eQTL studies: from bulk tissues to single cells

Jingfei Zhang^a,
Hongyu Zhao^b

a.
Information Systems and Operations Management, Emory University, Atlanta, GA 30322, USA
b.
Department of Biostatistics, Yale School of Public Health, New Haven, CT 208034, USA

Funds: Zhang’s research is supported by NSF DMS-2015190 and DMS-2210469. Zhao’s research is supported in part by NIH R01 GM134005 and R56 AG074015.

摘要

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Abstract: An expression quantitative trait locus (eQTL) is a chromosomal region where genetic variants are associated with the expression levels of specific genes that can be both nearby or distant. The identifications of eQTLs for different tissues, cell types, and contexts have led to a better understanding of the dynamic regulations of gene expressions and implications of functional genes and variants for complex traits and diseases. Although most eQTL studies have been performed on data collected from bulk tissues, recent studies have demonstrated the importance of cell-type-specific and context-dependent gene regulations in biological processes and disease mechanisms. In this review, we discuss statistical methods that have been developed to enable the detection of cell-type-specific and context-dependent eQTLs from bulk tissues, purified cell types, and single cells. We also discuss the limitations of the current methods and future research opportunities.
- eQTL /
- Bulk samples /
- Single cell /
- Tissues /
- Cell-type-specific /
- Context-dependent

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参考文献(81)

[1]	Abell, N.S., DeGorter, M.K., Gloudemans, M.J., Greenwald, E., Smith, K.S., He, Z.,Montgomery, S.B., 2022. Multiple causal variants underlie genetic associations in humans. Science 375, 1247-1254.
[2]	Aguirre-Gamboa, R., de Klein, N., di Tommaso, J., Claringbould, A., van der Wijst, M.G., de Vries, D., Brugge, H., Oelen, R., Vosa, U., Zorro, M.M., et al., 2020. Deconvolution of bulk blood eqtl effects into immune cell subpopulations. BMC Bioinformatics 21, 243.
[3]	Argelaguet, R., Velten, B., Arnol, D., Dietrich, S., Zenz, T., Marioni, J.C., Buettner, F., Huber, W.,Stegle, O., 2018. Multi-omics factor analysis-a framework for unsupervised integration of multi-omics data sets. Mol Syst Biol 14, e8124.
[4]	Avila Cobos, F., Alquicira-Hernandez, J., Powell, J.E., Mestdagh, P.,De Preter, K., 2020. Benchmarking of cell type deconvolution pipelines for transcriptomics data. Nat Commun 11, 5650.
[5]	Aygun, N., Elwell, A.L., Liang, D., Lafferty, M.J., Cheek, K.E., Courtney, K.P., Mory, J., Hadden-Ford, E., Krupa, O., de la Torre-Ubieta, L., et al., 2021. Brain-trait-associated variants impact cell-type-specific gene regulation during neurogenesis. Am J Hum Genet 108, 1647-1668.
[6]	Bryois, J., Calini, D., Macnair, W., Foo, L., Urich, E., Ortmann, W., Iglesias, V.A., Selvaraj, S., Nutma, E., Marzin, M., 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, 1104-1112.
[7]	Chen, L., Ge, B., Casale, F.P., Vasquez, L., Kwan, T., Garrido-Martin, D., Watt, S., Yan, Y., Kundu, K., Ecker, S., et al., 2016. Genetic drivers of epigenetic and transcriptional variation in human immune cells. Cell 167, 1398-1414 e1324.
[8]	Connally, N.J., Nazeen, S., Lee, D., Shi, H., Stamatoyannopoulos, J., Chun, S., Cotsapas, C., Cassa, C.A.,Sunyaev, S.R., 2022. The missing link between genetic association and regulatory function. Elife 11.
[9]	Consortium, E.P., Moore, J.E., Purcaro, M.J., Pratt, H.E., Epstein, C.B., Shoresh, N., Adrian, J., Kawli, T., Davis, C.A., Dobin, A., et al., 2020. Expanded encyclopaedias of DNA elements in the human and mouse genomes. Nature 583, 699-710.
[10]	Consortium, G.T., 2020. The gtex consortium atlas of genetic regulatory effects across human tissues. Science 369, 1318-1330.
[11]	Cuomo, A.S.E., Heinen, T., Vagiaki, D., Horta, D., Marioni, J.C.,Stegle, O., 2022. Cellregmap: A statistical framework for mapping context-specific regulatory variants using scrna-seq. Mol Syst Biol 18, e10663.
[12]	Cuomo, A.S.E., Seaton, D.D., McCarthy, D.J., Martinez, I., Bonder, M.J., Garcia-Bernardo, J., Amatya, S., Madrigal, P., Isaacson, A., Buettner, F., et al., 2020. Publisher correction: Single-cell rna-sequencing of differentiating ips cells reveals dynamic genetic effects on gene expression. Nat Commun 11, 1572.
[13]	Dong, X., Li, X., Chang, T.W., Scherzer, C.R., Weiss, S.T.,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, 4269-4271.
[14]	Donovan, M.K.R., D'Antonio-Chronowska, A., D'Antonio, M.,Frazer, K.A., 2020. Cellular deconvolution of gtex tissues powers discovery of disease and cell-type associated regulatory variants. Nat Commun 11, 955.
[15]	Ek, W.E., Rask-Andersen, M., Karlsson, T., Enroth, S., Gyllensten, U.,Johansson, A., 2018. Genetic variants influencing phenotypic variance heterogeneity. Hum Mol Genet 27, 799-810.
[16]	Flutre, T., Wen, X., Pritchard, J.,Stephens, M., 2013. A statistical framework for joint eqtl analysis in multiple tissues. PLoS Genet 9, e1003486.
[17]	Gamazon, E.R., Wheeler, H.E., Shah, K.P., Mozaffari, S.V., Aquino-Michaels, K., Carroll, R.J., Eyler, A.E., Denny, J.C., Consortium, G.T., Nicolae, D.L., et al., 2015. A gene-based association method for mapping traits using reference transcriptome data. Nat Genet 47, 1091-1098.
[18]	Giambartolomei, C., Zhenli Liu, J., Zhang, W., Hauberg, M., Shi, H., Boocock, J., Pickrell, J., Jaffe, A.E., CommonMind, C., Pasaniuc, B., et al., 2018. A bayesian framework for multiple trait colocalization from summary association statistics. Bioinformatics 34, 2538-2545.
[19]	Gusev, A., Ko, A., Shi, H., Bhatia, G., Chung, W., Penninx, B.W., Jansen, R., de Geus, E.J., Boomsma, D.I., Wright, F.A., et al., 2016. Integrative approaches for large-scale transcriptome-wide association studies. Nat Genet 48, 245-252.
[20]	Hormozdiari, F., Gazal, S., van de Geijn, B., Finucane, H.K., Ju, C.J., Loh, P.R., Schoech, A., Reshef, Y., Liu, X., O'Connor, L., et al., 2018. Leveraging molecular quantitative trait loci to understand the genetic architecture of diseases and complex traits. Nat Genet 50, 1041-1047.
[21]	Hormozdiari, F., van de Bunt, M., Segre, A.V., Li, X., Joo, J.W.J., Bilow, M., Sul, J.H., Sankararaman, S., Pasaniuc, B.,Eskin, E., 2016. Colocalization of gwas and eqtl signals detects target genes. Am J Hum Genet 99, 1245-1260.
[22]	Hu, Y., Li, M., Lu, Q., Weng, H., Wang, J., Zekavat, S.M., Yu, Z., Li, B., Gu, J., Muchnik, S., et al., 2019. A statistical framework for cross-tissue transcriptome-wide association analysis. Nat Genet 51, 568-576.
[23]	Hulse, A.M.,Cai, J.J., 2013. Genetic variants contribute to gene expression variability in humans. Genetics 193, 95-108.
[24]	Ionita-Laza, I., McCallum, K., Xu, B.,Buxbaum, J.D., 2016. A spectral approach integrating functional genomic annotations for coding and noncoding variants. Nat Genet 48, 214-220.
[25]	Jerber, J., Seaton, D.D., Cuomo, A.S.E., Kumasaka, N., Haldane, J., Steer, J., Patel, M., Pearce, D., Andersson, M., Bonder, M.J., et al., 2021. Population-scale single-cell rna-seq profiling across dopaminergic neuron differentiation. Nat Genet 53, 304-312.
[26]	Jonkers, I.H.,Wijmenga, C., 2017. Context-specific effects of genetic variants associated with autoimmune disease. Hum Mol Genet 26, R185-R192.
[27]	Kalita, C.A.,Gusev, A., 2022. Decaf: A novel method to identify cell-type specific regulatory variants and their role in cancer risk. Genome Biol 23, 152.
[28]	Kerimov, N., Hayhurst, J.D., Peikova, K., Manning, J.R., Walter, P., Kolberg, L., Samovica, M., Sakthivel, M.P., Kuzmin, I., Trevanion, S.J., et al., 2021. A compendium of uniformly processed human gene expression and splicing quantitative trait loci. Nat Genet 53, 1290-1299.
[29]	Kim-Hellmuth, S., Aguet, F., Oliva, M., Munoz-Aguirre, M., Kasela, S., Wucher, V., Castel, S.E., Hamel, A.R., Vinuela, A., Roberts, A.L., et al., 2020. Cell type-specific genetic regulation of gene expression across human tissues. Science 369.
[30]	Kindt, A.S., Navarro, P., Semple, C.A.,Haley, C.S., 2013. The genomic signature of trait-associated variants. BMC Genomics 14, 108.
[31]	Kircher, M., Witten, D.M., Jain, P., O'Roak, B.J., Cooper, G.M.,Shendure, J., 2014. A general framework for estimating the relative pathogenicity of human genetic variants. Nat Genet 46, 310-315.
[32]	Kumasaka, N., Knights, A.J.,Gaffney, D.J., 2016. Fine-mapping cellular qtls with rasqual and atac-seq. Nat Genet 48, 206-213.
[33]	Li, Z., Li, X., Zhou, H., Gaynor, S.M., Selvaraj, M.S., Arapoglou, T., Quick, C., Liu, Y., Chen, H., Sun, R., et al., 2022. A framework for detecting noncoding rare-variant associations of large-scale whole-genome sequencing studies. Nat Methods 19, 1599-1611.
[34]	Liang, Y., Aguet, F., Barbeira, A.N., Ardlie, K.,Im, H.K., 2021. A scalable unified framework of total and allele-specific counts for cis-qtl, fine-mapping, and prediction. Nat Commun 12, 1424.
[35]	Little, P., Zhabotynsky, V., Li, Y., Lin, D.,Sun, W., 2022. Cell type-speci c expression quantitative trait loci. https://doiorg/101101/20220331486605.
[36]	Liu, L., Zeng, P., Xue, F., Yuan, Z.,Zhou, X., 2021. Multi-trait transcriptome-wide association studies with probabilistic mendelian randomization. Am J Hum Genet 108, 240-256.
[37]	Lu, Q., Hu, Y., Sun, J., Cheng, Y., Cheung, K.H.,Zhao, H., 2015. A statistical framework to predict functional non-coding regions in the human genome through integrated analysis of annotation data. Sci Rep 5, 10576.
[38]	Lu, Q., Powles, R.L., Abdallah, S., Ou, D., Wang, Q., Hu, Y., Lu, Y., Liu, W., Li, B., Mukherjee, S., et al., 2017. Systematic tissue-specific functional annotation of the human genome highlights immune-related DNA elements for late-onset alzheimer's disease. PLoS Genet 13, e1006933.
[39]	Lu, Q., Powles, R.L., Wang, Q., He, B.J.,Zhao, H., 2016. Integrative tissue-specific functional annotations in the human genome provide novel insights on many complex traits and improve signal prioritization in genome wide association studies. PLoS Genet 12, e1005947.
[40]	Mandric, I., Schwarz, T., Majumdar, A., Hou, K., Briscoe, L., Perez, R., Subramaniam, M., Hafemeister, C., Satija, R., Ye, C.J., et al., 2020. Optimized design of single-cell rna sequencing experiments for cell-type-specific eqtl analysis. Nat Commun 11, 5504.
[41]	Marderstein, A.R., Davenport, E.R., Kulm, S., Van Hout, C.V., Elemento, O.,Clark, A.G., 2021. Leveraging phenotypic variability to identify genetic interactions in human phenotypes. Am J Hum Genet 108, 49-67.
[42]	Nathan, A., Asgari, S., Ishigaki, K., Valencia, C., Amariuta, T., Luo, Y., Beynor, J.I., Baglaenko, Y., Suliman, S., Price, A.L., et al., 2022. Single-cell eqtl models reveal dynamic t cell state dependence of disease loci. Nature 606, 120-128.
[43]	Neavin, D., Nguyen, Q., Daniszewski, M.S., Liang, H.H., Chiu, H.S., Wee, Y.K., Senabouth, A., Lukowski, S.W., Crombie, D.E., Lidgerwood, G.E., 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.
[44]	Nicolae, D.L., Gamazon, E., Zhang, W., Duan, S., Dolan, M.E.,Cox, N.J., 2010. Trait-associated snps are more likely to be eqtls: Annotation to enhance discovery from gwas. PLoS Genet 6, e1000888.
[45]	Oelen, R., de Vries, D.H., Brugge, H., Gordon, M.G., Vochteloo, M., single-cell e, Q.c., Consortium, B., Ye, C.J., Westra, H.J., Franke, L., 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.
[46]	Okamoto, J., Wang, L., Yin, X., Luca, F., Pique-Regi, R., Helms, A., Im, H.K., Morrison, J.,Wen, X., 2023. Probabilistic integration of transcriptome-wide association studies and colocalization analysis identifies key molecular pathways of complex traits. Am J Hum Genet 110, 44-57.
[47]	Oliva, M., Demanelis, K., Lu, Y., Chernoff, M., Jasmine, F., Ahsan, H., Kibriya, M.G., Chen, L.S.,Pierce, B.L., 2023. DNA methylation qtl mapping across diverse human tissues provides molecular links between genetic variation and complex traits. Nat Genet 55, 112-122.
[48]	Ongen, H., Buil, A., Brown, A.A., Dermitzakis, E.T.,Delaneau, O., 2016. Fast and efficient qtl mapper for thousands of molecular phenotypes. Bioinformatics 32, 1479-1485
[49]	Ota, M., Nagafuchi, Y., Hatano, H., Ishigaki, K., Terao, C., Takeshima, Y., Yanaoka, H., Kobayashi, S., Okubo, M., Shirai, H., et al., 2021. Dynamic landscape of immune cell-specific gene regulation in immune-mediated diseases. Cell 184, 3006-3021 e3017.
[50]	Patel, D., Zhang, X., Farrell, J.J., Chung, J., Stein, T.D., Lunetta, K.L.,Farrer, L.A., 2021. Cell-type-specific expression quantitative trait loci associated with alzheimer disease in blood and brain tissue. Transl Psychiatry 11, 250.
[51]	Psych, E.C., Akbarian, S., Liu, C., Knowles, J.A., Vaccarino, F.M., Farnham, P.J., Crawford, G.E., Jaffe, A.E., Pinto, D., Dracheva, S., et al., 2015. The psychencode project. Nat Neurosci 18, 1707-1712.
[52]	Richardson, T.G., Hemani, G., Gaunt, T.R., Relton, C.L.,Davey Smith, G., 2020. A transcriptome-wide mendelian randomization study to uncover tissue-dependent regulatory mechanisms across the human phenome. Nat Commun 11, 185.
[53]	Ritchie, G.R., Dunham, I., Zeggini, E.,Flicek, P., 2014. Functional annotation of noncoding sequence variants. Nat Methods 11, 294-296.
[54]	Roadmap Epigenomics, C., Kundaje, A., Meuleman, W., Ernst, J., Bilenky, M., Yen, A., Heravi-Moussavi, A., Kheradpour, P., Zhang, Z., Wang, J., et al., 2015. Integrative analysis of 111 reference human epigenomes. Nature 518, 317-330.
[55]	Sarkar, A.K., Tung, P.Y., Blischak, J.D., Burnett, J.E., Li, Y.I., Stephens, M.,Gilad, Y., 2019. Discovery and characterization of variance qtls in human induced pluripotent stem cells. PLoS Genet 15, e1008045.
[56]	Schmiedel, B.J., Gonzalez-Colin, C., Fajardo, V., Rocha, J., Madrigal, A., Ramirez-Suastegui, C., Bhattacharyya, S., Simon, H., Greenbaum, J.A., Peters, B., 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.
[57]	Schmiedel, B.J., Singh, D., Madrigal, A., Valdovino-Gonzalez, A.G., White, B.M., Zapardiel-Gonzalo, J., Ha, B., Altay, G., Greenbaum, J.A., McVicker, G., et al., 2018. Impact of genetic polymorphisms on human immune cell gene expression. Cell 175, 1701-1715 e1716.
[58]	Shabalin, A.A., 2012. Matrix eqtl: Ultra fast eqtl analysis via large matrix operations. Bioinformatics 28, 1353-1358.
[59]	Song, X., Ji, J., Rothstein, J.H., Alexeeff, S.E., Sakoda, L.C., Sistig, A., Achacoso, N., Jorgenson, E., Whittemore, A.S., Klein, R.J., et al., 2023. Mixcan: A framework for cell-type-aware transcriptome-wide association studies with an application to breast cancer. Nat Commun 14, 377.
[60]	Soskic, B., Cano-Gamez, E., Smyth, D.J., Ambridge, K., Ke, Z., Matte, J.C., Bossini-Castillo, L., Kaplanis, J., Ramirez-Navarro, L., Lorenc, A., et al., 2022. Immune disease risk variants regulate gene expression dynamics during cd4(+) t cell activation. Nat Genet 54, 817-826.
[61]	Strober, B.J., Tayeb, K., Popp, J., Qi, G., Gordon, M.G., Perez, R., Ye, C.J.,Battle, A., 2022. Uncovering context-specific genetic-regulation of gene expression from single-cell rnasequencing using latent-factor models. bioRxiv https://doiorg/101101/20221222521678.
[62]	Su, C., Xu, Z., Shan, X., Cai, B., Zhao, H.,Zhang, J., 2022a. Cell-type-specific co-expression inference from single cell rna-sequencing data. doi: 101101/20221213520181.
[63]	Su, C., Zhang, J.,Zhao, H., 2022b. Csnet: Estimating cell-type-specific gene co-expression networks from bulk gene expression data. https://doiorg/101101/20211221473558.
[64]	Sun, W., 2012. A statistical framework for eqtl mapping using rna-seq data. Biometrics 68, 1-11.
[65]	Urbut, S.M., Wang, G., Carbonetto, P.,Stephens, M., 2019. Flexible statistical methods for estimating and testing effects in genomic studies with multiple conditions. Nat Genet 51, 187-195.
[66]	van der Wijst, M.G.P., Brugge, H., de Vries, D.H., Deelen, P., Swertz, M.A., LifeLines Cohort, S., Consortium, B.,Franke, L., 2018. Single-cell rna sequencing identifies celltype-specific cis-eqtls and co-expression qtls. Nat Genet 50, 493-497.
[67]	Wang, S.K., Nair, S., Li, R., Kraft, K., Pampari, A., Patel, A., Kang, J.B., Luong, C., Kundaje, A.,Chang, H.Y., 2022. Single-cell multiome of the human retina and deep learning nominate causal variants in complex eye diseases. Cell Genom 2.
[68]	Wang, Y.,Zhao, H., 2022. Non-linear archetypal analysis of single-cell rna-seq data by deep autoencoders. PLoS Comput Biol 18, e1010025.
[69]	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, 1114-1129.
[70]	Westra, H.J., Arends, D., Esko, T., Peters, M.J., Schurmann, C., Schramm, K., Kettunen, J., Yaghootkar, H., Fairfax, B.P., Andiappan, A.K., et al., 2015. Cell specific eqtl analysis without sorting cells. PLoS Genet 11, e1005223.
[71]	Xie, D.,Wang, J., 2022. Robust statistical inference for cell type deconvolution. arXiv:220206420.
[72]	Yankovitz, G., Cohn, O., Bacharach, E., Peshes-Yaloz, N., Steuerman, Y., Iraqi, F.A.,Gat-Viks, I., 2021. Leveraging the cell lineage to predict cell-type specificity of regulatory variation from bulk genomics. Genetics 217.
[73]	Yao, D.W., O'Connor, L.J., Price, A.L.,Gusev, A., 2020. Quantifying genetic effects on disease mediated by assayed gene expression levels. Nat Genet 52, 626-633.
[74]	Yazar, S., Alquicira-Hernandez, J., Wing, K., Senabouth, A., Gordon, M.G., Andersen, S., Lu, Q., Rowson, A., Taylor, T.R.P., Clarke, L., et al., 2022. Single-cell eqtl mapping identifies cell type-specific genetic control of autoimmune disease. Science 376, eabf3041.
[75]	Young, A.M.H., Kumasaka, N., Calvert, F., Hammond, T.R., Knights, A., Panousis, N., Park, J.S., Schwartzentruber, J., Liu, J., Kundu, K., et al., 2021. A map of transcriptional heterogeneity and regulatory variation in human microglia. Nat Genet 53, 861-868.
[76]	Yuan, Z., Zhu, H., Zeng, P., Yang, S., Sun, S., Yang, C., Liu, J.,Zhou, X., 2020. Testing and controlling for horizontal pleiotropy with probabilistic mendelian randomization in transcriptome-wide association studies. Nat Commun 11, 3861.
[77]	Zhabotynsky, V., Huang, L., Little, P., Hu, Y.J., Pardo-Manuel de Villena, F., Zou, F.,Sun, W., 2022. Eqtl mapping using allele-specific count data is computationally feasible, powerful, and provides individual-specific estimates of genetic effects. PLoS Genet 18, e1010076.
[78]	Zhang, T., Choi, J., Dilshat, R., Einarsdottir, B.O., Kovacs, M.A., Xu, M., Malasky, M., Chowdhury, S., Jones, K., Bishop, D.T., et al., 2021. Cell-type-specific meqtls extend melanoma gwas annotation beyond eqtls and inform melanocyte gene-regulatory mechanisms. Am J Hum Genet 108, 1631-1646.
[79]	Zhang, T., Choi, J., Kovacs, M.A., Shi, J., Xu, M., Program, N.C.S., Melanoma Meta-Analysis, C., Goldstein, A.M., Trower, A.J., Bishop, D.T., et al., 2018. Cell-type-specific eqtl of primary melanocytes facilitates identification of melanoma susceptibility genes. Genome Res 28, 1621-1635.
[80]	Zhernakova, D.V., Deelen, P., Vermaat, M., van Iterson, M., van Galen, M., Arindrarto, W., van 't Hof, P., Mei, H., van Dijk, F., Westra, H.J., et al., 2017. Identification of context-dependent expression quantitative trait loci in whole blood. Nat Genet 49, 139-145.
[81]	Zhou, D., Jiang, Y., Zhong, X., Cox, N.J., Liu, C.,Gamazon, E.R., 2020. A unified framework for joint-tissue transcriptome-wide association and mendelian randomization analysis. Nat Genet 52, 1239-1246.