Chen, Hongjie

Abstract Studies have identified genetic and epidemiologic factors associated with mammographic density (MD) phenotypes. However, MD-associated genetic variants only account for a small proportion of the total estimated heritability. Interrogating interactions between genetic and epidemiologic factors could potentially identify additional MD-associated loci, expand our understanding of the genetic basis of MD phenotypes, and clarify how epidemiologic factors modulate relationships between genetic variants and MD. We conducted six separate genome-wide, gene–environment (GxE) interaction analyses, applying 2 degrees of freedom (df) and 1df interaction tests, for each of three MD phenotypes (percent density, dense area (DA), and nondense area (NDA)). The six epidemiologic factors considered were height, ever parous, parity, ever menopausal hormone therapy, ever breastfeeding, and months of breastfeeding. We included European ancestry participants from multiple studies within the Markers of Density consortium and the Breast Cancer Association Consortium (n = 4895-16 218 depending on specific analyses). We identified 11 loci with genome-wide significant (P < 5 × 10−8) interaction tests including two novel common genetic signals interacting with parity (8p21.2) and ever breastfeeding (19p13.2) for NDA. Our results suggest that epidemiologic risk factors might influence relationships between common genetic variants and MD phenotypes at particular genomic loci.

Abstract Background The shared inherited genetic contribution to risk of different cancers is not fully known. In this study, we leverage results from 12 cancer genome-wide association studies (GWAS) to quantify pairwise genome-wide genetic correlations across cancers and identify novel cancer susceptibility loci. Methods We collected GWAS summary statistics for 12 solid cancers based on 376 759 participants with cancer and 532 864 participants without cancer of European ancestry. The included cancer types were breast, colorectal, endometrial, esophageal, glioma, head and neck, lung, melanoma, ovarian, pancreatic, prostate, and renal cancers. We conducted cross-cancer GWAS and transcriptome-wide association studies to discover novel cancer susceptibility loci. Finally, we assessed the extent of variant-specific pleiotropy among cancers at known and newly identified cancer susceptibility loci. Results We observed widespread but modest genome-wide genetic correlations across cancers. In cross-cancer GWAS and transcriptome-wide association studies, we identified 15 novel cancer susceptibility loci. Additionally, we identified multiple variants at 77 distinct loci with strong evidence of being associated with at least 2 cancer types by testing for pleiotropy at known cancer susceptibility loci. Conclusions Overall, these results suggest that some genetic risk variants are shared among cancers, though much of cancer heritability is cancer-specific and thus tissue-specific. The increase in statistical power associated with larger sample sizes in cross-disease analysis allows for the identification of novel susceptibility regions. Future studies incorporating data on multiple cancer types are likely to identify additional regions associated with the risk of multiple cancer types.

Genome-wide association studies (GWAS) have identified more than 200 susceptibility loci for breast cancer, but these variants explain less than a fifth of the disease risk. Although gene–environment interactions have been proposed to account for some of the remaining heritability, few studies have empirically assessed this. We obtained genotype and risk factor data from 46,060 cases and 47,929 controls of European ancestry from population-based studies within the Breast Cancer Association Consortium (BCAC). We built gene expression prediction models for 4,864 genes with a significant (P < 0.01) heritable component using the transcriptome and genotype data from the Genotype-Tissue Expression (GTEx) project. We leveraged predicted gene expression information to investigate the interactions between gene-centric genetic variation and 14 established risk factors in association with breast cancer risk, using a mixed-effects score test. After adjusting for number of tests using Bonferroni correction, no interaction remained statistically significant. The strongest interaction observed was between the predicted expression of the C13orf45 gene and age at first full-term pregnancy (PGXE = 4.44 × 10−6). In this transcriptome-informed genome-wide gene–environment interaction study of breast cancer, we found no strong support for the role of gene expression in modifying the associations between established risk factors and breast cancer risk. Our study suggests a limited role of gene–environment interactions in breast cancer risk.

Abstract Background: Radiologically dense area (DA) on a mammogram represents the stromal and epithelial tissue in the breast, while non-dense area (NDA) represents adipose tissue. Quantitative measurements of DA, NDA as well as the proportion of dense area in the breast (percent density, PD) adjusted for age and body mass index (BMI) have all been associated with breast cancer (BC) risk. Previous genome-wide association studies (GWAS) have identified genetic loci that show association with both mammographic density (MD) phenotypes and BC, but little is known about the genome-wide and local genetic correlation between MD phenotypes and BC. Methods: We estimated the genome-wide and local genetic correlation between MD phenotypes adjusted for age and BMI and BC, leveraging GWAS summary statistics for MD phenotypes based on 12,866 women (MODE consortium) and for BC based on 122,977 cases and 105,974 controls (BCAC consortium). We also conducted partitioned heritability analysis to identify specific functional annotations that contribute to MD heritability. Results: We observed a negative genetic correlation between DA and NDA (-0.62, p=0.002). Both DA and PD showed positive genetic correlations with overall BC (DA: rg=0.24, p=0.004; PD: rg=0.27, p=0.0001), estrogen receptor-positive (ER+) BC (DA: rg=0.23, p=0.005; PD: rg=0.23, p=0.001), and ER- BC (DA: rg=0.26, p=0.01; PD: rg=0.32, p=0.0006). In contrast, NDA showed a nonsignificant negative genetic correlation with BC (rg=-0.11, p=0.21). Local genetic correlation analyses between MD phenotypes and BC reflected known findings including positive correlation at 10q21 region between both PD and DA and BC (both p<5x10-7), negative correlation at 22q13.1 between DA and BC (p=4.6x10-6), and positive correlation at 8p11.23 between NDA and BC (p=2.8x10-6). Two known BC regions that have not previously been associated with MD showed suggestive genetic correlations between BC and DA: 1q21.2 (negative genetic correlation, p=1.3x10-4) and 12q21.31 (positive genetic correlation, p=6.7x10-4). In exploratory analyses estimating the genome-wide genetic correlation between MD and hormone-related phenotypes in UK Biobank (n=337,199), we observed nominally significant positive genetic correlations between PD and benign mammary dysplasia (rg=0.45, p=0.01), NDA and endometriosis (rg=0.45, p=0.02), and negative genetic correlation between NDA and menstrual cycle length (rg=-0.30, p=0.05). We identified multiple functional annotations that contribute to MD heritability including histone mark H3K27ac that marks active enhancers (~2,5-fold enrichment in heritability for both DA and NDA) and DNAsel Hypersensitive Sites (DHS, 9.1-fold enrichment in heritability for DA). Conclusion: Our results provide evidence of shared genetic basis between mammographic density and breast cancer. Citation Format: Hongjie Chen, Julie Douglas, William Gordon, Deborah Thompson, Fergus Couch, Peter Fasching, Per Hall, John Hopper, Gertraud Maskarenic, Marina Pollan, Jennifer Stone, Rulla Tamimi, Sara Lindstroem. Evidence for a shared genetic basis between breast cancer and mammographic density phenotypes [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2018; 2018 Apr 14-18; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2018;78(13 Suppl):Abstract nr 224.