TY - JOUR TI - Evaluation of in silico predictors on short nucleotide variants in HBA1, HBA2, and HBB associated with haemoglobinopathies AU - Tamana, Stella AU - Xenophontos, Maria AU - Minaidou, Anna AU - Stephanou, Coralea AU - Harteveld, Cornelis L AU - Bento, Celeste AU - Traeger-Synodinos, Joanne AU - Fylaktou, Irene AU - Yasin, Norafiza Mohd AU - Abdul Hamid, Faidatul Syazlin AU - Esa, Ezalia AU - Halim-Fikri, Hashim AU - Zilfalil, Bin Alwi AU - Kakouri, Andrea C AU - ClinGen Hemoglobinopathy Variant Curation Expert Panel AU - Kleanthous, Marina AU - Kountouris, Petros A2 - Baiocchi, Robert A2 - Zaidi, Mone A2 - Hamid, Mohammad VL - 11 PY - 2022 DA - 2022/12/01 SP - e79713 C1 - eLife 2022;11:e79713 DO - 10.7554/eLife.79713 UR - https://doi.org/10.7554/eLife.79713 AB - 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. KW - variant classification KW - in silico prediction KW - haemoglobinopathies KW - globin genes KW - thalassaemia JF - eLife SN - 2050-084X PB - eLife Sciences Publications, Ltd ER -