1932

Abstract

Recent advancements in single-cell technologies have enabled expression quantitative trait locus (eQTL) analysis across many individuals at single-cell resolution. Compared with bulk RNA sequencing, which averages gene expression across cell types and cell states, single-cell assays capture the transcriptional states of individual cells, including fine-grained, transient, and difficult-to-isolate populations at unprecedented scale and resolution. Single-cell eQTL (sc-eQTL) mapping can identify context-dependent eQTLs that vary with cell states, including some that colocalize with disease variants identified in genome-wide association studies. By uncovering the precise contexts in which these eQTLs act, single-cell approaches can unveil previously hidden regulatory effects and pinpoint important cell states underlying molecular mechanisms of disease. Here, we present an overview of recently deployed experimental designs in sc-eQTL studies. In the process, we consider the influence of study design choices such as cohort, cell states, and ex vivo perturbations. We then discuss current methodologies, modeling approaches, and technical challenges as well as future opportunities and applications.

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2023-08-25
2024-12-06
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Literature Cited

  1. 1.
    Aguiar VRC, César J, Delaneau O, Dermitzakis ET, Meyer D. 2019. Expression estimation and eQTL mapping for HLA genes with a personalized pipeline. PLOS Genet. 15:e1008091
    [Google Scholar]
  2. 2.
    Ahmed S, Rattray M, Boukouvalas A. 2019. GrandPrix: scaling up the Bayesian GPLVM for single-cell data. Bioinformatics 35:47–54
    [Google Scholar]
  3. 3.
    Alquicira-Hernandez J, Sathe A, Ji HP, Nguyen Q, Powell JE. 2019. scPred: accurate supervised method for cell-type classification from single-cell RNA-seq data. Genome Biol. 20:264
    [Google Scholar]
  4. 4.
    Anderson CA, Pettersson FH, Clarke GM, Cardon LR, Morris AP, Zondervan KT. 2010. Data quality control in genetic case-control association studies. Nat. Protoc. 5:1564–73
    [Google Scholar]
  5. 5.
    Aquino Y, Bisiaux A, Li Z, O'Neill M, Mendoza-Revilla J et al. 2022. Environmental and genetic drivers of population differences in SARS-CoV-2 immune responses. bioRxiv 2022.11.22.517073. https://doi.org/10.1101/2022.11.22.517073
    [Crossref]
  6. 6.
    Aran D, Looney AP, Liu L, Wu E, Fong V et al. 2019. Reference-based analysis of lung single-cell sequencing reveals a transitional profibrotic macrophage. Nat. Immunol. 20:163–72
    [Google Scholar]
  7. 7.
    Argelaguet R, Velten B, Arnol D, Dietrich S, Zenz T et al. 2018. Multi-Omics Factor Analysis—a framework for unsupervised integration of multi-omics data sets. Mol. Syst. Biol. 14:e8124
    [Google Scholar]
  8. 8.
    Armstrong AW, Gooderham M, Warren RB, Papp KA, Strober B et al. 2023. Deucravacitinib versus placebo and apremilast in moderate to severe plaque psoriasis: efficacy and safety results from the 52-week, randomized, double-blinded, placebo-controlled phase 3 POETYK PSO-1 trial. J. Am. Acad. Dermatol. 88:29–39
    [Google Scholar]
  9. 9.
    Azodi CB, Zappia L, Oshlack A, McCarthy DJ. 2021. splatPop: simulating population scale single-cell RNA sequencing data. Genome Biol. 22:341
    [Google Scholar]
  10. 10.
    Baechler EC, Batliwalla FM, Karypis G, Gaffney PM, Ortmann WA et al. 2003. Interferon-inducible gene expression signature in peripheral blood cells of patients with severe lupus. PNAS 100:2610–15
    [Google Scholar]
  11. 11.
    Bakken TE, Hodge RD, Miller JA, Yao Z, Nguyen TN et al. 2018. Single-nucleus and single-cell transcriptomes compared in matched cortical cell types. PLOS ONE 13:e0209648
    [Google Scholar]
  12. 12.
    Baran Y, Bercovich A, Sebe-Pedros A, Lubling Y, Giladi A et al. 2019. MetaCell: analysis of single-cell RNA-seq data using K-nn graph partitions. Genome Biol. 20:206
    [Google Scholar]
  13. 13.
    Barreiro LB, Tailleux L, Pai AA, Gicquel B, Marioni JC, Gilad Y. 2012. Deciphering the genetic architecture of variation in the immune response to Mycobacterium tuberculosis infection. PNAS 109:1204–9
    [Google Scholar]
  14. 14.
    Ben-David E, Boocock J, Guo L, Zdraljevic S, Bloom JS, Kruglyak L. 2021. Whole-organism eQTL mapping at cellular resolution with single-cell sequencing. eLife 10:e65857
    [Google Scholar]
  15. 15.
    Bossini-Castillo L, Glinos DA, Kunowska N, Golda G, Lamikanra AA et al. 2022. Immune disease variants modulate gene expression in regulatory CD4+ T cells. Cell Genom. 2:100117
    [Google Scholar]
  16. 16.
    Browning SR, Browning BL. 2011. Haplotype phasing: existing methods and new developments. Nat. Rev. Genet. 12:703–14
    [Google Scholar]
  17. 17.
    Bryois J, Buil A, Ferreira PG, Panousis NI, Brown AA et al. 2017. Time-dependent genetic effects on gene expression implicate aging processes. Genome Res. 27:545–52
    [Google Scholar]
  18. 18.
    Bryois J, Calini D, Macnair W, Foo L, Urich E et al. 2022. Cell-type-specific cis-eQTLs in eight human brain cell types identify novel risk genes for psychiatric and neurological disorders. Nat. Neurosci. 25:1104–12
    [Google Scholar]
  19. 19.
    Butler A, Hoffman P, Smibert P, Papalexi E, Satija R. 2018. Integrating single-cell transcriptomic data across different conditions, technologies, and species. Nat. Biotechnol. 36:411–20
    [Google Scholar]
  20. 20.
    Cano-Gamez E, Trynka G. 2020. From GWAS to function: using functional genomics to identify the mechanisms underlying complex diseases. Front. Genet. 11:424
    [Google Scholar]
  21. 21.
    Castel SE, Aguet F, Mohammadi P, GTEx Consort., Ardlie KG, Lappalainen T. 2020. A vast resource of allelic expression data spanning human tissues. Genome Biol. 21:234
    [Google Scholar]
  22. 22.
    Chen G, Ning B, Shi T. 2019. Single-cell RNA-seq technologies and related computational data analysis. Front. Genet. 10:317
    [Google Scholar]
  23. 23.
    Chen L, Ge B, Casale FP, Vasquez L, Kwan T et al. 2016. Genetic drivers of epigenetic and transcriptional variation in human immune cells. Cell 167:1398–414.e24
    [Google Scholar]
  24. 24.
    Chen S, Lake BB, Zhang K. 2019. High-throughput sequencing of the transcriptome and chromatin accessibility in the same cell. Nat. Biotechnol. 37:1452–57
    [Google Scholar]
  25. 25.
    Chun S, Casparino A, Patsopoulos NA, Croteau-Chonka DC, Raby BA et al. 2017. Limited statistical evidence for shared genetic effects of eQTLs and autoimmune-disease-associated loci in three major immune-cell types. Nat. Genet. 49:600–5
    [Google Scholar]
  26. 26.
    Clarke ZA, Andrews TS, Atif J, Pouyabahar D, Innes BT et al. 2021. Tutorial: guidelines for annotating single-cell transcriptomic maps using automated and manual methods. Nat. Protoc. 16:2749–64
    [Google Scholar]
  27. 27.
    Connally NJ, Nazeen S, Lee D, Shi H, Stamatoyannopoulos J et al. 2022. The missing link between genetic association and regulatory function. eLife 11:e74970
    [Google Scholar]
  28. 28.
    Crowell HL, Soneson C, Germain P-L, Calini D, Collin L et al. 2020. muscat detects subpopulation-specific state transitions from multi-sample multi-condition single-cell transcriptomics data. Nat. Commun. 11:6077
    [Google Scholar]
  29. 29.
    Cuomo ASE, Alvari G, Azodi CB, Single-Cell eQTLGen Consort., McCarthy DJ, Bonder MJ 2021. Optimizing expression quantitative trait locus mapping workflows for single-cell studies. Genome Biol. 22:188
    [Google Scholar]
  30. 30.
    Cuomo ASE, Heinen T, Vagiaki D, Horta D, Marioni JC, Stegle O. 2022. CellRegMap: a statistical framework for mapping context-specific regulatory variants using scRNA-seq. Mol. Syst. Biol. 18:e10663
    [Google Scholar]
  31. 31.
    Cuomo ASE, Seaton DD, McCarthy DJ, Martinez I, Bonder MJ et al. 2020. Single-cell RNA-sequencing of differentiating iPS cells reveals dynamic genetic effects on gene expression. Nat. Commun. 11:810
    [Google Scholar]
  32. 32.
    Cutolo M, Sulli A, Paolino S, Pizzorni C. 2016. CTLA-4 blockade in the treatment of rheumatoid arthritis: an update. Expert Rev. Clin. Immunol. 12:417–25
    [Google Scholar]
  33. 33.
    Dannemann M, Prüfer K, Kelso J. 2017. Functional implications of Neandertal introgression in modern humans. Genome Biol. 18:61
    [Google Scholar]
  34. 34.
    Darby CA, Stubbington MJT, Marks PJ, Martínez Barrio Á, Fiddes IT. 2020. scHLAcount: allele-specific HLA expression from single-cell gene expression data. Bioinformatics 36:3905–6
    [Google Scholar]
  35. 35.
    Das S, Forer L, Schönherr S, Sidore C, Locke AE et al. 2016. Next-generation genotype imputation service and methods. Nat. Genet. 48:1284–87
    [Google Scholar]
  36. 36.
    Davenport EE, Amariuta T, Gutierrez-Arcelus M, Slowikowski K, Westra H-J et al. 2018. Discovering in vivo cytokine-eQTL interactions from a lupus clinical trial. Genome Biol. 19:168
    [Google Scholar]
  37. 37.
    de Vries DH, Matzaraki V, Bakker OB, Brugge H, Westra H-J et al. 2020. Integrating GWAS with bulk and single-cell RNA-sequencing reveals a role for LY86 in the anti-Candida host response. PLOS Pathog. 16:e1008408
    [Google Scholar]
  38. 38.
    Denisenko E, Guo BB, Jones M, Hou R, de Kock L et al. 2020. Systematic assessment of tissue dissociation and storage biases in single-cell and single-nucleus RNA-seq workflows. Genome Biol. 21:130
    [Google Scholar]
  39. 39.
    DeTomaso D, Jones MG, Subramaniam M, Ashuach T, Ye CJ, Yosef N. 2019. Functional interpretation of single cell similarity maps. Nat. Commun. 10:4376
    [Google Scholar]
  40. 40.
    Ding J, Adiconis X, Simmons SK, Kowalczyk MS, Hession CC et al. 2020. Systematic comparison of single-cell and single-nucleus RNA-sequencing methods. Nat. Biotechnol. 38:737–46
    [Google Scholar]
  41. 41.
    Dong X, Li X, Chang T-W, Scherzer CR, Weiss ST, Qiu W. 2021. powerEQTL: an R package and shiny application for sample size and power calculation of bulk tissue and single-cell eQTL analysis. Bioinformatics 37:4269–71
    [Google Scholar]
  42. 42.
    Donovan MKR, D'Antonio-Chronowska A, D'Antonio M, Frazer KA 2020. Cellular deconvolution of GTEx tissues powers discovery of disease and cell-type associated regulatory variants. Nat. Commun. 11:955
    [Google Scholar]
  43. 43.
    Elorbany R, Popp JM, Rhodes K, Strober BJ, Barr K et al. 2022. Single-cell sequencing reveals lineage-specific dynamic genetic regulation of gene expression during human cardiomyocyte differentiation. PLOS Genet. 18:e1009666
    [Google Scholar]
  44. 44.
    Fairfax BP, Humburg P, Makino S, Naranbhai V, Wong D et al. 2014. Innate immune activity conditions the effect of regulatory variants upon monocyte gene expression. Science 343:1246949
    [Google Scholar]
  45. 45.
    Fairfax BP, Makino S, Radhakrishnan J, Plant K, Leslie S et al. 2012. Genetics of gene expression in primary immune cells identifies cell type-specific master regulators and roles of HLA alleles. Nat. Genet. 44:502–10
    [Google Scholar]
  46. 46.
    Fan S, Hansen MEB, Lo Y, Tishkoff SA. 2016. Going global by adapting local: a review of recent human adaptation. Science 354:54–59
    [Google Scholar]
  47. 47.
    Farber DL. 2021. Tissues, not blood, are where immune cells function. Nature 593:506–9
    [Google Scholar]
  48. 48.
    Farh KK-H, Marson A, Zhu J, Kleinewietfeld M, Housley WJ et al. 2015. Genetic and epigenetic fine mapping of causal autoimmune disease variants. Nature 518:337–43
    [Google Scholar]
  49. 49.
    Fehrmann RSN, Jansen RC, Veldink JH, Westra H-J, Arends D et al. 2011. Trans-eQTLs reveal that independent genetic variants associated with a complex phenotype converge on intermediate genes, with a major role for the HLA. PLOS Genet. 7:e1002197
    [Google Scholar]
  50. 50.
    Garrido-Martín D, Borsari B, Calvo M, Reverter F, Guigó R. 2021. Identification and analysis of splicing quantitative trait loci across multiple tissues in the human genome. Nat. Commun. 12:727
    [Google Scholar]
  51. 51.
    Giambartolomei C, Liu JZ, Zhang W, Hauberg M, Shi H et al. 2018. A Bayesian framework for multiple trait colocalization from summary association statistics. Bioinformatics 34:2538–45
    [Google Scholar]
  52. 52.
    Giambartolomei C, Vukcevic D, Schadt EE, Franke L, Hingorani AD et al. 2014. Bayesian test for colocalisation between pairs of genetic association studies using summary statistics. PLOS Genet. 10:e1004383
    [Google Scholar]
  53. 53.
    Grieshaber-Bouyer R, Radtke FA, Cunin P, Stifano G, Levescot A et al. 2021. The neutrotime transcriptional signature defines a single continuum of neutrophils across biological compartments. Nat. Commun. 12:2856
    [Google Scholar]
  54. 54.
    GTEx Consort 2020. The GTEx Consortium atlas of genetic regulatory effects across human tissues. Science 369:1318–30
    [Google Scholar]
  55. 55.
    Gutierrez-Arcelus M, Baglaenko Y, Arora J, Hannes S, Luo Y et al. 2020. Allele-specific expression changes dynamically during T cell activation in HLA and other autoimmune loci. Nat. Genet. 52:247–53
    [Google Scholar]
  56. 56.
    Gutierrez-Arcelus M, Rich SS, Raychaudhuri S. 2016. Autoimmune diseases—connecting risk alleles with molecular traits of the immune system. Nat. Rev. Genet. 17:160–74
    [Google Scholar]
  57. 57.
    Gutierrez-Arcelus M, Teslovich N, Mola AR, Polidoro RB, Nathan A et al. 2019. Lymphocyte innateness defined by transcriptional states reflects a balance between proliferation and effector functions. Nat. Commun. 10:687
    [Google Scholar]
  58. 58.
    Hagemann-Jensen M, Ziegenhain C, Sandberg R. 2022. Scalable single-cell RNA sequencing from full transcripts with Smart-seq3xpress. Nat. Biotechnol. 40:1452–57
    [Google Scholar]
  59. 59.
    Hahaut V, Pavlinic D, Carbone W, Schuierer S, Balmer P et al. 2022. Fast and highly sensitive full-length single-cell RNA sequencing using FLASH-seq. Nat. Biotechnol. 40:1447–51
    [Google Scholar]
  60. 60.
    Heaton H, Talman AM, Knights A, Imaz M, Gaffney DJ et al. 2020. Souporcell: robust clustering of single-cell RNA-seq data by genotype without reference genotypes. Nat. Methods 17:615–20
    [Google Scholar]
  61. 61.
    Heinen T, Secchia S, Reddington JP, Zhao B, Furlong EEM, Stegle O. 2022. scDALI: modeling allelic heterogeneity in single cells reveals context-specific genetic regulation. Genome Biol. 23:8
    [Google Scholar]
  62. 62.
    Hie B, Peters J, Nyquist SK, Shalek AK, Berger B, Bryson BD. 2020. Computational methods for single-cell RNA sequencing. Annu. Rev. Biomed. Data Sci. 3:339–64
    [Google Scholar]
  63. 63.
    Hormozdiari F, van de Bunt M, Segrè AV, Li X, Joo JWJ et al. 2016. Colocalization of GWAS and eQTL signals detects target genes. Am. J. Hum. Genet. 99:1245–60
    [Google Scholar]
  64. 64.
    Hu X, Kim H, Raj T, Brennan PJ, Trynka G et al. 2014. Regulation of gene expression in autoimmune disease loci and the genetic basis of proliferation in CD4+ effector memory T cells. PLOS Genet. 10:e1004404
    [Google Scholar]
  65. 65.
    Ilicic T, Kim JK, Kolodziejczyk AA, Bagger FO, McCarthy DJ et al. 2016. Classification of low quality cells from single-cell RNA-seq data. Genome Biol. 17:29
    [Google Scholar]
  66. 66.
    Jagoda E, Xue JR, Reilly SK, Dannemann M, Racimo F et al. 2022. Detection of Neanderthal adaptively introgressed genetic variants that modulate reporter gene expression in human immune cells. Mol. Biol. Evol. 39:msab304
    [Google Scholar]
  67. 67.
    Jerber J, Seaton DD, Cuomo ASE, Kumasaka N, Haldane J et al. 2021. Population-scale single-cell RNA-seq profiling across dopaminergic neuron differentiation. Nat. Genet. 53:304–12
    [Google Scholar]
  68. 68.
    Kamariza M, Crawford L, Jones D, Finucane H. 2021. Misuse of the term “trans-ethnic” in genomics research. Nat. Genet. 53:1520–21
    [Google Scholar]
  69. 69.
    Kaminow B, Yunusov D, Dobin A. 2021. STARsolo: accurate, fast and versatile mapping/quantification of single-cell and single-nucleus RNA-seq data. bioRxiv 2021.05.05.442755. https://doi.org/10.1101/2021.05.05.442755
    [Crossref]
  70. 70.
    Kang HM, Subramaniam M, Targ S, Nguyen M, Maliskova L et al. 2018. Multiplexed droplet single-cell RNA-sequencing using natural genetic variation. Nat. Biotechnol. 36:89–94
    [Google Scholar]
  71. 71.
    Kang JB, Nathan A, Weinand K, Zhang F, Millard N et al. 2021. Efficient and precise single-cell reference atlas mapping with Symphony. Nat. Commun. 12:5890
    [Google Scholar]
  72. 72.
    Kasela S, Kisand K, Tserel L, Kaleviste E, Remm A et al. 2017. Pathogenic implications for autoimmune mechanisms derived by comparative eQTL analysis of CD4+ versus CD8+ T cells. PLOS Genet. 13:e1006643
    [Google Scholar]
  73. 73.
    Kashima Y, Sakamoto Y, Kaneko K, Seki M, Suzuki Y, Suzuki A. 2020. Single-cell sequencing techniques from individual to multiomics analyses. Exp. Mol. Med. 52:1419–27
    [Google Scholar]
  74. 74.
    Kerimov N, Hayhurst JD, Peikova K, Manning JR, Walter P et al. 2021. A compendium of uniformly processed human gene expression and splicing quantitative trait loci. Nat. Genet. 53:1290–99
    [Google Scholar]
  75. 75.
    Kim-Hellmuth S, Aguet F, Oliva M, Muñoz-Aguirre M, Kasela S et al. 2020. Cell type-specific genetic regulation of gene expression across human tissues. Science 369:eaaz8528
    [Google Scholar]
  76. 76.
    Kim-Hellmuth S, Bechheim M, Pütz B, Mohammadi P, Nédélec Y et al. 2017. Genetic regulatory effects modified by immune activation contribute to autoimmune disease associations. Nat. Commun. 8:266
    [Google Scholar]
  77. 77.
    Kiselev VY, Andrews TS, Hemberg M. 2019. Challenges in unsupervised clustering of single-cell RNA-seq data. Nat. Rev. Genet. 20:273–82
    [Google Scholar]
  78. 78.
    Kiselev VY, Yiu A, Hemberg M. 2018. scmap: projection of single-cell RNA-seq data across data sets. Nat. Methods 15:359–62
    [Google Scholar]
  79. 79.
    Knowles DA, Burrows CK, Blischak JD, Patterson KM, Serie DJ et al. 2018. Determining the genetic basis of anthracycline-cardiotoxicity by molecular response QTL mapping in induced cardiomyocytes. eLife 7:33480
    [Google Scholar]
  80. 80.
    Korsunsky I, Millard N, Fan J, Slowikowski K, Zhang F et al. 2019. Fast, sensitive and accurate integration of single-cell data with Harmony. Nat. Methods 16:1289–96
    [Google Scholar]
  81. 81.
    Kumasaka N, Rostom R, Huang N, Polanski K, Meyer KB et al. 2021. Mapping interindividual dynamics of innate immune response at single-cell resolution. bioRxiv 2021.09.01.457774. https://doi.org/10.1101/2021.09.01.457774
    [Crossref]
  82. 82.
    Kundu K, Tardaguila M, Mann AL, Watt S, Ponstingl H et al. 2022. Genetic associations at regulatory phenotypes improve fine-mapping of causal variants for 12 immune-mediated diseases. Nat. Genet. 54:251–62
    [Google Scholar]
  83. 83.
    Lähnemann D, Köster J, Szczurek E, McCarthy DJ, Hicks SC et al. 2020. Eleven grand challenges in single-cell data science. Genome Biol. 21:31
    [Google Scholar]
  84. 84.
    Lee J, Hyeon DY, Hwang D. 2020. Single-cell multiomics: technologies and data analysis methods. Exp. Mol. Med. 52:1428–42
    [Google Scholar]
  85. 85.
    Lee MN, Ye C, Villani A-C, Raj T, Li W et al. 2014. Common genetic variants modulate pathogen-sensing responses in human dendritic cells. Science 343:1246980
    [Google Scholar]
  86. 86.
    Li S, Schmid KT, de Vries D, Korshevniuk M, Oelen R et al. 2022. Identification of genetic variants that impact gene co-expression relationships using large-scale single-cell data. bioRxiv 2022.04.20.488925. https://doi.org/10.1101/2022.04.20.488925
    [Crossref]
  87. 87.
    Liu H, Prashant NM, Spurr LF, Bousounis P, Alomran N et al. 2021. scReQTL: an approach to correlate SNVs to gene expression from individual scRNA-seq datasets. BMC Genom. 22:40
    [Google Scholar]
  88. 88.
    Liu X, Li YI, Pritchard JK. 2019. Trans effects on gene expression can drive omnigenic inheritance. Cell 177:1022–34.e6
    [Google Scholar]
  89. 89.
    Lopez R, Regier J, Cole MB, Jordan MI, Yosef N. 2018. Deep generative modeling for single-cell transcriptomics. Nat. Methods 15:1053–58
    [Google Scholar]
  90. 90.
    Lotfollahi M, Naghipourfar M, Luecken MD, Khajavi M, Büttner M et al. 2022. Mapping single-cell data to reference atlases by transfer learning. Nat. Biotechnol. 40:121–30
    [Google Scholar]
  91. 91.
    Luckheeram RV, Zhou R, Verma AD, Xia B. 2012. CD4+ T cells: differentiation and functions. Clin. Dev. Immunol. 2012:925135
    [Google Scholar]
  92. 92.
    Luecken MD, Theis FJ. 2019. Current best practices in single-cell RNA-seq analysis: a tutorial. Mol. Syst. Biol. 15:e8746
    [Google Scholar]
  93. 93.
    Ma T, Li H, Zhang X. 2022. Discovering single-cell eQTLs from scRNA-seq data only. Gene 829:146520
    [Google Scholar]
  94. 94.
    Mandric I, Schwarz T, Majumdar A, Hou K, Briscoe L et al. 2020. Optimized design of single-cell RNA sequencing experiments for cell-type-specific eQTL analysis. Nat. Commun. 11:5504
    [Google Scholar]
  95. 95.
    Marchini J, Howie B. 2010. Genotype imputation for genome-wide association studies. Nat. Rev. Genet. 11:499–511
    [Google Scholar]
  96. 96.
    Matzaraki V, Kumar V, Wijmenga C, Zhernakova A. 2017. The MHC locus and genetic susceptibility to autoimmune and infectious diseases. Genome Biol. 18:76
    [Google Scholar]
  97. 97.
    Maurano MT, Humbert R, Rynes E, Thurman RE, Haugen E et al. 2012. Systematic localization of common disease-associated variation in regulatory DNA. Science 337:1190–95
    [Google Scholar]
  98. 98.
    Melsted P, Booeshaghi AS, Liu L, Gao F, Lu L et al. 2021. Modular, efficient and constant-memory single-cell RNA-seq preprocessing. Nat. Biotechnol. 39:813–18
    [Google Scholar]
  99. 99.
    Millard N, Korsunsky I, Weinand K, Fonseka CY, Nathan A et al. 2021. Maximizing statistical power to detect differentially abundant cell states with scPOST. Cell Rep. Methods 1:100120
    [Google Scholar]
  100. 100.
    Mo A, Marigorta UM, Arafat D, Chan LHK, Ponder L et al. 2018. Disease-specific regulation of gene expression in a comparative analysis of juvenile idiopathic arthritis and inflammatory bowel disease. Genome Med. 10:48
    [Google Scholar]
  101. 101.
    Moltke I, Grarup N, Jørgensen ME, Bjerregaard P, Treebak JT et al. 2014. A common Greenlandic TBC1D4 variant confers muscle insulin resistance and type 2 diabetes. Nature 512:190–93
    [Google Scholar]
  102. 102.
    Mostafavi H, Spence JP, Naqvi S, Pritchard JK. 2022. Limited overlap of eQTLs and GWAS hits due to systematic differences in discovery. bioRxiv 2022.05.07.491045. https://doi.org/10.1101/2022.05.07.491045
    [Crossref]
  103. 103.
    Moyerbrailean GA, Richards AL, Kurtz D, Kalita CA, Davis GO et al. 2016. High-throughput allele-specific expression across 250 environmental conditions. Genome Res. 26:1627–38
    [Google Scholar]
  104. 104.
    Mu Z, Wei W, Fair B, Miao J, Zhu P, Li YI. 2021. The impact of cell type and context-dependent regulatory variants on human immune traits. Genome Biol. 22:122
    [Google Scholar]
  105. 105.
    Naranbhai V, Fairfax BP, Makino S, Humburg P, Wong D et al. 2015. Genomic modulators of gene expression in human neutrophils. Nat. Commun. 6:7545
    [Google Scholar]
  106. 106.
    Nathan A, Asgari S, Ishigaki K, Valencia C, Amariuta T et al. 2022. Single-cell eQTL models reveal dynamic T cell state dependence of disease loci. Nature 606:120–28
    [Google Scholar]
  107. 107.
    Nathan A, Beynor JI, Baglaenko Y, Suliman S, Ishigaki K et al. 2021. Multimodally profiling memory T cells from a tuberculosis cohort identifies cell state associations with demographics, environment and disease. Nat. Immunol. 22:781–93
    [Google Scholar]
  108. 108.
    Neavin D, Nguyen Q, Daniszewski MS, Liang HH, Chiu HS et al. 2021. Single cell eQTL analysis identifies cell type-specific genetic control of gene expression in fibroblasts and reprogrammed induced pluripotent stem cells. Genome Biol. 22:76
    [Google Scholar]
  109. 109.
    Nédélec Y, Sanz J, Baharian G, Szpiech ZA, Pacis A et al. 2016. Genetic ancestry and natural selection drive population differences in immune responses to pathogens. Cell 167:657–69.e21
    [Google Scholar]
  110. 110.
    Oelen R, de Vries DH, Brugge H, Gordon MG, Vochteloo M et al. 2022. Single-cell RNA-sequencing of peripheral blood mononuclear cells reveals widespread, context-specific gene expression regulation upon pathogenic exposure. Nat. Commun. 13:3267
    [Google Scholar]
  111. 111.
    O'Neill MB, Quach H, Pothlichet J, Aquino Y, Bisiaux A et al. 2021. Single-cell and bulk RNA-sequencing reveal differences in monocyte susceptibility to influenza A virus infection between Africans and Europeans. Front. Immunol. 12:768189
    [Google Scholar]
  112. 112.
    Osorio D, Cai JJ. 2021. Systematic determination of the mitochondrial proportion in human and mice tissues for single-cell RNA-sequencing data quality control. Bioinformatics 37:963–67
    [Google Scholar]
  113. 113.
    Ota M, Nagafuchi Y, Hatano H, Ishigaki K, Terao C et al. 2021. Dynamic landscape of immune cell-specific gene regulation in immune-mediated diseases. Cell 184:3006–21.e17
    [Google Scholar]
  114. 114.
    Patel D, Zhang X, Farrell JJ, Chung J, Stein TD et al. 2021. Cell-type-specific expression quantitative trait loci associated with Alzheimer disease in blood and brain tissue. Transl. Psychiatry 11:250
    [Google Scholar]
  115. 115.
    Perez RK, Gordon MG, Subramaniam M, Kim MC, Hartoularos GC et al. 2022. Single-cell RNA-seq reveals cell type-specific molecular and genetic associations to lupus. Science 376:eabf1970
    [Google Scholar]
  116. 116.
    Picelli S, Faridani OR, Björklund AK, Winberg G, Sagasser S, Sandberg R. 2014. Full-length RNA-seq from single cells using Smart-seq2. Nat. Protoc. 9:171–81
    [Google Scholar]
  117. 117.
    Pierce BL, Tong L, Chen LS, Rahaman R, Argos M et al. 2014. Mediation analysis demonstrates that trans-eQTLs are often explained by cis-mediation: a genome-wide analysis among 1,800 South Asians. PLOS Genet. 10:e1004818
    [Google Scholar]
  118. 118.
    Pool A-H, Poldsam H, Chen S, Thomson M, Oka Y. 2022. Enhanced recovery of single-cell RNA-sequencing reads for missing gene expression data. bioRxiv 2022.04.26.489449. https://doi.org/10.1101/2022.04.26.489449
    [Crossref]
  119. 119.
    Qi T, Wu Y, Fang H, Zhang F, Liu S et al. 2022. Genetic control of RNA splicing and its distinct role in complex trait variation. Nat. Genet. 54:1355–63
    [Google Scholar]
  120. 120.
    Quach H, Rotival M, Pothlichet J, Loh Y-HE, Dannemann M et al. 2016. Genetic adaptation and Neandertal admixture shaped the immune system of human populations. Cell 167:643–56.e17
    [Google Scholar]
  121. 121.
    Randolph HE, Fiege JK, Thielen BK, Mickelson CK, Shiratori M et al. 2021. Genetic ancestry effects on the response to viral infection are pervasive but cell type specific. Science 374:1127–33
    [Google Scholar]
  122. 122.
    Rao DA, Gurish MF, Marshall JL, Slowikowski K, Fonseka CY et al. 2017. Pathologically expanded peripheral T helper cell subset drives B cells in rheumatoid arthritis. Nature 542:110–14
    [Google Scholar]
  123. 123.
    Rao S, Yao Y, Bauer DE. 2021. Editing GWAS: experimental approaches to dissect and exploit disease-associated genetic variation. Genome Med. 13:41
    [Google Scholar]
  124. 124.
    Reid JE, Wernisch L. 2016. Pseudotime estimation: deconfounding single cell time series. Bioinformatics 32:2973–80
    [Google Scholar]
  125. 125.
    Sakaue S, Gurajala S, Curtis M, Luo Y, Choi W et al. 2022. A statistical genetics guide to identifying HLA alleles driving complex disease. bioRxiv 2022.08.24.504550. https://doi.org/10.1101/2022.08.24.504550
    [Crossref]
  126. 126.
    Sarkar AK, Stephens M. 2021. Separating measurement and expression models clarifies confusion in single-cell RNA sequencing analysis. Nat. Genet. 53:770–77
    [Google Scholar]
  127. 127.
    Sarkar AK, Tung P-Y, Blischak JD, Burnett JE, Li YI et al. 2019. Discovery and characterization of variance QTLs in human induced pluripotent stem cells. PLOS Genet. 15:e1008045
    [Google Scholar]
  128. 128.
    Schmid KT, Höllbacher B, Cruceanu C, Böttcher A, Lickert H et al. 2021. scPower accelerates and optimizes the design of multi-sample single cell transcriptomic studies. Nat. Commun. 12:6625
    [Google Scholar]
  129. 129.
    Schmiedel BJ, Gonzalez-Colin C, Fajardo V, Rocha J, Madrigal A et al. 2022. Single-cell eQTL analysis of activated T cell subsets reveals activation and cell type-dependent effects of disease-risk variants. Sci. Immunol. 7:eabm2508
    [Google Scholar]
  130. 130.
    Sheng X, Guan Y, Ma Z, Wu J, Liu H et al. 2021. Mapping the genetic architecture of human traits to cell types in the kidney identifies mechanisms of disease and potential treatments. Nat. Genet. 53:1322–33
    [Google Scholar]
  131. 131.
    Slyper M, Porter CBM, Ashenberg O, Waldman J, Drokhlyansky E et al. 2020. A single-cell and single-nucleus RNA-Seq toolbox for fresh and frozen human tumors. Nat. Med. 26:792–802
    [Google Scholar]
  132. 132.
    Smillie CS, Biton M, Ordovas-Montanes J, Sullivan KM, Burgin G et al. 2019. Intra- and inter-cellular rewiring of the human colon during ulcerative colitis. Cell 178:714–30.e22
    [Google Scholar]
  133. 133.
    Soskic B, Cano-Gamez E, Smyth DJ, Ambridge K, Ke Z et al. 2022. Immune disease risk variants regulate gene expression dynamics during CD4+ T cell activation. Nat. Genet. 54:817–26
    [Google Scholar]
  134. 134.
    Stegle O, Parts L, Piipari M, Winn J, Durbin R. 2012. Using probabilistic estimation of expression residuals (PEER) to obtain increased power and interpretability of gene expression analyses. Nat. Protoc. 7:500–7
    [Google Scholar]
  135. 135.
    Stoeckius M, Hafemeister C, Stephenson W, Houck-Loomis B, Chattopadhyay PK et al. 2017. Simultaneous epitope and transcriptome measurement in single cells. Nat. Methods 14:865–68
    [Google Scholar]
  136. 136.
    Stoeckius M, Zheng S, Houck-Loomis B, Hao S, Yeung BZ et al. 2018. Cell hashing with barcoded antibodies enables multiplexing and doublet detection for single cell genomics. Genome Biol. 19:224
    [Google Scholar]
  137. 137.
    Strober BJ, Elorbany R, Rhodes K, Krishnan N, Tayeb K et al. 2019. Dynamic genetic regulation of gene expression during cellular differentiation. Science 364:1287–90
    [Google Scholar]
  138. 138.
    Stuart T, Butler A, Hoffman P, Hafemeister C, Papalexi E et al. 2019. Comprehensive integration of single-cell data. Cell 177:1888–902.e21
    [Google Scholar]
  139. 139.
    Stubbington MJT, Rozenblatt-Rosen O, Regev A, Teichmann SA. 2017. Single-cell transcriptomics to explore the immune system in health and disease. Science 358:58–63
    [Google Scholar]
  140. 140.
    Swanson E, Lord C, Reading J, Heubeck AT, Genge PC et al. 2021. Simultaneous trimodal single-cell measurement of transcripts, epitopes, and chromatin accessibility using TEA-seq. eLife 10:63632
    [Google Scholar]
  141. 141.
    Tan Y, Cahan P. 2019. SingleCellNet: a computational tool to classify single cell RNA-seq data across platforms and across species. Cell Syst. 9:207–13.e2
    [Google Scholar]
  142. 142.
    Tian L, Jabbari JS, Thijssen R, Gouil Q, Amarasinghe SL et al. 2021. Comprehensive characterization of single-cell full-length isoforms in human and mouse with long-read sequencing. Genome Biol. 22:310
    [Google Scholar]
  143. 143.
    Townes FW, Hicks SC, Aryee MJ, Irizarry RA. 2019. Feature selection and dimension reduction for single-cell RNA-Seq based on a multinomial model. Genome Biol. 20:295
    [Google Scholar]
  144. 144.
    Tran HTN, Ang KS, Chevrier M, Zhang X, Lee NYS et al. 2020. A benchmark of batch-effect correction methods for single-cell RNA sequencing data. Genome Biol. 21:12
    [Google Scholar]
  145. 145.
    Trapnell C. 2015. Defining cell types and states with single-cell genomics. Genome Res. 25:1491–98
    [Google Scholar]
  146. 146.
    Umans BD, Battle A, Gilad Y. 2021. Where are the disease-associated eQTLs?. Trends Genet. 37:109–24
    [Google Scholar]
  147. 147.
    van der Wijst MGP, Brugge H, de Vries DH, Deelen P, Swertz MA et al. 2018. Single-cell RNA sequencing identifies celltype-specific cis-eQTLs and co-expression QTLs. Nat. Genet. 50:493–97
    [Google Scholar]
  148. 148.
    Vieira Braga FA, Miragaia RJ. 2019. Tissue handling and dissociation for single-cell RNA-Seq. Single Cell Methods V Proserpio 9–21. New York: Humana
    [Google Scholar]
  149. 149.
    Vieth B, Ziegenhain C, Parekh S, Enard W, Hellmann I. 2017. powsimR: power analysis for bulk and single cell RNA-seq experiments. Bioinformatics 33:3486–88
    [Google Scholar]
  150. 150.
    Vochteloo M, Deelen P, Vink B, BIOS Consort., Tsai EA et al. 2022. Unbiased identification of unknown cellular and environmental factors that mediate eQTLs using principal interaction component analysis. bioRxiv 2022.07.28.501849. https://doi.org/10.1101/2022.07.28.501849
    [Crossref]
  151. 151.
    Võsa U, Claringbould A, Westra H-J, Bonder MJ, Deelen P et al. 2021. Large-scale cis- and trans-eQTL analyses identify thousands of genetic loci and polygenic scores that regulate blood gene expression. Nat. Genet. 53:1300–10
    [Google Scholar]
  152. 152.
    Ward MC, Banovich NE, Sarkar A, Stephens M, Gilad Y. 2021. Dynamic effects of genetic variation on gene expression revealed following hypoxic stress in cardiomyocytes. eLife 10:e57345
    [Google Scholar]
  153. 153.
    Westra H-J, Arends D, Esko T, Peters MJ, Schurmann C et al. 2015. Cell specific eQTL analysis without sorting cells. PLOS Genet. 11:e1005223
    [Google Scholar]
  154. 154.
    Westra H-J, Peters MJ, Esko T, Yaghootkar H, Schurmann C et al. 2013. Systematic identification of trans eQTLs as putative drivers of known disease associations. Nat. Genet. 45:1238–43
    [Google Scholar]
  155. 155.
    Wills QF, Livak KJ, Tipping AJ, Enver T, Goldson AJ et al. 2013. Single-cell gene expression analysis reveals genetic associations masked in whole-tissue experiments. Nat. Biotechnol. 31:748–52
    [Google Scholar]
  156. 156.
    Wojcik GL, Graff M, Nishimura KK, Tao R, Haessler J et al. 2019. Genetic analyses of diverse populations improves discovery for complex traits. Nature 570:514–18
    [Google Scholar]
  157. 157.
    Wolock SL, Lopez R, Klein AM. 2019. Scrublet: computational identification of cell doublets in single-cell transcriptomic data. Cell Syst. 8:281–91.e9
    [Google Scholar]
  158. 158.
    Wu H, Kirita Y, Donnelly EL, Humphreys BD. 2019. Advantages of single-nucleus over single-cell RNA sequencing of adult kidney: rare cell types and novel cell states revealed in fibrosis. J. Am. Soc. Nephrol. 30:23–32
    [Google Scholar]
  159. 159.
    Xu C, Lopez R, Mehlman E, Regier J, Jordan MI, Yosef N. 2021. Probabilistic harmonization and annotation of single-cell transcriptomics data with deep generative models. Mol. Syst. Biol. 17:e9620
    [Google Scholar]
  160. 160.
    Xue A, Yazar S, Neavin D, Powell JE. 2022. Pitfalls and opportunities for applying PEER factors in single-cell eQTL analyses. bioRxiv 2022.08.02.502566. https://doi.org/10.1101/2022.08.02.502566
    [Crossref]
  161. 161.
    Yao DW, O'Connor LJ, Price AL, Gusev A 2020. Quantifying genetic effects on disease mediated by assayed gene expression levels. Nat. Genet. 52:626–33
    [Google Scholar]
  162. 162.
    Yap CX, Lloyd-Jones L, Holloway A, Smartt P, Wray NR et al. 2018. Trans-eQTLs identified in whole blood have limited influence on complex disease biology. Eur. J. Hum. Genet. 26:1361–68
    [Google Scholar]
  163. 163.
    Yazar S, Alquicira-Hernandez J, Wing K, Senabouth A, Gordon MG et al. 2022. Single-cell eQTL mapping identifies cell type-specific genetic control of autoimmune disease. Science 376:eabf3041
    [Google Scholar]
  164. 164.
    Yong J, Johnson JD, Arvan P, Han J, Kaufman RJ. 2021. Therapeutic opportunities for pancreatic β-cell ER stress in diabetes mellitus. Nat. Rev. Endocrinol. 17:455–67
    [Google Scholar]
  165. 165.
    Yoo T, Joo SK, Kim HJ, Kim HY, Sim H et al. 2021. Disease-specific eQTL screening reveals an anti-fibrotic effect of AGXT2 in non-alcoholic fatty liver disease. J. Hepatol. 75:514–23
    [Google Scholar]
  166. 166.
    Young AMH, Kumasaka N, Calvert F, Hammond TR, Knights A et al. 2021. A map of transcriptional heterogeneity and regulatory variation in human microglia. Nat. Genet. 53:861–68
    [Google Scholar]
  167. 167.
    Zappia L, Phipson B, Oshlack A. 2017. Splatter: simulation of single-cell RNA sequencing data. Genome Biol. 18:174
    [Google Scholar]
  168. 168.
    Zeng H. 2022. What is a cell type and how to define it?. Cell 185:2739–55
    [Google Scholar]
  169. 169.
    Zhang F, Jonsson AH, Nathan A, Wei K, Millard N et al. 2022. Cellular deconstruction of inflamed synovium defines diverse inflammatory phenotypes in rheumatoid arthritis. bioRxiv 2022.02.25.481990. https://doi.org/10.1101/2022.02.25.481990
    [Crossref]
  170. 170.
    Zhang F, Wei K, Slowikowski K, Fonseka CY, Rao DA et al. 2019. Defining inflammatory cell states in rheumatoid arthritis joint synovial tissues by integrating single-cell transcriptomics and mass cytometry. Nat. Immunol. 20:928–42
    [Google Scholar]
  171. 171.
    Zhang X, Li T, Liu F, Chen Y, Yao J et al. 2019. Comparative analysis of droplet-based ultra-high-throughput single-cell RNA-seq systems. Mol. Cell 73:130–42.e5
    [Google Scholar]
  172. 172.
    Zhang Y, Yang HT, Kadash-Edmondson K, Pan Y, Pan Z et al. 2020. Regional variation of splicing QTLs in human brain. Am. J. Hum. Genet. 107:196–210
    [Google Scholar]
  173. 173.
    Zhang Z, Luo D, Zhong X, Choi JH, Ma Y et al. 2019. SCINA: a semi-supervised subtyping algorithm of single cells and bulk samples. Genes 10:531
    [Google Scholar]
  174. 174.
    Zhou S, Butler-Laporte G, Nakanishi T, Morrison DR, Afilalo J et al. 2021. A Neanderthal OAS1 isoform protects individuals of European ancestry against COVID-19 susceptibility and severity. Nat. Med. 27:659–67
    [Google Scholar]
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