BOOGIE: Predicting Blood Groups from High Throughput Sequencing Data

Over the last decade, we have witnessed an incredible growth in the amount of available genotype data due to high throughput sequencing (HTS) techniques. This information may be used to predict phenotypes of medical relevance, and pave the way towards personalized medicine. Blood phenotypes (e.g. ABO and Rh) are a purely genetic trait that has been extensively studied for decades, with currently over thirty known blood groups. Given the public availability of blood group data, it is of interest to predict these phenotypes from HTS data which may translate into more accurate blood typing in clinical practice. Here we propose BOOGIE, a fast predictor for the inference of blood groups from single nucleotide variant (SNV) databases. We focus on the prediction of thirty blood groups ranging from the well known ABO and Rh, to the less studied Junior or Diego. BOOGIE correctly predicted the blood group with 94% accuracy for the Personal Genome Project whole genome profiles where good quality SNV annotation was available. Additionally, our tool produces a high quality haplotype phase, which is of interest in the context of ethnicity-specific polymorphisms or traits. The versatility and simplicity of the analysis make it easily interpretable and allow easy extension of the protocol towards other phenotypes. BOOGIE can be down-loaded from URL http://protein.bio.unipd.it/download/

Comparative molecular analysis of the Drosophila olfactory subsystems identifies a support cell-expressed Osiris protein required for pheromone sensitivity

BACKGROUND: The nose of most animals comprises multiple sensory subsystems, which are defined by the expression of different olfactory receptor families. Drosophila melanogaster antennae contain two morphologically and functionally distinct subsystems that express odorant receptors (Ors) or ionotropic receptors (Irs). Although these receptors have been thoroughly characterized in this species, the subsystem-specific expression and roles of other genes are much less well-understood. RESULTS: Here we generate subsystem-specific transcriptomic datasets to identify hundreds of genes, encoding diverse protein classes, that are selectively enriched in either Or or Ir subsystems. Using single-cell antennal transcriptomic data and RNA in situ hybridization, we find that most neuronal genes—other than sensory receptor genes—are broadly expressed within the subsystems. By contrast, we identify many non-neuronal genes that exhibit highly selective expression, revealing substantial molecular heterogeneity in the non-neuronal cellular components of the olfactory subsystems. We characterize one Or subsystem-specific non-neuronal molecule, Osiris 8 (Osi8), a conserved member of a large, insect-specific family of transmembrane proteins. Osi8 is expressed in the membranes of tormogen support cells of pheromone-sensing trichoid sensilla. Loss of Osi8 does not have obvious impact on trichoid sensillar development or basal neuronal activity, but abolishes high sensitivity responses to pheromone ligands. CONCLUSIONS: This work identifies a new protein required for insect pheromone detection, emphasizes the importance of support cells in neuronal sensory functions, and provides a resource for future characterization of other olfactory subsystem-specific genes. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12915-022-01425-w

Explanation for the ABO blood group mispredictions.

<p>In two cases the solution requires an improved weighting scheme, while in the latter two there is no definitive answer.</p><p>Explanation for the ABO blood group mispredictions.</p

Sample haplotype table extracted for the ABO system.

<p>Sample haplotype table extracted for the ABO system.</p

Fraction of dbSNP variants annotated for each blood group.

<p>The most important groups for the transfusion (grey bars) have good annotation in BGMUT, explaining the quality of our results. Conversely, most variants in the less studied systems still need further research for a proper automatic annotation.</p

Schematic BOOGIE overview.

<p>The tool requires just genotype data and an haplotype table for its execution. Genotype must be specified in a tabular file similar to VCF format, where chromosome, genomic position, nucleotides ad zygosity are specified. Haplotypes are defined in a tabular file, and each row specify the expected SNVs of a target phenotype. See README file of the application for format details. BOOGIE search for key variants in the input genotype, and optimize their assignment to a haplotypes with known phenotype according to the 1-nearest neighbour algorithm. The SNV permutation with best score is the one with highest phenotype likelihood.</p

Sample ABO gene exonic mutations and phenotpe.

<p>Given the input variants, BOOGIE select all the key mutations specified in the Haplotype table for ABO group that can play a role (in chromosome 9). The tool assigns all heterozygous SNVs to the same chromatid, as this represents the most likely haplotype. As a result, the corresponding proteins will express either the A102 or B101 blood group. Genetic analysis suggests that both antigens will be present in PGP sample hu604D39, which is confirmed by a serological test. It should be noted that the SNVs reported use hg19 as reference, thus differing from the BGMUT definition of A and B blood groups. In addition, not all variants reported are necessary for the final phenotype, as some of them are common for different groups.</p

Predicted blood group distribution for the PGP full genome dataset.

<p>For each PGP sample, the possible occurrence of uncommon blood groups is highlighted. The prediction is based on the observation of coding SNVs known to be associated with uncommon blood groups. When no known variant is found, the phenotype is assumed to be the reference one. The existence of non-coding or new uncharacterized variants relevant for a blood system can influence BOOGIE, leading to some false negative predictions.</p