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

doi:10.1126/science.aaz8528

. 2020 Sep 11;369(6509):eaaz8528.

doi: 10.1126/science.aaz8528.

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

Sarah Kim-Hellmuth^#^{1

2

3}, François Aguet^#⁴, Meritxell Oliva^{5

6}, Manuel Muñoz-Aguirre^{7

8}, Silva Kasela^{2

3}, Valentin Wucher⁷, Stephane E Castel^{2

3}, Andrew R Hamel^{4

9}, Ana Viñuela^{10

11

12

13}, Amy L Roberts¹⁰, Serghei Mangul^{14

15}, Xiaoquan Wen¹⁶, Gao Wang¹⁷, Alvaro N Barbeira⁵, Diego Garrido-Martín⁷, Brian B Nadel¹⁸, Yuxin Zou¹⁹, Rodrigo Bonazzola⁵, Jie Quan²⁰, Andrew Brown^{11

21}, Angel Martinez-Perez²², José Manuel Soria²²; GTEx Consortium; Gad Getz^{4

23

24}, Emmanouil T Dermitzakis^{11

12

13}, Kerrin S Small¹⁰, Matthew Stephens¹⁷, Hualin S Xi²⁵, Hae Kyung Im⁵, Roderic Guigó^{7

26}, Ayellet V Segrè^{4

9}, Barbara E Stranger^{5

27}, Kristin G Ardlie⁴, Tuuli Lappalainen^{28

3}

Collaborators, Affiliations

Collaborators

GTEx Consortium:
François Aguet, Shankara Anand, Kristin G Ardlie, Stacey Gabriel, Gad A Getz, Aaron Graubert, Kane Hadley, Robert E Handsaker, Katherine H Huang, Seva Kashin, Xiao Li, Daniel G MacArthur, Samuel R Meier, Jared L Nedzel, Duyen T Nguyen, Ayellet V Segrè, Ellen Todres, Brunilda Balliu, Alvaro N Barbeira, Alexis Battle, Rodrigo Bonazzola, Andrew Brown, Christopher D Brown, Stephane E Castel, Donald F Conrad, Daniel J Cotter, Nancy Cox, Sayantan Das, Olivia M de Goede, Emmanouil T Dermitzakis, Jonah Einson, Barbara E Engelhardt, Eleazar Eskin, Tiffany Y Eulalio, Nicole M Ferraro, Elise D Flynn, Laure Fresard, Eric R Gamazon, Diego Garrido-Martín, Nicole R Gay, Michael J Gloudemans, Roderic Guigó, Andrew R Hame, Yuan He, Paul J Hoffman, Farhad Hormozdiari, Lei Hou, Hae Kyung Im, Brian Jo, Silva Kasela, Manolis Kellis, Sarah Kim-Hellmuth, Alan Kwong, Tuuli Lappalainen, Xin Li, Yanyu Liang, Serghei Mangul, Pejman Mohammadi, Stephen B Montgomery, Manuel Muñoz-Aguirre, Daniel C Nachun, Andrew B Nobel, Meritxell Oliva, YoSon Park, Yongjin Park, Princy Parsana, Abhiram S Rao, Ferran Reverter, John M Rouhana, Chiara Sabatti, Ashis Saha, Matthew Stephens, Barbara E Stranger, Benjamin J Strober, Nicole A Teran, Ana Viñuela, Gao Wang, Xiaoquan Wen, Fred Wright, Valentin Wucher, Yuxin Zou, Pedro G Ferreira, Gen Li, Marta Melé, Esti Yeger-Lotem, Mary E Barcus, Debra Bradbury, Tanya Krubit, Jeffrey A McLean, Liqun Qi, Karna Robinson, Nancy V Roche, Anna M Smith, Leslie Sobin, David E Tabor, Anita Undale, Jason Bridge, Lori E Brigham, Barbara A Foster, Bryan M Gillard, Richard Hasz, Marcus Hunter, Christopher Johns, Mark Johnson, Ellen Karasik, Gene Kopen, William F Leinweber, Alisa McDonald, Michael T Moser, Kevin Myer, Kimberley D Ramsey, Brian Roe, Saboor Shad, Jeffrey A Thomas, Gary Walters, Michael Washington, Joseph Wheeler, Scott D Jewell, Daniel C Rohrer, Dana R Valley, David A Davis, Deborah C Mash, Philip A Branton, Laura K Barker, Heather M Gardiner, Maghboeba Mosavel, Laura A Siminoff, Paul Flicek, Maximilian Haeussler, Thomas Juettemann, W James Kent, Christopher M Lee, Conner C Powell, Kate R Rosenbloom, Magali Ruffier, Dan Sheppard, Kieron Taylor, Stephen J Trevanion, Daniel R Zerbino, Nathan S Abell, Joshua Akey, Lin Chen, Kathryn Demanelis, Jennifer A Doherty, Andrew P Feinberg, Kasper D Hansen, Peter F Hickey, Farzana Jasmine, Lihua Jiang, Rajinder Kaul, Muhammad G Kibriya, Jin Billy Li, Qin Li, Shin Lin, Sandra E Linder, Brandon L Pierce, Lindsay F Rizzardi, Andrew D Skol, Kevin S Smith, Michael Snyder, John Stamatoyannopoulos, Hua Tang, Meng Wang, Latarsha J Carithers, Ping Guan, Susan E Koester, A Roger Little, Helen M Moore, Concepcion R Nierras, Abhi K Rao, Jimmie B Vaught, Simona Volpi

Affiliations

¹ Statistical Genetics, Max Planck Institute of Psychiatry, Munich, Germany. skimhellmuth@gmail.com tlappalainen@nygenome.org.
² New York Genome Center, New York, NY, USA.
³ Department of Systems Biology, Columbia University, New York, NY, USA.
⁴ The Broad Institute of MIT and Harvard, Cambridge, MA, USA.
⁵ Section of Genetic Medicine, Department of Medicine, University of Chicago, Chicago, IL, USA.
⁶ Department of Public Health Sciences, University of Chicago, Chicago, IL, USA.
⁷ Centre for Genomic Regulation (CRG), The Barcelona Institute for Science and Technology, Barcelona, Catalonia, Spain.
⁸ Department of Statistics and Operations Research, Universitat Politècnica de Catalunya (UPC), Barcelona, Catalonia, Spain.
⁹ Ocular Genomics Institute, Massachusetts Eye and Ear, Harvard Medical School, Boston, MA, USA.
¹⁰ Department of Twin Research and Genetic Epidemiology, King's College London, London, UK.
¹¹ Department of Genetic Medicine and Development, University of Geneva Medical School, Geneva, Switzerland.
¹² Institute for Genetics and Genomics in Geneva (iGE3), University of Geneva, Geneva, Switzerland.
¹³ Swiss Institute of Bioinformatics, Geneva, Switzerland.
¹⁴ Department of Computer Science, University of California, Los Angeles, Los Angeles, CA, USA.
¹⁵ Department of Clinical Pharmacy, School of Pharmacy, University of Southern California, Los Angeles, Los Angeles, CA, USA.
¹⁶ Department of Biostatistics, University of Michigan, Ann Arbor, MI, USA.
¹⁷ Department of Human Genetics, University of Chicago, Chicago, IL, USA.
¹⁸ Department of Molecular, Cellular, and Developmental Biology, University of California, Los Angeles, Los Angeles, CA, USA.
¹⁹ Department of Statistics, University of Chicago, Chicago, IL, USA.
²⁰ Inflammation & Immunology, Pfizer, Cambridge, MA, USA.
²¹ Population Health and Genomics, University of Dundee, Dundee, Scotland, UK.
²² Unit of Genomic of Complex Diseases, Institut d'Investigació Biomèdica Sant Pau (IIB-Sant Pau), Barcelona, Spain.
²³ Cancer Center and Department of Pathology, Massachusetts General Hospital, Boston, MA, USA.
²⁴ Harvard Medical School, Boston, MA, USA.
²⁵ Foundational Neuroscience Center, AbbVie, Cambridge, MA, USA.
²⁶ Universitat Pompeu Fabra (UPF), Barcelona, Catalonia, Spain.
²⁷ Center for Genetic Medicine, Department of Pharmacology, Northwestern University, Feinberg School of Medicine, Chicago, IL, USA.
²⁸ New York Genome Center, New York, NY, USA. skimhellmuth@gmail.com tlappalainen@nygenome.org.

^# Contributed equally.

PMID: 32913075
PMCID: PMC8051643
DOI: 10.1126/science.aaz8528

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

Sarah Kim-Hellmuth et al. Science. 2020.

. 2020 Sep 11;369(6509):eaaz8528.

doi: 10.1126/science.aaz8528.

Authors

Collaborators

GTEx Consortium:
François Aguet, Shankara Anand, Kristin G Ardlie, Stacey Gabriel, Gad A Getz, Aaron Graubert, Kane Hadley, Robert E Handsaker, Katherine H Huang, Seva Kashin, Xiao Li, Daniel G MacArthur, Samuel R Meier, Jared L Nedzel, Duyen T Nguyen, Ayellet V Segrè, Ellen Todres, Brunilda Balliu, Alvaro N Barbeira, Alexis Battle, Rodrigo Bonazzola, Andrew Brown, Christopher D Brown, Stephane E Castel, Donald F Conrad, Daniel J Cotter, Nancy Cox, Sayantan Das, Olivia M de Goede, Emmanouil T Dermitzakis, Jonah Einson, Barbara E Engelhardt, Eleazar Eskin, Tiffany Y Eulalio, Nicole M Ferraro, Elise D Flynn, Laure Fresard, Eric R Gamazon, Diego Garrido-Martín, Nicole R Gay, Michael J Gloudemans, Roderic Guigó, Andrew R Hame, Yuan He, Paul J Hoffman, Farhad Hormozdiari, Lei Hou, Hae Kyung Im, Brian Jo, Silva Kasela, Manolis Kellis, Sarah Kim-Hellmuth, Alan Kwong, Tuuli Lappalainen, Xin Li, Yanyu Liang, Serghei Mangul, Pejman Mohammadi, Stephen B Montgomery, Manuel Muñoz-Aguirre, Daniel C Nachun, Andrew B Nobel, Meritxell Oliva, YoSon Park, Yongjin Park, Princy Parsana, Abhiram S Rao, Ferran Reverter, John M Rouhana, Chiara Sabatti, Ashis Saha, Matthew Stephens, Barbara E Stranger, Benjamin J Strober, Nicole A Teran, Ana Viñuela, Gao Wang, Xiaoquan Wen, Fred Wright, Valentin Wucher, Yuxin Zou, Pedro G Ferreira, Gen Li, Marta Melé, Esti Yeger-Lotem, Mary E Barcus, Debra Bradbury, Tanya Krubit, Jeffrey A McLean, Liqun Qi, Karna Robinson, Nancy V Roche, Anna M Smith, Leslie Sobin, David E Tabor, Anita Undale, Jason Bridge, Lori E Brigham, Barbara A Foster, Bryan M Gillard, Richard Hasz, Marcus Hunter, Christopher Johns, Mark Johnson, Ellen Karasik, Gene Kopen, William F Leinweber, Alisa McDonald, Michael T Moser, Kevin Myer, Kimberley D Ramsey, Brian Roe, Saboor Shad, Jeffrey A Thomas, Gary Walters, Michael Washington, Joseph Wheeler, Scott D Jewell, Daniel C Rohrer, Dana R Valley, David A Davis, Deborah C Mash, Philip A Branton, Laura K Barker, Heather M Gardiner, Maghboeba Mosavel, Laura A Siminoff, Paul Flicek, Maximilian Haeussler, Thomas Juettemann, W James Kent, Christopher M Lee, Conner C Powell, Kate R Rosenbloom, Magali Ruffier, Dan Sheppard, Kieron Taylor, Stephen J Trevanion, Daniel R Zerbino, Nathan S Abell, Joshua Akey, Lin Chen, Kathryn Demanelis, Jennifer A Doherty, Andrew P Feinberg, Kasper D Hansen, Peter F Hickey, Farzana Jasmine, Lihua Jiang, Rajinder Kaul, Muhammad G Kibriya, Jin Billy Li, Qin Li, Shin Lin, Sandra E Linder, Brandon L Pierce, Lindsay F Rizzardi, Andrew D Skol, Kevin S Smith, Michael Snyder, John Stamatoyannopoulos, Hua Tang, Meng Wang, Latarsha J Carithers, Ping Guan, Susan E Koester, A Roger Little, Helen M Moore, Concepcion R Nierras, Abhi K Rao, Jimmie B Vaught, Simona Volpi

Affiliations

¹ Statistical Genetics, Max Planck Institute of Psychiatry, Munich, Germany. skimhellmuth@gmail.com tlappalainen@nygenome.org.
² New York Genome Center, New York, NY, USA.
³ Department of Systems Biology, Columbia University, New York, NY, USA.
⁴ The Broad Institute of MIT and Harvard, Cambridge, MA, USA.
⁵ Section of Genetic Medicine, Department of Medicine, University of Chicago, Chicago, IL, USA.
⁶ Department of Public Health Sciences, University of Chicago, Chicago, IL, USA.
⁷ Centre for Genomic Regulation (CRG), The Barcelona Institute for Science and Technology, Barcelona, Catalonia, Spain.
⁸ Department of Statistics and Operations Research, Universitat Politècnica de Catalunya (UPC), Barcelona, Catalonia, Spain.
⁹ Ocular Genomics Institute, Massachusetts Eye and Ear, Harvard Medical School, Boston, MA, USA.
¹⁰ Department of Twin Research and Genetic Epidemiology, King's College London, London, UK.
¹¹ Department of Genetic Medicine and Development, University of Geneva Medical School, Geneva, Switzerland.
¹² Institute for Genetics and Genomics in Geneva (iGE3), University of Geneva, Geneva, Switzerland.
¹³ Swiss Institute of Bioinformatics, Geneva, Switzerland.
¹⁴ Department of Computer Science, University of California, Los Angeles, Los Angeles, CA, USA.
¹⁵ Department of Clinical Pharmacy, School of Pharmacy, University of Southern California, Los Angeles, Los Angeles, CA, USA.
¹⁶ Department of Biostatistics, University of Michigan, Ann Arbor, MI, USA.
¹⁷ Department of Human Genetics, University of Chicago, Chicago, IL, USA.
¹⁸ Department of Molecular, Cellular, and Developmental Biology, University of California, Los Angeles, Los Angeles, CA, USA.
¹⁹ Department of Statistics, University of Chicago, Chicago, IL, USA.
²⁰ Inflammation & Immunology, Pfizer, Cambridge, MA, USA.
²¹ Population Health and Genomics, University of Dundee, Dundee, Scotland, UK.
²² Unit of Genomic of Complex Diseases, Institut d'Investigació Biomèdica Sant Pau (IIB-Sant Pau), Barcelona, Spain.
²³ Cancer Center and Department of Pathology, Massachusetts General Hospital, Boston, MA, USA.
²⁴ Harvard Medical School, Boston, MA, USA.
²⁵ Foundational Neuroscience Center, AbbVie, Cambridge, MA, USA.
²⁶ Universitat Pompeu Fabra (UPF), Barcelona, Catalonia, Spain.
²⁷ Center for Genetic Medicine, Department of Pharmacology, Northwestern University, Feinberg School of Medicine, Chicago, IL, USA.
²⁸ New York Genome Center, New York, NY, USA. skimhellmuth@gmail.com tlappalainen@nygenome.org.

^# Contributed equally.

PMID: 32913075
PMCID: PMC8051643
DOI: 10.1126/science.aaz8528

Abstract

The Genotype-Tissue Expression (GTEx) project has identified expression and splicing quantitative trait loci in cis (QTLs) for the majority of genes across a wide range of human tissues. However, the functional characterization of these QTLs has been limited by the heterogeneous cellular composition of GTEx tissue samples. We mapped interactions between computational estimates of cell type abundance and genotype to identify cell type-interaction QTLs for seven cell types and show that cell type-interaction expression QTLs (eQTLs) provide finer resolution to tissue specificity than bulk tissue cis-eQTLs. Analyses of genetic associations with 87 complex traits show a contribution from cell type-interaction QTLs and enables the discovery of hundreds of previously unidentified colocalized loci that are masked in bulk tissue.

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Figures

**Fig. 1.. Study design of mapping cell type ieQTLs and isQTLs in GTEx v8 project.**
(A) Illustration of 43 cell type-tissue pairs included in the GTEx v8 project. See (1) for the full list of tissues included in the GTEx v8 project; two brain regions (frontal cortex and cerebellum) were sampled in replicates. Cell types with median xCell enrichment score > 0.1 within a tissue were used (fig. S2). (B) Schematic representation of a cell type interaction eQTL and sQTL. RNA-seq coverage is depicted in gray, blue, and red, representing different genotypes. Differences in coverage between genotypes, corresponding to a QTL effect, are only observed with high cell type enrichment. The scatter plot illustrates the regression model used to identify iQTLs, where the dots represent individual samples. (C) Example cell type ieQTL and isQTL. The *CNTN1* eQTL effect in not sun-exposed skin is associated with keratinocyte abundance (p = 4.1 × 10⁻¹⁹; left panel). The *TNFRSF1A* sQTL effect in whole blood is associated with neutrophil abundance but is only detected in samples with lower neutrophil abundances (p = 6.7 × 10⁻⁷⁸; right panel). Each data point represents an individual and is colored by genotype. Cell type enrichment scores and gene expression were inverse normal transformed, and intron excision ratios were standardized. The regression lines from the interaction model illustrate how the QTL effect is modulated by cell type enrichment.

**Fig. 2.. Cell type ieQTL and isQTL discovery.**
(A) Number of cell type ieQTLs (left panel) and isQTLs (right panel) discovered in each cell type-tissue combination at FDR < 5%. Bar labels show the number of ieQTLs and isQTLs, respectively. See Fig. 1A for the legend of tissue colors. (B) Proportion of cell type ieQTLs that validated in ASE data. Validation was defined as ieQTLs for which the Pearson correlation between allelic fold-change (aFC) estimates from ASE and cell type estimates was nominally significant (P < 0.05). Tissue abbreviations are provided in table S2. Bar labels indicate the number of ieQTLs with validation/number of ieQTLs tested.

**Fig. 3.. Cell type ieQTLs contribute to *cis-*eQTL tissue specificity.**
(A) Coefficients from logistic regression models of *cis-*eQTL tissue sharing, where epithelial cell ieQTL status is one of the predictors: All significant top *cis-*eQTLs per tissue were annotated based on if they were also a significant ieQTL for a given cell type. The coefficients represent the log(odds ratio) that an eQTL is active in a replication tissue given a predictor. Chromatin states were defined using matched Epigenomics Roadmap tissues and the 15-state ChromHMM (37). Genomic annotations, conservation, and overlaps with Ensembl regulatory build TF, CTCF, and DHS peaks are also included. Bars represent the 95% confidence interval. (B) Proportion of cell type ieQTL-genes (ieGenes) among tissue-specific and tissue-shared eGenes. An eGene is considered tissue-specific if its eQTL had a MASH local false sign rate (LFSR, equivalent to FDR) < 0.05 only in the cell type ieQTL tissue (or tissue type) otherwise it is considered tissue-shared. Results of all 43 cell type-tissue combinations are shown. See Fig. 1A for the legend of tissue colors. (**C+D**) Tissue activity of cell type ieQTLs and eQTLs, where a cell type ieQTL and eQTL was considered active in a tissue if it had an LFSR < 0.05 (left panel). Pairwise tissue-sharing of ieQTLs (middle panel) or lead standard *cis-*eQTLs (right panel) respectively. The color-coded sharing signal is the proportion of significant QTLs (LFSR < 0.05) that are shared in magnitude (within a factor of 2) and sign between two tissues.

**Fig. 4.. Cell type iQTLs are enriched for GWAS signals.**
**(A)** Distribution of adjusted GWAS fold-enrichment of 23×87 (top panel) and 7×87 (bottom panel) tissue-trait combinations using the most significant iQTL or standard QTL per eGene/sGene. (B) Adjusted GWAS fold-enrichments of 87 GWAS traits among iQTLs on the x-axis and standard QTLs on the y-axis. Filled circles indicate significant GWAS enrichment among iQTLs at P < 0.05 (Bonferroni-corrected). Colors represent GWAS categories of the 87 GWAS traits (see table S3).

**Fig. 5.. Cell type iQTLs improve GWAS-QTL matching.**
**(A)** Proportion of cell type ieQTLs (left panel) or isQTLs (right panel) with evidence of colocalization using COLOC posterior probabilities (PP4 > 0.5), for ieQTLs and isQTL at FDR < 0.4. Color saturation indicates if a trait colocalized with the cell type iQTL only (dark), the *cis*-QTL only (light) or both QTLs (medium). Bar labels indicate the number of cell type iQTLs with evidence of colocalization (either as iQTL or *cis*-QTL)/number of iQTLs tested. (B) Summary of all QTL-trait colocalizations from (A). (C) Association p-values in the *DHX58* locus for an asthma GWAS (top), standard heart left ventricle *cis*-eQTL (middle) and myocyte ieQTL (bottom), and in the *KREMEN1* locus for a birth weight GWAS (top), standard subcutaneous adipose *cis*-eQTL (middle), and adipocyte ieQTL (bottom). (D) Association p-values in the *CDHR5* locus for an eosinophil count GWAS (top), standard small intestine terminal ileum *cis*-sQTL (middle) and epithelial cell isQTL (bottom), and in the *ATP5SL* locus for a standing height GWAS (top), standard heart left ventricle *cis*-sQTL (middle), and myocyte isQTL (bottom).

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Comment in

Reaching completion for GTEx.
Burgess DJ. Burgess DJ. Nat Rev Genet. 2020 Dec;21(12):717. doi: 10.1038/s41576-020-00296-7. Nat Rev Genet. 2020. PMID: 33060849 No abstract available.

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References

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[1] GTEx Consortium, The GTEx Consortium atlas of genetic regulatory effects across human tissues. bioRxiv. 7, 1860–1832 (2019). - PMC - PubMed

[2] GTEx Consortium, The GTEx Consortium atlas of genetic regulatory effects across human tissues. bioRxiv. 7, 1860–1832 (2019). - PMC - PubMed

[3] Võsa U et al., Unraveling the polygenic architecture of complex traits using blood eQTL meta-analysis. bioRxiv, 1–57 (2018).

[4] Võsa U et al., Unraveling the polygenic architecture of complex traits using blood eQTL meta-analysis. bioRxiv, 1–57 (2018).

[5] Joehanes R et al., Integrated genome-wide analysis of expression quantitative trait loci aids interpretation of genomic association studies. Genome Biol. 18, 16 (2017). - PMC - PubMed

[6] Joehanes R et al., Integrated genome-wide analysis of expression quantitative trait loci aids interpretation of genomic association studies. Genome Biol. 18, 16 (2017). - PMC - PubMed

[7] Kirsten H et al., Dissecting the genetics of the human transcriptome identifies novel trait-related trans-eQTLs and corroborates the regulatory relevance of non-protein coding loci†. Human Molecular Genetics. 24, 4746–4763 (2015). - PMC - PubMed

[8] Kirsten H et al., Dissecting the genetics of the human transcriptome identifies novel trait-related trans-eQTLs and corroborates the regulatory relevance of non-protein coding loci†. Human Molecular Genetics. 24, 4746–4763 (2015). - PMC - PubMed

[9] Buil A et al., Gene-gene and gene-environment interactions detected by transcriptome sequence analysis in twins. Nat. Genet. 47, 88–91 (2014). - PMC - PubMed

[10] Buil A et al., Gene-gene and gene-environment interactions detected by transcriptome sequence analysis in twins. Nat. Genet. 47, 88–91 (2014). - PMC - PubMed

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

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

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