Large-Scale Pathway-Based Analysis of Bladder Cancer Genome-Wide Association Data from Five Studies of European Background

Pathway analysis of genome-wide association studies (GWAS) offer a unique opportunity to collectively evaluate genetic variants with effects that are too small to be detected individually. We applied a pathway analysis to a bladder cancer GWAS containing data from 3,532 cases and 5,120 controls of European background (n = 5 studies). Thirteen hundred and ninety-nine pathways were drawn from five publicly available resources (Biocarta, Kegg, NCI-PID, HumanCyc, and Reactome), and we constructed 22 additional candidate pathways previously hypothesized to be related to bladder cancer. In total, 1421 pathways, 5647 genes and ∼90,000 SNPs were included in our study. Logistic regression model adjusting for age, sex, study, DNA source, and smoking status was used to assess the marginal trend effect of SNPs on bladder cancer risk. Two complementary pathway-based methods (gene-set enrichment analysis [GSEA], and adapted rank-truncated product [ARTP]) were used to assess the enrichment of association signals within each pathway. Eighteen pathways were detected by either GSEA or ARTP at P≤0.01. To minimize false positives, we used the I2 statistic to identify SNPs displaying heterogeneous effects across the five studies. After removing these SNPs, seven pathways (‘Aromatic amine metabolism’ [PGSEA = 0.0100, PARTP = 0.0020], ‘NAD biosynthesis’ [PGSEA = 0.0018, PARTP = 0.0086], ‘NAD salvage’ [PARTP = 0.0068], ‘Clathrin derived vesicle budding’ [PARTP = 0.0018], ‘Lysosome vesicle biogenesis’ [PGSEA = 0.0023, PARTP<0.00012], ’Retrograde neurotrophin signaling’ [PGSEA = 0.00840], and ‘Mitotic metaphase/anaphase transition’ [PGSEA = 0.0040]) remained. These pathways seem to belong to three fundamental cellular processes (metabolic detoxification, mitosis, and clathrin-mediated vesicles). Identification of the aromatic amine metabolism pathway provides support for the ability of this approach to identify pathways with established relevance to bladder carcinogenesis

Genome-wide association analysis of more than 120,000 individuals identifies 15 new susceptibility loci for breast cancer.

Genome-wide association studies (GWAS) and large-scale replication studies have identified common variants in 79 loci associated with breast cancer, explaining ∼14% of the familial risk of the disease. To identify new susceptibility loci, we performed a meta-analysis of 11 GWAS, comprising 15,748 breast cancer cases and 18,084 controls together with 46,785 cases and 42,892 controls from 41 studies genotyped on a 211,155-marker custom array (iCOGS). Analyses were restricted to women of European ancestry. We generated genotypes for more than 11 million SNPs by imputation using the 1000 Genomes Project reference panel, and we identified 15 new loci associated with breast cancer at P < 5 × 10(-8). Combining association analysis with ChIP-seq chromatin binding data in mammary cell lines and ChIA-PET chromatin interaction data from ENCODE, we identified likely target genes in two regions: SETBP1 at 18q12.3 and RNF115 and PDZK1 at 1q21.1. One association appears to be driven by an amino acid substitution encoded in EXO1.BCAC is funded by Cancer Research UK (C1287/A10118, C1287/A12014) and by the European Community's Seventh Framework Programme under grant agreement 223175 (HEALTH-F2-2009-223175) (COGS). Meetings of the BCAC have been funded by the European Union COST programme (BM0606). Genotyping on the iCOGS array was funded by the European Union (HEALTH-F2-2009-223175), Cancer Research UK (C1287/A10710, C8197/A16565), the Canadian Institutes of Health Research (CIHR) for the CIHR Team in Familial Risks of Breast Cancer program and the Ministry of Economic Development, Innovation and Export Trade of Quebec, grant PSR-SIIRI-701. Combination of the GWAS data was supported in part by the US National Institutes of Health (NIH) Cancer Post-Cancer GWAS initiative, grant 1 U19 CA148065-01 (DRIVE, part of the GAME-ON initiative). For a full description of funding and acknowledgments, see the Supplementary Note.This is the author accepted manuscript. The final version is available from NPG via http://dx.doi.org/10.1038/ng.324

Genome-wide associations for birth weight and correlations with adult disease

Birth weight (BW) has been shown to be influenced by both fetal and maternal factors and in observational studies is reproducibly associated with future risk of adult metabolic diseases including type 2 diabetes (T2D) and cardiovascular disease1. These life-course associations have often been attributed to the impact of an adverse early life environment. Here, we performed a multi-ancestry genome-wide association study (GWAS) meta-analysis of BW in 153,781 individuals, identifying 60 loci where fetal genotype was associated with BW (P < 5 × 10−8). Overall, approximately 15% of variance in BW was captured by assays of fetal genetic variation. Using genetic association alone, we found strong inverse genetic correlations between BW and systolic blood pressure (Rg = −0.22, P = 5.5 × 10−13), T2D (Rg = −0.27, P = 1.1 × 10−6) and coronary artery disease (Rg = −0.30, P = 6.5 × 10−9). In addition, using large -cohort datasets, we demonstrated that genetic factors were the major contributor to the negative covariance between BW and future cardiometabolic risk. Pathway analyses indicated that the protein products of genes within BW-associated regions were enriched for diverse processes including insulin signalling, glucose homeostasis, glycogen biosynthesis and chromatin remodelling. There was also enrichment of associations with BW in known imprinted regions (P = 1.9 × 10−4). We demonstrate that life-course associations between early growth phenotypes and adult cardiometabolic disease are in part the result of shared genetic effects and identify some of the pathways through which these causal genetic effects are mediated

Interactions between genetic variants and breast cancer risk factors in the breast and prostate cancer cohort consortium.

Background: Recently, several genome-wide association studies have identified various genetic susceptibility loci for breast cancer. Relatively little is known about the possible interactions between these loci and the established risk factors for breast cancer. Methods: To assess interactions between single-nucleotide polymorphisms (SNPs) and established risk factors, we prospectively collected DNA samples and questionnaire data from 8576 breast cancer case subjects and 11 892 control subjects nested within the National Cancer Institute's Breast and Prostate Cancer Cohort Consortium (BPC3). We genotyped 17 germline SNPs (FGFR2-rs2981582, FGFR2-rs3750817, TNRC9-rs3803662, 2q35-rs13387042, MAP3K1-rs889312, 8q24-rs13281615, CASP8-rs1045485, LSP1-rs3817198, COL1A1-rs2075555, COX11-rs6504950, RNF146-rs2180341, 6q25-rs2046210, SLC4A7-rs4973768, NOTCH2-rs11249433, 5p12-rs4415084, 5p12-rs10941679, RAD51L1-rs999737), and odds ratios were estimated by logistic regression to confirm previously reported associations with breast cancer risk. We performed likelihood ratio test to assess interactions between 17 SNPs and nine established risk factors (age at menarche, parity, age at menopause, use of hormone replacement therapy, family history, height, body mass index, smoking status, and alcohol consumption), and a correction for multiple testing of 153 tests (adjusted P value threshold = .05/153 = 3 × 10-4) was done. Case-case comparisons were performed for possible differential associations of polymorphisms by subgroups of tumor stage, estrogen and progesterone receptor status, and age at diagnosis. All statistical tests were two-sided. Results: We confirmed the association of 14 SNPs with breast cancer risk (Ptrend = 2.57 × 1023 -3.96 × 10 219). Three SNPs (LSP1-rs3817198, COL1A1-rs2075555, and RNF146-rs2180341) did not show association with breast cancer risk. After accounting for multiple testing, no statistically significant interactions were detected between the 17 SNPs and the nine risk factors. We also confirmed that SNPs in FGFR2 and TNRC9 were associated with greater risk of estrogen receptor-positive than estrogen receptor-negative breast cancer (P heterogeneity = .0016 for FGFR2-rs2981582 and P heterogeneity = .0053 for TNRC9-rs3803662). SNP 5p12-rs10941679 was statistically significantly associated with greater risk of progesterone receptor-positive than progesterone receptor-negative breast cancer (P heterogeneity = .0028). Conclusion: This study does not support the hypothesis that known common breast cancer susceptibility loci strongly modify the associations between established risk factors and breast cancer

Interactions between genetic variants and breast cancer risk factors in the breast and prostate cancer cohort consortium

Background: Recently, several genome-wide association studies have identified various genetic susceptibility loci for breast cancer. Relatively little is known about the possible interactions between these loci and the established risk factors for breast cancer. Methods: To assess interactions between single-nucleotide polymorphisms (SNPs) and established risk factors, we prospectively collected DNA samples and questionnaire data from 8576 breast cancer case subjects and 11 892 control subjects nested within the National Cancer Institute's Breast and Prostate Cancer Cohort Consortium (BPC3). We genotyped 17 germline SNPs (FGFR2-rs2981582, FGFR2-rs3750817, TNRC9-rs3803662, 2q35-rs13387042, MAP3K1-rs889312, 8q24-rs13281615, CASP8-rs1045485, LSP1-rs3817198, COL1A1-rs2075555, COX11-rs6504950, RNF146-rs2180341, 6q25-rs2046210, SLC4A7-rs4973768, NOTCH2-rs11249433, 5p12-rs4415084, 5p12-rs10941679, RAD51L1-rs999737), and odds ratios were estimated by logistic regression to confirm previously reported associations with breast cancer risk. We performed likelihood ratio test to assess interactions between 17 SNPs and nine established risk factors (age at menarche, parity, age at menopause, use of hormone replacement therapy, family history, height, body mass index, smoking status, and alcohol consumption), and a correction for multiple testing of 153 tests (adjusted P value threshold = .05/153 = 3 × 10-4) was done. Case-case comparisons were performed for possible differential associations of polymorphisms by subgroups of tumor stage, estrogen and progesterone receptor status, and age at diagnosis. All statistical tests were two-sided. Results: We confirmed the association of 14 SNPs with breast cancer risk (Ptrend = 2.57 × 1023 -3.96 × 10 219). Three SNPs (LSP1-rs3817198, COL1A1-rs2075555, and RNF146-rs2180341) did not show association with breast cancer risk. After accounting for multiple testing, no statistically significant interactions were detected between the 17 SNPs and the nine risk factors. We also confirmed that SNPs in FGFR2 and TNRC9 were associated with greater risk of estrogen receptor-positive than estrogen receptor-negative breast cancer (P heterogeneity = .0016 for FGFR2-rs2981582 and P heterogeneity = .0053 for TNRC9-rs3803662). SNP 5p12-rs10941679 was statistically significantly associated with greater risk of progesterone receptor-positive than progesterone receptor-negative breast cancer (P heterogeneity = .0028). Conclusion: This study does not support the hypothesis that known common breast cancer susceptibility loci strongly modify the associations between established risk factors and breast cancer. © Oxford University Press 2011