Characterization of Blood Group Variants in an Omani Population by Comparison of Whole Genome Sequencing and Serology.

Although blood group variation was first described over a century ago, our understanding of the genetic variation affecting antigenic expression on the red blood cell surface in many populations is lacking. This deficit limits the ability to accurately type patients, especially as serological testing is not available for all described blood groups, and targeted genotyping panels may lack rare or population-specific variants. Here, we perform serological assays across 24 antigens and whole genome sequencing on 100 Omanis, a population underrepresented in genomic databases. We inferred blood group phenotypes using the most commonly typed genetic variants. The comparison of serological to inferred phenotypes resulted in an average concordance of 96.9%. Among the 22 discordances, we identify seven known variants in four blood groups that, to our knowledge, have not been previously reported in Omanis. Incorporating these variants for phenotype inference, concordance increases to 98.8%. Additionally, we describe five candidate variants in the Lewis, Lutheran, MNS, and P1 blood groups that may affect antigenic expression, although further functional confirmation is required. Notably, we identify several blood group alleles most common in African populations, likely introduced to Oman by gene flow over the last thousand years. These findings highlight the need to evaluate individual populations and their population history when considering variants to include in genotype panels for blood group typing. This research will inform future work in blood banks and transfusion services.

Imputation of race and ethnicity categories using genetic ancestry from real-world genomic testing data.

The incompleteness of race and ethnicity information in real-world data (RWD) hampers its utility in promoting healthcare equity. This study introduces two methods-one heuristic and the other machine learning-based-to impute race and ethnicity from genetic ancestry using tumor profiling data. Analyzing de-identified data from over 100,000 cancer patients sequenced with the Tempus xT panel, we demonstrate that both methods outperform existing geolocation and surname-based methods, with the machine learning approach achieving high recall (range: 0.859-0.993) and precision (range: 0.932-0.981) across four mutually exclusive race and ethnicity categories. This work presents a novel pathway to enhance RWD utility in studying racial disparities in healthcare.

Rapid Evolution of Glycan Recognition Receptors Reveals an Axis of Host-Microbe Arms Races beyond Canonical Protein-Protein Interfaces.

Detection of microbial pathogens is a primary function of many mammalian immune proteins. This is accomplished through the recognition of diverse microbial-produced macromolecules including proteins, nucleic acids, and carbohydrates. Pathogens subvert host defenses by rapidly changing these structures to avoid detection, placing strong selective pressures on host immune proteins that repeatedly adapt to remain effective. Signatures of rapid evolution have been identified in numerous immunity proteins involved in the detection of pathogenic protein substrates, but whether similar signals can be observed in host proteins engaged in interactions with other types of pathogen-derived molecules has received less attention. This focus on protein-protein interfaces has largely obscured the study of fungi as contributors to host-pathogen conflicts, despite their importance as a formidable class of vertebrate pathogens. Here, we provide evidence that mammalian immune receptors involved in the detection of microbial glycans have been subject to recurrent positive selection. We find that rapidly evolving sites in these genes cluster in key functional domains involved in carbohydrate recognition. Further, we identify convergent patterns of substitution and evidence for balancing selection in one particular gene, MelLec, which plays a critical role in controlling invasive fungal disease. Our results also highlight the power of evolutionary analyses to reveal uncharacterized interfaces of host-pathogen conflict by identifying genes, like CLEC12A, with strong signals of positive selection across mammalian lineages. These results suggest that the realm of interfaces shaped by host-microbe conflicts extends beyond the world of host-viral protein-protein interactions and into the world of microbial glycans and fungi.