Fine-mapping of the HNF1B multicancer locus identifies candidate variants that mediate endometrial cancer risk

Abstract

Common variants in the hepatocyte nuclear factor 1 homeobox B (HNF1B) gene are associated with the risk of Type II diabetes and multiple cancers. Evidence to date indicates that cancer risk may be mediated via genetic or epigenetic effects on HNF1B gene expression. We previously found single-nucleotide polymorphisms (SNPs) at the HNF1B locus to be associated with endometrial cancer, and now report extensive fine-mapping and in silico and laboratory analyses of this locus. Analysis of 1184 genotyped and imputed SNPs in 6608 Caucasian cases and 37 925 controls, and 895 Asian cases and 1968 controls, revealed the best signal of association for SNP rs11263763 (P = 8.4 × 10−14, odds ratio = 0.86, 95% confidence interval = 0.82–0.89), located within HNF1B intron 1. Haplotype analysis and conditional analyses provide no evidence of further independent endometrial cancer risk variants at this locus. SNP rs11263763 genotype was associated with HNF1B mRNA expression but not with HNF1B methylation in endometrial tumor samples from The Cancer Genome Atlas. Genetic analyses prioritized rs11263763 and four other SNPs in high-to-moderate linkage disequilibrium as the most likely causal SNPs. Three of these SNPs map to the extended HNF1B promoter based on chromatin marks extending from the minimal promoter region. Reporter assays demonstrated that this extended region reduces activity in combination with the minimal HNF1B promoter, and that the minor alleles of rs11263763 or rs8064454 are associated with decreased HNF1B promoter activity. Our findings provide evidence for a single signal associated with endometrial cancer risk at the HNF1B locus, and that risk is likely mediated via altered HNF1B gene expression.

Genetic Risk Score Mendelian Randomization Shows that Obesity Measured as Body Mass Index, but not Waist:Hip Ratio, Is Causal for Endometrial Cancer

“…We analyzed four datasets from separate studies contributing to the Endometrial Cancer Association Consortium (ECAC), as detailed previously (18, 19), and as summarized in Supplementary Table 1). The first three comprised GWAS datasets genotyped using Illumina genotyping arrays, from Australia (“ANECS/QIMR/HCS”: 606 cases, 3,083 controls), and the UK (“SEARCH/WTCCC”, 681 cases, 5,190 controls (18, 20)); “NSECG/CORGI”, 919 cases, 894 controls(19, 21)).…”

“…The first three comprised GWAS datasets genotyped using Illumina genotyping arrays, from Australia (“ANECS/QIMR/HCS”: 606 cases, 3,083 controls), and the UK (“SEARCH/WTCCC”, 681 cases, 5,190 controls (18, 20)); “NSECG/CORGI”, 919 cases, 894 controls(19, 21)). The fourth dataset (“iCOGS”) was genotyped using the ‘iCOGs’ custom Illumina Infinium iSelect genotyping array comprising 211,155 SNPs chosen for follow-up and fine-mapping of hormonal cancer GWAS hits, and included 4,402 cases recruited from 11 separate studies from 7 countries, and 28,758 controls from the same countries.…”

Comprehensive genetic assessment of the ESR1 locus identifies a risk region for endometrial cancer

“…All studies have the relevant IRB approval in each country in accordance with the principles embodied in the Declaration of Helsinki, and informed consent was obtained from all participants. Details of iCOGS genotyping of endometrial cancer cases and control samples can be found in Supplementary Table 1 and in Painter et al (Painter, et al 2014). All cases and controls selected for analysis were of European ancestry, as defined by Identity-By-State (IBS) scores between study individuals and individuals in HapMap (http://hapmap.ncbi.nlm.nih.gov/).…”

“…Additionally, three Caucasian GWAS datasets (ANECS, SEARCH and NSECG) were as previously described, totalling 2,206 cases and 9,167 controls after quality control. (Painter et al 2014; Spurdle, et al 2011). Overall, there were 6,607 endometrial cancer cases and 37,925 controls included in the meta-analysis of the four datasets (ANECS, SEARCH and NSECG GWAS datasets and the iCOGS dataset).…”

“…The GWAS datasets were each analysed as a single stratum, with adjustment for the first two (ANECS and NSECG) and three (SEARCH) principal components. For the iCOGS dataset, analyses were performed adjusting for strata and for the first ten principal components, as previously described (Painter et al 2014). The numbers of principal components included in the analyses were selected to adequately account for population stratification in each of the datasets.…”