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Identification of rare and common regulatory variants in pluripotent cells using population-scale transcriptomics

Nature Genetics volume 53, pages 313–321 (2021)Cite this article

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Abstract

Induced pluripotent stem cells (iPSCs) are an established cellular system to study the impact of genetic variants in derived cell types and developmental contexts. However, in their pluripotent state, the disease impact of genetic variants is less well known. Here, we integrate data from 1,367 human iPSC lines to comprehensively map common and rare regulatory variants in human pluripotent cells. Using this population-scale resource, we report hundreds of new colocalization events for human traits specific to iPSCs, and find increased power to identify rare regulatory variants compared with somatic tissues. Finally, we demonstrate how iPSCs enable the identification of causal genes for rare diseases.

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Fig. 1: Map of cis genetic regulation in human induced pluripotent cells.
Fig. 2: Linking rare variants to gene expression outliers.
Fig. 3: Colocalization of disease and trait variants with iPSC eQTLs.
Fig. 4: Integration of common-variant colocalization analyses with outlier-associated rare variants.

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Data availability

All data used in the study are available from the Sequence Read Archive, dbGaP or the European Nucleotide Archive; the full data availability is provided in Supplementary Table 1. Supplementary Table 2 provides a description of the samples used in the study. Full summary statistics on significant eQTLs can be obtained from Zenodo (https://doi.org/10.5281/zenodo.4005576). The colocalization results are accessible from the LocusCompare portal (http://locuscompare.com/). We used eQTL summary statistics from GTEx (v7; https://gtexportal.org/home/datasets/) and the BIOS cohort (https://genenetwork.nl/biosqtlbrowser/). We further used GWAS summary statistics from the NHGRI-EBI GWAS catalog (Ensembl v92; https://www.ebi.ac.uk/gwas/), Phenome scanner (v2; http://www.phenoscanner.medschl.cam.ac.uk/) and GWAS studies aggregated in the LocusCompare study. We downloaded the GTEx colocalization results via LocusCompare. Other external data sources are referenced in the Methods and the main text. Source data are provided with this paper.

Code availability

Code produced for the following analyses is available on GitHub: eQTL mapping (https://github.com/PMBio/hipsci_pipeline/tree/master/limix_QTL_pipeline/), gene expression outlier analysis (https://github.com/csmail/i2qtl_outlier/) and colocalization analysis (https://github.com/mikegloudemans/ipsc-coloc).

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Acknowledgements

The authors thank the HipSci, iPSCORE, GENSIPS and PhLiPS cohorts for their input into the study and (early) access to the data. This work was supported by: a strategic award from the Wellcome Trust and UK Medical Research Council (WT098503), the EMBL Interdisciplinary Postdoc (EI3POD) program under Marie Skłodowska-Curie Actions COFUND (grant no. 664726 to M.J.B. and D.S.); National Institutes of Health (NIH; T32 LM012409 to C.S.; T15 LM01127 to D.J.; U01 HL107388-01, P30DK116074 and SPO 130829 to I.C.-O.; HL107442, DK105541, DK112155 to K.A.F.); Stanford Graduate Fellowship (to M.J.G.); National Natural Science Foundation of China (31970554 to X.L.); National Key R&D Program of China (2019YFC1315804 to X.L.); Shanghai Municipal Science and Technology Major Project (2017SHZDZX01 to X.L.); EBI–Sanger Postdoctoral Fellowship (to N.C.); National Science Foundation Graduate Research Fellowship (to N.M.F.); and California Institute for Regenerative Medicine (GC1R-06673 to K.A.F.). Research in the Stegle laboratory is supported by the BMBF, the Volkswagen Foundation and the European Union (ERC project DECODE). S.B.M. is supported by NIH grants U01HG009431, R01HL142015, R01HG008150, R01AG066490 and U01HG009080. Research in the Knowles laboratory is supported by NIH grants DK116750, DK120565, DK106236, DK107437 and HL107388. We thank the staff in the Cellular Genetics and Phenotyping and Sequencing core facilities at the Wellcome Trust Sanger Institute. Work at the Wellcome Trust Sanger Institute was further supported by Wellcome Trust grant WT090851. Several datasets used in this study were sourced from the NextGen Consortium, available from the NCBI database of Genotypes and Phenotypes (dbGaP): iPSCORE (phs000924.v4.p1 and phs001325.v3.p1); PhLiPS (phs001341.v1.p1); and GENESiPS (phs001139.v1.p1). This work, in part, used supercomputing resources provided by the Stanford Genetics Bioinformatics Service Center, supported by NIH S10 instrumentation grant S10OD023452. Research reported in this manuscript was in part supported by the NIH Common Fund, through the Office of Strategic Coordination/Office of the NIH Director under award no. U01HG007708. The content is solely the responsibility of the authors and does not necessarily represent the official views of the NIH.

Author information

Author notes

  1. These authors contributed equally: Marc Jan Bonder, Craig Smail.

  2. These authors jointly supervised this work: Stephen B. Montgomery, Oliver Stegle.

Authors and Affiliations

  1. European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Trust Genome Campus, Cambridge, UK

    Marc Jan Bonder, Bogdan Mirauta, Daniel D. Seaton, Na Cai, Danilo Horta, Marc Jan Bonder, Daniel Seaton & Oliver Stegle

  2. European Molecular Biology Laboratory, Genome Biology Unit, Heidelberg, Germany

    Marc Jan Bonder, Marc Jan Bonder & Oliver Stegle

  3. Division of Computational Genomics and Systems Genetics, German Cancer Research Center (DKFZ), Heidelberg, Germany

    Marc Jan Bonder, Marc Jan Bonder & Oliver Stegle

  4. Department of Biomedical Data Science, Stanford University School of Medicine, Stanford, CA, USA

    Craig Smail, Michael J. Gloudemans & Nicole M. Ferraro

  5. Genomic Medicine Center, Children’s Mercy Research Institute and Children’s Mercy Kansas City, Kansas City, MO, USA

    Craig Smail

  6. Department of Pathology, Stanford University School of Medicine, Stanford, CA, USA

    Laure Frésard & Stephen B. Montgomery

  7. Biomedical Sciences Graduate Program, University of California, San Diego, La Jolla, CA, USA

    David Jakubosky & David A. Jakubosky

  8. Department of Biomedical Informatics, University of California, San Diego, La Jolla, CA, USA

    David Jakubosky & David A. Jakubosky

  9. Department of Pediatrics and Rady Children’s Hospital, University of California, San Diego, La Jolla, CA, USA

    Matteo D’Antonio & Kelly A. Frazer

  10. CAS Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai, China

    Xin Li

  11. Division of Cardiovascular Medicine, Stanford University School of Medicine, Stanford, CA, USA

    Ivan Carcamo-Orive, Matthew T. Wheeler & Joshua W. Knowles

  12. Wellcome Sanger Institute, Wellcome Trust Genome Campus, Cambridge, UK

    Na Cai, Helena Kilpinen & Oliver Stegle

  13. Helmholtz Pioneer Campus, Helmholtz Zentrum München, Neuherberg, Germany

    Na Cai

  14. UCL Great Ormond Street Institute of Child Health, University College London, London, UK

    Dara Vakili & Helena Kilpinen

  15. Faculty of Medicine, Imperial College London, London, UK

    Dara Vakili

  16. Stanford Center for Undiagnosed Diseases, Stanford University, Stanford, CA, USA

    Chunli Zhao, Diane B. Zastrow, Devon E. Bonner & Matthew T. Wheeler

  17. Faculty of Biological and Environmental Sciences, University of Helsinki, Helsinki, Finland

    Helena Kilpinen

  18. Helsinki Institute of Life Science (HiLIFE), University of Helsinki, Helsinki, Finland

    Helena Kilpinen

  19. Institute for Genomic Medicine, University of California, San Diego, La Jolla, CA, USA

    Erin N. Smith & Kelly A. Frazer

  20. Department of Genetics, Stanford University School of Medicine, Stanford, CA, USA

    Stephen B. Montgomery

  21. Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA

    Christopher D. Brown & YoSon Park

Authors
  1. Marc Jan Bonder
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  2. Craig Smail
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  3. Michael J. Gloudemans
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  4. Laure Frésard
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  5. David Jakubosky
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  6. Matteo D’Antonio
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  7. Xin Li
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  8. Nicole M. Ferraro
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  9. Ivan Carcamo-Orive
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  10. Bogdan Mirauta
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  11. Daniel D. Seaton
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  12. Na Cai
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  15. Chunli Zhao
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  18. Matthew T. Wheeler
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  19. Helena Kilpinen
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  20. Joshua W. Knowles
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  21. Erin N. Smith
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  22. Kelly A. Frazer
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  23. Stephen B. Montgomery
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  24. Oliver Stegle
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Consortia

HipSci Consortium

  • Marc Jan Bonder
  • , Helena Kilpinen
  • , Daniel Seaton
  •  & Oliver Stegle

iPSCORE consortium

  • Matteo D’Antonio
  • , Kelly A. Frazer
  • , David A. Jakubosky
  •  & Erin N. Smith

Undiagnosed Diseases Network

  • Chunli Zhao
  • , Diane B. Zastrow
  • , Devon E. Bonner
  •  & Matthew T. Wheeler

PhLiPS consortium

  • Christopher D. Brown
  •  & YoSon Park

Contributions

The main analyses and data preparations were performed by M.J.B. and C.S.; D.J. and M.D. performed SV calling and analysis; N.M.F. performed GTEx (v7) data processing; L.F. and X.L. performed rare-variant annotation and interpretation; H.K. and D.V. annotated and validated the rare-disease variants and genes for the HipSci rare-disease samples; M.J.G. performed the colocalization analysis; M.J.B., D.S., B.M. and D.H. developed the eQTL software; C.Z. generated iPSC lines for Undiagnosed Diseases Network rare-disease samples; N.C., I.C.-O. and Y.P. assisted with data processing and analysis; M.J.B., C.S., M.J.G., S.B.M. and O.S. wrote the manuscript; I.C.-O., N.C., N.M.F., K.A.F., L.F., M.J.G., D.J., M.T.W., D.B.Z. and B.M. assisted in editing the manuscript; M.J.B., C.S., E.S., K.A.F., O.S. and S.B.M. conceived and oversaw the study.

Corresponding authors

Correspondence to Marc Jan Bonder, Craig Smail, Stephen B. Montgomery or Oliver Stegle.

Ethics declarations

Competing interests

S.B.M. is a member of the Scientific Advisory Board of Myome. All other authors declare no competing interests.

Additional information

Peer review information Nature Genetics thanks Gabriel Hoffman and Vijay Sankaran for their contribution to the peer review of this work.

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Extended data

Extended Data Fig. 1 Replication and consistency of effect sizes of eQTL discovered in the the original iPSC studies and replicated in i2QTL.

Shown are scatter plots between eQTL effect size estimates in this study (i2QTL, x-axis) versus effect size estimates from previous studies (y-axis). Dots correspond to eQTL discovered in the respective study. Black: eQTL with consistent effect direction. Red: eQTL with discordant effect direction. Replication defined at nominal P < 0.05 in i2QTL. a, Replication of HipSci in i2QTL. 70.4% of the effects are replicated; 98% of the eQTL have concordant effect direction. Differences in the approach for estimating effect sizes result in the observed variation. b, Replication of iPSCORE in i2QTL. 81% of the effects are replicated; 98.7% of the eQTL have concordant effect direction. Notably only SNP eQTL were considered whereas SVs were not considered for replication. c, Replication of GENESiPS in i2QTL; 76.7% of the effects are replicated; 99.5% of the effects have concordant effect direction. d, Replication of PhiLiPS in i2QTL. 76.8% of the effects are replicated; 97.5% of the effects have concordant effect direction.

Extended Data Fig. 2 Identification of multiple independent eQTL for the same gene using stepwise regression.

a, Histogram of the number of independent eQTL effects identified for individual eGenes. Up to 12 independent effects were identified. b, Zoom-in view displaying the number of eGenes with 5 to 8 independent effects. c, Zoom-in view displaying the number eGenes with 8 to 12 independent effects.

Extended Data Fig. 3 Sharing of lead eQTL signals between cell types and studies, considering i2QTL, GTEX and the iPS differentiation study from Cuomo et al.

a, Pairwise sharing of lead eQTL signals in i2QTL (iPSC) and 48 GTEx tissues. Shown is the fraction of shared eQTL signals relative to the total number of common genes and lead eQTL variants in the two respective maps. Shared eQTL signals are defined as eQTL with concordant effect direction and absolute effect size within a factor of two (Methods). b, Distribution of pairwise sharing as in A of iPSCs versus GTEx tissues (blue, N = 48 comparisons) versus pairwise sharing between GTEx tissues (red, N = 2,401 comparisons).c, Pairwise sharing of lead eQTL signals in i2QTL (iPSC) and 48 GTEx tissues as in A, however additionally including single-cell eQTL form Cuomo et al. in iPSCs (iPSCsc), differentiated cell types (mesendo, endoderm). d, Distribution of pairwise sharing as in C, considering iPSCs and differentiated cell types (bulk left, followed by iPSC single cell), mesendoderm (cyan), and endoderm (green) versus GTex tissues (N = 48 comparisons). During differentiation genetic signals in iPSC become more similar to those in GTEx tissues. Data in panels b and d are displayed as violin- and boxplot with the midpoint corresponding to the median, the lower and upper edges of the box to the first and third quartiles and the whiskers corresponding to the IQR ×1.5.

Extended Data Fig. 4 Properties of distal (trans) gene-level eQTL.

a, Dot plot of N = 862 trans-eQTL detected in iPSC (FDR < 10%). Dots correspond to individual trans-eQTL, with color denoting the variant category (blue: cis-eQTL, red: GWAS variant, purple: cis-eQTL and GWAS variant). b, Breakdown of unique trans-eQTL variants (N = 193) across different variant annotations. Darker shaded colors denote trans-eQTL linked to variants that have more than one annotation; lighter shades correspond variants that can be unique assigned to a given variant category. c, Mediation analysis of trans-eQTL, considering variants that are linked to a cis eQTL. The outer pie denotes the annotation of the underlying trans variant: GWAS only variants (n = 7, white), cis eQTL variants (n = 186, dark-blue). The inner pie displays results from the mediation analysis for individual trans variants: n = 7 trans eQTL are exclusively linked to GWAS variants and hence not mediated (white, ‘GWAS only trans-eQTL’); n = 58 trans eQTL variants are not significantly linked to mediation with any RNA trait (‘Non-significant link to any cis-eQTL’); 86 are exclusively linked to gene-level abundance (‘Only linked to gene-level cis-eQTL’), 7 are exclusively linked a RNA trait other than gene-level abundance (‘Linked only to non gene-level cis-eQTL’); 35 are linked to gene-level abundance and at least one additional RNA trait(‘Linked both to gene-level and other level cis-eQTL’).

Extended Data Fig. 5 Analysis of the trans-eQTL hotspot at ELF2.

a, Schematic representation of the genetic loci around ELF2 (left) and NAA15 (right). SNPs are annotated by evidence of cis-eQTL regulation on different traits (blue: transcript-ratio eQTL on ELF2, purple: gene-level eQTL on EFL2, green: splice eQTL on ELF2, brown: the APA eQTL on NAA15). b, LD structure between cis-eQTL variants implicated in the hotspot, annotated by cis-eQTL type as in a. c-f. Lead cis-eQTL effects by RNA trait for ELF2 and NAA15 across SNPs linked to downstream trans-eQTLs(n = 682 samples). Data are represented as a violin- and boxplot with the midpoint corresponding to the median, the lower and upper edges of the box to the first and third quartiles, the whiskers represent the interquartile range ×1.5. C. Splicing cis-eQTL on ELF2. d. Gene level cis-eQTL on ELF2. E. Transcript-ratio cis-eQTL on ELF2. F. Alternative polyadenilation cis-eQTL on NAA15. g, Co-expression network of genes that are controlled by the trans-eQTL linked to the hotspot at ELF2 (N = 37 genes), including ELF2 itself (center). Color of the bounding box around Genes denotes the cis-eQTL variant that drives the corresponding trans effect (colors as in d-g). Genes with multiple trans regulators are depicted with multiple colored rings. h, Replication of trans effects at ELF2 in a single cell differentiation study (Cuomo et al.). Shown are trans-eQTL effect sizes (beta’s) for the 12 ELF2-linked trans targets that show significant replication (defined as P < 0.05 and consistent effect direction) in any of the Cuomo et al tissues. From left to right: eQTL effect size in; the i2QTL study (discovery); in undifferentiated iPSC profiled using scRNA-seq; in mesendoderm profiled using scRNA-seq and in definitive endoderm profiled using scRNA-seq. Significant replication are indicated with a red asterisk.

Extended Data Fig. 6 Enrichment for rare, deleterious SNPs in iPSC and GTEx tissues.

Comparison of enrichments for singleton, highly-deleterious (CADD > 25) SNPs in iPSC versus GTEx v7 tissues analogous to Fig. 2b, however with an additional adjustment for the number of expressed genes. Displayed are enrichments for 10,000 random draws of 50 samples, controlling for the number of expressed genes (genes subset at a fixed number across tissues, N = 500 genes). Strongest enrichment is observed in iPSC. The data are represented as a boxplot where the middle line corresponds to the median, the lower and upper edges of the box corresponding to the first and third quartiles, the whiskers represent the interquartile range (IQR) ×1.5 and beyond the whiskers are outlier points.

Extended Data Fig. 7 Expression level of rare disease genes in iPSC versus GTEx tissues.

a, Distribution of gene expression level of XX rare disease genes (log10(FPKM + 1)) in i2QTL iPSC and 17 GTEx tissues with a median expression level of at least 1 FPKM (red dashed line). Expression in i2QTL highlighted in teal, GTEx tissues in yellow. Disease genes are expressed in iPSCs and only difficult to biopsy tissues in GTEx display higher expression levels. n = 2,952 biologically independent samples. Data are represented as boxplots with the middle line corresponding to the median, the lower and upper edges of the box to the first and third quartiles, the whiskers to the interquartile range (IQR) ×1.5. b, Expression level of genes in different curated gene lists, comparing i2QTL iPSC (left), all GTEx tissues (middle) and GTEx whole blod (right). Shown is the fraction of genes in each category for two expression bins: [0,1) FPKM expression absent or lowly expressed; [1,1e + 12) FPKM gene expressed.

Extended Data Fig. 8 Additional results from the colocalization analysis of eQTL and GWAS traits.

a, Summary of colocalization results for the Coronary artery Disease GWAS (van der Harst et al 2018). 36 out of 93 GWAS loci were identified as colocalized with an eQTL of at least one RNA trait (38.7%). Shown are the number of colocalization events for eQTL of different RNA traits. From left to right: any eQTL type (All), gene-level eQTL (Gene), exon eQTL (Exon), transcript eQTL (Transcript), splicing eQTL (Splicing), APA eQTL (APA). The number of GWAS colocalization that are uniquely linked to a given RNA trait eQTL are displayed using a triangle and the total number of colocalizations per trait is depicted as a circle. b, Colocalization between a gene-level eQTL for POLR1B and a GWAS hit (rs7586668:C > T) for Height. Left: Manhatten plots displaying the local association signal for the eQTL on POLR1B (bottom) and the GWAS signal on Height (top). Right: Scatter plot of negative log P-values for the GWAS signal (x-axis) for BMI versus the POLR1B eQTL signal (y-axis) for the corresponding region.

Extended Data Fig. 9 Enrichment for large-effect outlier-associated rare variants in colocalized genes.

Absolute effect size of GWAS trait associations for iPSC outlier- and non-outlier-associated variants, considering genes with varying degree of evidence for colocalization with common eQTL variants.

Extended Data Fig. 10 Optimization of the number of PEER factors to adjust for confounding expression heterogeneity.

Shown is the number of genes with at least one gene-level eQTL (eGenes) for the top 3,000 highest expressed genes in iPSC for increasing number of PEER factors. PEER factors are adjusted for as additional fixed effect covariates. The vertical red line denotes the number of PEER factors considered in all i2QTL analyses.

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Bonder, M.J., Smail, C., Gloudemans, M.J. et al. Identification of rare and common regulatory variants in pluripotent cells using population-scale transcriptomics. Nat Genet 53, 313–321 (2021). https://doi.org/10.1038/s41588-021-00800-7

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