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

Sudhir Jadhao; Candice L Davison; Eileen V Roulis; Elizna M Schoeman; Mayur Divate; Mitchel Haring; Chris Williams; Arvind Jaya Shankar; Simon Lee; Natalie M Pecheniuk; David O Irving; Catherine A Hyland; Robert L Flower; Shivashankar H Nagaraj

doi:10.1016/j.ebiom.2021.103759

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

EBioMedicine. 2022 Feb:76:103759. doi: 10.1016/j.ebiom.2021.103759. Epub 2022 Jan 14.

Affiliations

¹ Centre for Genomics and Personalised Health, Queensland University of Technology, Brisbane, Queensland 4059, Australia.
² Australian Red Cross Lifeblood Research and Development, Brisbane, Queensland, Australia.
³ Australian Red Cross Lifeblood Research and Development, Brisbane, Queensland, Australia; Faculty of Health, Queensland University of Technology, Brisbane, Australia.
⁴ Office of eResearch, Queensland University of Technology, Brisbane, Queensland 4059, Australia.
⁵ School of Biomedical Sciences, Queensland University of Technology, Brisbane, Australia.
⁶ Research and Development, Australian Red Cross Blood Service, Sydney, New South Wales, Australia.
⁷ Centre for Genomics and Personalised Health, Queensland University of Technology, Brisbane, Queensland 4059, Australia; Translational Research Institute, Brisbane, Australia. Electronic address: shiv.nagaraj@qut.edu.au.

Abstract

Background: While blood transfusion is an essential cornerstone of hematological care, patients requiring repetitive transfusion remain at persistent risk of alloimmunization due to the diversity of human blood group polymorphisms. Despite the promise, user friendly methods to accurately identify blood types from next-generation sequencing data are currently lacking. To address this unmet need, we have developed RBCeq, a novel genetic blood typing algorithm to accurately identify 36 blood group systems.

Methods: RBCeq can predict complex blood groups such as RH, and ABO that require identification of small indels and copy number variants. RBCeq also reports clinically significant, rare, and novel variants with potential clinical relevance that may lead to the identification of novel blood group alleles.

Findings: The RBCeq algorithm demonstrated 99·07% concordance when validated on 402 samples which included 29 antigens with serology and 9 antigens with SNP-array validation in 14 blood group systems and 59 antigens validation on manual predicted phenotype from variant call files. We have also developed a user-friendly web server that generates detailed blood typing reports with advanced visualization (https://www.rbceq.org/).

Interpretation: RBCeq will assist blood banks and immunohematology laboratories by overcoming existing methodological limitations like scalability, reproducibility, and accuracy when genotyping and phenotyping in multi-ethnic populations. This Amazon Web Services (AWS) cloud based platform has the potential to reduce pre-transfusion testing time and to increase sample processing throughput, ultimately improving quality of patient care.

Funding: This work was supported in part by Advance Queensland Research Fellowship, MRFF Genomics Health Futures Mission (76,757), and the Australian Red Cross LifeBlood. The Australian governments fund the Australian Red Cross Lifeblood for the provision of blood, blood products and services to the Australian community.

Keywords: Alloimmunization; Blood groups; Next-generation sequencing; Population genomics; Red blood cell antigens; Single nucleotide polymorphism; Transfusion.

MeSH terms

Algorithms
Australia
Blood Group Antigens* / genetics
Blood Grouping and Crossmatching*
Genotype
Humans
Reproducibility of Results

Substances

Blood Group Antigens