02478nas a2200325   4500008004100000022001400041245015000055210006900205260000900274300000700283490000600290520144200296653001401738653000901752653001901761100002301780700001701803700001901820700002401839700002601863700002001889700002801909700002601937700002601963700002001989700002502009700002002034700001902054856007902073       2015                        eng d  a1755-879400aIdentification of epistatic interactions through genome-wide association studies in sporadic medullary and juvenile papillary thyroid carcinomas.0 aIdentification of epistatic interactions through genomewide asso  c2015  a830 v83 aBACKGROUND: The molecular mechanisms leading to sporadic medullary thyroid carcinoma (sMTC) and juvenile papillary thyroid carcinoma (PTC), two rare tumours of the thyroid gland, remain poorly understood. Genetic studies on thyroid carcinomas have been conducted, although just a few loci have been systematically associated. Given the difficulties to obtain single-loci associations, this work expands its scope to the study of epistatic interactions that could help to understand the genetic architecture of complex diseases and explain new heritable components of genetic risk. METHODS: We carried out the first screening for epistasis by Multifactor-Dimensionality Reduction (MDR) in genome-wide association study (GWAS) on sMTC and juvenile PTC, to identify the potential simultaneous involvement of pairs of variants in the disease. RESULTS: We have identified two significant epistatic gene interactions in sMTC (CHFR-AC016582.2 and C8orf37-RNU1-55P) and three in juvenile PTC (RP11-648k4.2-DIO1, RP11-648k4.2-DMGDH and RP11-648k4.2-LOXL1). Interestingly, each interacting gene pair included a non-coding RNA, providing thus support to the relevance that these elements are increasingly gaining to explain carcinoma development and progression. CONCLUSIONS: Overall, this study contributes to the understanding of the genetic basis of thyroid carcinoma susceptibility in two different case scenarios such as sMTC and juvenile PTC.10aepistasis10aGWAS10aThyroid cancer1 aLuzón-Toro, Berta1 aBleda, Marta1 aNavarro, Elena1 aGarcía-Alonso, Luz1 aRuiz-Ferrer, Macarena1 aMedina, Ignacio1 aMartín-Sánchez, Marta1 aGonzalez, Cristina, Y1 aFernández, Raquel, M1 aTorroglosa, Ana1 aAntiňolo, Guillermo1 aDopazo, Joaquin1 aBorrego, Salud  uhttp://bmcmedgenomics.biomedcentral.com/articles/10.1186/s12920-015-0160-702519nas a2200373   4500008004100000022001400041245006800055210006500123260001500188300000800203490000600211520145200217653000901669653001601678653002101694653002701715100002601742700001701768700002301785700002401808700001901832700002201851700002001873700002401893700001601917700002501933700002301958700002401981700002602005700002502031700002102056700001902077856004902096       2013                        eng d  a1750-117200aPathways systematically associated to Hirschsprung’s disease.0 aPathways systematically associated to Hirschsprung s disease  c2013 Dec 2  a1870 v83 aDespite it has been reported that several loci are involved in Hirschsprung’s disease, the molecular basis of the disease remains yet essentially unknown. The study of collective properties of modules of functionally-related genes provides an efficient and sensitive statistical framework that can overcome sample size limitations in the study of rare diseases. Here, we present the extension of a previous study of a Spanish series of HSCR trios to an international cohort of 162 HSCR trios to validate the generality of the underlying functional basis of the Hirschsprung’s disease mechanisms previously found. The Pathway-Based Analysis (PBA) confirms a strong association of gene ontology (GO) modules related to signal transduction and its regulation, enteric nervous system (ENS) formation and other processes related to the disease. In addition, network analysis recovers sub-networks significantly associated to the disease, which contain genes related to the same functionalities, thus providing an independent validation of these findings. The functional profiles of association obtained for patients populations from different countries were compared to each other. While gene associations were different at each series, the main functional associations were identical in all the five populations. These observations would also explain the reported low reproducibility of associations of individual disease genes across populations.10aGWAS10aHirschprung10anetwork analysis10aPathway Based Analysis1 aFernández, Raquel, M1 aBleda, Marta1 aLuzón-Toro, Berta1 aGarcía-Alonso, Luz1 aArnold, Stacey1 aSribudiani, Yunia1 aBesmond, Claude1 aLantieri, Francesca1 aDoan, Betty1 aCeccherini, Isabella1 aLyonnet, Stanislas1 aHofstra, Robert, Mw1 aChakravarti, Aravinda1 aAntiňolo, Guillermo1 aDopazo, Joaquín1 aBorrego, Salud  uhttp://www.ojrd.com/content/8/1/187/abstract02554nas a2200361   4500008004100000245014100041210006900182260001600251300003100267490000700298520145100305653001501756653002001771653001501791653001001806653000801816653000901824100002001833700002101853700001701874700002101891700001801912700001601930700002301946700002901969700002701998700002002025700002302045700002002068700002002088700002102108856006302129       2010                        eng d00aBabelomics: an integrative platform for the analysis of transcriptomics, proteomics and genomic data with advanced functional profiling.0 aBabelomics an integrative platform for the analysis of transcrip  c2010 May 16  aW210-W213. Featured in NAR0 v383 a<p>Babelomics is a response to the growing necessity of integrating and analyzing different types of genomic data in an environment that allows an easy functional interpretation of the results. Babelomics includes a complete suite of methods for the analysis of gene expression data that include normalization (covering most commercial platforms), pre-processing, differential gene expression (case-controls, multiclass, survival or continuous values), predictors, clustering; large-scale genotyping assays (case controls and TDTs, and allows population stratification analysis and correction). All these genomic data analysis facilities are integrated and connected to multiple options for the functional interpretation of the experiments. Different methods of functional enrichment or gene set enrichment can be used to understand the functional basis of the experiment analyzed. Many sources of biological information, which include functional (GO, KEGG, Biocarta, Reactome, etc.), regulatory (Transfac, Jaspar, ORegAnno, miRNAs, etc.), text-mining or protein-protein interaction modules can be used for this purpose. Finally a tool for the de novo functional annotation of sequences has been included in the system. This provides support for the functional analysis of non-model species. Mirrors of Babelomics or command line execution of their individual components are now possible. Babelomics is available at http://www.babelomics.org.</p>10ababelomics10agene expression10agenotyping10agepas10aGSA10aGWAS1 aMedina, Ignacio1 aCarbonell, José1 aPulido, Luis1 aMadeira, Sara, C1 aGoetz, Stefan1 aConesa, Ana1 aTárraga, Joaquín1 aPascual-Montano, Alberto1 aNogales-Cadenas, Ruben1 aSantoyo, Javier1 aGarcía, Francisco1 aMarbà, Martina1 aMontaner, David1 aDopazo, Joaquín  uhttp://nar.oxfordjournals.org/content/38/suppl_2/W210.full02872nas a2200241   4500008004100000245013000041210006900171300000700240490000600247520211000253653001302363653000902376653000802385100001702393700001802410700001302428700001402441700002002455700001502475700001502490700001902505856010602524       2008                        eng d00aBiological processes, properties and molecular wiring diagrams of candidate low-penetrance breast cancer susceptibility genes0 aBiological processes properties and molecular wiring diagrams of  a620 v13 a<p>ABSTRACT: BACKGROUND: Recent advances in whole-genome association studies (WGASs) for human cancer risk are beginning to provide the part lists of low-penetrance susceptibility genes. However, statistical analysis in these studies is complicated by the vast number of genetic variants examined and the weak effects observed, as a result of which constraints must be incorporated into the study design and analytical approach. In this scenario, biological attributes beyond the adjusted statistics generally receive little attention and, more importantly, the fundamental biological characteristics of low-penetrance susceptibility genes have yet to be determined. METHODS: We applied an integrative approach for identifying candidate low-penetrance breast cancer susceptibility genes, their characteristics and molecular networks through the analysis of diverse sources of biological evidence. RESULTS: First, examination of the distribution of Gene Ontology terms in ordered WGAS results identified asymmetrical distribution of Cell Communication and Cell Death processes linked to risk. Second, analysis of 11 different types of molecular or functional relationships in genomic and proteomic data sets defined the &quot;omic&quot; properties of candidate genes: i/ differential expression in tumors relative to normal tissue; ii/ somatic genomic copy number changes correlating with gene expression levels; iii/ differentially expressed across age at diagnosis; and iv/ expression changes after BRCA1 perturbation. Finally, network modeling of the effects of variants on germline gene expression showed higher connectivity than expected by chance between novel candidates and with known susceptibility genes, which supports functional relationships and provides mechanistic hypotheses of risk. CONCLUSION: This study proposes that cell communication and cell death are major biological processes perturbed in risk of breast cancer conferred by low-penetrance variants, and defines the common omic properties, molecular interactions and possible functional effects of candidate genes and proteins.</p>10agene set10aGWAS10aSNP1 aBonifaci, N.1 aBerenguer, A.1 aDiez, J.1 aReina, O.1 aMedina, Ignacio1 aDopazo, J.1 aMoreno, V.1 aPujana, M., A.  uhttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=19094230