Abstract
In epigenome-wide association studies, cell-type composition often differs between cases and controls, yielding associations that simply tag cell type rather than reveal fundamental biology. Current solutions require actual or estimated cell-type composition—information not easily obtainable for many samples of interest. We propose a method, FaST-LMM-EWASher, that automatically corrects for cell-type composition without the need for explicit knowledge of it, and then validate our method by comparison with the state-of-the-art approach. Corresponding software is available from http://www.microsoft.com/science/.
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Liu, Y. et al. Nat. Biotechnol. 31, 142–147 (2013).
Koestler, D.C. et al. Epigenetics 8, 816–826 (2013).
Reinius, L.E. et al. PLoS ONE 7, e41361 (2012).
Lam, L.L. et al. Proc. Natl. Acad. Sci. USA 109 (suppl. 2), 17253–17260 (2012).
Rakyan, V. K., Down, T.A., Balding, D. J. & Beck, S. Nat. Rev. Genet. 12, 529–541 (2011).
Lippert, C. et al. Nat. Methods 8, 833–835 (2011).
Listgarten, J. et al. Nat. Methods 9, 525–526 (2012).
Lippert, C. et al. Sci. Rep. 3, 1815 (2013).
Houseman, E.A. et al. BMC Bioinformatics 13, 86 (2012).
Devlin, B., Roeder, K. & Wasserman, L. Theor. Popul. Biol. 60, 155–166 (2001).
Heichman, K.A. & Warren, J.D. Clin. Chem. Lab. Med. 50, 1707–1721 (2012).
Hanavadi, S., Martin, T.A., Watkins, G., Mansel, R.E. & Jiang, W.G. Ann. Surg. Oncol. 13, 802–808 (2006).
The Cancer Genome Atlas Network. Nature 490, 61–70 (2012).
Bortsov, A.V. et al. Anesthesiology 116, 896–902 (2012).
Huang, B. et al. Oncogene 31, 527–534 (2012).
Balding, D.J. Nat. Rev. Genet. 7, 781–791 (2006).
Devlin, B. & Roeder, K. Biometrics 55, 997–1004 (1999).
Yang, J. et al. Eur. J. Hum. Genet. 19, 807–812 (2011).
Price, A.L., Zaitlen, N.A., Reich, D. & Patterson, N. Nat. Rev. Genet. 11, 459–463 (2010).
Huang, D.W., Sherman, B.T. & Lempicki, R.A. Nucleic Acids Res. 37, 1–13 (2009).
Huang, D.W., Sherman, B.T. & Lempicki, R.A. Nat. Protoc. 4, 44–57 (2009).
Acknowledgements
We thank Y. Yamanaka for helpful feedback on the manuscript and D. Koestler, M. Kobor, G. Quon for useful discussions.
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J.Z. and J.L. designed research, performed research, contributed analytic tools, analyzed data and wrote the paper. C.L. and D.H. contributed analytic tools. M.A. designed research.
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J.Z., C.L., D.H. and J.L. were employees of Microsoft at the time this work was performed.
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Zou, J., Lippert, C., Heckerman, D. et al. Epigenome-wide association studies without the need for cell-type composition. Nat Methods 11, 309–311 (2014). https://doi.org/10.1038/nmeth.2815
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DOI: https://doi.org/10.1038/nmeth.2815
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