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

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.

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.

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.

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

ABSTRACT

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.

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.

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.

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.

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 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.

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.

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.

Is Gene-Size an Issue for the Diagnosis of Skeletal Muscle Disorders?

Human genes have a variable length. Those having a coding sequence of extraordinary length and a high number of exons were almost impossible to sequence using the traditional Sanger-based gene-by-gene approach. High-throughput sequencing has partly overcome the size-related technical issues, enabling a straightforward, rapid and relatively inexpensive analysis of large genes.

Several large genes (e.g. TTN, NEB, RYR1, DMD) are recognized as disease-causing in patients with skeletal muscle diseases. However, because of their sheer size, the clinical interpretation of variants in these genes is probably the most challenging aspect of the high-throughput genetic investigation in the field of skeletal muscle diseases.

The main aim of this review is to discuss the technical and interpretative issues related to the diagnostic investigation of large genes and to reflect upon the current state of the art and the future advancements in the field.

Strong functional data for pathogenicity or neutrality classify BRCA2 DNA-binding-domain variants of uncertain significance

Determination of the clinical relevance of rare germline variants of uncertain significance (VUSs) in the BRCA2 cancer predisposition gene remains a challenge as a result of limited availability of data for use in classification models. However, laboratory-based functional data derived from validated functional assays of known sensitivity and specificity may influence the interpretation of VUSs. We evaluated 252 missense VUSs from the BRCA2 DNA-binding domain by using a homology-directed DNA repair (HDR) assay and identified 90 as non-functional and 162 as functional. The functional assay results were integrated with other available data sources into an ACMG/AMP rules-based classification framework used by a hereditary cancer testing laboratory. Of the 186 missense variants observed by the testing laboratory, 154 were classified as VUSs without functional data. However, after applying protein functional data, 86% (132/154) of the VUSs were reclassified as either likely pathogenic/pathogenic (39/132) or likely benign/benign (93/132), which impacted testing results for 1,900 individuals. These results indicate that validated functional assay data can have a substantial impact on VUS classification and associated clinical management for many individuals with inherited alterations in BRCA2.

Massively Parallel Sequencing for Rare Genetic Disorders: Potential and Pitfalls

There have been two major eras in the history of gene discovery. The first was the era of linkage analysis, with approximately 1,300 disease-related genes identified by positional cloning by the turn of the millennium. The second era has been powered by two major breakthroughs: the publication of the human genome and the development of massively parallel sequencing (MPS). MPS has greatly accelerated disease gene identification, such that disease genes that would have taken years to map previously can now be determined in a matter of weeks. Additionally, the number of affected families needed to map a causative gene and the size of such families have fallen: de novo mutations, previously intractable by linkage analysis, can be identified through sequencing of the parent-child trio, and genes for recessive disease can be identified through MPS even of a single affected individual. MPS technologies include whole exome sequencing (WES), whole genome sequencing (WGS), and panel sequencing, each with their strengths. While WES has been responsible for most gene discoveries through MPS, WGS is superior in detecting copy number variants, chromosomal rearrangements, and repeat-rich regions. Panels are commonly used for diagnostic purposes as they are extremely cost-effective and generate manageable quantities of data, with no risk of unexpected findings. However, in instances of diagnostic uncertainty, it can be challenging to choose the right panel, and in these circumstances WES has a higher diagnostic yield. MPS has ethical, social, and legal implications, many of which are common to genetic testing generally but amplified due to the magnitude of data (e.g., relationship misattribution, identification of variants of uncertain significance, and genetic discrimination); others are unique to WES and WGS technologies (e.g., incidental or secondary findings). Nonetheless, MPS is rapidly translating into clinical practice as an extremely useful part of the clinical armamentarium.

Family history assessment significantly enhances delivery of precision medicine in the genomics era

BACKGROUND

Family history has traditionally been an essential part of clinical care to assess health risks. However, declining sequencing costs have precipitated a shift towards genomics-first approaches in population screening programs rendering the value of family history unknown. We evaluated the utility of incorporating family history information for genomic sequencing selection.

METHODS

To ascertain the relationship between family histories on such population-level initiatives, we analysed whole genome sequences of 1750 research participants with no known pre-existing conditions, of which half received comprehensive family history assessment of up to four generations, focusing on 95 cancer genes.

RESULTS

Amongst the 1750 participants, 866 (49.5%) had high-quality standardised family history available. Within this group, 73 (8.4%) participants had an increased family history risk of cancer (increased FH risk cohort) and 1 in 7 participants (n = 10/73) carried a clinically actionable variant inferring a sixfold increase compared with 1 in 47 participants (n = 17/793) assessed at average family history cancer risk (average FH risk cohort) (p = 0.00001) and a sevenfold increase compared to 1 in 52 participants (n = 17/884) where family history was not available (FH not available cohort) (p = 0.00001). The enrichment was further pronounced (up to 18-fold) when assessing only the 25 cancer genes in the American College of Medical Genetics (ACMG) Secondary Findings (SF) genes. Furthermore, 63 (7.3%) participants had an increased family history cancer risk in the absence of an apparent clinically actionable variant.

CONCLUSIONS

These findings demonstrate that the collection and analysis of comprehensive family history and genomic data are complementary and in combination can prioritise individuals for genomic analysis. Thus, family history remains a critical component of health risk assessment, providing important actionable data when implementing genomics screening programs.

TRIAL REGISTRATION

ClinicalTrials.gov NCT02791152 . Retrospectively registered on May 31, 2016.