Dataset Information

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

ABSTRACT: 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 American College of Medical Genetics and Genomics (ACMG)/Association for Molecular Pathology (AMP) guidelines is challenging and computational evidence can provide valuable input about their functional annotation. While many in silico predictors have already been developed, their performance varies for different genes and diseases. In this study, we evaluate 31 in silico predictors using a dataset of 1627 variants in HBA1, HBA2, and HBB. By varying the decision threshold for each tool, we analyse their performance (a) as binary classifiers of pathogenicity and (b) by using different non-overlapping pathogenic and benign thresholds for their optimal use in the ACMG/AMP framework. Our results show that 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. This study provides evidence about the optimal use of computational tools in globin gene clusters under the ACMG/AMP framework.

SUBMITTER: Tamana S

PROVIDER: S-EPMC9731569 | biostudies-literature | 2022 Dec

REPOSITORIES: biostudies-literature

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

Tamana Stella S Xenophontos Maria M Minaidou Anna A Stephanou Coralea C Harteveld Cornelis L CL Bento Celeste C Traeger-Synodinos Joanne J Fylaktou Irene I Yasin Norafiza Mohd NM Abdul Hamid Faidatul Syazlin FS Esa Ezalia E Halim-Fikri Hashim H Zilfalil Bin Alwi BA Kakouri Andrea C AC Kleanthous Marina M Kountouris Petros P

eLife 20221201

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 American College of Medical Genetics and Genomics (ACMG)/Association for Molecular Pathology (AMP) guidelines is challenging and computational evidence can provide valuable input about their functional annotation. While many in silico predictors have already been developed, their performance varies for ...[more]

PMID: 36453528

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Project description:Germline BRCA2 loss-of function (LOF) variants identified by clinical genetic testing predispose to breast, ovarian, prostate and pancreatic cancer. However, variants of uncertain significance (VUS) (n>5000) limit the clinical use of testing results. Thus, there is an urgent need for functional characterization and clinical classification of all BRCA2 variants. Here we report on comprehensive saturation genome editing-based functional characterization of 99% of all possible single nucleotide variants (SNVs) in the BRCA2 DNA Binding Domain hotspot for pathogenic missense variants that is encoded by exons 15 to 26. The assay was based on deep sequence analysis of HAP1 haploid cells endogenously targeted using a CRISPR/cas9 knockin approach. A total of 6959 SNVs were characterized for effects on cell survival. The assay was validated relative to nonsense and synonymous variants, ClinVar pathogenic/likely pathogenic and benign/likely benign variants and homology directed repair cell based DNA repair assay results, all of which showed >94% sensitivity and specificity. Variants were assigned posterior probabilities of pathogenicity using a VarCall two component Bayesian mixture model and were further grouped according to ACMG strength of evidence under the PS3/BS3 rule resulting in Benign Strong (n=5430), Benign Moderate (n=190), Benign Supporting (n=61), VUS (122), Pathogenic Strong (n=1021), Pathogenic Moderate (n=88), and Pathogenic Supporting (n=47). Breast cancer case-control association studies showed that pooled SNVs encoding functionally pathogenic missense variants were associated with increased risks of breast (odds ratio (OR)3.81, 95%CI: 2.88-5.07) and ovarian cancer (OR 5.93, 95%CI: 4.12-8.52). The functional data were also combined with other sources of information in the ClinGen BRCA1/2 VCEP ACMG/AMP-classification model. A total of 785 SNVs, including 261 missense SNVs, were classified as pathogenic or likely pathogenic, while 5566 SNVs, including 3786 missense SNVs, were classified as benign or likely benign. These classified variants can now be used for risk assessment and clinical care of variant carriers.

Dataset Information

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

Publications

Evaluation of in silico predictors on short nucleotide variants in <i>HBA1</i>, <i>HBA2</i>, and <i>HBB</i> associated with haemoglobinopathies.

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