@article {19094230, title = {Biological processes, properties and molecular wiring diagrams of candidate low-penetrance breast cancer susceptibility genes}, journal = {BMC Med Genomics}, volume = {1}, year = {2008}, note = {

Bonifaci, Nuria Berenguer, Antoni Diez, Javier Reina, Oscar Medina, Ignacio Dopazo, Joaquin Moreno, Victor Pujana, Miguel Angel England BMC medical genomics BMC Med Genomics. 2008 Dec 18;1:62.

}, pages = {62}, abstract = {

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 \"omic\" 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.

}, keywords = {gene set, GWAS, SNP}, url = {http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve\&db=PubMed\&dopt=Citation\&list_uids=19094230}, author = {Bonifaci, N. and Berenguer, A. and Diez, J. and Reina, O. and Medina, Ignacio and Dopazo, J. and Moreno, V. and Pujana, M. A.} } @article {17597009, title = {CLEAR-test: combining inference for differential expression and variability in microarray data analysis}, journal = {J Biomed Inform}, volume = {41}, number = {1}, year = {2008}, note = {

Valls, Joan Grau, Monica Sole, Xavier Hernandez, Pilar Montaner, David Dopazo, Joaquin Peinado, Miguel A Capella, Gabriel Moreno, Victor Pujana, Miguel Angel Comparative Study Research Support, Non-U.S. Gov{\textquoteright}t United States Journal of biomedical informatics J Biomed Inform. 2008 Feb;41(1):33-45. Epub 2007 May 17.

}, pages = {33-45}, abstract = {

A common goal of microarray experiments is to detect genes that are differentially expressed under distinct experimental conditions. Several statistical tests have been proposed to determine whether the observed changes in gene expression are significant. The t-test assigns a score to each gene on the basis of changes in its expression relative to its estimated variability, in such a way that genes with a higher score (in absolute values) are more likely to be significant. Most variants of the t-test use the complete set of genes to influence the variance estimate for each single gene. However, no inference is made in terms of the variability itself. Here, we highlight the problem of low observed variances in the t-test, when genes with relatively small changes are declared differentially expressed. Alternatively, the z-test could be used although, unlike the t-test, it can declare differentially expressed genes with high observed variances. To overcome this, we propose to combine the z-test, which focuses on large changes, with a chi(2) test to evaluate variability. We call this procedure CLEAR-test and we provide a combined p-value that offers a compromise between both aspects. Analysis of three publicly available microarray datasets reveals the greater performance of the CLEAR-test relative to the t-test and alternative methods. Finally, empirical and simulated data analyses demonstrate the greater reproducibility and statistical power of the CLEAR-test and z-test with respect to current alternative methods. In addition, the CLEAR-test improves the z-test by capturing reproducible genes with high variability.

}, keywords = {*Algorithms Artificial Intelligence *Data Interpretation, Statistical Gene Expression Profiling/*methods Gene Expression Regulation/*physiology Oligonucleotide Array Sequence Analysis/*methods Proteome/*metabolism Signal Transduction/*physiology}, url = {http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve\&db=PubMed\&dopt=Citation\&list_uids=17597009}, author = {Valls, J. and Grau, M. and Sole, X. and Hernandez, P. and Montaner, D. and Dopazo, J. and Peinado, M. A. and Capella, G. and Moreno, V. and Pujana, M. A.} }