Typing and copy number determination for <scp>HLA‐DRB3</scp>, ‐<scp>DRB4</scp> and ‐<scp>DRB5</scp> from next‐generation sequencing data

Zhang, Y.; Song, Yumo; Mo, Xingguo; Yang, Huangming; Wang, J.; Zhang, Lu; Zhang, T.

doi:10.1111/tan.12966

“…For example, the observed variation in gene expression within and between populations involving functional paralogous HLA-DRB genes is likely to be accounted for by the variable number of HLA-DRB loci dependent on haplotype that vary in frequency across populations ( Gonzalez-Galarza et al , 2015 ; Zhang et al , 2017 ). This variation is reflected in the occurrence of these genes among the reference haplotypes ( HLA-DRB1 is present in seven out of the eight available reference haplotypes, whereas HLA-DRB3 is only present on COX and QBL, HLA-DRB4 on MCF and SSTO and HLA-DRB5 on PGF) and in population studies where, for example, African ancestry populations have high frequency of HLA-DRB3 and low HLA-DRB4 ( Zhang et al , 2017 ) consistent with our observations at the level of gene expression. We also provide estimates of differences in gene expression specific to individual HLA types involving six classical HLA genes, with HLA typing information extracted from RNA-seq data by HLA typing tools.…”

Section: Discussionmentioning

confidence: 99%

AltHapAlignR: improved accuracy of RNA-seq analyses through the use of alternative haplotypes

Plant

¹

,

Humburg

²

,

Knight

³

2018

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MotivationReliance on mapping to a single reference haplotype currently limits accurate estimation of allele or haplotype-specific expression using RNA-sequencing, notably in highly polymorphic regions such as the major histocompatibility complex.ResultsWe present AltHapAlignR, a method incorporating alternate reference haplotypes to generate gene- and haplotype-level estimates of transcript abundance for any genomic region where such information is available. We validate using simulated and experimental data to quantify input allelic ratios for major histocompatibility complex haplotypes, demonstrating significantly improved correlation with ground truth estimates of gene counts compared to standard single reference mapping. We apply AltHapAlignR to RNA-seq data from 462 individuals, showing how significant underestimation of expression of the majority of classical human leukocyte antigen genes using conventional mapping can be corrected using AltHapAlignR to allow more accurate quantification of gene expression for individual alleles and haplotypes.Availability and implementationSource code freely available at https://github.com/jknightlab/AltHapAlignR.Supplementary information Supplementary data are available at Bioinformatics online.

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“…To address this, we sought to predict the number of HLA-DRB3 -DRB4, and -DRB5 copies prior to assessing allele accuracy. Zhang et al previously showed that copy numbers could be predicted from targeted sequencing using the ratio of HLA-DRB345 reads to HLA-DRB1 reads (25). Using a similar approach, we trained a K-nearest neighbor (kNN) classifier to predict the number of HLA-DBR3, -DRB4, and -DRB5 alleles based on their relative read abundance (Figure S3A).…”

Section: Increasing Accuracy Of Hla-drb345 Predictionsmentioning

confidence: 99%

Prediction of HLA genotypes from single-cell transcriptome data

Solomon

¹

,

Zheng

²

,

Dillon

³

et al. 2023

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The human leukocyte antigen (HLA) locus plays a central role in adaptive immune function and has significant clinical implications for tissue transplant compatibility and allelic disease associations. Studies using bulk-cell RNA sequencing have demonstrated that HLA transcription may be regulated in an allele-specific manner and single-cell RNA sequencing (scRNA-seq) has the potential to better characterize these expression patterns. However, quantification of allele-specific expression (ASE) for HLA loci requires sample-specific reference genotyping due to extensive polymorphism. While genotype prediction from bulk RNA sequencing is well described, the feasibility of predicting HLA genotypes directly from single-cell data is unknown. Here we evaluate and expand upon several computational HLA genotyping tools by comparing predictions from human single-cell data to gold-standard, molecular genotyping. The highest 2-field accuracy averaged across all loci was 76% by arcasHLA and increased to 86% using a composite model of multiple genotyping tools. We also developed a highly accurate model (AUC 0.93) for predicting HLA-DRB345 copy number in order to improve genotyping accuracy of the HLA-DRB locus. Genotyping accuracy improved with read depth and was reproducible at repeat sampling. Using a metanalytic approach, we also show that HLA genotypes from PHLAT and OptiType can generate ASE ratios that are highly correlated (R2 = 0.8 and 0.94, respectively) with those derived from gold-standard genotyping.

show abstract

“…To address this, we sought to predict the number of HLA-DRB3 -DRB4, and -DRB5 copies prior to assessing allele accuracy. Zhang et al previously showed that copy numbers could be predicted from targeted sequencing using the ratio of HLA-DRB345 reads to HLA-DRB1 reads [24]. Using a similar approach, we trained a K-nearest neighbor (kNN) classifier to predict the number of HLA-DBR3, -DRB4, and -DRB5 alleles based on their relative read abundance (Figure S3A).…”

Section: Increasing Accuracy Of Hla-drb345 Predictionsmentioning

confidence: 99%

Prediction of HLA genotypes from single-cell transcriptome data

Solomon

¹

,

Zheng

²

,

Dillon

³

et al. 2022

Preprint

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The human leukocyte antigen (HLA) locus plays a central role in adaptive immune function and has significant clinical implications for tissue transplant compatibility and allelic disease associations. Studies using bulk-cell RNA sequencing have demonstrated that HLA transcription may be regulated in an allele-specific manner and single-cell RNA sequencing (scRNA-seq) has the potential to better characterize these expression patterns. However, quantification of allele-specific expression (ASE) for HLA loci requires sample-specific reference genotyping due to extensive polymorphism. While genotype prediction from bulk RNA sequencing is well described, the feasibility of predicting HLA genotypes directly from single-cell data is unknown. Here we evaluate and expand upon several computational HLA genotyping tools by comparing predictions from human single-cell data to gold-standard, molecular genotyping. The highest 2-field accuracy averaged across all loci was 76% by arcasHLA and increased to 86% using a composite model of multiple genotyping tools. We also developed a highly accurate model (AUC 0.93) for predicting HLA-DRB345 copy number in order to improve genotyping accuracy of the HLA-DRB locus. Genotyping accuracy improved with read depth and was reproducible at repeat sampling. Using a metanalytic approach, we also show that HLA genotypes from PHLAT and OptiType can generate ASE ratios that are highly correlated (R2 = 0.8 and 0.94, respectively) with those derived from gold-standard genotyping.

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Typing and copy number determination for HLA‐DRB3, ‐DRB4 and ‐DRB5 from next‐generation sequencing data

Abstract: Our method for DRB3/4/5 typing has high accuracy. It is a good supplement to regular HLA typing and could help in disease studies, medical applications and human population diversity studies.

Cited by 7 publications

References 29 publications

AltHapAlignR: improved accuracy of RNA-seq analyses through the use of alternative haplotypes

AltHapAlignR: improved accuracy of RNA-seq analyses through the use of alternative haplotypes

Prediction of HLA genotypes from single-cell transcriptome data

Prediction of HLA genotypes from single-cell transcriptome data

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