Abstract
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 nonoverlapping 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 nonmissense variants. Moreover, SpliceAl 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.
Competing Interest Statement
The authors have declared no competing interest.
