Functional Characterization of Genetic Variant Effects on Expression

Literature Cited

1. 1.

   Cooper DN, Krawczak M. 1996. Human gene mutation database. Hum. Genet. 98:5629
   [Google Scholar]
2. 2.

   Landrum MJ, Lee JM, Riley GR, Jang W, Rubinstein WS et al. 2014. ClinVar: public archive of relationships among sequence variation and human phenotype. Nucleic Acids Res 42:D980–85
   [Google Scholar]
3. 3.

   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]
4. 4.

   Musunuru K, Strong A, Frank-Kamenetsky M, Lee NE, Ahfeldt T et al. 2010. From noncoding variant to phenotype via SORT1 at the 1p13 cholesterol locus. Nature 466:7307714–19
   [Google Scholar]
5. 5.

   Smemo S, Tena JJ, Kim K-H, Gamazon ER, Sakabe NJ et al. 2014. Obesity-associated variants within FTO form long-range functional connections with IRX3. Nature 507:7492371–75
   [Google Scholar]
6. 6.

   Schmiedel BJ, Seumois G, Samaniego-Castruita D, Cayford J, Schulten V et al. 2016. 17q21 asthma-risk variants switch CTCF binding and regulate IL-2 production by T cells. Nat. Commun. 7:13426
   [Google Scholar]
7. 7.

   Zhu D-L, Chen X-F, Hu W-X, Dong S-S, Lu B-J et al. 2018. Multiple functional variants at 13q14 risk locus for osteoporosis regulate RANKL expression through long-range super-enhancer. J. Bone Miner. Res. 33:71335–46
   [Google Scholar]
8. 8.

   Sobreira DR, Joslin AC, Zhang Q, Williamson I, Hansen GT et al. 2021. Extensive pleiotropism and allelic heterogeneity mediate metabolic effects of IRX3 and IRX5. Science 372:65461085–91
   [Google Scholar]
9. 9.

   Xu M, Mehl L, Zhang T, Thakur R, Sowards H et al. 2021. A UVB-responsive common variant at chromosome band 7p21.1 confers tanning response and melanoma risk via regulation of the aryl hydrocarbon receptor. AHR. Am. J. Hum. Genet. 108:91611–30
   [Google Scholar]
10. 10.

    Lukacs GL, Verkman AS. 2012. CFTR: folding, misfolding and correcting the ΔF508 conformational defect. Trends Mol. Med. 18:281–91
    [Google Scholar]
11. 11.

    Kurosaki T, Popp MW, Maquat LE. 2019. Quality and quantity control of gene expression by nonsense-mediated mRNA decay. Nat. Rev. Mol. Cell Biol. 20:7406–20
    [Google Scholar]
12. 12.

    Brandt M, Gokden A, Ziosi M, Lappalainen T. 2020. A polyclonal allelic expression assay for detecting regulatory effects of transcript variants. Genome Med 12:79
    [Google Scholar]
13. 13.

    Tushev G, Glock C, Heumüller M, Biever A, Jovanovic M, Schuman EM. 2018. Alternative 3′ UTRs modify the localization, regulatory potential, stability, and plasticity of mRNAs in neuronal compartments. Neuron 98:3495–511.e6
    [Google Scholar]
14. 14.

    Vejnar CE, Abdel Messih M, Takacs CM, Yartseva V, Oikonomou P et al. 2019. Genome wide analysis of 3′ UTR sequence elements and proteins regulating mRNA stability during maternal-to-zygotic transition in zebrafish. Genome Res 29:71100–14
    [Google Scholar]
15. 15.

    López-Martínez A, Soblechero-Martín P, de-la-Puente-Ovejero L, Nogales-Gadea G, Arechavala-Gomeza V. 2020. An overview of alternative splicing defects implicated in myotonic dystrophy type I. Genes 11:91109
    [Google Scholar]
16. 16.

    Kilpinen H, Waszak SM, Gschwind AR, Raghav SK, Witwicki RM et al. 2013. Coordinated effects of sequence variation on DNA binding, chromatin structure, and transcription. Science 342:6159744–47
    [Google Scholar]
17. 17.

    McVicker G, van de Geijn B, Degner JF, Cain CE, Banovich NE et al. 2013. Identification of genetic variants that affect histone modifications in human cells. Science 342:6159747–49
    [Google Scholar]
18. 18.

    Kasowski M, Kyriazopoulou-Panagiotopoulou S, Grubert F, Zaugg JB, Kundaje A et al. 2013. Extensive variation in chromatin states across humans. Science 342:6159750–52
    [Google Scholar]
19. 19.

    Waszak SM, Delaneau O, Gschwind AR, Kilpinen H, Raghav SK et al. 2015. Population variation and genetic control of modular chromatin architecture in humans. Cell 162:51039–50
    [Google Scholar]
20. 20.

    Grubert F, Zaugg JB, Kasowski M, Ursu O, Spacek DV et al. 2015. Genetic control of chromatin states in humans involves local and distal chromosomal interactions. Cell 162:51051–65
    [Google Scholar]
21. 21.

    Cheung VG, Conlin LK, Weber TM, Arcaro M, Jen K-Y et al. 2003. Natural variation in human gene expression assessed in lymphoblastoid cells. Nat. Genet. 33:3422–25
    [Google Scholar]
22. 22.

    Stranger BE, Forrest MS, Clark AG, Minichiello MJ, Deutsch S et al. 2005. Genome-wide associations of gene expression variation in humans. PLOS Genet 1:6e78
    [Google Scholar]
23. 23.

    Stranger BE, Nica AC, Forrest MS, Dimas A, Bird CP et al. 2007. Population genomics of human gene expression. Nat. Genet. 39:101217–24
    [Google Scholar]
24. 24.

    Spielman RS, Bastone LA, Burdick JT, Morley M, Ewens WJ, Cheung VG. 2007. Common genetic variants account for differences in gene expression among ethnic groups. Nat. Genet. 39:2226–31
    [Google Scholar]
25. 25.

    Emilsson V, Thorleifsson G, Zhang B, Leonardson AS, Zink F et al. 2008. Genetics of gene expression and its effect on disease. Nature 452:7186423–28
    [Google Scholar]
26. 26.

    Gaffney DJ, Veyrieras J-B, Degner JF, Pique-Regi R, Pai AA et al. 2012. Dissecting the regulatory architecture of gene expression QTLs. Genome Biol 13:R7
    [Google Scholar]
27. 27.

    Lappalainen T, Sammeth M, Friedländer MR, ‘ t Hoen PAC, Monlong J et al. 2013. Transcriptome and genome sequencing uncovers functional variation in humans. Nature 501:7468506–11
    [Google Scholar]
28. 28.

    GTEx Consort 2020. The GTEx Consortium atlas of genetic regulatory effects across human tissues. Science 369:65091318–30
    [Google Scholar]
29. 29.

    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. 53:91290–99
    [Google Scholar]
30. 30.

    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 7:8e1002197
    [Google Scholar]
31. 31.

    GTEx Consort. Aguet F, Brown AA, Castel SE, Davis JR et al. 2017. Genetic effects on gene expression across human tissues. Nature 550:7675204–13
    [Google Scholar]
32. 32.

    Ongen H, Buil A, Dermitzakis ET, Delaneau O, Brown AA. 2016. Fast and efficient QTL mapper for thousands of molecular phenotypes. Bioinformatics 32:101479–85
    [Google Scholar]
33. 33.

    Stegle O, Parts L, Durbin R, Winn J 2010. A Bayesian framework to account for complex non-genetic factors in gene expression levels greatly increases power in eQTL studies. PLOS Comput. Biol. 6:5e1000770
    [Google Scholar]
34. 34.

    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. 7:3500–7
    [Google Scholar]
35. 35.

    Rouhani F, Kumasaka N, de Brito MC, Bradley A, Vallier L, Gaffney D. 2014. Genetic background drives transcriptional variation in human induced pluripotent stem cells. PLOS Genet 10:6e1004432
    [Google Scholar]
36. 36.

    Battle A, Mostafavi S, Zhu X, Potash JB, Weissman MM et al. 2013. Characterizing the genetic basis of transcriptome diversity through RNA-sequencing of 922 individuals. Genome Res 24:14–24
    [Google Scholar]
37. 37.

    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. 44:5502–10
    [Google Scholar]
38. 38.

    Raj T, Rothamel K, Mostafavi S, Ye C, Lee MN et al. 2014. Polarization of the effects of autoimmune and neurodegenerative risk alleles in leukocytes. Science 344:6183519–23
    [Google Scholar]
39. 39.

    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]
40. 40.

    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 167:51398–414.e24
    [Google Scholar]
41. 41.

    Schmiedel BJ, Singh D, Madrigal A, Valdovino-Gonzalez AG, White BM et al. 2018. Impact of genetic polymorphisms on human immune cell gene expression. Cell 175:61701–15.e16
    [Google Scholar]
42. 42.

    Chandra V, Bhattacharyya S, Schmiedel BJ, Madrigal A, Gonzalez-Colin C et al. 2021. Promoter-interacting expression quantitative trait loci are enriched for functional genetic variants. Nat. Genet. 53:110–19
    [Google Scholar]
43. 43.

    GTEx Consort 2015. The Genotype-Tissue Expression (GTEx) pilot analysis: multitissue gene regulation in humans. Science 348:6235648–60
    [Google Scholar]
44. 44.

    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 15:4e1008045
    [Google Scholar]
45. 45.

    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]
46. 46.

    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. 53:3304–12
    [Google Scholar]
47. 47.

    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]
48. 48.

    van der Wijst M, de Vries DH, Groot HE, Trynka G, Hon CC et al. 2020. The single-cell eQTLGen consortium. eLife 9:e52155
    [Google Scholar]
49. 49.

    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]
50. 50.

    Li YI, van de Geijn B, Raj A, Knowles DA, Petti AA et al. 2016. RNA splicing is a primary link between genetic variation and disease. Science 352:6285600–4
    [Google Scholar]
51. 51.

    Kimura K, Wakamatsu A, Suzuki Y, Ota T, Nishikawa T et al. 2006. Diversification of transcriptional modulation: large-scale identification and characterization of putative alternative promoters of human genes. Genome Res 16:55–65
    [Google Scholar]
52. 52.

    Garieri M, Delaneau O, Santoni F, Fish RJ, Mull D et al. 2017. The effect of genetic variation on promoter usage and enhancer activity. Nat. Commun. 8:1358
    [Google Scholar]
53. 53.

    Alasoo K, Rodrigues J, Danesh J, Freitag DF, Paul DS, Gaffney DJ. 2019. Genetic effects on promoter usage are highly context-specific and contribute to complex traits. eLife 8:e41673
    [Google Scholar]
54. 54.

    Heinzen EL, Ge D, Cronin KD, Maia JM, Shianna KV et al. 2008. Tissue-specific genetic control of splicing: implications for the study of complex traits. PLOS Biol 6:12e1000001
    [Google Scholar]
55. 55.

    Monlong J, Calvo M, Ferreira PG, Guigó R. 2014. Identification of genetic variants associated with alternative splicing using sQTLseekeR. Nat. Commun. 5:4698
    [Google Scholar]
56. 56.

    Chick JM, Munger SC, Simecek P, Huttlin EL, Choi K et al. 2016. Defining the consequences of genetic variation on a proteome-wide scale. Nature 534:7608500–5
    [Google Scholar]
57. 57.

    Mirauta BA, Seaton DD, Bensaddek D, Brenes A, Bonder MJ et al. 2020. Population-scale proteome variation in human induced pluripotent stem cells. eLife 9:e57390
    [Google Scholar]
58. 58.

    He B, Shi J, Wang X, Jiang H, Zhu H-J. 2020. Genome-wide pQTL analysis of protein expression regulatory networks in the human liver. BMC Biol 18:97
    [Google Scholar]
59. 59.

    Robins C, Liu Y, Fan W, Duong DM, Meigs J et al. 2021. Genetic control of the human brain proteome. Am. J. Hum. Genet. 108:3400–10
    [Google Scholar]
60. 60.

    Battle A, Khan Z, Wang SH, Mitrano A, Ford MJ et al. 2015. Impact of regulatory variation from RNA to protein. Science 347:6222664–67
    [Google Scholar]
61. 61.

    Degner JF, Pai AA, Pique-Regi R, Veyrieras J-B, Gaffney DJ et al. 2012. DNase I sensitivity QTLs are a major determinant of human expression variation. Nature 482:7385390–94
    [Google Scholar]
62. 62.

    Ding Z, Ni Y, Timmer SW, Lee B-K, Battenhouse A et al. 2014. Quantitative genetics of CTCF binding reveal local sequence effects and different modes of X-chromosome association. PLOS Genet 10:11e1004798
    [Google Scholar]
63. 63.

    Tehranchi AK, Myrthil M, Martin T, Hie BL, Golan D, Fraser HB. 2016. Pooled ChIP-Seq links variation in transcription factor binding to complex disease risk. Cell 165:3730–41
    [Google Scholar]
64. 64.

    Brandt M, Lappalainen T. 2017. SnapShot: discovering genetic regulatory variants by QTL analysis. Cell 171:4980.e1
    [Google Scholar]
65. 65.

    Abell NS, DeGorter MK, Gloudemans M, Greenwald E, Smith KS et al. 2021. Multiple causal variants underlie genetic associations in humans. bioRxiv 10.1101/2021.05.24.445471. https://doi.org/10.1101/2021.05.24.445471
    [Crossref]
66. 66.

    Mouri K, Guo MH, de Boer CG, Newby GA, Gentili M et al. 2021. Prioritization of autoimmune disease-associated genetic variants that perturb regulatory element activity in T cells. bioRxiv 10.1101/2021.05.30.445673. https://doi.org/10.1101/2021.05.30.445673
    [Crossref]
67. 67.

    Hormozdiari F, Kostem E, Kang EY, Pasaniuc B, Eskin E. 2014. Identifying causal variants at loci with multiple signals of association. Genetics 198:2497–508
    [Google Scholar]
68. 68.

    Brown AA, Viñuela A, Delaneau O, Spector TD, Small KS, Dermitzakis ET. 2017. Predicting causal variants affecting expression by using whole-genome sequencing and RNA-seq from multiple human tissues. Nat. Genet. 49:121747–51
    [Google Scholar]
69. 69.

    Wang G, Sarkar A, Carbonetto P, Stephens M. 2020. A simple new approach to variable selection in regression, with application to genetic fine mapping. J. R. Stat. Soc. B 82:51273–300
    [Google Scholar]
70. 70.

    Wen X, Lee Y, Luca F, Pique-Regi R. 2016. Efficient integrative multi-SNP association analysis via deterministic approximation of posteriors. Am. J. Hum. Genet. 98:61114–29
    [Google Scholar]
71. 71.

    Lee Y, Luca F, Pique-Regi R, Wen X 2018. Bayesian multi-SNP genetic association analysis: control of FDR and use of summary statistics. bioRxiv 10.1101/316471. https://doi.org/10.1101/316471
    [Crossref]
72. 72.

    Weissbrod O, Hormozdiari F, Benner C, Cui R, Ulirsch J et al. 2020. Functionally informed fine-mapping and polygenic localization of complex trait heritability. Nat. Genet. 52:121355–63
    [Google Scholar]
73. 73.

    Wang QS, Kelley DR, Ulirsch J, Kanai M, Sadhuka S et al. 2021. Leveraging supervised learning for functionally informed fine-mapping of cis-eQTLs identifies an additional 20,913 putative causal eQTLs. Nat. Commun. 12:3394
    [Google Scholar]
74. 74.

    de Wet JR, Wood KV, DeLuca M, Helinski DR, Subramani S. 1987. Firefly luciferase gene: structure and expression in mammalian cells. Mol. Cell. Biol. 7:2725–37
    [Google Scholar]
75. 75.

    Brasier AR, Tate JE, Habener JF. 1989. Optimized use of the firefly luciferase assay as a reporter gene in mammalian cell lines. Biotechniques 7:101116–22
    [Google Scholar]
76. 76.

    Melnikov A, Murugan A, Zhang X, Tesileanu T, Wang L et al. 2012. Systematic dissection and optimization of inducible enhancers in human cells using a massively parallel reporter assay. Nat. Biotechnol. 30:3271–77
    [Google Scholar]
77. 77.

    Kheradpour P, Ernst J, Melnikov A, Rogov P, Wang L et al. 2013. Systematic dissection of regulatory motifs in 2000 predicted human enhancers using a massively parallel reporter assay. Genome Res 23:5800–11
    [Google Scholar]
78. 78.

    van Arensbergen J, Pagie L, FitzPatrick VD, de Haas M, Baltissen MP et al. 2019. High-throughput identification of human SNPs affecting regulatory element activity. Nat. Genet. 51:71160–69
    [Google Scholar]
79. 79.

    Jinek M, Chylinski K, Fonfara I, Hauer M, Doudna JA, Charpentier E. 2012. A programmable dual-RNA-guided DNA endonuclease in adaptive bacterial immunity. Science 337:6096816–21
    [Google Scholar]
80. 80.

    Ran FA, Hsu PD, Wright J, Agarwala V, Scott DA, Zhang F. 2013. Genome engineering using the CRISPR-Cas9 system. Nat. Protoc. 8:112281–308
    [Google Scholar]
81. 81.

    Zerbino DR, Wilder SP, Johnson N, Juettemann T, Flicek PR. 2015. The Ensembl Regulatory Build. Genome Biol 16:56
    [Google Scholar]
82. 82.

    McLaren W, Gil L, Hunt SE, Riat HS, Ritchie GRS et al. 2016. The Ensembl Variant Effect Predictor. Genome Biol 17:122
    [Google Scholar]
83. 83.

    Cunningham F, Achuthan P, Akanni W, Allen J, Amode MR et al. 2019. Ensembl 2019. Nucleic Acids Res 47:D1D745–51
84. 84.

    EMBL (Europ. Mol. Biol. Lab.)-EBI (Europ. Bioinform. Inst.) 2021. The Ensembl Regulatory Build Ensembl http://may2021.archive.ensembl.org
85. 85.

    Boyle AP, Davis S, Shulha HP, Meltzer P, Margulies EH et al. 2008. High-resolution mapping and characterization of open chromatin across the genome. Cell 132:2311–22
    [Google Scholar]
86. 86.

    Buenrostro JD, Giresi PG, Zaba LC, Chang HY, Greenleaf WJ. 2013. Transposition of native chromatin for fast and sensitive epigenomic profiling of open chromatin, DNA-binding proteins and nucleosome position. Nat. Methods 10:121213–18
    [Google Scholar]
87. 87.

    Barski A, Cuddapah S, Cui K, Roh T-Y, Schones DE et al. 2007. High-resolution profiling of histone methylations in the human genome. Cell 129:4823–37
    [Google Scholar]
88. 88.

    Heintzman ND, Hon GC, Hawkins RD, Kheradpour P, Stark A et al. 2009. Histone modifications at human enhancers reflect global cell-type-specific gene expression. Nature 459:7243108–12
    [Google Scholar]
89. 89.

    ENCODE Proj. Consort 2012. An integrated encyclopedia of DNA elements in the human genome. Nature 489:741457–74
    [Google Scholar]
90. 90.

    PsychENCODE Consort 2018. Revealing the brain's molecular architecture. Science 362:64201262–63
    [Google Scholar]
91. 91.

    Roadmap Epigenom. Consort. Kundaje A, Meuleman W, Ernst J, Bilenky M et al. 2015. Integrative analysis of 111 reference human epigenomes. Nature 518:7539317–30
    [Google Scholar]
92. 92.

    Stunnenberg HG, Int. Hum. Epigenome Consort., Hirst M 2016. The International Human Epigenome Consortium: a blueprint for scientific collaboration and discovery. Cell 167:51145–49
    [Google Scholar]
93. 93.

    Swinstead EE, Paakinaho V, Presman DM, Hager GL. 2016. Pioneer factors and ATP-dependent chromatin remodeling factors interact dynamically: a new perspective. Bioessays 38:111150–57
    [Google Scholar]
94. 94.

    Lambert SA, Jolma A, Campitelli LF, Das PK, Yin Y et al. 2018. The human transcription factors. Cell 172:4650–65
    [Google Scholar]
95. 95.

    Johnson DS, Mortazavi A, Myers RM, Wold B. 2007. Genome-wide mapping of in vivo protein-DNA interactions. Science 316:58301497–502
    [Google Scholar]
96. 96.

    Chen J, Rozowsky J, Galeev TR, Harmanci A, Kitchen R et al. 2016. A uniform survey of allele-specific binding and expression over 1000-Genomes-Project individuals. Nat. Commun. 7:11101
    [Google Scholar]
97. 97.

    Abramov S, Boytsov A, Bykova D, Penzar DD, Yevshin I et al. 2021. Landscape of allele-specific transcription factor binding in the human genome. Nat. Commun. 12:2751
    [Google Scholar]
98. 98.

    Garner MM, Revzin A. 1981. A gel electrophoresis method for quantifying the binding of proteins to specific DNA regions: application to components of the Escherichia coli lactose operon regulatory system. Nucleic Acids Res 9:133047–60
    [Google Scholar]
99. 99.

    Leblanc B, Moss T, eds. 2009. DNA-Protein Interactions: Principles and Protocols New York: Humana Press. , 3rd ed..
100. 100.

     Tuerk C, Gold L. 1990. Systematic evolution of ligands by exponential enrichment: RNA ligands to bacteriophage T4 DNA polymerase. Science 249:4968505–10
     [Google Scholar]
101. 101.

     Jolma A, Kivioja T, Toivonen J, Cheng L, Wei G et al. 2010. Multiplexed massively parallel SELEX for characterization of human transcription factor binding specificities. Genome Res 20:6861–73
     [Google Scholar]
102. 102.

     Slattery M, Riley T, Liu P, Abe N, Gomez-Alcala P et al. 2011. Cofactor binding evokes latent differences in DNA binding specificity between Hox proteins. Cell 147:61270–82
     [Google Scholar]
103. 103.

     Yan J, Qiu Y, Ribeiro Dos Santos AM, Yin Y, Li YE et al. 2021. Systematic analysis of binding of transcription factors to noncoding variants. Nature 591:7848147–51
     [Google Scholar]
104. 104.

     Jolma A, Yin Y, Nitta KR, Dave K, Popov A et al. 2015. DNA-dependent formation of transcription factor pairs alters their binding specificity. Nature 527:7578384–88
     [Google Scholar]
105. 105.

     Kheradpour P, Kellis M. 2014. Systematic discovery and characterization of regulatory motifs in ENCODE TF binding experiments. Nucleic Acids Res 42:52976–87
     [Google Scholar]
106. 106.

     Kulakovskiy IV, Vorontsov IE, Yevshin IS, Sharipov RN, Fedorova AD et al. 2018. HOCOMOCO: towards a complete collection of transcription factor binding models for human and mouse via large-scale ChIP-Seq analysis. Nucleic Acids Res 46:D1D252–59
     [Google Scholar]
107. 107.

     Mathelier A, Fornes O, Arenillas DJ, Chen C-Y, Denay G et al. 2016. JASPAR 2016: a major expansion and update of the open-access database of transcription factor binding profiles. Nucleic Acids Res 44:D1D110–15
     [Google Scholar]
108. 108.

     Jolma A, Yan J, Whitington T, Toivonen J, Nitta KR et al. 2013. DNA-binding specificities of human transcription factors. Cell 152:1–2327–39
     [Google Scholar]
109. 109.

     Bucher P. 1990. Weight matrix descriptions of four eukaryotic RNA polymerase II promoter elements derived from 502 unrelated promoter sequences. J. Mol. Biol. 212:4563–78
     [Google Scholar]
110. 110.

     Rastogi C, Rube HT, Kribelbauer JF, Crocker J, Loker RE et al. 2018. Accurate and sensitive quantification of protein-DNA binding affinity. PNAS 115:16E3692–701
     [Google Scholar]
111. 111.

     Zhou J, Troyanskaya OG. 2015. Predicting effects of noncoding variants with deep learning-based sequence model. Nat. Methods 12:10931–34
     [Google Scholar]
112. 112.

     Urbut SM, Wang G, Carbonetto P, Stephens M. 2019. Flexible statistical methods for estimating and testing effects in genomic studies with multiple conditions. Nat. Genet. 51:187–95
     [Google Scholar]
113. 113.

     He Y, Chhetri SB, Arvanitis M, Srinivasan K, Aguet F et al. 2020. sn-spMF: Matrix factorization informs tissue-specific genetic regulation of gene expression. Genome Biol 21:235
     [Google Scholar]
114. 114.

     Mizuno A, Okada Y. 2019. Biological characterization of expression quantitative trait loci (eQTLs) showing tissue-specific opposite directional effects. Eur. J. Hum. Genet. 27:111745–56
     [Google Scholar]
115. 115.

     Gutierrez-Arcelus M, Ongen H, Lappalainen T, Montgomery SB, Buil A et al. 2015. Tissue-specific effects of genetic and epigenetic variation on gene regulation and splicing. PLOS Genet 11:1e1004958
     [Google Scholar]
116. 116.

     Kilpinen H, Goncalves A, Leha A, Afzal V, Alasoo K et al. 2017. Common genetic variation drives molecular heterogeneity in human iPSCs. Nature 546:7658370–75
     [Google Scholar]
117. 117.

     Oh JH, Kim YJ, Moon S, Nam H-Y, Jeon J-P et al. 2013. Genotype instability during long-term subculture of lymphoblastoid cell lines. J. Hum. Genet. 58:16–20
     [Google Scholar]
118. 118.

     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 13:3e1006643
     [Google Scholar]
119. 119.

     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]
120. 120.

     Zhang T, Choi J, Kovacs MA, Shi J, Xu M et al. 2018. Cell-type-specific eQTL of primary melanocytes facilitates identification of melanoma susceptibility genes. Genome Res 28:111621–35
     [Google Scholar]
121. 121.

     Viñuela A, Varshney A, van de Bunt M, Prasad RB, Asplund O et al. 2020. Genetic variant effects on gene expression in human pancreatic islets and their implications for T2D. Nat. Commun. 11:4912
     [Google Scholar]
122. 122.

     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. 53:6861–68
     [Google Scholar]
123. 123.

     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. 50:4493–97
     [Google Scholar]
124. 124.

     Madissoon E, Wilbrey-Clark A, Miragaia RJ, Saeb-Parsy K, Mahbubani KT et al. 2019. scRNA-seq assessment of the human lung, spleen, and esophagus tissue stability after cold preservation. Genome Biol 21:1
     [Google Scholar]
125. 125.

     Westra H-J, Arends D, Esko T, Peters MJ, Schurmann C et al. 2015. Cell specific eQTL analysis without sorting cells. PLOS Genet 11:5e1005223
     [Google Scholar]
126. 126.

     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]
127. 127.

     Aran D, Hu Z, Butte AJ. 2017. xCell: digitally portraying the tissue cellular heterogeneity landscape. Genome Biol 18:220
     [Google Scholar]
128. 128.

     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 369:6509aaz8528
     [Google Scholar]
129. 129.

     Aguirre-Gamboa R, de Klein N, di Tommaso J, Claringbould A, van der Wijst MG et al. 2020. Deconvolution of bulk blood eQTL effects into immune cell subpopulations. BMC Bioinform. 21:243
     [Google Scholar]
130. 130.

     Park Y, He L, Davila-Velderrain J, Hou L, Mohammadi S et al. 2021. Single-cell deconvolution of 3,000 post-mortem brain samples for eQTL and GWAS dissection in mental disorders. bioRxiv 10.1101/2021.01.21.426000. https://doi.org/10.1101/2021.01.21.426000
     [Crossref]
131. 131.

     Knowles DA, Davis JR, Edgington H, Raj A, Favé M-J et al. 2017. Allele-specific expression reveals interactions between genetic variation and environment. Nat. Methods 14:699–702
     [Google Scholar]
132. 132.

     Taylor DL, Knowles DA, Scott LJ, Ramirez AH, Casale FP et al. 2018. Interactions between genetic variation and cellular environment in skeletal muscle gene expression. PLOS ONE 13:4e0195788–17
     [Google Scholar]
133. 133.

     Maranville JC, Luca F, Richards AL, Wen X, Witonsky DB et al. 2011. Interactions between glucocorticoid treatment and cis-regulatory polymorphisms contribute to cellular response phenotypes. PLOS Genet 7:7e1002162
     [Google Scholar]
134. 134.

     Smirnov DA, Brady L, Halasa K, Morley M, Solomon S, Cheung VG. 2012. Genetic variation in radiation-induced cell death. Genome Res 22:2332–39
     [Google Scholar]
135. 135.

     Kariuki SN, Maranville JC, Baxter SS, Jeong C, Nakagome S et al. 2016. Mapping variation in cellular and transcriptional response to 1,25-dihydroxyvitamin D3 in peripheral blood mononuclear cells. PLOS ONE 11:7e0159779
     [Google Scholar]
136. 136.

     Moyerbrailean GA, Richards AL, Kurtz D, Kalita CA, Davis GO et al. 2016. High-throughput allele-specific expression across 250 environmental conditions. Genome Res 26:121627–38
     [Google Scholar]
137. 137.

     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:e33480
     [Google Scholar]
138. 138.

     Findley AS, Richards AL, Petrini C, Alazizi A, Doman E et al. 2019. Interpreting coronary artery disease risk through gene-environment interactions in gene regulation. Genetics 213:2651–63
     [Google Scholar]
139. 139.

     Findley AS, Monziani A, Richards AL, Rhodes K, Ward MC et al. 2021. Functional dynamic genetic effects on gene regulation are specific to particular cell types and environmental conditions. eLife 10:e67077
     [Google Scholar]
140. 140.

     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]
141. 141.

     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 109:41204–9
     [Google Scholar]
142. 142.

     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 343:61751246949
     [Google Scholar]
143. 143.

     Kim S, Becker J, Bechheim M, Kaiser V, Noursadeghi M et al. 2014. Characterizing the genetic basis of innate immune response in TLR4-activated human monocytes. Nat. Commun. 5:5236
     [Google Scholar]
144. 144.

     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 343:61751246980
     [Google Scholar]
145. 145.

     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 167:3657–69.e21
     [Google Scholar]
146. 146.

     Alasoo K, Rodrigues J, Mukhopadhyay S, Knights AJ, Mann AL et al. 2018. Shared genetic effects on chromatin and gene expression indicate a role for enhancer priming in immune response. Nat. Genet. 50:3424–31
     [Google Scholar]
147. 147.

     Brandt M, Kim-Hellmuth S, Ziosi M, Gokden A, Wolman A et al. 2020. An autoimmune disease risk variant: a trans master regulatory effect mediated by IRF1 under immune stimulation?. PLOS Genet 17:7e1009684
     [Google Scholar]
148. 148.

     Randolph HE, Mu Z, Fiege JK, Thielen BK, Grenier J-C et al. 2020. Single-cell RNA-sequencing reveals pervasive but highly cell type-specific genetic ancestry effects on the response to viral infection. bioRxiv 10.1101/2020.12.21.423830. https://doi.org/10.1101/2020.12.21.423830
     [Crossref]
149. 149.

     Strober BJ, Elorbany R, Rhodes K, Krishnan N, Tayeb K et al. 2019. Dynamic genetic regulation of gene expression during cellular differentiation. Science 364:64471287–90
     [Google Scholar]
150. 150.

     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. 52:3247–53
     [Google Scholar]
151. 151.

     Kubota N, Suyama M. 2021. Functional variants in hematopoietic transcription factor footprints and their roles in the risk of immune system diseases. bioRxiv 10.1101/2021.03.22.436360. https://doi.org/10.1101/2021.03.22.436360
     [Crossref]
152. 152.

     Zhernakova DV, Deelen P, Vermaat M, van Iterson M, van Galen M et al. 2016. Identification of context-dependent expression quantitative trait loci in whole blood. Nat. Genet. 49:139–45
     [Google Scholar]
153. 153.

     Flynn E, Tsu AL, Kasela S, Kim-Hellmuth S, Aguet F et al. 2021. Transcription factor regulation of eQTL activity across individuals and tissues. PLOS Genet 18:1e1009719
     [Google Scholar]
154. 154.

     Finucane HK, Bulik-Sullivan B, Gusev A, Trynka G, Reshef Y et al. 2015. Partitioning heritability by functional annotation using genome-wide association summary statistics. Nat. Genet. 47:111228–35
     [Google Scholar]
155. 155.

     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 10:5e1004383–15
     [Google Scholar]
156. 156.

     Hormozdiari F, van de Bunt M, Segre AV, Li X, Joo JWJ et al. 2016. Colocalization of GWAS and eQTL signals detects target genes. Am. J. Hum. Genet. 99:61245–60
     [Google Scholar]
157. 157.

     Wen X, Pique-Regi R, Luca F. 2017. Integrating molecular QTL data into genome-wide genetic association analysis: probabilistic assessment of enrichment and colocalization. PLOS Genet 13:3e1006646–25
     [Google Scholar]
158. 158.

     Wallace C. 2020. Eliciting priors and relaxing the single causal variant assumption in colocalisation analyses. PLOS Genet 16:4e1008720
     [Google Scholar]
159. 159.

     Barbeira AN, Bonazzola R, Gamazon ER, Liang Y, Park Y et al. 2021. Exploiting the GTEx resources to decipher the mechanisms at GWAS loci. Genome Biol 22:49
     [Google Scholar]
160. 160.

     Umans BD, Battle A, Gilad Y. 2021. Where are the disease-associated eQTLs?. Trends Genet 37:2109–24
     [Google Scholar]
161. 161.

     Gamazon ER, GTEx Consort., Wheeler HE, Shah KP, Mozaffari SV et al. 2015. A gene-based association method for mapping traits using reference transcriptome data. Nat. Genet. 47:1091–98
     [Google Scholar]
162. 162.

     Gusev A, Ko A, Shi H, Bhatia G, Chung W et al. 2016. Integrative approaches for large-scale transcriptome-wide association studies. Nat. Genet. 48:3245–52
     [Google Scholar]
163. 163.

     Barbeira AN, Dickinson SP, Bonazzola R, Zheng J, Wheeler HE et al. 2018. Exploring the phenotypic consequences of tissue specific gene expression variation inferred from GWAS summary statistics. Nat. Commun. 9:1825
     [Google Scholar]
164. 164.

     Wainberg M, Sinnott-Armstrong N, Mancuso N, Barbeira AN, Knowles DA et al. 2019. Opportunities and challenges for transcriptome-wide association studies. Nat. Genet. 51:4592–99
     [Google Scholar]
165. 165.

     ENCODE Proj. Consort., Moore JE, Purcaro MJ, Pratt HE, Epstein CB et al. 2020. Expanded encyclopaedias of DNA elements in the human and mouse genomes. Nature 583:699–710
     [Google Scholar]
166. 166.

     Moore JE, Pratt HE, Purcaro MJ, Weng Z. 2020. A curated benchmark of enhancer-gene interactions for evaluating enhancer-target gene prediction methods. Genome Biol 21:17
     [Google Scholar]
167. 167.

     Zhang H. 2018. Lysosomal acid lipase and lipid metabolism: new mechanisms, new questions, and new therapies. Curr. Opin. Lipidol. 29:3218–23
     [Google Scholar]