Yusha Liu1, Keith A Baggerly2, Elias Orouji3, Ganiraju Manyam2, Huiqin Chen2, Michael Lam4, Jennifer S Davis5, Michael S Lee6, Bradley M Broom2, David G Menter4, Kunal Rai3, Scott Kopetz4, Jeffrey S Morris7. 1. Department of Human Genetics, The University of Chicago, Chicago, IL 60637, USA. 2. Department of Bioinformatics and Computational Biology, M.D. Anderson Cancer Center, Houston, TX 77030, USA. 3. Department of Genomic Medicine, M.D. Anderson Cancer Center, Houston, TX 77030, USA. 4. Department of Gastrointestinal Medical Oncology, M.D. Anderson Cancer Center, Houston, TX 77030, USA. 5. Department of Epidemiology, M.D. Anderson Cancer Center, Houston, TX 77030, USA. 6. Department of Medicine, The University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA. 7. Department of Biostatistics, Epidemiology and Informatics, The University of Pennsylvania, Philadelphia, PA 19104-6021, USA.
Abstract
MOTIVATION: DNA methylation is a key epigenetic factor regulating gene expression. While promoter methylation has been well studied, recent publications have revealed that functionally important methylation also occurs in intergenic and distal regions, and varies across genes and tissue types. Given the growing importance of inter-platform integrative genomic analyses, there is an urgent need to develop methods to discover and characterize gene-level relationships between methylation and expression. RESULTS: We introduce a novel sequential penalized regression approach to identify methylation-expression quantitative trait loci (methyl-eQTLs), a term that we have coined to represent, for each gene and tissue type, a sparse set of CpG loci best explaining gene expression and accompanying weights indicating direction and strength of association. Using TCGA and MD Anderson colorectal cohorts to build and validate our models, we demonstrate our strategy better explains expression variability than current commonly used gene-level methylation summaries. The methyl-eQTLs identified by our approach can be used to construct gene-level methylation summaries that are maximally correlated with gene expression for use in integrative models, and produce a tissue-specific summary of which genes appear to be strongly regulated by methylation. Our results introduce an important resource to the biomedical community for integrative genomics analyses involving DNA methylation. AVAILABILITY AND IMPLEMENTATION: We produce an R Shiny app (https://rstudio-prd-c1.pmacs.upenn.edu/methyl-eQTL/) that interactively presents methyl-eQTL results for colorectal, breast, and pancreatic cancer. The source R code for this work is provided in the supplement. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
MOTIVATION: DNA methylation is a key epigenetic factor regulating gene expression. While promoter methylation has been well studied, recent publications have revealed that functionally important methylation also occurs in intergenic and distal regions, and varies across genes and tissue types. Given the growing importance of inter-platform integrative genomic analyses, there is an urgent need to develop methods to discover and characterize gene-level relationships between methylation and expression. RESULTS: We introduce a novel sequential penalized regression approach to identify methylation-expression quantitative trait loci (methyl-eQTLs), a term that we have coined to represent, for each gene and tissue type, a sparse set of CpG loci best explaining gene expression and accompanying weights indicating direction and strength of association. Using TCGA and MD Anderson colorectal cohorts to build and validate our models, we demonstrate our strategy better explains expression variability than current commonly used gene-level methylation summaries. The methyl-eQTLs identified by our approach can be used to construct gene-level methylation summaries that are maximally correlated with gene expression for use in integrative models, and produce a tissue-specific summary of which genes appear to be strongly regulated by methylation. Our results introduce an important resource to the biomedical community for integrative genomics analyses involving DNA methylation. AVAILABILITY AND IMPLEMENTATION: We produce an R Shiny app (https://rstudio-prd-c1.pmacs.upenn.edu/methyl-eQTL/) that interactively presents methyl-eQTL results for colorectal, breast, and pancreatic cancer. The source R code for this work is provided in the supplement. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
Authors: Louise B Thingholm; Lars Andersen; Enes Makalic; Melissa C Southey; Mads Thomassen; Lise Lotte Hansen Journal: Front Genet Date: 2016-02-01 Impact factor: 4.599
Authors: Rafael A Irizarry; Christine Ladd-Acosta; Andrew P Feinberg; Bo Wen; Zhijin Wu; Carolina Montano; Patrick Onyango; Hengmi Cui; Kevin Gabo; Michael Rongione; Maree Webster; Hong Ji; James Potash; Sarven Sabunciyan Journal: Nat Genet Date: 2009-01-18 Impact factor: 38.330
Authors: Justin Guinney; Rodrigo Dienstmann; Xin Wang; Aurélien de Reyniès; Andreas Schlicker; Charlotte Soneson; Laetitia Marisa; Paul Roepman; Gift Nyamundanda; Paolo Angelino; Brian M Bot; Jeffrey S Morris; Iris M Simon; Sarah Gerster; Evelyn Fessler; Felipe De Sousa E Melo; Edoardo Missiaglia; Hena Ramay; David Barras; Krisztian Homicsko; Dipen Maru; Ganiraju C Manyam; Bradley Broom; Valerie Boige; Beatriz Perez-Villamil; Ted Laderas; Ramon Salazar; Joe W Gray; Douglas Hanahan; Josep Tabernero; Rene Bernards; Stephen H Friend; Pierre Laurent-Puig; Jan Paul Medema; Anguraj Sadanandam; Lodewyk Wessels; Mauro Delorenzi; Scott Kopetz; Louis Vermeulen; Sabine Tejpar Journal: Nat Med Date: 2015-10-12 Impact factor: 53.440