title: Integration Analysis of Three Omics Data Using Penalized Regression Methods: An Application to Bladder Cancer.
abstract: Omics data integration is becoming necessary to investigate the genomic mechanisms involved in complex diseases. During the integration process, many challenges arise such as data heterogeneity, the smaller number of individuals in comparison to the number of parameters, multicollinearity, and interpretation and validation of results due to their complexity and lack of knowledge about biological processes. To overcome some of these issues, innovative statistical approaches are being developed. In this work, we propose a permutation-based method to concomitantly assess significance and correct by multiple testing with the MaxT algorithm. This was applied with penalized regression methods (LASSO and ENET) when exploring relationships between common genetic variants, DNA methylation and gene expression measured in bladder tumor samples. The overall analysis flow consisted of three steps: (1) SNPs/CpGs were selected per each gene probe within 1Mb window upstream and downstream the gene; (2) LASSO and ENET were applied to assess the association between each expression probe and the selected SNPs/CpGs in three multivariable models (SNP, CPG, and Global models, the latter integrating SNPs and CPGs); and (3) the significance of each model was assessed using the permutation-based MaxT method. We identified 48 genes whose expression levels were significantly associated with both SNPs and CPGs. Importantly, 36 (75%) of them were replicated in an independent data set (TCGA) and the performance of the proposed method was checked with a simulation study. We further support our results with a biological interpretation based on an enrichment analysis. The approach we propose allows reducing computational time and is flexible and easy to implement when analyzing several types of omics data. Our results highlight the importance of integrating omics data by applying appropriate statistical strategies to discover new insights into the complex genetic mechanisms involved in disease conditions.
authors.affiliation: Genetic and Molecular Epidemiology Group, Spanish National Cancer Research Centre (CNIO), Madrid, Spain.|Systems and Modeling Unit-BIO3, Montefiore Institute, Liège, Belgium.
authors.forename: Silvia
authors.initials: S
authors.lastname: Pineda
authors.affiliation: Epithelial Carcinogenesis Group, Spanish National Cancer Research Centre (CNIO), Madrid, Spain.|Departament de Ciències Experimentals i de la Salut, Universitat Pompeu Fabra, Barcelona, Spain.
authors.forename: Francisco X
authors.initials: FX
authors.lastname: Real
authors.affiliation: Centre for Research in Environmental Epidemiology (CREAL) and Parc de Salut Mar, Barcelona, Spain.
authors.forename: Manolis
authors.initials: M
authors.lastname: Kogevinas
authors.affiliation: Servicio de Oncología, Hospital Universitario Ramon y Cajal, Madrid, and Servicio de Oncología, Hospital Universitario de Elche, Alicante, Spain.
authors.forename: Alfredo
authors.initials: A
authors.lastname: Carrato
authors.affiliation: Division of Cancer Epidemiology and Genetics, National Cancer Institute, Department of Health and Human Services, Bethesda, Maryland, United States of America.
authors.forename: Stephen J
authors.initials: SJ
authors.lastname: Chanock
authors.affiliation: Genetic and Molecular Epidemiology Group, Spanish National Cancer Research Centre (CNIO), Madrid, Spain.
authors.forename: Núria
authors.initials: N
authors.lastname: Malats
authors.affiliation: Systems and Modeling Unit-BIO3, Montefiore Institute, Liège, Belgium.|Systems Biology and Chemical Biology, GIGA-R, Liège, Belgium.
authors.forename: Kristel
authors.initials: K
authors.lastname: Van Steen
mesh_terms: D000465:Algorithms; D018899:CpG Islands / Q000235:genetics; D019175:DNA Methylation / Q000235:genetics*; D015972:Gene Expression Regulation, Neoplastic; D020022:Genetic Predisposition to Disease; D023281:Genomics; D006801:Humans; D009363:Neoplasm Proteins / Q000096:biosynthesis* / Q000235:genetics; D020641:Polymorphism, Single Nucleotide / Q000235:genetics; D012984:Software; D001749:Urinary Bladder Neoplasms / Q000235:genetics* / Q000473:pathology
chemical_list: D009363:Neoplasm Proteins