, Binchuan, Dali, and Yangbi, are selected for the study and analysis. The phase standard deviation (STD), phase-height correlation, and global navigation satellite system (GNSS) data were used to evaluate the effect of tropospheric delay correction by integrating topographic, seasonal, and meteorological factors. The results show that all three methods can attenuate the tropospheric delay, but the correction effect varies with spatial and temporal characteristics.

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.