Prediction of HLA-DQ in deceased donors and its clinical significance in kidney transplantation

Soo Kyung Kim, John Jeongseok Yang, Sang Hyun Hwang, Heungsup Sung, Sung Shin, Sun Young Ko, Heung Bum Oh

Research output: Contribution to journalArticlepeer-review

3 Scopus citations

Abstract

Background: HLA-DQ typing in deceased donors is not mandatory in Korea. Therefore, when patients develop DQ antibodies after kidney transplantation (KT) from deceased donor, it is impossible to determine whether they are donor-specific antibodies (DSA). We developed DQ prediction programs for the HLA gene and evaluated their clinical utility. Methods: Two HLA-DQ prediction programs were developed: one based on Lewontin's linkage disequilibrium (LD) and haplotype frequency and the other on an artificial neural network (ANN). Low-resolution HLA-A, -B, -DR, and -DQ typing data of 5,603 Korean patients were analyzed in terms of haplotype frequency and used to develop an ANN DQ prediction program. Predicted DQ (pDQ) genotype accuracy was analyzed using the typed DQ data of 403 patients. pDQ DSA agreement, sensitivity, specificity, and false-negative rate was evaluated using 1,970 single-antigen bead assays performed on 885 KT recipients. The clinical significance of DQ and pDQ DSA was evaluated in 411 KT recipients. Results: pDQ genotype accuracies were 75.4% (LD algorithm) and 75.7% (ANN). When the second most likely pDQ (LD algorithm) was also considered, the genotype accuracy increased to 92.6%. pDQ DSA (LD algorithm) agreement, sensitivity, specificity, and false-negative rate were 97.5%, 97.3%, 98.6%, and 2.4%, respectively. The antibody-mediated rejection treatment frequency was significantly higher in DQ or pDQ DSA-positive patients than in DQ or pDQ DSA-negative patients (P<0.001). Conclusions: Our DQ prediction programs showed good accuracy and could aid DQ DSA detection in patients who had undergone deceased donor KT without donor HLA-DQ typing.

Original languageEnglish
Pages (from-to)190-197
Number of pages8
JournalAnnals of Laboratory Medicine
Volume41
Issue number2
DOIs
StatePublished - Mar 2020

Bibliographical note

Publisher Copyright:
© 2020 Seoul National University, Institute for Cognitive Science. All rights reserved.

Keywords

  • Artificial neural network
  • Donor-specific antibody
  • HLA-DQ
  • Kidney transplantation
  • Linkage disequilibrium

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