IDEAS home Printed from https://ideas.repec.org/a/plo/pcbi00/1000642.html
   My bibliography  Save this article

A Bayesian Partition Method for Detecting Pleiotropic and Epistatic eQTL Modules

Author

Listed:
  • Wei Zhang
  • Jun Zhu
  • Eric E Schadt
  • Jun S Liu

Abstract

Studies of the relationship between DNA variation and gene expression variation, often referred to as “expression quantitative trait loci (eQTL) mapping”, have been conducted in many species and resulted in many significant findings. Because of the large number of genes and genetic markers in such analyses, it is extremely challenging to discover how a small number of eQTLs interact with each other to affect mRNA expression levels for a set of co-regulated genes. We present a Bayesian method to facilitate the task, in which co-expressed genes mapped to a common set of markers are treated as a module characterized by latent indicator variables. A Markov chain Monte Carlo algorithm is designed to search simultaneously for the module genes and their linked markers. We show by simulations that this method is more powerful for detecting true eQTLs and their target genes than traditional QTL mapping methods. We applied the procedure to a data set consisting of gene expression and genotypes for 112 segregants of S. cerevisiae. Our method identified modules containing genes mapped to previously reported eQTL hot spots, and dissected these large eQTL hot spots into several modules corresponding to possibly different biological functions or primary and secondary responses to regulatory perturbations. In addition, we identified nine modules associated with pairs of eQTLs, of which two have been previously reported. We demonstrated that one of the novel modules containing many daughter-cell expressed genes is regulated by AMN1 and BPH1. In conclusion, the Bayesian partition method which simultaneously considers all traits and all markers is more powerful for detecting both pleiotropic and epistatic effects based on both simulated and empirical data.Author Summary: Genome-wide association studies (GWAS) have yielded several causal genes for many human diseases. However, the mechanisms underlying how DNA variations affect disease phenotypes have not been well understood in many cases. Gene expression is intermediate between DNA and clinical endpoints. Linking DNA variation and gene expression variation, often referred to as “expression quantitative trait loci (eQTL) mapping”, has yielded clues of mechanisms and pathways by which DNA variations impact phenotypes. Because of the large number of genes and genetic markers in such analyses, it is extremely challenging to discover how a small number of eQTLs interact with each other to affect mRNA expression levels for a set of co-regulated genes. We present a Bayesian method to identify genetic interactions and more eQTLs by treating co-expressed genes as a module. Our method provides a tool to study genetic interactions in human disease models.

Suggested Citation

  • Wei Zhang & Jun Zhu & Eric E Schadt & Jun S Liu, 2010. "A Bayesian Partition Method for Detecting Pleiotropic and Epistatic eQTL Modules," PLOS Computational Biology, Public Library of Science, vol. 6(1), pages 1-10, January.
    • Handle: RePEc:plo:pcbi00:1000642
    DOI: 10.1371/journal.pcbi.1000642
    as

    Download full text from publisher

    File URL: https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1000642
    Download Restriction: no
    File URL: https://journals.plos.org/ploscompbiol/article/file?id=10.1371/journal.pcbi.1000642&type=printable
    Download Restriction: no
    File URL: https://libkey.io/10.1371/journal.pcbi.1000642?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. C. M. Kendziorski & M. Chen & M. Yuan & H. Lan & A. D. Attie, 2006. "Statistical Methods for Expression Quantitative Trait Loci (eQTL) Mapping," Biometrics, The International Biometric Society, vol. 62(1), pages 19-27, March.
    2. Yanqing Chen & Jun Zhu & Pek Yee Lum & Xia Yang & Shirly Pinto & Douglas J. MacNeil & Chunsheng Zhang & John Lamb & Stephen Edwards & Solveig K. Sieberts & Amy Leonardson & Lawrence W. Castellini & Su, 2008. "Variations in DNA elucidate molecular networks that cause disease," Nature, Nature, vol. 452(7186), pages 429-435, March.
    3. Rachel B. Brem & John D. Storey & Jacqueline Whittle & Leonid Kruglyak, 2005. "Genetic interactions between polymorphisms that affect gene expression in yeast," Nature, Nature, vol. 436(7051), pages 701-703, August.
    4. Michael Morley & Cliona M. Molony & Teresa M. Weber & James L. Devlin & Kathryn G. Ewens & Richard S. Spielman & Vivian G. Cheung, 2004. "Genetic analysis of genome-wide variation in human gene expression," Nature, Nature, vol. 430(7001), pages 743-747, August.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Lin Yuan & Chang-An Yuan & De-Shuang Huang, 2017. "FAACOSE: A Fast Adaptive Ant Colony Optimization Algorithm for Detecting SNP Epistasis," Complexity, Hindawi, vol. 2017, pages 1-10, September.
    2. Bo Jiang & Jun S. Liu, 2015. "Bayesian Partition Models for Identifying Expression Quantitative Trait Loci," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 110(512), pages 1350-1361, December.
    3. Gang Fang & Majda Haznadar & Wen Wang & Haoyu Yu & Michael Steinbach & Timothy R Church & William S Oetting & Brian Van Ness & Vipin Kumar, 2012. "High-Order SNP Combinations Associated with Complex Diseases: Efficient Discovery, Statistical Power and Functional Interactions," PLOS ONE, Public Library of Science, vol. 7(4), pages 1-15, April.
    4. Brown Andrew Anand & Richardson Sylvia & Whittaker John, 2011. "Application of the Lasso to Expression Quantitative Trait Loci Mapping," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 10(1), pages 1-35, March.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Bo Jiang & Jun S. Liu, 2015. "Bayesian Partition Models for Identifying Expression Quantitative Trait Loci," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 110(512), pages 1350-1361, December.
    2. Enrico Petretto & Leonardo Bottolo & Sarah R Langley & Matthias Heinig & Chris McDermott-Roe & Rizwan Sarwar & Michal Pravenec & Norbert Hübner & Timothy J Aitman & Stuart A Cook & Sylvia Richardson, 2010. "New Insights into the Genetic Control of Gene Expression using a Bayesian Multi-tissue Approach," PLOS Computational Biology, Public Library of Science, vol. 6(4), pages 1-13, April.
    3. Jin Hyun Ju & Sushila A Shenoy & Ronald G Crystal & Jason G Mezey, 2017. "An independent component analysis confounding factor correction framework for identifying broad impact expression quantitative trait loci," PLOS Computational Biology, Public Library of Science, vol. 13(5), pages 1-26, May.
    4. Hui-Min Wang & Ching-Lin Hsiao & Ai-Ru Hsieh & Ying-Chao Lin & Cathy S J Fann, 2012. "Constructing Endophenotypes of Complex Diseases Using Non-Negative Matrix Factorization and Adjusted Rand Index," PLOS ONE, Public Library of Science, vol. 7(7), pages 1-12, July.
    5. Julia Schröder & Vitalia Schüller & Andrea May & Christian Gerges & Mario Anders & Jessica Becker & Timo Hess & Nicole Kreuser & René Thieme & Kerstin U Ludwig & Tania Noder & Marino Venerito & Lothar, 2019. "Identification of loci of functional relevance to Barrett’s esophagus and esophageal adenocarcinoma: Cross-referencing of expression quantitative trait loci data from disease-relevant tissues with gen," PLOS ONE, Public Library of Science, vol. 14(12), pages 1-12, December.
    6. Emma Pierson & the GTEx Consortium & Daphne Koller & Alexis Battle & Sara Mostafavi, 2015. "Sharing and Specificity of Co-expression Networks across 35 Human Tissues," PLOS Computational Biology, Public Library of Science, vol. 11(5), pages 1-19, May.
    7. Kai Wang & Manikandan Narayanan & Hua Zhong & Martin Tompa & Eric E Schadt & Jun Zhu, 2009. "Meta-analysis of Inter-species Liver Co-expression Networks Elucidates Traits Associated with Common Human Diseases," PLOS Computational Biology, Public Library of Science, vol. 5(12), pages 1-16, December.
    8. Yixin Fang & Yang Feng & Ming Yuan, 2014. "Regularized principal components of heritability," Computational Statistics, Springer, vol. 29(3), pages 455-465, June.
    9. Won Jun Lee & Sang Cheol Kim & Jung-Ho Yoon & Sang Jun Yoon & Johan Lim & You-Sun Kim & Sung Won Kwon & Jeong Hill Park, 2016. "Meta-Analysis of Tumor Stem-Like Breast Cancer Cells Using Gene Set and Network Analysis," PLOS ONE, Public Library of Science, vol. 11(2), pages 1-20, February.
    10. Wei Sun, 2012. "A Statistical Framework for eQTL Mapping Using RNA-seq Data," Biometrics, The International Biometric Society, vol. 68(1), pages 1-11, March.
    11. Witten Daniela M & Tibshirani Robert J., 2009. "Extensions of Sparse Canonical Correlation Analysis with Applications to Genomic Data," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 8(1), pages 1-29, June.
    12. Devansh Pandey & Mariana Harris & Nandita R. Garud & Vagheesh M. Narasimhan, 2024. "Leveraging ancient DNA to uncover signals of natural selection in Europe lost due to admixture or drift," Nature Communications, Nature, vol. 15(1), pages 1-13, December.
    13. Valur Emilsson & Elias F. Gudmundsson & Thorarinn Jonmundsson & Brynjolfur G. Jonsson & Michael Twarog & Valborg Gudmundsdottir & Zhiguang Li & Nancy Finkel & Stephen Poor & Xin Liu & Robert Esterberg, 2022. "A proteogenomic signature of age-related macular degeneration in blood," Nature Communications, Nature, vol. 13(1), pages 1-15, December.
    14. Lingxue Zhang & Seyoung Kim, 2014. "Learning Gene Networks under SNP Perturbations Using eQTL Datasets," PLOS Computational Biology, Public Library of Science, vol. 10(2), pages 1-20, February.
    15. Federico Innocenti & Gregory M Cooper & Ian B Stanaway & Eric R Gamazon & Joshua D Smith & Snezana Mirkov & Jacqueline Ramirez & Wanqing Liu & Yvonne S Lin & Cliona Moloney & Shelly Force Aldred & Nat, 2011. "Identification, Replication, and Functional Fine-Mapping of Expression Quantitative Trait Loci in Primary Human Liver Tissue," PLOS Genetics, Public Library of Science, vol. 7(5), pages 1-16, May.
    16. Xiaolin Xiao & Aida Moreno-Moral & Maxime Rotival & Leonardo Bottolo & Enrico Petretto, 2014. "Multi-tissue Analysis of Co-expression Networks by Higher-Order Generalized Singular Value Decomposition Identifies Functionally Coherent Transcriptional Modules," PLOS Genetics, Public Library of Science, vol. 10(1), pages 1-16, January.
    17. Cipolli III, William & Hanson, Timothy & McLain, Alexander C., 2016. "Bayesian nonparametric multiple testing," Computational Statistics & Data Analysis, Elsevier, vol. 101(C), pages 64-79.
    18. Koen J F Verhoeven & George Casella & Lauren M McIntyre, 2010. "Epistasis: Obstacle or Advantage for Mapping Complex Traits?," PLOS ONE, Public Library of Science, vol. 5(8), pages 1-12, August.
    19. Benjamin A Logsdon & Jason Mezey, 2010. "Gene Expression Network Reconstruction by Convex Feature Selection when Incorporating Genetic Perturbations," PLOS Computational Biology, Public Library of Science, vol. 6(12), pages 1-13, December.
    20. Barbara E Stranger & Stephen B Montgomery & Antigone S Dimas & Leopold Parts & Oliver Stegle & Catherine E Ingle & Magda Sekowska & George Davey Smith & David Evans & Maria Gutierrez-Arcelus & Alkes P, 2012. "Patterns of Cis Regulatory Variation in Diverse Human Populations," PLOS Genetics, Public Library of Science, vol. 8(4), pages 1-13, April.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:plo:pcbi00:1000642. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: ploscompbiol (email available below). General contact details of provider: https://journals.plos.org/ploscompbiol/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.