Breast cancer risk variants at 6q25 display different phenotype associations and regulate ESR1, RMND1 and CCDC170

Abstract

We analyzed 3,872 common genetic variants across the ESR1 locus (encoding estrogen receptor α) in 118,816 subjects from three international consortia. We found evidence for at least five independent causal variants, each associated with different phenotype sets, including estrogen receptor (ER+ or ER−) and human ERBB2 (HER2+ or HER2−) tumor subtypes, mammographic density and tumor grade. The best candidate causal variants for ER− tumors lie in four separate enhancer elements, and their risk alleles reduce expression of ESR1, RMND1 and CCDC170, whereas the risk alleles of the strongest candidates for the remaining independent causal variant disrupt a silencer element and putatively increase ESR1 and RMND1 expression.

A dementia-associated risk variant near TMEM106B alters chromatin architecture and gene expression

“…Furthermore, while GWAS causal variants have previously been implicated in specific allele-15! specific long-range chromatin interactions 47,[81][82][83][84] , here we report a variant that may directly 16! affect higher-order chromatin architecture.…”

“…disease risk may confer their effects by altering the expression levels of nearby genes 7,[42][43][44][45][46][47][48][49] . In …”

Modifiers of breast and ovarian cancer risks for BRCA1 and BRCA2 mutation carriers

“…Fine-mapping studies, in which a much denser selection of SNPs in susceptibility loci is genotyped and analysed using multivariable (conditional) 23:10 models, can highlight potentially causal variants, although extensive laboratory work is often required to demonstrate their function. These experiments are not feasible for all loci, and those that are, are often laborious and sometimes equivocal in identifying a single causal variant or even a single target gene (Dunning et al 2016, Lawrenson et al 2016). These fine-mapping studies often identify additional independent associations.…”

“…Genotypes at many millions of common variants across the genome can be genotyped or imputed with high accuracy using largescale genotyping arrays, using reference panels from the 1000 Genomes Project (Auton et al 2015). This approach has been applied with great success in cancer epidemiology in the general population, with GWAS having identified more than 100 common susceptibility variants for breast cancer , Hunter et al 2007, Stacey et al 2007, Ahmed et al 2009, Thomas et al 2009, Antoniou et al 2010b, Turnbull et al 2010, Cai et al 2011, Fletcher et al 2011, Ghoussaini et al 2012, Hein et al 2012, Long et al 2012, Siddiq et al 2012, Bojesen et al 2013, French et al 2013, Garcia-Closas et al 2013, Gaudet et al 2013, Meyer et al 2013, Cai et al 2014, Milne et al 2014a, Orr et al 2015, Couch et al 2016, Dunning et al 2016, Lawrenson et al 2016, Zheng et al 2009) and 22 for ovarian cancer (Song et al 2009, Bolton et al 2010, Goode et al 2010, Bojesen et al 2013, Permuth-Wey et al 2013, Pharoah et al 2013, Kuchenbaecker et al 2015.…”

“…In this context, within CIMBA, four approaches have been applied to identify loci associated with breast and ovarian cancer for mutation carriers: (i) GWAS for breast and ovarian cancer specifically performed in samples of BRCA1 and BRCA2 mutation carriers (Antoniou et al 2010b, Gaudet et al 2013; (ii) association 23:10 studies to assess common breast and ovarian cancer susceptibility alleles identified in the general population as potential modifiers of risk for mutation carriers (Antoniou et al 2008b, Kuchenbaecker et al 2014; (iii) meta-analyses of GWAS performed in BRCA1 and BRCA2 mutation carriers with GWAS of related phenotypes in the general population (for example, combining studies of breast cancer risk for BRCA1 mutation carriers with those of oestrogen receptor-negative breast cancer in general population or studies of ovarian cancer risk for BRCA1 and BRCA2 mutation carriers with GWAS of serous ovarian cancer in the general population) (Kuchenbaecker et al 2015, Couch et al 2016; and iv) finescale mapping of risk-modifying loci identified through GWAS approaches to fully characterise the associations with all genetic variants at these loci (Bojesen et al 2013, Dunning et al 2016, Lawrenson et al 2016.…”