Modeling the ACMG/AMP variant classification guidelines as a Bayesian classification framework

Functional analysis of a novel nonsense PPP1R12A variant in a Chinese family with infantile epilepsy

BACKGROUND

Defects in PPP1R12A can lead to genitourinary and/or brain malformation syndrome (GUBS). GUBS is primarily characterized by neurological or genitourinary system abnormalities, but a few reported cases are associated with neonatal seizures. Here, we report a case of a female newborn with neonatal seizures caused by a novel variant in PPP1R12A, aiming to enhance the clinical and variant data of genetic factors related to epilepsy in early life.

METHODS

Whole-exome and Sanger sequencing were used for familial variant assessment, and bioinformatics was employed to annotate the variant. A structural model of the mutant protein was simulated using molecular dynamics (MD), and the free binding energy between PPP1R12A and PPP1CB was analyzed. A mutant plasmid was constructed, and mutant protein expression was analyzed using western blotting (WB), and the interaction between the mutant and PPP1CB proteins using co-immunoprecipitation (Co-IP) experiments.

RESULTS

The patient experienced tonic-clonic seizures on the second day after birth. Genetic testing revealed a heterozygous variant in PPP1R12A, NM_002480.3:c.2533 C > T (p.Arg845Ter). Both parents had the wild-type gene. MD suggested that loss of the C-terminal structure in the mutant protein altered its structural stability and increased the binding energy with PPP1CB, indicating unstable protein-protein interactions. On WB, a low-molecular-weight band was observed, indicating that the protein was truncated. Co-IP indicated that the mutant protein no longer interacted with PPP1CB, indicating an effect on the structural stability of the myosin phase complex.

CONCLUSION

The PPP1R12A c.2533 C > T variant may explain the neonatal seizures in the present case. The findings of this study expand the spectrum of PPP1R12A variants and highlight the potential significance of truncated proteins in the pathogenesis of GUBS.

Large-scale application of ClinGen-InSiGHT APC-specific ACMG/AMP variant classification criteria leads to substantial reduction in VUS

Pathogenic constitutional APC variants underlie familial adenomatous polyposis, the most common hereditary gastrointestinal polyposis syndrome. To improve variant classification and resolve the interpretative challenges of variants of uncertain significance (VUSs), APC-specific variant classification criteria were developed by the ClinGen-InSiGHT Hereditary Colorectal Cancer/Polyposis Variant Curation Expert Panel (VCEP) based on the criteria of the American College of Medical Genetics and Genomics and the Association for Molecular Pathology (ACMG/AMP). A streamlined algorithm using the APC-specific criteria was developed and applied to assess all APC variants in ClinVar and the International Society for Gastrointestinal Hereditary Tumours (InSiGHT) international reference APC Leiden Open Variation Database (LOVD) variant database, which included a total of 10,228 unique APC variants. Among the ClinVar and LOVD variants with an initial classification of (likely) benign or (likely) pathogenic, 94% and 96% remained in their original categories, respectively. In contrast, 41% ClinVar and 61% LOVD VUSs were reclassified into clinically meaningful classes, the vast majority as (likely) benign. The total number of VUSs was reduced by 37%. In 24 out of 37 (65%) promising APC variants that remained VUS despite evidence for pathogenicity, a data-mining-driven work-up allowed their reclassification as (likely) pathogenic. These results demonstrated that the application of APC-specific criteria substantially reduced the number of VUSs in ClinVar and LOVD. The study also demonstrated the feasibility of a systematic approach to variant classification in large datasets, which might serve as a generalizable model for other gene- or disease-specific variant interpretation initiatives. It also allowed for the prioritization of VUSs that will benefit from in-depth evidence collection. This subset of APC variants was approved by the VCEP and made publicly available through ClinVar and LOVD for widespread clinical use.

The PS4-likelihood ratio calculator: flexible allocation of evidence weighting for case-control data in variant classification

BACKGROUND

The 2015 American College of Medical Genetics/Association of Molecular Pathology (ACMG/AMP) variant classification framework specifies that case-control observations can be scored as 'strong' evidence (PS4) towards pathogenicity.

METHODS

We developed the PS4-likelihood ratio calculator (PS4-LRCalc) for quantitative evidence assignment based on the observed variant frequencies in cases and controls. Binomial likelihoods are computed for two models, each defined by prespecified OR thresholds. Model 1 represents the hypothesis of association between variant and phenotype (eg, OR≥5) and model 2 represents the hypothesis of non-association (eg, OR≤1).

RESULTS

PS4-LRCalc enables continuous quantitation of evidence for variant classification expressed as a likelihood ratio (LR), which can be log-converted into log LR (evidence points). Using PS4-LRCalc, observed data can be used to quantify evidence towards either pathogenicity or benignity. Variants can also be evaluated against models of different penetrance. The approach is applicable to balanced data sets generated for more common phenotypes and smaller data sets more typical in very rare disease variant evaluation.

CONCLUSION

PS4-LRCalc enables flexible evidence quantitation on a continuous scale for observed case-control data. The converted LR is amenable to incorporation into the now widely used 2018 updated Bayesian ACMG/AMP framework.

Calibration of additional computational tools expands ClinGen recommendation options for variant classification with PP3/BP4 criteria

Purpose

We previously developed an approach to calibrate computational tools for clinical variant classification, updating recommendations for the reliable use of variant impact predictors to provide evidence strength up to Strong . A new generation of tools using distinctive approaches have since been released, and these methods must be independently calibrated for clinical application.

Method

Using our local posterior probability-based calibration and our established data set of ClinVar pathogenic and benign variants, we determined the strength of evidence provided by three new tools (AlphaMissense, ESM1b, VARITY) and calibrated scores meeting each evidence strength. Results All three tools reached the Strong level of evidence for variant pathogenicity and Moderate for benignity, though sometimes for few variants. Compared to previously recommended tools, these yielded at best only modest improvements in the tradeoffs of evidence strength and false positive predictions.

Conclusion

At calibrated thresholds, three new computational predictors provided evidence for variant pathogenicity at similar strength to the four previously recommended predictors (and comparable with functional assays for some variants). This calibration broadens the scope of computational tools for application in clinical variant classification. Their new approaches offer promise for future advancement of the field.

Specifications of the ACMG/AMP variant curation guidelines for the analysis of germline ATM sequence variants

The ClinGen Hereditary Breast, Ovarian, and Pancreatic Cancer (HBOP) Variant Curation Expert Panel (VCEP) is composed of internationally recognized experts in clinical genetics, molecular biology, and variant interpretation. This VCEP made specifications for the American College of Medical Genetics and Association for Molecular Pathology (ACMG/AMP) guidelines for the ataxia telangiectasia mutated (ATM) gene according to the ClinGen protocol. These gene-specific rules for ATM were modified from the ACMG/AMP guidelines and were tested against 33 ATM variants of various types and classifications in a pilot curation phase. The pilot revealed a majority agreement between the HBOP VCEP classifications and the ClinVar-deposited classifications. Six pilot variants had conflicting interpretations in ClinVar, and re-evaluation with the VCEP's ATM-specific rules resulted in four that were classified as benign, one as likely pathogenic, and one as a variant of uncertain significance (VUS) by the VCEP, improving the certainty of interpretations in the public domain. Overall, 28 of the 33 pilot variants were not VUS, leading to an 85% classification rate. The ClinGen-approved, modified rules demonstrated value for improved interpretation of variants in ATM.

Assigning credit where it is due: an information content score to capture the clinical value of multiplexed assays of variant effect

BACKGROUND

A variant can be pathogenic or benign with relation to a human disease. Current classification categories from benign to pathogenic reflect a probabilistic summary of the current understanding. A primary metric of clinical utility for multiplexed assays of variant effect (MAVE) is the number of variants that can be reclassified from uncertain significance (VUS). However, a gap in this measure of utility is that it underrepresents the information gained from MAVEs. The aim of this study was to develop an improved quantification metric for MAVE utility. We propose adopting an information content approach that includes data that does not reclassify variants will better reflect true information gain. We adopted an information content approach to evaluate the information gain, in bits, for MAVEs of BRCA1, PTEN, and TP53. Here, one bit represents the amount of information required to completely classify a single variant starting from no information.

RESULTS

BRCA1 MAVEs produced a total of 831.2 bits of information, 6.58% of the total missense information in BRCA1 and a 22-fold increase over the information that only contributed to VUS reclassification. PTEN MAVEs produced 2059.6 bits of information which represents 32.8% of the total missense information in PTEN and an 85-fold increase over the information that contributed to VUS reclassification. TP53 MAVEs produced 277.8 bits of information which represents 6.22% of the total missense information in TP53 and a 3.5-fold increase over the information that contributed to VUS reclassification.

CONCLUSIONS

An information content approach will more accurately portray information gained through MAVE mapping efforts than by counting the number of variants reclassified. This information content approach may also help define the impact of guideline changes that modify the information definitions used to classify groups of variants.

Evidence-based recommendations for gene-specific ACMG/AMP variant classification from the ClinGen ENIGMA BRCA1 and BRCA2 Variant Curation Expert Panel

The ENIGMA research consortium develops and applies methods to determine clinical significance of variants in hereditary breast and ovarian cancer genes. An ENIGMA BRCA1/2 classification sub-group, formed in 2015 as a ClinGen external expert panel, evolved into a ClinGen internal Variant Curation Expert Panel (VCEP) to align with Food and Drug Administration recognized processes for ClinVar contributions. The VCEP reviewed American College of Medical Genetics and Genomics/Association of Molecular Pathology (ACMG/AMP) classification criteria for relevance to interpreting BRCA1 and BRCA2 variants. Statistical methods were used to calibrate evidence strength for different data types. Pilot specifications were tested on 40 variants and documentation revised for clarity and ease of use. The original criterion descriptions for 13 evidence codes were considered non-applicable or overlapping with other criteria. Scenario of use was extended or re-purposed for eight codes. Extensive analysis and/or data review informed specification descriptions and weights for all codes. Specifications were applied to pilot variants with pre-existing ClinVar classification as follows: 13 uncertain significance or conflicting, 14 pathogenic and/or likely pathogenic, and 13 benign and/or likely benign. Review resolved classification for 11/13 uncertain significance or conflicting variants and retained or improved confidence in classification for the remaining variants. Alignment of pre-existing ENIGMA research classification processes with ACMG/AMP classification guidelines highlighted several gaps in the research processes and the baseline ACMG/AMP criteria. Calibration of evidence strength was key to justify utility and strength of different data types for gene-specific application. The gene-specific criteria demonstrated value for improving ACMG/AMP-aligned classification of BRCA1 and BRCA2 variants.

Co-observation of germline pathogenic variants in breast cancer predisposition genes: Results from analysis of the BRIDGES sequencing dataset

Co-observation of a gene variant with a pathogenic variant in another gene that explains the disease presentation has been designated as evidence against pathogenicity for commonly used variant classification guidelines. Multiple variant curation expert panels have specified, from consensus opinion, that this evidence type is not applicable for the classification of breast cancer predisposition gene variants. Statistical analysis of sequence data for 55,815 individuals diagnosed with breast cancer from the BRIDGES sequencing project was undertaken to formally assess the utility of co-observation data for germline variant classification. Our analysis included expected loss-of-function variants in 11 breast cancer predisposition genes and pathogenic missense variants in BRCA1, BRCA2, and TP53. We assessed whether co-observation of pathogenic variants in two different genes occurred more or less often than expected under the assumption of independence. Co-observation of pathogenic variants in each of BRCA1, BRCA2, and PALB2 with the remaining genes was less frequent than expected. This evidence for depletion remained after adjustment for age at diagnosis, study design (familial versus population-based), and country. Co-observation of a variant of uncertain significance in BRCA1, BRCA2, or PALB2 with a pathogenic variant in another breast cancer gene equated to supporting evidence against pathogenicity following criterion strength assignment based on the likelihood ratio and showed utility in reclassification of missense BRCA1 and BRCA2 variants identified in BRIDGES. Our approach has applicability for assessing the value of co-observation as a predictor of variant pathogenicity in other clinical contexts, including for gene-specific guidelines developed by ClinGen Variant Curation Expert Panels.

Calibration of variant effect predictors on genome-wide data masks heterogeneous performance across genes

In silico variant effect predictions are available for nearly all missense variants but played a minimal role in clinical variant classification because they were deemed to provide only supporting evidence. Recently, the ClinGen Sequence Variant Interpretation (SVI) Working Group updated recommendations for variant effect prediction use. By analyzing control pathogenic and benign variants across all genes, they were able to compute evidence strength for predictor score intervals with some intervals generating moderate, strong, or even very strong evidence. However, this genome-wide approach could obscure heterogeneous predictor performance in different genes. We quantified the gene-by-gene performance of two top predictors, REVEL and BayesDel, by analyzing control variants in each predictor score interval in 3,668 disease-relevant genes. Approximately 10% of intervals had sufficient control variants for analysis, and ∼70% of these intervals exceeded the maximum number of incorrect predictions implied by the SVI recommendations. These trending discordant intervals arose owing to the divergence of the gene-specific distribution of predictions from the genome-wide distribution, suggesting that gene-specific calibration is needed in many cases. Approximately 22% of ClinVar missense variants of uncertain significance in genes we analyzed (REVEL = 100,629, BayesDel = 71,928) had predictions in trending discordant intervals. Thus, genome-wide calibrations could result in many variants receiving inappropriate evidence strength. To facilitate a review of the SVI's calibrations, we developed a web application enabling visualization of gene-specific predictions and trending concordant and discordant intervals.

Analysis of more than 400,000 women provides case-control evidence for BRCA1 and BRCA2 variant classification

Clinical genetic testing identifies variants causal for hereditary cancer, information that is used for risk assessment and clinical management. Unfortunately, some variants identified are of uncertain clinical significance (VUS), complicating patient management. Case-control data is one evidence type used to classify VUS, and previous findings indicate that case-control likelihood ratios (LRs) outperform odds ratios for variant classification. As an initiative of the Evidence-based Network for the Interpretation of Germline Mutant Alleles (ENIGMA) Analytical Working Group we analyzed germline sequencing data of BRCA1 and BRCA2 from 96,691 female breast cancer cases and 303,925 unaffected controls from three studies: the BRIDGES study of the Breast Cancer Association Consortium, the Cancer Risk Estimates Related to Susceptibility consortium, and the UK Biobank. We observed 11,227 BRCA1 and BRCA2 variants, with 6,921 being coding, covering 23.4% of BRCA1 and BRCA2 VUS in ClinVar and 19.2% of ClinVar curated (likely) benign or pathogenic variants. Case-control LR evidence was highly consistent with ClinVar assertions for (likely) benign or pathogenic variants; exhibiting 99.1% sensitivity and 95.4% specificity for BRCA1 and 92.2% sensitivity and 86.6% specificity for BRCA2. This approach provides case-control evidence for 785 unclassified variants, that can serve as a valuable element for clinical classification.

Next-generation phenotyping in Nigerian children with Cornelia de Lange syndrome

Next-generation phenotyping (NGP) can be used to compute the similarity of dysmorphic patients to known syndromic diseases. So far, the technology has been evaluated in variant prioritization and classification, providing evidence for pathogenicity if the phenotype matched with other patients with a confirmed molecular diagnosis. In a Nigerian cohort of individuals with facial dysmorphism, we used the NGP tool GestaltMatcher to screen portraits prior to genetic testing and subjected individuals with high similarity scores to exome sequencing (ES). Here, we report on two individuals with global developmental delay, pulmonary artery stenosis, and genital and limb malformations for whom GestaltMatcher yielded Cornelia de Lange syndrome (CdLS) as the top hit. ES revealed a known pathogenic nonsense variant, NM_133433.4: c.598C>T; p.(Gln200*), as well as a novel frameshift variant c.7948dup; p.(Ile2650Asnfs*11) in NIPBL. Our results suggest that NGP can be used as a screening tool and thresholds could be defined for achieving high diagnostic yields in ES. Training the artificial intelligence (AI) with additional cases of the same ethnicity might further increase the positive predictive value of GestaltMatcher.

Functional characterization of 2,832 JAG1 variants supports reclassification for Alagille syndrome and improves guidance for clinical variant interpretation

Pathogenic variants in the JAG1 gene are a primary cause of the multi-system disorder Alagille syndrome. Although variant detection rates are high for this disease, there is uncertainty associated with the classification of missense variants that leads to reduced diagnostic yield. Consequently, up to 85% of reported JAG1 missense variants have uncertain or conflicting classifications. We generated a library of 2,832 JAG1 nucleotide variants within exons 1-7, a region with a high number of reported missense variants, and designed a high-throughput assay to measure JAG1 membrane expression, a requirement for normal function. After calibration using a set of 175 known or predicted pathogenic and benign variants included within the variant library, 486 variants were characterized as functionally abnormal (n = 277 abnormal and n = 209 likely abnormal), of which 439 (90.3%) were missense. We identified divergent membrane expression occurring at specific residues, indicating that loss of the wild-type residue itself does not drive pathogenicity, a finding supported by structural modeling data and with broad implications for clinical variant classification both for Alagille syndrome and globally across other disease genes. Of 144 uncertain variants reported in patients undergoing clinical or research testing, 27 had functionally abnormal membrane expression, and inclusion of our data resulted in the reclassification of 26 to likely pathogenic. Functional evidence augments the classification of genomic variants, reducing uncertainty and improving diagnostics. Inclusion of this repository of functional evidence during JAG1 variant reclassification will significantly affect resolution of variant pathogenicity, making a critical impact on the molecular diagnosis of Alagille syndrome.

Genebe.net: Implementation and validation of an automatic ACMG variant pathogenicity criteria assignment

We present GeneBe, an online platform streamlining the automated application of American College of Medical Genetics and Genomics (ACMG), Association for Molecular Pathology (AMP), and the College of American Pathologists (CAP) criteria for assessment of pathogenicity of genetic variants. GeneBe utilizes automated algorithms that evaluate 17 criteria from 28, closely aligning with current guidelines and leveraging data from diverse sources, including ClinVar. The user-friendly web interface enables manual refinement of assignments for specific criteria based on site-collected data. Our algorithm demonstrates a high correlation (r = 0.90) of assigned pathogenicity scores compared to expert assessments from the ClinGen Evidence Repository and substantial concordance with ClinVar verdict assignments (κ = 0.69). Comparative analysis with other published tools reveals that GeneBe performs similarly to VarSome while being superior over TAPES and InterVar. In contrast to some other tools, GeneBe's web implementation is tracker-free and third-party request-free, safeguarding user privacy. Additionally, GeneBe offers an Application Programming Interface (API) for enhanced flexibility and integration into existing workflows and is provided free of charge for research purposes. GeneBe is available at https://genebe.net.

Multisite Validation of a Functional Assay to Adjudicate SCN5A Brugada Syndrome-Associated Variants

BACKGROUND

Brugada syndrome is an inheritable arrhythmia condition that is associated with rare, loss-of-function variants in SCN5A. Interpreting the pathogenicity of SCN5A missense variants is challenging, and ≈79% of SCN5A missense variants in ClinVar are currently classified as variants of uncertain significance. Automated patch clamp technology enables high-throughput functional studies of ion channel variants and can provide evidence for variant reclassification.

METHODS

An in vitro SCN5A-Brugada syndrome automated patch clamp assay was independently performed at Vanderbilt University Medical Center and Victor Chang Cardiac Research Institute. The assay was calibrated according to ClinGen Sequence Variant Interpretation recommendations using high-confidence variant controls (n=49). Normal and abnormal ranges of function were established based on the distribution of benign variant assay results. Odds of pathogenicity values were derived from the experimental results according to ClinGen Sequence Variant Interpretation recommendations. The calibrated assay was then used to study SCN5A variants of uncertain significance observed in 4 families with Brugada syndrome and other arrhythmia phenotypes associated with SCN5A loss-of-function.

RESULTS

Variant channel parameters generated independently at the 2 research sites showed strong correlations, including peak INa density (R2=0.86). The assay accurately distinguished benign controls (24/25 concordant variants) from pathogenic controls (23/24 concordant variants). Odds of pathogenicity values were 0.042 for normal function and 24.0 for abnormal function, corresponding to strong evidence for both American College of Medical Genetics and Genomics/Association for Molecular Pathology benign and pathogenic functional criteria (BS3 and PS3, respectively). Application of the assay to 4 clinical SCN5A variants of uncertain significance revealed loss-of-function for 3/4 variants, enabling reclassification to likely pathogenic.

CONCLUSIONS

This validated high-throughput assay provides clinical-grade functional evidence to aid the classification of current and future SCN5A-Brugada syndrome variants of uncertain significance.

Assessment of the evidence yield for the calibrated PP3/BP4 computational recommendations

PURPOSE

To investigate the number of rare missense variants observed in human genome sequences by ACMG/AMP PP3/BP4 evidence strength, following the ClinGen-calibrated PP3/BP4 computational recommendations.

METHODS

Missense variants from the genome sequences of 300 probands from the Rare Genomes Project with suspected rare disease were analyzed using computational prediction tools that were able to reach PP3_Strong and BP4_Moderate evidence strengths (BayesDel, MutPred2, REVEL, and VEST4). The numbers of variants at each evidence strength were analyzed across disease-associated genes and genome-wide.

RESULTS

From a median of 75.5 rare (≤1% allele frequency) missense variants in disease-associated genes per proband, a median of one reached PP3_Strong, 3-5 PP3_Moderate, and 3-5 PP3_Supporting. Most were allocated BP4 evidence (median 41-49 per proband) or were indeterminate (median 17.5-19 per proband). Extending the analysis to all protein-coding genes genome-wide, the number of variants reaching PP3_Strong score thresholds increased approximately 2.6-fold compared with disease-associated genes, with a median per proband of 1-3 PP3_Strong, 8-16 PP3_Moderate, and 10-17 PP3_Supporting.

CONCLUSION

A small number of variants per proband reached PP3_Strong and PP3_Moderate in 3424 disease-associated genes. Although not the intended use of the recommendations, this was also observed genome-wide. Use of PP3/BP4 evidence as recommended from calibrated computational prediction tools in the clinical diagnostic laboratory is unlikely to inappropriately contribute to the classification of an excessive number of variants as pathogenic or likely pathogenic by ACMG/AMP rules.

Using metabolic abnormalities of carriers in the neonatal period to evaluate the pathogenicity of variants of uncertain significance in methylmalonic acidemia

Objective

To accurately verify the pathogenicity of variants of uncertain significance (VUS) in MUT and MMACHC genes through mass spectrometry and silico analysis.

Methods

This multicenter retrospective study included 35 participating units (ClinicalTrials.gov ID: NCT06183138). A total of 3,071 newborns (within 7 days of birth) were sorted into carrying pathogenic/likely pathogenic (P/LP) variants and carrying VUS, non-variant groups. Differences in metabolites among the groups were calculated using statistical analyses. Changes in conservatism, free energy, and interaction force of MMUT and MMACHC variants were analyzed using silico analysis.

Results

The percentage of those carrying VUS cases was 68.15% (659/967). In the MMUT gene variant, we found that C3, C3/C2, and C3/C0 levels in those carrying the P/LP variant group were higher than those in the non-variant group (p < 0.000). The conservative scores of those carrying the P/LP variant group were >7. C3, C3/C0, and C3/C2 values of newborns carrying VUS (c.1159A>C and c.1286A>G) were significantly higher than those of the non-variant group and the remaining VUS newborns (p < 0.005). The conservative scores of c.1159A>C and c.1286A>G calculated by ConSurf analysis were 9 and 7, respectively. Unfortunately, three MMA patients with c.1159A>C died during the neonatal period; their C3, C3/C0, C3/C2, and MMA levels were significantly higher than those of the controls.

Conclusion

Common variants of methylmalonic acidemia in the study population were categorized as VUS. In the neonatal period, the metabolic biomarkers of those carrying the P/LP variant group of the MUT gene were significantly higher than those in the non-variant group. If the metabolic biomarkers of those carrying VUS are also significantly increased, combined with silico analysis the VUS may be elevated to a likely pathogenic variant. The results also suggest that mass spectrometry and silico analysis may be feasible screening methods for verifying the pathogenicity of VUS in other inherited metabolic diseases.

Prenatal genetic diagnosis of fetuses with dextrocardia using whole exome sequencing in a tertiary center

To evaluate the genetic etiology of fetal dextrocardia, associated ultrasound anomalies, and perinatal outcomes, we investigated the utility of whole exome sequencing (WES) for prenatal diagnosis of dextrocardia. Fetuses with dextrocardia were prospectively collected between January 2016 and December 2022. Trio-WES was performed on fetuses with dextrocardia, following normal karyotyping and/or chromosomal microarray analysis (CMA) results. A total of 29 fetuses with dextrocardia were collected, including 27 (93.1%) diagnosed with situs inversus totalis and 2 (6.9%) with situs inversus partialis. Cardiac malformations were present in nine cases, extra-cardiac anomalies were found in seven cases, and both cardiac and extra-cardiac malformations were identified in one case. The fetal karyotypes and CMA results of 29 cases were normal. Of the 29 cases with dextrocardia, 15 underwent WES, and the other 14 cases refused. Of the 15 cases that underwent WES, clinically relevant variants were identified in 5/15 (33.3%) cases, including the diagnostic variants DNAH5, DNAH11, LRRC56, PEX10, and ZIC3, which were verified by Sanger sequencing. Of the 10 cases with non-diagnostic results via WES, eight (80%) chose to continue the pregnancies. Of the 29 fetuses with dextrocardia, 10 were terminated during pregnancy, and 19 were live born. Fetal dextrocardia is often accompanied by cardiac and extra-cardiac anomalies, and fetal dextrocardia accompanied by situs inversus is associated with a high risk of primary ciliary dyskinesia. Trio-WES is recommended following normal karyotyping and CMA results because it can improve the diagnostic utility of genetic variants of fetal dextrocardia, accurately predict fetal prognosis, and guide perinatal management and the reproductive decisions of affected families.

Regional-specific calibration enables application of computational evidence for clinical classification of 5' cis-regulatory variants in Mendelian disease

To date, clinical genetic testing for Mendelian disease variants has focused heavily on exonic coding and intronic gene regions. This multi-step study was undertaken to provide an evidence base for selecting and applying computational approaches for use in clinical classification of 5' cis-regulatory region variants. Curated datasets of clinically reported disease-causing 5' cis-regulatory region variants and variants from matched genomic regions in population controls were used to calibrate six bioinformatic tools as predictors of variant pathogenicity. Likelihood ratio estimates were aligned to code weights following ClinGen recommendations for application of the American College of Medical Genetics and Genomics and the Association for Molecular Pathology (ACMG/AMP) classification scheme. Considering code assignment across all reference dataset variants, performance was best for CADD (81.2%) and REMM (81.5%). Optimized thresholds provided moderate evidence toward pathogenicity (CADD, REMM) and moderate (CADD) or supporting (REMM) evidence against pathogenicity. Both sensitivity and specificity of prediction were improved when further categorizing variants based on location in an EPDnew-defined promoter region. Combining predictions (CADD, REMM, and location in a promoter region) increased specificity at the expense of sensitivity. Importantly, the optimal CADD thresholds for assigning ACMG/AMP codes PP3 (≥10) and BP4 (≤8) were vastly different from recommendations for protein-coding variants (PP3 ≥25.3; BP4 ≤22.7); CADD <22.7 would incorrectly assign BP4 for >90% of reported disease-causing cis-regulatory region variants. Our results demonstrate the need to consider a tiered approach and tailored score thresholds to optimize bioinformatic impact prediction for clinical classification of 5' cis-regulatory region variants.

Saturation genome editing of BAP1 functionally classifies somatic and germline variants

Many variants that we inherit from our parents or acquire de novo or somatically are rare, limiting the precision with which we can associate them with disease. We performed exhaustive saturation genome editing (SGE) of BAP1, the disruption of which is linked to tumorigenesis and altered neurodevelopment. We experimentally characterized 18,108 unique variants, of which 6,196 were found to have abnormal functions, and then used these data to evaluate phenotypic associations in the UK Biobank. We also characterized variants in a large population-ascertained tumor collection, in cancer pedigrees and ClinVar, and explored the behavior of cancer-associated variants compared to that of variants linked to neurodevelopmental phenotypes. Our analyses demonstrated that disruptive germline BAP1 variants were significantly associated with higher circulating levels of the mitogen IGF-1, suggesting a possible pathological mechanism and therapeutic target. Furthermore, we built a variant classifier with >98% sensitivity and specificity and quantify evidence strengths to aid precision variant interpretation.

Comparison of the ABC and ACMG systems for variant classification

The ABC and ACMG variant classification systems were compared by asking mainly European clinical laboratories to classify variants in 10 challenging cases using both systems, and to state if the variant in question would be reported as a relevant result or not as a measure of clinical utility. In contrast to the ABC system, the ACMG system was not made to guide variant reporting but to determine the likelihood of pathogenicity. Nevertheless, this comparison is justified since the ACMG class determines variant reporting in many laboratories. Forty-three laboratories participated in the survey. In seven cases, the classification system used did not influence the reporting likelihood when variants labeled as "maybe report" after ACMG-based classification were included. In three cases of population frequent but disease-associated variants, there was a difference in favor of reporting after ABC classification. A possible reason is that ABC step C (standard variant comments) allows a variant to be reported in one clinical setting but not another, e.g., based on Bayesian-based likelihood calculation of clinical relevance. Finally, the selection of ACMG criteria was compared between 36 laboratories. When excluding criteria used by less than four laboratories (<10%), the average concordance rate was 46%. Taken together, ABC-based classification is more clear-cut than ACMG-based classification since molecular and clinical information is handled separately, and variant reporting can be adapted to the clinical question and phenotype. Furthermore, variants do not get a clinically inappropriate label, like pathogenic when not pathogenic in a clinical context, or variant of unknown significance when the significance is known.

Considerations for reporting variants in novel candidate genes identified during clinical genomic testing

Since the first novel gene discovery for a Mendelian condition was made via exome sequencing, the rapid increase in the number of genes known to underlie Mendelian conditions coupled with the adoption of exome (and more recently, genome) sequencing by diagnostic testing labs has changed the landscape of genomic testing for rare diseases. Specifically, many individuals suspected to have a Mendelian condition are now routinely offered clinical ES. This commonly results in a precise genetic diagnosis but frequently overlooks the identification of novel candidate genes. Such candidates are also less likely to be identified in the absence of large-scale gene discovery research programs. Accordingly, clinical laboratories have both the opportunity, and some might argue a responsibility, to contribute to novel gene discovery, which should, in turn, increase the diagnostic yield for many conditions. However, clinical diagnostic laboratories must necessarily balance priorities for throughput, turnaround time, cost efficiency, clinician preferences, and regulatory constraints and often do not have the infrastructure or resources to effectively participate in either clinical translational or basic genome science research efforts. For these and other reasons, many laboratories have historically refrained from broadly sharing potentially pathogenic variants in novel genes via networks such as Matchmaker Exchange, much less reporting such results to ordering providers. Efforts to report such results are further complicated by a lack of guidelines for clinical reporting and interpretation of variants in novel candidate genes. Nevertheless, there are myriad benefits for many stakeholders, including patients/families, clinicians, and researchers, if clinical laboratories systematically and routinely identify, share, and report novel candidate genes. To facilitate this change in practice, we developed criteria for triaging, sharing, and reporting novel candidate genes that are most likely to be promptly validated as underlying a Mendelian condition and translated to use in clinical settings.

Considerations for reporting variants in novel candidate genes identified during clinical genomic testing

Since the first novel gene discovery for a Mendelian condition was made via exome sequencing (ES), the rapid increase in the number of genes known to underlie Mendelian conditions coupled with the adoption of exome (and more recently, genome) sequencing by diagnostic testing labs has changed the landscape of genomic testing for rare disease. Specifically, many individuals suspected to have a Mendelian condition are now routinely offered clinical ES. This commonly results in a precise genetic diagnosis but frequently overlooks the identification of novel candidate genes. Such candidates are also less likely to be identified in the absence of large-scale gene discovery research programs. Accordingly, clinical laboratories have both the opportunity, and some might argue a responsibility, to contribute to novel gene discovery which should in turn increase the diagnostic yield for many conditions. However, clinical diagnostic laboratories must necessarily balance priorities for throughput, turnaround time, cost efficiency, clinician preferences, and regulatory constraints, and often do not have the infrastructure or resources to effectively participate in either clinical translational or basic genome science research efforts. For these and other reasons, many laboratories have historically refrained from broadly sharing potentially pathogenic variants in novel genes via networks like Matchmaker Exchange, much less reporting such results to ordering providers. Efforts to report such results are further complicated by a lack of guidelines for clinical reporting and interpretation of variants in novel candidate genes. Nevertheless, there are myriad benefits for many stakeholders, including patients/families, clinicians, researchers, if clinical laboratories systematically and routinely identify, share, and report novel candidate genes. To facilitate this change in practice, we developed criteria for triaging, sharing, and reporting novel candidate genes that are most likely to be promptly validated as underlying a Mendelian condition and translated to use in clinical settings.

Critical assessment of missense variant effect predictors on disease-relevant variant data

Regular, systematic, and independent assessment of computational tools used to predict the pathogenicity of missense variants is necessary to evaluate their clinical and research utility and suggest directions for future improvement. Here, as part of the sixth edition of the Critical Assessment of Genome Interpretation (CAGI) challenge, we assess missense variant effect predictors (or variant impact predictors) on an evaluation dataset of rare missense variants from disease-relevant databases. Our assessment evaluates predictors submitted to the CAGI6 Annotate-All-Missense challenge, predictors commonly used by the clinical genetics community, and recently developed deep learning methods for variant effect prediction. To explore a variety of settings that are relevant for different clinical and research applications, we assess performance within different subsets of the evaluation data and within high-specificity and high-sensitivity regimes. We find strong performance of many predictors across multiple settings. Meta-predictors tend to outperform their constituent individual predictors; however, several individual predictors have performance similar to that of commonly used meta-predictors. The relative performance of predictors differs in high-specificity and high-sensitivity regimes, suggesting that different methods may be best suited to different use cases. We also characterize two potential sources of bias. Predictors that incorporate allele frequency as a predictive feature tend to have reduced performance when distinguishing pathogenic variants from very rare benign variants, and predictors supervised on pathogenicity labels from curated variant databases often learn label imbalances within genes. Overall, we find notable advances over the oldest and most cited missense variant effect predictors and continued improvements among the most recently developed tools, and the CAGI Annotate-All-Missense challenge (also termed the Missense Marathon) will continue to assess state-of-the-art methods as the field progresses. Together, our results help illuminate the current clinical and research utility of missense variant effect predictors and identify potential areas for future development.

Multi-dimensional Insight into the Coexistence of Pathogenic Genes for ADAR1 and TSC2: Careful Consideration is Essential for Interpretation of ADAR1 Variants

Aicardi-Goutières syndrome 6 (AGS6) is a serious auto-immunization-associated acute neurologic decompensation. AGS6 manifests as acute onset of severe generalized dystonia of limbs and developmental regression secondary to febrile illness mostly. Dyschromatosis symmetrica hereditaria (DSH), as pigmentary genodermatosis, is a characterized mixture of hyperpigmented and hypopigmented macules. Both AGS6 and DSH are associated with ADAR1 pathogenic variants. To explore the etiology of a proband with developmental regression with mixture of hyperpigmentation and hypopigmentation macules, we used the trio-WES. Later, to clarify the association between variants and diseases, we used guidelines of ACMG for variants interpretation and quantitative Real-time PCR for verifying elevated expression levels of interferon-stimulated genes, separately. By WES, we detected 2 variants in ADAR1 and a variant in TSC2, respectively, were NM_001111.5:c.1096_1097del, NM_001111.5:c.518A>G, and NM_000548.5:c.1864C>T. Variants interpretation suggested that these 3 variants were both pathogenic. Expression levels of interferon-stimulated genes also elevated as expected. We verified the co-occurrence of pathogenic variants of ADAR1 and TSC2 in AGS6 patients with DSH. Our works contributed to the elucidation of ADAR1 pathogenic mechanism, given the specific pathogenic mechanism of ADAR1, and it is necessary to consider with caution when variants were found in ADAR1.

Specifications of the ACMG/AMP variant curation guidelines for the analysis of germline ATM sequence variants

The ClinGen Hereditary Breast, Ovarian and Pancreatic Cancer (HBOP) Variant Curation Expert Panel (VCEP) is composed of internationally recognized experts in clinical genetics, molecular biology and variant interpretation. This VCEP made specifications for ACMG/AMP guidelines for the ataxia telangiectasia mutated (ATM) gene according to the Food and Drug Administration (FDA)-approved ClinGen protocol. These gene-specific rules for ATM were modified from the American College of Medical Genetics and Association for Molecular Pathology (ACMG/AMP) guidelines and were tested against 33 ATM variants of various types and classifications in a pilot curation phase. The pilot revealed a majority agreement between the HBOP VCEP classifications and the ClinVar-deposited classifications. Six pilot variants had conflicting interpretations in ClinVar and reevaluation with the VCEP's ATM-specific rules resulted in four that were classified as benign, one as likely pathogenic and one as a variant of uncertain significance (VUS) by the VCEP, improving the certainty of interpretations in the public domain. Overall, 28 the 33 pilot variants were not VUS leading to an 85% classification rate. The ClinGen-approved, modified rules demonstrated value for improved interpretation of variants in ATM.

Analyzing the functional effects of DNA variants with gene editing

Continual advancements in genomics have led to an ever-widening disparity between the rate of discovery of genetic variants and our current understanding of their functions and potential roles in disease. Systematic methods for phenotyping DNA variants are required to effectively translate genomics data into improved outcomes for patients with genetic diseases. To make the biggest impact, these approaches must be scalable and accurate, faithfully reflect disease biology, and define complex disease mechanisms. We compare current methods to analyze the function of variants in their endogenous DNA context using genome editing strategies, such as saturation genome editing, base editing and prime editing. We discuss how these technologies can be linked to high-content readouts to gain deep mechanistic insights into variant effects. Finally, we highlight key challenges that need to be addressed to bridge the genotype to phenotype gap, and ultimately improve the diagnosis and treatment of genetic diseases.

Comparative analysis of in-silico tools in identifying pathogenic variants in dominant inherited retinal diseases

Inherited retinal diseases (IRDs) are a group of rare genetic eye conditions that cause blindness. Despite progress in identifying genes associated with IRDs, improvements are necessary for classifying rare autosomal dominant (AD) disorders. AD diseases are highly heterogenous, with causal variants being restricted to specific amino acid changes within certain protein domains, making AD conditions difficult to classify. Here, we aim to determine the top-performing in-silico tools for predicting the pathogenicity of AD IRD variants. We annotated variants from ClinVar and benchmarked 39 variant classifier tools on IRD genes, split by inheritance pattern. Using area-under-the-curve (AUC) analysis, we determined the top-performing tools and defined thresholds for variant pathogenicity. Top-performing tools were assessed using genome sequencing on a cohort of participants with IRDs of unknown etiology. MutScore achieved the highest accuracy within AD genes, yielding an AUC of 0.969. When filtering for AD gain-of-function and dominant negative variants, BayesDel had the highest accuracy with an AUC of 0.997. Five participants with variants in NR2E3, RHO, GUCA1A, and GUCY2D were confirmed to have dominantly inherited disease based on pedigree, phenotype, and segregation analysis. We identified two uncharacterized variants in GUCA1A (c.428T>A, p.Ile143Thr) and RHO (c.631C>G, p.His211Asp) in three participants. Our findings support using a multi-classifier approach comprised of new missense classifier tools to identify pathogenic variants in participants with AD IRDs. Our results provide a foundation for improved genetic diagnosis for people with IRDs.

Application of whole exome sequencing in the diagnosis of muscular disorders: a study of Taiwanese pediatric patients

Background

Muscular dystrophies and congenital myopathies encompass various inherited muscular disorders that present diagnostic challenges due to clinical complexity and genetic heterogeneity.

Methods

This study aimed to investigate the use of whole exome sequencing (WES) in diagnosing muscular disorders in pediatric patients in Taiwan. Out of 161 pediatric patients suspected to have genetic/inherited myopathies, 115 received a molecular diagnosis through conventional tests, single gene testing, and gene panels. The remaining 46 patients were divided into three groups: Group 1 (multiplex ligation-dependent probe amplification-negative Duchenne muscular dystrophy) with three patients (6.5%), Group 2 (various forms of muscular dystrophies) with 21 patients (45.7%), and Group 3 (congenital myopathies) with 22 patients (47.8%).

Results

WES analysis of these groups found pathogenic variants in 100.0% (3/3), 57.1% (12/21), and 68.2% (15/22) of patients in Groups 1 to 3, respectively. WES had a diagnostic yield of 65.2% (30 patients out of 46), detecting 30 pathogenic or potentially pathogenic variants across 28 genes.

Conclusion

WES enables the diagnosis of rare diseases with symptoms and characteristics similar to congenital myopathies and muscular dystrophies, such as muscle weakness. Consequently, this approach facilitates targeted therapy implementation and appropriate genetic counseling.

Exome sequencing in every pregnancy? Results of trio exome sequencing in structurally normal fetuses

OBJECTIVE

This study aimed to assess the detection rate of clinically significant results of prenatal exome sequencing (pES) in low-risk pregnancies and apparently normal fetuses in non-consanguineous couples.

METHODS

A retrospective analysis of pES conducted at a single center from January 2020 to September 2023 was performed. Genetic counseling was provided, and detailed medical histories were obtained. High-risk pregnancies were excluded due to major ultrasound anomalies, sonographic soft markers, abnormal maternal biochemical screening, or family history suggestive of monogenic diseases as well as cases with pathogenic and likely pathogenic (P/LP) chromosomal microarray results. Exome analysis focused on ∼2100 genes associated with Mendelian genetic disorders. Variant analysis and classification followed the American College of Medical Genetics and Genomics (ACMG) guidelines.

RESULTS

Among 1825 pES conducted, 1020 low-risk cases revealed 28 fetuses (2.7%) with potentially clinically significant variants indicating known monogenic diseases, primarily de novo dominant variants (64%). Among these 28 cases, 9 fetuses (0.9%) had the potential for severe phenotypes, including shortened lifespan and intellectual disability, and another 12 had the potential for milder phenotypes. Seven cases were reported with variants of uncertain significance (VUS) that, according to the ACMG criteria, leaned toward LP, constituting 0.7% of the entire cohort. Termination of pregnancy was elected in 13 out of 1020 cases (1.2%) in the cohort, including 7/9 in the severe phenotypes group, 2/12 in the milder phenotype group, and 4/7 in the VUS group.

CONCLUSION

The 2.7% detection rate highlights the significant contribution of pES in low-risk pregnancies. However, it necessitates rigorous analysis, and comprehensive genetic counseling before and after testing.

Systematic large-scale application of ClinGen InSiGHT APC -specific ACMG/AMP variant classification criteria substantially alleviates the burden of variants of uncertain significance in ClinVar and LOVD databases

Background

Pathogenic constitutional APC variants underlie familial adenomatous polyposis, the most common hereditary gastrointestinal polyposis syndrome. To improve variant classification and resolve the interpretative challenges of variants of uncertain significance (VUS), APC-specific ACMG/AMP variant classification criteria were developed by the ClinGen-InSiGHT Hereditary Colorectal Cancer/Polyposis Variant Curation Expert Panel (VCEP).

Methods

A streamlined algorithm using the APC -specific criteria was developed and applied to assess all APC variants in ClinVar and the InSiGHT international reference APC LOVD variant database.

Results

A total of 10,228 unique APC variants were analysed. Among the ClinVar and LOVD variants with an initial classification of (Likely) Benign or (Likely) Pathogenic, 94% and 96% remained in their original categories, respectively. In contrast, 41% ClinVar and 61% LOVD VUS were reclassified into clinically actionable classes, the vast majority as (Likely) Benign. The total number of VUS was reduced by 37%. In 21 out of 36 (58%) promising APC variants that remained VUS despite evidence for pathogenicity, a data mining-driven work-up allowed their reclassification as (Likely) Pathogenic.

Conclusions

The application of APC -specific criteria substantially reduced the number of VUS in ClinVar and LOVD. The study also demonstrated the feasibility of a systematic approach to variant classification in large datasets, which might serve as a generalisable model for other gene-/disease-specific variant interpretation initiatives. It also allowed for the prioritization of VUS that will benefit from in-depth evidence collection. This subset of APC variants was approved by the VCEP and made publicly available through ClinVar and LOVD for widespread clinical use.

Exome sequencing reveals genetic heterogeneity and clinically actionable findings in children with cerebral palsy

Cerebral palsy (CP) is the most common motor disability in children. To ascertain the role of major genetic variants in the etiology of CP, we conducted exome sequencing on a large-scale cohort with clinical manifestations of CP. The study cohort comprised 505 girls and 1,073 boys. Utilizing the current gold standard in genetic diagnostics, 387 of these 1,578 children (24.5%) received genetic diagnoses. We identified 412 pathogenic and likely pathogenic (P/LP) variants across 219 genes associated with neurodevelopmental disorders, and 59 P/LP copy number variants. The genetic diagnostic rate of children with CP labeled at birth with perinatal asphyxia was higher than the rate in children without asphyxia (P = 0.0033). Also, 33 children with CP manifestations (8.5%, 33 of 387) had findings that were clinically actionable. These results highlight the need for early genetic testing in children with CP, especially those with risk factors like perinatal asphyxia, to enable evidence-based medical decision-making.

Clinical implications of VUS reclassification in a single-centre series from application of ACMG/AMP classification rules specified for BRCA1/2

BACKGROUND

BRCA1/2 testing is crucial to guide clinical decisions in patients with hereditary breast/ovarian cancer, but detection of variants of uncertain significance (VUSs) prevents proper management of carriers. The ENIGMA (Evidence-based Network for the Interpretation of Germline Mutant Alleles) BRCA1/2 Variant Curation Expert Panel (VCEP) has recently developed BRCA1/2 variant classification guidelines consistent with ClinGen processes, specified against the ACMG/AMP (American College of Medical Genetics and Genomics/Association for Molecular-Pathology) classification framework.

METHODS

The ClinGen-approved BRCA1/2-specified ACMG/AMP classification guidelines were applied to BRCA1/2 VUSs identified from 2011 to 2022 in a series of patients, retrieving information from the VCEP documentation, public databases, literature and ENIGMA unpublished data. Then, we critically re-evaluated carrier families based on new results and checked consistency of updated classification with main sources for clinical interpretation of BRCA1/2 variants.

RESULTS

Among 166 VUSs detected in 231 index cases, 135 (81.3%) found in 197 index cases were classified by applying BRCA1/2-specified ACMG/AMP criteria: 128 (94.8%) as Benign/Likely Benign and 7 (5.2%) as Pathogenic/Likely Pathogenic. The average time from the first report as 'VUS' to classification using this approach was 49.4 months. Considering that 15 of these variants found in 64 families had already been internally reclassified prior to this work, this study provided 121 new reclassifications among the 151 (80.1%) remaining VUSs, relevant to 133/167 (79.6%) families.

CONCLUSIONS

These results demonstrated the effectiveness of new BRCA1/2 ACMG/AMP classification guidelines for VUS classification within a clinical cohort, and their important clinical impact. Furthermore, they suggested a cadence of no more than 3 years for regular review of VUSs, which however requires time, expertise and resources.

Guidelines for releasing a variant effect predictor

Computational methods for assessing the likely impacts of mutations, known as variant effect predictors (VEPs), are widely used in the assessment and interpretation of human genetic variation, as well as in other applications like protein engineering. Many different VEPs have been released to date, and there is tremendous variability in their underlying algorithms and outputs, and in the ways in which the methodologies and predictions are shared. This leads to considerable challenges for end users in knowing which VEPs to use and how to use them. Here, to address these issues, we provide guidelines and recommendations for the release of novel VEPs. Emphasising open-source availability, transparent methodologies, clear variant effect score interpretations, standardised scales, accessible predictions, and rigorous training data disclosure, we aim to improve the usability and interpretability of VEPs, and promote their integration into analysis and evaluation pipelines. We also provide a large, categorised list of currently available VEPs, aiming to facilitate the discovery and encourage the usage of novel methods within the scientific community.

Challenges and approaches to calibrating patient phenotype as evidence for cancer gene variant classification under ACMG/AMP guidelines

Since first publication of the American College of Medical Genetics and Genomics/Association for Medical Pathology (ACMG/AMP) variant classification guidelines, additional recommendations for application of certain criteria have been released (https://clinicalgenome.org/docs/), to improve their application in the diagnostic setting. However, none have addressed use of the PS4 and PP4 criteria, capturing patient presentation as evidence towards pathogenicity. Application of PS4 can be done through traditional case-control studies, or "proband counting" within or across clinical testing cohorts. Review of the existing PS4 and PP4 specifications for Hereditary Cancer Gene Variant Curation Expert Panels revealed substantial differences in the approach to defining specifications. Using BRCA1, BRCA2 and TP53 as exemplar genes, we calibrated different methods proposed for applying the "PS4 proband counting" criterion. For each approach, we considered limitations, non-independence with other ACMG/AMP criteria, broader applicability, and variability in results for different datasets. Our findings highlight inherent overlap of proband-counting methods with ACMG/AMP frequency codes, and the importance of calibration to derive dataset-specific code weights that can account for potential between-dataset differences in ascertainment and other factors. Our work emphasizes the advantages and generalizability of logistic regression analysis over simple proband-counting approaches to empirically determine the relative predictive capacity and weight of various personal clinical features in the context of multigene panel testing, for improved variant interpretation. We also provide a general protocol, including instructions for data formatting and a web-server for analysis of personal history parameters, to facilitate dataset-specific calibration analyses required to use such data for germline variant classification.

Mutational scanning of CRX classifies clinical variants and reveals biochemical properties of the transcriptional effector domain

Cone-Rod Homeobox, encoded by CRX, is a transcription factor (TF) essential for the terminal differentiation and maintenance of mammalian photoreceptors. Structurally, CRX comprises an ordered DNA-binding homeodomain and an intrinsically disordered transcriptional effector domain. Although a handful of human variants in CRX have been shown to cause several different degenerative retinopathies with varying cone and rod predominance, as with most human disease genes the vast majority of observed CRX genetic variants are uncharacterized variants of uncertain significance (VUS). We performed a deep mutational scan (DMS) of nearly all possible single amino acid substitution variants in CRX, using an engineered cell-based transcriptional reporter assay. We measured the ability of each CRX missense variant to transactivate a synthetic fluorescent reporter construct in a pooled fluorescence-activated cell sorting assay and compared the activation strength of each variant to that of wild-type CRX to compute an activity score, identifying thousands of variants with altered transcriptional activity. We calculated a statistical confidence for each activity score derived from multiple independent measurements of each variant marked by unique sequence barcodes, curating a high-confidence list of nearly 2,000 variants with significantly altered transcriptional activity compared to wild-type CRX. We evaluated the performance of the DMS assay as a clinical variant classification tool using gold-standard classified human variants from ClinVar, and determined that activity scores could be used to identify pathogenic variants with high specificity. That this performance could be achieved using a synthetic reporter assay in a foreign cell type, even for a highly cell type-specific TF like CRX, suggests that this approach shows promise for DMS of other TFs that function in cell types that are not easily accessible. Per-position average activity scores closely aligned to a predicted structure of the ordered homeodomain and demonstrated position-specific residue requirements. The intrinsically disordered transcriptional effector domain, by contrast, displayed a qualitatively different pattern of substitution effects, following compositional constraints without specific residue position requirements in the peptide chain. The observed compositional constraints of the effector domain were consistent with the acidic exposure model of transcriptional activation. Together, the results of the CRX DMS identify molecular features of the CRX effector domain and demonstrate clinical utility for variant classification.

An AI-based approach driven by genotypes and phenotypes to uplift the diagnostic yield of genetic diseases

Identifying disease-causing variants in Rare Disease patients' genome is a challenging problem. To accomplish this task, we describe a machine learning framework, that we called "Suggested Diagnosis", whose aim is to prioritize genetic variants in an exome/genome based on the probability of being disease-causing. To do so, our method leverages standard guidelines for germline variant interpretation as defined by the American College of Human Genomics (ACMG) and the Association for Molecular Pathology (AMP), inheritance information, phenotypic similarity, and variant quality. Starting from (1) the VCF file containing proband's variants, (2) the list of proband's phenotypes encoded in Human Phenotype Ontology terms, and optionally (3) the information about family members (if available), the "Suggested Diagnosis" ranks all the variants according to their machine learning prediction. This method significantly reduces the number of variants that need to be evaluated by geneticists by pinpointing causative variants in the very first positions of the prioritized list. Most importantly, our approach proved to be among the top performers within the CAGI6 Rare Genome Project Challenge, where it was able to rank the true causative variant among the first positions and, uniquely among all the challenge participants, increased the diagnostic yield of 12.5% by solving 2 undiagnosed cases.

Recommendations for laboratory workflow that better support centralised amalgamation of genomic variant data: findings from CanVIG-UK national molecular laboratory survey

BACKGROUND

National and international amalgamation of genomic data offers opportunity for research and audit, including analyses enabling improved classification of variants of uncertain significance. Review of individual-level data from National Health Service (NHS) testing of cancer susceptibility genes (2002-2023) submitted to the National Disease Registration Service revealed heterogeneity across participating laboratories regarding (1) the structure, quality and completeness of submitted data, and (2) the ease with which that data could be assembled locally for submission.

METHODS

In May 2023, we undertook a closed online survey of 51 clinical scientists who provided consensus responses representing all 17 of 17 NHS molecular genetic laboratories in England and Wales which undertake NHS diagnostic analyses of cancer susceptibility genes. The survey included 18 questions relating to 'next-generation sequencing workflow' (11), 'variant classification' (3) and 'phenotypical context' (4).

RESULTS

Widely differing processes were reported for transfer of variant data into their local LIMS (Laboratory Information Management System), for the formatting in which the variants are stored in the LIMS and which classes of variants are retained in the local LIMS. Differing local provisions and workflow for variant classifications were also reported, including the resources provided and the mechanisms by which classifications are stored.

CONCLUSION

The survey responses illustrate heterogeneous laboratory workflow for preparation of genomic variant data from local LIMS for centralised submission. Workflow is often labour-intensive and inefficient, involving multiple manual steps which introduce opportunities for error. These survey findings and adoption of the concomitant recommendations may support improvement in laboratory dataflows, better facilitating submission of data for central amalgamation.

Explicable prioritization of genetic variants by integration of rule-based and machine learning algorithms for diagnosis of rare Mendelian disorders

BACKGROUND

In the process of finding the causative variant of rare diseases, accurate assessment and prioritization of genetic variants is essential. Previous variant prioritization tools mainly depend on the in-silico prediction of the pathogenicity of variants, which results in low sensitivity and difficulty in interpreting the prioritization result. In this study, we propose an explainable algorithm for variant prioritization, named 3ASC, with higher sensitivity and ability to annotate evidence used for prioritization. 3ASC annotates each variant with the 28 criteria defined by the ACMG/AMP genome interpretation guidelines and features related to the clinical interpretation of the variants. The system can explain the result based on annotated evidence and feature contributions.

RESULTS

We trained various machine learning algorithms using in-house patient data. The performance of variant ranking was assessed using the recall rate of identifying causative variants in the top-ranked variants. The best practice model was a random forest classifier that showed top 1 recall of 85.6% and top 3 recall of 94.4%. The 3ASC annotates the ACMG/AMP criteria for each genetic variant of a patient so that clinical geneticists can interpret the result as in the CAGI6 SickKids challenge. In the challenge, 3ASC identified causal genes for 10 out of 14 patient cases, with evidence of decreased gene expression for 6 cases. Among them, two genes (HDAC8 and CASK) had decreased gene expression profiles confirmed by transcriptome data.

CONCLUSIONS

3ASC can prioritize genetic variants with higher sensitivity compared to previous methods by integrating various features related to clinical interpretation, including features related to false positive risk such as quality control and disease inheritance pattern. The system allows interpretation of each variant based on the ACMG/AMP criteria and feature contribution assessed using explainable AI techniques.

Enhancing Variant Prioritization in VarFish through On-Premise Computational Facial Analysis

Genomic variant prioritization is crucial for identifying disease-associated genetic variations. Integrating facial and clinical feature analyses into this process enhances performance. This study demonstrates the integration of facial analysis (GestaltMatcher) and Human Phenotype Ontology analysis (CADA) within VarFish, an open-source variant analysis framework. Challenges related to non-open-source components were addressed by providing an open-source version of GestaltMatcher, facilitating on-premise facial analysis to address data privacy concerns. Performance evaluation on 163 patients recruited from a German multi-center study of rare diseases showed PEDIA's superior accuracy in variant prioritization compared to individual scores. This study highlights the importance of further benchmarking and future integration of advanced facial analysis approaches aligned with ACMG guidelines to enhance variant classification.

Implementing Whole Genome Sequencing (WGS) in Clinical Practice: Advantages, Challenges, and Future Perspectives

The integration of whole genome sequencing (WGS) into all aspects of modern medicine represents the next step in the evolution of healthcare. Using this technology, scientists and physicians can observe the entire human genome comprehensively, generating a plethora of new sequencing data. Modern computational analysis entails advanced algorithms for variant detection, as well as complex models for classification. Data science and machine learning play a crucial role in the processing and interpretation of results, using enormous databases and statistics to discover new and support current genotype-phenotype correlations. In clinical practice, this technology has greatly enabled the development of personalized medicine, approaching each patient individually and in accordance with their genetic and biochemical profile. The most propulsive areas include rare disease genomics, oncogenomics, pharmacogenomics, neonatal screening, and infectious disease genomics. Another crucial application of WGS lies in the field of multi-omics, working towards the complete integration of human biomolecular data. Further technological development of sequencing technologies has led to the birth of third and fourth-generation sequencing, which include long-read sequencing, single-cell genomics, and nanopore sequencing. These technologies, alongside their continued implementation into medical research and practice, show great promise for the future of the field of medicine.

Functional analysis and clinical classification of 462 germline BRCA2 missense variants affecting the DNA binding domain

Variants of uncertain significance (VUSs) in BRCA2 are a common result of hereditary cancer genetic testing. While more than 4,000 unique VUSs, comprised of missense or intronic variants, have been identified in BRCA2, the few missense variants now classified clinically as pathogenic or likely pathogenic are predominantly located in the region encoding the C-terminal DNA binding domain (DBD). We report on functional evaluation of the influence of 462 BRCA2 missense variants affecting the DBD on DNA repair activity of BRCA2 using a homology-directed DNA double-strand break repair assay. Of these, 137 were functionally abnormal, 313 were functionally normal, and 12 demonstrated intermediate function. Comparisons with other functional studies of BRCA2 missense variants yielded strong correlations. Sequence-based in silico prediction models had high sensitivity, but limited specificity, relative to the homology-directed repair assay. Combining the functional results with clinical and genetic data in an American College of Medical Genetics (ACMG)/Association for Molecular Pathology (AMP)-like variant classification framework from a clinical testing laboratory, after excluding known splicing variants and functionally intermediate variants, classified 431 of 442 (97.5%) missense variants (129 as pathogenic/likely pathogenic and 302 as benign/likely benign). Functionally abnormal variants classified as pathogenic by ACMG/AMP rules were associated with a slightly lower risk of breast cancer (odds ratio [OR] 5.15, 95% confidence interval [CI] 3.43-7.83) than BRCA2 DBD protein truncating variants (OR 8.56, 95% CI 6.03-12.36). Overall, functional studies of BRCA2 variants using validated assays substantially improved the variant classification yield from ACMG/AMP models and are expected to improve clinical management of many individuals found to harbor germline BRCA2 missense VUS.

Assessment of the evidence yield for the calibrated PP3/BP4 computational recommendations

Purpose

To investigate the number of rare missense variants observed in human genome sequences by ACMG/AMP PP3/BP4 evidence strength, following the calibrated PP3/BP4 computational recommendations.

Methods

Missense variants from the genome sequences of 300 probands from the Rare Genomes Project with suspected rare disease were analyzed using computational prediction tools able to reach PP3_Strong and BP4_Moderate evidence strengths (BayesDel, MutPred2, REVEL, and VEST4). The numbers of variants at each evidence strength were analyzed across disease-associated genes and genome-wide.

Results

From a median of 75.5 rare (≤1% allele frequency) missense variants in disease-associated genes per proband, a median of one reached PP3_Strong, 3-5 PP3_Moderate, and 3-5 PP3_Supporting. Most were allocated BP4 evidence (median 41-49 per proband) or were indeterminate (median 17.5-19 per proband). Extending the analysis to all protein-coding genes genome-wide, the number of PP3_Strong variants increased approximately 2.6-fold compared to disease-associated genes, with a median per proband of 1-3 PP3_Strong, 8-16 PP3_Moderate, and 10-17 PP3_Supporting.

Conclusion

A small number of variants per proband reached PP3_Strong and PP3_Moderate in 3,424 disease-associated genes, and though not the intended use of the recommendations, also genome-wide. Use of PP3/BP4 evidence as recommended from calibrated computational prediction tools in the clinical diagnostic laboratory is unlikely to inappropriately contribute to the classification of an excessive number of variants as Pathogenic or Likely Pathogenic by ACMG/AMP rules.

Clinical application of prospective whole-exome sequencing in the diagnosis of genetic disease: Experience of a regional disease center in South Korea

INTRODUCTION

Next-generation sequencing helps clinicians diagnose patients with suspected genetic disorders. The current study aimed to investigate the diagnostic yield and clinical utility of prospective whole-exome sequencing (WES) in rare diseases.

METHODS

WES was performed in 92 patients who presented with clinical symptoms suggestive of genetic disorders. The WES data were analyzed using an in-house developed software. The patients' phenotypic characteristics were classified according to the human phenotype ontology.

RESULTS

WES detected 64 variants, 13 were classified as pathogenic, 26 as likely pathogenic, and 25 as variants of uncertain significance. In 57 patients with these variants, 30 were identified as causal variants. The diagnostic yield was higher in patients with abnormalities in joint mobility and skin morphology than in those with cerebellar hypoplasia/atrophy, epilepsy, global developmental delay, dysmorphic features/facial dysmorphisms, and chronic kidney disease/abnormal renal morphology.

CONCLUSION

In this study, a WES-based variant interpretation system was employed to provide a definitive diagnosis for 28.3% of the patients suspected of having genetic disorders. WES is particularly useful for diagnosing rare diseases with symptoms that affect more than one system, when targeted genetic panels are difficult to employ.

Rapid genomic sequencing for genetic disease diagnosis and therapy in intensive care units: a review

Single locus (Mendelian) diseases are a leading cause of childhood hospitalization, intensive care unit (ICU) admission, mortality, and healthcare cost. Rapid genome sequencing (RGS), ultra-rapid genome sequencing (URGS), and rapid exome sequencing (RES) are diagnostic tests for genetic diseases for ICU patients. In 44 studies of children in ICUs with diseases of unknown etiology, 37% received a genetic diagnosis, 26% had consequent changes in management, and net healthcare costs were reduced by $14,265 per child tested by URGS, RGS, or RES. URGS outperformed RGS and RES with faster time to diagnosis, and higher rate of diagnosis and clinical utility. Diagnostic and clinical outcomes will improve as methods evolve, costs decrease, and testing is implemented within precision medicine delivery systems attuned to ICU needs. URGS, RGS, and RES are currently performed in <5% of the ~200,000 children likely to benefit annually due to lack of payor coverage, inadequate reimbursement, hospital policies, hospitalist unfamiliarity, under-recognition of possible genetic diseases, and current formatting as tests rather than as a rapid precision medicine delivery system. The gap between actual and optimal outcomes in children in ICUs is currently increasing since expanded use of URGS, RGS, and RES lags growth in those likely to benefit through new therapies. There is sufficient evidence to conclude that URGS, RGS, or RES should be considered in all children with diseases of uncertain etiology at ICU admission. Minimally, diagnostic URGS, RGS, or RES should be ordered early during admissions of critically ill infants and children with suspected genetic diseases.

CAGI, the Critical Assessment of Genome Interpretation, establishes progress and prospects for computational genetic variant interpretation methods

BACKGROUND

The Critical Assessment of Genome Interpretation (CAGI) aims to advance the state-of-the-art for computational prediction of genetic variant impact, particularly where relevant to disease. The five complete editions of the CAGI community experiment comprised 50 challenges, in which participants made blind predictions of phenotypes from genetic data, and these were evaluated by independent assessors.

RESULTS

Performance was particularly strong for clinical pathogenic variants, including some difficult-to-diagnose cases, and extends to interpretation of cancer-related variants. Missense variant interpretation methods were able to estimate biochemical effects with increasing accuracy. Assessment of methods for regulatory variants and complex trait disease risk was less definitive and indicates performance potentially suitable for auxiliary use in the clinic.

CONCLUSIONS

Results show that while current methods are imperfect, they have major utility for research and clinical applications. Emerging methods and increasingly large, robust datasets for training and assessment promise further progress ahead.

Short-read whole genome sequencing identifies causative variants in most individuals with previously unexplained aniridia

BACKGROUND

Classic aniridia is a highly penetrant autosomal dominant disorder characterised by congenital absence of the iris, foveal hypoplasia, optic disc anomalies and progressive opacification of the cornea. >90% of cases of classic aniridia are caused by heterozygous, loss-of-function variants affecting the PAX6 locus.

METHODS

Short-read whole genome sequencing was performed on 51 (39 affected) individuals from 37 different families who had screened negative for mutations in the PAX6 coding region.

RESULTS

Likely causative mutations were identified in 22 out of 37 (59%) families. In 19 out of 22 families, the causative genomic changes have an interpretable deleterious impact on the PAX6 locus. Of these 19 families, 1 has a novel heterozygous PAX6 frameshift variant missed on previous screens, 4 have single nucleotide variants (SNVs) (one novel) affecting essential splice sites of PAX6 5' non-coding exons and 2 have deep intronic SNV (one novel) resulting in gain of a donor splice site. In 12 out of 19, the causative variants are large-scale structural variants; 5 have partial or whole gene deletions of PAX6, 3 have deletions encompassing critical PAX6 cis-regulatory elements, 2 have balanced inversions with disruptive breakpoints within the PAX6 locus and 2 have complex rearrangements disrupting PAX6. The remaining 3 of 22 families have deletions encompassing FOXC1 (a known cause of atypical aniridia). Seven of the causative variants occurred de novo and one cosegregated with familial aniridia. We were unable to establish inheritance status in the remaining probands. No plausibly causative SNVs were identified in PAX6 cis-regulatory elements.

CONCLUSION

Whole genome sequencing proves to be an effective diagnostic test in most individuals with previously unexplained aniridia.

An overview of actionable and potentially actionable TSC1 and TSC2 germline variants in an online Database

Tuberous Sclerosis Complex (TSC) is caused by loss of function germline variants in the TSC1 or TSC2 tumor suppressor genes. Genetic testing for the detection of pathogenic variants in either TSC1 or TSC2 was implemented as a diagnostic criterion for TSC. However, TSC molecular diagnosis can be challenging due to the absence of variant hotspots and the high number of variants described. This review aimed to perform an overview of TSC1/2 variants submitted in the ClinVar database. Variants of uncertain significance (VUS), missense and single nucleotide variants were the most frequent in clinical significance (37-40%), molecular consequence (37%-39%) and variation type (82%-83%) categories in ClinVar in TSC1 and TSC2 variants, respectively. Frameshift and nonsense VUS have potential for pathogenic reclassification if further functional and segregation studies were performed. Indeed, there were few functional assays deposited in the database and literature. In addition, we did not observe hotspots for variation and many variants presented conflicting submissions regarding clinical significance. This study underscored the importance of disseminating molecular diagnostic results in a public database to render the information largely accessible and promote accurate diagnosis. We encourage the performance of functional studies evaluating the pathogenicity of TSC1/2 variants.

Gene-specific ACMG/AMP classification criteria for germline APC variants: Recommendations from the ClinGen InSiGHT Hereditary Colorectal Cancer/Polyposis Variant Curation Expert Panel

PURPOSE

The Hereditary Colorectal Cancer/Polyposis Variant Curation Expert Panel (VCEP) was established by the International Society for Gastrointestinal Hereditary Tumours and the Clinical Genome Resource, who set out to develop recommendations for the interpretation of germline APC variants underlying Familial Adenomatous Polyposis, the most frequent hereditary polyposis syndrome.

METHODS

Through a rigorous process of database analysis, literature review, and expert elicitation, the APC VCEP derived gene-specific modifications to the ACMG/AMP (American College of Medical Genetics and Genomics and Association for Molecular Pathology) variant classification guidelines and validated such criteria through the pilot classification of 58 variants.

RESULTS

The APC-specific criteria represented gene- and disease-informed specifications, including a quantitative approach to allele frequency thresholds, a stepwise decision tool for truncating variants, and semiquantitative evaluations of experimental and clinical data. Using the APC-specific criteria, 47% (27/58) of pilot variants were reclassified including 14 previous variants of uncertain significance (VUS).

CONCLUSION

The APC-specific ACMG/AMP criteria preserved the classification of well-characterized variants on ClinVar while substantially reducing the number of VUS by 56% (14/25). Moving forward, the APC VCEP will continue to interpret prioritized lists of VUS, the results of which will represent the most authoritative variant classification for widespread clinical use.

Unraveling Complexities in Genetically Elusive Long QT Syndrome

Genetic testing has become standard of care for patients with long QT syndrome (LQTS), providing diagnostic, prognostic, and therapeutic information for both probands and their family members. However, up to a quarter of patients with LQTS do not have identifiable Mendelian pathogenic variants in the currently known LQTS-associated genes. This absence of genetic confirmation, intriguingly, does not lessen the severity of LQTS, with the prognosis in these gene-elusive patients with unequivocal LQTS mirroring genotype-positive patients in the limited data available. Such a conundrum instigates an exploration into the causes of corrected QT interval (QTc) prolongation in these cases, unveiling a broad spectrum of potential scenarios and mechanisms. These include multiple environmental influences on QTc prolongation, exercise-induced repolarization abnormalities, and the profound implications of the constantly evolving nature of genetic testing and variant interpretation. In addition, the rapid advances in genetics have the potential to uncover new causal genes, and polygenic risk factors may aid in the diagnosis of high-risk patients. Navigating this multifaceted landscape requires a systematic approach and expert knowledge, integrating the dynamic nature of genetics and patient-specific influences for accurate diagnosis, management, and counseling of patients. The role of a subspecialized expert cardiogenetic clinic is paramount in evaluation to navigate this complexity. Amid these intricate aspects, this review outlines potential causes of gene-elusive LQTS. It also provides an outline for the evaluation of patients with negative and inconclusive genetic test results and underscores the need for ongoing adaptation and reassessment in our understanding of LQTS, as the complexities of gene-elusive LQTS are increasingly deciphered.

Diagnostic Outcomes of Concurrent DNA and RNA Sequencing in Individuals Undergoing Hereditary Cancer Testing

Importance

Personalized surveillance, prophylaxis, and cancer treatment options for individuals with hereditary cancer predisposition are informed by results of germline genetic testing. Improvements to genomic technology, such as the availability of RNA sequencing, may increase identification of individuals eligible for personalized interventions by improving the accuracy and yield of germline testing.

Objective

To assess the cumulative association of paired DNA and RNA testing with detection of disease-causing germline genetic variants and resolution of variants of uncertain significance (VUS).

Design, Setting, and Participants

Paired DNA and RNA sequencing was performed on individuals undergoing germline testing for hereditary cancer indication at a single diagnostic laboratory from March 2019 through April 2020. Demographic characteristics, clinical data, and test results were curated as samples were received, and changes to variant classification were assessed over time. Data analysis was performed from May 2020 to June 2023.

Main Outcomes and Measures

Main outcomes were increase in diagnostic yield, decrease in VUS rate, the overall results by variant type, the association of RNA evidence with variant classification, and the corresponding predicted effect on cancer risk management.

Results

A total of 43 524 individuals were included (median [range] age at testing, 54 [2-101] years; 37 373 female individuals [85.7%], 6224 male individuals [14.3%], and 2 individuals of unknown sex [<0.1%]), with 43 599 tests. A total of 2197 (5.0%) were Ashkenazi Jewish, 1539 (3.5%) were Asian, 3077 (7.1%) were Black, 2437 (5.6%) were Hispanic, 27 793 (63.7%) were White, and 2049 (4.7%) were other race, and for 4507 individuals (10.3%), race and ethnicity were unknown. Variant classification was impacted in 549 individuals (1.3%). Medically significant upgrades were made in 97 individuals, including 70 individuals who had a variant reclassified from VUS to pathogenic/likely pathogenic (P/LP) and 27 individuals who had a novel deep intronic P/LP variant that would not have been detected using DNA sequencing alone. A total of 93 of 545 P/LP splicing variants (17.1%) were dependent on RNA evidence for classification, and 312 of 439 existing splicing VUS (71.1%) were resolved by RNA evidence. Notably, the increase in positive rate (3.1%) and decrease in VUS rate (-3.9%) was higher in Asian, Black, and Hispanic individuals combined compared to White individuals (1.6%; P = .02; and -2.5%; P < .001).

Conclusions and Relevance

Findings of this diagnostic study demonstrate that the ability to perform RNA sequencing concurrently with DNA sequencing represents an important advancement in germline genetic testing by improving detection of novel variants and classification of existing variants. This expands the identification of individuals with hereditary cancer predisposition and increases opportunities for personalization of therapeutics and surveillance.