Use of a Novel Nonparametric Version of DEPTH to Identify Genomic Regions Associated with Prostate Cancer Risk.

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.National Health and Medical Research Council Australia (Grant ID: 1033452, Senior Principal Research Fellowship, Senior Research Fellowship), Cancer Research UK (Grant IDs: C5047/A7357, C1287/A10118, C1287/A5260, C5047/A3354, C5047/A10692, C16913/A6135 and C16913/A6835), Prostate Research Campaign UK (now Prostate Cancer UK), The Institute of Cancer Research and The Everyman Campaign, The National Cancer Research Network UK, The National Cancer Research Institute (NCRI) UK, National Institute for Health Research funding to the NIHR Biomedical Research Centre at The Institute of Cancer Research and The Royal Marsden NHS Foundation Trust, Prostate Cancer Research Program of Cancer Council Victoria from The National Health and Medical Research Council, Australia (Grant IDs: 126402, 209057, 251533, 396414, 450104, 504700, 504702, 504715, 623204, 940394, 614296), VicHealth, Cancer Council Victoria, The Prostate Cancer Foundation of Australia, The Whitten Foundation, PricewaterhouseCoopers, Tattersall’sThis is the author accepted manuscript. The final version is available from the American Association for Cancer Research via http://dx.doi.org/10.1158/1055-9965.EPI-16-030

Comparison of 6q25 Breast Cancer Hits from Asian and European Genome Wide Association Studies in the Breast Cancer Association Consortium (BCAC)

Peer reviewe

Comparison of 6q25 breast cancer hits from asian and european genome wide association studies in the breast cancer association consortium (bcac)

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 = 7.6x10(-14) in Asians and 1.09 (95% CI 1.07-1.11), p = 6.8x10(-18) in Europeans. rs12662670: OR (G/T) = 1.29 (95% CI 1.19-1.41), p = 1.2x10(-9) in Asians and 1.12 (95% CI 1.08-1.17), p = 3.8x10(-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 = 1.8x10(-17) versus OR (ER+) = 1.07 (95% CI 1.04-1.1), p = 1.3x10(-7), p(heterogeneity) = 5.1x10(-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

Table S9 from Using DEPendency of Association on the Number of Top Hits (DEPTH) as a Complementary Tool to Identify Novel Colorectal Cancer Susceptibility Loci

Supplementary Table S9 shows the number of risk regions that are common to both CCFR and GECCO in stage 3 DEPTH analysis, as well as whether they are detected by logistic regression analysis or were in previous CRC GWAS studies.</p

Table S1 from Using DEPendency of Association on the Number of Top Hits (DEPTH) as a Complementary Tool to Identify Novel Colorectal Cancer Susceptibility Loci

Supplementary Table S1 shows the original number of cases and controls in both the CCFR and GECCO datasets.</p

Table S7 from Using DEPendency of Association on the Number of Top Hits (DEPTH) as a Complementary Tool to Identify Novel Colorectal Cancer Susceptibility Loci

Supplementary Table S7 shows the number of risk regions that are common to both CCFR and GECCO datasets in stage 1 DEPTH analysis, as well as whether they are detected by logistic regression analysis or were in previous CRC GWAS studies.</p

Table S11 from Using DEPendency of Association on the Number of Top Hits (DEPTH) as a Complementary Tool to Identify Novel Colorectal Cancer Susceptibility Loci

Supplementary Table S11 shows the ranking of SNPs at the HLA locus.</p

Table S8 from Using DEPendency of Association on the Number of Top Hits (DEPTH) as a Complementary Tool to Identify Novel Colorectal Cancer Susceptibility Loci

Supplementary Table S8 shows the number of risk regions that are common to both CCFR and GECCO in stage 2 DEPTH analysis, as well as whether they are detected by logistic regression analysis or were in previous CRC GWAS studies.</p

Figure S5 from Using DEPendency of Association on the Number of Top Hits (DEPTH) as a Complementary Tool to Identify Novel Colorectal Cancer Susceptibility Loci

Supplementary Figure S5 showing the QQ and Manhattan plots of conventional GWAS on the GECCO and CCFR datasets</p

Table S2 from Using DEPendency of Association on the Number of Top Hits (DEPTH) as a Complementary Tool to Identify Novel Colorectal Cancer Susceptibility Loci

Supplementary Table S2 shows the QC and harmonisation steps for both the GECCO and CCFR datasets</p