Toggle Main Menu Toggle Search

Open Access padlockePrints

Cell type–specific genetic regulation of gene expression across human tissues

Lookup NU author(s): Dr Ana ViñuelaORCiD


Full text for this publication is not currently held within this repository. Alternative links are provided below where available.


© 2020 American Association for the Advancement of Science. All rights reserved.INTRODUCTION: Efforts to map quantitative trait loci (QTLs) across human tissues by the GTEx Consortium and others have identified expression and splicing QTLs (eQTLs and sQTLs, respectively) for a majority of genes. However, these studies were largely performed with gene expression measurements from bulk tissue samples, thus obscuring the cellular specificity of genetic regulatory effects and in turn limiting their functional interpretation. Identifying the cell type (or types) in which a QTL is active will be key to uncovering the molecular mechanisms that underlie complex trait variation. Recent studies demonstrated the feasibility of identifying cell type–specific QTLs from bulk tissue RNA-sequencing data by using computational estimates of cell type proportions. To date, such approaches have only been applied to a limited number of cell types and tissues. By applying this methodology to GTEx tissues for a diverse set of cell types, we aim to characterize the cellular specificity of genetic effects across human tissues and to describe the contribution of these effects to complex traits. RATIONALE: A growing number of in silico cell type deconvolution methods and associated reference panels with cell type–specific marker genes enable the robust estimation of the enrichment of specific cell types from bulk tissue gene expression data. We benchmarked and used enrichment estimates for seven cell types (adipocytes, epithelial cells, hepatocytes, keratinocytes, myocytes, neurons, and neutrophils) across 35 tissues from the GTEx project to map QTLs that are specific to at least one cell type. We mapped such cell type–interaction QTLs for expression and splicing (ieQTLs and isQTLs, respectively) by testing for interactions between genotype and cell type enrichment. RESULTS: Using 43 pairs of tissues and cell types, we found 3347 protein-coding and long intergenic noncoding RNA (lincRNA) genes with an ieQTL and 987 genes with an isQTL (at 5% false discovery rate in each pair). To validate these findings, we tested the QTLs for replication in available external datasets and applied an independent validation using allele-specific expression from eQTL heterozygotes. We analyzed the cell type–interaction QTLs for patterns of tissue sharing and found that ieQTLs are enriched for genes with tissue-specific eQTLs and are generally not shared across unrelated tissues, suggesting that tissue-specific eQTLs originate in tissue-specific cell types. Last, we tested the ieQTLs and isQTLs for colocalization with genetic associations for 87 complex traits. We show that cell type–interaction QTLs are enriched for complex trait associations and identify colocalizations for hundreds of loci that were undetected in bulk tissue, corresponding to an increase of >50% over colocalizations with standard QTLs. Our results also reveal the cellular specificity and potential origin for a similar number of colocalized standard QTLs. CONCLUSION: The ieQTLs and isQTLs identified for seven cell types across GTEx tissues suggest that the large majority of cell type–specific QTLs remains to be discovered. Our colocalization results indicate that comprehensive mapping of cell type–specific QTLs will be highly valuable for gaining a mechanistic understanding of complex trait associations. We anticipate that the approaches presented here will complement studies mapping QTLs in single cells.

Publication metadata

Author(s): Kim-Hellmuth S, Aguet F, Oliva M, Muñoz-Aguirre M, Kasela S, Wucher V, Castel SE, Hamel AR, Viñuela A, Roberts AL, Mangul S, Wen X, Wang G, Barbeira AN, Garrido-Martin D, Nadel BB, Zou Y, Bonazzola R, Quan J, Brown A, Martinez-Perez A, Soria JM, Getz G, Dermitzakis ET, Small KS, Stephens M, Xi HS, Im HK, Guigó R, Segrè AV, Stranger BE, Ardlie KG, Lappalainen T, Anand S, Gabriel S, Getz GA, Graubert A, Hadley K, Handsaker RE, Huang KH, Kashin S, Li X, MacArthur DG, Meier SR, Nedzel JL, Nguyen DT, Todres E, Balliu B, Battle A, Brown CD, Conrad DF, Cotter DJ, Cox N, Das S, de Goede O, Einson J, Engelhardt BE, Eskin E, Eulalio TY, Ferraro NM, Flynn ED, Fresard L, Gamazon ER, Gay NR, Gloudemans MJ, Hame AR, He Y, Hoffman PJ, Hormozdiari F, Hou L, Jo B, Kellis M, Kwong A, Li X, Liang Y, Mohammadi P, Montgomery SB, Nachun DC, Nobel AB, Park Y, Park Y, Parsana P, Rao AS, Reverter F, Rouhana JM, Sabatti C, Saha A, Skol AD, Strober BJ, Teran NA, Wright F, Ferreira PG, Li G, Melé M, Yeger-Lotem E, Barcus ME, Bradbury D, Krubit T, McLean JA, Qi L, Robinson K, Roche NV, Smith AM, Sobin L, Tabor DE, Undale A, Bridge J, Brigham LE, Foster BA, Gillard BM, Hasz R, Hunter M, Johns C, Johnson M, Karasik E, Kopen G, Leinweber WF, McDonald A, Moser MT, Myer K, Ramsey KD, Roe B, Shad S, Thomas JA, Walters G, Washington M, Wheeler J, Jewell SD, Rohrer DC, Valley DR, Davis DA, Mash DC, Branton PA, Barker LK, Gardiner HM, Mosavel M, Siminoff LA, Flicek P, Haeussler M, Juettemann T, Kent WJ, Lee CM, Powell CC, Rosenbloom KR, Ruffier M, Sheppard D, Taylor K, Trevanion SJ, Zerbino DR, Abell NS, Akey J, Chen L, Demanelis K, Doherty JA, Feinberg AP, Hansen KD, Hickey PF, Jasmine F, Jiang L, Kaul R, Kibriya MG, Li JB, Li Q, Lin S, Linder SE, Pierce BL, Rizzardi LF, Smith KS, Snyder M, Stamatoyannopoulos J, Tang H, Wang M, Carithers LJ, Guan P, Koester SE, Little AR, Moore HM, Nierras CR, Rao AK, Vaught JB, Volpi S

Publication type: Article

Publication status: Published

Journal: Science

Year: 2020

Volume: 369

Issue: 6509

Online publication date: 11/09/2020

Acceptance date: 31/07/2020

ISSN (print): 0036-8075

ISSN (electronic): 1095-9203

Publisher: American Association for the Advancement of Science


DOI: 10.1126/SCIENCE.AAZ8528

PubMed id: 32913075


Altmetrics provided by Altmetric