Browsing by Author "Rosli, Nordiana"
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- Functional characterisation of putative Cis-Regulatory Risk Loci for breast cancerPublication . Rosli, Nordiana; Maia, Ana Teresa; Xavier, JoanaAt present, 94 breast cancer susceptibility loci have been discovered from genome-wide association studies (GWAS). The next step is to identify the causal risk variant, the target gene and to understand the underlying disease mechanism. Studies revealed that most of the variants discovered by GWAS are cis-acting regulatory. Cis-acting regulatory variants can be identified most efficiently by differential allelic expression (DAE) analysis. A DAE genome-wide mapping was done in normal breast tissue, which was cross-compared with GWAS breast cancer data. 19 loci associated with risk and with evidence of cis-regulation were identified, including the 5q14.2 locus that has one SNP associated with risk - rs7707921, and five SNPs displaying DAE across three genes: ATG10, RPS23 and ATP6AP1L. The aim of this thesis is to set out to map the regulatory variants responsible for the DAE signals in the 5q14.2 locus and to determine which one(s) is (are) associated with risk for breast cancer. We performed in silico analysis using data obtained through publically accessible databases, to identify candidate regulatory SNPs (rSNPs) that could be responsible for the DAE and determine if they may be associated with risk to breast cancer. Experimental in vitro analysis by EMSA and analysis of available ChIP-seq data was also conducted in order to investigate possible interactions between candidate rSNPs and transcription factors (TFs). In this study, three SNPs rs226198, rs6880209 and rs17247678 were identified as potential cis-acting regulators of ATG10, RPS23 and ATP6AP1L. Henceforth, we propose a risk model based on our findings: Binding of c-Myc and POL2 to the common allele of rs226198 and rs6880209 lead to over expression of RPS23 and under expression of ATG10, respectively, whereas, binding of STAT3 and c-FOS to rs17247678 lead to under expression of ATP6AP1L, increasing the risk for breast cancer.
- Identification of candidate causal variants and target genes at 41 breast cancer risk loci through differential allelic expression analysisPublication . Gonçalves de Gouveia Maia Xavier, Joana; Magno, Ramiro; Russell, Roslin; Almeida, Bernardo P. de; Jacinta-Fernandes, Ana; Duarte, André; Besouro-Duarte, André; Dunning, Mark; Samarajiwa, Shamith; O’Reilly, Martin; MARQUES MAIA DE ALMEIDA, JOSE ANTONIO; Rocha, Cátia L.; Rosli, Nordiana; Ponder, Bruce A. J.; Maia, Ana-TeresaUnderstanding breast cancer genetic risk relies on identifying causal variants and candidate target genes in risk loci identified by genome-wide association studies (GWAS), which remains challenging. Since most loci fall in active gene regulatory regions, we developed a novel approach facilitated by pinpointing the variants with greater regulatory potential in the disease’s tissue of origin. Through genome-wide differential allelic expression (DAE) analysis, using microarray data from 64 normal breast tissue samples, we mapped the variants associated with DAE (daeQTLs). Then, we intersected these with GWAS data to reveal candidate risk regulatory variants and analysed their cis-acting regulatory potential. Finally, we validated our approach by extensive functional analysis of the 5q14.1 breast cancer risk locus. We observed widespread gene expression regulation by cis-acting variants in breast tissue, with 65% of coding and noncoding expressed genes displaying DAE (daeGenes). We identified over 54 K daeQTLs for 6761 (26%) daeGenes, including 385 daeGenes harbouring variants previously associated with BC risk. We found 1431 daeQTLs mapped to 93 different loci in strong linkage disequilibrium with risk-associated variants (risk-daeQTLs), suggesting a link between risk-causing variants and cis-regulation. There were 122 risk-daeQTL with stronger cis-acting potential in active regulatory regions with protein binding evidence. These variants mapped to 41 risk loci, of which 29 had no previous report of target genes and were candidates for regulating the expression levels of 65 genes. As validation, we identified and functionally characterised five candidate causal variants at the 5q14.1 risk locus targeting the ATG10 and ATP6AP1L genes, likely acting via modulation of alternative transcription and transcription factor binding. Our study demonstrates the power of DAE analysis and daeQTL mapping to identify causal regulatory variants and target genes at breast cancer risk loci, including those with complex regulatory landscapes. It additionally provides a genome-wide resource of variants associated with DAE for future functional studies.