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

  1. Stella Tamana
  2. Maria Xenophontos
  3. Anna Minaidou
  4. Coralea Stephanou
  5. Cornelis L Harteveld
  6. Celeste Bento
  7. Joanne Traeger-Synodinos
  8. Irene Fylaktou
  9. Norafiza Mohd Yasin
  10. Faidatul Syazlin Abdul Hamid
  11. Ezalia Esa
  12. Hashim Halim-Fikri
  13. Bin Alwi Zilfalil
  14. Andrea C Kakouri
  15. ClinGen Hemoglobinopathy VCEP
  16. Marina Kleanthous
  17. Petros Kountouris  Is a corresponding author
  1. Cyprus Institute of Neurology and Genetics, Cyprus
  2. Leiden University Medical Center, Netherlands
  3. Centro Hospitalar e Universitário de Coimbra, Portugal
  4. National and Kapodistrian University of Athens, Greece
  5. Ministry of Health Malaysia, Malaysia
  6. 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

  1. Stella Tamana

    Molecular Genetics Thalassaemia Department, Cyprus Institute of Neurology and Genetics, Nicosia, Cyprus
    Competing interests
    The authors declare that no competing interests exist.
  2. Maria Xenophontos

    Molecular Genetics Thalassaemia Department, Cyprus Institute of Neurology and Genetics, Nicosia, Cyprus
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0001-5978-0193
  3. Anna Minaidou

    Molecular Genetics Thalassaemia Department, Cyprus Institute of Neurology and Genetics, Nicosia, Cyprus
    Competing interests
    The authors declare that no competing interests exist.
  4. Coralea Stephanou

    Molecular Genetics Thalassaemia Department, Cyprus Institute of Neurology and Genetics, Nicosia, Cyprus
    Competing interests
    The authors declare that no competing interests exist.
  5. Cornelis L Harteveld

    Leiden University Medical Center, Leiden, Netherlands
    Competing interests
    The authors declare that no competing interests exist.
  6. Celeste Bento

    Centro Hospitalar e Universitário de Coimbra, Coimbra, Portugal
    Competing interests
    The authors declare that no competing interests exist.
  7. Joanne Traeger-Synodinos

    Laboratory of Medical Genetics, National and Kapodistrian University of Athens, Athens, Greece
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-1860-5628
  8. Irene Fylaktou

    First Department of Pediatrics, National and Kapodistrian University of Athens, Athens, Greece
    Competing interests
    The authors declare that no competing interests exist.
  9. Norafiza Mohd Yasin

    Haematology Unit, Ministry of Health Malaysia, Selangor, Malaysia
    Competing interests
    The authors declare that no competing interests exist.
  10. Faidatul Syazlin Abdul Hamid

    Haematology Unit, Ministry of Health Malaysia, Selangor, Malaysia
    Competing interests
    The authors declare that no competing interests exist.
  11. Ezalia Esa

    Haematology Unit, Ministry of Health Malaysia, Selangor, Malaysia
    Competing interests
    The authors declare that no competing interests exist.
  12. Hashim Halim-Fikri

    Malaysian Node of the Human Variome Project, Universiti Sains Malaysia, Kelantan, Malaysia
    Competing interests
    The authors declare that no competing interests exist.
  13. Bin Alwi Zilfalil

    Human Genome Centre, Universiti Sains Malaysia, Kelantan, Malaysia
    Competing interests
    The authors declare that no competing interests exist.
  14. Andrea C Kakouri

    Molecular Genetics Thalassaemia Department, Cyprus Institute of Neurology and Genetics, Nicosia, Cyprus
    Competing interests
    The authors declare that no competing interests exist.
  15. ClinGen Hemoglobinopathy VCEP

  16. Marina Kleanthous

    Molecular Genetics Thalassaemia Department, Cyprus Institute of Neurology and Genetics, Nicosia, Cyprus
    Competing interests
    The authors declare that no competing interests exist.
  17. Petros Kountouris

    Molecular Genetics Thalassaemia Department, Cyprus Institute of Neurology and Genetics, Nicosia, Cyprus
    For correspondence
    petrosk@cing.ac.cy
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0003-2681-4355

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

  1. Robert Baiocchi, The Ohio State University, United States

Version history

  1. Preprint posted: April 9, 2022 (view preprint)
  2. Received: April 23, 2022
  3. Accepted: October 31, 2022
  4. Accepted Manuscript published: December 1, 2022 (version 1)
  5. 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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  1. Stella Tamana
  2. Maria Xenophontos
  3. Anna Minaidou
  4. Coralea Stephanou
  5. Cornelis L Harteveld
  6. Celeste Bento
  7. Joanne Traeger-Synodinos
  8. Irene Fylaktou
  9. Norafiza Mohd Yasin
  10. Faidatul Syazlin Abdul Hamid
  11. Ezalia Esa
  12. Hashim Halim-Fikri
  13. Bin Alwi Zilfalil
  14. Andrea C Kakouri
  15. ClinGen Hemoglobinopathy VCEP
  16. Marina Kleanthous
  17. Petros Kountouris
(2022)
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

Share this article

https://doi.org/10.7554/eLife.79713

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