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- Colocalised genetic associations reveal alternative splicing variants as candidate causal links for breast cancer risk in 10 LociPublication . Duarte, André; Carrasqueiro, Beatriz; Vieira de Sousa, Cármen Sofia; Gonçalves de Gouveia Maia Xavier, Joana; Maia, Ana-TeresaSimple Summary Hundreds of common genetic variants have been linked to breast cancer, but their exact mechanisms of action remain unclear. Understanding these mechanisms could lead to better prevention strategies and improved survival rates. Our study focused on how these variants influence splicing-a process by which a gene's coding elements are rearranged to produce different proteins. By analysing data from healthy breast tissue, we identified 43 variants within twelve genes associated with both alternative splicing and breast cancer risk. We then used advanced computational tools and existing experimental data to explore the biological significance of these findings.Abstract Genome-wide association studies (GWASs) have revealed numerous loci associated with breast cancer risk, yet the precise causal variants, their impact on molecular mechanisms, and the affected genes often remain elusive. We hypothesised that specific variants exert their influence by affecting cis-regulatory alternative splice elements. An analysis of splicing quantitative trait loci (sQTL) in healthy breast tissue from female individuals identified multiple variants linked to alterations in splicing ratios. Through colocalisation analysis, we pinpointed 43 variants within twelve genes that serve as candidate causal links between sQTL and GWAS findings. In silico splice analysis highlighted a potential mechanism for three genes-FDPS, SGCE, and MRPL11-where variants in proximity to or on the splice site modulate usage, resulting in alternative splice transcripts. Further in vitro/vivo studies are imperative to fully understand how these identified changes contribute to breast oncogenesis. Moreover, investigating their potential as biomarkers for breast cancer risk could enhance screening strategies and early detection methods 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.