Student or educator? Sign up with your school email and get 6 months of Pro free.
Cited by 424

The DOI matches its Crossref record; 5 of the 5 references it cites were found in a registry or an index.

HIBAG—HLA genotype imputation with attribute bagging

Open access
Zheng, X, Shen, J, Cox, C, Wakefield, J C, Ehm, M G, Nelson, M R, Weir, B S·2014·The Pharmacogenomics Journal

Abstract

Genotyping of classical human leukocyte antigen (HLA) alleles is an essential tool in the analysis of diseases and adverse drug reactions with associations mapping to the major histocompatibility complex (MHC). However, deriving high-resolution HLA types subsequent to whole-genome single-nucleotide polymorphism (SNP) typing or sequencing is often cost prohibitive for large samples. An alternative approach takes advantage of the extended haplotype structure within the MHC to predict HLA alleles using dense SNP genotypes, such as those available from genome-wide SNP panels. Current methods for HLA imputation are difficult to apply or may require the user to have access to large training data sets with SNP and HLA types. We propose HIBAG, HLA Imputation using attribute BAGging, that makes predictions by averaging HLA-type posterior probabilities over an ensemble of classifiers built on bootstrap samples. We assess the performance of HIBAG using our study data (n=2668 subjects of European ancestry) as a training set and HLA data from the British 1958 birth cohort study (n≈1000 subjects) as independent validation samples. Prediction accuracies for HLA-A, B, C, DRB1 and DQB1 range from 92.2% to 98.1% using a set of SNP markers common to the Illumina 1M Duo, OmniQuad, OmniExpress, 660K and 550K platforms. HIBAG performed well compared with the other two leading methods, HLA*IMP and BEAGLE. This method is implemented in a freely available HIBAG R package that includes pre-fit classifiers for European, Asian, Hispanic and African ancestries, providing a readily available imputation approach without the need to have access to large training data sets.

Overview

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1038/tpj.2013.18

Cites

5 of 5 references verified0 citation errors · 0 not checkable

Statistics

Verification

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Citations

Cites

Show all 5 references

Research works printed but not linked: 26

  1. Shiina T, Hosomichi K, Inoko H, Kulski J . The HLA genomic loci map: expression, interaction, diversity and disease. J Human Genet 2009; 54: 15–39. · DOI · Paper page
  2. Robinson J, Halliwell J, McWilliam H, Lopez R, Parham P, Marsh S . The IMGT/HLA database. Nucleic Acids Res 2013; 41: 1222–1227. · DOI
  3. Evseeva I, Nicodemus K, Bonilla C, Tonks S, Bodmer W . Linkage disequilibrium and age of HLA region SNPs in relation to classic HLA gene alleles within Europe. Eur J Hum Genet 2010; 18: 924–932. · DOI
  4. Leslie S, Donnelly P, McVean G . A statistical method for predicting classical HLA alleles from SNP data. Am J Hum Genet 2008; 82: 48–56. · DOI · Paper page
  5. Dilthey AT, Moutsianas L, Leslie S, McVean G . HLA*IMP – an integrated framework for imputing classical HLA alleles from SNP genotypes. Bioinformatics 2011; 27: 968–972. · DOI
  6. Browning S, Browning B . Rapid and accurate haplotype phasing and missing-data inference for whole-genome association studies by use of localized haplotype clustering. Am J Hum Genet 2007; 81: 1084–1097. · DOI · Paper page
  7. McVean G, Cardin N . Approximating the coalescent with recombination. Philos Trans R Soc Lond B Biol Sci 2005; 360: 1387–1393. · DOI · Paper page
  8. Stephens M, Scheet P . Accounting for decay of linkage disequilibrium in haplotype inference and missing-data imputation. Am J Hum Genet 2005; 76: 449–462. · DOI · Paper page
  9. Scheet P, Stephens M . A fast and flexible statistical model for large-scale population genotype data: applications to inferring missing genotypes and haplotypic phase. Am J Hum Genet 2006; 78: 629–644. · DOI
  10. Li Y, Willer C, Ding J, Scheet P, Abecasis G . MaCH: using sequence and genotype data to estimate haplotypes and unobserved genotypes. Genet Epidemiol 2010; 34: 816–834. · DOI · Paper page
  11. Browning S, Browning B . Haplotype phasing: existing methods and new developments. Nat Rev Genet 2011; 12: 703–714. · DOI · Paper page
  12. Raychaudhuri S, Sandor C, Stahl E, Freudenberg J, Lee H, Jia X et al. Five amino acids in three HLA proteins explain most of the association between MHC and seropositive rheumatoid arthritis. Nat Genet 2012; 44: 291–296. · DOI · Paper page
  13. Breiman L . Random forests. Mach Learn 2001; 45: 5–32. · DOI · Paper page
  14. Bryll R, Gutierrez-Osuna R, Quek F . Attribute bagging: improving accuracy of classifier ensembles by using random feature subsets. Pattern Recognit 2003; 36: 1291–1302. · DOI · Paper page
  15. Horton R, Wilming L, Rand V, Lovering R, Bruford E, Khodiyar V et al. Gene map of the extended human MHC. Nat Rev Genet 2004; 5: 889–899. · DOI · Paper page
  16. Breiman L . Heuristics of instability and stabilization in model selection. Ann Stat 1996; 24: 2350–2383. · DOI · Paper page
  17. Hill W . Tests for association of gene frequencies at several loci in random mating diploid populations. Biometrics 1975; 31: 881–888. · DOI
  18. Long J, Williams R, Urbanek M . An E-M algorithm and testing strategy for multiple-locus haplotypes. Am J Hum Genet 1995; 56: 799–810.
  19. Qin Z, Niu T, Liu J . Partition-ligation-expectation-maximization algorithm for haplotype inference with single-nucleotide polymorphisms. Am J Hum Genet 2002; 71: 1242–1247. · DOI · Paper page
  20. Niu T, Qin Z, Xu X, Liu J . Bayesian haplotype inference for multiple linked single-nucleotide polymorphisms. Am J Hum Genet 2002; 70: 157–169. · DOI
  21. Nejentsev S, Howson J, Walker N, Szeszko J, Field S, Stevens H et al. Localization of type 1 diabetes susceptibility to the MHC class I genes HLA-B and HLA-A. Nature 2007; 450: 887–892. · DOI · Paper page
  22. Hetherington S, Hughes A, Mosteller M, Shortino D, Baker K, Spreen W et al. Genetic variations in HLA-B region and hypersensitivity reactions to abacavir. Lancet 2002; 359: 1121–1122. · DOI
  23. Hung S, Chung W, Liou L, Chu C, Lin M, Huang H et al. HLA-B*5801 allele as a genetic marker for severe cutaneous adverse reactions caused by allopurinol. Proc Natl Acad Sci USA 2005; 102: 4134–4139. · DOI · Paper page
  24. Maiers M, Gragert L, Klitz W . High-resolution HLA alleles and haplotypes in the United States population. Human Immunol 2007; 68: 779–788. · DOI
  25. Browning S . Multilocus association mapping using variable-length Markov chains. Am J Hum Genet 2006; 78: 903–913. · DOI
  26. Buhlmann P, Wyner A . Variable length markov chains. Ann Stat 1999; 27: 480–513. · DOI

Cited by

Metadata

Authors
Zheng, X., Shen, J., Cox, C., Wakefield, J. C., Ehm, M. G., Nelson, M. R., & Weir, B. S.
Venue
The Pharmacogenomics Journal
Published
2013-05-28
Volume
14
Issue
2
Pages
192-200
ISSN
  • 1470-269X (ISSN-L)
  • 1473-1150
OpenAlex
W2069587618
Cited by
424
References
5
FWCI
11.53
Type
journal article
Language
en
Open access
hybrid

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review

Data sources

Bibliographic data: Semantic Scholar, OpenAlex, Crossref.

Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.