VPOT: A Customizable Variant Prioritization Ordering Tool for Annotated Variants

Variant Impact Predictor database (VIPdb), version 2: Trends from 25 years of genetic variant impact predictors

Background

Variant interpretation is essential for identifying patients' disease-causing genetic variants amongst the millions detected in their genomes. Hundreds of Variant Impact Predictors (VIPs), also known as Variant Effect Predictors (VEPs), have been developed for this purpose, with a variety of methodologies and goals. To facilitate the exploration of available VIP options, we have created the Variant Impact Predictor database (VIPdb).

Results

The Variant Impact Predictor database (VIPdb) version 2 presents a collection of VIPs developed over the past 25 years, summarizing their characteristics, ClinGen calibrated scores, CAGI assessment results, publication details, access information, and citation patterns. We previously summarized 217 VIPs and their features in VIPdb in 2019. Building upon this foundation, we identified and categorized an additional 186 VIPs, resulting in a total of 403 VIPs in VIPdb version 2. The majority of the VIPs have the capacity to predict the impacts of single nucleotide variants and nonsynonymous variants. More VIPs tailored to predict the impacts of insertions and deletions have been developed since the 2010s. In contrast, relatively few VIPs are dedicated to the prediction of splicing, structural, synonymous, and regulatory variants. The increasing rate of citations to VIPs reflects the ongoing growth in their use, and the evolving trends in citations reveal development in the field and individual methods.

Conclusions

VIPdb version 2 summarizes 403 VIPs and their features, potentially facilitating VIP exploration for various variant interpretation applications.

Availability

VIPdb version 2 is available at https://genomeinterpretation.org/vipdb.

Computational Tools to Assist in Analyzing Effects of the SERPINA1 Gene Variation on Alpha-1 Antitrypsin (AAT)

In the rapidly advancing field of bioinformatics, the development and application of computational tools to predict the effects of single nucleotide variants (SNVs) are shedding light on the molecular mechanisms underlying disorders. Also, they hold promise for guiding therapeutic interventions and personalized medicine strategies in the future. A comprehensive understanding of the impact of SNVs in the SERPINA1 gene on alpha-1 antitrypsin (AAT) protein structure and function requires integrating bioinformatic approaches. Here, we provide a guide for clinicians to navigate through the field of computational analyses which can be applied to describe a novel genetic variant. Predicting the clinical significance of SERPINA1 variation allows clinicians to tailor treatment options for individuals with alpha-1 antitrypsin deficiency (AATD) and related conditions, ultimately improving the patient's outcome and quality of life. This paper explores the various bioinformatic methodologies and cutting-edge approaches dedicated to the assessment of molecular variants of genes and their product proteins using SERPINA1 and AAT as an example.

DMD-Associated Dilated Cardiomyopathy: Genotypes, Phenotypes, and Phenocopies

BACKGROUND

Variants in the DMD gene, that encodes the cytoskeletal protein, dystrophin, cause a severe form of dilated cardiomyopathy (DCM) associated with high rates of heart failure, heart transplantation, and ventricular arrhythmias. Improved early detection of individuals at risk is needed.

METHODS

Genetic testing of 40 male probands with a potential X-linked genetic cause of primary DCM was undertaken using multi-gene panel sequencing, multiplex polymerase chain reaction, and array comparative genomic hybridization. Variant location was assessed with respect to dystrophin isoform patterns and exon usage. Telomere length was evaluated as a marker of myocardial dysfunction in left ventricular tissue and blood.

RESULTS

Four pathogenic/likely pathogenic DMD variants were found in 5 probands (5/40: 12.5%). Only one rare variant was identified by gene panel testing with 3 additional multi-exon deletion/duplications found following targeted assays for structural variants. All of the pathogenic/likely pathogenic DMD variants involved dystrophin exons that had percent spliced-in scores >90, indicating high levels of constitutive expression in the human adult heart. Fifteen DMD variant-negative probands (15/40: 37.5%) had variants in autosomal genes including TTN, BAG3, LMNA, and RBM20. Myocardial telomere length was reduced in patients with DCM irrespective of genotype. No differences in blood telomere length were observed between genotype-positive family members with/without DCM and controls.

CONCLUSIONS

Primary genetic testing using multi-gene panels has a low yield and specific assays for structural variants are required if DMD-associated cardiomyopathy is suspected. Distinguishing X-linked causes of DCM from autosomal genes that show sex differences in clinical presentation is crucial for informed family management.

eyeVarP: A computational framework for the identification of pathogenic variants specific to eye disease

PURPOSE

Disease-specific pathogenic variant prediction tools that differentiate pathogenic variants from benign have been improved through disease specificity recently. However, they have not been evaluated on disease-specific pathogenic variants compared with other diseases, which would help to prioritize disease-specific variants from several genes or novel genes. Thus, we hypothesize that features of pathogenic variants alone would provide a better model.

METHODS

We developed an eye disease-specific variant prioritization tool (eyeVarP), which applied the random forest algorithm to the data set of pathogenic variants of eye diseases and other diseases. We also developed the VarP tool and generalized pipeline to filter missense and insertion-deletion variants and predict their pathogenicity from exome or genome sequencing data, thus we provide a complete computational procedure.

RESULTS

eyeVarP outperformed pan disease-specific tools in identifying eye disease-specific pathogenic variants under the top 10. VarP outperformed 12 pathogenicity prediction tools with an accuracy of 95% in correctly identifying the pathogenicity of missense and insertion-deletion variants. The complete pipeline would help to develop disease-specific tools for other genetic disorders.

CONCLUSION

eyeVarP performs better in identifying eye disease-specific pathogenic variants using pathogenic variant features and gene features. Implementing such complete computational procedure would significantly improve the clinical variant interpretation for specific diseases.

mvPPT: A Highly Efficient and Sensitive Pathogenicity Prediction Tool for Missense Variants

Next-generation sequencing technologies both boost the discovery of variants in the human genome and exacerbate the challenges of pathogenic variant identification. In this study, we developed Pathogenicity Prediction Tool for missense variants (mvPPT), a highly sensitive and accurate missense variant classifier based on gradient boosting. mvPPT adopts high-confidence training sets with a wide spectrum of variant profiles, and extracts three categories of features, including scores from existing prediction tools, frequencies (allele frequencies, amino acid frequencies, and genotype frequencies), and genomic context. Compared with established predictors, mvPPT achieves superior performance in all test sets, regardless of data source. In addition, our study also provides guidance for training set and feature selection strategies, as well as reveals highly relevant features, which may further provide biological insights into variant pathogenicity. mvPPT is freely available at http://www.mvppt.club/.

Phenotype-aware prioritisation of rare Mendelian disease variants

A molecular diagnosis from the analysis of sequencing data in rare Mendelian diseases has a huge impact on the management of patients and their families. Numerous patient phenotype-aware variant prioritisation (VP) tools have been developed to help automate this process, and shorten the diagnostic odyssey, but performance statistics on real patient data are limited. Here we identify, assess, and compare the performance of all up-to-date, freely available, and programmatically accessible tools using a whole-exome, retinal disease dataset from 134 individuals with a molecular diagnosis. All tools were able to identify around two-thirds of the genetic diagnoses as the top-ranked candidate, with LIRICAL performing best overall. Finally, we discuss the challenges to overcome most cases remaining undiagnosed after current, state-of-the-art practices.

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.

Exploring the Genetic Architecture of Spontaneous Coronary Artery Dissection Using Whole-Genome Sequencing

Background:

Spontaneous coronary artery dissection (SCAD) is a cause of acute coronary syndrome that predominantly affects women. Its pathophysiology remains unclear but connective tissue disorders (CTD) and other vasculopathies have been observed in many SCAD patients. A genetic component for SCAD is increasingly appreciated, although few genes have been robustly implicated. We sought to clarify the genetic cause of SCAD using targeted and genome-wide methods in a cohort of sporadic cases to identify both common and rare disease-associated variants.

Methods:

A cohort of 91 unrelated sporadic SCAD cases was investigated for rare, deleterious variants in genes associated with either SCAD or CTD, while new candidate genes were sought using rare variant collapsing analysis and identification of novel loss-of-function variants in genes intolerant to such variation. Finally, 2 SCAD polygenic risk scores were applied to assess the contribution of common variants.

Results:

We identified 10 cases with at least one rare, likely disease-causing variant in CTD-associated genes, although only one had a CTD phenotype. No genes were significantly associated with SCAD from genome-wide collapsing analysis, however, enrichment for TGF (transforming growth factor)-β signaling pathway genes was found with analysis of 24 genes harboring novel loss-of-function variants. Both polygenic risk scores demonstrated that sporadic SCAD cases have a significantly elevated genetic SCAD risk compared with controls.

Conclusions:

SCAD shares some genetic overlap with CTD, even in the absence of any major CTD phenotype. Consistent with a complex genetic architecture, SCAD patients also have a higher burden of common variants than controls.

Identifying adaptive alleles in the human genome: from selection mapping to functional validation

The suite of phenotypic diversity across geographically distributed human populations is the outcome of genetic drift, gene flow, and natural selection throughout human evolution. Human genetic variation underlying local biological adaptations to selective pressures is incompletely characterized. With the emergence of population genetics modeling of large-scale genomic data derived from diverse populations, scientists are able to map signatures of natural selection in the genome in a process known as selection mapping. Inferred selection signals further can be used to identify candidate functional alleles that underlie putative adaptive phenotypes. Phenotypic association, fine mapping, and functional experiments facilitate the identification of candidate adaptive alleles. Functional investigation of candidate adaptive variation using novel techniques in molecular biology is slowly beginning to unravel how selection signals translate to changes in biology that underlie the phenotypic spectrum of our species. In addition to informing evolutionary hypotheses of adaptation, the discovery and functional annotation of adaptive alleles also may be of clinical significance. While selection mapping efforts in non-European populations are growing, there remains a stark under-representation of diverse human populations in current public genomic databases, of both clinical and non-clinical cohorts. This lack of inclusion limits the study of human biological variation. Identifying and functionally validating candidate adaptive alleles in more global populations is necessary for understanding basic human biology and human disease.