Hundreds of heritable traits and diseases that are caused by germline aberrations in ubiquitously expressed genes manifest in a remarkably limited number of cell types and tissues across the body. Unravelling mechanisms that govern their tissue-specific manifestations is critical for our understanding of disease aetiologies and may direct efforts to develop treatments. Owing to recent advances in high-throughput technologies and open resources, data and tools are now available to approach this enigmatic phenomenon at large scales, both computationally and experimentally. Here, we discuss the large prevalence of tissue-selective traits and diseases, describe common molecular mechanisms underlying their tissue-selective manifestation and present computational strategies and publicly available resources for elucidating the molecular basis of their genotype–phenotype relationships.
Subscribe to Journal
Get full journal access for 1 year
only $22.08 per issue
All prices are NET prices.
VAT will be added later in the checkout.
Rent or Buy article
Get time limited or full article access on ReadCube.
All prices are NET prices.
Lage, K. et al. A large-scale analysis of tissue-specific pathology and gene expression of human disease genes and complexes. Proc. Natl Acad. Sci. USA 105, 20870–20875 (2008). A pioneering large-scale analysis of expression mechanisms in the context of tissue-selective hereditary diseases.
Barshir, R. et al. Role of duplicate genes in determining the tissue-selectivity of hereditary diseases. PLOS Genet. 14, e1007327 (2018).
Barshir, R., Shwartz, O., Smoly, I. Y. & Yeger-Lotem, E. Comparative analysis of human tissue interactomes reveals factors leading to tissue-specific manifestation of hereditary diseases. PLOS Comput. Biol. 10, e1003632 (2014). Tissue-specific protein interaction maps and their relationship to the tissue selectivity of hereditary diseases.
Bamshad, M. J. et al. Exome sequencing as a tool for Mendelian disease gene discovery. Nat. Rev. Genet. 12, 745–755 (2011).
Moaven, N., Tayebi, N., Goldin, E. & Sidransky, E. Rare Diseases Advances in Predictive, Preventive and Personalised Medicine 69-90 (Springer Netherlands, 2015).
Holmans, P. A., Massey, T. H. & Jones, L. Genetic modifiers of Mendelian disease: Huntington’s disease and the trinucleotide repeat disorders. Hum. Mol. Genet. 26, R83–R90 (2017).
Hernandez, D. G., Reed, X. & Singleton, A. B. Genetics in Parkinson disease: Mendelian versus non-Mendelian inheritance. J. Neurochem. 139 (Suppl. 1), 59–74 (2016).
Goedert, M., Jakes, R. & Spillantini, M. G. The synucleinopathies: twenty years on. J. Parkinsons Dis. 7 (Suppl. 1), S51–S69 (2017).
Reynolds, R. H. et al. Moving beyond neurons: the role of cell type-specific gene regulation in Parkinson’s disease heritability. NPJ Parkinsons Dis. 5, 6 (2019).
Li, X. & Zhang, P. Genetic determinants of myocardial dysfunction. J. Med. Genet. 54, 1–10 (2017).
Montoro, D. T. et al. A revised airway epithelial hierarchy includes CFTR-expressing ionocytes. Nature 560, 319–324 (2018). Example of a cell type-specific mechanism observed via single-cell mapping of a healthy tissue.
Su, A. I. et al. A gene atlas of the mouse and human protein-encoding transcriptomes. Proc. Natl Acad. Sci. USA 101, 6062–6067 (2004).
Jongeneel, C. V. et al. An atlas of human gene expression from massively parallel signature sequencing (MPSS). Genome Res. 15, 1007–1014 (2005).
GTEx Consortium. et al. Genetic effects on gene expression across human tissues. Nature 550, 204–213 (2017). An unprecedented resource of transcriptomes and eQTLs across physiological human tissues.
Uhlen, M. et al. Transcriptomics resources of human tissues and organs. Mol. Syst. Biol. 12, 862 (2016).
Rozenblatt-Rosen, O., Stubbington, M. J. T., Regev, A. & Teichmann, S. A. The human cell atlas: from vision to reality. Nature 550, 451–453 (2017).
Collado-Torres, L. et al. Reproducible RNA-seq analysis using recount2. Nat. Biotechnol. 35, 319–321 (2017).
Uhlen, M. et al. Proteomics. Tissue-based map of the human proteome. Science 347, 1260419 (2015). An unprecedented proteomic resource across physiological human tissues.
Wilhelm, M. et al. Mass-spectrometry-based draft of the human proteome. Nature 509, 582–587 (2014).
ENCODE Project Consortium. An integrated encyclopedia of DNA elements in the human genome. Nature 489, 57–74 (2012). An expansive resource of epigenetic and regulatory signals in human cells.
Andersson, R. et al. An atlas of active enhancers across human cell types and tissues. Nature 507, 455–461 (2014).
Roadmap Epigenomics Consortium et al. Integrative analysis of 111 reference human epigenomes. Nature 518, 317–330 (2015). An expansive resource of epigenetic signals in human cells.
Schmitt, A. D. et al. A compendium of chromatin contact maps reveals spatially active regions in the human genome. Cell Rep. 17, 2042–2059 (2016).
Barbeira, A. N. et al. Exploring the phenotypic consequences of tissue specific gene expression variation inferred from GWAS summary statistics. Nat. Commun. 9, 1825 (2018).
Yao, V. et al. An integrative tissue-network approach to identify and test human disease genes. Nat. Biotechnol. 36, 1091–1099 (2018).
Plasschaert, L. W. et al. A single-cell atlas of the airway epithelium reveals the CFTR-rich pulmonary ionocyte. Nature 560, 377–381 (2018). Example of a cell type-specific mechanism observed via single-cell mapping of a healthy tissue.
Sharma, A. et al. A disease module in the interactome explains disease heterogeneity, drug response and captures novel pathways and genes in asthma. Hum. Mol. Genet. 24, 3005–3020 (2015).
Huttlin, E. L. et al. Architecture of the human interactome defines protein communities and disease networks. Nature 545, 505–509 (2017).
Kveler, K. et al. Immune-centric network of cytokines and cells in disease context identified by computational mining of PubMed. Nat. Biotechnol. 36, 651–659 (2018).
Gandal, M. J. et al. Shared molecular neuropathology across major psychiatric disorders parallels polygenic overlap. Science 359, 693–697 (2018).
Greene, C. S. et al. Understanding multicellular function and disease with human tissue-specific networks. Nat. Genet. 47, 569–576 (2015). Tissue-specific networks of functional molecular relationships and an online tool.
Davis, C. A. et al. The Encyclopedia of DNA Elements (ENCODE): data portal update. Nucleic Acids Res. 46, D794–D801 (2018).
Lizio, M. et al. Update of the FANTOM web resource: high resolution transcriptome of diverse cell types in mammals. Nucleic Acids Res. 45, D737–D743 (2017).
Ramskold, D., Wang, E. T., Burge, C. B. & Sandberg, R. An abundance of ubiquitously expressed genes revealed by tissue transcriptome sequence data. PLOS Comput. Biol. 5, e1000598 (2009).
Amberger, J., Bocchini, C. A., Scott, A. F. & Hamosh, A. McKusick’s online Mendelian Inheritance in Man (OMIM). Nucleic Acids Res. 37, D793–D796 (2009). A comprehensive resource for Mendelian disorders.
Kohler, S. et al. The human phenotype ontology in 2017. Nucleic Acids Res. 45, D865–D876 (2017).
Kibbe, W. A. et al. Disease ontology 2015 update: an expanded and updated database of human diseases for linking biomedical knowledge through disease data. Nucleic Acids Res. 43, D1071–D1078 (2015).
Finucane, H. K. et al. Heritability enrichment of specifically expressed genes identifies disease-relevant tissues and cell types. Nat. Genet. 50, 621–629 (2018).
Zou, Y. M., Lu, D., Liu, L. P., Zhang, H. H. & Zhou, Y. Y. Olfactory dysfunction in Alzheimer’s disease. Neuropsychiatr. Dis. Treat. 12, 869–875 (2016).
Rave-Harel, N. et al. The molecular basis of partial penetrance of splicing mutations in cystic fibrosis. Am. J. Hum. Genet. 60, 87–94 (1997).
Antoniou, A. et al. Average risks of breast and ovarian cancer associated with BRCA1 or BRCA2 mutations detected in case series unselected for family history: a combined analysis of 22 studies. Am. J. Hum. Genet. 72, 1117–1130 (2003).
Concannon, P., Rich, S. S. & Nepom, G. T. Genetics of type 1A diabetes. N. Engl. J. Med. 360, 1646–1654 (2009).
Bokhari, S. R. A., Zulfiqar, H. & Mansur, A. Bartter Syndrome (StatPearls Publishing, 2019).
Basha, O. et al. Differential network analysis of human tissue interactomes highlights tissue-selective processes and genetic disorder genes. Preprint at bioRxiv https://doi.org/10.1101/612143 (2019).
Gamazon, E. R. et al. Using an atlas of gene regulation across 44 human tissues to inform complex disease- and trait-associated variation. Nat. Genet. 50, 956–967 (2018).
Menche, J. et al. Disease networks. Uncovering disease-disease relationships through the incomplete interactome. Science 347, 1257601 (2015).
Marbach, D. et al. Tissue-specific regulatory circuits reveal variable modular perturbations across complex diseases. Nat. Methods 13, 366–370 (2016).
Gracanin, A., Dreijerink, K. M., van der Luijt, R. B., Lips, C. J. & Hoppener, J. W. Tissue selectivity in multiple endocrine neoplasia type 1-associated tumorigenesis. Cancer Res. 69, 6371–6374 (2009).
Dyson, N. J. RB1: a prototype tumor suppressor and an enigma. Genes. Dev. 30, 1492–1502 (2016).
Park, J. et al. Single-cell transcriptomics of the mouse kidney reveals potential cellular targets of kidney disease. Science 360, 758–763 (2018).
Sonawane, A. R. et al. Understanding tissue-specific gene regulation. Cell Rep. 21, 1077–1088 (2017).
Reyes, A. & Huber, W. Alternative start and termination sites of transcription drive most transcript isoform differences across human tissues. Nucleic Acids Res. 46, 582–592 (2018).
Ungewitter, E. & Scrable, H. 40p53 controls the switch from pluripotency to differentiation by regulating IGF signaling in ESCs. Genes. Dev. 24, 2408–2419 (2010).
Kim, H. K., Pham, M. H. C., Ko, K. S., Rhee, B. D. & Han, J. Alternative splicing isoforms in health and disease. Pflugers Arch. 470, 995–1016 (2018).
Raj, T. et al. Integrative transcriptome analyses of the aging brain implicate altered splicing in Alzheimer’s disease susceptibility. Nat. Genet. 50, 1584–1592 (2018).
Sedic, M. et al. Haploinsufficiency for BRCA1 leads to cell-type-specific genomic instability and premature senescence. Nat. Commun. 6, 7505 (2015).
Edfors, F. et al. Gene-specific correlation of RNA and protein levels in human cells and tissues. Mol. Syst. Biol. 12, 883 (2016).
Bateman, J. F., Freddi, S., Nattrass, G. & Savarirayan, R. Tissue-specific RNA surveillance? Nonsense-mediated mRNA decay causes collagen X haploinsufficiency in Schmid metaphyseal chondrodysplasia cartilage. Hum. Mol. Genet. 12, 217–225 (2003).
Rivas, M. A. et al. Human genomics. Effect of predicted protein-truncating genetic variants on the human transcriptome. Science 348, 666–669 (2015).
Klabonski, L., Zha, J., Senthilkumar, L. & Gidalevitz, T. A bystander mechanism explains the specific phenotype of a broadly expressed misfolded protein. PLOS Genet. 12, e1006450 (2016).
DeLuna, A. et al. Exposing the fitness contribution of duplicated genes. Nat. Genet. 40, 676–681 (2008).
Conant, G. C. & Wagner, A. Duplicate genes and robustness to transient gene knock-downs in Caenorhabditis elegans. Proc. Biol. Sci. 271, 89–96 (2004).
White, J. K. et al. Genome-wide generation and systematic phenotyping of knockout mice reveals new roles for many genes. Cell 154, 452–464 (2013).
Wang, T. et al. Identification and characterization of essential genes in the human genome. Science 350, 1096–1101 (2015).
Diss, G., Ascencio, D., DeLuna, A. & Landry, C. R. Molecular mechanisms of paralogous compensation and the robustness of cellular networks. J. Exp. Zool. B Mol. Dev. Evol. 322, 488–499 (2014).
Lan, X. & Pritchard, J. K. Coregulation of tandem duplicate genes slows evolution of subfunctionalization in mammals. Science 352, 1009–1013 (2016).
Aoidi, R., Maltais, A. & Charron, J. Functional redundancy of the kinases MEK1 and MEK2: rescue of the Mek1 mutant phenotype by Mek2 knock-in reveals a protein threshold effect. Sci. Signal. 9, ra9 (2016).
Yamauchi, Y. et al. Two genes substitute for the mouse Y chromosome for spermatogenesis and reproduction. Science 351, 514–516 (2016).
Sambamoorthy, G. & Raman, K. Understanding the evolution of functional redundancy in metabolic networks. Bioinformatics 34, i981–i987 (2018).
Sameith, K. et al. A high-resolution gene expression atlas of epistasis between gene-specific transcription factors exposes potential mechanisms for genetic interactions. BMC Biol. 13, 112 (2015).
Taylor, M. B. & Ehrenreich, I. M. Higher-order genetic interactions and their contribution to complex traits. Trends Genet. 31, 34–40 (2015).
Kuzmin, E. et al. Systematic analysis of complex genetic interactions. Science 360, eaao1729 (2018).
El-Brolosy, M. A. et al. Genetic compensation triggered by mutant mRNA degradation. Nature 568, 193–197 (2019).
Ma, Z. et al. PTC-bearing mRNA elicits a genetic compensation response via Upf3a and COMPASS components. Nature 568, 259–263 (2019).
Jdey, W. et al. Drug-driven synthetic lethality: bypassing tumor cell genetics with a combination of AsiDNA and PARP inhibitors. Clin. Cancer Res. 23, 1001–1011 (2017).
Lee, J. S. et al. Harnessing synthetic lethality to predict the response to cancer treatment. Nat. Commun. 9, 2546 (2018).
Finkel, R. S. et al. Nusinersen versus sham control in infantile-onset spinal muscular atrophy. N. Engl. J. Med. 377, 1723–1732 (2017).
Mercuri, E. et al. Nusinersen versus sham control in later-onset spinal muscular atrophy. N. Engl. J. Med. 378, 625–635 (2018).
Visscher, P. M. et al. 10 years of GWAS discovery: biology, function, and translation. Am. J. Hum. Genet. 101, 5–22 (2017).
Claussnitzer, M. et al. FTO obesity variant circuitry and adipocyte browning in humans. N. Engl. J. Med. 373, 895–907 (2015). Deciphering the tissue-specific regulatory mechanism underlying a disease variant via cross-tissue exploration.
Maurano, M. T. et al. Systematic localization of common disease-associated variation in regulatory DNA. Science 337, 1190–1195 (2012).
Boyle, E. A., Li, Y. I. & Pritchard, J. K. An expanded view of complex traits: from polygenic to omnigenic. Cell 169, 1177–1186 (2017).
Trynka, G. et al. Chromatin marks identify critical cell types for fine mapping complex trait variants. Nat. Genet. 45, 124–130 (2013).
Parker, S. C. J. et al. Chromatin stretch enhancer states drive cell-specific gene regulation and harbor human disease risk variants. Proc. Natl Acad. Sci. USA 110, 17921–17926 (2013).
Albert, F. W. & Kruglyak, L. The role of regulatory variation in complex traits and disease. Nat. Rev. Genet. 16, 197–212 (2015).
Dixon, J. R. et al. Topological domains in mammalian genomes identified by analysis of chromatin interactions. Nature 485, 376–380 (2012).
Nora, E. P. et al. Spatial partitioning of the regulatory landscape of the X-inactivation centre. Nature 485, 381–385 (2012).
Lupianez, D. G. et al. Disruptions of topological chromatin domains cause pathogenic rewiring of gene-enhancer interactions. Cell 161, 1012–1025 (2015).
Kaiser, V. B. & Semple, C. A. When TADs go bad: chromatin structure and nuclear organisation in human disease. F1000Res 6, 314 (2017).
Bossi, A. & Lehner, B. Tissue specificity and the human protein interaction network. Mol. Syst. Biol. 5, 260 (2009).
Ilsley, J. L., Sudol, M. & Winder, S. J. The interaction of dystrophin with β-dystroglycan is regulated by tyrosine phosphorylation. Cell Signal. 13, 625–632 (2001).
Sotgia, F. et al. Caveolin-3 directly interacts with the C-terminal tail of β-dystroglycan. Identification of a central WW-like domain within caveolin family members. J. Biol. Chem. 275, 38048–38058 (2000).
Sahni, N. et al. Widespread macromolecular interaction perturbations in human genetic disorders. Cell 161, 647–660 (2015).
Zhong, Q. et al. Edgetic perturbation models of human inherited disorders. Mol. Syst. Biol. 5, 321 (2009).
Ozen, H. Glycogen storage diseases: new perspectives. World J. Gastroenterol. 13, 2541–2553 (2007).
Hartwell, L. H., Hopfield, J. J., Leibler, S. & Murray, A. W. From molecular to modular cell biology. Nature 402, C47–C52 (1999).
Feldman, I., Rzhetsky, A. & Vitkup, D. Network properties of genes harboring inherited disease mutations. Proc. Natl Acad. Sci. USA 105, 4323–4328 (2008).
Kitsak, M. et al. Tissue specificity of human disease module. Sci. Rep. 6, 35241 (2016).
Emig, D. & Albrecht, M. Tissue-specific proteins and functional implications. J. Proteome Res. 10, 1893–1903 (2011).
Sasanuma, H. et al. BRCA1 ensures genome integrity by eliminating estrogen-induced pathological topoisomerase II–DNA complexes. Proc. Natl Acad. Sci. USA 115, E10642–E10651 (2018).
Da Mesquita, S. et al. Insights on the pathophysiology of Alzheimer’s disease: the crosstalk between amyloid pathology, neuroinflammation and the peripheral immune system. Neurosci. Biobehav. Rev. 68, 547–562 (2016).
Pelz, L., Purfurst, B. & Rathjen, F. G. The cell adhesion molecule BT-IgSF is essential for a functional blood-testis barrier and male fertility in mice. J. Biol. Chem. 292, 21490–21503 (2017).
Faria, A. M. C., Reis, B. S. & Mucida, D. Tissue adaptation: implications for gut immunity and tolerance. J. Exp. Med. 214, 1211–1226 (2017).
Liu, Y., Ma, C. & Zhang, N. Tissue-specific control of tissue-resident memory T cells. Crit. Rev. Immunol. 38, 79–103 (2018).
Okabe, Y. & Medzhitov, R. Tissue biology perspective on macrophages. Nat. Immunol. 17, 9–17 (2016).
Nakayama, T. et al. Tissue-specific and time-dependent clonal expansion of ENU-induced mutant cells in gpt delta mice. Environ. Mol. Mutagen. 58, 592–606 (2017).
Cardoso-Moreira, M. et al. Gene expression across mammalian organ development. Nature 571, 505–509 (2019).
Newman, J. R. B. et al. Disease-specific biases in alternative splicing and tissue-specific dysregulation revealed by multitissue profiling of lymphocyte gene expression in type 1 diabetes. Genome Res. 27, 1807–1815 (2017).
Puram, S. V. et al. Single-cell transcriptomic analysis of primary and metastatic tumor ecosystems in head and neck cancer. Cell 171, 1611–1624. e24 (2017).
Sumner, C. J. & Crawford, T. O. Two breakthrough gene-targeted treatments for spinal muscular atrophy: challenges remain. J. Clin. Invest. 128, 3219–3227 (2018).
MacArthur, J. et al. The new NHGRI-EBI catalog of published genome-wide association studies (GWAS Catalog). Nucleic Acids Res. 45, D896–D901 (2017).
Rath, A. et al. Representation of rare diseases in health information systems: the Orphanet approach to serve a wide range of end users. Hum. Mutat. 33, 803–808 (2012).
Han, X. et al. Mapping the mouse cell atlas by microwell-seq. Cell 172, 1091–1107 e17 (2018).
Tabula Muris, C. et al. Single-cell transcriptomics of 20 mouse organs creates a Tabula Muris. Nature 562, 367–372 (2018).
Saunders, A. et al. Molecular diversity and specializations among the cells of the adult mouse brain. Cell 174, 1015–1030. e16 (2018).
Gamazon, E. R. et al. A gene-based association method for mapping traits using reference transcriptome data. Nat. Genet. 47, 1091–1098 (2015).
Breeze, C. E. et al. eFORGE: a tool for identifying cell type-specific signal in epigenomic data. Cell Rep. 17, 2137–2150 (2016).
Hormozdiari, F. et al. Colocalization of GWAS and eQTL signals detects target genes. Am. J. Hum. Genet. 99, 1245–1260 (2016).
Lappalainen, T. & Greally, J. M. Associating cellular epigenetic models with human phenotypes. Nat. Rev. Genet. 18, 441–451 (2017).
Bujold, D. et al. The International Human Epigenome Consortium Data Portal. Cell Syst. 3, 496–499. e2 (2016).
Yardimci, G. G. & Noble, W. S. Software tools for visualizing Hi-C data. Genome Biol. 18, 26 (2017).
Wang, Y. et al. The 3D Genome Browser: a web-based browser for visualizing 3D genome organization and long-range chromatin interactions. Genome Biol. 19, 151 (2018).
Dunham, I., Kulesha, E., Iotchkova, V., Morganella, S. & E, B. FORGE: a tool to discover cell specific enrichments of GWAS associated SNPs in regulatory regions. F1000 Res. 4, 18 (2015).
Oz-Levi, D. et al. Noncoding deletions reveal a gene that is critical for intestinal function. Nature 571, 107–111 (2019).
Yeger-Lotem, E. & Sharan, R. Human protein interaction networks across tissues and diseases. Front. Genet. 6, 257 (2015).
Hekselman, I., Sharon, M., Basha, O. & Yeger-Lotem, E. Analyzing Network Data in Biology and Medicine (ed Pržulj, N.) 459–489 (Cambridge Univ. Press, 2019).
Schwanhausser, B. et al. Global quantification of mammalian gene expression control. Nature 473, 337–342 (2011).
Magger, O., Waldman, Y. Y., Ruppin, E. & Sharan, R. Enhancing the prioritization of disease-causing genes through tissue specific protein interaction networks. PLOS Comput. Biol. 8, e1002690 (2012).
Basha, O. et al. The tissueNet v.2 database: a quantitative view of protein-protein interactions across human tissues. Nucleic Acids Res. 45, D427–D431 (2017).
Basha, O., Shpringer, R., Argov, C. M. & Yeger-Lotem, E. The DifferentialNet database of differential protein-protein interactions in human tissues. Nucleic Acids Res. 46, D522–D526 (2018).
Fabregat, A. et al. The reactome pathway knowledgebase. Nucleic Acids Res. 46, D649–D655 (2018).
Jerby, L., Shlomi, T. & Ruppin, E. Computational reconstruction of tissue-specific metabolic models: application to human liver metabolism. Mol. Syst. Biol. 6, 401 (2010).
Ji, X. et al. Identification of susceptibility pathways for the role of chromosome 15q25.1 in modifying lung cancer risk. Nat. Commun. 9, 3221 (2018).
Schultz, A. & Qutub, A. A. Reconstruction of tissue-specific metabolic networks using CORDA. PLOS Comput. Biol. 12, e1004808 (2016).
Niemi, M. E. K. et al. Common genetic variants contribute to risk of rare severe neurodevelopmental disorders. Nature 562, 268–271 (2018).
Hamazaki, T., El Rouby, N., Fredette, N. C., Santostefano, K. E. & Terada, N. Concise review: induced pluripotent stem cell research in the era of precision medicine. Stem Cells 35, 545–550 (2017).
Clevers, H. Modeling development and disease with organoids. Cell 165, 1586–1597 (2016).
Pennisi, E. Development cell by cell. Science 362, 1344–1345 (2018).
Grunwald, H. A. et al. Super-Mendelian inheritance mediated by CRISPR-Cas9 in the female mouse germline. Nature 566, 105–109 (2019).
Haigis, K. M., Cichowski, K. & Elledge, S. J. Tissue-specificity in cancer: the rule, not the exception. Science 363, 1150–1151 (2019).
Bastarache, L. et al. Phenotype risk scores identify patients with unrecognized Mendelian disease patterns. Science 359, 1233–1239 (2018).
Ongen, H. et al. Estimating the causal tissues for complex traits and diseases. Nat. Genet. 49, 1676–1683 (2017).
Leach, K., Conigrave, A. D., Sexton, P. M. & Christopoulos, A. Towards tissue-specific pharmacology: insights from the calcium-sensing receptor as a paradigm for GPCR (patho)physiological bias. Trends Pharmacol. Sci. 36, 215–225 (2015).
Khanna, H. et al. A common allele in RPGRIP1L is a modifier of retinal degeneration in ciliopathies. Nat. Genet. 41, 739–745 (2009).
Lakhani, C. M. et al. Repurposing large health insurance claims data to estimate genetic and environmental contributions in 560 phenotypes. Nat. Genet. 51, 327–334 (2019).
eGTEx Project. Enhancing GTEx by bridging the gaps between genotype, gene expression, and disease. Nat. Genet. 49, 1664–1670 (2017).
Boisset, J. C. et al. Mapping the physical network of cellular interactions. Nat. Methods 15, 547–553 (2018).
Zhou, X. et al. Circuit design features of a stable two-cell system. Cell 172, 744–757. e17 (2018).
Tomasetti, C., Li, L. & Vogelstein, B. Stem cell divisions, somatic mutations, cancer etiology, and cancer prevention. Science 355, 1330–1334 (2017).
Schneider, G., Schmidt-Supprian, M., Rad, R. & Saur, D. Tissue-specific tumorigenesis: context matters. Nat. Rev. Cancer 17, 239–253 (2017).
Sack, L. M. et al. Profound tissue specificity in proliferation control underlies cancer drivers and aneuploidy patterns. Cell 173, 499–514.e23 (2018).
Ostrow, S. L., Barshir, R., DeGregori, J., Yeger-Lotem, E. & Hershberg, R. Cancer evolution is associated with pervasive positive selection on globally expressed genes. PLOS Genet. 10, e1004239 (2014).
Schaefer, M. H. & Serrano, L. Cell type-specific properties and environment shape tissue specificity of cancer genes. Sci. Rep. 6, 20707 (2016).
Kryuchkova-Mostacci, N. & Robinson-Rechavi, M. A benchmark of gene expression tissue-specificity metrics. Brief. Bioinform. 18, 205–214 (2017).
Kim, M. S. et al. A draft map of the human proteome. Nature 509, 575–581 (2014).
Ludwig, N. et al. Distribution of miRNA expression across human tissues. Nucleic Acids Res. 44, 3865–3877 (2016).
Leung, Y. Y. et al. DASHR: database of small human noncoding RNAs. Nucleic Acids Res. 44, D216–D222 (2016).
de Rie, D. et al. An integrated expression atlas of miRNAs and their promoters in human and mouse. Nat. Biotechnol. 35, 872–878 (2017).
Liu, X., Yu, X., Zack, D. J., Zhu, H. & Qian, J. TiGER: a database for tissue-specific gene expression and regulation. BMC Bioinformatics 9, 271 (2008).
Buniello, A. et al. The NHGRI-EBI GWAS Catalog of published genome-wide association studies, targeted arrays and summary statistics 2019. Nucleic Acids Res. 47, D1005–D1012 (2019).
Pinero, J. et al. DisGeNET: a comprehensive platform integrating information on human disease-associated genes and variants. Nucleic Acids Res. 45, D833–D839 (2017).
Alanis-Lobato, G., Andrade-Navarro, M. A. & Schaefer, M. H. HIPPIE v2.0: enhancing meaningfulness and reliability of protein-protein interaction networks. Nucleic Acids Res. 45, D408–D414 (2017).
Micale, G., Ferro, A., Pulvirenti, A. & Giugno, R. SPECTRA: an integrated knowledge base for comparing tissue and tumor-specific PPI networks in human. Front. Bioeng. Biotechnol. 3, 58 (2015).
Pierson, E. et al. Sharing and specificity of co-expression networks across 35 human tissues. PLOS Comput. Biol. 11, e1004220 (2015).
We thank A. Rudich, V. Chalifa-Caspi, A. Monsonego and members of the Yeger-Lotem lab for their helpful comments.
The authors declare no competing interests.
Peer review information
Nature Reviews Genetics thanks M. Kuijjer and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
3D Genome: http://promoter.bx.psu.edu/hi-c/index.html
Database of Small Human Non-coding RNAs (DASHR): http://dashr2.lisanwanglab.org/index.php
Disease Ontology: http://disease-ontology.org/
Encyclopedia of DNA Elements (ENCODE): https://www.encodeproject.org/
Functional Annotation of the Mammalian Genome (FANTOM5): http://fantom.gsc.riken.jp/5/
Genetic Network Analysis Tool (GNAT): http://mostafavilab.stat.ubc.ca/gnat/#
Genotype-Tissue Expression (GTEx): https://gtexportal.org/home/
Human Cell Atlas: https://www.humancellatlas.org/
Human Phenotype Ontology (HPO): https://hpo.jax.org/app/
Human Protein Atlas (HPA): https://www.proteinatlas.org/
Human Proteome Map: http://www.humanproteomemap.org/index.php
US National Human Genome Research Institute–European Bioinformatics Institute (NHGRI–EBI) GWAS Catalogue: https://www.ebi.ac.uk/gwas/
Online Mendelian Inheritance in Man (OMIM): https://www.omim.org/
Roadmap Epigenomics Project: http://www.roadmapepigenomics.org/
Single Cell portal: https://portals.broadinstitute.org/single_cell
SPECific Tissue/Tumour Related PPI networks Analyser (SPECTRA): https://alpha.dmi.unict.it/spectra/
Tissue-specific Gene Expression and Regulation (TiGER): http://bioinfo.wilmer.jhu.edu/tiger/
- Heritable traits and diseases
Phenotypes with a heritable monogenic or polygenic component due to inherited or de novo germline aberrations present throughout the body.
- Germline aberrations
Aberrations (inherited or de novo) that are common to all cells harbouring the individual’s genome.
- Causal genes
Genes containing a variant that was found to lead to disease.
- Pathogenic tissues
The tissues that elicit the disease.
- Complex traits
Traits caused by variations in multiple genes or non-coding genomic regions potentially in combination with other factors such as environmental exposure or lifestyle.
- Genome-wide association studies
(GWAS). Studies that scan genetic variants across individuals to identify variants that are significantly associated with a trait (the variants are known as risk alleles when they are associated with disease occurrence).
A trait caused by variation in a single gene.
- Susceptible tissue
The tissue that manifests a trait or disease.
- Tissue-exclusive expression
When expression of a gene exceeds a predefined cut-off in a single tissue. This is in contrast to tissue-selective expression, which refers to expression in a subset of tissues (>1 tissue).
- Preferential expression
When expression of a gene is elevated in a certain tissue relative to its expression in other tissues.
A homologous gene present in the same organism, typically having redundant functionality.
- Expression quantitative trait loci
(eQTLs). Genomic regions containing DNA sequence variants that influence the mRNA expression level of a gene.
About this article
Cite this article
Hekselman, I., Yeger-Lotem, E. Mechanisms of tissue and cell-type specificity in heritable traits and diseases. Nat Rev Genet 21, 137–150 (2020). https://doi.org/10.1038/s41576-019-0200-9