• A Corrigendum to this article was published on 14 June 2017

Abstract

Technology utilizing human induced pluripotent stem cells (iPS cells) has enormous potential to provide improved cellular models of human disease. However, variable genetic and phenotypic characterization of many existing iPS cell lines limits their potential use for research and therapy. Here we describe the systematic generation, genotyping and phenotyping of 711 iPS cell lines derived from 301 healthy individuals by the Human Induced Pluripotent Stem Cells Initiative. Our study outlines the major sources of genetic and phenotypic variation in iPS cells and establishes their suitability as models of complex human traits and cancer. Through genome-wide profiling we find that 5–46% of the variation in different iPS cell phenotypes, including differentiation capacity and cellular morphology, arises from differences between individuals. Additionally, we assess the phenotypic consequences of genomic copy-number alterations that are repeatedly observed in iPS cells. In addition, we present a comprehensive map of common regulatory variants affecting the transcriptome of human pluripotent cells.

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Acknowledgements

This work was funded with a strategic award from the Wellcome Trust and UK Medical Research Council (WT098503). 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. H.K. is supported by a MRC eMedLab Medical Bioinformatics career development award from the UK Medical Research Council (MR/L016311/1). F.M.W. acknowledges financial support from the Department of Health via the NIHR Biomedical Research Centre award to Guy’s & St Thomas’ National Health Service Foundation Trust in partnership with King’s College London and King’s College Hospital NHS Foundation Trust. We acknowledge the participation of all NIHR Cambridge BioResource volunteers, and thank the NIHR Cambridge BioResource centre staff for their contribution. We thank the National Institute for Health Research and NHS Blood and Transplant. The NIHR/Wellcome Trust Cambridge Clinical Research Facility supported the volunteer recruitment. We acknowledge Life Science Technologies Corporation as the provider of Cytotune. We thank F.-J. Müller for insights regarding the PluriTest method, and the GTEx consortium for making raw data and intermediate results available.

Author information

Author notes

    • Helena Kilpinen
    •  & Andreas Leha

    Present addresses: UCL Great Ormond Street Institute of Child Health, University College London, London WC1N 1EH, UK (H.K.); Department of Medical Statistics, University Medical Center Göttingen, Humboldtallee 32, 37073 Göttingen, Germany (A.L.).

    • Helena Kilpinen
    • , Angela Goncalves
    • , Oliver Stegle
    •  & Daniel J. Gaffney

    These authors contributed equally to this work.

    • Fiona M. Watt
    • , Richard Durbin
    • , Oliver Stegle
    •  & Daniel J. Gaffney

    These authors jointly supervised this work.

Affiliations

  1. European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridge CB10 1SD, UK

    • Helena Kilpinen
    • , Francesco Paolo Casale
    • , Adam Faulconbridge
    • , Peter W. Harrison
    • , Davis McCarthy
    • , Ian Streeter
    • , Laura Clarke
    • , Ewan Birney
    •  & Oliver Stegle
  2. Wellcome Trust Sanger Institute, Wellcome Genome Campus, Hinxton, Cambridge CB10 1SA, UK

    • Angela Goncalves
    • , Andreas Leha
    • , Kaur Alasoo
    • , Sendu Bala
    • , Petr Danecek
    • , Shane A. McCarthy
    • , Yasin Memari
    • , Alice Mann
    • , Chukwuma A. Agu
    • , Alex Alderton
    • , Rachel Nelson
    • , Sarah Harper
    • , Minal Patel
    • , Alistair White
    • , Sharad R. Patel
    • , Reena Halai
    • , Christopher M. Kirton
    • , Anja Kolb-Kokocinski
    • , Willem H. Ouwehand
    • , Ludovic Vallier
    • , Richard Durbin
    •  & Daniel J. Gaffney
  3. Centre for Gene Regulation & Expression, School of Life Sciences, University of Dundee, Dundee, DD1 5EH, UK

    • Vackar Afzal
    • , Dalila Bensaddek
    •  & Angus I. Lamond
  4. Department of Haematology, University of Cambridge, Cambridge Biomedical Campus, Cambridge CB2 0XY, UK

    • Sofie Ashford
    •  & Willem H. Ouwehand
  5. Centre for Stem Cells & Regenerative Medicine, King’s College London, Tower Wing, Guy’s Hospital, Great Maze Pond, London SE1 9RT, UK

    • Oliver J. Culley
    • , Annie Kathuria
    • , Ruta Meleckyte
    • , Nathalie Moens
    • , Davide Danovi
    •  & Fiona M. Watt
  6. St Vincent’s Institute of Medical Research, 41 Victoria Parade, Fitzroy, Victoria 3065, Australia

    • Davis McCarthy
  7. Wellcome Trust and MRC Cambridge Stem Cell Institute and Biomedical Research Centre, Anne McLaren Laboratory, Department of Surgery, University of Cambridge, Cambridge CB2 0SZ, UK

    • Filipa Soares
    •  & Ludovic Vallier
  8. UCL Great Ormond Street Institute of Child Health, University College London, London WC1N 1EH, UK

    • Philip Beales
  9. NHS Blood and Transplant, Cambridge Biomedical Campus, Cambridge CB2 0PT, UK

    • Willem H. Ouwehand

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Contributions

H.K., A.G., O.S. and D.J.G. wrote the paper with input from all authors. H.K., A.G., D.B., Y.M., I.S., P.D., D.M., A.A., M.P., A.M., D.D., A.I.L., O.S. and D.G. contributed to the Supplementary Information. H.K., A.G., A.L., F.P.C., P.D., D.M., K.A. and D.D. analysed the data. S.A. and W.H.O. managed and supervised collection of research volunteer samples. F.S., C.A.A., A.A., R.N., S.H., M.P., S.R.P., A.W. and C.M.K. generated iPS cell lines, tier 1 assay data, cell growth data, RNA-seq and methylation data. V.A. and D.B. generated and processed the proteomics data. A.L., O.J.C., R.M., N.M. and D.D. generated and processed the high-content cellular imaging data. S.A.M., S.B. and Y.M. carried out initial data quality control and bioinformatics processing/pipelines. A.F., P.W.H., I.S. and L.C. curated and managed data and the project website. R.H. and A.K.-K. coordinated the project. D.D., P.B., W.H.O., E.B., L.V., A.I.L., F.M.W., R.D., O.S. and D.G. supervised and designed the research. H.K. and A.G. contributed equally to this work; O.S. and D.J.G. contributed equally to this work.

Competing interests

The authors declare no competing financial interests.

Corresponding authors

Correspondence to Fiona M. Watt or Richard Durbin or Oliver Stegle or Daniel J. Gaffney.

Reviewer Information Nature thanks E. Dermitzakis, S. Montgomery and the other anonymous reviewer(s) 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

Supplementary information

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  1. 1.

    Supplementary Information

    This file contains supplementary information and methods.

Excel files

  1. 1.

    Supplementary Table 1

    Sample meta data for the HipSci cell lines used in this publication. This is a subset of HipSci's full catalogue of cell lines and data, which can be queried at http://www.hipsci.org/lines.

  2. 2.

    Supplementary Table 2

    CNA results. (a) CNA locations (b) Significance of CNA recurrence over 200 kb genome windows (c) Properties of the recurrent CNAs, including: peak region, overlap with chromatin fragile sites, cis (same chromosome from the CNA) and trans (different chromosome) regulated genes (i.e. genes differentially expressed between copy-number 2 and 3 lines), and top candidate genes (identified as described in the main text). (d) Genome-wide association of copy numbers at recurrent CNAs with gene expression (e) Pathway enrichment analysis of genes regulated in trans by the chromosome 17 recurrent CNA region.

  3. 3.

    Supplementary Table 3

    Gene expression variance components analysis. Fraction of variance explained by the factors considered for each expression array probe.

  4. 4.

    Supplementary Table 4

    iPSC eQTL results. (a) eGene level summary of the cis-eQTLs discovered with different sample sets in this study. (b) eQTL results for primary and secondary lead eQTL variants of HipSci RNA-seq iPSC eGenes at FDR < 5% (N = 6,631). Primary and secondary eQTLs are defined by the column ‘primary_eQTL’. The column ‘iPSC_specific’ defines whether the eQTL is iPSC-specific. Columns ‘N_proxies_used’ and ‘proxy_positions’ give the total number and positions of proxy variants that were tested in the tissue-specific analysis. Additionally, the column ‘overlaps_CNA’ indicates whether the eQTL lead variant overlaps with a recurrent iPSC CNA.

  5. 5.

    Supplementary Table 5

    Tissue information. (a) Description of the tissue data used in this study to define tissuespecific eQTLs (GTEx V6p, HipSci), including the embryonic origin of each tissue and number of tissue-specific eQTLs identified for each tissue. (b) Summary of iPSC eQTL replication tests in the tissue-specific analysis, showing for each replication tissue how often proxy variants (‘ld_buddy’, ‘best_proxy’) were tested instead of the same lead variant (‘same_as_lead’).

  6. 6.

    Supplementary Table 6

    iPSC eQTL overlap with disease-associated variants. (a) All disease-associated variants in the NHGRI-EBI GWAS catalogue (release 2016-04-10) which are tagged by an iPSC eQTL (lead variant or r2 > 0.8 proxy). For proxy matches, all eQTLs for which the variant is a proxy (r2 > 0.8) are shown. (b) Disease-associated variants in the GWAS catalogue that are lead eQTL variants in iPSCs (subset of (a)). For each variant, the number of high-LD proxies it has is listed (‘N_HIGH_LD_PROXIES’). (c) Individual traits in the GWAS catalogue for which iPSC eQTLs show a significant enrichment (BH-adjusted empirical P < 0.05, derived from 100 random sets of matched variants; Methods). Shown are traits with minimum five variants tagged by iPSC eQTLs. (d) Results of the colocalisation analysis for 14 traits.

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