Sickle cell anemia (SCA) is an inherited disease affecting the hemoglobin that is particularly common in sub-Saharan Africa. Although monogenic, phenotypes are markedly heterogeneous in terms of severity and life span. Hydroxyurea is still the most common treatment for these patients, and the response to treatment is highly variable and seems to be an inherited trait. Therefore, identifying the variants that might predict hydroxyurea response is important for identifying patients who will have a poorer or non-response to treatment, and the ones that are more prone to suffer from severe side effects. In the present pharmacogenetic study, we analyzed the exons of 77 genes described in the literature as potentially associated with hydroxyurea metabolism in Angolan children treated with hydroxyurea and evaluated the drug response considering fetal hemoglobin levels, other hematological and biochemical parameters, hemolysis, number of vaso-occlusive crises and hospitalizations. Thirty variants were identified in 18 of those genes as possibly associated with drug response, five of them in gene DCHS2. Other polymorphisms in this gene were also associated with hematological, biochemical and clinical parameters. Further research examining the maximum tolerated dose and fixed dose with a larger sample size is necessary to corroborate these findings.

The accurate detection of genetic variants is critical for advancing genomics research and precision medicine. However, this task remains challenging due to pervasive sequencing errors and complex genomic regions. The choice of variant calling software significantly influences results, creating a need for clear, evidence-based guidance. This study aims to provide a performance evaluation and a clear, evidence-based guide for selecting variant callers by benchmarking seven widely used tools, GATK, FreeBayes, DeepVariant, Samtools, Strelka2, Octopus, and Varscan2, highlighting their algorithmic trade-offs. The well-characterized NA12878 genome from the Genome in a Bottle consortium was analyzed. High-coverage whole-genome sequencing data was processed with each variant caller, and the resulting variant calling files were benchmarked against a gold-standard reference. Performance was assessed using precision, recall, and F1-score on a chromosome 20 subset and on full whole-genome data. The analysis revealed that DeepVariant’s deep learning approach achieved the highest precision (0.7869) and F1-score (0.8754) on chromosome 20. For whole-genome analysis, Strelka2 excelled in precision (0.8326), while Octopus demonstrated superior recall (0.9838). FreeBayes exhibited high sensitivity but lower precision, underscoring a key trade-off. There is no universally superior variant caller; the optimal choice depends on the specific research objectives, whether prioritizing precision, recall, or computational efficiency. This study serves as a crucial evidence-based resource for researchers and clinicians, enabling informed tool selection.