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

Moslemi, Camous; Sækmose, Susanne G.; Larsen, Rune; Bay, Jakob T.; Brodersen, Thorsten; Didriksen, Maria; Hjalgrim, Henrik; Banasik, Karina; Nielsen, Kaspar R.; Bruun, Mie T.; Dowsett, Joseph; Dinh, Khoa M.; Mikkelsen, Susan; Mikkelsen, Christina; Hansen, Thomas F.; Ullum, Henrik; Erikstrup, Christian; Brunak, Søren; Krogfelt, Karen Angeliki; Storry, Jill R.; Ostrowski, Sisse R.; Olsson, Martin L.; Pedersen, Ole B.

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

Mark

Moslemi, Camous ; Sækmose, Susanne G. ; Larsen, Rune ; Bay, Jakob T. ; Brodersen, Thorsten ; Didriksen, Maria ; Hjalgrim, Henrik ; Banasik, Karina ; Nielsen, Kaspar R. and Bruun, Mie T. , et al. (2023) In Transfusion 63(12). p.2297-2310

Abstract: 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... (More); 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.
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Please use this url to cite or link to this publication: https://lup.lub.lu.se/record/5b2e5dd2-0fd0-4751-8787-af47b0ecdfb6

author

Moslemi, Camous ; Sækmose, Susanne G. ; Larsen, Rune ; Bay, Jakob T. ; Brodersen, Thorsten ; Didriksen, Maria ; Hjalgrim, Henrik ; Banasik, Karina ; Nielsen, Kaspar R. and Bruun, Mie T. , et al. (More)

Moslemi, Camous ; Sækmose, Susanne G. ; Larsen, Rune ; Bay, Jakob T. ; Brodersen, Thorsten ; Didriksen, Maria ; Hjalgrim, Henrik ; Banasik, Karina ; Nielsen, Kaspar R. ; Bruun, Mie T. ; Dowsett, Joseph ; Dinh, Khoa M. ; Mikkelsen, Susan ; Mikkelsen, Christina ; Hansen, Thomas F. ; Ullum, Henrik ; Erikstrup, Christian ; Brunak, Søren ; Krogfelt, Karen Angeliki ; Storry, Jill R. ^LU ; Ostrowski, Sisse R. ; Olsson, Martin L. ^LU

and Pedersen, Ole B. (Less)

organization

publishing date

2023

type

Contribution to journal

publication status

published

subject

Hematology

keywords

ABO, blood group systems, blood groups, Danish blood type rates, Danish population, Denmark, Diego, Dombrock, donor blood typing, Duffy, erythrocyte antigens, genetic blood typing, Kell, Kidd, Knops, Lewis, Lutheran, MNS, P1PK, Rh, secretor, Vel, Yt

in

Transfusion

volume

63

issue

12

pages

2297 - 2310

publisher

Wiley-Blackwell

external identifiers

pmid:37921035
scopus:85175740684

ISSN

0041-1132

DOI

10.1111/trf.17575

language

English

LU publication?

yes

id

5b2e5dd2-0fd0-4751-8787-af47b0ecdfb6

date added to LUP

2023-12-04 14:28:03

date last changed

2024-04-17 10:38:09

@article{5b2e5dd2-0fd0-4751-8787-af47b0ecdfb6,
  abstract     = {{<p>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 &gt;99.5% in most cases: A, B, C/c, Co<sup>a</sup>/Co<sup>b</sup>, Do<sup>a</sup>/Do<sup>b</sup>, E/e, Jk<sup>a</sup>/Jk<sup>b</sup>, Kn<sup>a</sup>/Kn<sup>b</sup>, Kp<sup>a</sup>/Kp<sup>b</sup>, M/N, S/s, Sd<sup>a</sup>, Se, and Yt<sup>a</sup>/Yt<sup>b</sup>, while some performed slightly worse: Fy<sup>a</sup>/Fy<sup>b</sup>, K/k, Lu<sup>a</sup>/Lu<sup>b</sup>, and Vel ~99%–98% and C<sup>W</sup> and P<sub>1</sub> ~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.</p>}},
  author       = {{Moslemi, Camous and Sækmose, Susanne G. and Larsen, Rune and Bay, Jakob T. and Brodersen, Thorsten and Didriksen, Maria and Hjalgrim, Henrik and Banasik, Karina and Nielsen, Kaspar R. and Bruun, Mie T. and Dowsett, Joseph and Dinh, Khoa M. and Mikkelsen, Susan and Mikkelsen, Christina and Hansen, Thomas F. and Ullum, Henrik and Erikstrup, Christian and Brunak, Søren and Krogfelt, Karen Angeliki and Storry, Jill R. and Ostrowski, Sisse R. and Olsson, Martin L. and Pedersen, Ole B.}},
  issn         = {{0041-1132}},
  keywords     = {{ABO; blood group systems; blood groups; Danish blood type rates; Danish population; Denmark; Diego; Dombrock; donor blood typing; Duffy; erythrocyte antigens; genetic blood typing; Kell; Kidd; Knops; Lewis; Lutheran; MNS; P1PK; Rh; secretor; Vel; Yt}},
  language     = {{eng}},
  number       = {{12}},
  pages        = {{2297--2310}},
  publisher    = {{Wiley-Blackwell}},
  series       = {{Transfusion}},
  title        = {{Genetic prediction of 33 blood group phenotypes using an existing genotype dataset}},
  url          = {{http://dx.doi.org/10.1111/trf.17575}},
  doi          = {{10.1111/trf.17575}},
  volume       = {{63}},
  year         = {{2023}},
}

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