REVEL and BayesDel outperform other in silico meta-predictors for clinical variant classification

Study of the impact of ClinGen Revisions on ACMG/AMP variant semi-automatic classification for Rare Diseases diagnosis

With the rapid development of massive sequencing technologies, the analysis of genetic variants for clinical diagnosis has exponentially escalated, particularly in the context of Rare Diseases (RDs). Diagnosing them involves identifying the genetic variants responsible for the underlying pathology development. In 2015, the American College of Medical Genetics (ACMG) established a set of recommendations to assess the evidence associated with each variant, aiming to achieve a standardized five tier classification. Over the past 5 years, ClinGen, the NIH-funded Clinical Genome Resource, has reviewed these criteria in order to make variant classification a more reproducible and rigorous process. This paper examines the impact of ClinGen-Rev modifications on variant classification, comparing them with the ACMG-2015 original recommendations. After analyzing sets of genetic variants, extracted from VCFs samples, using both criteria, we observed a change in 8.0 % of the clinical verdicts for these variants. ClinGen-Rev modifications correctly categorized 89.2 % of the curated variants, representing a significant improvement compared to the 65.6 % achieved by ACMG-2015. We also analyzed the modifications impact in a real like clinical setting, showing a significant overall reduction of VUS variants and thus potential reduction in analysis time. Finally, we discuss the underlying reasons for the most relevant changes in terms of specific labels and present their implications on the prioritization and selection process of variants, identifying some recommendations of key significant importance.

Whole-exome sequencing and Drosophila modelling reveal mutated genes and pathways contributing to human ovarian failure

BACKGROUND

Ovarian failure (OF) is a multifactorial, complex disease presented by up to 1% of women under 40 years of age. Despite 90% of patients being diagnosed with idiopathic OF, the underlying molecular mechanisms remain unknown, making it difficult to personalize treatments for these patients in the clinical setting. Studying the presence and/or accumulation of SNVs at the gene/pathway levels will help describe novel genes and characterize disrupted biological pathways linked with ovarian failure.

METHODS

Ad-hoc case-control SNV screening conducted from 2020 to 2023 of 150 VCF files WES data included Spanish IVF patients with (n = 118) and without (n = 32) OF (< 40 years of age; mean BMI 22.78) along with GnomAD (n = 38,947) and IGSR (n = 1,271; 258 European female VCF) data for pseudo-control female populations. SNVs were prioritized according to their predicted deleteriousness, frequency in genomic databases, and proportional differences across populations. A burden test was performed to reveal genes with a higher presence of SNVs in the OF cohort in comparison to control and pseudo-control groups. Systematic in-silico analyses were performed to assess the potential disruptions caused by the mutated genes in relevant biological pathways. Finally, genes with orthologues in Drosophila melanogaster were considered to experimentally validate the potential impediments to ovarian function and reproductive potential.

RESULTS

Eighteen genes had a higher presence of SNVs in the OF population (FDR < 0.05). AK2, CDC27, CFTR, CTBP2, KMT2C, and MTCH2 were associated with OF for the first time and their silenced/knockout forms reduced fertility in Drosophila. We also predicted the disruption of 29 sub-pathways across four signalling pathways (FDR < 0.05). These sub-pathways included the metaphase to anaphase transition during oocyte meiosis, inflammatory processes related to necroptosis, DNA repair mismatch systems and the MAPK signalling cascade.

CONCLUSIONS

This study sheds light on the underlying molecular mechanisms of OF, providing novel associations for six genes and OF-related infertility, setting a foundation for further biomarker development, and improving precision medicine in infertility.

Using AI-predicted protein structures as a reference to predict loss-of-function activity in tumor suppressor breast cancer genes

Background

The loss-of-function (LOF) classification of most missense variants in tumor suppressor breast cancer genes BRCA1, BRCA2, PALB2, and RAD51C remains unclassified and confounds clinical actionability. Classifying these variants is challenging due to their rarity, leading clinicians to rely on in silico predictive methods. Protein stability changes are associated with function, making stability predictors valuable. Stability predictions upon missense variant perturbations require high-resolution protein structures. However, the availability of these high-resolution structures is lacking. This study explores using generative AI to predict high-resolution protein structures, which can then be analyzed with in silico protein stability prediction methods to assess LOF activity in ordered regions of the protein. This study also determines the appropriate in silico protein stability and dedicated in silico missense prediction methods in dbNSFP v4.7 database to predict LOF activity in ordered regions of these four genes. Functional classifications from homology recombination DNA repair (HDR) assays and variant classifications from the ClinVar database provide a reliable dataset for evaluating the performance of these in silico prediction methods.

Results

Complex AlphaFold2 structures of the BRCA1-C terminal (BRCT) domain and the DNA-binding (DB) domain of BRCA2, analyzed using protein stability tool FoldX predicts LOF activity from missense variants significantly better than experimentally-derived structures in ordered regions. The BRCT domain achieved an Area Under the Curve (AUC)= 0.861 (95 % CI:0.858-0.863) and AUC= 0.842 (95 % CI:0.840-0.845), while the DB domain achieved an AUC= 0.836 (95 % CI:0.8322-0.841), compared to AUC= 0.847 (95 % CI:0.844-0.850) and AUC= 0.835 (95 % CI:0.832-0.837) from the BRCT domain, and AUC= 0.830 (95 % CI:0.821-0.8320) from the DB domain from experimentally-derived structures. Protein stability does not predict LOF activity from missense variants better than dedicated in silico missense predictors. Overall, we find that AlphaMissense ranks highly, with an average AUC= 0.890 (95 % CI 0.886-0.895) from ordered regions across these four cancer genes, compared to all other in silico missense predictors present in the dbNSFP database.

Conclusions

The study reveals that generative AI protein predicted structures can outperform experimentally-derived structures in evaluating LOF activity from predicted protein stability in ordered regions of genes BRCA1, BRCA2, PALB2 and RAD51C. The study also highlights the predictive performance of AlphaMissense as the premier in silico missense prediction method to predict LOF activity from missense variants in these four tumor suppressor breast cancer genes. The code for this study can be downloaded for free on GitHub (https://github.com/rohandavidg/CarePred).

Exploring Aerobic Energy Metabolism in Breast Cancer: A Mutational Profile of Glycolysis and Oxidative Phosphorylation

Energy metabolism is a fundamental aspect of the aggressiveness and invasiveness of breast cancer (BC), the neoplasm that most affects women worldwide. Nonetheless, the impact of genetic somatic mutations on glycolysis and oxidative phosphorylation (OXPHOS) genes in BC remains unclear. To fill these gaps, the mutational profiles of 205 screened genes related to glycolysis and OXPHOS in 968 individuals with BC from The Cancer Genome Atlas (TCGA) project were performed. We carried out analyses to characterize the mutational profile of BC, assess the clonality of tumors, identify somatic mutation co-occurrence, and predict the pathogenicity of these alterations. In total, 408 mutations in 132 genes related to the glycolysis and OXPHOS pathways were detected. The PGK1, PC, PCK1, HK1, DONSON, GPD1, NDUFS1, and FOXRED1 genes are also associated with the tumorigenesis process in other types of cancer, as are the genes BRCA1, BRCA2, and HMCN1, which had been previously described as oncogenes in BC, with whom the target genes of this work were associated. Seven mutations were identified and highlighted due to the high pathogenicity, which are present in more than one of our results and are documented in the literature as being correlated with other diseases. These mutations are rs267606829 (FOXRED1), COSV53860306 (HK1), rs201634181 (NDUFS1), rs774052186 (DONSON), rs119103242 (PC), rs1436643226 (PC), and rs104894677 (ETFB). They could be further investigated as potential biomarkers for diagnosis, prognosis, and treatment of BC patients.

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.

Marginal Contribution of Pathogenic RAD51D Germline Variants to Pakistani Early-Onset and Familial Breast/Ovarian Cancer Patients

Introduction

RAD51D has been reported as a breast cancer (BC) and ovarian cancer (OC) predisposition gene, particularly among Caucasian populations. We studied the prevalence of RAD51D variants in Pakistani BC/OC patients.

Materials and Methods

In total, 371 young or familial BC/OC patients were thoroughly analyzed for RAD51D sequence variants using denaturing high-performance liquid chromatography pursued by DNA sequencing of differentially eluted amplicons. We also assessed the pathogenic effects of novel variants using in-silico algorithms. All detected RAD51D variants were investigated in 400 unaffected controls.

Results

No pathogenic RAD51D variant was detected. However, we identified nine unique heterozygous variants. Of these, two missense variants (p.Pro10Leu and p.Ile311Asn) and one intronic variant (c.481-26_23delGTTC) were classified as in silico-predicted variants of uncertain significance, with a frequency of 0.8% (3/371). The p.Pro10Leu variant was detected in a 28-year-old female BC patient of Punjabi ethnic background, whose mother and maternal cousin had BCs at ages 53 and 40, respectively. This variant was also detected in 1/400 (0.25%) healthy controls, where the control subject's daughter had acute lymphoblastic leukemia. The p.Ile311Asn variant was identified in a female BC patient at age 29 of Punjabi ethnicity and in 1/400 (0.25%) healthy controls, where the control subject's daughter had Hodgkin's disease at age 14. A novel intronic variant, c.481-26_-23delGTTC, was found in a 30-year-old Punjabi female BC patient but not in 400 healthy controls.

Conclusion

No pathogenic RAD51D variant was identified in the current study. Our study data suggested a negligible association of RAD51D variants with BC/OC risk in Pakistani women.

Using computational approaches to enhance the interpretation of missense variants in the PAX6 gene

The PAX6 gene encodes a highly-conserved transcription factor involved in eye development. Heterozygous loss-of-function variants in PAX6 can cause a range of ophthalmic disorders including aniridia. A key molecular diagnostic challenge is that many PAX6 missense changes are presently classified as variants of uncertain significance. While computational tools can be used to assess the effect of genetic alterations, the accuracy of their predictions varies. Here, we evaluated and optimised the performance of computational prediction tools in relation to PAX6 missense variants. Through inspection of publicly available resources (including HGMD, ClinVar, LOVD and gnomAD), we identified 241 PAX6 missense variants that were used for model training and evaluation. The performance of ten commonly used computational tools was assessed and a threshold optimization approach was utilized to determine optimal cut-off values. Validation studies were subsequently undertaken using PAX6 variants from a local database. AlphaMissense, SIFT4G and REVEL emerged as the best-performing predictors; the optimized thresholds of these tools were 0.967, 0.025, and 0.772, respectively. Combining the prediction from these top-three tools resulted in lower performance compared to using AlphaMissense alone. Tailoring the use of computational tools by employing optimized thresholds specific to PAX6 can enhance algorithmic performance. Our findings have implications for PAX6 variant interpretation in clinical settings.

Novel BRAT1 variant associated with neurodevelopmental disorder with cerebellar atrophy and seizure: Case report and a literature review

The BRAT1 gene plays a crucial role in RNA metabolism and brain development, and mutations in this gene have been associated with neurodevelopmental disorders. The variability in the clinical presentation of BRAT1-related disorders is highlighted, emphasizing the importance of considering this condition in the differential diagnosis of neurodevelopmental disorders. This study aimed to identify a causative variant in an Iranian patient affected by developmental delay, speech delay, seizure, and clubfoot through whole exome sequencing (WES) followed by Sanger sequencing. The WES revealed a novel biallelic variant of the BRAT1, c.398A>G (p.His133Arg), in the patient, which segregated within the family. A literature review suggests that the phenotypic variability associated with BRAT1 mutations is likely due to multiple factors, including the location and type of mutation, the specific functions of the protein, and the influence of other genetic and environmental factors. The phenotypic variability of BRAT1-related disorders underscores the importance of considering BRAT1-related disorders in the differential diagnosis of epileptic encephalopathy with rigidity. These findings provide important insights into the role of BRAT1 in neurodevelopmental disorders and highlight the potential clinical implications of identifying and characterizing novel variants in this gene.

Menke-Hennekam syndrome; delineation of domain-specific subtypes with distinct clinical and DNA methylation profiles

CREB-binding protein (CBP, encoded by CREBBP) and its paralog E1A-associated protein (p300, encoded by EP300) are involved in histone acetylation and transcriptional regulation. Variants that produce a null allele or disrupt the catalytic domain of either protein cause Rubinstein-Taybi syndrome (RSTS), while pathogenic missense and in-frame indel variants in parts of exons 30 and 31 cause phenotypes recently described as Menke-Hennekam syndrome (MKHK). To distinguish MKHK subtypes and define their characteristics, molecular and extended clinical data on 82 individuals (54 unpublished) with variants affecting CBP (n = 71) or p300 (n = 11) (NP_004371.2 residues 1,705-1,875 and NP_001420.2 residues 1,668-1,833, respectively) were summarized. Additionally, genome-wide DNA methylation profiles were assessed in DNA extracted from whole peripheral blood from 54 individuals. Most variants clustered closely around the zinc-binding residues of two zinc-finger domains (ZZ and TAZ2) and within the first α helix of the fourth intrinsically disordered linker (ID4) of CBP/p300. Domain-specific methylation profiles were discerned for the ZZ domain in CBP/p300 (found in nine out of 10 tested individuals) and TAZ2 domain in CBP (in 14 out of 20), while a domain-specific diagnostic episignature was refined for the ID4 domain in CBP/p300 (in 21 out of 21). Phenotypes including intellectual disability of varying degree and distinct physical features were defined for each of the regions. These findings demonstrate existence of at least three MKHK subtypes, which are domain specific (MKHK-ZZ, MKHK-TAZ2, and MKHK-ID4) rather than gene specific (CREBBP/EP300). DNA methylation episignatures enable stratification of molecular pathophysiologic entities within a gene or across a family of paralogous genes.

Artificial Intelligence-Driven Prediction Revealed CFTR Associated with Therapy Outcome of Breast Cancer: A Feasibility Study

INTRODUCTION

In silico tools capable of predicting the functional consequences of genomic differences between individuals, many of which are AI-driven, have been the most effective over the past two decades for non-synonymous single nucleotide variants (nsSNVs). When appropriately selected for the purpose of the study, a high predictive performance can be expected. In this feasibility study, we investigate the distribution of nsSNVs with an allele frequency below 5%. To classify the putative functional consequence, a tier-based filtration led by AI-driven predictors and scoring system was implemented to the overall decision-making process, resulting in a list of prioritised genes.

METHODS

The study has been conducted on breast cancer patients of homogeneous ethnicity. Germline rare variants have been sequenced in genes that influence pharmacokinetic parameters of anticancer drugs or molecular signalling pathways in cancer. After AI-driven functional pathogenicity classification and data mining in pharmacogenomic (PGx) databases, variants were collapsed to the gene level and ranked according to their putative deleterious role.

RESULTS

In breast cancer patients, seven of the twelve genes prioritised based on the predictions were found to be associated with response to oncotherapy, histological grade, and tumour subtype. Most importantly, we showed that the group of patients with at least one rare nsSNVs in cystic fibrosis transmembrane conductance regulator (CFTR) had significantly reduced disease-free (log rank, p = 0.002) and overall survival (log rank, p = 0.006).

CONCLUSION

AI-driven in silico analysis with PGx data mining provided an effective approach navigating for functional consequences across germline genetic background, which can be easily integrated into the overall decision-making process for future studies. The study revealed a statistically significant association with numerous clinicopathological parameters, including treatment response. Our study indicates that CFTR may be involved in the processes influencing the effectiveness of oncotherapy or in the malignant progression of the disease itself.

Clinical manifestations and spermatogenesis outcomes in Chinese patients with congenital hypogonadotropic hypogonadism caused by inherited or de novo FGFR1 mutations

Fibroblast growth factor receptor 1 ( FGFR1 ) mutations are associated with congenital hypogonadotropic hypogonadism (CHH) through inheritance or spontaneous occurrence. We detected FGFR1 mutations in a Chinese cohort of 210 CHH patients at Peking Union Medical College Hospital (Beijing, China) using next-generation and Sanger sequencing. We assessed missense variant pathogenicity using six bioinformatics tools and compared clinical features and treatment outcomes between inherited and de novo mutation groups. Among 19 patients with FGFR1 mutations, three were recurrent, and 16 were novel variants. Sixteen of the novel mutations were likely pathogenic according to the American College of Medical Genetics and Genomics (ACMG) guidelines, with the prevalent P366L variant. The majority of FGFR1 mutations was inherited (57.9%), with frameshift mutations exclusive to the de novo mutation group. The inherited mutation group had a lower incidence of cryptorchidism, short stature, and skeletal deformities. In the inherited mutation group, luteinizing hormone (LH) levels were 0.5 IU l -1 , follicle-stimulating hormone (FSH) levels were 1.0 IU l -1 , and testosterone levels were 1.3 nmol l -1 . In contrast, the de novo group had LH levels of 0.2 IU l -1 , FSH levels of 0.5 IU l -1 , and testosterone levels of 0.9 nmol l -1 , indicating milder hypothalamus-pituitary-gonadal axis (HPGA) functional deficiency in the inherited group. The inherited mutation group showed a tendency toward higher spermatogenesis rates. In conclusion, this study underscores the predominance of inherited FGFR1 mutations and their association with milder HPGA dysfunction compared to de novo mutations, contributing to our understanding of the genetic and clinical aspects of FGFR1 mutations.

Functional and Molecular Characterization of New SPTLC1 Missense Variants in Patients with Hereditary Sensory and Autonomic Neuropathy Type 1 (HSAN1)

Hereditary sensory and autonomic neuropathy type 1 is an autosomal dominant neuropathy caused by the SPTLC1 or SPTLC2 variants. These variants modify the preferred substrate of serine palmitoyl transferase, responsible for the first step of de novo sphingolipids synthesis, leading to accumulation of cytotoxic deoxysphingolipids. Diagnosis of HSAN1 is based on clinical symptoms, mainly progressive loss of distal sensory keep, and genetic analysis. Aim: Identifying new SPTLC1 or SPTLC2 "gain-of-function" variants raises the question as to their pathogenicity. This work focused on characterizing six new SPTLC1 variants using in silico prediction tools, new meta-scores, 3D modeling, and functional testing to establish their pathogenicity. Methods: Variants from six patients with HSAN1 were studied. In silico, CADD and REVEL scores and the 3D modeling software MITZLI were used to characterize the pathogenic effect of the variants. Functional tests based on plasma sphingolipids quantification (total deoxysphinganine, ceramides, and dihydroceramides) were performed by tandem mass spectrometry. Results: In silico predictors did not provide very contrasting results when functional tests discriminated the different variants according to their impact on deoxysphinganine level or canonical sphingolipids synthesis. Two SPTLC1 variants were newly described as pathogenic: SPTLC1 NM_006415.4:c.998A>G and NM_006415.4:c.1015G>A. Discussion: The combination of the different tools provides arguments to establish the pathogenicity of these new variants. When available, functional testing remains the best option to establish the in vivo impact of a variant. Moreover, the comprehension of metabolic dysregulation offers opportunities to develop new therapeutic strategies for these genetic disorders.

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.

Comprehensive splicing analysis of the alternatively spliced CHEK2 exons 8 and 10 reveals three enhancer/silencer-rich regions and 38 spliceogenic variants

Splicing is controlled by a large set of regulatory elements (SREs) including splicing enhancers and silencers, which are involved in exon recognition. Variants at these motifs may dysregulate splicing and trigger loss-of-function transcripts associated with disease. Our goal here was to study the alternatively spliced exons 8 and 10 of the breast cancer susceptibility gene CHEK2. For this purpose, we used a previously published minigene with exons 6-10 that produced the expected minigene full-length transcript and replicated the naturally occurring events of exon 8 [Δ(E8)] and exon 10 [Δ(E10)] skipping. We then introduced 12 internal microdeletions of exons 8 and 10 by mutagenesis in order to map SRE-rich intervals by splicing assays in MCF-7 cells. We identified three minimal (10-, 11-, 15-nt) regions essential for exon recognition: c.863_877del [ex8, Δ(E8): 75%] and c.1073_1083del and c.1083_1092del [ex10, Δ(E10): 97% and 62%, respectively]. Then 87 variants found within these intervals were introduced into the wild-type minigene and tested functionally. Thirty-eight of them (44%) impaired splicing, four of which (c.883G>A, c.883G>T, c.884A>T, and c.1080G>T) induced negligible amounts (<5%) of the minigene full-length transcript. Another six variants (c.886G>A, c.886G>T, c.1075G>A, c.1075G>T, c.1076A>T, and c.1078G>T) showed significantly strong impacts (20-50% of the minigene full-length transcript). Thirty-three of the 38 spliceogenic variants were annotated as missense, three as nonsense, and two as synonymous, underlying the fact that any exonic change is capable of disrupting splicing. Moreover, c.883G>A, c.883G>T, and c.884A>T were classified as pathogenic/likely pathogenic variants according to ACMG/AMP (American College of Medical Genetics and Genomics/Association for Molecular Pathology)-based criteria. © 2024 The Authors. The Journal of Pathology published by John Wiley & Sons Ltd on behalf of The Pathological Society of Great Britain and Ireland.

Identification of potential disease-associated variants in idiopathic generalized epilepsy using targeted sequencing

Many questions remain regarding the genetics of idiopathic generalized epilepsy (IGE), a subset of genetic generalized epilepsy (GGE). We aimed to identify the candidate coding variants of epilepsy panel genes in a cohort of affected individuals, using variant frequency information from a control cohort of the same region. We performed whole-exome sequencing analysis of 121 individuals and 10 affected relatives, focusing on variants of 950 candidate genes associated with epilepsy according to the Genes4Epilepsy curated panel. We identified 168 candidate variants (CVs) in 137 of 950 candidate genes in 88 of 121 affected individuals with IGE, of which 61 were novel variants. Notably, we identified five CVs in known GGE-associated genes (CHD2, GABRA1, RORB, SCN1A, and SCN1B) in five individuals and CVs shared by affected individuals in each of four family cases for other epilepsy candidate genes. The results of this study demonstrate that IGE is a disease with high heterogeneity and provide IGE-associated CVs whose pathogenicity should be proven by future studies, including advanced functional analysis. The low detection rate of CVs in the GGE-associated genes (4.1%) in this study suggests the current incompleteness of the Genes4Epilepsy panel for the diagnosis of IGE in clinical practice.

Identification of a Novel Variant in CC2D1A Gene Linked to Autosomal Recessive Intellectual Disability 3 in an Iranian Family and Investigating the Structure and Pleiotropic Effects of this Gene

Objectives

Intellectual disability (ID) represents a significant health challenge due to its diverse and intricate nature. A multitude of genes play a role in brain development and function, with defects in these genes potentially leading to ID. Considering that many of these genes have yet to be identified, and those identified have only been found in a small number of patients, no complete description of the phenotype created by these genes is available. CC2D1A is one of the genes whose loss-of-function mutation leads to a rare form of non-syndromic ID-3(OMIM*610055), and four pathogenic variants have been reported in this gene so far.

Materials & Methods

n the current study, two affected females were included with an initial diagnosis of ID who were from an Iranian family with consanguineous marriage. Whole-exome sequencing was used to identify the probable genetic defects. The Genotypic and phenotypic characteristics of the patients were compared with a mutation in the CC2D1A gene, and then the structure of the gene and its reported variants were investigated.

Results

The patients carried a novel homozygous splicing variant (NM_017721, c.1641+1G>A) in intron 14, which is pathogenic according to the ACMG guideline. Loss-of-function mutations in CC2D1A have severe phenotypic consequences such as ID, autism spectrum disorder (ASD), and seizures. However, missense mutations lead to ASD with or without ID, and in some patients, they cause ciliopathy.

Conclusion

This study reports the fifth novel, probably pathogenic variant in the CC2D1A gene. Comparing the clinical and molecular genetic features of the patients with loss-of-function mutation helped to describe the phenotype caused by this gene more precisely. Investigating the CC2D1A gene's mutations and structure revealed that it performs multiple functions. The DM14 domain appears more pivotal in triggering severe clinical symptoms, including ID, than the C2 domain.

Evaluating the use of paralogous protein domains to increase data availability for missense variant classification

BACKGROUND

Classification of rare missense variants remains an ongoing challenge in genomic medicine. Evidence of pathogenicity is often sparse, and decisions about how to weigh different evidence classes may be subjective. We used a Bayesian variant classification framework to investigate the performance of variant co-localisation, missense constraint, and aggregating data across paralogous protein domains ("meta-domains").

METHODS

We constructed a database of all possible coding single nucleotide variants in the human genome and used PFam predictions to annotate structurally-equivalent positions across protein domains. We counted the number of pathogenic and benign missense variants at these equivalent positions in the ClinVar database, calculated a regional constraint score for each meta-domain, and assessed this approach versus existing missense constraint metrics for classifying variant pathogenicity and benignity.

RESULTS

Alternative pathogenic missense variants at the same amino acid position in the same protein provide strong evidence of pathogenicity (positive likelihood ratio, LR+  = 85). Additionally, clinically annotated pathogenic or benign missense variants at equivalent positions in different proteins can provide moderate evidence of pathogenicity (LR+  = 7) or benignity (LR+  = 5), respectively. Applying these approaches sequentially (through PM5) increases sensitivity for classifying pathogenic missense variants from 27 to 41%. Missense constraint can also provide strong evidence of pathogenicity for some variants, but its absence provides no evidence of benignity.

CONCLUSIONS

We propose using structurally equivalent positions across related protein domains from different genes to augment evidence for variant co-localisation when classifying novel missense variants. Additionally, we advocate adopting a numerical evidence-based approach to integrating diverse data in variant interpretation.

Early-onset diabetes in Africa: A mini-review of the current genetic profile

Early-onset diabetes is poorly diagnosed partly due to its heterogeneity and variable presentations. Although several genes have been associated with the disease, these genes are not well studied in Africa. We sought to identify the major neonatal, early childhood, juvenile, or early-onset diabetes genes in Africa; and evaluate the available molecular methods used for investigating these gene variants. A literature search was conducted on PubMed, Scopus, Africa-Wide Information, and Web of Science databases. The retrieved records were screened and analyzed to identify genetic variants associated with early-onset diabetes. Although 319 records were retrieved, 32 were considered for the current review. Most of these records (22/32) were from North Africa. The disease condition was genetically heterogenous with most cases possessing unique gene variants. We identified 22 genes associated with early-onset diabetes, 9 of which had variants (n = 19) classified as pathogenic or likely pathogenic (PLP). Among the PLP variants, IER3IP1: p.(Leu78Pro) was the variant with the highest number of cases. There was limited data from West Africa, hence the contribution of genetic variability to early-onset diabetes in Africa could not be comprehensively evaluated. It is worth mentioning that most studies were focused on natural products as antidiabetics and only a few studies reported on the genetics of the disease. ABCC8 and KCNJ11 were implicated as major contributors to early-onset diabetes gene networks. Gene ontology analysis of the network associated ion channels, impaired glucose tolerance, and decreased insulin secretions to the disease. Our review highlights 9 genes from which PLP variants have been identified and can be considered for the development of an African diagnostic panel. There is a gap in early-onset diabetes genetic research from sub-Saharan Africa which is much needed to develop a comprehensive, efficient, and cost-effective genetic panel that will be useful in clinical practice on the continent and among the African diasporas.

Accurate long-read sequencing identified GBA1 as major risk factor in the Luxembourgish Parkinson's study

Heterozygous variants in the glucocerebrosidase GBA1 gene are an increasingly recognized risk factor for Parkinson's disease (PD). Due to the GBAP1 pseudogene, which shares 96% sequence homology with the GBA1 coding region, accurate variant calling by array-based or short-read sequencing methods remains a major challenge in understanding the genetic landscape of GBA1-associated PD. We analyzed 660 patients with PD, 100 patients with Parkinsonism and 808 healthy controls from the Luxembourg Parkinson's study, sequenced using amplicon-based long-read DNA sequencing technology. We found that 12.1% (77/637) of PD patients carried GBA1 variants, with 10.5% (67/637) of them carrying known pathogenic variants (including severe, mild, risk variants). In comparison, 5% (34/675) of the healthy controls carried GBA1 variants, and among them, 4.3% (29/675) were identified as pathogenic variant carriers. We found four GBA1 variants in patients with atypical parkinsonism. Pathogenic GBA1 variants were 2.6-fold more frequently observed in PD patients compared to controls (OR = 2.6; CI = [1.6,4.1]). Three novel variants of unknown significance (VUS) were identified. Using a structure-based approach, we defined a potential risk prediction method for VUS. This study describes the full landscape of GBA1-related parkinsonism in Luxembourg, showing a high prevalence of GBA1 variants as the major genetic risk for PD. Although the long-read DNA sequencing technique used in our study may be limited in its effectiveness to detect potential structural variants, our approach provides an important advancement for highly accurate GBA1 variant calling, which is essential for providing access to emerging causative therapies for GBA1 carriers.

Sequencing-based functional assays for classification of BRCA2 variants in mouse ESCs

Sequencing of genes, such as BRCA1 and BRCA2, is recommended for individuals with a personal or family history of early onset and/or bilateral breast and/or ovarian cancer or a history of male breast cancer. Such sequencing efforts have resulted in the identification of more than 17,000 BRCA2 variants. The functional significance of most variants remains unknown; consequently, they are called variants of uncertain clinical significance (VUSs). We have previously developed mouse embryonic stem cell (mESC)-based assays for functional classification of BRCA2 variants. We now developed a next-generation sequencing (NGS)-based approach for functional evaluation of BRCA2 variants using pools of mESCs expressing 10-25 BRCA2 variants from a given exon. We use this approach for functional evaluation of 223 variants listed in ClinVar. Our functional classification of BRCA2 variants is concordant with the classification reported in ClinVar or those reported by other orthogonal assays.

The hospital Israelita Albert Einstein standards for constitutional sequence variants classification: version 2023

BACKGROUND

Next-generation sequencing has had a significant impact on genetic disease diagnosis, but the interpretation of the vast amount of genomic data it generates can be challenging. To address this, the American College of Medical Genetics and Genomics and the Association for Molecular Pathology have established guidelines for standardized variant interpretation. In this manuscript, we present the updated Hospital Israelita Albert Einstein Standards for Constitutional Sequence Variants Classification, incorporating modifications from leading genetics societies and the ClinGen initiative.

RESULTS

First, we standardized the scientific publications, documents, and other reliable sources for this document to ensure an evidence-based approach. Next, we defined the databases that would provide variant information for the classification process, established the terminology for molecular findings, set standards for disease-gene associations, and determined the nomenclature for classification criteria. Subsequently, we defined the general rules for variant classification and the Bayesian statistical reasoning principles to enhance this process. We also defined bioinformatics standards for automated classification. Our workgroup adhered to gene-specific rules and workflows curated by the ClinGen Variant Curation Expert Panels whenever available. Additionally, a distinct set of specifications for criteria modulation was created for cancer genes, recognizing their unique characteristics.

CONCLUSIONS

The development of an internal consensus and standards for constitutional sequence variant classification, specifically adapted to the Brazilian population, further contributes to the continuous refinement of variant classification practices. The aim of these efforts from the workgroup is to enhance the reliability and uniformity of variant classification.

How human genetic context can inform pathogenicity classification: FGFR1 variation in idiopathic hypogonadotropic hypogonadism

Precision medicine requires precise genetic variant interpretation, yet many disease-associated genes have unresolved variants of unknown significance (VUS). We analyzed variants in a well-studied gene, FGFR1, a common cause of Idiopathic Hypogonadotropic Hypogonadism (IHH) and examined whether regional genetic enrichment of missense variants could improve variant classification. FGFR1 rare sequence variants (RSVs) were examined in a large cohort to (i) define regional genetic enrichment, (ii) determine pathogenicity based on the American College of Medical Genetics/Association for Molecular Pathology (ACMG/AMP) variant classification framework, and (iii) characterize the phenotype of FGFR1 variant carriers by variant classification. A total of 143 FGFR1 RSVs were identified in 175 IHH probands (n = 95 missense, n = 48 protein-truncating variants). FGFR1 missense RSVs showed regional enrichment across biologically well-defined domains: D1, D2, D3, and TK domains and linker regions (D2-D3, TM-TK). Using these defined regions of enrichment to augment the ACMG/AMP classification reclassifies 37% (20/54) of FGFR1 missense VUS as pathogenic or likely pathogenic (PLP). Non-proband carriers of FGFR1 missense VUS variants that were reclassified as PLP were more likely to express IHH or IHH-associated phenotypes [anosmia or delayed puberty] than non-proband carriers of FGFR1 missense variants that remained as VUS (76.9% vs 34.7%, p = 0.035). Using the largest cohort of FGFR1 variant carriers, we show that integration of regional genetic enrichment as moderate evidence for pathogenicity improves the classification of VUS and that reclassified variants correlated with phenotypic expressivity. The addition of regional genetic enrichment to the ACMG/AMP guidelines may improve clinical variant interpretation.

Finding significance: New perspectives in variant classification of the RAD51 regulators, BRCA2 and beyond

For many individuals harboring a variant of uncertain functional significance (VUS) in a homologous recombination (HR) gene, their risk of developing breast and ovarian cancer is unknown. Integral to the process of HR are BRCA1 and regulators of the central HR protein, RAD51, including BRCA2, PALB2, RAD51C and RAD51D. Due to advancements in sequencing technology and the continued expansion of cancer screening panels, the number of VUS identified in these genes has risen significantly. Standard practices for variant classification utilize different types of predictive, population, phenotypic, allelic and functional evidence. While variant analysis is improving, there remains a struggle to keep up with demand. Understanding the effects of an HR variant can aid in preventative care and is critical for developing an effective cancer treatment plan. In this review, we discuss current perspectives in the classification of variants in the breast and ovarian cancer genes BRCA1, BRCA2, PALB2, RAD51C and RAD51D.

A genetic study of a Brazilian cohort of patients with X-linked hypophosphatemia reveals no correlation between genotype and phenotype

Aim

X-linked hypophosphatemia (XLH) is the most common inherited form of rickets, and it is caused by pathogenic inactivating variants of the phosphate-regulating endopeptidase homolog X-linked (PHEX) gene. The main purpose of this study is to identify the presence of a genotype-phenotype correlation in a cohort of XLH patients.

Methods

This is a retrospective study including patients diagnosed with hypophosphatemic rickets, confirmed by clinical, radiological, and laboratory findings. Medical records were reviewed for phenotypic analyses. Genomic DNA was extracted from the peripheral blood lymphocytes, and PHEX sequencing was performed by exomic NGS sequencing. The Wilcoxon rank-sum test and the two-tailed Fisher's exact test were employed for the statistical analyses of this study.

Results

A total of 41 patients were included in this study, and 63.41% (26/41) of the patients were female. The mutation analyses identified 29.27% missense variants and 29.72% nonsense variants, most of them were considered deleterious (66.41%). Six novel deleterious variants in the PHEX gene were detected in seven patients. The median concentrations of pretreatment serum calcium, phosphorus, and parathyroid hormone (PTH) were not significantly different among patients with different genotypes. An orthopedic surgery due to bone deformity was required in 57.69%.

Conclusions

Our analysis did not identify any specific genotype as a predictor. No significant genotype-phenotype correlation was found, suggesting that the recognition of subjacent pathogenic mutation in the PHEX gene may have limited prognostic value. Despite this finding, genetic testing may be useful for identifying affected individuals early and providing appropriate treatment.

Male breast cancer risk associated with pathogenic variants in genes other than BRCA1/2: an Italian case-control study

BACKGROUND

Germline pathogenic variants (PVs) in BRCA1/2 genes are associated with breast cancer (BC) risk in both women and men. Multigene panel testing is being increasingly used for BC risk assessment, allowing the identification of PVs in genes other than BRCA1/2. While data on actionable PVs in other cancer susceptibility genes are now available in female BC, reliable data are still lacking in male BC (MBC). This study aimed to provide the patterns, prevalence and risk estimates associated with PVs in non-BRCA1/2 genes for MBC in order to improve BC prevention for male patients.

METHODS

We performed a large case-control study in the Italian population, including 767 BRCA1/2-negative MBCs and 1349 male controls, all screened using a custom 50 cancer gene panel.

RESULTS

PVs in genes other than BRCA1/2 were significantly more frequent in MBCs compared with controls (4.8% vs 1.8%, respectively) and associated with a threefold increased MBC risk (OR: 3.48, 95% CI: 1.88-6.44; p < 0.0001). PV carriers were more likely to have personal (p = 0.03) and family (p = 0.02) history of cancers, not limited to BC. PALB2 PVs were associated with a sevenfold increased MBC risk (OR: 7.28, 95% CI: 1.17-45.52; p = 0.034), and ATM PVs with a fivefold increased MBC risk (OR: 4.79, 95% CI: 1.12-20.56; p = 0.035).

CONCLUSIONS

This study highlights the role of PALB2 and ATM PVs in MBC susceptibility and provides risk estimates at population level. These data may help in the implementation of multigene panel testing in MBC patients and inform gender-specific BC risk management and decision making for patients and their families.

Gene-specific machine learning for pathogenicity prediction of rare BRCA1 and BRCA2 missense variants

Machine learning-based pathogenicity prediction helps interpret rare missense variants of BRCA1 and BRCA2, which are associated with hereditary cancers. Recent studies have shown that classifiers trained using variants of a specific gene or a set of genes related to a particular disease perform better than those trained using all variants, due to their higher specificity, despite the smaller training dataset size. In this study, we further investigated the advantages of "gene-specific" machine learning compared to "disease-specific" machine learning. We used 1068 rare (gnomAD minor allele frequency (MAF) < 0.005) missense variants of 28 genes associated with hereditary cancers for our investigation. Popular machine learning classifiers were employed: regularized logistic regression, extreme gradient boosting, random forests, support vector machines, and deep neural networks. As features, we used MAFs from multiple populations, functional prediction and conservation scores, and positions of variants. The disease-specific training dataset included the gene-specific training dataset and was > 7 × larger. However, we observed that gene-specific training variants were sufficient to produce the optimal pathogenicity predictor if a suitable machine learning classifier was employed. Therefore, we recommend gene-specific over disease-specific machine learning as an efficient and effective method for predicting the pathogenicity of rare BRCA1 and BRCA2 missense variants.

Practical Utility of Liquid Biopsies for Evaluating Genomic Alterations in Castration-Resistant Prostate Cancer

Traditional tissue-based assessments of genomic alterations in castration-resistant prostate cancer (CRPC) can be challenging. To evaluate the real-world clinical utility of liquid biopsies for the evaluation of genomic alterations in CRPC, we preemptively collected available plasma samples and archival tissue samples from patients that were being treated for clinically confirmed CRPC. The cell-free DNA (cfDNA) and tumor tissue DNA were analyzed using the AlphaLiquid^®100-HRR panel. Plasma samples from a total of 87 patients were included in this study. Somatic mutations from cfDNA were detected in 78 (89.7%) patients, regardless of the presence of overt metastasis or concomitant treatment given at the time of plasma sample collection. Twenty-three patients were found to have known deleterious somatic or germline mutations in HRR genes from their cfDNA. Archival tissue samples from 33 (37.9%) patients were available for comparative analysis. Tissue sequencing was able to yield an NGS result in only 51.5% of the tissue samples. The general sensitivity of cfDNA for detecting somatic mutations in tissues was 71.8%, but important somatic/germline mutations in HRR genes were found to have a higher concordance (100%). Liquid biopsies can be a reasonable substitute for tissue biopsies in CRPC patients when evaluating genomic alterations.

SRSF1 haploinsufficiency is responsible for a syndromic developmental disorder associated with intellectual disability

SRSF1 (also known as ASF/SF2) is a non-small nuclear ribonucleoprotein (non-snRNP) that belongs to the arginine/serine (R/S) domain family. It recognizes and binds to mRNA, regulating both constitutive and alternative splicing. The complete loss of this proto-oncogene in mice is embryonically lethal. Through international data sharing, we identified 17 individuals (10 females and 7 males) with a neurodevelopmental disorder (NDD) with heterozygous germline SRSF1 variants, mostly de novo, including three frameshift variants, three nonsense variants, seven missense variants, and two microdeletions within region 17q22 encompassing SRSF1. Only in one family, the de novo origin could not be established. All individuals featured a recurrent phenotype including developmental delay and intellectual disability (DD/ID), hypotonia, neurobehavioral problems, with variable skeletal (66.7%) and cardiac (46%) anomalies. To investigate the functional consequences of SRSF1 variants, we performed in silico structural modeling, developed an in vivo splicing assay in Drosophila, and carried out episignature analysis in blood-derived DNA from affected individuals. We found that all loss-of-function and 5 out of 7 missense variants were pathogenic, leading to a loss of SRSF1 splicing activity in Drosophila, correlating with a detectable and specific DNA methylation episignature. In addition, our orthogonal in silico, in vivo, and epigenetics analyses enabled the separation of clearly pathogenic missense variants from those with uncertain significance. Overall, these results indicated that haploinsufficiency of SRSF1 is responsible for a syndromic NDD with ID due to a partial loss of SRSF1-mediated splicing activity.

Genomic Strategies in Mitochondrial Diagnostics

Pathogenic variants in both mitochondrial and nuclear genes contribute to the clinical and genetic heterogeneity of mitochondrial diseases. There are now pathogenic variants in over 300 nuclear genes linked to human mitochondrial diseases. Nonetheless, diagnosing mitochondrial disease with a genetic outcome remains challenging. However, there are now many strategies that help us to pinpoint causative variants in patients with mitochondrial disease. This chapter describes some of the approaches and recent advancements in gene/variant prioritization using whole-exome sequencing (WES).

Calibration of computational tools for missense variant pathogenicity classification and ClinGen recommendations for PP3/BP4 criteria

Recommendations from the American College of Medical Genetics and Genomics and the Association for Molecular Pathology (ACMG/AMP) for interpreting sequence variants specify the use of computational predictors as "supporting" level of evidence for pathogenicity or benignity using criteria PP3 and BP4, respectively. However, score intervals defined by tool developers, and ACMG/AMP recommendations that require the consensus of multiple predictors, lack quantitative support. Previously, we described a probabilistic framework that quantified the strengths of evidence (supporting, moderate, strong, very strong) within ACMG/AMP recommendations. We have extended this framework to computational predictors and introduce a new standard that converts a tool's scores to PP3 and BP4 evidence strengths. Our approach is based on estimating the local positive predictive value and can calibrate any computational tool or other continuous-scale evidence on any variant type. We estimate thresholds (score intervals) corresponding to each strength of evidence for pathogenicity and benignity for thirteen missense variant interpretation tools, using carefully assembled independent data sets. Most tools achieved supporting evidence level for both pathogenic and benign classification using newly established thresholds. Multiple tools reached score thresholds justifying moderate and several reached strong evidence levels. One tool reached very strong evidence level for benign classification on some variants. Based on these findings, we provide recommendations for evidence-based revisions of the PP3 and BP4 ACMG/AMP criteria using individual tools and future assessment of computational methods for clinical interpretation.

Insights on variant analysis in silico tools for pathogenicity prediction

Molecular biology is currently a fast-advancing science. Sequencing techniques are getting cheaper, but the interpretation of genetic variants requires expertise and computational power, therefore is still a challenge. Next-generation sequencing releases thousands of variants and to classify them, researchers propose protocols with several parameters. Here we present a review of several in silico pathogenicity prediction tools involved in the variant prioritization/classification process used by some international protocols for variant analysis and studies evaluating their efficiency.

Phenotypic screening models for rapid diagnosis of genetic variants and discovery of personalized therapeutics

Precision medicine strives for highly individualized treatments for disease under the notion that each individual's unique genetic makeup and environmental exposures imprints upon them not only a disposition to illness, but also an optimal therapeutic approach. In the realm of rare disorders, genetic predisposition is often the predominant mechanism driving disease presentation. For such, mostly, monogenic disorders, a causal gene to phenotype association is likely. As a result, it becomes important to query the patient's genome for the presence of pathogenic variations that are likely to cause the disease. Determining whether a variant is pathogenic or not is critical to these analyses and can be challenging, as many disease-causing variants are novel and, ergo, have no available functional data to help categorize them. This problem is exacerbated by the need for rapid evaluation of pathogenicity, since many genetic diseases present in young children who will experience increased morbidity and mortality without rapid diagnosis and therapeutics. Here, we discuss the utility of animal models, with a focus mainly on C. elegans, as a contrast to tissue culture and in silico approaches, with emphasis on how these systems are used in determining pathogenicity of variants with uncertain significance and then used to screen for novel therapeutics.

An additive destabilising effect of compound T60I and V122I substitutions in ATTRv amyloidosis

BACKGROUND

The amyloidogenic transthyretin (TTR) variant, V122I, occurs in 4% of the African American population and frequently presents as a restricted cardiomyopathy. While heterozygosity for TTR V122I predominates, several compound heterozygous cases have been previously described. Herein, we detail features of ATTRv amyloidosis associated with novel compound heterozygous TTR mutation, T60I/V122I and provide evidence supporting the amyloidogenecity of T60I.

METHODS

A 63-year-old African American female presented with atrial fibrillation, congestive heart failure, autonomic and peripheral neuropathy. In vitro studies of TTR T60I and V122I were undertaken to compare the biophysical properties of the proteins.

RESULTS

Congophilic deposits in a rectal biopsy were immunohistochemically positive for TTR. Serum screening by isoelectric focussing revealed two TTR variants in the absence of wild-type protein. DNA sequencing identified compound heterozygous TTR gene mutations, c.239C > T and c.424G > A. Adipose amyloid deposits were composed of both T60I and V122I. While kinetic stabilities of T60I and V122I variants were similar, distinct thermodynamic stabilities and amyloid growth kinetics were observed.

CONCLUSIONS

This report provides clinical and experimental results supporting the amyloidogenic nature of a novel TTR T60I variant. In vitro data indicate that the destabilising effect of individual T60I and V122I variants appears to be additive rather than synergistic.

Genome interpretation using in silico predictors of variant impact

Estimating the effects of variants found in disease driver genes opens the door to personalized therapeutic opportunities. Clinical associations and laboratory experiments can only characterize a tiny fraction of all the available variants, leaving the majority as variants of unknown significance (VUS). In silico methods bridge this gap by providing instant estimates on a large scale, most often based on the numerous genetic differences between species. Despite concerns that these methods may lack reliability in individual subjects, their numerous practical applications over cohorts suggest they are already helpful and have a role to play in genome interpretation when used at the proper scale and context. In this review, we aim to gain insights into the training and validation of these variant effect predicting methods and illustrate representative types of experimental and clinical applications. Objective performance assessments using various datasets that are not yet published indicate the strengths and limitations of each method. These show that cautious use of in silico variant impact predictors is essential for addressing genome interpretation challenges.

Two novel mutations in ALDH18A1 and SPG11 gene found by whole-exome sequencing in spastic paraplegia disease patients in Iran

Hereditary spastic paraplegia is a not common inherited neurological disorder with heterogeneous clinical expressions. ALDH18A1 (located on 10q24.1) gene-related spastic paraplegias (SPG9A and SPG9B) are rare metabolic disorders caused by dominant and recessive mutations that have been found recently. Autosomal recessive hereditary spastic paraplegia is a common and clinical type of familial spastic paraplegia linked to the SPG11 locus (locates on 15q21.1). There are different symptoms of spastic paraplegia, such as muscle atrophy, moderate mental retardation, short stature, balance problem, and lower limb weakness. Our first proband involves a 45 years old man and our second proband involves a 20 years old woman both are affected by spastic paraplegia disease. Genomic DNA was extracted from the peripheral blood of the patients, their parents, and their siblings using a filter-based methodology and quantified and used for molecular analysis and sequencing. Sequencing libraries were generated using Agilent SureSelect Human All ExonV7 kit, and the qualified libraries are fed into NovaSeq 6000 Illumina sequencers. Sanger sequencing was performed by an ABI prism 3730 sequencer. Here, for the first time, we report two cases, the first one which contains likely pathogenic NM_002860: c.475C>T: p.R159X mutation of the ALDH18A1 and the second one has likely pathogenic NM_001160227.2: c.5454dupA: p.Glu1819Argfs Ter11 mutation of the SPG11 gene and also was identified by the whole-exome sequencing and confirmed by Sanger sequencing. Our aim with this study was to confirm that these two novel variants are direct causes of spastic paraplegia.

Computational approaches for predicting variant impact: An overview from resources, principles to applications

One objective of human genetics is to unveil the variants that contribute to human diseases. With the rapid development and wide use of next-generation sequencing (NGS), massive genomic sequence data have been created, making personal genetic information available. Conventional experimental evidence is critical in establishing the relationship between sequence variants and phenotype but with low efficiency. Due to the lack of comprehensive databases and resources which present clinical and experimental evidence on genotype-phenotype relationship, as well as accumulating variants found from NGS, different computational tools that can predict the impact of the variants on phenotype have been greatly developed to bridge the gap. In this review, we present a brief introduction and discussion about the computational approaches for variant impact prediction. Following an innovative manner, we mainly focus on approaches for non-synonymous variants (nsSNVs) impact prediction and categorize them into six classes. Their underlying rationale and constraints, together with the concerns and remedies raised from comparative studies are discussed. We also present how the predictive approaches employed in different research. Although diverse constraints exist, the computational predictive approaches are indispensable in exploring genotype-phenotype relationship.

Minigene-based splicing analysis and ACMG/AMP-based tentative classification of 56 ATM variants

The ataxia telangiectasia-mutated (ATM) protein is a major coordinator of the DNA damage response pathway. ATM loss-of-function variants are associated with 2-fold increased breast cancer risk. We aimed at identifying and classifying spliceogenic ATM variants detected in subjects of the large-scale sequencing project BRIDGES. A total of 381 variants at the intron-exon boundaries were identified, 128 of which were predicted to be spliceogenic. After further filtering, we ended up selecting 56 variants for splicing analysis. Four functional minigenes (mgATM) spanning exons 4-9, 11-17, 25-29, and 49-52 were constructed in the splicing plasmid pSAD. Selected variants were genetically engineered into the four constructs and assayed in MCF-7/HeLa cells. Forty-eight variants (85.7%) impaired splicing, 32 of which did not show any trace of the full-length (FL) transcript. A total of 43 transcripts were identified where the most prevalent event was exon/multi-exon skipping. Twenty-seven transcripts were predicted to truncate the ATM protein. A tentative ACMG/AMP (American College of Medical Genetics and Genomics/Association for Molecular Pathology)-based classification scheme that integrates mgATM data allowed us to classify 29 ATM variants as pathogenic/likely pathogenic and seven variants as likely benign. Interestingly, the likely pathogenic variant c.1898+2T>G generated 13% of the minigene FL-transcript due to the use of a noncanonical GG-5'-splice-site (0.014% of human donor sites). Circumstantial evidence in three ATM variants (leakiness uncovered by our mgATM analysis together with clinical data) provides some support for a dosage-sensitive expression model in which variants producing ≥30% of FL-transcripts would be predicted benign, while variants producing ≤13% of FL-transcripts might be pathogenic. © 2022 The Authors. The Journal of Pathology published by John Wiley & Sons Ltd on behalf of The Pathological Society of Great Britain and Ireland.

Clinical significance of genetic variation in hypertrophic cardiomyopathy: comparison of computational tools to prioritize missense variants

Hypertrophic cardiomyopathy (HCM) is a common heart disease associated with sudden cardiac death. Early diagnosis is critical to identify patients who may benefit from implantable cardioverter defibrillator therapy. Although genetic testing is an integral part of the clinical evaluation and management of patients with HCM and their families, in many cases the genetic analysis fails to identify a disease-causing mutation. This is in part due to difficulties in classifying newly detected rare genetic variants as well as variants-of-unknown-significance (VUS). Multiple computational algorithms have been developed to predict the potential pathogenicity of genetic variants, but their relative performance in HCM has not been comprehensively assessed. Here, we compared the performance of 39 currently available prediction tools in distinguishing between high-confidence HCM-causing missense variants and benign variants, and we developed an easy-to-use-tool to perform variant prediction benchmarks based on annotated VCF files (VETA). Our results show that tool performance increases after HCM-specific calibration of thresholds. After excluding potential biases due to circularity type I issues, we identified ClinPred, MISTIC, FATHMM, MPC and MetaLR as the five best performer tools in discriminating HCM-associated variants. We propose combining these tools in order to prioritize unknown HCM missense variants that should be closely followed-up in the clinic.

Defining pathogenicity of NOTCH2 variants for diagnosis of Alagille syndrome type 2 using a large cohort of patients

BACKGROUND AND AIMS

Alagille syndrome (ALGS) type 2 caused by mutations in NOTCH2 has genotypic and phenotypic heterogeneity. Diagnosis in some atypical patients with isolated hepatic presentation could be missed.

METHODS

Using 2087 patients with paediatric liver manifestations, NOTCH2 allele frequencies, in-silico prediction, protein domains and clinical features were analysed to define the pathogenicity of NOTCH2 variants for diagnosis of ALGS type 2.

RESULTS

Among 2087 patients with paediatric liver manifestations, significantly more NOTCH2 variants were absent in gnomAD in patients with elevated γ-glutamyltransferase (GGT) (p = .041). Significantly more NOTCH2 variants which were absent in gnomAD were located in protein functional domains (p = .038). When missense variants were absent in gnomAD and predicted to be pathogenic by at least three out of seven in-silico tools, they were found to be significantly associated with liver manifestations with elevated GGT (p = .003). Comparing this to patients with likely benign (LB) variants, the patients with likely-pathogenic (LP) variants have significantly more liver manifestations with elevated GGT (p = .0001). Significantly more patients with LP variants had extra-hepatic phenotypes of ALGS compared with those patients with LB variants (p = .0004).

CONCLUSION

When NOTCH2 variants are absent in gnomAD, null variants and missense variants which were predicted to be pathogenic by at least three in-silico tools could be considered pathogenic in patients with high GGT chronic liver diseases.

CaMKII Inhibition Attenuates Distinct Gain-of-Function Effects Produced by Mutant Nav1.6 Channels and Reduces Neuronal Excitability

Aberrant Nav1.6 activity can induce hyperexcitability associated with epilepsy. Gain-of-function mutations in the SCN8A gene encoding Nav1.6 are linked to epilepsy development; however, the molecular mechanisms mediating these changes are remarkably heterogeneous and may involve post-translational regulation of Nav1.6. Because calcium/calmodulin-dependent protein kinase II (CaMKII) is a powerful modulator of Nav1.6 channels, we investigated whether CaMKII modulates disease-linked Nav1.6 mutants. Whole-cell voltage clamp recordings in ND7/23 cells show that CaMKII inhibition of the epilepsy-related mutation R850Q largely recapitulates the effects previously observed for WT Nav1.6. We also characterized a rare missense variant, R639C, located within a regulatory hotspot for CaMKII modulation of Nav1.6. Prediction software algorithms and electrophysiological recordings revealed gain-of-function effects for R639C mutant channel activity, including increased sodium currents and hyperpolarized activation compared to WT Nav1.6. Importantly, the R639C mutation ablates CaMKII phosphorylation at a key regulatory site, T642, and, in contrast to WT and R850Q channels, displays a distinct response to CaMKII inhibition. Computational simulations demonstrate that modeled neurons harboring the R639C or R850Q mutations are hyperexcitable, and simulating the effects of CaMKII inhibition on Nav1.6 activity in modeled neurons differentially reduced hyperexcitability. Acute CaMKII inhibition may represent a promising mechanism to attenuate gain-of-function effects produced by Nav1.6 mutations.

Parental segregation study reveals rare benign and likely benign variants in a Brazilian cohort of rare diseases

Genomic studies may generate massive amounts of data, bringing interpretation challenges. Efforts for the differentiation of benign and pathogenic variants gain importance. In this article, we used segregation analysis and other molecular data to reclassify to benign or likely benign several rare clinically curated variants of autosomal dominant inheritance from a cohort of 500 Brazilian patients with rare diseases. This study included only symptomatic patients who had undergone molecular investigation with exome sequencing for suspected diseases of genetic etiology. Variants clinically suspected as the causative etiology and harbored by genes associated with highly-penetrant conditions of autosomal dominant inheritance underwent Sanger confirmation in the proband and inheritance pattern determination because a "de novo" event was expected. Among all 327 variants studied, 321 variants were inherited from asymptomatic parents. Considering segregation analysis, we have reclassified 51 rare variants as benign and 211 as likely benign. In our study, the inheritance of a highly penetrant variant expected to be de novo for pathogenicity assumption was considered as a non-segregation and, therefore, a key step for benign or likely benign classification. Studies like ours may help to identify rare benign variants and improve the correct interpretation of genetic findings.

Federated analysis of BRCA1 and BRCA2 variation in a Japanese cohort

More than 40% of the germline variants in ClinVar today are variants of uncertain significance (VUSs). These variants remain unclassified in part because the patient-level data needed for their interpretation is siloed. Federated analysis can overcome this problem by "bringing the code to the data": analyzing the sensitive patient-level data computationally within its secure home institution and providing researchers with valuable insights from data that would not otherwise be accessible. We tested this principle with a federated analysis of breast cancer clinical data at RIKEN, derived from the BioBank Japan repository. We were able to analyze these data within RIKEN's secure computational framework without the need to transfer the data, gathering evidence for the interpretation of several variants. This exercise represents an approach to help realize the core charter of the Global Alliance for Genomics and Health (GA4GH): to responsibly share genomic data for the benefit of human health.

Case Report: Diagnosis of Mucopolysaccharidosis Type IVA With Compound Heterozygous Galactosamine-6 Sulfatase Variants and Biopsy of Replaced Femoral Heads

INTRODUCTION

Mucopolysaccharidosis Type IVA (MPS IVA) or Morquio A Syndrome, is a rare metabolic disorder caused by compromised galactosamine-6 sulfatase (GALNS) encoded by GALNS gene (NM_000512.5), leading to keratin sulfate (KS), and chondroitin-6-sulfate accumulation in various organs. We present a 17-year-old woman with progressive bilateral hip pain and radiographic evidence of spondyloepiphyseal dysplasia.

METHODS

Diagnosis of MPS IVA was made based on whole-exome sequencing (WES) of blood samples collected from the patient and family members, high urinary glycosaminoglycan excretion, supportive clinical manifestations, radiographic examinations, including whole-body X-rays, cervical MRI, and pelvic CT. The patient underwent bilateral total hip arthroplasties sequentially, at a 1-month interval. Femoral heads were preserved for the micro-CT (μCT) analysis and the osteochondral histology examination.

RESULTS

The patient presented with multiple skeletal deformities, including vertebras and long bone deformities. WES disclosed compound heterozygous variants at exon 11 (c.1156C>T) and exon 12 (c.1288C>G) of the GALNS (NM_000512.5). The μCT analysis revealed significant bone quantity loss and microarchitectural change in both weight-bearing area (WBA) and non-weight-bearing area (NWBA) of the femoral heads, while histological analysis showed structural abnormity of articular cartilage in the WBA of the femoral heads.

CONCLUSION

We have found compound heterozygous variants of GALNS. This is also the first study to report the microarchitectural and histological changes of both subchondral bone and articular cartilage of the femoral head in a patient with MPS IVA.

Closing the gap: Systematic integration of multiplexed functional data resolves variants of uncertain significance in BRCA1, TP53, and PTEN

Clinical interpretation of missense variants is challenging because the majority identified by genetic testing are rare and their functional effects are unknown. Consequently, most variants are of uncertain significance and cannot be used for clinical diagnosis or management. Although not much can be done to ameliorate variant rarity, multiplexed assays of variant effect (MAVEs), where thousands of single-nucleotide variant effects are simultaneously measured experimentally, provide functional evidence that can help resolve variants of unknown significance (VUSs). However, a rigorous assessment of the clinical value of multiplexed functional data for variant interpretation is lacking. Thus, we systematically combined previously published BRCA1, TP53, and PTEN multiplexed functional data with phenotype and family history data for 324 VUSs identified by a single diagnostic testing laboratory. We curated 49,281 variant functional scores from MAVEs for these three genes and integrated four different TP53 multiplexed functional datasets into a single functional prediction for each variant by using machine learning. We then determined the strength of evidence provided by each multiplexed functional dataset and reevaluated 324 VUSs. Multiplexed functional data were effective in driving variant reclassification when combined with clinical data, eliminating 49% of VUSs for BRCA1, 69% for TP53, and 15% for PTEN. Thus, multiplexed functional data, which are being generated for numerous genes, are poised to have a major impact on clinical variant interpretation.

Performance of meta-predictors for the classification of MED13L missense variations, implication of raw parameters

MED13L syndrome is a rare congenital disorder comprising moderate intellectual disability, hypotonia and facial dysmorphism. Whole exome or genome sequencing in patients with non-specific neurodevelopmental disorders leads to identification of an increasing number of MED13L missense variations of unknown signification. The aim of our study was to identify relevant annotation parameters enhancing discrimination between candidate pathogenic or neutral missense variations, and to assess the performance of seven meta-predictor algorithms: BayesDel, CADD, DANN, FATHMM-XF, M-CAP, MISTIC and REVEL for the classification of MED13L missense variants. Significant differences were identified for five parameters: global conservation through verPhyloP and verPhCons scores; physico-chemical difference between amino acids estimated by Grantham scores; conservation of residues between MED13L and MED13 protein; proximity to phosphorylation sites for pathogenic variations. Among the seven selected in-silico tools, BayesDel, REVEL, and MISTIC provided the most interesting performances to discriminate pathogenic from neutral missense variations. Individual gene parameter studies with MED13L have provided expertise on elements of annotation improving meta-predictor choices. The in-silico approach allows us to make valuable hypotheses to predict the involvement of these amino acids in MED13L pathogenic missense variations.

Genetic Determinants and Genotype-Phenotype Correlations in Vietnamese Patients With Dilated Cardiomyopathy

BACKGROUND

Dilated cardiomyopathy (DCM) is an important cause of heart failure and cardiac transplantation. This study determined the prevalence of DCM-associated genes and evaluated the genotype-phenotype correlation in Vietnamese patients.

METHODS AND RESULTS

This study analyzed 58 genes from 230 patients. The study cohort consisted of 64.3% men; age at diagnosis 47.9±13.7 years; familial (10.9%) and sporadic DCM (82.2%). The diagnostic yield was 23.5%, 44.0% in familial and 19.6% in sporadic DCM.TTNtruncating variants (TTNtv) were predominant (46.4%), followed byTPM1,DSP,LMNA,MYBPC3,MYH6,MYH7,DES,TNNT2,ACTC1,ACTN2,BAG3,DMD,FKTN,PLN,TBX5,RBM20,TCAP(2-6%). Familial DCM, genotype-positive andTTNtv-positive patients were younger than those with genotype-negative and sporadic DCM. Genotype-positive patients displayed a decreased systolic blood pressure and left ventricular wall thickness compared to genotype-negative patients. Genotype-positive patients, particularly those withTTNtv, had a family history of DCM, higher left atrial volume index and body mass index, and lower right ventricle-fractional area change than genotype-negative patients. Genotype-positive patients reached the combined outcomes more frequently and at a younger age than genotype-negative patients. Major cardiac events occurred more frequently in patients positive with genes other thanTTNtv.

CONCLUSIONS

The study findings provided an overview of Vietnamese DCM patients' genetic profile and suggested that management of environmental factors may be beneficial for DCM patients.

Genetic Analysis of Tryptophan Metabolism Genes in Sporadic Amyotrophic Lateral Sclerosis

The essential amino acid tryptophan (TRP) is the initiating metabolite of the kynurenine pathway (KP), which can be upregulated by inflammatory conditions in cells. Neuroinflammation-triggered activation of the KP and excessive production of the KP metabolite quinolinic acid are common features of multiple neurodegenerative diseases, including amyotrophic lateral sclerosis (ALS). In addition to its role in the KP, genes involved in TRP metabolism, including its incorporation into proteins, and synthesis of the neurotransmitter serotonin, have also been genetically and functionally linked to these diseases. ALS is a late onset neurodegenerative disease that is classified as familial or sporadic, depending on the presence or absence of a family history of the disease. Heritability estimates support a genetic basis for all ALS, including the sporadic form of the disease. However, the genetic basis of sporadic ALS (SALS) is complex, with the presence of multiple gene variants acting to increase disease susceptibility and is further complicated by interaction with potential environmental factors. We aimed to determine the genetic contribution of 18 genes involved in TRP metabolism, including protein synthesis, serotonin synthesis and the KP, by interrogating whole-genome sequencing data from 614 Australian sporadic ALS cases. Five genes in the KP (AFMID, CCBL1, GOT2, KYNU, HAAO) were found to have either novel protein-altering variants, and/or a burden of rare protein-altering variants in SALS cases compared to controls. Four genes involved in TRP metabolism for protein synthesis (WARS) and serotonin synthesis (TPH1, TPH2, MAOA) were also found to carry novel variants and/or gene burden. These variants may represent ALS risk factors that act to alter the KP and lead to neuroinflammation. These findings provide further evidence for the role of TRP metabolism, the KP and neuroinflammation in ALS disease pathobiology.

Identification of LRRK2 missense variants in the accelerating medicines partnership Parkinson's disease cohort

Pathogenic missense variants in the leucine-rich repeat kinase 2 (LRRK2) gene have been identified through linkage analysis in familial Parkinson disease (PD). Subsequently, other missense variants with lower effect sizes on PD risk have emerged, as well as non-coding polymorphisms (e.g. rs76904798) enriched in PD cases in genome-wide association studies. Here we leverage recent whole-genome sequences from the Accelerating Medicines Partnership-Parkinson's Disease (AMP-PD) and the Genome Aggregation (gnomAD) databases to characterize novel missense variants in LRRK2 and explore their relationships with known pathogenic and PD-linked missense variants. Using a computational prediction tool that successfully classifies known pathogenic LRRK2 missense variants, we describe an online web-based resource that catalogs characteristics of over 1200 LRRK2 missense variants of unknown significance. Novel high-pathogenicity scoring variants, some identified exclusively in PD cases, tightly cluster within the ROC-COR-Kinase domains. Structure-function predictions support that some of these variants exert gain-of-function effects with respect to LRRK2 kinase activity. In AMP-PD participants, all p.R1441G carriers (N = 89) are also carriers of the more common PD-linked variant p.M1646T. In addition, nearly all carriers of the PD-linked p.N2081D missense variant are also carriers of the LRRK2 PD-risk variant rs76904798. These results provide a compendium of LRRK2 missense variants and how they associate with one another. While the pathogenic p.G2019S variant is by far the most frequent high-pathogenicity scoring variant, our results suggest that ultra-rare missense variants may have an important cumulative impact in increasing the number of individuals with LRRK2-linked PD.

Improving the clinical interpretation of missense variants in X linked genes using structural analysis

BACKGROUND

Improving the clinical interpretation of missense variants can increase the diagnostic yield of genomic testing and lead to personalised management strategies. Currently, due to the imprecision of bioinformatic tools that aim to predict variant pathogenicity, their role in clinical guidelines remains limited. There is a clear need for more accurate prediction algorithms and this study aims to improve performance by harnessing structural biology insights. The focus of this work is missense variants in a subset of genes associated with X linked disorders.

METHODS

We have developed a protein-specific variant interpreter (ProSper) that combines genetic and protein structural data. This algorithm predicts missense variant pathogenicity by applying machine learning approaches to the sequence and structural characteristics of variants.

RESULTS

ProSper outperformed seven previously described tools, including meta-predictors, in correctly evaluating whether or not variants are pathogenic; this was the case for 11 of the 21 genes associated with X linked disorders that met the inclusion criteria for this study. We also determined gene-specific pathogenicity thresholds that improved the performance of VEST4, REVEL and ClinPred, the three best-performing tools out of the seven that were evaluated; this was the case in 11, 11 and 12 different genes, respectively.

CONCLUSION

ProSper can form the basis of a molecule-specific prediction tool that can be implemented into diagnostic strategies. It can allow the accurate prioritisation of missense variants associated with X linked disorders, aiding precise and timely diagnosis. In addition, we demonstrate that gene-specific pathogenicity thresholds for a range of missense prioritisation tools can lead to an increase in prediction accuracy.