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Background: We have developed a genome-wide association study 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., 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, penalized 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. Cancer Epidemiol Biomarkers Prev; 25(12); 1619–24. ©2016 AACR.

List of the previously identified 100 independent prostate cancer susceptibility SNPs.

Plot of the first two principal components for the 7,920 participants in the iCOGS dataset.

Technical details of the DEPTH procedure.

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, we demonstrate 4 discrete overlapping signals by utilising forward selection logistic regression and LASSO techniques. Two of these signals are centred 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. Analysis of 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 Computational and Systems Biology of Cancer; Feb 8-11 2015; San Francisco, CA. Philadelphia (PA): AACR; Cancer Res 2015;75(22 Suppl 2):Abstract nr B1-52.

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.

Background: We have developed a genome-wide association study 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., 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, penalized 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. Cancer Epidemiol Biomarkers Prev; 25(12); 1619–24. ©2016 AACR.

Background Clustering of breast and colorectal cancer has been observed within some families and cannot be explained by chance or known high-risk mutations in major susceptibility genes. Potential shared genetic susceptibility between breast and colorectal cancer, not explained by high-penetrance genes, has been postulated. We hypothesized that yet undiscovered genetic variants predispose to a breast-colorectal cancer phenotype. Methods To identify variants associated with a breast-colorectal cancer phenotype, we analyzed genome-wide association study (GWAS) data from cases and controls that met the following criteria: cases (n = 985) were women with breast cancer who had one or more first- or second-degree relatives with colorectal cancer, men/women with colorectal cancer who had one or more first- or second-degree relatives with breast cancer, and women diagnosed with both breast and colorectal cancer. Controls (n = 1769), were unrelated, breast and colorectal cancer-free, and age- and sex- frequency-matched to cases. After imputation, 6,220,060 variants were analyzed using the discovery set and variants associated with the breast-colorectal cancer phenotype at P

Parathyroid Hormone-Like Hormone (PTHLH) is an important regulatory gene encoding the product Parathyroid Hormone-related Protein (PTHrP). Among many biological roles, it has been demonstrated to be essential to the development of multiple tissue-types, the regulation of foetal calcium levels, and the metastasis of breast cancer to bone. Its region has GWAS associations with breast cancer, breast size, height, type 2 diabetes mellitis, neural development, cardiac arrest, and several immunological phenotypes. They extend both up- and down-stream of PTHLH, however their driving molecular mechanisms remain obscure. Analysis of datasets including Hi-C, ChIA-PET, IM-PET and ChIP-seq suggests PTHLH sits within a 1.3Mb Topologically Associating Domain (TAD) containing 4 other protein-coding genes (pc-genes) CCDC91, MRPS35, MANSC4 and KLHL42. PTHLH itself rests between two 220kb inducible sub-TADs that likely comprise regulatory archipelagos directed primarily at its regulation. There are several other inducible loops in the TAD. All protein-coding genes within the TAD feature an Activated Chromatin Hub (ACH) nearby their 5’. The 3D-structure of the TAD appears to be supported by a system of permanent and dynamic tether points producing primary and inducible chromatin loops. These result in all pc-genes’ ACHs being 30 to 60kb from each other. RNA Pol II (RNAPII) ChIA-PET data suggests there is an inclusive hierarchical regulatory structure within the TAD, with substantial interaction between the ACHs of respective pc-genes that is likely facilitated by the 3D conformation. Citation Format: Adam N. Freeman, Michael A. Henderson, Miroslaw K. Kapuscinski, John Hopper, T John Martin. The 3D chromatin structure of the PTHLH region comprises a dynamic hierarchical looping complex that approximates the protein-coding genes and facilitates promoter and enhancer promiscuity. [abstract]. In: Proceedings of the 107th Annual Meeting of the American Association for Cancer Research; 2016 Apr 16-20; New Orleans, LA. Philadelphia (PA): AACR; Cancer Res 2016;76(14 Suppl):Abstract nr 1975.

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

The first wave of cancer genome-wide association studies (GWAS) have revealed tens of independent loci marked by common variants of unknown or likely no functional significance that explain about 5-10% of familial risk for the particular disease. The approach taken to date has been conservative, and only a fraction of information has yet to be extracted from these expensive enterprises. For example, the Bonferroni procedure for selecting candidate phase II SNPs ignores many SNPs that happen to fail an extremely low p-value threshold. While this procedure does guarantee control of false positives, it seems counterintuitive to the purpose of phase I, which is to generate hypotheses based on promising candidates. Researchers have generally combined data from the discovery phase I and other phases and used ‘genome-wide thresholds’ based on assuming all SNPs are independent. Linkage disequilibrium (LD) makes it problematic to differentiate a real signal from highly correlated proxy signals. Most published GWAS do not examine SNP interactions due to: (a) the high computational complexity of computing pvalues for the interaction terms, and (b) the typically low power to detect significant interactions. It is plausible that more information should be extracted if: (i) higher order interactions are fitted, (ii) highly selected cases and controls are used in phase I, (iii) large replication studies are used, especially if involving existing GWAS data, (iv) the non-independence of SNPs is taken into account using, e.g. BEAGLE CALL or haplotype analyses, (v) focus is on candidate gene pathways, and/or functional SNPs, and (vi) rarer and more SNPs, such as is available from the Illumina 5M SNP chip, are used. We will illustrate these ideas using data from a GWAS of early-onset breast cancers, enriched for those with a family history, and a GWAS using extremes sample of extremes for mammographic density. We will also discuss the design of a large international breast cancer GWAS using the Illumina 5M SNP chip, phase I cases enriched for family history, population-based phase II cases and controls, population-based family study of candidate SNPs, and GxG analyses using ‘massively parallel’ super computing.

The 6q25.1 locus was first identified via a genome-wide association study (GWAS) in Chinese women and marked by single nucleotide polymorphism (SNP) rs2046210, approximately 180 Kb upstream of ESR1. There have been conflicting reports about the association of this locus with breast cancer in Europeans, and a GWAS in Europeans identified a different SNP, tagged here by rs12662670. We examined the associations of both SNPs in up to 61,689 cases and 58,822 controls from forty-four studies collaborating in the Breast Cancer Association Consortium, of which four studies were of Asian and 39 of European descent. Logistic regression was used to estimate odds ratios (OR) and 95% confidence intervals (CI). Case-only analyses were used to compare SNP effects in Estrogen Receptor positive (ER+) versus negative (ER-) tumours. Models including both SNPs were fitted to investigate whether the SNP effects were independent. Both SNPs are significantly associated with breast cancer risk in both ethnic groups. Per-allele ORs are higher in Asian than in European studies [rs2046210: OR (A/G) = 1.36 (95% CI 1.26-1.48), p = 7.6×10(-14) in Asians and 1.09 (95% CI 1.07-1.11), p = 6.8×10(-18) in Europeans. rs12662670: OR (G/T) = 1.29 (95% CI 1.19-1.41), p = 1.2×10(-9) in Asians and 1.12 (95% CI 1.08-1.17), p = 3.8×10(-9) in Europeans]. SNP rs2046210 is associated with a significantly greater risk of ER- than ER+ tumours in Europeans [OR (ER-) = 1.20 (95% CI 1.15-1.25), p = 1.8×10(-17) versus OR (ER+) = 1.07 (95% CI 1.04-1.1), p = 1.3×10(-7), p(heterogeneity) = 5.1×10(-6)]. In these Asian studies, by contrast, there is no clear evidence of a differential association by tumour receptor status. Each SNP is associated with risk after adjustment for the other SNP. These results suggest the presence of two variants at 6q25.1 each independently associated with breast cancer risk in Asians and in Europeans. Of these two, the one tagged by rs2046210 is associated with a greater risk of ER- tumours. ispartof: PLoS One vol:7 issue:8 ispartof: location:United States status: published

Most published GWAS do not examine SNP interactions due to the high computational complexity of computing p-values for the interaction terms. Our aim is to utilize supercomputing resources to apply complex statistical techniques to the world's accumulating GWAS, epidemiology, survival and pathology data to uncover more information about genetic and environmental risk, biology and aetiology. We performed the Bayesian Posterior Probability test on a pseudo data set with 500,000 single nucleotide polymorphism and 100 samples as proof of principle. We carried out strong scaling simulations on 2 to 4,096 processing cores with factor 2 increments in partition size. On two processing cores, the run time is 317h, i.e. almost two weeks, compared to less than 10 minutes on 4,096 processing cores. The speedup factor is 2,020 that is very close to the theoretical value of 2,048. This work demonstrates the feasibility of performing exhaustive higher order analysis of GWAS studies using independence testing for contingency tables. We are now in a position to employ supercomputers with hundreds of thousands of threads for higher order analysis of GWAS data using complex statistics.