Large deletions and small insertions and deletions in the factor VIII gene predict unfavorable immune tolerance induction outcome in people with severe hemophilia A and high-responding inhibitors.

INTRODUCTION: Hemophilia A is an inherited bleeding disorder caused by pathogenic variants in the factor VIII gene (F8), which leads to factor VIII (FVIII) deficiency. Immune tolerance induction (ITI) is a therapeutic approach to eradicate alloantibodies (inhibitors) against exogenous FVIII in people with inherited hemophilia A. Few studies have evaluated the role of F8 variants on ITI outcome. MATERIAL AND METHODS: We included people with severe hemophilia A (FVIII ˂ 1 international units/dL) and high-responding inhibitors (≥ 5 Bethesda units/mL lifelong) who underwent a first course of ITI. Socio-demographic, clinical and laboratory data were collected. ITI outcomes were defined as total, partial successes, and failure. Detection of intron 1 and 22 inversions was performed by polymerase-chain reaction, followed by F8 sequencing. RESULTS: We included 168 people with inherited hemophilia A and high-responding inhibitors, median age 6 years at ITI start. Intron 22 inversion was the most prevalent variant (53.6 %), followed by nonsense (16.1 %), small insertion/deletion (11.3 %), and large deletion (10.7 %). In comparison with intron 22 inversion, the odds of ITI failure were 15.5 times higher (odds ratio [OR] 15.50; 95 % confidence interval [95 % CI] 4.59-71.30) and 4.25 times higher (95 % CI, 1.53-12.3) among carriers of F8 large deletions and small insertions and deletions, respectively. CONCLUSION: F8 large deletions and small insertions/deletions predicted ITI failure after a first course of ITI in patients with severe hemophilia A and high-responding inhibitors. This is the first study to show F8 large deletions and small insertions/deletions as predictors of ITI failure.

NAToRA, a relatedness-pruning method to minimize the loss of dataset size in genetic and omics analyses.

Genetic and omics analyses frequently require independent observations, which is not guaranteed in real datasets. When relatedness cannot be accounted for, solutions involve removing related individuals (or observations) and, consequently, a reduction of available data. We developed a network-based relatedness-pruning method that minimizes dataset reduction while removing unwanted relationships in a dataset. It uses node degree centrality metric to identify highly connected nodes (or individuals) and implements heuristics that approximate the minimal reduction of a dataset to allow its application to complex datasets. When compared with two other popular population genetics methodologies (PLINK and KING), NAToRA shows the best combination of removing all relatives while keeping the largest possible number of individuals in all datasets tested and also, with similar effects on the allele frequency spectrum and Principal Component Analysis than PLINK and KING. NAToRA is freely available, both as a standalone tool that can be easily incorporated as part of a pipeline, and as a graphical web tool that allows visualization of the relatedness networks. NAToRA also accepts a variety of relationship metrics as input, which facilitates its use. We also release a genealogies simulator software used for different tests performed in this study.