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Mapping the dynamic genetic regulatory architecture of HLA genes at single-cell resolution

Nature Genetics volume 55, pages 2255–2268 (2023)Cite this article

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Abstract

The human leukocyte antigen (HLA) locus plays a critical role in complex traits spanning autoimmune and infectious diseases, transplantation and cancer. While coding variation in HLA genes has been extensively documented, regulatory genetic variation modulating HLA expression levels has not been comprehensively investigated. Here we mapped expression quantitative trait loci (eQTLs) for classical HLA genes across 1,073 individuals and 1,131,414 single cells from three tissues. To mitigate technical confounding, we developed scHLApers, a pipeline to accurately quantify single-cell HLA expression using personalized reference genomes. We identified cell-type-specific cis-eQTLs for every classical HLA gene. Modeling eQTLs at single-cell resolution revealed that many eQTL effects are dynamic across cell states even within a cell type. HLA-DQ genes exhibit particularly cell-state-dependent effects within myeloid, B and T cells. For example, a T cell HLA-DQA1 eQTL (rs3104371) is strongest in cytotoxic cells. Dynamic HLA regulation may underlie important interindividual variability in immune responses.

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Fig. 1: Overview of study and scHLApers pipeline.
Fig. 2: Quantifying single-cell HLA expression using scHLApers.
Fig. 3: eQTLs for classical HLA genes from pseudobulk analysis.
Fig. 4: Integrating single cells into a unified cell state embedding across datasets.
Fig. 5: Identifying dynamic eQTLs by modeling single cells.
Fig. 6: Dynamic HLA-DQ eQTLs in myeloid and B cells.

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Data availability

The GRCh38 reference genome (primary assembly) and Gencode v38 annotation file can be downloaded at https://www.gencodegenes.org/human/release_38.html. For the synovium dataset, the single-cell expression data are available on Synapse at https://doi.org/10.7303/syn52297840. Genotype data are available on the Arthritis and Autoimmune and Related Diseases Knowledge Portal (ARK Portal, https://arkportal.synapse.org/Explore/Datasets/DetailsPage?id=syn52297840). For intestine, the raw scRNA-seq data (bam files) was obtained from the Broad Data Use Oversight System (DUOS) (dataset name: Ulcerative_Colitis_in_Colon_Regev_Xavier); the genotype data are available on dbGaP (phs001642). For PBMC-cultured, the raw scRNA-seq data (FASTQ files) was obtained from GEO (PRJNA682434), and the imputed low-pass WGS data is publicly available at SRA (PRJNA736483) and Zenodo (https://doi.org/10.5281/zenodo.4273999). For PBMC-blood (OneK1K cohort), both the raw scRNA-seq data (bam files) and genotyping data are publicly available on GEO (GSE196830). The reprocessed versions of all scRNA-seq count matrices from this study after realignment with scHLApers are publicly available on Figshare (https://doi.org/10.6084/m9.figshare.24311335).

Code availability

Code and tutorials to run the scHLApers pipeline (v1.0) are available on GitHub (https://github.com/immunogenomics/scHLApers) and Zenodo (https://doi.org/10.5281/zenodo.10003910). Scripts for reproducing analyses in the manuscript are also available on GitHub (https://github.com/immunogenomics/hla2023) and Zenodo (https://doi.org/10.5281/zenodo.10003911).

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Acknowledgements

We thank A. Dobin, H. Randolph, H. Lau, C. Stevens and members of the Raychaudhuri Lab, in particular A. Gupta and Y. Baglaenko, for their helpful input and discussions. This work was funded by the National Institutes of Health grants T32GM007753 and T32GM144273 (J.B.K., L.R. and K.A.L.), F30AI172238 (J.B.K.), T32HG002295 (A.Z.S. and L.R.), T32AR007530 (A.N.), F30AI157385 (L.R.), R01AR063759 (S.R.), U01HG012009 (S.R.) and UC2AR081023 (S.R.). The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health. This project also received funding from the MGH Center for the Study of Inflammatory Bowel Disease grant DK-43351 (R.J.X.), a fellowship from the Fok Foundation (J.E.P.), the Arthritis National Research Foundation (M.G.-A.), Gilead Sciences Research Scholar grant (M.G.-A.), Lupus Research Alliance (M.G.-A.) and a Kennedy Trust KTRR Senior Research Fellowship (KENN202109) (Y.L).

Author information

Authors and Affiliations

  1. Center for Data Sciences, Brigham and Women’s Hospital, Boston, MA, USA

    Joyce B. Kang, Amber Z. Shen, Saisriram Gurajala, Aparna Nathan, Laurie Rumker, Cristian Valencia, Kaitlyn A. Lagattuta, Fan Zhang, Michelle Curtis, Kazuyoshi Ishigaki, Ilya Korsunsky, Joseph Mears, Nghia Millard, Yakir Reshef, Kathryn Weinand, Qian Xiao, Yang Luo, Saori Sakaue & Soumya Raychaudhuri

  2. Division of Genetics, Department of Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA, USA

    Joyce B. Kang, Amber Z. Shen, Saisriram Gurajala, Aparna Nathan, Laurie Rumker, Cristian Valencia, Kaitlyn A. Lagattuta, Fan Zhang, Michelle Curtis, Kazuyoshi Ishigaki, Ilya Korsunsky, Joseph Mears, Nghia Millard, Yakir Reshef, Kathryn Weinand, Qian Xiao, Yang Luo, Saori Sakaue & Soumya Raychaudhuri

  3. Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA

    Joyce B. Kang, Amber Z. Shen, Saisriram Gurajala, Aparna Nathan, Laurie Rumker, Cristian Valencia, Kaitlyn A. Lagattuta, Fan Zhang, Michelle Curtis, Kazuyoshi Ishigaki, Ilya Korsunsky, Katherine P. Liao, Joseph Mears, Nghia Millard, Yakir Reshef, Kamil Slowikowski, Kathryn Weinand, Qian Xiao, Yang Luo, Saori Sakaue & Soumya Raychaudhuri

  4. Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA

    Joyce B. Kang, Amber Z. Shen, Saisriram Gurajala, Aparna Nathan, Laurie Rumker, Vitor R. C. Aguiar, Cristian Valencia, Kaitlyn A. Lagattuta, Fan Zhang, Michelle Curtis, Kazuyoshi Ishigaki, Ilya Korsunsky, Joseph Mears, Nghia Millard, Yakir Reshef, Kamil Slowikowski, Kathryn Weinand, Qian Xiao, Maria Gutierrez-Arcelus, Yang Luo, Saori Sakaue & Soumya Raychaudhuri

  5. Division of Rheumatology, Inflammation, and Immunity, Department of Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA, USA

    Joyce B. Kang, Amber Z. Shen, Saisriram Gurajala, Aparna Nathan, Laurie Rumker, Cristian Valencia, Kaitlyn A. Lagattuta, Fan Zhang, Anna Helena Jonsson, William Apruzzese, Adam Chicoine, Michelle Curtis, Ellen M. Gravallese, Kazuyoshi Ishigaki, Gregory Keras, Ilya Korsunsky, Zhihan J. Li, Yuhong Li, Katherine P. Liao, Kathryne E. Marks, Joseph Mears, Nghia Millard, Yakir Reshef, Gerald F. M. Watts, Kevin Wei, Kathryn Weinand, Dana Weisenfeld, Qian Xiao, Zhu Zhu, Deepak A. Rao, Michael B. Brenner, Yang Luo, Saori Sakaue & Soumya Raychaudhuri

  6. Division of Immunology, Boston Children’s Hospital, Harvard Medical School, Boston, MA, USA

    Vitor R. C. Aguiar & Maria Gutierrez-Arcelus

  7. Division of Rheumatology and the Center for Health Artificial Intelligence, University of Colorado School of Medicine, Aurora, CO, USA

    Fan Zhang

  8. Garvan Institute of Medical Research, Sydney, New South Wales, Australia

    Seyhan Yazar, Jose Alquicira-Hernandez & Joseph E. Powell

  9. Division of Gastroenterology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA

    Hamed Khalili & Ashwin N. Ananthakrishnan

  10. Harvard T. H. Chan School of Public Health, Boston, MA, USA

    Karthik Jagadeesh & Kushal Dey

  11. Computational and Systems Biology Program, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, New York, NY, USA

    Kushal Dey

  12. Physiology, Biophysics and Systems Biology Program, Weill Cornell Medicine, New York, NY, USA

    Kushal Dey

  13. Psychiatric and Neurodevelopmental Genetics Unit, Massachusetts General Hospital, Boston, MA, USA

    Mark J. Daly

  14. The Stanley Center for Psychiatric Research, The Broad Institute of MIT and Harvard, Cambridge, MA, USA

    Mark J. Daly

  15. Institute for Molecular Medicine Finland (FIMM), University of Helsinki, Helsinki, Finland

    Mark J. Daly

  16. Analytic and Translational Genetics Unit, Department of Medicine, Massachusetts General Hospital, Boston, MA, USA

    Mark J. Daly

  17. Klarman Cell Observatory, Broad Institute of Harvard and MIT, Cambridge, MA, USA

    Ramnik J. Xavier

  18. Center for Computational and Integrative Biology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA

    Ramnik J. Xavier

  19. Department of Molecular Biology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA

    Ramnik J. Xavier

  20. Hospital for Special Surgery, New York, NY, USA

    S. Louis Bridges Jr., Vivian P. Bykerk, Susan M. Goodman, Lionel B. Ivashkiv, Amit Lakhanpal, Ian Mantel, Dana E. Orange, Anvita Singaraju, Melanie H. Smith & Laura T. Donlin

  21. Weill Cornell Medicine, New York, NY, USA

    S. Louis Bridges Jr., Vivian P. Bykerk, Susan M. Goodman, Lionel B. Ivashkiv, Amit Lakhanpal, Ian Mantel, Anvita Singaraju & Laura T. Donlin

  22. Department of Medicine, University of Rochester Medical Center, Rochester, NY, USA

    Jennifer Albrecht, Jennifer L. Barnas, Debbie Campbell, Nida Meednu, Javier Rangel-Moreno, Christopher Ritchlin, Darren Tabechian & Jennifer H. Anolik

  23. Kennedy Institute of Rheumatology, University of Oxford, Oxford, UK

    Yang Luo

  24. Division of Rheumatology, University of Colorado School of Medicine, Aurora, CO, USA

    Nirmal Banda, Kevin D. Deane, V. Michael Holers, Larry W. Moreland & Jennifer A. Seifert

  25. Division of Rheumatology, Columbia University College of Physicians and Surgeons, New York, NY, USA

    Joan M. Bathon & Laura Geraldino-Pardilla

  26. Division of Rheumatology, Cedars-Sinai Medical Center, Los Angeles, CA, USA

    Ami Ben-Artzi & Michael H. Weisman

  27. Department of Pathology and Laboratory Medicine, University of Rochester Medical Center, Rochester, NY, USA

    Brendan F. Boyce

  28. Rheumatology Research Group, Institute for Inflammation and Ageing, University of Birmingham, Birmingham, UK

    David L. Boyle, Hayley L. Carr, Andrew Filer, Lindsy Forbess, Mark Maybury, Saba Nayar, Karim Raza, Ilfita Sahbudin & Dagmar Scheel-Toellner

  29. NIHR Birmingham Biomedical Research Center and Clinical Research Facility, University of Birmingham, Queen Elizabeth Hospital, Birmingham, UK

    Hayley L. Carr, Andrew Filer, Saba Nayar, Karim Raza & Dagmar Scheel-Toellner

  30. Division of Rheumatology, Allergy and Immunology, University of California, San Diego, La Jolla, CA, USA

    Arnold Ceponis & Gary S. Firestein

  31. Department of Radiology, University of Pittsburgh Medical Center, Pittsburgh, PA, USA

    Andrew Cordle & Aaron Wyse

  32. Department of Pathology and Laboratory Medicine, Hospital for Special Surgery, New York, NY, USA

    Edward DiCarlo

  33. Division of Allergy, Immunology, and Transplantation, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, MD, USA

    Patrick Dunn

  34. Northrop Grumman Health Solutions, Rockville, MD, USA

    Patrick Dunn

  35. Birmingham Tissue Analytics, Institute of Translational Medicine, University of Birmingham, Birmingham, UK

    Andrew Filer & Saba Nayar

  36. Feinstein Institute for Medical Research, Northwell Health, Manhasset, New York, NY, USA

    Peter K. Gregersen & Diane Horowitz

  37. Department of Arthritis & Clinical Immunology, Oklahoma Medical Research Foundation, Oklahoma City, OK, USA

    Joel M. Guthridge & Judith A. James

  38. Division of Clinical Immunology and Rheumatology, Department of Medicine, University of Alabama at Birmingham, Birmingham, AL, USA

    Laura B. Hughes

  39. Laboratory for Human Immunogenetics, RIKEN Center for Integrative Medical Sciences, Yokohama, Japan

    Kazuyoshi Ishigaki

  40. Department of Surgery, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA, USA

    James A. Lederer

  41. Centre for Experimental Medicine & Rheumatology, William Harvey Research Institute, Queen Mary University of London, London, UK

    Myles Lewis, Alessandra Nerviani, Costantino Pitzalis & Felice Rivellese

  42. Barts Health NHS Trust, Barts Biomedical Research Centre, National Institute for Health and Care Research, London, UK

    Myles Lewis, Alessandra Nerviani, Costantino Pitzalis & Felice Rivellese

  43. Division of Rheumatology, Department of Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL, USA

    Arthur M. Mandelin II & Harris Perlman

  44. Department of Biostatistics and Computational Biology, University of Rochester School of Medicine and Dentistry, Rochester, NY, USA

    Andrew McDavid

  45. Division of Rheumatology and Clinical Immunology, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA

    Mandy J. McGeachy & Larry W. Moreland

  46. Laboratory of Molecular Neuro-Oncology, The Rockefeller University, New York, NY, USA

    Dana E. Orange

  47. Department of Biomedical Sciences, Humanitas University and Humanitas Research Hospital, Milan, Italy

    Costantino Pitzalis

  48. Division of Immunology and Rheumatology, Institute for Immunity, Transplantation and Infection, Stanford University School of Medicine, Stanford, CA, USA

    William H. Robinson, Paul J. Utz & Michael H. Weisman

  49. Center for Immunology and Inflammatory Diseases, Department of Medicine, Massachusetts General Hospital, Boston, MA, USA

    Kamil Slowikowski

  50. MGH Cancer Center, Boston, MA, USA

    Kamil Slowikowski

Authors
  1. Joyce B. Kang
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  2. Amber Z. Shen
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  3. Saisriram Gurajala
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  6. Vitor R. C. Aguiar
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  8. Kaitlyn A. Lagattuta
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  9. Fan Zhang
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  14. Ashwin N. Ananthakrishnan
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  27. Soumya Raychaudhuri
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Consortia

Accelerating Medicines Partnership Program: Rheumatoid Arthritis and Systemic Lupus Erythematosus (AMP RA/SLE) Network

  • Jennifer Albrecht
  • , William Apruzzese
  • , Nirmal Banda
  • , Jennifer L. Barnas
  • , Joan M. Bathon
  • , Ami Ben-Artzi
  • , Brendan F. Boyce
  • , David L. Boyle
  • , S. Louis Bridges Jr.
  • , Vivian P. Bykerk
  • , Debbie Campbell
  • , Hayley L. Carr
  • , Arnold Ceponis
  • , Adam Chicoine
  • , Andrew Cordle
  • , Michelle Curtis
  • , Kevin D. Deane
  • , Edward DiCarlo
  • , Patrick Dunn
  • , Andrew Filer
  • , Gary S. Firestein
  • , Lindsy Forbess
  • , Laura Geraldino-Pardilla
  • , Susan M. Goodman
  • , Ellen M. Gravallese
  • , Peter K. Gregersen
  • , Joel M. Guthridge
  • , V. Michael Holers
  • , Diane Horowitz
  • , Laura B. Hughes
  • , Kazuyoshi Ishigaki
  • , Lionel B. Ivashkiv
  • , Judith A. James
  • , Gregory Keras
  • , Ilya Korsunsky
  • , Amit Lakhanpal
  • , James A. Lederer
  • , Myles Lewis
  • , Zhihan J. Li
  • , Yuhong Li
  • , Katherine P. Liao
  • , Arthur M. Mandelin II
  • , Ian Mantel
  • , Kathryne E. Marks
  • , Mark Maybury
  • , Andrew McDavid
  • , Mandy J. McGeachy
  • , Joseph Mears
  • , Nida Meednu
  • , Nghia Millard
  • , Larry W. Moreland
  • , Saba Nayar
  • , Alessandra Nerviani
  • , Dana E. Orange
  • , Harris Perlman
  • , Costantino Pitzalis
  • , Javier Rangel-Moreno
  • , Karim Raza
  • , Yakir Reshef
  • , Christopher Ritchlin
  • , Felice Rivellese
  • , William H. Robinson
  • , Ilfita Sahbudin
  • , Anvita Singaraju
  • , Jennifer A. Seifert
  • , Kamil Slowikowski
  • , Melanie H. Smith
  • , Darren Tabechian
  • , Dagmar Scheel-Toellner
  • , Paul J. Utz
  • , Gerald F. M. Watts
  • , Kevin Wei
  • , Kathryn Weinand
  • , Dana Weisenfeld
  • , Michael H. Weisman
  • , Aaron Wyse
  • , Qian Xiao
  •  & Zhu Zhu

Contributions

J.B.K. and S.R. conceived the study. J.B.K., A.Z.S. and Y.L. developed the scHLApers pipeline. J.B.K., S.S. and S.G. performed HLA imputation and eQTL analysis. J.B.K. performed analysis and integration of the single-cell data. L.R. post-processed the PBMC-blood dataset. A.N., V.R.C.A., C.V., K.A.L. and M.G.-A. helped interpret data and analyses. F.Z., A.H.J., S.Y., J.A.-H., H.K., A.N.A., K.J., K.D., AMP RA/SLE, M.J.D., R.J.X., L.T.D., J.H.A., J.E.P., D.A.R. and M.B.B. generated and helped interpret data resources. S.R. supervised the project. J.B.K. and S.R. composed the initial manuscript draft. All authors provided critical intellectual feedback and participated in interpreting the data and revising the manuscript.

Corresponding author

Correspondence to Soumya Raychaudhuri.

Ethics declarations

Competing interests

J.B.K. is a consultant to Aditum Bio. R.J.X. is co-founder of Jnana Therapeutics and Celsius Therapeutics, scientific advisory board member at Nestlé, and board director at MoonLake Immunotherapeutics; these organizations had no roles in this study. M.B.B. is a consultant to GSK, 4FO Ventures, Third Rock Ventures and consultant and founder of Mestag Therapeutics. S.R. is a scientific advisor to Pfizer, Janssen and Sonoma Biotherapeutics, a founder of Mestag Therapeutics, and a consultant for AbbVie, Sanofi, Biogen and Nimbus Therapeutics. The remaining authors declare no competing interests.

Peer review

Peer review information

Nature Genetics thanks Patrick Gaffney and Keishi Fujio for their contribution to the peer review of this work. Peer reviewer reports are available.

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Extended data

Extended Data Fig. 1 Correcting HLA expression estimation bias with scHLApers.

a, Schematic showing how high HLA gene polymorphism leads to bias in read alignment to a single reference genome. Consider two hypothetical individuals who are either homozygous for HLA-DRB1 allele X (orange) or allele Y (blue), where the reference allele is X. Reads from X will align perfectly to the reference, leading to accurate HLA-DRB1 quantification. However, for Y, reads will fail to align to the reference due to discordant sequence content, leading to unmapped reads and underestimation of expression. b, Percentage change in expression (total UMIs for HLA gene per individual, y-axis) across cohorts (synovium, n = 69 individuals; intestine, n = 22; PBMC-cultured, n = 73; PBMC-blood, n = 909). c, Percentage change in estimated expression (total UMIs for HLA gene per individual, y-axis) in synovium (n = 69) as a function of the mean (between the individual’s two alleles) Levenshtein distance relative to the GRCh38 reference allele at the 3’ end of each gene (x-axis). For b and c, dashed horizontal red lines denote no change. Fitted linear regression line (blue) shown with 95% confidence region. d, Heatmap showing the alignment of reads to each gene in scHLApers (rows) versus where the same read aligned (‘came from’) in the standard pipeline (columns) for synovium (top) and PBMC-cultured (bottom). Columns include HLA genes, other regions in the extended MHC, or unmapped reads. Rows sum to 100%, and a darker color indicates that more of the reads aligning to a given gene in scHLApers came from the corresponding location in the standard pipeline. e, Phylogenetic tree derived from a multiple sequence alignment of HLA-C allelic genomic sequences. The reference allele is C*07:02. Yellow box shows alleles similar to the reference (‘reference-like’). Boxplot on right shows the change in HLA-B estimated UMI counts summed across cells from each sample (y-axis) compared to the genotype for HLA-C in terms of dosage of ‘reference-like’ alleles (x-axis), across n = 1,073 individuals from all cohorts. For b and e, boxplot center line represents median, lower/upper box limits represent 25/75% quantiles, whiskers extend to box limit ±1.5 × IQR, and outlying points are plotted individually.

Extended Data Fig. 2 Concordance of eQTLs with bulk RNA-seq, differential allelic expression, and read alignment visualization.

a, Concordance between the effect sizes of lead HLA eQTLs identified in the multi-cohort pseudobulk model for B cells (this study, y-axis) and the same variant’s effect in LCLs identified through bulk RNA-seq eQTL analysis (Aguiar et al., x-axis). Because not all lead variants in this study were directly comparable due to different sets of tested variants, we tested the concordance of the most significant variant present in both datasets (triangles indicate that the exact lead variant in this study was also tested in Aguiar et al., whereas circles indicate ‘substitute’ lead variants was used for comparison). b, HLA-B expression in myeloid cells (top, n = 861 individuals) and HLA-C expression in B cells (bottom, n = 909), showing mean log(CP10k + 1)-normalized expression (y-axis) across cells for each individual in PBMC-blood by allele (x-axis). Each individual’s expression value is plotted once if they are homozygous (red) and twice if heterozygous (tan) for each allele (imputed dosage is rounded to the nearest integer). The black diamonds show the mean value for each allele (used to order the x-axis). c, Integrative Genomics Viewer (IGV) screenshots showing read alignments for alleles HLA-B*15:01 and HLA-C*07:01, associated with lower expression of the respective genes, for a representative individual in synovium.

Extended Data Fig. 3 Personalization improves eQTL effect size estimates.

a, Comparison of eQTL effect size estimates calculated using expression quantified by scHLApers (x-axis) vs. standard pipeline (y-axis). Each dot represents one of 12,045 MHC-wide genetic variants tested using the pseudobulk eQTL model per cell type (color). Pearson correlation is labeled for each gene. b, Example of eQTL effect correction through the use of corrected expression estimates, shown for HLA-DRB1 in B cells. eQTL effect sizes (y-axis) estimated for MHC variants along Chr. 6 (x-axis), shown for standard pipeline (top), scHLApers pipeline (middle), and the magnitude of difference between the betas from the two pipelines (bottom). The variant with the largest correction in estimated eQTL effect (HLA-DRB1*07:01) is labeled in orange, and the lead variant in the scHLApers pipeline (rs9271117) is labeled in blue. c, Boxplots visualizing the eQTL effects across individuals for HLA-DRB1*07:01 (left) and rs9271117 (right) using HLA-DRB1 expression estimates from the standard (top) vs. scHLApers (bottom) pipelines. Increased dosage of the ALT allele (x-axis) vs. HLA-DRB1 expression in B cells (y-axis: units are residual of inverse normal transformed mean log(CP10k + 1)-normalized expression across cells after regressing out covariates), across n = 1,069 individuals total (synovium, n = 65; intestine, n = 22; PBMC-cultured, n = 73; PBMC-blood, n = 909), plotted by dataset (color). For HLA-DRB1*07:01, ‘A’ denotes absence of the allele, and ‘T’ denotes presence (rather than REF/ALT nucleotides). Nominal Wald P-values are derived from linear regression (two-sided test).

Extended Data Fig. 4 Testing single-cell NBME model for concordance with pseudobulk and for calibration for genotype-cell-state interactions.

a-e, The models in a-c test genotype main effects, whereas d and e test genotype-cell-state interaction. a,b, Concordance of genotype main effect estimates (a) and significance of genotype main effect (b) between the NBME model (y-axis) and the pseudobulk model for the PBMC-blood dataset (x-axis) across all cell types and classical HLA genes. c, Power of the NBME single-cell eQTL model to detect regulatory effects across allele frequencies. The proportion of simulations where the null hypothesis was appropriately rejected at α = 5 × 10−8 (y-axis) in the presence of a simulated eQTL effect across 1000 simulations. Simulations were run across a range of eQTL allele frequencies (x-axis) and effect sizes (colors) using the PBMC-blood myeloid data and HLA-DQA1 expression. d,e, We permuted cell state (10 hPCs as a block) for 1,000 tests and obtained interaction P-values from a one-sided likelihood ratio test (LRT) comparing to the null model without G×hPC interaction terms. Q-Q plots showing statistical calibration (compared to uniform P-values) for PME model (d) versus NBME model (e) when testing for cell state interactions for representative class I (HLA-A) and class II (HLA-DPA1) genes in myeloid cells in PBMC-blood. The red line is the identity line. The histograms below show distributions of LRT P-values for HLA-DPA1.

Supplementary information

Supplementary Information

Supplementary notes and Figs. 1–15.

Reporting Summary

Peer Review File

Supplementary Tables

Supplementary Tables 1–15.

Supplementary Data 1

Multi-cohort pseudobulk eQTL full results for myeloid, B and T cells. Results from testing each of 12,050 for association with classical HLA gene expression in each cell type (total 8 genes × 3 cell types × 12,050 variants = 289,200 tests) in the multi-cohort pseudobulk linear model. Columns list the variants in multi-cohort analysis, cell type, gene, effect size of variant on covariate-corrected standardized gene expression (β), standard error of β estimate, nominal Wald P value from linear regression (two-sided test), and REF and ALT alleles. For metadata about each tested variant, see Supplementary Table 7.

Supplementary Data 2

Multi-cohort pseudobulk conditional analysis results. Results from conditional analysis identifying eQTLs, conditioning on the lead variant(s) from previous round(s). Columns list the variant, cell type, gene, round of conditional analysis (conditional_iter, ranging from 1 to 4 for primary to quaternary effects), effect size of eQTL (β), standard error of β estimate, and nominal Wald P-value from linear regression (two-sided test). Includes only variants with nominal P < 0.05 to reduce file size. For metadata about each tested variant, see Supplementary Table 7.

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Kang, J.B., Shen, A.Z., Gurajala, S. et al. Mapping the dynamic genetic regulatory architecture of HLA genes at single-cell resolution. Nat Genet 55, 2255–2268 (2023). https://doi.org/10.1038/s41588-023-01586-6

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