Resources and tools for rare disease variant interpretation

Licata, Luana; Via, Allegra; Turina, Paola; Babbi, Giulia; Benevenuta, Silvia; Carta, Claudio; Casadio, Rita; Cicconardi, A; Facchiano, Angelo; Fariselli, Piero; Giordano, Deborah; Isidori, Federica; Martelli, Pier Luigi; Pascarella, Stefano; Pinelli, Michele; Pippucci, Tommaso; Russo, Roberta; Savojardo, Castrense; Scafuri, Bernardina; Valeriani, Lucrezia; Capriotti, Emidio

doi:10.3389/fmolb.2023.1169109

Cited by 4 publications

(2 citation statements)

References 212 publications

(259 reference statements)

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“…In terms of rare diseases, Licata and colleagues have written an exhaustive guide on how to improve variant prioritization in rare disorders. [ 65 ]. Comprehensive reviews covering differences between concept, usage, and accuracy of various variant prioritization tools were written by Katsonis et al [ 66 ] and Ghosh et al [ 67 ].…”

Section: Computational Tools For Predicting the Outcome Of Snvsmentioning

confidence: 99%

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

Mróz,

Pelc,

Mitusińska

et al. 2024

Genes

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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.

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Section: Computational Tools For Predicting the Outcome Of Snvsmentioning

confidence: 99%

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

Mróz,

Pelc,

Mitusińska

et al. 2024

Genes

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“…Since that time, laboratory diagnosis has become the definitive means of distinguishing rare developmental disabilities in many cases. This has been facilitated by the number of exomes and genomes available for comparison (https://gnomad.broadinstitute.org/, accessed on 7 April 2024) as well as improved in silico prediction tools available for the analysis of likely pathogenic variants [17].…”

Section: Introductionmentioning

confidence: 99%

Rare Genetic Developmental Disabilities: Mabry Syndrome (MIM 239300) Index Cases and Glycophosphatidylinositol (GPI) Disorders

Thompson,

Knaus

2024

Genes

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The case report by Mabry et al. (1970) of a family with four children with elevated tissue non-specific alkaline phosphatase, seizures and profound developmental disability, became the basis for phenotyping children with the features that became known as Mabry syndrome. Aside from improvements in the services available to patients and families, however, the diagnosis and treatment of this, and many other developmental disabilities, did not change significantly until the advent of massively parallel sequencing. As more patients with features of the Mabry syndrome were identified, exome and genome sequencing were used to identify the glycophosphatidylinositol (GPI) biosynthesis disorders (GPIBDs) as a group of congenital disorders of glycosylation (CDG). Biallelic variants of the phosphatidylinositol glycan (PIG) biosynthesis, type V (PIGV) gene identified in Mabry syndrome became evidence of the first in a phenotypic series that is numbered HPMRS1-6 in the order of discovery. HPMRS1 [MIM: 239300] is the phenotype resulting from inheritance of biallelic PIGV variants. Similarly, HPMRS2 (MIM 614749), HPMRS5 (MIM 616025) and HPMRS6 (MIM 616809) result from disruption of the PIGO, PIGW and PIGY genes expressed in the endoplasmic reticulum. By contrast, HPMRS3 (MIM 614207) and HPMRS4 (MIM 615716) result from disruption of post attachment to proteins PGAP2 (HPMRS3) and PGAP3 (HPMRS4). The GPI biosynthesis disorders (GPIBDs) are currently numbered GPIBD1-21. Working with Dr. Mabry, in 2020, we were able to use improved laboratory diagnostics to complete the molecular diagnosis of patients he had originally described in 1970. We identified biallelic variants of the PGAP2 gene in the first reported HPMRS patients. We discuss the longevity of the Mabry syndrome index patients in the context of the utility of pyridoxine treatment of seizures and evidence for putative glycolipid storage in patients with HPMRS3. From the perspective of the laboratory innovations made that enabled the identification of the HPMRS phenotype in Dr. Mabry’s patients, the need for treatment innovations that will benefit patients and families affected by developmental disabilities is clear.

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

Zucca,

Nicora,

De Paoli

et al. 2024

Hum. Genet.

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

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Resources and tools for rare disease variant interpretation

Cited by 4 publications

References 212 publications

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

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

Rare Genetic Developmental Disabilities: Mabry Syndrome (MIM 239300) Index Cases and Glycophosphatidylinositol (GPI) Disorders

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

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