In silico blood genotyping from exome sequencing data

Background: Over the last decade, we have witnessed an incredible growth in the field of exome and genome sequencing. This information can be used to predict phenotypes for a number of traits of medical relevance. Here, we have focused on the identification of blood cell traits, developing BOOGIE, a tool recognizing relevant mutations through genome analysis and interpreting them in several blood traits important for transfusions. Results: In our method, we extract relevant mutation data and annotate a genome with ANNOVAR. These variants are then directly compared with our knowledge base, containing association rules between mutations and phenotypes for the ten major blood groups: ABO, Rh, Duffy, Kell, Diego, Kidd, Lewis, Lutheran, MNS and Bombay. Whenever a match is found, it is used to predict the related phenotype and list causative mutations. The decision process is implemented as an expert system, automatically performing the logical reasoning connected to the genome variants. Interactions with other proteins and enzymes are easily kept into account during the full process, e.g. for the Bombay phenotype. This rare and easily misclassified genetic trait involves three blood groups, making blood donations potentially lethal. Conclusions: BOOGIE was tested on Personal Genome Project (PGP) data. The blood traits for genomes with available ABO and Rh annotation were correctly predicted in between 86% and 100% of cases. The analysis is very efficient, making it suitable for genome scale diagnostic applications in personalized medicine. The versatility and simplicity of the analysis make it easily interpretable and allows easy extension of the protocol towards other blood related traits.