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A machine-learning method for biobank-scale genetic prediction of blood group antigens

Hyvärinen, Kati ; Haimila, Katri ; Moslemi, Camous ; Biobank, Blood Service ; Olsson, Martin L. LU orcid ; Ostrowski, Sisse R. ; Pedersen, Ole B. ; Erikstrup, Christian ; Partanen, Jukka and Ritari, Jarmo (2024) In PLoS Computational Biology 20(3).
Abstract

A key element for successful blood transfusion is compatibility of the patient and donor red blood cell (RBC) antigens. Precise antigen matching reduces the risk for immunization and other adverse transfusion outcomes. RBC antigens are encoded by specific genes, which allows developing computational methods for determining antigens from genomic data. We describe here a classification method for determining RBC antigens from genotyping array data. Random forest models for 39 RBC antigens in 14 blood group systems and for human platelet antigen (HPA)-1 were trained and tested using genotype and RBC antigen and HPA-1 typing data available for 1,192 blood donors in the Finnish Blood Service Biobank. The algorithm and models were further... (More)

A key element for successful blood transfusion is compatibility of the patient and donor red blood cell (RBC) antigens. Precise antigen matching reduces the risk for immunization and other adverse transfusion outcomes. RBC antigens are encoded by specific genes, which allows developing computational methods for determining antigens from genomic data. We describe here a classification method for determining RBC antigens from genotyping array data. Random forest models for 39 RBC antigens in 14 blood group systems and for human platelet antigen (HPA)-1 were trained and tested using genotype and RBC antigen and HPA-1 typing data available for 1,192 blood donors in the Finnish Blood Service Biobank. The algorithm and models were further evaluated using a validation cohort of 111,667 Danish blood donors. In the Finnish test data set, the median (interquartile range [IQR]) balanced accuracy for 39 models was 99.9 (98.9-100)%. We were able to replicate 34 out of 39 Finnish models in the Danish cohort and the median (IQR) balanced accuracy for classifications was 97.1 (90.1-99.4)%. When applying models trained with the Danish cohort, the median (IQR) balanced accuracy for the 40 Danish models in the Danish test data set was 99.3 (95.1-99.8)%. The RBC antigen and HPA-1 prediction models demonstrated high overall accuracies suitable for probabilistic determination of blood groups and HPA-1 at biobank- scale. Furthermore, population-specific training cohort increased the accuracies of the models. This stand-alone and freely available method is applicable for research and screening for antigen-negative blood donors.

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author
; ; ; ; ; ; ; ; and
organization
publishing date
type
Contribution to journal
publication status
published
subject
in
PLoS Computational Biology
volume
20
issue
3
article number
e1011977
publisher
Public Library of Science (PLoS)
external identifiers
  • pmid:38512997
  • scopus:85188441904
ISSN
1553-734X
DOI
10.1371/journal.pcbi.1011977
language
English
LU publication?
yes
id
891ab985-175f-49ca-b158-fccf09488a90
date added to LUP
2024-04-16 12:46:12
date last changed
2024-04-30 14:19:17
@article{891ab985-175f-49ca-b158-fccf09488a90,
  abstract     = {{<p>A key element for successful blood transfusion is compatibility of the patient and donor red blood cell (RBC) antigens. Precise antigen matching reduces the risk for immunization and other adverse transfusion outcomes. RBC antigens are encoded by specific genes, which allows developing computational methods for determining antigens from genomic data. We describe here a classification method for determining RBC antigens from genotyping array data. Random forest models for 39 RBC antigens in 14 blood group systems and for human platelet antigen (HPA)-1 were trained and tested using genotype and RBC antigen and HPA-1 typing data available for 1,192 blood donors in the Finnish Blood Service Biobank. The algorithm and models were further evaluated using a validation cohort of 111,667 Danish blood donors. In the Finnish test data set, the median (interquartile range [IQR]) balanced accuracy for 39 models was 99.9 (98.9-100)%. We were able to replicate 34 out of 39 Finnish models in the Danish cohort and the median (IQR) balanced accuracy for classifications was 97.1 (90.1-99.4)%. When applying models trained with the Danish cohort, the median (IQR) balanced accuracy for the 40 Danish models in the Danish test data set was 99.3 (95.1-99.8)%. The RBC antigen and HPA-1 prediction models demonstrated high overall accuracies suitable for probabilistic determination of blood groups and HPA-1 at biobank- scale. Furthermore, population-specific training cohort increased the accuracies of the models. This stand-alone and freely available method is applicable for research and screening for antigen-negative blood donors.</p>}},
  author       = {{Hyvärinen, Kati and Haimila, Katri and Moslemi, Camous and Biobank, Blood Service and Olsson, Martin L. and Ostrowski, Sisse R. and Pedersen, Ole B. and Erikstrup, Christian and Partanen, Jukka and Ritari, Jarmo}},
  issn         = {{1553-734X}},
  language     = {{eng}},
  number       = {{3}},
  publisher    = {{Public Library of Science (PLoS)}},
  series       = {{PLoS Computational Biology}},
  title        = {{A machine-learning method for biobank-scale genetic prediction of blood group antigens}},
  url          = {{http://dx.doi.org/10.1371/journal.pcbi.1011977}},
  doi          = {{10.1371/journal.pcbi.1011977}},
  volume       = {{20}},
  year         = {{2024}},
}