RESUMEN
Analysis of a subset of case-control sporadic breast cancer data, [from the National Cancer Institute's Cancer Genetic Markers of Susceptibility (CGEMS) initiative], focusing on 18 breast cancer-related genes with 304 SNPs, indicates that there are many interesting interactions that form two- and three-way networks in which BRCA1 plays a dominant and central role. The apparent interactions of BRCA1 with many other genes suggests the conjecture that BRCA1 serves as a protective gene and that some mutations in it or in related genes may prevent it from carrying out this protective function even if the patients are not carriers of known cancer-predisposing BRCA1 mutations. The method of analysis features the evaluation of the effect of a gene by averaging the effects of the SNPs covered by that gene. Marginal methods that test one gene at a time fail to show any effect. That may be related to the fact that each of these 18 genes adds very little to the risk of cancer. Analysis that relates the ratio of interactions to the maximum of the first-order effects discovers significant gene pairs and triplets.
Asunto(s)
Neoplasias de la Mama/genética , Redes Reguladoras de Genes/fisiología , Genes BRCA1/fisiología , Proteína BRCA1/genética , Estudios de Casos y Controles , Biología Computacional , Receptor alfa de Estrógeno/genética , Femenino , Predisposición Genética a la Enfermedad , Humanos , Polimorfismo de Nucleótido Simple , Proteínas Proto-Oncogénicas/genética , Proteínas Proto-Oncogénicas p21(ras) , Proteínas Supresoras de Tumor/genética , Ubiquitina-Proteína Ligasas/genética , Proteínas ras/genéticaRESUMEN
The papers in presentation group 2 of Genetic Analysis Workshop 15 (GAW15) conducted association analyses of rheumatoid arthritis data. The analyses were carried out primarily in the data provided by the North American Rheumatoid Arthritis Consortium (NARAC). One group conducted analyses in the data provided by the Canadian Rheumatoid Arthritis Genetics Study (CRAGS). Analysis strategies included genome-wide scans, the examination of candidate genes, and investigations of a region of interest on chromosome 18q21. Most authors employed relatively new methods, proposed extensions of existing methods, or introduced completely novel methods for aspects of association analysis. There were several common observations; a group of papers using a variety of methods found stronger association, on chromosomes 6 and 18 and in candidate gene PTPN22 among women with early onset. Generally, models that considered haplotypes or multiple markers showed stronger evidence for association than did single marker analyses.
Asunto(s)
Artritis Reumatoide/genética , Algoritmos , Cromosomas Humanos Par 18 , Cromosomas Humanos Par 6 , Genoma Humano , Haplotipos , Humanos , Fenotipo , Proteína Tirosina Fosfatasa no Receptora Tipo 22/genéticaRESUMEN
BACKGROUND: The mRNA expression levels of genes have been shown to have discriminating power for the classification of breast cancer. Studying the heritability of gene expression levels on breast cancer related transcripts can lead to the identification of shared common regulators and inter-regulation patterns, which would be important for dissecting the etiology of breast cancer. RESULTS: We applied multilocus association genome-wide scans to 18 breast cancer related transcripts and combined the results with traditional linkage scans. Regulatory hotspots for these transcripts were identified and some inter-regulation patterns were observed. We also derived evidence on interacting genetic regulatory loci shared by a number of these transcripts. CONCLUSION: In this paper, by restricting to a set of related genes, we were able to employ a more detailed multilocus approach that evaluates both marginal and interaction association signals at each single-nucleotide polymorphism. Interesting inter-regulation patterns and significant overlaps of genetic regulators between transcripts were observed. Interaction association results returned more expression quantitative trait locus hotspots that are significant.
RESUMEN
BACKGROUND: Rheumatoid arthritis (RA, MIM 180300) is a common and complex inflammatory disorder. The North American Rheumatoid Arthritis Consortium (NARAC) data, as part of the Genetic Analysis Workshop 15 data, consists of both genome scan and candidate gene studies on RA patients. RESULTS: We applied the backward genotype-trait association (BGTA) algorithm to capture marginal and gene x gene interaction effects of multiple susceptibility loci on RA disease status. A two-stage screening approach was used for the genome scan, whereas a comprehensive study of all possible subsets was conducted for the candidate genes. For the genome scan, we constructed an association network among 39 genetic loci that demonstrated strong signals, 19 of which have been reported in the RA literature. For the candidate genes, we found strong signals for PTPN22 and SUMO4. Based on significant association evidence, we built an association network among the loci of PTPN22, PADI4, DLG5, SLC22A4, SUMO4, and CARD15. To control for false positives, we used permutation tests to constrain the family-wise type I error rate to 1%. CONCLUSION: Using the BGTA algorithm, we identified genetic loci and candidate genes that were associated with RA susceptibility and association networks among them. For the first time, we report possible interactions between single-nucleotide polymorphisms/genes, which may be useful for biological interpretation.