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Digital RNA allelotyping reveals tissue-specific and allele-specific gene expression in human

Nature Methods volume 6, pages 613618 (2009) | Download Citation

Abstract

We developed a digital RNA allelotyping method for quantitatively interrogating allele-specific gene expression. This method involves ultra-deep sequencing of padlock-captured single-nucleotide polymorphisms (SNPs) from the transcriptome. We characterized four cell lines established from two human subjects in the Personal Genome Project. Approximately 11–22% of the heterozygous mRNA-associated SNPs showed allele-specific expression in each cell line and 4.3–8.5% were tissue-specific, suggesting the presence of tissue-specific cis regulation. When we applied allelotyping to two pairs of sibling human embryonic stem cell lines, the sibling lines were more similar in allele-specific expression than were the genetically unrelated lines. We found that the variation of allelic ratios in gene expression among different cell lines was primarily explained by genetic variations, much more so than by specific tissue types or growth conditions. Comparison of expressed SNPs on the sense and antisense transcripts suggested that allelic ratios are primarily determined by cis-regulatory mechanisms on the sense transcripts.

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Acknowledgements

We thank C. Ludka and Narimene Lekmine for assistance for Illumina sequencing. This study was supported by National Human Genome Research Institute (P50-HG003170); National Heart, Lung and Blood Institute (R01-HL094963); the Broad Institute and Personal Genome Project donations (to G.M.C.); and the new faculty startup fund from the University of California at San Diego (to K.Z.). J.D. was sponsored by a California Institute of Regenerative Medicine postdoctoral fellowship.

Author information

Author notes

    • Kun Zhang
    • , Jin Billy Li
    •  & Yuan Gao

    These authors contributed equally to this work.

Affiliations

  1. Department of Bioengineering, University of California at San Diego, La Jolla, California, USA.

    • Kun Zhang
    • , Jie Deng
    •  & Zhe Li
  2. Department of Genetics, Harvard Medical School, Boston, Massachusetts, USA.

    • Jin Billy Li
    • , Je-Hyuk Lee
    • , John Aach
    •  & George M Church
  3. Center for the Study of Biological Complexity, Virginia Commonwealth University, Richmond, Virginia, USA.

    • Yuan Gao
    •  & Bin Xie
  4. Department of Computer Science, Virginia Commonwealth University, Richmond, Virginia, USA.

    • Yuan Gao
  5. The Stowers Medical Institute, Harvard Stem Cell Institute and Department of Molecular and Cellular Biology, Harvard University, Cambridge, Massachusetts, USA.

    • Dieter Egli
    •  & Kevin Eggan
  6. Genomics Solution Unit, Agilent Technologies Inc., Santa Clara, California, USA.

    • Emily M Leproust

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Contributions

K.Z. and J.B.L. developed and optimized the digital allelotyping method; J.D., Z.L. and J.L. participated in the experiments; D.E. and K.E. provided DNA/RNA of hESCs; E.M.L. provided oligonucleotide libraries; Y.G. and B.X. performed Illumina sequencing. K.Z., J.B.L. and J.A. performed data analysis; and K.Z. and G.M.C. oversaw the project.

Competing interests

G.M.C. has advisory roles, royalties and/or equity holding in several next-generation sequencing companies. He donates these funds to PersonalGenomes.org.

E.M.L. is an employee of Agilent Technologies Inc.

Corresponding authors

Correspondence to Kun Zhang or George M Church.

Supplementary information

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    Supplementary Text and Figures

    Supplementary Figures 1–6, Supplementary Table 1 and Supplementary Note

Excel files

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    Supplementary Table 2

    Detailed information of the CES27k probe set.

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DOI

https://doi.org/10.1038/nmeth.1357

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