Genetic blood typing by high throughput sequencing
Editorial Commentary

Genetic blood typing by high throughput sequencing

Meng Cai1,2, Daniel Xin Zhang2,3, Chingyee Moo1,2, Jiahai Shi1,2

1Department of Biomedical Sciences, City University of Hong Kong, Hong Kong, China; 2City University of Hong Kong Shenzhen Research Institute, Shenzhen 518000, China; 3Jockey Club College of Veterinary Medicine and Life Sciences, City University of Hong Kong, Hong Kong, China

Correspondence to: Jiahai Shi. Department of Biomedical Sciences, City University of Hong Kong, Hong Kong, China. Email: jiahai.shi@cityu.edu.hk.

Provenance: This is an Invited Editorial commissioned by the Section Editor Dr. Wenxu Hong (Shenzhen Blood Center, Shenzhen, China).

Comment on: Montemayor-Garcia C, Karagianni P, Stiles DA, et al. Genomic coordinates and continental distribution of 120 blood group variants reported by the 1000 Genomes Project. Transfusion 2018;58:2693-704.


Received: 04 March 2019; Accepted: 15 March 2019; Published: 18 March 2019.

doi: 10.21037/aob.2019.03.01


Accurate blood typing is an essential step to minimize alloimmunization caused by blood transfusion. For patients with chronic anemia, such as sickle cell disease and thalassemia, multiple transfusions are indispensable in the long run. However, the management of transfusion remains challenging in clinical practice (1). Limited accuracy of serological tests can cause mismatches and the resulting alloimmunization can be life-threatening (2). Weak or partial expression of antigen might also confuse the blood typing (3). Therefore, conventional blood typing using serology may not best suit the clinical demands on the rise and genetic blood group testing would be a better alternative to complement the current serological methods.

Conventional serological methods are based on the immunoreactions between transmembrane antigens of red blood cells and antibodies in the serum. Most of time, serological tests provide rapid and reliable results in an inexpensive fashion. However, there are cases where blood typing based on serology fails and leads to blood type mismatches. With the advancement of molecular techniques, commercial microarray- based platforms are clinically available for identifying single nucleotide polymorphisms (SNPs) in blood group antigen coding genes (4,5). The detection of SNPs can compensate for the less common variants which serological methods do not routinely cover. However, the current SNP typing method cannot fully identify the existing variations especially the unreported ones and is not applicable to detect rare chromosomal rearrangement events such as large insertion, deletion or hybridization (6). Therefore, more comprehensive database of blood group antigen coding genes are urgently warranted for precise blood typing.

As multiple next-generation sequencing (NGS) platforms have been commercialized since 2005, NGS has become a powerful tool for discovery of novel genetic variants in both clinical and scientific research. Stabentheiner and colleagues (7) applied NGS in blood group research first in RHD genotyping and provided a proof of concept in 2010. Then NGS was applied in whole blood group detection. In 2015, Giollo and colleagues (8) proposed a predictor, BOOGIE, for the inference of blood groups from SNV databases and achieved a 94% concordance for the Personal Genome Project database. The DNA data need to be analyzed in advance. Lane and colleagues (9) improved the accuracy of the whole genome sequencing typing algorithm, BloodTyper, by using iteratively learning method to compare the genotyping result with the traditional serological and SNP results. The initial concordance with the serological and SNP results was 99.5% across the 20 genomes from the MedSeq Project randomized controlled trial. The improvement of the following round, 99.8% concordant, of the remaining 90 MedSeq genomes was due to the discordances addressed. The final algorithm was 99.9% concordant for 200 genomes with low depth of coverage, 15×, from INTERVAL study after adjustment. Compared with BOOGIE, BloodTyper get full blood group profiles from genomic data automatically and obtain a higher concordance. However, a larger cohort of samples will be needed for further evaluation of the algorithm and more attention should be paid to rare blood groups, especially those with high homologous sequence, in future investigations.

The existing antibody-based serological tests do not cover all major blood groups that may pose an alloimmunization risk in blood transfusion (10). Besides that, serological method is usually labor-intensive and time-consuming. The abnormal antibodies caused by multiple pregnancies or previous blood transfusions may lead to the false conclusions of serological blood typing. The microarray-based SNP tests are established on the foundation of previous studies and therefore is not suitable for discovery of new variants. The major defect of this method lies in its inability to detect structural changes at chromosomal level. Targeted exome sequencing in blood group genotyping has been studied (11,12) due to its lower cost as compared to the whole genome sequencing. Despite the advantages of low cost, the method cannot detect splice variants and missing non-coding variants those are important for blood group genes (13,14). On the one hand, the blood group analysis using whole genome sequencing provides the complete profiles in one go and can be used for rare blood group discovery (15). NGS allows the detection of the unknown single nucleotide variations (SNVs) which is unavailable when using conventional serological tests or microarray-based platforms. On the other hand, NGS obtains a massive quantity of information all at once (16) and the abnormal antibodies that are misleading in serological test can be avoided in NGS analysis, too. However, the algorithm of the massive data processing remains the most challenging issue. The cost of sequencing, data storage and labor force need to be taken into consideration as well. From the perspective of NGS principles, the limitations of extensive use in blood group research include the short-read length and the high cost of experiment and data analysis (17,18). Along with the development of biological and computational sciences (19), we stay positive on the great expectation that the shortcomings of NGS will be overcome and that the accuracy can be improved in the foreseeable future. In Lane’s research (9), only part of the known antigens was studied and the copy number analyses require further optimizations. In spite of the limitations, the study provides an exceptional idea of automatic analysis of whole genome sequencing for blood group typing and related medical applications.


Acknowledgements

Funding: This project is funded by the Hong Kong Health, and Medical Research Fund (grant 05160296), the Hong Kong Research Grants Council (grant 21101218), the National Natural Science Foundation of China (grant 81770099) and Shenzhen Science and Technology Innovation Fund (grant JCYJ20170413115637100).


Footnote

Conflicts of Interest: The authors have no conflicts of interest to declare.


References

  1. Chou ST, Flanagan JM, Vege S, et al. Whole-exome sequencing for RH genotyping and alloimmunization risk in children with sickle cell anemia. Blood Adv 2017;1:1414-22. [Crossref] [PubMed]
  2. Avent ND. Large scale blood group genotyping. Transfus Clin Biol 2007;14:10-5. [Crossref] [PubMed]
  3. Johnsen JM. Using red blood cell genomics in transfusion medicine. Hematology Am Soc Hematol Educ Program 2015;2015:168-76. [Crossref] [PubMed]
  4. Veldhuisen B, van der Schoot CE, de Haas M. Blood group genotyping: from patient to high-throughput donor screening. Vox Sang 2009;97:198-206. [Crossref] [PubMed]
  5. Denomme GA. Prospects for the provision of genotyped blood for transfusion. Br J Haematol 2013;163:3-9. [Crossref] [PubMed]
  6. Reid ME, Denomme GA. DNA-based methods in the immunohematology reference laboratory. Transfus Apher Sci 2011;44:65-72. [Crossref] [PubMed]
  7. Stabentheiner S, Danzer M, Niklas N, et al. Overcoming methodical limits of standard RHD genotyping by next-generation sequencing. Vox Sang 2011;100:381-8. [Crossref] [PubMed]
  8. Giollo M, Minervini G, Scalzotto M, et al. BOOGIE: Predicting Blood Groups from High Throughput Sequencing Data. PLoS One 2015;10:e0124579. [Crossref] [PubMed]
  9. Lane WJ, Westhoff CM, Gleadall NS, et al. Automated typing of red blood cell and platelet antigens: a whole-genome sequencing study. Lancet Haematol 2018;5:e241-51. [Crossref] [PubMed]
  10. Chou ST, Liem RI, Thompson AA. Challenges of alloimmunization in patients with haemoglobinopathies. Br J Haematol 2012;159:394-404. [Crossref] [PubMed]
  11. Schoeman EM, Lopez GH, McGowan EC, et al. Evaluation of targeted exome sequencing for 28 protein-based blood group systems, including the homologous gene systems, for blood group genotyping. Transfusion 2017;57:1078-88. [Crossref] [PubMed]
  12. Schoeman EM, Roulis EV, Liew YW, et al. Targeted exome sequencing defines novel and rare variants in complex blood group serology cases for a red blood cell reference laboratory setting. Transfusion 2018;58:284-93. [Crossref] [PubMed]
  13. Giardine B, Riemer C, Hefferon T, et al. PhenCode: connecting ENCODE data with mutations and phenotype. Hum Mutat 2007;28:554-62. [Crossref] [PubMed]
  14. Wagner FF, Flegel WA. RHD gene deletion occurred in the Rhesus box. Blood 2000;95:3662-8. [PubMed]
  15. Jakobsen MA, Dellgren C, Sheppard C, et al. The use of next-generation sequencing for the determination of rare blood group genotypes. Transfus Med 2017. [Epub ahead of print]. [Crossref] [PubMed]
  16. Johnsen JM, Nickerson DA, Reiner AP. Massively parallel sequencing: the new frontier of hematologic genomics. Blood 2013;122:3268-75. [Crossref] [PubMed]
  17. Treangen TJ, Salzberg SL. Repetitive DNA and next-generation sequencing: computational challenges and solutions. Nat Rev Genet 2011;13:36-46. [Crossref] [PubMed]
  18. Wu PC, Pai SC, Chen PL. Blood group genotyping goes next generation: featuring ABO, RH and MNS. ISBT Sci Ser 2018;13:290-7. [Crossref]
  19. Goodwin S, McPherson JD, McCombie WR. Coming of age: ten years of next-generation sequencing technologies. Nat Rev Genet 2016;17:333-51. [Crossref] [PubMed]
doi: 10.21037/aob.2019.03.01
Cite this article as: Cai M, Zhang DX, Moo C, Shi J. Genetic blood typing by high throughput sequencing. Ann Blood 2019;4:6.