Suchergebnisse

To access publisher's full text version of this article click on the hyperlink below ; Genome-wide association studies have identified breast cancer risk variants in over 150 genomic regions, but the mechanisms underlying risk remain largely unknown. These regions were explored by combining association analysis with in silico genomic feature annotations. We defined 205 independent risk-associated signals with the set of credible causal variants in each one. In parallel, we used a Bayesian approach (PAINTOR) that combines genetic association, linkage disequilibrium and enriched genomic features to determine variants with high posterior probabilities of being causal. Potentially causal variants were significantly over-represented in active gene regulatory regions and transcription factor binding sites. We applied our INQUSIT pipeline for prioritizing genes as targets of those potentially causal variants, using gene expression (expression quantitative trait loci), chromatin interaction and functional annotations. Known cancer drivers, transcription factors and genes in the developmental, apoptosis, immune system and DNA integrity checkpoint gene ontology pathways were over-represented among the highest-confidence target genes. ; European Union's Horizon 2020 Research and Innovation Programme under Marie Sklodowska-Curie grant PERSPECTIVE project (Government of Canada through Genome Canada) Canadian Institutes of Health Research (CIHR) PERSPECTIVE project ('Ministere de l'Economie de la Science et de l'Innovation du Quebec' (Genome Quebec) PERSPECTIVE project (Quebec Breast Cancer Foundation) NCI Genetic Associations and Mechanisms in Oncology (GAME-ON) initiative United States Department of Health & Human Services National Institutes of Health (NIH) - USA Cancer Research UK European Community (EC) European Union (EU) Canadian Institutes of Health Research (CIHR) Ministry of Economic Development, Innovation and Export Trade of Quebec United States Department of Health & Human Services National Institutes of ...