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

Research output: Contribution to journal › Journal article › Research › peer-review

Standard

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.

In: Transfusion, Vol. 63, No. 12, 2023, p. 2297-2310.

Research output: Contribution to journal › Journal article › Research › peer-review

Harvard

Moslemi, C, Sækmose, SG, Larsen, R, Bay, JT, Brodersen, T, Didriksen, M, Hjalgrim, H, Banasik, K, Nielsen, KR, Bruun, MT, Dowsett, J, Dinh, KM, Mikkelsen, S, Mikkelsen, C, Hansen, TF, Ullum, H, Erikstrup, C, Brunak, S, Krogfelt, KA, Storry, JR, Ostrowski, SR, Olsson, ML & Pedersen, OB 2023, 'Genetic prediction of 33 blood group phenotypes using an existing genotype dataset', Transfusion, vol. 63, no. 12, pp. 2297-2310. https://doi.org/10.1111/trf.17575

APA

Moslemi, C., Sækmose, S. G., Larsen, R., Bay, J. T., Brodersen, T., Didriksen, M., Hjalgrim, H., Banasik, K., Nielsen, K. R., Bruun, M. T., Dowsett, J., Dinh, K. M., Mikkelsen, S., Mikkelsen, C., Hansen, T. F., Ullum, H., Erikstrup, C., Brunak, S., Krogfelt, K. A., ... Pedersen, O. B. (2023). Genetic prediction of 33 blood group phenotypes using an existing genotype dataset. Transfusion, 63(12), 2297-2310. https://doi.org/10.1111/trf.17575

Vancouver

Moslemi C, Sækmose SG, Larsen R, Bay JT, Brodersen T, Didriksen M et al. Genetic prediction of 33 blood group phenotypes using an existing genotype dataset. Transfusion. 2023;63(12):2297-2310. https://doi.org/10.1111/trf.17575

Author

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. In: Transfusion. 2023 ; Vol. 63, No. 12. pp. 2297-2310.

Bibtex

@article{272f130c39f349a38e787dc4c35c8b99,
title = "Genetic prediction of 33 blood group phenotypes using an existing genotype dataset",
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 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, Coa/Cob, Doa/Dob, E/e, Jka/Jkb, Kna/Knb, Kpa/Kpb, M/N, S/s, Sda, Se, and Yta/Ytb, while some performed slightly worse: Fya/Fyb, K/k, Lua/Lub, and Vel ~99%–98% and CW 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.",
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",
author = "Camous Moslemi and S{\ae}kmose, {Susanne G.} and Rune Larsen and Bay, {Jakob T.} and Thorsten Brodersen and Maria Didriksen and Henrik Hjalgrim and Karina Banasik and Nielsen, {Kaspar R.} and Bruun, {Mie T.} and Joseph Dowsett and Dinh, {Khoa M.} and Susan Mikkelsen and Christina Mikkelsen and Hansen, {Thomas F.} and Henrik Ullum and Christian Erikstrup and S{\o}ren Brunak and Krogfelt, {Karen Angeliki} and Storry, {Jill R.} and Ostrowski, {Sisse R.} and Olsson, {Martin L.} and Pedersen, {Ole B.}",
note = "Publisher Copyright: {\textcopyright} 2023 The Authors. Transfusion published by Wiley Periodicals LLC on behalf of AABB.",
year = "2023",
doi = "10.1111/trf.17575",
language = "English",
volume = "63",
pages = "2297--2310",
journal = "Transfusion",
issn = "0041-1132",
publisher = "Wiley-Blackwell",
number = "12",

}

RIS

TY - JOUR

T1 - Genetic prediction of 33 blood group phenotypes using an existing genotype dataset

AU - Moslemi, Camous

AU - Sækmose, Susanne G.

AU - Larsen, Rune

AU - Bay, Jakob T.

AU - Brodersen, Thorsten

AU - Didriksen, Maria

AU - Hjalgrim, Henrik

AU - Banasik, Karina

AU - Nielsen, Kaspar R.

AU - Bruun, Mie T.

AU - Dowsett, Joseph

AU - Dinh, Khoa M.

AU - Mikkelsen, Susan

AU - Mikkelsen, Christina

AU - Hansen, Thomas F.

AU - Ullum, Henrik

AU - Erikstrup, Christian

AU - Brunak, Søren

AU - Krogfelt, Karen Angeliki

AU - Storry, Jill R.

AU - Ostrowski, Sisse R.

AU - Olsson, Martin L.

AU - Pedersen, Ole B.

N1 - Publisher Copyright: © 2023 The Authors. Transfusion published by Wiley Periodicals LLC on behalf of AABB.

PY - 2023

Y1 - 2023

N2 - 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, Coa/Cob, Doa/Dob, E/e, Jka/Jkb, Kna/Knb, Kpa/Kpb, M/N, S/s, Sda, Se, and Yta/Ytb, while some performed slightly worse: Fya/Fyb, K/k, Lua/Lub, and Vel ~99%–98% and CW 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.

AB - 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, Coa/Cob, Doa/Dob, E/e, Jka/Jkb, Kna/Knb, Kpa/Kpb, M/N, S/s, Sda, Se, and Yta/Ytb, while some performed slightly worse: Fya/Fyb, K/k, Lua/Lub, and Vel ~99%–98% and CW 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.

KW - ABO

KW - blood group systems

KW - blood groups

KW - Danish blood type rates

KW - Danish population

KW - Denmark

KW - Diego

KW - Dombrock

KW - donor blood typing

KW - Duffy

KW - erythrocyte antigens

KW - genetic blood typing

KW - Kell

KW - Kidd

KW - Knops

KW - Lewis

KW - Lutheran

KW - MNS

KW - P1PK

KW - Rh

KW - secretor

KW - Vel

KW - Yt

U2 - 10.1111/trf.17575

DO - 10.1111/trf.17575

M3 - Journal article

C2 - 37921035

AN - SCOPUS:85175740684

VL - 63

SP - 2297

EP - 2310

JO - Transfusion

JF - Transfusion

SN - 0041-1132

IS - 12

ER -

ID: 372967678