Science - USA (2022-04-08)

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  1. Y.-F. Wanget al., Identification of 38 novel loci for systemic
    lupus erythematosus and genetic heterogeneity between
    ancestral groups.Nat. Commun. 12 , 772 (2021). doi:10.1038/
    s41467-021-21049-y; pmid: 33536424

  2. J. D. Cooperet al., Meta-analysis of genome-wide association
    study data identifies additional type 1 diabetes risk loci.
    Nat. Genet. 40 , 1399–1401 (2008). doi:10.1038/ng.249;
    pmid: 18978792

  3. K. Kimet al., High-density genotyping of immune loci in
    Koreans and Europeans identifies eight new rheumatoid
    arthritis risk loci.Ann. Rheum. Dis. 74 , e13 (2015).
    doi:10.1136/annrheumdis-2013-204749; pmid: 24532676

  4. A. E. Rentonet al., A genome-wide association study
    of myasthenia gravis.JAMA Neurol. 72 , 396– 404
    (2015). doi:10.1001/jamaneurol.2014.4103;
    pmid: 25643325

  5. R. Roychoudhuriet al., BACH2 represses effector programmes
    to stabilize Treg-mediated immune homeostasis - a new
    target in tumor immunotherapy?J. Immunother. Cancer 1 ,
    O14 (2013). doi:10.1186/2051-1426-1-S1-O14

  6. M. Mediciet al., Identification of novel genetic loci
    associated with thyroid peroxidase antibodies and
    clinical thyroid disease.PLOS Genet. 10 , e1004123
    (2014). doi:10.1371/journal.pgen.1004123;
    pmid: 24586183

  7. C. J. Lessardet al., Variants at multiple loci implicated in both
    innate and adaptive immune responses are associated with
    Sjögren’s syndrome.Nat. Genet. 45 , 1284–1292 (2013).
    doi:10.1038/ng.2792; pmid: 24097067

  8. A. Márquezet al., Meta-analysis of Immunochip data of
    four autoimmune diseases reveals novel single-disease
    and cross-phenotype associations.Genome Med. 10 ,
    97 (2018). doi:10.1186/s13073-018-0604-8;
    pmid: 30572963

  9. K. R. Simpfendorferet al., Autoimmune disease-associated
    haplotypes of BLK exhibit lowered thresholds for B cell
    activation and expansion of Ig class-switched B cells.
    Arthritis Rheumatol. 67 , 2866–2876 (2015). doi:10.1002/
    art.39301; pmid: 26246128

  10. A. V. Korennykhet al., The unfolded protein response
    signals through high-order assembly of Ire1.Nature
    457 , 687–693 (2009). doi:10.1038/nature07661;
    pmid: 19079236

  11. K. P. K. Leeet al., Structure of the dual enzyme Ire1 reveals the
    basis for catalysis and regulation in nonconventional RNA
    splicing.Cell 132 , 89–100 (2008). doi:10.1016/
    j.cell.2007.10.057; pmid: 18191223

  12. T. Beckeret al., Structure of monomeric yeast and mammalian
    Sec61 complexes interacting with the translating ribosome.
    Science 326 , 1369–1373 (2009). doi:10.1126/science.1178535;
    pmid: 19933108

  13. K. U. Kalies, E. Hartmann, Membrane topology of the 12- and
    the 25-kDa subunits of the mammalian signal peptidase
    complex.J. Biol. Chem. 271 , 3925–3929 (1996). doi:10.1074/
    jbc.271.7.3925; pmid: 8632014

  14. Z. Zhuet al., Shared genetic and experimental links between
    obesity-related traits and asthma subtypes in UK Biobank.


J. Allergy Clin. Immunol. 145 , 537–549 (2020). doi:10.1016/
j.jaci.2019.09.035; pmid: 31669095


  1. S. W. Janget al., Homeobox protein Hhex negatively regulates
    Treg cells by inhibiting Foxp3 expression and function.
    Proc. Natl. Acad. Sci. U.S.A. 116 , 25790–25799 (2019).
    doi:10.1073/pnas.1907224116; pmid: 31792183

  2. T. C. Tuet al., CD160 is essential for NK-mediated IFN-g
    production.J. Exp. Med. 212 , 415–429 (2015). doi:10.1084/
    jem.20131601; pmid: 25711213

  3. M. Korsgrenet al., Natural killer cells determine development
    of allergen-induced eosinophilic airway inflammation in mice.
    J. Exp. Med. 189 , 553–562 (1999). doi:10.1084/jem.189.3.553;
    pmid: 9927517

  4. S. Matsubaraet al., IL-2 and IL-18 attenuation of airway
    hyperresponsiveness requires STAT4, IFN-g, and natural killer
    cells.Am. J. Respir. Cell Mol. Biol. 36 , 324–332 (2007).
    doi:10.1165/rcmb.2006-0231OC; pmid: 17038663

  5. C. Mitchell, K. Provost, N. Niu, R. Homer, L. Cohn, IFN-gacts on
    the airway epithelium to inhibit local and systemic pathology in
    allergic airway disease.J. Immunol. 187 , 3815–3820 (2011).
    doi:10.4049/jimmunol.1100436; pmid: 21873527

  6. Z. Zhuet al., Integration of summary data from GWAS
    and eQTL studies predicts complex trait gene targets.
    Nat. Genet. 48 , 481–487 (2016). doi:10.1038/ng.3538;
    pmid: 27019110

  7. A. Dobinet al., STAR: Ultrafast universal RNA-seq aligner.
    Bioinformatics 29 , 15–21 (2013). doi:10.1093/bioinformatics/
    bts635; pmid: 23104886

  8. H. M. Kanget al., Multiplexed droplet single-cell RNA-sequencing
    using natural genetic variation.Nat. Biotechnol. 36 , 89–94 (2018).
    doi:10.1038/nbt.4042; pmid: 29227470

  9. S. L. Wolock, R. Lopez, A. M. Klein, Scrublet: Computational
    identification of cell doublets in single-cell transcriptomic data.
    Cell Syst. 8 , 281–291.e9 (2019). doi:10.1016/
    j.cels.2018.11.005; pmid: 30954476

  10. T. Stuartet al., Comprehensive integration of single-cell data.
    Cell 177 , 1888–1902.e21 (2019). doi:10.1016/j.cell.2019.05.031;
    pmid: 31178118

  11. C. Hafemeister, R. Satija, Normalization and variance
    stabilization of single-cell RNA-seq data using regularized
    negative binomial regression.Genome Biol. 20 , 296 (2019).
    doi:10.1186/s13059-019-1874-1; pmid: 31870423

  12. K. R. Moonet al., Visualizing structure and transitions in
    high-dimensional biological data.Nat. Biotechnol. 37 ,
    1482 – 1492 (2019). doi:10.1038/s41587-019-0336-3;
    pmid: 31796933

  13. K. Streetet al., Slingshot: Cell lineage and pseudotime
    inference for single-cell transcriptomics.BMC Genomics
    19 , 477 (2018). doi:10.1186/s12864-018-4772-0;
    pmid: 29914354

  14. A. Autonet al., A global reference for human genetic variation.
    Nature 526 , 68–74 (2015). doi:10.1038/nature15393;
    pmid: 26432245

  15. S. Yazar, J. A. Hernandez, Population-scale single-cell eQTL
    mapping identifies cell type specific genetic control of
    autoimmune disease. Zenodo (2021); .doi:10.5281/
    zenodo.5084395


ACKNOWLEDGMENTS
We thank the participants of this study and are grateful to
D. MacArthur and C. Goodnow, who provided feedback about
this work.Funding:This research was supported by a National
Health and Medical Research Council Research Fellowship (S.Y.),
Practitioner Fellowship (A.W.H.), Career Development Fellowship
(J.E.P., 1107599), and Investigator Fellowship (J.E.P., 1175781).
K.A.F. is supported by the Alex Gadomski Fellowship, funded by
Maddie Riewoldt’s Vision. Additional grant support was provided by
the National Health and Medical Research Council (1150144 and
1143163), the Australian Research Council (180101405), and
the Royal Hobart Hospital Research Foundation.Author
contributions:Conceptualization: J.E.P., A.W.H.; Methodology:
S.Y., J.A.-H., K.W., A.S., S.A., K.A.F., A.W.H., J.E.P.; Resources: K.W.,
M.G.G., Q.L., A.R., T.R.P.T., L.C., K.M., C.C., A.L.C., C.J.Y.,
K.A.F., A.W.H., J.E.P.; Data analysis: S.Y., J.A.-H., K.W., A.S.,
M.G.G., K.A.F., A.W.H., J.E.P.; Writing–original draft: S.Y., J.A.-H.,
K.W., K.A.F., A.W.H., J.E.P.; Writing–review and editing: All other
authors; Supervision and project administration: A.W.H., J.E.P.;
Funding acquisition: C.J.Y., A.W.H., J.E.P.Competing interests:
The authors declare no competing interests.Data and materials
availability:OneK1K single-cell gene expression and genotype
data are available via Gene Expression Omnibus (GSE196830).
The cell by gene data are available at Human Cell Atlas (HCA)
(https://cellxgene.cziscience.com/collections/dde06e0f-ab3b-
46be-96a2-a8082383c4a1). GWAS summary statistics for MS are
available from the Multiple Sclerosis Genetics Consortium (https://
imsgc.net/). 1000 Genome Phase 3 (02.05.2013 release) data are
available from (http://ftp.ebi.ac.uk/1000g/ftp/). Summary statistics
for IBD, CD, RA, AS, and T1DM GWASs are available for public
access in the following repositories: (i) for IBD and CD from the
IBD Genetics Consortium website (www.ibdgenetics.org), (ii) for RA
from the Japanese Encyclopedia of Genetic associations by RIKEN
(http://jenger.riken.jp/en/result), and (iii) for AS and T1DM from
Pan-UK Biobank (https://pan.ukbb.broadinstitute.org/). The SLE
GWAS summary statistics data can be downloaded from the following
link:http://urr.cat/data/GWAS_SLE_summaryStats.zip. There are no
known limitations or data transfer agreements regarding access to
the summary data described above. Data used to replicate the single-
cell eQTLs are available via Gene Expression Omnibus (GSE174188),
and genotype data are available via dbGap (46198). Cell by gene
data are available at HCA (https://cellxgene.cziscience.com/
collections/436154da-bcf1-4130-9c8b-120ff9a888f2). Analysis code is
available on Zenodo ( 84 ).

SUPPLEMENTARY MATERIALS
science.org/doi/10.1126/science.abf3041
Materials and Methods
Figs. S1 to S23
Tables S1 to S19
References ( 85 Ð 108 )
MDAR Reproducibility Checklist

16 October 2020; accepted 22 February 2022
10.1126/science.abf3041

Yazaret al.,Science 376 , eabf3041 (2022) 8 April 2022 14 of 14


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