Genetic prediction of 33 blood group phenotypes using an existing genotype dataset

Background: Accurate blood type data are essential for blood bank management, but due to costs, few of 43 blood group systems are routinely determined in Danish blood banks. However, a more comprehensive dataset of blood types is useful in scenarios such as rare blood type allocation. We aimed to investigate the viability and accuracy of predicting blood types by leveraging an existing dataset of imputed genotypes for two cohorts of approximately 90,000 each (Danish Blood Donor Study and Copenhagen Biobank) and present a more comprehensive overview of blood types for our Danish donor cohort.

Study design and methods: Blood types were predicted from genome array data using known variant determinants. Prediction accuracy was confirmed by comparing with preexisting serological blood types. The Vel blood group was used to test the viability of using genetic prediction to narrow down the list of candidate donors with rare blood types.

Results: Predicted phenotypes showed a high balanced accuracy >99.5% in most cases: A, B, C/c, Co^a /Co^b , Do^a /Do^b , E/e, Jk^a /Jk^b , Kn^a /Kn^b , Kp^a /Kp^b , M/N, S/s, Sd^a , Se, and Yt^a /Yt^b , while some performed slightly worse: Fy^a /Fy^b , K/k, Lu^a /Lu^b , and Vel ~99%-98% and C^W and P₁ ~96%. Genetic prediction identified 70 potential Vel negatives in our cohort, 64 of whom were confirmed correct using polymerase chain reaction (negative predictive value: 91.5%).

Discussion: High genetic prediction accuracy in most blood groups demonstrated the viability of generating blood types using preexisting genotype data at no cost and successfully narrowed the pool of potential individuals with the rare Vel-negative phenotype from 180,000 to 70.