<?xml version="1.0" encoding="UTF-8"?><xml><records><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Maroñas, Olalla</style></author><author><style face="normal" font="default" size="100%">Latorre, Ana</style></author><author><style face="normal" font="default" size="100%">Dopazo, Joaquin</style></author><author><style face="normal" font="default" size="100%">Pirmohamed, Munir</style></author><author><style face="normal" font="default" size="100%">Rodríguez-Antona, Cristina</style></author><author><style face="normal" font="default" size="100%">Siest, Gérard</style></author><author><style face="normal" font="default" size="100%">Carracedo, Ángel</style></author><author><style face="normal" font="default" size="100%">LLerena, Adrián</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Progress in pharmacogenetics: consortiums and new strategies.</style></title><secondary-title><style face="normal" font="default" size="100%">Drug Metab Pers Ther</style></secondary-title><alt-title><style face="normal" font="default" size="100%">Drug Metab Pers Ther</style></alt-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Cooperative Behavior</style></keyword><keyword><style  face="normal" font="default" size="100%">Genome-Wide Association Study</style></keyword><keyword><style  face="normal" font="default" size="100%">High-Throughput Screening Assays</style></keyword><keyword><style  face="normal" font="default" size="100%">Humans</style></keyword><keyword><style  face="normal" font="default" size="100%">Patient Care Team</style></keyword><keyword><style  face="normal" font="default" size="100%">pharmacogenetics</style></keyword><keyword><style  face="normal" font="default" size="100%">Polymorphism, Single Nucleotide</style></keyword><keyword><style  face="normal" font="default" size="100%">Precision Medicine</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2016</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2016 Mar</style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">31</style></volume><pages><style face="normal" font="default" size="100%">17-23</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;Pharmacogenetics (PGx), as a field dedicated to achieving the goal of personalized medicine (PM), is devoted to the study of genes involved in inter-individual response to drugs. Due to its nature, PGx requires access to large samples; therefore, in order to progress, the formation of collaborative consortia seems to be crucial. Some examples of this collective effort are the European Society of Pharmacogenomics and personalized Therapy and the Ibero-American network of Pharmacogenetics. As an emerging field, one of the major challenges that PGx faces is translating their discoveries from research bench to bedside. The development of genomic high-throughput technologies is generating a revolution and offers the possibility of producing vast amounts of genome-wide single nucleotide polymorphisms for each patient. Moreover, there is a need of identifying and replicating associations of new biomarkers, and, in addition, a greater effort must be invested in developing regulatory organizations to accomplish a correct standardization. In this review, we outline the current progress in PGx using examples to highlight both the importance of polymorphisms and the research strategies for their detection. These concepts need to be applied together with a proper dissemination of knowledge to improve clinician and patient understanding, in a multidisciplinary team-based approach. &lt;/p&gt;</style></abstract><issue><style face="normal" font="default" size="100%">1</style></issue><custom1><style face="normal" font="default" size="100%">https://www.ncbi.nlm.nih.gov/pubmed/26913460?dopt=Abstract</style></custom1></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Avila-Fernandez, Almudena</style></author><author><style face="normal" font="default" size="100%">Perez-Carro, Raquel</style></author><author><style face="normal" font="default" size="100%">Corton, Marta</style></author><author><style face="normal" font="default" size="100%">Lopez-Molina, Maria Isabel</style></author><author><style face="normal" font="default" size="100%">Campello, Laura</style></author><author><style face="normal" font="default" size="100%">Garanto, Alejandro</style></author><author><style face="normal" font="default" size="100%">Fernandez-Sanchez, Laura</style></author><author><style face="normal" font="default" size="100%">Duijkers, Lonneke</style></author><author><style face="normal" font="default" size="100%">Lopez-Martinez, Miguel Angel</style></author><author><style face="normal" font="default" size="100%">Riveiro-Alvarez, Rosa</style></author><author><style face="normal" font="default" size="100%">da Silva, Luciana Rodrigues Jacy</style></author><author><style face="normal" font="default" size="100%">Sanchez-Alcudia, Rocío</style></author><author><style face="normal" font="default" size="100%">Martin-Garrido, Esther</style></author><author><style face="normal" font="default" size="100%">Reyes, Noelia</style></author><author><style face="normal" font="default" size="100%">Garcia-Garcia, Francisco</style></author><author><style face="normal" font="default" size="100%">Dopazo, Joaquin</style></author><author><style face="normal" font="default" size="100%">Garcia-Sandoval, Blanca</style></author><author><style face="normal" font="default" size="100%">Collin, Rob W J</style></author><author><style face="normal" font="default" size="100%">Cuenca, Nicolas</style></author><author><style face="normal" font="default" size="100%">Ayuso, Carmen</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Whole-exome sequencing reveals ZNF408 as a new gene associated with autosomal recessive retinitis pigmentosa with vitreal alterations.</style></title><secondary-title><style face="normal" font="default" size="100%">Hum Mol Genet</style></secondary-title><alt-title><style face="normal" font="default" size="100%">Hum Mol Genet</style></alt-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Amino Acid Sequence</style></keyword><keyword><style  face="normal" font="default" size="100%">Animals</style></keyword><keyword><style  face="normal" font="default" size="100%">Chlorocebus aethiops</style></keyword><keyword><style  face="normal" font="default" size="100%">Chromosome Mapping</style></keyword><keyword><style  face="normal" font="default" size="100%">COS Cells</style></keyword><keyword><style  face="normal" font="default" size="100%">DNA-Binding Proteins</style></keyword><keyword><style  face="normal" font="default" size="100%">Exome</style></keyword><keyword><style  face="normal" font="default" size="100%">Genome-Wide Association Study</style></keyword><keyword><style  face="normal" font="default" size="100%">High-Throughput Nucleotide Sequencing</style></keyword><keyword><style  face="normal" font="default" size="100%">Homozygote</style></keyword><keyword><style  face="normal" font="default" size="100%">Humans</style></keyword><keyword><style  face="normal" font="default" size="100%">Molecular Sequence Data</style></keyword><keyword><style  face="normal" font="default" size="100%">Mutant Proteins</style></keyword><keyword><style  face="normal" font="default" size="100%">Pedigree</style></keyword><keyword><style  face="normal" font="default" size="100%">Retina</style></keyword><keyword><style  face="normal" font="default" size="100%">Retinal Cone Photoreceptor Cells</style></keyword><keyword><style  face="normal" font="default" size="100%">Retinal Rod Photoreceptor Cells</style></keyword><keyword><style  face="normal" font="default" size="100%">Retinitis pigmentosa</style></keyword><keyword><style  face="normal" font="default" size="100%">Transcription Factors</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2015</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2015 Jul 15</style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">24</style></volume><pages><style face="normal" font="default" size="100%">4037-48</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;Retinitis pigmentosa (RP) is a group of progressive inherited retinal dystrophies that cause visual impairment as a result of photoreceptor cell death. RP is heterogeneous, both clinically and genetically making difficult to establish precise genotype-phenotype correlations. In a Spanish family with autosomal recessive RP (arRP), homozygosity mapping and whole-exome sequencing led to the identification of a homozygous mutation (c.358_359delGT; p.Ala122Leufs*2) in the ZNF408 gene. A screening performed in 217 additional unrelated families revealed another homozygous mutation (c.1621C&gt;T; p.Arg541Cys) in an isolated RP case. ZNF408 encodes a transcription factor that harbors 10 predicted C2H2-type fingers thought to be implicated in DNA binding. To elucidate the ZNF408 role in the retina and the pathogenesis of these mutations we have performed different functional studies. By immunohistochemical analysis in healthy human retina, we identified that ZNF408 is expressed in both cone and rod photoreceptors, in a specific type of amacrine and ganglion cells, and in retinal blood vessels. ZNF408 revealed a cytoplasmic localization and a nuclear distribution in areas corresponding with the euchromatin fraction. Immunolocalization studies showed a partial mislocalization of the p.Arg541Cys mutant protein retaining part of the WT protein in the cytoplasm. Our study demonstrates that ZNF408, previously associated with Familial Exudative Vitreoretinopathy (FEVR), is a new gene causing arRP with vitreous condensations supporting the evidence that this protein plays additional functions into the human retina. &lt;/p&gt;</style></abstract><issue><style face="normal" font="default" size="100%">14</style></issue><custom1><style face="normal" font="default" size="100%">https://www.ncbi.nlm.nih.gov/pubmed/25882705?dopt=Abstract</style></custom1></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">García-Alonso, Luz</style></author><author><style face="normal" font="default" size="100%">Alonso, Roberto</style></author><author><style face="normal" font="default" size="100%">Vidal, Enrique</style></author><author><style face="normal" font="default" size="100%">Amadoz, Alicia</style></author><author><style face="normal" font="default" size="100%">De Maria, Alejandro</style></author><author><style face="normal" font="default" size="100%">Minguez, Pablo</style></author><author><style face="normal" font="default" size="100%">Medina, Ignacio</style></author><author><style face="normal" font="default" size="100%">Dopazo, Joaquin</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Discovering the hidden sub-network component in a ranked list of genes or proteins derived from genomic experiments.</style></title><secondary-title><style face="normal" font="default" size="100%">Nucleic Acids Res</style></secondary-title><alt-title><style face="normal" font="default" size="100%">Nucleic Acids Res</style></alt-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Bipolar Disorder</style></keyword><keyword><style  face="normal" font="default" size="100%">Fanconi Anemia</style></keyword><keyword><style  face="normal" font="default" size="100%">Gene Regulatory Networks</style></keyword><keyword><style  face="normal" font="default" size="100%">Genes, Neoplasm</style></keyword><keyword><style  face="normal" font="default" size="100%">Genome-Wide Association Study</style></keyword><keyword><style  face="normal" font="default" size="100%">Genomics</style></keyword><keyword><style  face="normal" font="default" size="100%">Humans</style></keyword><keyword><style  face="normal" font="default" size="100%">Protein Interaction Mapping</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2012</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2012 Nov 01</style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">40</style></volume><pages><style face="normal" font="default" size="100%">e158</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;Genomic experiments (e.g. differential gene expression, single-nucleotide polymorphism association) typically produce ranked list of genes. We present a simple but powerful approach which uses protein-protein interaction data to detect sub-networks within such ranked lists of genes or proteins. We performed an exhaustive study of network parameters that allowed us concluding that the average number of components and the average number of nodes per component are the parameters that best discriminate between real and random networks. A novel aspect that increases the efficiency of this strategy in finding sub-networks is that, in addition to direct connections, also connections mediated by intermediate nodes are considered to build up the sub-networks. The possibility of using of such intermediate nodes makes this approach more robust to noise. It also overcomes some limitations intrinsic to experimental designs based on differential expression, in which some nodes are invariant across conditions. The proposed approach can also be used for candidate disease-gene prioritization. Here, we demonstrate the usefulness of the approach by means of several case examples that include a differential expression analysis in Fanconi Anemia, a genome-wide association study of bipolar disorder and a genome-scale study of essentiality in cancer genes. An efficient and easy-to-use web interface (available at http://www.babelomics.org) based on HTML5 technologies is also provided to run the algorithm and represent the network.&lt;/p&gt;</style></abstract><issue><style face="normal" font="default" size="100%">20</style></issue><custom1><style face="normal" font="default" size="100%">https://www.ncbi.nlm.nih.gov/pubmed/22844098?dopt=Abstract</style></custom1></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Fernández, Raquel Ma</style></author><author><style face="normal" font="default" size="100%">Bleda, Marta</style></author><author><style face="normal" font="default" size="100%">Núñez-Torres, Rocío</style></author><author><style face="normal" font="default" size="100%">Medina, Ignacio</style></author><author><style face="normal" font="default" size="100%">Luzón-Toro, Berta</style></author><author><style face="normal" font="default" size="100%">García-Alonso, Luz</style></author><author><style face="normal" font="default" size="100%">Torroglosa, Ana</style></author><author><style face="normal" font="default" size="100%">Marbà, Martina</style></author><author><style face="normal" font="default" size="100%">Enguix-Riego, Ma Valle</style></author><author><style face="normal" font="default" size="100%">Montaner, David</style></author><author><style face="normal" font="default" size="100%">Antiňolo, Guillermo</style></author><author><style face="normal" font="default" size="100%">Dopazo, Joaquin</style></author><author><style face="normal" font="default" size="100%">Borrego, Salud</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Four new loci associations discovered by pathway-based and network analyses of the genome-wide variability profile of Hirschsprung's disease.</style></title><secondary-title><style face="normal" font="default" size="100%">Orphanet J Rare Dis</style></secondary-title><alt-title><style face="normal" font="default" size="100%">Orphanet J Rare Dis</style></alt-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Female</style></keyword><keyword><style  face="normal" font="default" size="100%">Genetic Predisposition to Disease</style></keyword><keyword><style  face="normal" font="default" size="100%">Genome-Wide Association Study</style></keyword><keyword><style  face="normal" font="default" size="100%">Genotype</style></keyword><keyword><style  face="normal" font="default" size="100%">Hirschsprung Disease</style></keyword><keyword><style  face="normal" font="default" size="100%">Humans</style></keyword><keyword><style  face="normal" font="default" size="100%">Male</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2012</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2012 Dec 28</style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">7</style></volume><pages><style face="normal" font="default" size="100%">103</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;Finding gene associations in rare diseases is frequently hampered by the reduced numbers of patients accessible. Conventional gene-based association tests rely on the availability of large cohorts, which constitutes a serious limitation for its application in this scenario. To overcome this problem we have used here a combined strategy in which a pathway-based analysis (PBA) has been initially conducted to prioritize candidate genes in a Spanish cohort of 53 trios of short-segment Hirschsprung's disease. Candidate genes have been further validated in an independent population of 106 trios. The study revealed a strong association of 11 gene ontology (GO) modules related to signal transduction and its regulation, enteric nervous system (ENS) formation and other HSCR-related processes. Among the preselected candidates, a total of 4 loci, RASGEF1A, IQGAP2, DLC1 and CHRNA7, related to signal transduction and migration processes, were found to be significantly associated to HSCR. Network analysis also confirms their involvement in the network of already known disease genes. This approach, based on the study of functionally-related gene sets, requires of lower sample sizes and opens new opportunities for the study of rare diseases.&lt;/p&gt;</style></abstract><custom1><style face="normal" font="default" size="100%">https://www.ncbi.nlm.nih.gov/pubmed/23270508?dopt=Abstract</style></custom1></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Serra, François</style></author><author><style face="normal" font="default" size="100%">Arbiza, Leonardo</style></author><author><style face="normal" font="default" size="100%">Dopazo, Joaquin</style></author><author><style face="normal" font="default" size="100%">Dopazo, Hernán</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Natural selection on functional modules, a genome-wide analysis.</style></title><secondary-title><style face="normal" font="default" size="100%">PLoS Comput Biol</style></secondary-title><alt-title><style face="normal" font="default" size="100%">PLoS Comput Biol</style></alt-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Animals</style></keyword><keyword><style  face="normal" font="default" size="100%">Databases, Genetic</style></keyword><keyword><style  face="normal" font="default" size="100%">Drosophila</style></keyword><keyword><style  face="normal" font="default" size="100%">Genome, Insect</style></keyword><keyword><style  face="normal" font="default" size="100%">Genome-Wide Association Study</style></keyword><keyword><style  face="normal" font="default" size="100%">Genomics</style></keyword><keyword><style  face="normal" font="default" size="100%">Mammals</style></keyword><keyword><style  face="normal" font="default" size="100%">Phylogeny</style></keyword><keyword><style  face="normal" font="default" size="100%">Selection, Genetic</style></keyword><keyword><style  face="normal" font="default" size="100%">Sequence Analysis, DNA</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2011</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2011 Mar</style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">7</style></volume><pages><style face="normal" font="default" size="100%">e1001093</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;Classically, the functional consequences of natural selection over genomes have been analyzed as the compound effects of individual genes. The current paradigm for large-scale analysis of adaptation is based on the observed significant deviations of rates of individual genes from neutral evolutionary expectation. This approach, which assumed independence among genes, has not been able to identify biological functions significantly enriched in positively selected genes in individual species. Alternatively, pooling related species has enhanced the search for signatures of selection. However, grouping signatures does not allow testing for adaptive differences between species. Here we introduce the Gene-Set Selection Analysis (GSSA), a new genome-wide approach to test for evidences of natural selection on functional modules. GSSA is able to detect lineage specific evolutionary rate changes in a notable number of functional modules. For example, in nine mammal and Drosophilae genomes GSSA identifies hundreds of functional modules with significant associations to high and low rates of evolution. Many of the detected functional modules with high evolutionary rates have been previously identified as biological functions under positive selection. Notably, GSSA identifies conserved functional modules with many positively selected genes, which questions whether they are exclusively selected for fitting genomes to environmental changes. Our results agree with previous studies suggesting that adaptation requires positive selection, but not every mutation under positive selection contributes to the adaptive dynamical process of the evolution of species.&lt;/p&gt;</style></abstract><issue><style face="normal" font="default" size="100%">3</style></issue><custom1><style face="normal" font="default" size="100%">https://www.ncbi.nlm.nih.gov/pubmed/21390268?dopt=Abstract</style></custom1></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Bonifaci, Núria</style></author><author><style face="normal" font="default" size="100%">Górski, Bohdan</style></author><author><style face="normal" font="default" size="100%">Masojć, Bartlomiej</style></author><author><style face="normal" font="default" size="100%">Wokołorczyk, Dominika</style></author><author><style face="normal" font="default" size="100%">Jakubowska, Anna</style></author><author><style face="normal" font="default" size="100%">Dębniak, Tadeusz</style></author><author><style face="normal" font="default" size="100%">Berenguer, Antoni</style></author><author><style face="normal" font="default" size="100%">Serra Musach, Jordi</style></author><author><style face="normal" font="default" size="100%">Brunet, Joan</style></author><author><style face="normal" font="default" size="100%">Dopazo, Joaquin</style></author><author><style face="normal" font="default" size="100%">Narod, Steven A</style></author><author><style face="normal" font="default" size="100%">Lubiński, Jan</style></author><author><style face="normal" font="default" size="100%">Lázaro, Conxi</style></author><author><style face="normal" font="default" size="100%">Cybulski, Cezary</style></author><author><style face="normal" font="default" size="100%">Pujana, Miguel Angel</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Exploring the link between germline and somatic genetic alterations in breast carcinogenesis.</style></title><secondary-title><style face="normal" font="default" size="100%">PLoS One</style></secondary-title><alt-title><style face="normal" font="default" size="100%">PLoS One</style></alt-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Adult</style></keyword><keyword><style  face="normal" font="default" size="100%">Bone Morphogenetic Protein Receptors, Type I</style></keyword><keyword><style  face="normal" font="default" size="100%">Breast</style></keyword><keyword><style  face="normal" font="default" size="100%">Breast Neoplasms</style></keyword><keyword><style  face="normal" font="default" size="100%">Calcium-Calmodulin-Dependent Protein Kinases</style></keyword><keyword><style  face="normal" font="default" size="100%">Case-Control Studies</style></keyword><keyword><style  face="normal" font="default" size="100%">Cyclin-Dependent Kinases</style></keyword><keyword><style  face="normal" font="default" size="100%">Disease Progression</style></keyword><keyword><style  face="normal" font="default" size="100%">Estrogen Receptor alpha</style></keyword><keyword><style  face="normal" font="default" size="100%">Female</style></keyword><keyword><style  face="normal" font="default" size="100%">Gene Frequency</style></keyword><keyword><style  face="normal" font="default" size="100%">Genetic Predisposition to Disease</style></keyword><keyword><style  face="normal" font="default" size="100%">Genome-Wide Association Study</style></keyword><keyword><style  face="normal" font="default" size="100%">Genotype</style></keyword><keyword><style  face="normal" font="default" size="100%">Germ-Line Mutation</style></keyword><keyword><style  face="normal" font="default" size="100%">Humans</style></keyword><keyword><style  face="normal" font="default" size="100%">Odds Ratio</style></keyword><keyword><style  face="normal" font="default" size="100%">Poland</style></keyword><keyword><style  face="normal" font="default" size="100%">Polymorphism, Single Nucleotide</style></keyword><keyword><style  face="normal" font="default" size="100%">Protein Serine-Threonine Kinases</style></keyword><keyword><style  face="normal" font="default" size="100%">Protein-Tyrosine Kinases</style></keyword><keyword><style  face="normal" font="default" size="100%">Receptor Protein-Tyrosine Kinases</style></keyword><keyword><style  face="normal" font="default" size="100%">Receptor, EphA3</style></keyword><keyword><style  face="normal" font="default" size="100%">Receptor, EphA7</style></keyword><keyword><style  face="normal" font="default" size="100%">Receptor, EphB1</style></keyword><keyword><style  face="normal" font="default" size="100%">Risk Factors</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2010</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2010 Nov 22</style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">5</style></volume><pages><style face="normal" font="default" size="100%">e14078</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;Recent genome-wide association studies (GWASs) have identified candidate genes contributing to cancer risk through low-penetrance mutations. Many of these genes were unexpected and, intriguingly, included well-known players in carcinogenesis at the somatic level. To assess the hypothesis of a germline-somatic link in carcinogenesis, we evaluated the distribution of somatic gene labels within the ordered results of a breast cancer risk GWAS. This analysis suggested frequent influence on risk of genetic variation in loci encoding for &quot;driver kinases&quot; (i.e., kinases encoded by genes that showed higher somatic mutation rates than expected by chance and, therefore, whose deregulation may contribute to cancer development and/or progression). Assessment of these predictions using a population-based case-control study in Poland replicated the association for rs3732568 in EPHB1 (odds ratio (OR) = 0.79; 95% confidence interval (CI): 0.63-0.98; P(trend) = 0.031). Analyses by early age at diagnosis and by estrogen receptor α (ERα) tumor status indicated potential associations for rs6852678 in CDKL2 (OR = 0.32, 95% CI: 0.10-1.00; P(recessive) = 0.044) and rs10878640 in DYRK2 (OR = 2.39, 95% CI: 1.32-4.30; P(dominant) = 0.003), and for rs12765929, rs9836340, rs4707795 in BMPR1A, EPHA3 and EPHA7, respectively (ERα tumor status P(interaction)&lt;0.05). The identification of three novel candidates as EPH receptor genes might indicate a link between perturbed compartmentalization of early neoplastic lesions and breast cancer risk and progression. Together, these data may lay the foundations for replication in additional populations and could potentially increase our knowledge of the underlying molecular mechanisms of breast carcinogenesis.&lt;/p&gt;</style></abstract><issue><style face="normal" font="default" size="100%">11</style></issue><custom1><style face="normal" font="default" size="100%">https://www.ncbi.nlm.nih.gov/pubmed/21124932?dopt=Abstract</style></custom1></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Medina, Ignacio</style></author><author><style face="normal" font="default" size="100%">Montaner, David</style></author><author><style face="normal" font="default" size="100%">Bonifaci, Núria</style></author><author><style face="normal" font="default" size="100%">Pujana, Miguel Angel</style></author><author><style face="normal" font="default" size="100%">Carbonell, José</style></author><author><style face="normal" font="default" size="100%">Tárraga, Joaquín</style></author><author><style face="normal" font="default" size="100%">Al-Shahrour, Fátima</style></author><author><style face="normal" font="default" size="100%">Dopazo, Joaquin</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Gene set-based analysis of polymorphisms: finding pathways or biological processes associated to traits in genome-wide association studies.</style></title><secondary-title><style face="normal" font="default" size="100%">Nucleic Acids Res</style></secondary-title><alt-title><style face="normal" font="default" size="100%">Nucleic Acids Res</style></alt-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Biological Phenomena</style></keyword><keyword><style  face="normal" font="default" size="100%">Breast Neoplasms</style></keyword><keyword><style  face="normal" font="default" size="100%">Female</style></keyword><keyword><style  face="normal" font="default" size="100%">Genes</style></keyword><keyword><style  face="normal" font="default" size="100%">Genetic Variation</style></keyword><keyword><style  face="normal" font="default" size="100%">Genome-Wide Association Study</style></keyword><keyword><style  face="normal" font="default" size="100%">Humans</style></keyword><keyword><style  face="normal" font="default" size="100%">Polymorphism, Single Nucleotide</style></keyword><keyword><style  face="normal" font="default" size="100%">Software</style></keyword><keyword><style  face="normal" font="default" size="100%">User-Computer Interface</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2009</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2009 Jul</style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">37</style></volume><pages><style face="normal" font="default" size="100%">W340-4</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;Genome-wide association studies have become a popular strategy to find associations of genes to traits of interest. Despite the high-resolution available today to carry out genotyping studies, the success of its application in real studies has been limited by the testing strategy used. As an alternative to brute force solutions involving the use of very large cohorts, we propose the use of the Gene Set Analysis (GSA), a different analysis strategy based on testing the association of modules of functionally related genes. We show here how the Gene Set-based Analysis of Polymorphisms (GeSBAP), which is a simple implementation of the GSA strategy for the analysis of genome-wide association studies, provides a significant increase in the power testing for this type of studies. GeSBAP is freely available at http://bioinfo.cipf.es/gesbap/.&lt;/p&gt;</style></abstract><issue><style face="normal" font="default" size="100%">Web Server issue</style></issue><custom1><style face="normal" font="default" size="100%">https://www.ncbi.nlm.nih.gov/pubmed/19502494?dopt=Abstract</style></custom1></record></records></xml>