RESUMO
The ability to identify regulatory interactions that mediate gene expression changes through distal elements, such as risk loci, is transforming our understanding of how genomes are spatially organized and regulated. Capture Hi-C (CHi-C) is a powerful tool to delineate such regulatory interactions. However, primary analysis and downstream interpretation of CHi-C profiles remains challenging and relies on disparate tools with ad-hoc input/output formats and specific assumptions for statistical modeling. Here we present a data processing and interaction calling toolkit (CHiCANE), specialized for the analysis and meaningful interpretation of CHi-C assays. In this protocol, we demonstrate applications of CHiCANE to region capture Hi-C (rCHi-C) and promoter capture Hi-C (pCHi-C) libraries, followed by quality assessment of interaction peaks, as well as downstream analysis specific to rCHi-C and pCHi-C to aid functional interpretation. For a typical rCHi-C/pCHi-C dataset this protocol takes up to 3 d for users with a moderate understanding of R programming and statistical concepts, although this is dependent on dataset size and compute power available. CHiCANE is freely available at https://cran.r-project.org/web/packages/chicane .
Assuntos
Genômica/métodos , Sequências Reguladoras de Ácido Nucleico/genética , Elementos Facilitadores Genéticos/genética , Epigenoma , Genoma , Código das Histonas , Modelos Genéticos , Anotação de Sequência Molecular , Mutação/genética , Polimorfismo de Nucleotídeo Único/genética , Regiões Promotoras Genéticas , Locos de Características Quantitativas/genética , RNA Mensageiro/genética , RNA Mensageiro/metabolismo , Estatística como AssuntoRESUMO
Genome-wide association studies have identified more than 70 common variants that are associated with breast cancer risk. Most of these variants map to non-protein-coding regions and several map to gene deserts, regions of several hundred kilobases lacking protein-coding genes. We hypothesized that gene deserts harbor long-range regulatory elements that can physically interact with target genes to influence their expression. To test this, we developed Capture Hi-C (CHi-C), which, by incorporating a sequence capture step into a Hi-C protocol, allows high-resolution analysis of targeted regions of the genome. We used CHi-C to investigate long-range interactions at three breast cancer gene deserts mapping to 2q35, 8q24.21, and 9q31.2. We identified interaction peaks between putative regulatory elements ("bait fragments") within the captured regions and "targets" that included both protein-coding genes and long noncoding (lnc) RNAs over distances of 6.6 kb to 2.6 Mb. Target protein-coding genes were IGFBP5, KLF4, NSMCE2, and MYC; and target lncRNAs included DIRC3, PVT1, and CCDC26. For one gene desert, we were able to define two SNPs (rs12613955 and rs4442975) that were highly correlated with the published risk variant and that mapped within the bait end of an interaction peak. In vivo ChIP-qPCR data show that one of these, rs4442975, affects the binding of FOXA1 and implicate this SNP as a putative functional variant.