ALBERT

Description: Background The development of inhibitors directed against coagulation factor VIII (FVIII) is a common and severe complication of hemophilia replacement therapy, occurring in up to one third of patients with severe hemophilia A (HA). Inhibitors impair efficacy of replacement therapy, rendering management of bleeds difficult and prophylaxis unfeasible. There is evidence that various genetic risk factors play an important role in inhibitor development; in particular, F8 mutation type. However, patients sharing the same mutation may have different inhibitor risk suggesting the presence of other genetic risk factors. Because most variants in the genome are rare and since variants within the protein-coding area (i.e. the exome) are expected to have a strong effect on phenotypes, we designed an exome sequencing study with the goal of assessing the role of rare, functional variants of the exome in inhibitor development. Methods A total of 28 HA patients (8 pairs of related individuals and 12 unrelated subjects) underwent sequencing of the exome on Illumina HiSeq 2000 platforms following capture by hybridization on VCRome V2.1 liquid probes. Patients were selected among those followed at the Angelo Bianchi Bonomi Hemophilia and Thrombosis Center (Milan, Italy). Inclusion criteria were (a) diagnosis of severe HA, with plasmatic FVIII activity below 1%, and the presence of intron 22 or intron 1 inversion of F8. Mapping of reads onto the human reference genome (hg19/NCBI build 37.1) was performed by BWA and variant calling by GATK. We restricted our analysis to functional variants (i.e. missense, nonsense, splice-site variants and coding indels) that are rare in Caucasian populations (i.e. with a minor allele frequency below 0.5% in the European Americans of the exome variant server). For association analysis, we carried out burden-testing association using (a) SKAT-O test, as well as gene-based association by (b) Fisher’s exact testing and (c) mixed-effects logistic regression analysis adjusted for familial clustering. The lists of top 100-genes and genes with p