Parathyroid hormone-related protein (PTHrP), the product of the PTHLH gene, has long been implicated in breast cancer. Its expression is thought to favor and, potentially, facilitate metastasis to bone. Paradoxically, a prospective clinical study clinical suggests that its production in primary breast cancers is actually protective, affording a better prognosis than its absence. Multiple recent Genome-Wide Association Studies (GWAS) have confirmed a single susceptibility locus immediately upstream of the PTHLH gene to be associated with breast cancer. This was again reproduced in the recent iCOGS GWAS, which involved over 90,000 European subjects. In spite of a single SNP, rs10771399, being uniquely reported and reproduced by prior studies, we demonstrate 4 discrete overlapping signals by utilizing forward selection logistic regression and LASSO techniques. Two of these signals are centered and superimposed around rs10771399, ~40kbp upstream of PTHLH, a third lies a further 100kbp upstream, and the fourth lies a further 250kbp upstream over the next gene, CCDC91. While the causation of this signal remains elusive, multiple putatively contributory variations are captured by these signals. Citation Format: Adam N. Freeman, T John Martin, Michael A. Henderson, Enes Makalic, Miroslaw K. Kapuscinski, Daniel F. Schmidt, John Hopper. The iCOGS breast cancer GWAS reveals 4 unique signals in the PTHLH region in patients of European origin. [abstract]. In: Proceedings of the AACR Special Conference on Translation of the Cancer Genome; Feb 7-9, 2015; San Francisco, CA. Philadelphia (PA): AACR; Cancer Res 2015;75(22 Suppl 1):Abstract nr A1-10.

In this note we derive a simple Bayesian sampler for linear regression with the horseshoe hierarchy. A new interpretation of the horseshoe model is presented, and extensions to logistic regression and alternative hierarchies, such as horseshoe+, are discussed. Due to the conjugacy of the proposed hierarchy, Chib's algorithm may be used to easily compute the marginal likelihood of the model.

Parathyroid hormone-related protein (PTHrP), the product of the PTHLH gene, has long been implicated in breast cancer. Its expression is thought to favour and, potentially, facilitate metastasis to bone. Paradoxically, a prospective clinical study clinical suggests that its production in primary breast cancers is actually protective, affording a better prognosis than its absence. Multiple recent Genome-Wide Association Studies (GWAS) have confirmed a single susceptibility locus immediately upstream of the PTHLH gene to be associated with breast cancer. This was again reproduced in the recent iCOGS GWAS, which involved over 90,000 European subjects. In spite of a single SNP, rs10771399, being uniquely reported and reproduced by prior studies, comprehensive analysis of the region reveals multiple significant SNPs stretching for hundreds of kbp across the locus. Epistasis analysis of this region versus the entire iCOGS array was performed using Plink!. These results suggest multiple genes may be implicated in the causation of this signal. Many known functional partners of PTHLH were identified, including IPO8, RUNX1, RUNX3, S100P, SOX9, SOX11, and SOX14. Multiple putative functional partners of PTHLH were likewise identified, including PBXIP1, PMVK, KCNN3, STX12, PPP1R8, CAMTA1, PRKCZ, TGIF1, SPHKAP, MYC, and STAT3. When taken together, these results offer novel potential insights into the function of the locus at PTHLH in breast cancer that may be further investigated. Citation Format: Adam N. Freeman, Michael A. Henderson, T John Martin, Enes Makalic, Miroslaw K. Kapuscinski, Daniel Schmidt, John Hopper. Epistasis analysis of the PTHLH region in European subjects of the iCOGS breast cancer GWAS suggest multiple genes may be implicated in its role in breast cancer. [abstract]. In: Proceedings of the 106th Annual Meeting of the American Association for Cancer Research; 2015 Apr 18-22; Philadelphia, PA. Philadelphia (PA): AACR; Cancer Res 2015;75(15 Suppl):Abstract nr 1098. doi:10.1158/1538-7445.AM2015-1098

Many common diseases have a complex genetic basis in which large numbers of genetic variations combine with environmental factors to determine risk. However, quantifying such polygenic effects has been challenging. In order to address these difficulties we developed a global measure of the information content of an individual's genome relative to a reference population, which may be used to assess differences in global genome structure between cases and appropriate controls. Informally this measure, which we call relative genome information (RGI), quantifies the relative “disorder” of an individual's genome. In order to test its ability to predict disease risk we used RGI to compare single‐nucleotide polymorphism genotypes from two independent samples of women with early‐onset breast cancer with three independent sets of controls. We found that RGI was significantly elevated in both sets of breast cancer cases in comparison with all three sets of controls, with disease risk rising sharply with RGI. Furthermore, these differences are not due to associations with common variants at a small number of disease‐associated loci, but rather are due to the combined associations of thousands of markers distributed throughout the genome. Our results indicate that the information content of an individual's genome may be used to measure the risk of a complex disease, and suggest that early‐onset breast cancer has a strongly polygenic component.

Gordon Fehringer, Peter Kraft, Christopher A. Haiman , Paul Pharoah, Rosalind Eeles, Nilanjan Chatterjee, Fred Schumacher, Joellen Schildkraut, Paul Brennan, Heike Bickeböller, Richard Houlston, Maria Teresa Landi, Neil Caporaso, Angela Risch, Ali Amin Al Olama, Sonja I Berndt, Edward Giovannucci, Henrik Grönberg, Zsofia Kote-Jarai, Jing Ma, Kenneth Muir, Meir Stampfer, Victoria L. Stevens, Fredrik Wiklund, Walter Willett, Ellen L. Goode, Jenny Permuth-Wey, Harvey Risch, Brett M. Reid, Stephane Bezieau, Hermann Brenner, Andrew T. Chan, Thomas J. Hudson, Jonathan K Kocarnik, Polly A. Newcomb, Robert E. Schoen, Martha L. Slattery, Emily White, Muriel Adank on behalf of Hereditary Breast and Ovarian Cancer Research Group Netherlands (HEBON), Habibul Ahsan , Kristiina Aittomäki, Laura Baglietto, Sonja Berndt, Carl Blomquist, Federico Canzian, Kamila Czene , Isabel dos-Santos-Silva , A. Heather Eliassen, Jonine Figueroa, Dieter Flesch-Janys, Olivia Fletcher, Montserrat Garcia-Closas, Mia M. Gaudet, Nichola Johnson, Per Hall , Aditi Hazra, Rebecca Hein , Albert Hofman, John L. Hopper, Astrid Irwanto, Mattias Johansson, Rudolf Kaaks, Muhammad G. Kibriya, Peter Lichtner, Sara Lindström, Jianjun Liu, Eiliv Lund, Enes Makalic, Alfons Meindl, Bertram Müller-Myhsok, Taru A. Muranen, Heli Nevanlinna, Petra H. Peeters, Julian Peto, Ross L. Prentice, Nazneen Rahman, Maria Jose Sanchez, Daniel F. Schmidt, Rita K. Schmutzler, Melissa C. Southey, Rulla Tamimi, Ruth C. Travis, Clare Turnbull, Andre G. Uitterlinden, Zhaoming Wang, Alice S. Whittemore, Rose Yang, Wei Zheng , Thorunn Rafnar, Julius Gudmundsson, Simon N Stacey, Kari Stefansson, Patrick Sulem, The PRACTICAL Consortium, Ovarian Cancer Association Consortium (OCAC), Y. Ann Chen, Jonathan P. Tyrer, David C. Christiani, Yongyue Wei, African American Breast Cancer Consortium (AABC), African Ancestry Prostate Cancer Consortium (AAPC), Japanese American Prostate Cancer Consortium (JAPC), Latino American Breast Cancer Consortium (LABC), Latino American Prostate Cancer Consortium (LAPC), Hongbing Shen, Dr Zhibin Hu , Xiao-Ou Shu, Kouya Shiraishi, Atsushi Takahashi, Yohan Bossé, Ma'en Obeidat, David Nickle, Wim Timens, Matthew L. Freedman, Qiyuan Li, Daniela Seminara, Stephen J. Chanock, Jian Gong, Ulrike Peters, Stephen Gruber on behalf of Colorectal Transdisciplinary (CORECT) Study, Christopher I. Amos, Thomas A. Sellers, Douglas Easton, David J Hunter, Brian E. Henderson, Rayjean J Hung.

Genetic variations, such as single nucleotide polymorphisms (SNPs) in microRNAs (miRNA) or in the miRNA binding sites may affect the miRNA dependent gene expression regulation, which has been implicated in various cancers, including breast cancer, and may alter individual susceptibility to cancer. We investigated associations between miRNA related SNPs and breast cancer risk. First we evaluated 2,196 SNPs in a case-control study combining nine genome wide association studies (GWAS). Second, we further investigated 42 SNPs with suggestive evidence for association using 41,785 cases and 41,880 controls from 41 studies included in the Breast Cancer Association Consortium (BCAC). Combining the GWAS and BCAC data within a meta-analysis, we estimated main effects on breast cancer risk as well as risks for estrogen receptor (ER) and age defined subgroups. Five miRNA binding site SNPs associated significantly with breast cancer risk: rs1045494 (odds ratio (OR) 0.92; 95% confidence interval (CI): 0.88–0.96), rs1052532 (OR 0.97; 95% CI: 0.95–0.99), rs10719 (OR 0.97; 95% CI: 0.94–0.99), rs4687554 (OR 0.97; 95% CI: 0.95–0.99, and rs3134615 (OR 1.03; 95% CI: 1.01–1.05) located in the 3′ UTR of CASP8, HDDC3, DROSHA, MUSTN1, and MYCL1, respectively. DROSHA belongs to miRNA machinery genes and has a central role in initial miRNA processing. The remaining genes are involved in different molecular functions, including apoptosis and gene expression regulation. Further studies are warranted to elucidate whether the miRNA binding site SNPs are the causative variants for the observed risk effects.

Proceedings: AACR Annual Meeting 2014; April 5-9, 2014; San Diego, CA Parathyroid hormone-related protein (PTHrP), the product of the PTHLH gene, has long been implicated in the mechanism by which breast cancers establish and grow as metastases in bone. Paradoxically, a prospective clinical study clinical suggests that its production early in breast cancer in a cancer is actually protective. Multiple recent Genome-Wide Association Studies (GWAS) have confirmed a single susceptibility locus immediately upstream of the PTHLH gene to be associated with breast cancer. In this study we compare the findings of two GWAS, the ABCFS and the MCCS, along with a large imputed meta-analysis combining the ABCFS, BBCS, DFBBCS, MARIE, SASBAC, HEBCS, GC-HBOC, UK2, BPC3, BCFR and SardiNIA data. To better discern the signals of association with common variant Single Nucleotide Polymorphisms (SNPs) in the PTHLH gene region, we compare results from a conventional marginal analysis performed by plink with those from DepTH, a novel bootstrap permutation-based method that considers multiple contiguous SNPs. These are plotted on the genome and analysed in conjunction with hapmap, encode expression and regulation, genome-wide epistasis, eQTLs, and genetic data. Whereas the classic approach identifies only one SNP (“top hit”), our analysis suggests there are at least 7 discrete regions in and around PTHLH in which there are susceptibility variants, rare or common, with potentially independent functions on disease risk. In addition, exhaustive genome-wide epistasis demonstrates statistical interactions between PTHLH and multiple other gene regions including calcitonin and the vitamin D receptor in breast cancer. Citation Format: Adam N. Freeman, Terence ‘Jack’ Martin, Michael Henderson, Enes Makalic, Daniel F. Schmidt, Miroslaw K. Kapinski, Melissa C. Southey, Graham G. Giles, John L. Hopper. The parathyroid hormone-related peptide region likely features seven discrete breast cancer susceptibility loci. [abstract]. In: Proceedings of the 105th Annual Meeting of the American Association for Cancer Research; 2014 Apr 5-9; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2014;74(19 Suppl):Abstract nr 3530. doi:10.1158/1538-7445.AM2014-3530

Candidate variant association studies have been largely unsuccessful in identifying common breast cancer susceptibility variants, although most studies have been underpowered to detect associations of a realistic magnitude. We assessed 41 common non-synonymous single-nucleotide polymorphisms (nsSNPs) for which evidence of association with breast cancer risk had been previously reported. Case-control data were combined from 38 studies of white European women (46 450 cases and 42 600 controls) and analyzed using unconditional logistic regression. Strong evidence of association was observed for three nsSNPs: ATXN7-K264R at 3p21 [rs1053338, per allele OR = 1.07, 95% confidence interval (CI) = 1.04–1.10, P = 2.9 × 10−6], AKAP9-M463I at 7q21 (rs6964587, OR = 1.05, 95% CI = 1.03–1.07, P = 1.7 × 10−6) and NEK10-L513S at 3p24 (rs10510592, OR = 1.10, 95% CI = 1.07–1.12, P = 5.1 × 10−17). The first two associations reached genome-wide statistical significance in a combined analysis of available data, including independent data from nine genome-wide association studies (GWASs): for ATXN7-K264R, OR = 1.07 (95% CI = 1.05–1.10, P = 1.0 × 10−8); for AKAP9-M463I, OR = 1.05 (95% CI = 1.04–1.07, P = 2.0 × 10−10). Further analysis of other common variants in these two regions suggested that intronic SNPs nearby are more strongly associated with disease risk. We have thus identified a novel susceptibility locus at 3p21, and confirmed previous suggestive evidence that rs6964587 at 7q21 is associated with risk. The third locus, rs10510592, is located in an established breast cancer susceptibility region; the association was substantially attenuated after adjustment for the known GWAS hit. Thus, each of the associated nsSNPs is likely to be a marker for another, non-coding, variant causally related to breast cancer risk. Further fine-mapping and functional studies are required to identify the underlying risk-modifying variants and the genes through which they act.