Classification Of Missense Variants Research Articles

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.

Classification of missense variants in the N-methyl-d-aspartate receptor GRIN gene family as gain- or loss-of-function.

Advances in sequencing technology have generated a large amount of genetic data from patients with neurological conditions. These data have provided diagnosis of many rare diseases, including a number of pathogenic de novo missense variants in GRIN genes encoding N-methyl-d-aspartate receptors (NMDARs). To understand the ramifications for neurons and brain circuits affected by rare patient variants, functional analysis of the variant receptor is necessary in model systems. For NMDARs, this functional analysis needs to assess multiple properties in order to understand how variants could impact receptor function in neurons. One can then use these data to determine whether the overall actions will increase or decrease NMDAR-mediated charge transfer. Here, we describe an analytical and comprehensive framework by which to categorize GRIN variants as either gain-of-function (GoF) or loss-of-function (LoF) and apply this approach to GRIN2B variants identified in patients and the general population. This framework draws on results from six different assays that assess the impact of the variant on NMDAR sensitivity to agonists and endogenous modulators, trafficking to the plasma membrane, response time course and channel open probability. We propose to integrate data from multiple in vitro assays to arrive at a variant classification, and suggest threshold levels that guide confidence. The data supporting GoF and LoF determination are essential to assessing pathogenicity and patient stratification for clinical trials as personalized pharmacological and genetic agents that can enhance or reduce receptor function are advanced. This approach to functional variant classification can generalize to other disorders associated with missense variants.

The impact of machine learning models in reducing variants of uncertain significance for individuals from underrepresented populations who are undergoing testing for inborn errors of immunity

The classification of missense variants is challenging due to limited evidence. Consequently, many remain categorized as variants of uncertain significance (VUS). VUS are at the core of healthcare disparities, as individuals from race, ethnicity and ancestry (REA) populations underrepresented in large genomic databases and medical literature receive VUS more often. To generate definitive genetic results more equitably across REA groups, we sought to develop gene-specific machine learning (ML) models and evaluate their utility in genetic testing for inborn errors of immunity (IEI).From 1/1/2022 to 11/15/2022, gene-specific ML algorithms were validated and integrated at Invitae. We incorporated existing models such as SpliceAI, and developed our own by leveraging large datasets including gnomAD, AlphaFold protein structures, and others, to model variant effects. Evidence from these ML models were incorporated into Sherloc, a semi-quantitative variant interpretation framework based on ACMG/AMP guidelines. Only evidence that met a negative or positive predictive value >80% was incorporated during variant interpretation. At least 1 validated model was available for 232 genes during the study period. VUS reduction was calculated, stratified by REA groups.Of 19 784 US based individuals who underwent multi-gene panel testing for IEI, ~16 900 (85%) had ML evidence applied to at least one variant, and ~5700 (29%) individuals were from an underrepresented REA group. Models contributed to the classification of at least 1 benign/likely benign variant in ~10 300 (52%) individuals and at least 1 pathogenic/likely pathogenic variant in ~210 (1%) individuals. A higher percentage of Black (67%), Asian (56%) and Hispanic (60%) individuals had a definitive classification (P/LP or B/LB) that was dependent on ML evidence compared to White (51%) individuals by one-tailed, two-sample proportion test (p <0.03). The average amount of benign or pathogenic ML evidence applied per reclassified variant by Sherloc was similar across populations. Among individuals who had at least 1 variant with ML evidence applied, over 60% would have received a VUS variant without this evidence. ML modeling has demonstrated utility during clinical variant interpretation for IEI, particularly in underrepresented populations.

Germline Missense Variants in BRCA1: New Trends and Challenges for Clinical Annotation.

Genetic testing allows for the identification of germline DNA variations, which are associated with a significant increase in the risk of developing breast cancer (BC) and ovarian cancer (OC). Detection of a BRCA1 or BRCA2 pathogenic variant triggers several clinical management actions, which may include increased surveillance and prophylactic surgery for healthy carriers or treatment with the PARP inhibitor therapy for carriers diagnosed with cancer. Thus, standardized validated criteria for the annotation of BRCA1 and BRCA2 variants according to their pathogenicity are necessary to support clinical decision-making and ensure improved outcomes. Upon detection, variants whose pathogenicity can be inferred by the genetic code are typically classified as pathogenic, likely pathogenic, likely benign, or benign. Variants whose impact on function cannot be directly inferred by the genetic code are labeled as variants of uncertain clinical significance (VUS) and are evaluated by multifactorial likelihood models that use personal and family history of cancer, segregation data, prediction tools, and co-occurrence with a pathogenic BRCA variant. Missense variants, coding alterations that replace a single amino acid residue with another, are a class of variants for which determination of clinical relevance is particularly challenging. Here, we discuss current issues in the missense variant classification by following a typical life cycle of a BRCA1 missense variant through detection, annotation and information dissemination. Advances in massively parallel sequencing have led to a substantial increase in VUS findings. Although the comprehensive assessment and classification of missense variants according to their pathogenicity remains the bottleneck, new developments in functional analysis, high throughput assays, data sharing, and statistical models are rapidly changing this scenario.

BRCA1/2 missense mutations and the value of in-silico analyses

IntroductionThe clinical implications of genetic variants in BRCA1/2 in healthy and affected individuals are considerable. Variant interpretation, however, is especially challenging for missense variants. The majority of them are classified as variants of unknown clinical significance (VUS). Computational (in-silico) predictive programs are easy to access, but represent only one tool out of a wide range of complemental approaches to classify VUS. With this single-center study, we aimed to evaluate the impact of in-silico analyses in a spectrum of different BRCA1/2 missense variants. MethodsWe conducted mutation analysis of BRCA1/2 in 523 index patients with suspected hereditary breast and ovarian cancer (HBOC). Classification of the genetic variants was performed according to the German Consortium (GC)-HBOC database. Additionally, all missense variants were classified by the following three in-silico prediction tools: SIFT, Mutation Taster (MT2) and PolyPhen2 (PPH2). ResultsOverall 201 different variants, 68 of which constituted missense variants were ranked as pathogenic, neutral, or unknown. The classification of missense variants by in-silico tools resulted in a higher amount of pathogenic mutations (25% vs. 13.2%) compared to the GC-HBOC-classification. Altogether, more than fifty percent (38/68, 55.9%) of missense variants were ranked differently. Sensitivity of in-silico-tools for mutation prediction was 88.9% (PPH2), 100% (SIFT) and 100% (MT2). ConclusionWe found a relevant discrepancy in variant classification by using in-silico prediction tools, resulting in potential overestimation and/or underestimation of cancer risk. More reliable, notably gene-specific, prediction tools and functional tests are needed to improve clinical counseling.

Classification of Missense Variants in XRCC2 by Functional Analysis: Implications for Breast Cancer Association Studies.

XRCC2 belongs to the family of RAD51 paralogs, which are involved in homologous recombination repair (HRR) of DNA double-strand breaks. Variants in XRCC2 have recently been suggested to increase the risk for breast cancer. However, this finding could not be confirmed in a second study. Both case-control studies lacked power due to the rarity of deleterious XRCC2 mutations, and therefore rare missense variants predicted to be damaging by in silico analysis had been included in the analyses. But is incorporating purely in silico predictions for variant pathogenicity advised when conducting association studies? In this issue, Hilbers et al. (Hum Mutat 37:914–925, 2016) examined 23 previously reported XRCC2 missense variants with four different in silico prediction algorithms, including AlignGVGD, SIFT, PolyPhen2, and CADD, to estimate whether the variants were likely to be pathogenic. The functional effects of the XRCC2 missense variants were subsequently examined by testing their ability to complement the DNA repair phenotype of XRCC2-deficient cells, as well as measuring the efficiency of the variants to perform HRR in hamster and human cells. The experiments revealed that the in silico predictions correlated poorly with the observed functional effects. When the novel functional data were included in case-control studies, there no longer was an association with breast cancer. The authors conclude that if XRCC2 variants predispose to breast cancer, the associated variants are likely to be restricted to protein-truncating mutations as well as few missense variants. Next-generation sequencing is expected to increase the number of rare variants identified in novel cancer-predisposing genes, which poses challenges to risk estimation through patient cohorts and cosegregation analysis. The study by Hilbers et al. highlights the importance of functional analysis of variants of unknown clinical significance and demonstrates the pitfalls in establishing an association between rare missense variants and disease risk.

Missense mutations of BRCA1 gene affect the binding with p53 both in vitro and in vivo

Women with BRCA1 gene mutations have an increased risk for breast and ovarian cancer (BOC). Classification of missense variants as neutral or disease causing is still a challenge and has major implications for genetic counseling. BRCA1 is organized in an N-terminal ring-finger domain and two BRCT (breast cancer C-terminus) domains, involved in protein-protein interaction. The integrity of the C-terminal, BRCT repeat region is also critical for BRCA1 tumor suppressor function. Several molecular partners of BRCA1 have so far been identified; among them, the tumor suppressor protein p53 seems to play a major role. This study was aimed at evaluating the impact of two missense mutations, namely the W1837R and the S1841N, previously identified in BOC patients and located in the BRCT domain of the BRCA1 gene, on the binding capacity of this protein to p53. Co-immunoprecipitation assays of E. coli-expressed wild-type and mutated BRCTs challenged with a HeLa cell extract revealed, for the S1841N variant a significant reduction in the binding activity to p53, while the W1837R mutant showed an inverse effect. Furthermore, a clonogenic soft agar growth assay performed on HeLa cells stably transfected with either wild-type or mutant BRCA1 showed a marked decrease of the growth in wild-type BRCA1-overexpressing cells and in BRCA1S1841N-transfected cells, while no significant changes were detected in the BRCA1W1837R-transfected cells. These results demonstrate that: i) distinct single nucleotide changes in the BRCT domain of BRCA1 affect binding of this protein to the tumor suppressor p53, and ii) the two missense mutations here described are likely to play a role in breast tumorigenesis. We suggest that in vitro/in vivo experiments testing the effects of unclassified BRCA1 gene variants should therefore be taken in to consideration and that increased surveillance should be adopted in individuals bearing these two BRCA1 missense alterations.

Classification of BRCA1 missense variants of unknown clinical significance

Background:BRCA1 is a tumour suppressor with pleiotropic actions. Germline mutations in BRCA1 are responsible for a large proportion of breast–ovarian cancer families. Several missense variants have been identified throughout the...

Analysis of missense variation in human BRCA1 in the context of interspecific sequence variation

Introduction: Interpretation of results from mutation screening of tumour suppressor genes known to harbour high risk susceptibility mutations, such as APC, BRCA1, BRCA2, MLH1, MSH2, TP53, and PTEN, is becoming...