Liu C.,University of Washington |
Yang X.,The Broad Institute of MIT and Harvard |
Duffy B.,HLA Laboratory |
Mohanakumar T.,University of Washington |
And 3 more authors.
Nucleic Acids Research | Year: 2013
Human leukocyte antigen (HLA) typing at the allelic level can in theory be achieved using whole exome sequencing (exome-seq) data with no added cost but has been hindered by its computational challenge. We developed ATHLATES, a program that applies assembly, allele identification and allelic pair inference to short read sequences, and applied it to data from Illumina platforms. In 15 data sets with adequate coverage for HLA-A, -B, -C, -DRB1 and -DQB1 genes, ATHLATES correctly reported 74 out of 75 allelic pairs with an overall concordance rate of 99% compared with conventional typing. This novel approach should be broadly applicable to research and clinical laboratories. © The Author(s) 2013. Published by Oxford University Press.
Ou G.,Institute of Blood Transfusion |
Wang J.,Institute of Blood Transfusion |
Wang C.,Institute of Blood Transfusion |
Ji X.,Institute of Blood Transfusion |
Chen Q.,HLA Laboratory
Tissue Antigens | Year: 2014
The novel allele B*15:325 shows difference from B*15:02:01 at codon 127 resulting in changes from Asn to Ser. © 2014 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.
Dabin T.,Peking Union Medical College |
Jue W.,Peking Union Medical College |
Guojin O.,Peking Union Medical College |
Xingjie L.,HLA Laboratory |
Qiang C.,Peking Union Medical College
Tissue Antigens | Year: 2014
The novel allele HLA-DPB1*04:01:15 is different from DPB1*04:01:01:01 with one nucleotide at nt 351 (C>A) in exon 2. © 2014 John Wiley & Sons A/S.
Endres R.O.,HLA Laboratory
Tissue antigens | Year: 2013
Baxter-Lowe L.A.,HLA Laboratory |
Cecka M.,University of California at Los Angeles |
Kamoun M.,University of Pennsylvania |
Sinacore J.,Babylon Inc. |
Melcher M.L.,Stanford University
American Journal of Transplantation | Year: 2014
Multi-center kidney paired donation (KPD) is an exciting new transplant option that has not yet approached its full potential. One barrier to progress is accurate virtual crossmatching for KPD waitlists with many highly sensitized patients. Virtual crossmatch results from a large multi-center consortium, the National Kidney Registry (NKR), were analyzed to determine the effectiveness of flexible center-specific criteria for virtual crossmatching. Approximately two-thirds of the patients on the NKR waitlist are highly sensitized (>80% CPRA). These patients have antibodies against HLA-A (63%), HLA-B (66%), HLA-C (41%), HLA-DRB1 (60%), HLA-DRB3/4/5 (18-22%), HLA-DQB1 (54%) and HLA-DPB1 (26%). With donors typed for these loci before activation, 91% of virtual crossmatches accurately predicted an acceptable cell-based donor crossmatch. Failed virtual crossmatches were attributed to equivocal virtual crossmatches (46%), changes in HLA antibodies (21%), antibodies against HLA-DQA (6%), transcription errors (6%), suspected non-HLA antibodies (5%), allele-specific antibodies (1%) and unknown causes (15%). Some failed crossmatches could be prevented by modifiable factors such as more frequent assessment of HLA antibodies, DQA1 typing of donors and auditing data entry. Importantly, when transplant centers have flexibility to define crossmatch criteria, it is currently feasible to use virtual crossmatching for highly sensitized patients to reliably predict acceptable cell-based crossmatches. When centers have flexibility to define unacceptable antigens, virtual crossmatching can be used to reliably predict acceptable cell-based crossmatches for highly sensitized patients. © Copyright 2014 The American Society of Transplantation and the American Society of Transplant Surgeons.