AuthorKarnes, Jason H.
Shaffer, Christian M.
Glazer, Andrew M.
Steiner, Heidi E.
Mosley, Jonathan D.
Denny, Joshua C.
Phillips, Elizabeth J.
Roden, Dan M.
AffiliationUniv Arizona, Coll Pharm, Dept Pharm Practice & Sci
MetadataShow full item record
PublisherPUBLIC LIBRARY SCIENCE
CitationComparison of HLA allelic imputation programs 2017, 12 (2):e0172444 PLOS ONE
Rights© 2017 Karnes et al. This is an open access article distributed under the terms of the Creative Commons Attribution License.
Collection InformationThis item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at email@example.com.
AbstractImputation of human leukocyte antigen (HLA) alleles from SNP-level data is attractive due to importance of HLA alleles in human disease, widespread availability of genome-wide association study (GWAS) data, and expertise required for HLA sequencing. However, comprehensive evaluations of HLA imputations programs are limited. We compared HLA imputation results of HIBAG, SNP2HLA, and HLA* IMP: 02 to sequenced HLA alleles in 3,265 samples from BioVU, a de-identified electronic health record database coupled to a DNA biorepository. We performed four-digit HLA sequencing for HLA-A, -B, -C, -DRB1, -DPB1, and -DQB1 using long-read 454 FLX sequencing. All samples were genotyped using both the Illumina HumanExome BeadChip platform and a GWAS platform. Call rates and concordance rates were compared by platform, frequency of allele, and race/ethnicity. Overall concordance rates were similar between programs in European Americans (EA) (0.975 [SNP2HLA]; 0.939 [HLA* IMP: 02]; 0.976 [HIBAG]). SNP2HLA provided a significant advantage in terms of call rate and the number of alleles imputed. Concordance rates were lower overall for African Americans (AAs). These observations were consistent when accuracy was compared across HLA loci. All imputation programs performed similarly for low frequency HLA alleles. Higher concordance rates were observed when HLA alleles were imputed from GWAS platforms versus the HumanExome BeadChip, suggesting that high genomic coverage is preferred as input for HLA allelic imputation. These findings provide guidance on the best use of HLA imputation methods and elucidate their limitations.
NoteOpen Access Journal.
VersionFinal published version
SponsorsVanderbilt CTSA grant from NCATS/NIH [ULTR000445]; HumanExome BeadChip; NIH [RC2GM092618, U01HG004603, U19HL065962]; NCATS/NIH [UL1TR000445]; VUMC Clinical Pharmacology Training grant [T32 GM07569]; American Heart Association [16SDG29090005, 15POST22660017]; ACCP Research Institute Futures Grants Award from the American College of Clinical Pharmacy; [5U01GM092691-04]; [1R01AR062886-01]
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- Authors: Kuniholm MH, Xie X, Anastos K, Xue X, Reimers L, French AL, Gange SJ, Kassaye SG, Kovacs A, Wang T, Aouizerat BE, Strickler HD
- Issue date: 2016 Dec
- HIBAG--HLA genotype imputation with attribute bagging.
- Authors: Zheng X, Shen J, Cox C, Wakefield JC, Ehm MG, Nelson MR, Weir BS
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- Imputing amino acid polymorphisms in human leukocyte antigens.
- Authors: Jia X, Han B, Onengut-Gumuscu S, Chen WM, Concannon PJ, Rich SS, Raychaudhuri S, de Bakker PI
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- High-accuracy imputation for HLA class I and II genes based on high-resolution SNP data of population-specific references.
- Authors: Khor SS, Yang W, Kawashima M, Kamitsuji S, Zheng X, Nishida N, Sawai H, Toyoda H, Miyagawa T, Honda M, Kamatani N, Tokunaga K
- Issue date: 2015 Dec