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.

• MeSH
• Bayesova věta MeSH
• celogenomová asociační studie * MeSH
• genetická predispozice k nemoci * MeSH
• jednonukleotidový polymorfismus * MeSH
• lidé MeSH
• lokus kvantitativního znaku * MeSH
• mapování chromozomů metody   MeSH
• nádorové biomarkery genetika   MeSH
• nádory prsu genetika   MeSH
• regulační oblasti nukleových kyselin MeSH
• rizikové faktory MeSH
• vazebná nerovnováha MeSH
• Check Tag
• lidé MeSH
• ženské pohlaví MeSH
• Publikační typ
• časopisecké články MeSH
• práce podpořená grantem MeSH
• Research Support, N.I.H., Extramural MeSH