Evaluation of in silico predictors on short nucleotide variants in HBA1, HBA2 and HBB associated with haemoglobinopathies
- Cyprus Institute of Neurology and Genetics, Cyprus
- Leiden University Medical Center, Netherlands
- Centro Hospitalar e Universitário de Coimbra, Portugal
- National and Kapodistrian University of Athens, Greece
- Ministry of Health Malaysia, Malaysia
- Universiti Sains Malaysia, Malaysia
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 ACMG/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.
Data availability
All data generated or analysed during this study are included in Supporting File 2 and Supporting File 3. Supporting File 2 provides the full dataset and subsets used as input in the analysis (sheet names starting with "Input") as well as the results of the analysis (sheets starting with "On"). Supporting File 3 includes the finetuning analysis for specific tools and data subsets, as described in the manuscript.We make the source code for evaluating the tools and generating the figures presented herein, freely available at https://github.com/cing-mgt/evaluation-of-in-silico-predictors.
Article and author information
Author details
Joanne Traeger-Synodinos
Funding
Research and Innovation Foundation [Cyprus] (EXCELLENCE/1216/256)
- Maria Xenophontos
- Anna Minaidou
- Bin Alwi Zilfalil
- Marina Kleanthous
- Petros Kountouris
The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.
Reviewing Editor
- Robert Baiocchi, The Ohio State University, United States
Version history
- Preprint posted: April 9, 2022 (view preprint)
- Received: April 23, 2022
- Accepted: October 31, 2022
- Accepted Manuscript published: December 1, 2022 (version 1)
- Version of Record published: December 8, 2022 (version 2)
Copyright
© 2022, Tamana et al.
This article is distributed under the terms of the Creative Commons Attribution License permitting unrestricted use and redistribution provided that the original author and source are credited.
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Evaluation of in silico predictors on short nucleotide variants in HBA1, HBA2 and HBB associated with haemoglobinopathies
eLife 11:e79713.
https://doi.org/10.7554/eLife.79713
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