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Deep sequencing of the MHC region in the Chinese population contributes to studies of complex disease

Abstract

The human major histocompatibility complex (MHC) region has been shown to be associated with numerous diseases. However, it remains a challenge to pinpoint the causal variants for these associations because of the extreme complexity of the region. We thus sequenced the entire 5-Mb MHC region in 20,635 individuals of Han Chinese ancestry (10,689 controls and 9,946 patients with psoriasis) and constructed a Han-MHC database that includes both variants and HLA gene typing results of high accuracy. We further identified multiple independent new susceptibility loci in HLA-C, HLA-B, HLA-DPB1 and BTNL2 and an intergenic variant, rs118179173, associated with psoriasis and confirmed the well-established risk allele HLA-C*06:02. We anticipate that our Han-MHC reference panel built by deep sequencing of a large number of samples will serve as a useful tool for investigating the role of the MHC region in a variety of diseases and thus advance understanding of the pathogenesis of these disorders.

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Figure 1: Diversity of 29 MHC genes in the Han Chinese population.
Figure 2: Plots of stepwise conditional association of the variants for psoriasis in the MHC region.
Figure 3: HLA allele frequency in Han Chinese and European populations.

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Acknowledgements

We thank the faculty and staff at Anhui Medical University and BGI-Shenzhen who contributed to the Han-MHC project. We acknowledge grant support from the Key Program of the National Natural Science Foundation of China (81130031), the National Science Fund for Excellent Young Scholars (81222022), the Top-Notch Young Talents Program of China, the Pre-National Basic Research Program of China (973 plan; 2012CB722404), the National Natural Science Foundation of China (81573035, 81273301, 81271747, 81370044, 8157120504 and 81502713), the Natural Science Foundation of Anhui Province (1508085JGD05), the Program of Outstanding Talents of Anhui Medical University and the Shenzhen municipal government of China (CXZZ20140904154910774).

Author information

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Authors

Contributions

Xuejun Zhang and Jun Wang conceived the study and designed scientific objectives. Xuejun Zhang, Jun Wang, L.S., L.H., J. Liu and X. Zuo participated in the study design. L.S. and H.C. led the project and manuscript preparation. H.C., T.Z. and Xiaomin Liu managed the project. Xiaoguang Zhang, R.X., B.L., G.C., C.S., C. Zhu, X.F., M.Y., C. Zhang, L.Y., M.C., L.T., L.W., Y.X., S.Z., G.L., L.Z., Y. Wu, Z.Z., Y. Cui, Z.W., C. Yang, P.W., L.X., X.C., A.Z., X.G., F. Zhang, J.X., M.Z., J. Zheng, J. Zhang, X. Yu and S.Y. conducted sample selection and data management, undertook recruitment, collected phenotype data, undertook related data handling and calculations, managed recruitment and obtained biological samples. H.J., F.X., Xiao Liu, J. Wu and J. Li generated the sequence data. T.Z., Xiaomin Liu, Yuanwei Zhang, X.J., J.M., Q.L., J.S., X. Zhuang, H.S., Yijie Zhang, Y. Wang, H.X., M.B., Y. Chen, W.C., H.Y., Jian Wang and C. Ye performed polymorphism analysis and constructed the Han-MHC database. F. Zhou, H.C., T.Z., Xiaoguang Zhang, Xiaomin Liu, G.C., Yuanwei Zhang, X. Zheng, J.G., Y.S., X. Yin, Jianan Wang, T.K., X.X., Y.L. and L.H. conducted the association analysis. F. Zhou, H.C., X. Zuo, T.Z., Xiaoguang Zhang and Xiaomin Liu did most of the writing with contributions from all authors. All authors contributed to the final manuscript, with Xuejun Zhang, Jun Wang, L.S., L.H., F. Zhou, H.C., X. Zuo, T.Z., Xiaoguang Zhang and Xiaomin Liu having key roles.

Corresponding authors

Correspondence to Lennart Hammarström or Liangdan Sun or Jun Wang or Xuejun Zhang.

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The authors declare no competing financial interests.

Integrated supplementary information

Supplementary Figure 1 Distribution of healthy control samples included in the MHC sequencing study.

In total, 10,689 normal individuals were recruited in the sequencing stage.

Supplementary Figure 2 The average depth distribution along the MHC region of the sequenced samples.

Supplementary Figure 3 Workflow for the basic analysis pipeline used in this study.

Supplementary Figure 4 Distribution of the number of variants in 10,689 individuals.

The average SNP number in these samples approached 15,000, and the average indel number approached 2,000.

Supplementary Figure 5 Allelic frequencies of the eight most polymorphic genes.

Allelic frequencies for HLA-B, HLA-DRB1, HLA-C, HLA-A, MICA, HLA-DQB1, HLA-DQA1 and HLA-DPB1 (sorted on the basis of diversity and arranged from highest to lowest) in the Chinese population are given in each chart. Only the top 20 alleles for each gene are shown in the diagram.

Supplementary Figure 6 MHC haplotype frequency distribution.

Haplotypes are given as HLA A-B-C-DRB1-DQB1. Of the extended haplotypes, HLA A30-B13-C06-DR07-DQ02 was the most common haplotype with a frequency of 3.89%.

Supplementary Figure 7 The Pearson coefficient R between different geographical regions.

(ac) Results are shown for SNPs (a), amino acids and HLA types (b) and MHC haplotypes (c). Green, southern versus central China; black, northern versus central China; blue, southern versus northern China.

Supplementary Figure 8 The frequency of MHC haplotypes.

Only haplotypes with a frequency of ≥0.5% in the Han Chinese population are shown. The red line represents the overall frequency in the Han Chinese population. The blue, green and brown lines represent the frequency in northern, central and southern Han Chinese populations, respectively.

Supplementary Figure 9 Linkage map of HLA-DRB3, HLA-DRB4 and HLA-DRB5 with HLA-DRB1.

Supplementary Figure 10 HLA type imputation accuracy.

Supplementary Figure 11 The Pearson r2 value between imputed and standard alleles of the five classical HLA genes at different allele frequencies.

The mean r2 value is 0.97 for common alleles, 0.93 for low-frequency alleles and 0.81 for rare alleles.

Supplementary Figure 12 Functional annotation of an identified intergenic variant (rs118179173).

The first six rows show H3K4me3 (green) data for CD4+ memory primary cells, CD4+ naive primary cells, CD8+ memory primary cells, CD8+ naive primary cells, Treg primary cells and the GM12878 cell line (B lymphocyte, lymphoblastoid; International HapMap Project CEPH/Utah, European Caucasian, Epstein–Barr virus). The next five rows show H3K27ac (blue) data for CD4+ memory primary cells, CD4+ naive primary cells, CD8+ memory primary cells, CD8+ naive primary cells and the GM12878 cell line. Then, the next six rows show H3K36me3 (green) data for CD4+ memory primary cells, CD4+ naive primary cells, CD8+ memory primary cells, CD8+ naive primary cells, Treg primary cells and the GM12878 cell line. The chromatin states displayed are for CD4+ memory primary cells, CD4+ naive primary cells, CD8+ memory primary cells, CD8+ naive primary cells and the GM12878 cell line. The detailed color schemes for the chromatin states are listed below. Briefly, red corresponds to active transcriptional start sites (TSSs), yellow corresponds to enhancers, green corresponds to transcription and white corresponds to quiescent regions. DNase I hypersensitivity sites are for CD4+ primary cells, CD8+ primary cells, CD14+ monocytes, Treg, TH1 and TH2 cells, and the GM12878 cell line. All data are publicly available from ENCODE and NIH Roadmap. Raw data were plotted using the website http://epigenomegateway.wustl.edu/browser/.

Supplementary Figure 13 Three-dimensional ribbon model for HLA-B.

Key amino acid positions identified in psoriasis association analysis are highlighted.

Supplementary Figure 14 Example of a spurious variant site.

Supplementary Figure 15 The effect of purifying selection on variants.

(a) The relationship between MAFs and functional prediction scores from SIFT, PolyPhen-2, LRT and MutationTaster. Each prediction score showed a significant negative correlation with MAF in the studied samples. (b) The rare variant excess in most functional sequences, which varies systematically between types (for example, transcription factor motif variants have higher rare variant excess than splicing variants). Interestingly, the least conserved nonsynonymous variants show similar rare variant loads to UTR and synonymous regions, suggesting that these alternative transcripts are under very weak selective constraint.

Supplementary information

Supplementary Text and Figures

Supplementary Figures 1–15, Supplementary Tables 2, 4, 6, 7 and 9 and Supplementary Note. (PDF 2188 kb)

Supplementary Table 1

HLA type frequency. (XLSX 31 kb)

Supplementary Table 3

Selected tagging SNPs. (XLSX 33 kb)

Supplementary Table 5

MHC haplotype frequency. (XLSX 214 kb)

Supplementary Table 8

HLA types from sequencing data and the 1000 Genomes Project. (XLSX 13 kb)

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Zhou, F., Cao, H., Zuo, X. et al. Deep sequencing of the MHC region in the Chinese population contributes to studies of complex disease. Nat Genet 48, 740–746 (2016). https://doi.org/10.1038/ng.3576

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