The development and progression of cancer, a collection of diseases with complex genetic architectures, is facilitated by the interplay of multiple etiological factors. This complexity challenges the traditional single-platform study design and calls for an integrated approach to data analysis. However, integration of heterogeneous measurements of biological variation is a non-trivial exercise due to the diversity of the human genome and the variety of output data formats and genome coverage obtained from the commonly used molecular platforms. This review article will provide an introduction to integration strategies used for analyzing genetic risk factors for cancer. We critically examine the ability of these strategies to handle the complexity of the human genome and also accommodate information about the biological and functional interactions between the elements that have been measured—making the assessment of disease risk against a composite genomic factor possible. The focus of this review is to provide an overview and introduction to the main strategies and to discuss where there is a need for further development.

Background : We have developed a GWAS analysis method called DEPTH (DEPendency of association on the number of Top Hits) to identify genomic regions potentially associated with disease by considering overlapping groups of contiguous markers (e.g. single nucleotide polymorphisms, SNPs) across the genome. DEPTH is a machine learning algorithm for feature ranking of ultra-high dimensional datasets, built from well-established statistical tools such as bootstrapping, penalised regression and decision trees. Unlike marginal regression, which considers each SNP individually, the key idea behind DEPTH is to rank groups of SNPs in terms of their joint strength of association with the outcome. Our aim was to compare the performance of DEPTH with that of standard logistic regression analysis. Methods : We selected 1,854 prostate cancer cases and 1,894 controls from the UK for whom 541,129 SNPs were measured using the Illumina Infinium HumanHap550 array. Confirmation was sought using 4,152 cases and 2,874 controls, ascertained from the UK and Australia, for whom 211,155 SNPs were measured using the iCOGS Illumina Infinium array. Results : From the DEPTH analysis we identified 14 regions associated with prostate cancer risk that had been reported previously; five of which would not have been identified by conventional logistic regression. We also identified 112 novel putative susceptibility regions. Conclusions : DEPTH can reveal new risk-associated regions that would not have been identified using a conventional logistic regression analysis of individual SNPs. Impact : This study demonstrates that the DEPTH algorithm could identify additional genetic susceptibility regions that merit further investigation.

Common variants in 94 loci have been associated with breast cancer including 15 loci with genome-wide significant associations ( P o 5 (cid:2) 10 (cid:3) 8 ) with oestrogen receptor (ER)-negative breast cancer and BRCA1- associated breast cancer risk. In this study, to identify new ER-negative susceptibility loci, we performed a meta-analysis of 11 genome-wide association studies (GWAS) consisting of 4,939 ER-negative cases and 14,352 controls, combined with 7,333 ER-negative cases and 42,468 controls and 15,252 BRCA1 mutation carriers genotyped on the iCOGS array. We identify four previously unidentified loci including two loci at 13q22 near KLF5 , a 2p23.2 locus near WDR43 and a 2q33 locus near PPIL3 that display genome-wide significant associations with ER-negative breast cancer. In addition, 19 known breast cancer risk loci have genome-wide significant associations and 40 had moderate associations ( P o 0.05) with ER-negative disease. Using functional and eQTL studies we implicate TRMT61B and WDR43 at 2p23.2 and PPIL3 at 2q33 in ER-negative breast cancer aetiology. All ER-negative loci combined account for B 11% of familial relative risk for ER-negative disease and may contribute to improved ER-negative and BRCA1 breast cancer risk prediction.

AIM To examine whether peripheral blood methylation is associated with risk of developing mature B-cell neoplasms (MBCNs). MATERIALS & METHODS We conducted a case-control study nested within a large prospective cohort. Peripheral blood was collected from healthy participants. Cases of MBCN were identified by linkage to cancer registries. Methylation was measured using the Infinium(®) HumanMethylation450. RESULTS During a median of 10.6-year follow-up, 438 MBCN cases were evaluated. Global hypomethylation was associated with increased risk of MBCN (odds ratio: 2.27, [95% CI: 1.59-3.25]). Within high CpG promoter regions, hypermethylation was associated with increased risk (odds ratio: 1.76 [95% CI: 1.25-2.48]). Promoter hypermethylation was observed in HOXA9 and CDH1 genes. CONCLUSION Aberrant global DNA methylation is detectable in peripheral blood collected years before diagnosis and is associated with increased risk of MBCN, suggesting changes to DNA methylation are an early event in MBCN development.

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.