ITHANET: Information and database community portal for haemoglobinopathies

Kountouris, Petros; Stephanou, Coralea; Bento, Celeste; Fanis, Pavlos; Élion, Jacques; Ramesar, Raj; Zilfalil, Bin Alwi; Robinson, Helen M.; Traeger-Synodinos, Joanne; Lederer, Carsten W.; Kleanthous, Marina

doi:10.1101/209361

Cited by 10 publications

(69 citation statements)

References 28 publications

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“…Figure 1 shows a schematic representation of the main steps of our methodology. SNVs were retrieved from the IthaGenes database of the ITHANET portal 6,16 . The dataset includes all SNVs (≤50bp) curated in IthaGenes (access date: 05/02/2021) located in HBA1, HBA2, and HBB, excluding (i) disease-modifying variants, (ii) complex variants with multiple DNA changes found in cis, and (iii) variants whose genomic location is unclear, such as α-chain variants identified by protein studies without identifying the affected α-globin gene.…”

Section: Methodsmentioning

confidence: 99%

“…We selected 31 in silico predictors, including those recommended by ClinGen 3 and linked in the Variant Curation Interface (VCI) 15 , along with additional tools described in literature. A total of 1627 short nucleotide variants (SNVs) were retrieved from the IthaGenes database 6,16 and were annotated using a Delphi approach with respect to their pathogenicity by experts (co-authoring this study) involved in haemoglobinopathy molecular diagnosis in five different countries. The annotated pathogenicity of each SNV was then used to evaluate its predicted pathogenicity provided by in silico tools.…”

Section: Introductionmentioning

confidence: 99%

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Evaluation of in silico predictors on short nucleotide variants in HBA1, HBA2 and HBB associated with haemoglobinopathies

Tamana

Xenophontos

Minaidou

et al. 2022

Preprint

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Introduction: Haemoglobinopathies are the commonest monogenic diseases worldwide and are caused by variants in the globin gene clusters. With over 2400 variants detected to date, their interpretation using the ACMG/AMP guidelines is challenging, with computational evidence able to provide valuable input about their functional annotation. While many in silico predictors have already been developed, their performance varies for different genes and diseases. Materials and Methods: We evaluate 31 in silico predictors using a dataset of 1627 variants in HBA1, HBA2, and HBB. Through varying the decision threshold for each tool, we analyse their performance (a) as binary classifiers of pathogenicity, and (b) using different non-overlapping pathogenic and benign thresholds for their optimal use in the ACMG/AMP framework. Results: CADD, Eigen-PC, and REVEL are the overall top performers, with the former reaching moderate strength level for pathogenic prediction. Eigen-PC and REVEL achieve the highest accuracies for missense variants, while CADD is also a reliable predictor of non-missense variants. Moreover, SpliceAI is the top performing splicing predictor, reaching strong level of evidence, while GERP++ and phyloP are the most accurate conservation tools. Discussion: This study provides evidence about the optimal use of computational evidence in globin gene clusters under the ACMG/AMP framework.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Evaluation of in silico predictors on short nucleotide variants in HBA1, HBA2 and HBB associated with haemoglobinopathies

Tamana

Xenophontos

Minaidou

et al. 2022

Preprint

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“…β-thalassemia, a common genetic blood disorder worldwide, is caused by more than 200 mutations and a few deletions in the β-globin gene (HBB), resulting in a reduced synthesis (β + -thalassemia) or an absent synthesis (β 0 -thalassemia) of β-globin chain. [1][2][3][4] Patients with β-thalassemia suffer from anemia, growth retardation, bone deformities, and hepatosplenomegaly and, if left untreated, death. 5 β-thalassemia patients are dependent on lifelong blood transfusion combined with iron chelators.…”

Section: Introductionmentioning

confidence: 99%

Development of DNA controls for detection of β‐thalassemia mutations commonly found in Asian

Munkongdee

Nualkaew

Buasuwan

et al. 2020

Int J Lab Hematology

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Introduction: Several DNA-based approaches including a reverse dot-blot hybridization (RDB) have been established for detection of β-thalassemia genotypes to provide accurate genetic counseling and prenatal diagnosis for prevention and control of severe β-thalassemia. However, one of major concerns of these techniques is a risk of misdiagnosis due to a lack of DNA controls. Here, we constructed positive DNA controls for β-thalassemia genotyping in order to ensure that all steps in the analysis are performed properly. Methods: Four recombinant β-globin plasmids, including a normal sequence and three different mutant panels covering 10 common β-thalassemia mutations in Asia, were constructed by a conventional cloning method followed by sequential rounds of site-directed mutagenesis. These positive DNA controls were further validated by RDB analysis. Results: We demonstrated the applicability of established positive DNA controls for β-thalassemia genotyping in terms of accuracy and reproducibility by RDB analysis. We further combined three mutant β-globin plasmids into a single positive control, which showed positive signals for both normal and mutant probes of all tested mutations. Therefore, only two positive DNA controls, normal and combined mutant β-globin plasmids, are required for detecting 10 common β-thalassemia mutations by RDB, reducing the cost, time, and efforts in the routine diagnosis. Conclusion: The β-globin DNA controls established here provide efficient alternatives to a conventional DNA source from peripheral blood, which is more difficult to obtain. They also provide a platform for future development of β-globin plasmid controls with other mutations, which can also be suitable for other DNA-based approaches.

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“…The Hb protein complex comprises two α-like globin chains, encoded by genes in the α-globin locus (Chromosome: 16, Accession: NG_000006), and two β-like globin chains, encoded by genes in the β-globin locus (Chromosome: 11, Accession: NG_000007). The molecular pathology of haemoglobinopathies is traced to genetic defects in the two globin gene clusters, with more than 2000 different mutant alleles reported to date on the IthaGenes database of the ITHANET community portal [5,6]. These mutations can be grouped into those that impair globin chain synthesis, causing thalassaemia syndromes, and those that alter the structure of the Hb protein, causing structural haemoglobinopathies [7].…”

Section: Introductionmentioning

confidence: 99%

“…Due to the large volume of literature and the amount of time required to screen and collect relevant data, important information was bound to remain inaccessible to the broad scientific community. Over the past few years, the ITHANET community portal has been curating and annotating disease-modifying genes and variants [5,6], using rigorous literature monitoring. Gene-to-phenotype associations are manually reviewed from the literature by individual assessment and annotated in the IthaGenes database of the portal.…”

Section: Introductionmentioning

confidence: 99%

Genetic Modifiers at the Crossroads of Personalised Medicine for Haemoglobinopathies

Stephanou

Tamana

Minaidou

et al. 2019

JCM

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Haemoglobinopathies are common monogenic disorders with diverse clinical manifestations, partly attributed to the influence of modifier genes. Recent years have seen enormous growth in the amount of genetic data, instigating the need for ranking methods to identify candidate genes with strong modifying effects. Here, we present the first evidence-based gene ranking metric (IthaScore) for haemoglobinopathy-specific phenotypes by utilising curated data in the IthaGenes database. IthaScore successfully reflects current knowledge for well-established disease modifiers, while it can be dynamically updated with emerging evidence. Protein–protein interaction (PPI) network analysis and functional enrichment analysis were employed to identify new potential disease modifiers and to evaluate the biological profiles of selected phenotypes. The most relevant gene ontology (GO) and pathway gene annotations for (a) haemoglobin (Hb) F levels/Hb F response to hydroxyurea included urea cycle, arginine metabolism and vascular endothelial growth factor receptor (VEGFR) signalling, (b) response to iron chelators included xenobiotic metabolism and glucuronidation, and (c) stroke included cytokine signalling and inflammatory reactions. Our findings demonstrate the capacity of IthaGenes, together with dynamic gene ranking, to expand knowledge on the genetic and molecular basis of phenotypic variation in haemoglobinopathies and to identify additional candidate genes to potentially inform and improve diagnosis, prognosis and therapeutic management.

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ITHANET: Information and database community portal for haemoglobinopathies

Cited by 10 publications

References 28 publications

Evaluation of in silico predictors on short nucleotide variants in HBA1, HBA2 and HBB associated with haemoglobinopathies

Evaluation of in silico predictors on short nucleotide variants in HBA1, HBA2 and HBB associated with haemoglobinopathies

Development of DNA controls for detection of β‐thalassemia mutations commonly found in Asian

Genetic Modifiers at the Crossroads of Personalised Medicine for Haemoglobinopathies

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