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Whole-Transcriptome Causal Network Inference with Genomic and Transcriptomic Data

  • Lingfei WangEmail author
  • Tom Michoel
Protocol
Part of the Methods in Molecular Biology book series (MIMB, volume 1883)

Abstract

Reconstruction of causal gene networks can distinguish regulators from targets and reduce false positives by integrating genetic variations. Its recent developments in speed and accuracy have enabled whole-transcriptome causal network inference on a personal computer. Here, we demonstrate this technique with program Findr on 3000 genes from the Geuvadis dataset. Subsequent analysis reveals major hub genes in the reconstructed network.

Key words

Causal gene network Whole-transcriptome network Causal inference Genome–transcriptome variation 

Notes

Acknowledgements

Development of Findr was supported by grants from the BBSRC [BB/J004235/1, BB/M020053/1].

References

  1. 1.
    Goodwin S, McPherson JD, McCombie WR (2016) Coming of age: ten years of next-generation sequencing technologies. Nat Rev Genet 17(6):nrg.2016.49CrossRefGoogle Scholar
  2. 2.
    Lappalainen T, Sammeth M, Friedländer MR, ’t Hoen PA, Monlong J, Rivas MA, Gonzàlez-Porta M, Kurbatova N, Griebel T, Ferreira PG, Barann M, Wieland T, Greger L, van Iterson M, Almlöf J, Ribeca P, Pulyakhina I, Esser D, Giger T, Tikhonov A, Sultan M, Bertier G, MacArthur DG, Lek M, Lizano E, Buermans HPJ, Padioleau I, Schwarzmayr T, Karlberg O, Ongen H, Kilpinen H, Beltran S, Gut M, Kahlem K, Amstislavskiy V, Stegle O, Pirinen M, Montgomery SB, Donnelly P, McCarthy MI, Flicek P, Strom TM, The Geuvadis Consortium, Lehrach H, Schreiber S, Sudbrak R, Carracedo A, Antonarakis SE, Häsler R, Syvänen A-C, van Ommen G-J, Brazma A, Meitinger T, Rosenstiel P, Guigó R, Gut IG, Estivill X, Dermitzakis ET (2013) Transcriptome and genome sequencing uncovers functional variation in humans. Nature 501(7468):506–511CrossRefGoogle Scholar
  3. 3.
    The GTEx Consortium (2015) The Genotype-Tissue Expression (GTEx) pilot analysis: multitissue gene regulation in humans. Science 348(6235):648–660CrossRefGoogle Scholar
  4. 4.
    Ashley EA (2016) Towards precision medicine. Nat Rev Genet 17(9):507–522CrossRefGoogle Scholar
  5. 5.
    Schadt EE, Friend SH, Shaywitz DA (2009) A network view of disease and compound screening. Nat Rev Drug Discov 8(4): 286–295CrossRefGoogle Scholar
  6. 6.
    Talukdar HA, Foroughi Asl H, Jain RK, Ermel R, Ruusalepp A, Franzén O, Kidd BA, Readhead B, Giannarelli C, Kovacic JC, Ivert T, Dudley JT, Civelek M, Lusis AJ, Schadt EE, Skogsberg J, Michoel T, Björkegren JL (2016) Cross-tissue regulatory gene networks in coronary artery disease. Cell Syst 2(3): 196–208CrossRefGoogle Scholar
  7. 7.
    Rockman MV (2008) Reverse engineering the genotype–phenotype map with natural genetic variation. Nature 456(7223):738–744CrossRefGoogle Scholar
  8. 8.
    Lawlor DA, Harbord RM, Sterne JAC, Timpson N, Smith GD (2008) Mendelian randomization: using genes as instruments for making causal inferences in epidemiology. Stat Med 27(8):1133–1163CrossRefGoogle Scholar
  9. 9.
    Chen LS, Emmert-Streib F, Storey JD (2007) Harnessing naturally randomized transcription to infer regulatory relationships among genes. Genome Biol 8:R219CrossRefGoogle Scholar
  10. 10.
    Millstein J, Chen GK, Breton CV (2016) cit: hypothesis testing software for mediation analysis in genomic applications. Bioinformatics 32(15):2364–2365CrossRefGoogle Scholar
  11. 11.
    Greenland S (1980) The effect of misclassification in the presence of covariates. Am J Epidemiol 112(4):564–569CrossRefGoogle Scholar
  12. 12.
    Li Y, Tesson BM, Churchill GA, Jansen RC (2010) Critical reasoning on causal inference in genome-wide linkage and association studies. Trends Genet 26(12):493–498CrossRefGoogle Scholar
  13. 13.
    Cole SR, Platt RW, Schisterman EF, Chu H, Westreich D, Richardson D, Poole C (2010) Illustrating bias due to conditioning on a collider. Int J Epidemiol 39(2):417–420CrossRefGoogle Scholar
  14. 14.
    Wang L, Michoel T (2017) Efficient and accurate causal inference with hidden confounders from genome-transcriptome variation data. PLoS Comput Biol 13(8):e1005703CrossRefGoogle Scholar
  15. 15.
    Hemani G, Tilling K, Smith GD (2017) Orienting the causal relationship between imprecisely measured traits using genetic instruments. bioRxiv, pp 117101Google Scholar
  16. 16.
    Storey JD, Tibshirani R (2003) Statistical significance for genomewide studies. Proc Natl Acad Sci 100(16):9440–9445CrossRefGoogle Scholar
  17. 17.
    Shabalin AA (2012) Matrix eQTL: ultra fast eQTL analysis via large matrix operations. Bioinformatics 28(10):1353–1358CrossRefGoogle Scholar
  18. 18.
    Ongen H, Buil A, Brown AA, Dermitzakis ET, Delaneau O (2016) Fast and efficient QTL mapper for thousands of molecular phenotypes. Bioinformatics 32(10):1479–1485CrossRefGoogle Scholar
  19. 19.
    Strimmer K (2008) fdrtool: a versatile R package for estimating local and tail area-based false discovery rates. Bioinformatics 24(12):1461–1462CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Division of Genetics and Genomics, The Roslin InstituteThe University of EdinburghMidlothianUK
  2. 2.Current address: Computational Biology Unit, Department of InformaticsUniversity of BergenBergenNorway

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