RBCeq: A robust and scalable algorithm for accurate genetic blood typing.

Area of Science:

• Genetics
• Genomics
• Immunohematology

Background:

• Repetitive blood transfusions pose alloimmunization risks due to blood group polymorphisms.
• Current methods for genetic blood typing from next-generation sequencing data lack user-friendliness and accuracy.
• A need exists for advanced genetic blood typing solutions to improve patient care.

Purpose of the Study:

• To develop a novel, user-friendly genetic blood typing algorithm.
• To accurately identify a comprehensive set of blood group systems from sequencing data.
• To address limitations in current blood typing methodologies.

Main Methods:

• Developed RBCeq, a genetic blood typing algorithm.
• RBCeq identifies complex blood groups (e.g., RH, ABO) by analyzing small indels and copy number variants.
• The algorithm reports rare and novel variants with potential clinical relevance.

Main Results:

• RBCeq demonstrated 99.07% concordance in validation studies.
• Validated against serology (29 antigens) and SNP-array (9 antigens) across 14 blood group systems.
• A user-friendly web server (https://www.rbceq.org/) was developed for detailed reporting and visualization.

Conclusions:

• RBCeq overcomes limitations in scalability, reproducibility, and accuracy for genotyping and phenotyping in multi-ethnic populations.
• The AWS cloud-based platform can reduce pre-transfusion testing time.
• Increased sample processing throughput and improved patient care quality are anticipated.