Framework for the Integration of Genomics, Epigenomics and Transcriptomics in Complex DiseasesPineda S.a, f · Gomez-Rubio P.a · Picornell A.a · Bessonov K.f · Márquez M.a · Kogevinas M.c, d · Real F.X.b, e · Van Steen K.f, g · Malats N.a
aGenetic and Molecular Epidemiology Group, and bEpithelial Carcinogenesis Group, Spanish National Cancer Research Centre (CNIO), Madrid, cCentre for Research in Environmental Epidemiology (CREAL), dInstitut Municipal d'Investigació Mèdica - Hospital del Mar, and eDepartament de Ciències Experimentals i de la Salut, Universitat Pompeu Fabra, Barcelona, Spain; fSystems and Modeling Unit, Montefiore Institute, University of Liège, and gBioinformatics and Modeling, GIGA-R, University of Liège, Liège, Belgium
Nuria Malats, MD, MPH, PhD
Genetic and Molecular Epidemiology Group
Spanish National Cancer Research Centre (CNIO)
C/Melchor Fernández Almagro, 3, ES-28029 Madrid (Spain)
Prof. Kristel Van Steen, PhD, PhD
Systems and Modeling Unit, Montefiore Institute
University of Liège, Bât. B28 Bioinformatique, Grande Traverse 10
BE-4000 Liège (Belgium)
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Objectives: Different types of ‘-omics' data are becoming available in the post-genome era; still a single -omics assessment provides limited insights to understand the biological mechanism of complex diseases. Genomics, epigenomics and transcriptomics data provide insight into the molecular dysregulation of neoplastic diseases, among them urothelial bladder cancer (UBC). Here, we propose a detailed analytical framework necessary to achieve an adequate integration of the three sets of -omics data to ultimately identify previously hidden genetic mechanisms in UBC. Methods: We built a multi-staged framework to study possible pair-wise combinations and integrated the data in three-way relationships. SNP genotypes, CpG methylation levels and gene expression levels were determined for a total of 70 individuals with UBC and with fresh tumour tissue available. Results: We suggest two main hypothesis-based scenarios for gene regulation based on the -omics integration analysis, where DNA methylation affects gene expression and genetic variants co-regulate gene expression and DNA methylation. We identified several three-way trans-association ‘hotspots' that are found at the molecular level and that deserve further studies. Conclusions: The proposed integrative framework allowed us to identify relationships at the whole-genome level providing some new biological insights and highlighting the importance of integrating -omics data.
© 2015 S. Karger AG, Basel
- Heyn H, Sayols S, Moutinho C, et al: Linkage of DNA methylation quantitative trait loci to human cancer risk. Cell Rep 2014;7:331-338.
- Irizarry RA, Ladd-Acosta C, Wen B, et al: The human colon cancer methylome shows similar hypo- and hypermethylation at conserved tissue-specific CpG island shores. Nat Genet 2009;41:178-186.
- Jones PA: Functions of DNA methylation: islands, start sites, gene bodies and beyond. Nat Rev Genet 2012;13:484-492.
- Portela A, Esteller M: Epigenetic modifications and human disease. Nat Biotechnol 2010;28:1057-1068.
- Esteller M: Epigenetics in cancer. N Engl J Med 2008;358:1148-1159.
- Cheung VG, Spielman RS: Genetics of human gene expression: mapping DNA variants that influence gene expression. Nat Rev Genet 2009;10:595-604.
- Nica AC, Montgomery SB, Dimas AS, et al: Candidate causal regulatory effects by integration of expression QTLs with complex trait genetic associations. PLoS Genet 2010;6:e1000895.
- Nicolae DL, Gamazon E, Zhang W, et al: Trait-associated SNPs are more likely to be eQTLs: annotation to enhance discovery from GWAS. PLoS Genet 2010;6:e1000888.
- Pickrell JK, Marioni JC, Pai AA, et al: Understanding mechanisms underlying human gene expression variation with RNA sequencing. Nature 2010;464:768-772.
- Westra HJ, Peters MJ, Esko T, et al: Systematic identification of trans eQTLs as putative drivers of known disease associations. Nat Genet 2013;45:1238-1243.
- Zhernakova DV, de Klerk E, Westra H-J, et al: DeepSAGE reveals genetic variants associated with alternative polyadenylation and expression of coding and non-coding transcripts. PLoS Genet 2013;9:e1003594.
- Gibbs JR, van der Brug MP, Hernandez DG, et al: Abundant quantitative trait loci exist for DNA methylation and gene expression in human brain. PLoS Genet 2010;6:e1000952.
- Zhang D, Cheng L, Badner JA, et al: Genetic control of individual differences in gene-specific methylation in human brain. Am J Hum Genet 2010;86:411-419.
- Bell JT, Pai AA, Pickrell JK, et al: DNA methylation patterns associate with genetic and gene expression variation in HapMap cell lines. Genome Biol 2011;12:R10.
- Drong AW, Nicholson G, Hedman AK, et al: The presence of methylation quantitative trait loci indicates a direct genetic influence on the level of DNA methylation in adipose tissue. PLoS One 2013;8:e55923.
- Van Eijk KR, de Jong S, Boks MPM,et al: Genetic analysis of DNA methylation and gene expression levels in whole blood of healthy human subjects. BMC Genomics 2012;13:636.
- Liu Y, Ding J, Reynolds LM, et al: Methylomics of gene expression in human monocytes. Hum Mol Genet 2013;22:5065-5074.
- Li Q, Seo JH, Stranger B, et al: Integrative eQTL-based analyses reveal the biology of breast cancer risk loci. Cell 2013;152:633-641.
- Greenawalt DM, Sieberts SK, Cornelis MC, et al: Integrating genetic association, genetics of gene expression, and single nucleotide polymorphism set analysis to identify susceptibility loci for type 2 diabetes mellitus. Am J Epidemiol 2012;176:423-430.
- Bibikova M, Le J, Barnes B, et al: Genome-wide DNA methylation profiling using Infinium(R) assay. Epigenomics 2009;1:177-200.
- Du P, Zhang X, Huang C-C, et al: Comparison of Beta-value and M-value methods for quantifying methylation levels by microarray analysis. BMC Bioinformatics 2010;11:587.
- Chen Y, Choufani S, Ferreira JC, et al: Sequence overlap between autosomal and sex-linked probes on the Illumina HumanMethylation27 microarray. Genomics 2011;97:214-222.
- Kauffmann A, Gentleman R, Huber W: arrayQualityMetrics - a bioconductor package for quality assessment of microarray data. Bioinformatics 2009;25:415-416.
- Gautier L, Cope L, Bolstad BM, Irizarry RA: affy - analysis of Affymetrix GeneChip data at the probe level. Bioinformatics 2004;20:307-315.
Benjamini Y, Yekutieli D: The control of the false discovery rate in multiple testing under dependency. Ann Stat 2001;29:1165-1188.
- Krzywinski M, Schein J, Birol I, et al: Circos: an information aesthetic for comparative genomics. Genome Res 2009;19:1639-1645.
- You JS, Jones PA: Cancer genetics and epigenetics: two sides of the same coin? Cancer Cell 2012;22:9-20.
- Kanwal R, Gupta S: Epigenetic modifications in cancer. Clin Genet 2012;81:303-311.
- Bickmore WA, van Steensel B: Genome architecture: domain organization of interphase chromosomes. Cell 2013;152:1270-1284.
- Uchida K, Veeramachaneni R, Huey B, et al: Investigation of HOXA9 promoter methylation as a biomarker to distinguish oral cancer patients at low risk of neck metastasis. BMC Cancer 2014;14:353.
- Reinert T, Modin C, Castano FM, et al: Comprehensive genome methylation analysis in bladder cancer: identification and validation of novel methylated genes and application of these as urinary tumor markers. Clin Cancer Res 2011;17:5582-5592.
- Gilbert PM, Mouw JK, Unger MA, et al: HOXA9 regulates BRCA1 expression to modulate human breast tumor phenotype. J Clin Invest 2010;120:1535-1550.
- Guerrero-Preston R, Soudry E, Acero J, et al: NID2 and HOXA9 promoter hypermethylation as biomarkers for prevention and early detection in oral cavity squamous cell carcino- ma tissues and saliva. Cancer Prev Res (Phila) 2011;4:1061-1072.
- Wu Q, Lothe RA, Ahlquist T, et al: DNA methylation profiling of ovarian carcinomas and their in vitro models identifies HOXA9, HOXB5, SCGB3A1, and CRABP1 as novel targets. Mol Cancer 2007;6:45.
- Reinert T, Borre M, Christiansen A, et al: Diagnosis of bladder cancer recurrence based on urinary levels of EOMES, HOXA9, POU4F2, TWIST1, VIM, and ZNF154 hypermethylation. PLoS One 2012;7:e46297.
- ENCODE Project Consortium: The ENCODE (ENCyclopedia Of DNA Elements) Project. Science 2004;306:636-640.
- Fu Y-P, Kohaar I, Rothman N, et al: Common genetic variants in the PSCA gene influence gene expression and bladder cancer risk. Proc Natl Acad Sci USA 2012;109:4974-4979.
- Rothman N, Garcia-Closas M, Chatterjee N, et al: A multi-stage genome-wide association study of bladder cancer identifies multiple susceptibility loci. Nat Genet 2010;42:978-984.
- Visser-Grieve S, Hao Y, Yang X: Human homolog of Drosophila expanded, hEx, functions as a putative tumor suppressor in human cancer cell lines independently of the Hippo pathway. Oncogene 2012;31:1189-1195.
- Wagner JR, Busche S, Ge B, et al: The relationship between DNA methylation, genetic and expression inter-individual variation in untransformed human fibroblasts. Genome Biol 2014;15:R37.
- Moffatt MF, Gut IG, Demenais F, et al: A large-scale, consortium-based genomewide association study of asthma. N Engl J Med 2010;363:1211-1221.
- Hunter DJ, Kraft P, Jacobs KB, et al: A genome-wide association study identifies alleles in FGFR2 associated with risk of sporadic postmenopausal breast cancer. Nat Genet 2007;39:870-874.
- Qi W, White MC, Choi W, et al: Inhibition of inducible heat shock protein-70 (hsp72) enhances bortezomib-induced cell death in human bladder cancer cells. PLoS One 2013;8: e69509.
- Piazza R, Valletta S, Winkelmann N, et al: Recurrent SETBP1 mutations in atypical chronic myeloid leukemia. Nat Genet 2013;45:18-24.
- Makishima H, Yoshida K, Nguyen N, et al: Somatic SETBP1 mutations in myeloid malignancies. Nat Genet 2013;45:942-946.
- Ibragimova I, Dulaimi E, Slifker MJ, et al: A global profile of gene promoter methylation in treatment-naïve urothelial cancer. Epigenetics 2014;9:760-773.
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