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  • Open Access English
    Authors: 
    Caroline J. Bull; Joshua A. Bell; Neil Murphy; Eleanor Sanderson; George Davey Smith; Nicholas J. Timpson; Barbara L. Banbury; Demetrius Albanes; Sonja I. Berndt; Stéphane Bézieau; +68 more
    Countries: Netherlands, United Kingdom, Sweden, Netherlands, Spain
    Project: WT | The Avon Longitudinal Stu... (217065), NIH | Tissue and Pathology Reso... (1P50CA127003-01), NIH | Regional Oncology Researc... (3P30CA006973-53S2), NIH | SOUTHWEST ONCOLOGY GROUP ... (2U10CA037429-04), NIH | Cancer Survivorship: Decr... (1K05CA152715-01), NIH | Data sharing: the Colon C... (3U01CA167551-07S1), NIH | Accelerating Transdiscipl... (5R35CA197735-04), EC | PREVIEW (312057), NIH | GENETIC EPIDEMIOLOGIC STU... (5R01CA059045-04), NIH | USC COMPREHENSIVE CANCER ... (3P30CA014089-17S1),...

    Abstract Background Higher adiposity increases the risk of colorectal cancer (CRC), but whether this relationship varies by anatomical sub-site or by sex is unclear. Further, the metabolic alterations mediating the effects of adiposity on CRC are not fully understood. Methods We examined sex- and site-specific associations of adiposity with CRC risk and whether adiposity-associated metabolites explain the associations of adiposity with CRC. Genetic variants from genome-wide association studies of body mass index (BMI) and waist-to-hip ratio (WHR, unadjusted for BMI; N = 806,810), and 123 metabolites from targeted nuclear magnetic resonance metabolomics (N = 24,925), were used as instruments. Sex-combined and sex-specific Mendelian randomization (MR) was conducted for BMI and WHR with CRC risk (58,221 cases and 67,694 controls in the Genetics and Epidemiology of Colorectal Cancer Consortium, Colorectal Cancer Transdisciplinary Study, and Colon Cancer Family Registry). Sex-combined MR was conducted for BMI and WHR with metabolites, for metabolites with CRC, and for BMI and WHR with CRC adjusted for metabolite classes in multivariable models. Results In sex-specific MR analyses, higher BMI (per 4.2 kg/m2) was associated with 1.23 (95% confidence interval (CI) = 1.08, 1.38) times higher CRC odds among men (inverse-variance-weighted (IVW) model); among women, higher BMI (per 5.2 kg/m2) was associated with 1.09 (95% CI = 0.97, 1.22) times higher CRC odds. WHR (per 0.07 higher) was more strongly associated with CRC risk among women (IVW OR = 1.25, 95% CI = 1.08, 1.43) than men (IVW OR = 1.05, 95% CI = 0.81, 1.36). BMI or WHR was associated with 104/123 metabolites at false discovery rate-corrected P ≤ 0.05; several metabolites were associated with CRC, but not in directions that were consistent with the mediation of positive adiposity-CRC relations. In multivariable MR analyses, associations of BMI and WHR with CRC were not attenuated following adjustment for representative metabolite classes, e.g., the univariable IVW OR for BMI with CRC was 1.12 (95% CI = 1.00, 1.26), and this became 1.11 (95% CI = 0.99, 1.26) when adjusting for cholesterol in low-density lipoprotein particles. Conclusions Our results suggest that higher BMI more greatly raises CRC risk among men, whereas higher WHR more greatly raises CRC risk among women. Adiposity was associated with numerous metabolic alterations, but none of these explained associations between adiposity and CRC. More detailed metabolomic measures are likely needed to clarify the mechanistic pathways.

  • Open Access
    Authors: 
    Bull, Caroline J.; Bell, Joshua A.; Murphy, Neil; Sanderson, Eleanor; Smith, George Davey; Timpson, Nicholas J.; Banbury, Barbara L.; Albanes, Demetrius; Berndt, Sonja I.; Bézieau, Stéphane; +68 more
    Publisher: figshare
    Project: NIH | GWAS Identified Colorecta... (5R03CA153323-02), NIH | MOLECULAR EPIDEMIOLOGY OF... (5R01CA081488-05), NIH | Multiethnic Cohort Study ... (5R37CA054281-13), NIH | Metabolomic Strategies fo... (5R01CA207371-02), NIH | Administrative Core and E... (5P30DK034987-28), NHMRC | Risk factors for molecula... (509348), NHMRC | Epidemiology of Chronic D... (209057), UKRI | Mendelian randomization t... (MC_UU_00011/1), NIH | SOUTHWEST ONCOLOGY GROUP ... (2U10CA037429-04), NIH | Cancer Survivorship: Decr... (1K05CA152715-01),...

    Additional file 1: Table S1. Genetic variants used to instrument BMI, WHR and metabolites. Table S2. Assesment of instrument strength. Table S3. Colorectal cancer case distributions by study, sex and site. Table S4. LogOR colorectal cancer per SD higher BMI or WHR. Table S5. Beta change in NMR-detected metabolite per SD higher BMI or WHR. Table S6. LogOR colorectal cancer per SD higher BMI or WHR-driven NMR-detected metabolite. Table S7. Risk of overall colorectal cancer per SD higher adipose or metabolite trait, estimated using multivariable Mendelian randomization. Table S8. Posthoc investigations.

  • Open Access
    Authors: 
    Bull, Caroline J.; Bell, Joshua A.; Murphy, Neil; Sanderson, Eleanor; Smith, George Davey; Timpson, Nicholas J.; Banbury, Barbara L.; Albanes, Demetrius; Berndt, Sonja I.; Bézieau, Stéphane; +68 more
    Publisher: figshare
    Project: NIH | Long Term Multidisciplina... (5UM1CA186107-05), NIH | Diet, Activity, and Lifes... (5R01CA048998-15), NIH | AROMATIC AMINES AND COLON... (5R01CA060987-03), WT | Institutional Strategic S... (204813), NIH | MOFFITT CANCER CENTER SUP... (3P30CA076292-08S4), NIH | Epigenetic Events and Col... (5R01CA151993-03), NIH | The Colon Cancer Family R... (2U24CA074783-10), NIH | Epidemiologic Studies (5U19CA148107-04), NIH | The Epidemiology of Immun... (5R01CA197350-03), UKRI | Health of vegetarians (MR/M012190/1),...

    Additional file 2: Figure S1. Scatter plot of SNP-BMI and SNP-CRC associations. Figure S2. Scatter plot of SNP-BMI and SNP-CRC associations (female specific). Figure S3. Scatter plot of SNP-BMI and SNP-CRC associations (male specific). Figure S4. Scatter plot of SNP-BMI and SNP-colon cancer associations. Figure S5. Scatter plot of SNP-BMI and SNP-proximal colon cancer associations. Figure S6. Scatter plot of SNP-BMI and SNP-distal colon cancer associations. Figure S7. Scatter plot of SNP-BMI and SNP-rectal cancer associations. Figure S8. Forest plot showing individual SNP (black) and combined MR estimates (red; Egger and IVW) for the effect of BMI on CRC. Figure S9. Forest plot showing individual SNP (black) and combined MR estimates (red; Egger and IVW) for the effect of BMI on CRC (female specific). Figure S10. Forest plot showing individual SNP (black) and combined MR estimates (red; Egger and IVW) for the effect of BMI on CRC (male specific). Figure S11. Forest plot showing individual SNP (black) and combined MR estimates (red; Egger and IVW) for the effect of BMI on colon cancer. Figure S12. Forest plot showing individual SNP (black) and combined MR estimates (red; Egger and IVW) for the effect of BMI on proximal colon cancer. Figure S13. Forest plot showing individual SNP (black) and combined MR estimates (red; Egger and IVW) for the effect of BMI on distal colon cancer. Figure S14. Forest plot showing individual SNP (black) and combined MR estimates (red; Egger and IVW) for the effect of BMI on rectal cancer. Figure S15. Leave-one-out plot showing the association between BMI and CRC, following SNP-by-SNP removal from the model. Figure S16. Leave-one-out plot showing the association between BMI and CRC (femalespecific), following SNP-by-SNP removal from the model. Figure S17. Leave-one-out plot showing the association between BMI and CRC (malespecific), following SNP-by-SNP removal from the model. Figure S18. Leave-one-out plot showing the association between BMI and colon cancer, following SNP-by-SNP removal from the model. Figure S19. Leave-one-out plot showing the association between BMI and proximal colon cancer, following SNP-by-SNP removal from the model. Figure S20. Leave-one-out plot showing the association between BMI and distal colon cancer, following SNP-by-SNP removal from the model. Figure S21. Leave-one-out plot showing the association between BMI and rectal cancer, following SNP-by-SNP removal from the model. Figure S22. Scatter plot of SNP-WHR and SNP-CRC associations. Figure S23. Scatter plot of SNP-WHR and SNP-CRC associations (female specific). Figure S24. Scatter plot of SNP-WHR and SNP-CRC associations (male specific). Figure S25. Scatter plot of SNP-WHR and SNP-colon cancer associations. Figure S26. Scatter plot of SNP-WHR and SNP-proximal colon cancer associations. Figure S27. Scatter plot of SNP-WHR and SNP-distal colon cancer associations. Figure S28. Scatter plot of SNP-WHR and SNP-rectal cancer associations. Figure S29. Forest plot showing individual SNP (black) and combined MR estimates (red; Egger and IVW) for the effect of WHR on CRC. Figure S30. Forest plot showing individual SNP (black) and combined MR estimates (red; Egger and IVW) for the effect of WHR on CRC (female specific). Figure S31. Forest plot showing individual SNP (black) and combined MR estimates (red; Egger and IVW) for the effect of WHR on CRC (male specific). Figure S32. Forest plot showing individual SNP (black) and combined MR estimates (red; Egger and IVW) for the effect of WHR on colon cancer. Figure S33. Forest plot showing individual SNP (black) and combined MR estimates (red; Egger and IVW) for the effect of WHR on proximal colon cancer. Figure S34. Forest plot showing individual SNP (black) and combined MR estimates (red; Egger and IVW) for the effect of WHR on distal colon cancer. Figure S35. Forest plot showing individual SNP (black) and combined MR estimates (red; Egger and IVW) for the effect of WHR on rectal cancer. Figure S36. Leave-one-out plot showing the association between WHR and CRC, following SNP-by-SNP removal from the model. Figure S37. Leave-one-out plot showing the association between WHR and CRC, following SNP-by-SNP removal from the model (female specific). Figure S38. Leave-one-out plot showing the association between WHR and CRC, following SNP-by-SNP removal from the model (male specific). Figure S39. Leave-one-out plot showing the association between WHR and colon cancer, following SNP-by-SNP removal from the model. Figure S40. Leave-one-out plot showing the association between WHR and proximal colon cancer, following SNP-by-SNP removal from the model. Figure S41. Leave-one-out plot showing the association between WHR and distal colon cancer, following SNP-by-SNP removal from the model. Figure S42. Leave-one-out plot showing the association between WHR and rectal cancer, following SNP-by-SNP removal from the model. Figure S43. Effects of BMI and WHR on circulating metabolite levels (NMR-detected metabolites, 1 of 5), based on two-sample MR (IVW models) in summary GWAS consortia data. Figure S44. Effects of BMI and WHR on circulating metabolite levels (NMR-detected metabolites, 2 of 5), based on two-sample MR (IVW models) in summary GWAS consortia data. Figure S45. Effects of BMI and WHR on circulating metabolite levels (NMR-detected metabolites, 3 of 5), based on two-sample MR (IVW models) in summary GWAS consortia data. Figure S46. Effects of BMI and WHR on circulating metabolite levels (NMR-detected metabolites, 4 of 5), based on two-sample MR (IVW models) in summary GWAS consortia data. Figure S47. Effects of BMI and WHR on circulating metabolite levels (NMR-detected metabolites, 5 of 5), based on two-sample MR (IVW models) in summary GWAS consortia data. Figure S48. Power curves for MR analyses, based on samples sizes for colorectal cancer in the present study (black), Thrift et al., 2015 (blue) and Jarvis et al., 2016 (purple). Upper and lower power curves describe genetic instruments explaining 5% and 0.3% of variance respectively for each study.

  • Open Access English
    Authors: 
    Stephanie A. Bien; Yu Ru Su; David V. Conti; Tabitha A. Harrison; Conghui Qu; Xingyi Guo; Yingchang Lu; Demetrius Albanes; Paul L. Auer; Barbara L. Banbury; +68 more
    Publisher: Springer Science and Business Media LLC
    Countries: Denmark, Sweden, United Kingdom, Netherlands, Spain, Netherlands, Sweden
    Project: NIH | USC COMPREHENSIVE CANCER ... (3P30CA014089-17S1), NIH | Statistical analysis of g... (5R01MH101825-02), NIH | GENETIC EPIDEMIOLOGIC STU... (5R01CA059045-04), NIH | Tissue and Pathology Reso... (1P50CA127003-01), NHMRC | Risk factors for molecula... (509348), NIH | Comprehensive Colorectal ... (5R01CA206279-02), NIH | The Colon Cancer Family R... (2U24CA074783-10), NIH | Inflammation and Colorect... (5R01CA137178-05), NIH | Established Investigator ... (5K05CA154337-04), NIH | Core-004 (1P01CA196569-01A1),...

    Genome-wide association studies have reported 56 independently associated colorectal cancer (CRC) risk variants, most of which are non-coding and believed to exert their effects by modulating gene expression. The computational method PrediXcan uses cis-regulatory variant predictors to impute expression and perform gene-level association tests in GWAS without directly measured transcriptomes. In this study, we used reference datasets from colon (n=169) and whole blood (n=922) transcriptomes to test CRC association with genetically determined expression levels in a genome-wide analysis of 12,186 cases and 14,718 controls. Three novel associations were discovered from colon transverse models at FDR0.2 and further evaluated in an independent replication including 32,825 cases and 39,933 controls. After adjusting for multiple comparisons, we found statistically significant associations using colon transcriptome models with TRIM4 (discovery P=2.2x10(-4), replication P=0.01), and PYGL (discovery P=2.3x10(-4), replication P=6.7x10(-4)). Interestingly, both genes encode proteins that influence redox homeostasis and are related to cellular metabolic reprogramming in tumors, implicating a novel CRC pathway linked to cell growth and proliferation. Defining CRC risk regions as one megabase up- and downstream of one of the 56 independent risk variants, we defined 44 non-overlapping CRC-risk regions. Among these risk regions, we identified genes associated with CRC (P<0.05) in 34/44 CRC-risk regions. Importantly, CRC association was found for two genes in the previously reported 2q25 locus, CXCR1 and CXCR2, which are potential cancer therapeutic targets. These findings provide strong candidate genes to prioritize for subsequent laboratory follow-up of GWAS loci. This study is the first to implement PrediXcan in a large colorectal cancer study and findings highlight the utility of integrating transcriptome data in GWAS for discovery of, and biological insight into, risk loci. Correction in: HUMAN GENETICS, Genetic variant predictors of gene expression provide new insight into risk of colorectal cancer. Hum Genet 138, 789–791 (2019)DOI: 10.1007/s00439-019-02030-8

  • Open Access
    Authors: 
    Sonja Neumeyer; Barbara L. Banbury; Volker Arndt; Sonja I. Berndt; Stéphane Bézieau; Stephanie A. Bien; Daniel D. Buchanan; Katja Butterbach; Bette J. Caan; Peter T. Campbell; +41 more
    Publisher: Springer Science and Business Media LLC
    Countries: United Kingdom, Spain
    Project: NIH | Hormones and Colon Cancer... (5R01CA076366-07), NIH | Inflammation and Colorect... (5R01CA137178-05), NIH | Established Investigator ... (5K05CA154337-04), NIH | Genome Wide Association C... (5U01HG004446-04), NIH | Discovery and verificatio... (5R01CA189184-04), NIH | Tissue and Pathology Reso... (1P50CA127003-01), NIH | GENETIC EPIDEMIOLOGIC STU... (5R01CA059045-04), NIH | The Colon Cancer Family R... (2U24CA074783-10), NIH | Genomic Wide Association ... (7U01CA122839-03), NIH | Multiethnic Cohort Study ... (5R37CA054281-13),...

    BACKGROUND: Substantial evidence supports an association between use of menopausal hormone therapy and decreased colorectal cancer (CRC) risk, indicating a role of exogenous sex hormones in CRC development. However, findings on endogenous oestrogen exposure and CRC are inconsistent. METHODS: We used a Mendelian randomisation approach to test for a causal effect of age at menarche and age at menopause as surrogates for endogenous oestrogen exposure on CRC risk. Weighted genetic risk scores based on 358 single-nucleotide polymorphisms associated with age at menarche and 51 single-nucleotide polymorphisms associated with age at menopause were used to estimate the association with CRC risk using logistic regression in 12,944 women diagnosed with CRC and 10,741 women without CRC from three consortia. Sensitivity analyses were conducted to address pleiotropy and possible confounding by body mass index. RESULTS: Genetic risk scores for age at menarche (odds ratio per year 0.98, 95% confidence interval: 0.95-1.02) and age at menopause (odds ratio 0.98, 95% confidence interval: 0.94-1.01) were not significantly associated with CRC risk. The sensitivity analyses yielded similar results. CONCLUSIONS: Our study does not support a causal relationship between genetic risk scores for age at menarche and age at menopause and CRC risk.

  • Open Access
    Authors: 
    Jian Gong; Carolyn M Hutter; Polly A Newcomb; Cornelia M Ulrich; Stephanie A Bien; Peter T Campbell; John A Baron; Sonja I Berndt; Stephane Bezieau; Hermann Brenner; +39 more
    Publisher: Public Library of Science (PLoS)
    Countries: United States, United Kingdom
    Project: NIH | The Colon Cancer Family R... (2U24CA097735-06), NIH | A Whole Genome Admixture ... (3R01CA063464-09S1), NIH | Accelerating Transdiscipl... (5R35CA197735-04), NIH | Colon Cancer Family Regis... (4UM1CA167551-04), NIH | Multiethnic Cohort Study ... (5R37CA054281-13), NIH | Genome Wide Association C... (5U01HG004446-04), NIH | Genomic Wide Association ... (7U01CA122839-03), NIH | The Colon Cancer Family R... (2U24CA074783-10), NIH | PROSPECTIVE STUDY OF DIET... (3P01CA055075-10S1), NIH | Prospective Study on Sele... (5R01CA120582-08),...

    Genome-wide association studies (GWAS) have identified many genetic susceptibility loci for colorectal cancer (CRC). However, variants in these loci explain only a small proportion of familial aggregation, and there are likely additional variants that are associated with CRC susceptibility. Genome-wide studies of gene-environment interactions may identify variants that are not detected in GWAS of marginal gene effects. To study this, we conducted a genome-wide analysis for interaction between genetic variants and alcohol consumption and cigarette smoking using data from the Colon Cancer Family Registry (CCFR) and the Genetics and Epidemiology of Colorectal Cancer Consortium (GECCO). Interactions were tested using logistic regression. We identified interaction between CRC risk and alcohol consumption and variants in the 9q22.32/HIATL1 (Pinteraction = 1.76×10−8; permuted p-value 3.51x10-8) region. Compared to non-/occasional drinking light to moderate alcohol consumption was associated with a lower risk of colorectal cancer among individuals with rs9409565 CT genotype (OR, 0.82 [95% CI, 0.74–0.91]; P = 2.1×10−4) and TT genotypes (OR,0.62 [95% CI, 0.51–0.75]; P = 1.3×10−6) but not associated among those with the CC genotype (p = 0.059). No genome-wide statistically significant interactions were observed for smoking. If replicated our suggestive finding of a genome-wide significant interaction between genetic variants and alcohol consumption might contribute to understanding colorectal cancer etiology and identifying subpopulations with differential susceptibility to the effect of alcohol on CRC risk. Author Summary Alcohol consumption and smoking are associated with CRC risk. We performed a genome-wide analysis for interaction between genetic variants and alcohol consumption and cigarette smoking to identify potential new genetic regions associated with CRC. About 8,000 CRC cases and 8,800 controls were included in alcohol-related analysis and over 11,000 cases and 11,000 controls were involved in smoking-related analysis. We identified interaction between variants at 9q22.32/HIATL1 and alcohol consumption in relation to CRC risk (Pinteraction = 1.76×10−8). If replicated our suggested finding of the interaction between genetic variants and alcohol consumption might contribute to understanding colorectal cancer etiology and identifying subpopulations with differential susceptible to the effect of alcohol on CRC risk.

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Lifestyle, genetics and colonoscopy for colorectal cancer prevention (5K07CA190673-06)
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6 Research products, page 1 of 1
  • Open Access English
    Authors: 
    Caroline J. Bull; Joshua A. Bell; Neil Murphy; Eleanor Sanderson; George Davey Smith; Nicholas J. Timpson; Barbara L. Banbury; Demetrius Albanes; Sonja I. Berndt; Stéphane Bézieau; +68 more
    Countries: Netherlands, United Kingdom, Sweden, Netherlands, Spain
    Project: WT | The Avon Longitudinal Stu... (217065), NIH | Tissue and Pathology Reso... (1P50CA127003-01), NIH | Regional Oncology Researc... (3P30CA006973-53S2), NIH | SOUTHWEST ONCOLOGY GROUP ... (2U10CA037429-04), NIH | Cancer Survivorship: Decr... (1K05CA152715-01), NIH | Data sharing: the Colon C... (3U01CA167551-07S1), NIH | Accelerating Transdiscipl... (5R35CA197735-04), EC | PREVIEW (312057), NIH | GENETIC EPIDEMIOLOGIC STU... (5R01CA059045-04), NIH | USC COMPREHENSIVE CANCER ... (3P30CA014089-17S1),...

    Abstract Background Higher adiposity increases the risk of colorectal cancer (CRC), but whether this relationship varies by anatomical sub-site or by sex is unclear. Further, the metabolic alterations mediating the effects of adiposity on CRC are not fully understood. Methods We examined sex- and site-specific associations of adiposity with CRC risk and whether adiposity-associated metabolites explain the associations of adiposity with CRC. Genetic variants from genome-wide association studies of body mass index (BMI) and waist-to-hip ratio (WHR, unadjusted for BMI; N = 806,810), and 123 metabolites from targeted nuclear magnetic resonance metabolomics (N = 24,925), were used as instruments. Sex-combined and sex-specific Mendelian randomization (MR) was conducted for BMI and WHR with CRC risk (58,221 cases and 67,694 controls in the Genetics and Epidemiology of Colorectal Cancer Consortium, Colorectal Cancer Transdisciplinary Study, and Colon Cancer Family Registry). Sex-combined MR was conducted for BMI and WHR with metabolites, for metabolites with CRC, and for BMI and WHR with CRC adjusted for metabolite classes in multivariable models. Results In sex-specific MR analyses, higher BMI (per 4.2 kg/m2) was associated with 1.23 (95% confidence interval (CI) = 1.08, 1.38) times higher CRC odds among men (inverse-variance-weighted (IVW) model); among women, higher BMI (per 5.2 kg/m2) was associated with 1.09 (95% CI = 0.97, 1.22) times higher CRC odds. WHR (per 0.07 higher) was more strongly associated with CRC risk among women (IVW OR = 1.25, 95% CI = 1.08, 1.43) than men (IVW OR = 1.05, 95% CI = 0.81, 1.36). BMI or WHR was associated with 104/123 metabolites at false discovery rate-corrected P ≤ 0.05; several metabolites were associated with CRC, but not in directions that were consistent with the mediation of positive adiposity-CRC relations. In multivariable MR analyses, associations of BMI and WHR with CRC were not attenuated following adjustment for representative metabolite classes, e.g., the univariable IVW OR for BMI with CRC was 1.12 (95% CI = 1.00, 1.26), and this became 1.11 (95% CI = 0.99, 1.26) when adjusting for cholesterol in low-density lipoprotein particles. Conclusions Our results suggest that higher BMI more greatly raises CRC risk among men, whereas higher WHR more greatly raises CRC risk among women. Adiposity was associated with numerous metabolic alterations, but none of these explained associations between adiposity and CRC. More detailed metabolomic measures are likely needed to clarify the mechanistic pathways.

  • Open Access
    Authors: 
    Bull, Caroline J.; Bell, Joshua A.; Murphy, Neil; Sanderson, Eleanor; Smith, George Davey; Timpson, Nicholas J.; Banbury, Barbara L.; Albanes, Demetrius; Berndt, Sonja I.; Bézieau, Stéphane; +68 more
    Publisher: figshare
    Project: NIH | GWAS Identified Colorecta... (5R03CA153323-02), NIH | MOLECULAR EPIDEMIOLOGY OF... (5R01CA081488-05), NIH | Multiethnic Cohort Study ... (5R37CA054281-13), NIH | Metabolomic Strategies fo... (5R01CA207371-02), NIH | Administrative Core and E... (5P30DK034987-28), NHMRC | Risk factors for molecula... (509348), NHMRC | Epidemiology of Chronic D... (209057), UKRI | Mendelian randomization t... (MC_UU_00011/1), NIH | SOUTHWEST ONCOLOGY GROUP ... (2U10CA037429-04), NIH | Cancer Survivorship: Decr... (1K05CA152715-01),...

    Additional file 1: Table S1. Genetic variants used to instrument BMI, WHR and metabolites. Table S2. Assesment of instrument strength. Table S3. Colorectal cancer case distributions by study, sex and site. Table S4. LogOR colorectal cancer per SD higher BMI or WHR. Table S5. Beta change in NMR-detected metabolite per SD higher BMI or WHR. Table S6. LogOR colorectal cancer per SD higher BMI or WHR-driven NMR-detected metabolite. Table S7. Risk of overall colorectal cancer per SD higher adipose or metabolite trait, estimated using multivariable Mendelian randomization. Table S8. Posthoc investigations.

  • Open Access
    Authors: 
    Bull, Caroline J.; Bell, Joshua A.; Murphy, Neil; Sanderson, Eleanor; Smith, George Davey; Timpson, Nicholas J.; Banbury, Barbara L.; Albanes, Demetrius; Berndt, Sonja I.; Bézieau, Stéphane; +68 more
    Publisher: figshare
    Project: NIH | Long Term Multidisciplina... (5UM1CA186107-05), NIH | Diet, Activity, and Lifes... (5R01CA048998-15), NIH | AROMATIC AMINES AND COLON... (5R01CA060987-03), WT | Institutional Strategic S... (204813), NIH | MOFFITT CANCER CENTER SUP... (3P30CA076292-08S4), NIH | Epigenetic Events and Col... (5R01CA151993-03), NIH | The Colon Cancer Family R... (2U24CA074783-10), NIH | Epidemiologic Studies (5U19CA148107-04), NIH | The Epidemiology of Immun... (5R01CA197350-03), UKRI | Health of vegetarians (MR/M012190/1),...

    Additional file 2: Figure S1. Scatter plot of SNP-BMI and SNP-CRC associations. Figure S2. Scatter plot of SNP-BMI and SNP-CRC associations (female specific). Figure S3. Scatter plot of SNP-BMI and SNP-CRC associations (male specific). Figure S4. Scatter plot of SNP-BMI and SNP-colon cancer associations. Figure S5. Scatter plot of SNP-BMI and SNP-proximal colon cancer associations. Figure S6. Scatter plot of SNP-BMI and SNP-distal colon cancer associations. Figure S7. Scatter plot of SNP-BMI and SNP-rectal cancer associations. Figure S8. Forest plot showing individual SNP (black) and combined MR estimates (red; Egger and IVW) for the effect of BMI on CRC. Figure S9. Forest plot showing individual SNP (black) and combined MR estimates (red; Egger and IVW) for the effect of BMI on CRC (female specific). Figure S10. Forest plot showing individual SNP (black) and combined MR estimates (red; Egger and IVW) for the effect of BMI on CRC (male specific). Figure S11. Forest plot showing individual SNP (black) and combined MR estimates (red; Egger and IVW) for the effect of BMI on colon cancer. Figure S12. Forest plot showing individual SNP (black) and combined MR estimates (red; Egger and IVW) for the effect of BMI on proximal colon cancer. Figure S13. Forest plot showing individual SNP (black) and combined MR estimates (red; Egger and IVW) for the effect of BMI on distal colon cancer. Figure S14. Forest plot showing individual SNP (black) and combined MR estimates (red; Egger and IVW) for the effect of BMI on rectal cancer. Figure S15. Leave-one-out plot showing the association between BMI and CRC, following SNP-by-SNP removal from the model. Figure S16. Leave-one-out plot showing the association between BMI and CRC (femalespecific), following SNP-by-SNP removal from the model. Figure S17. Leave-one-out plot showing the association between BMI and CRC (malespecific), following SNP-by-SNP removal from the model. Figure S18. Leave-one-out plot showing the association between BMI and colon cancer, following SNP-by-SNP removal from the model. Figure S19. Leave-one-out plot showing the association between BMI and proximal colon cancer, following SNP-by-SNP removal from the model. Figure S20. Leave-one-out plot showing the association between BMI and distal colon cancer, following SNP-by-SNP removal from the model. Figure S21. Leave-one-out plot showing the association between BMI and rectal cancer, following SNP-by-SNP removal from the model. Figure S22. Scatter plot of SNP-WHR and SNP-CRC associations. Figure S23. Scatter plot of SNP-WHR and SNP-CRC associations (female specific). Figure S24. Scatter plot of SNP-WHR and SNP-CRC associations (male specific). Figure S25. Scatter plot of SNP-WHR and SNP-colon cancer associations. Figure S26. Scatter plot of SNP-WHR and SNP-proximal colon cancer associations. Figure S27. Scatter plot of SNP-WHR and SNP-distal colon cancer associations. Figure S28. Scatter plot of SNP-WHR and SNP-rectal cancer associations. Figure S29. Forest plot showing individual SNP (black) and combined MR estimates (red; Egger and IVW) for the effect of WHR on CRC. Figure S30. Forest plot showing individual SNP (black) and combined MR estimates (red; Egger and IVW) for the effect of WHR on CRC (female specific). Figure S31. Forest plot showing individual SNP (black) and combined MR estimates (red; Egger and IVW) for the effect of WHR on CRC (male specific). Figure S32. Forest plot showing individual SNP (black) and combined MR estimates (red; Egger and IVW) for the effect of WHR on colon cancer. Figure S33. Forest plot showing individual SNP (black) and combined MR estimates (red; Egger and IVW) for the effect of WHR on proximal colon cancer. Figure S34. Forest plot showing individual SNP (black) and combined MR estimates (red; Egger and IVW) for the effect of WHR on distal colon cancer. Figure S35. Forest plot showing individual SNP (black) and combined MR estimates (red; Egger and IVW) for the effect of WHR on rectal cancer. Figure S36. Leave-one-out plot showing the association between WHR and CRC, following SNP-by-SNP removal from the model. Figure S37. Leave-one-out plot showing the association between WHR and CRC, following SNP-by-SNP removal from the model (female specific). Figure S38. Leave-one-out plot showing the association between WHR and CRC, following SNP-by-SNP removal from the model (male specific). Figure S39. Leave-one-out plot showing the association between WHR and colon cancer, following SNP-by-SNP removal from the model. Figure S40. Leave-one-out plot showing the association between WHR and proximal colon cancer, following SNP-by-SNP removal from the model. Figure S41. Leave-one-out plot showing the association between WHR and distal colon cancer, following SNP-by-SNP removal from the model. Figure S42. Leave-one-out plot showing the association between WHR and rectal cancer, following SNP-by-SNP removal from the model. Figure S43. Effects of BMI and WHR on circulating metabolite levels (NMR-detected metabolites, 1 of 5), based on two-sample MR (IVW models) in summary GWAS consortia data. Figure S44. Effects of BMI and WHR on circulating metabolite levels (NMR-detected metabolites, 2 of 5), based on two-sample MR (IVW models) in summary GWAS consortia data. Figure S45. Effects of BMI and WHR on circulating metabolite levels (NMR-detected metabolites, 3 of 5), based on two-sample MR (IVW models) in summary GWAS consortia data. Figure S46. Effects of BMI and WHR on circulating metabolite levels (NMR-detected metabolites, 4 of 5), based on two-sample MR (IVW models) in summary GWAS consortia data. Figure S47. Effects of BMI and WHR on circulating metabolite levels (NMR-detected metabolites, 5 of 5), based on two-sample MR (IVW models) in summary GWAS consortia data. Figure S48. Power curves for MR analyses, based on samples sizes for colorectal cancer in the present study (black), Thrift et al., 2015 (blue) and Jarvis et al., 2016 (purple). Upper and lower power curves describe genetic instruments explaining 5% and 0.3% of variance respectively for each study.

  • Open Access English
    Authors: 
    Stephanie A. Bien; Yu Ru Su; David V. Conti; Tabitha A. Harrison; Conghui Qu; Xingyi Guo; Yingchang Lu; Demetrius Albanes; Paul L. Auer; Barbara L. Banbury; +68 more
    Publisher: Springer Science and Business Media LLC
    Countries: Denmark, Sweden, United Kingdom, Netherlands, Spain, Netherlands, Sweden
    Project: NIH | USC COMPREHENSIVE CANCER ... (3P30CA014089-17S1), NIH | Statistical analysis of g... (5R01MH101825-02), NIH | GENETIC EPIDEMIOLOGIC STU... (5R01CA059045-04), NIH | Tissue and Pathology Reso... (1P50CA127003-01), NHMRC | Risk factors for molecula... (509348), NIH | Comprehensive Colorectal ... (5R01CA206279-02), NIH | The Colon Cancer Family R... (2U24CA074783-10), NIH | Inflammation and Colorect... (5R01CA137178-05), NIH | Established Investigator ... (5K05CA154337-04), NIH | Core-004 (1P01CA196569-01A1),...

    Genome-wide association studies have reported 56 independently associated colorectal cancer (CRC) risk variants, most of which are non-coding and believed to exert their effects by modulating gene expression. The computational method PrediXcan uses cis-regulatory variant predictors to impute expression and perform gene-level association tests in GWAS without directly measured transcriptomes. In this study, we used reference datasets from colon (n=169) and whole blood (n=922) transcriptomes to test CRC association with genetically determined expression levels in a genome-wide analysis of 12,186 cases and 14,718 controls. Three novel associations were discovered from colon transverse models at FDR0.2 and further evaluated in an independent replication including 32,825 cases and 39,933 controls. After adjusting for multiple comparisons, we found statistically significant associations using colon transcriptome models with TRIM4 (discovery P=2.2x10(-4), replication P=0.01), and PYGL (discovery P=2.3x10(-4), replication P=6.7x10(-4)). Interestingly, both genes encode proteins that influence redox homeostasis and are related to cellular metabolic reprogramming in tumors, implicating a novel CRC pathway linked to cell growth and proliferation. Defining CRC risk regions as one megabase up- and downstream of one of the 56 independent risk variants, we defined 44 non-overlapping CRC-risk regions. Among these risk regions, we identified genes associated with CRC (P<0.05) in 34/44 CRC-risk regions. Importantly, CRC association was found for two genes in the previously reported 2q25 locus, CXCR1 and CXCR2, which are potential cancer therapeutic targets. These findings provide strong candidate genes to prioritize for subsequent laboratory follow-up of GWAS loci. This study is the first to implement PrediXcan in a large colorectal cancer study and findings highlight the utility of integrating transcriptome data in GWAS for discovery of, and biological insight into, risk loci. Correction in: HUMAN GENETICS, Genetic variant predictors of gene expression provide new insight into risk of colorectal cancer. Hum Genet 138, 789–791 (2019)DOI: 10.1007/s00439-019-02030-8

  • Open Access
    Authors: 
    Sonja Neumeyer; Barbara L. Banbury; Volker Arndt; Sonja I. Berndt; Stéphane Bézieau; Stephanie A. Bien; Daniel D. Buchanan; Katja Butterbach; Bette J. Caan; Peter T. Campbell; +41 more
    Publisher: Springer Science and Business Media LLC
    Countries: United Kingdom, Spain
    Project: NIH | Hormones and Colon Cancer... (5R01CA076366-07), NIH | Inflammation and Colorect... (5R01CA137178-05), NIH | Established Investigator ... (5K05CA154337-04), NIH | Genome Wide Association C... (5U01HG004446-04), NIH | Discovery and verificatio... (5R01CA189184-04), NIH | Tissue and Pathology Reso... (1P50CA127003-01), NIH | GENETIC EPIDEMIOLOGIC STU... (5R01CA059045-04), NIH | The Colon Cancer Family R... (2U24CA074783-10), NIH | Genomic Wide Association ... (7U01CA122839-03), NIH | Multiethnic Cohort Study ... (5R37CA054281-13),...

    BACKGROUND: Substantial evidence supports an association between use of menopausal hormone therapy and decreased colorectal cancer (CRC) risk, indicating a role of exogenous sex hormones in CRC development. However, findings on endogenous oestrogen exposure and CRC are inconsistent. METHODS: We used a Mendelian randomisation approach to test for a causal effect of age at menarche and age at menopause as surrogates for endogenous oestrogen exposure on CRC risk. Weighted genetic risk scores based on 358 single-nucleotide polymorphisms associated with age at menarche and 51 single-nucleotide polymorphisms associated with age at menopause were used to estimate the association with CRC risk using logistic regression in 12,944 women diagnosed with CRC and 10,741 women without CRC from three consortia. Sensitivity analyses were conducted to address pleiotropy and possible confounding by body mass index. RESULTS: Genetic risk scores for age at menarche (odds ratio per year 0.98, 95% confidence interval: 0.95-1.02) and age at menopause (odds ratio 0.98, 95% confidence interval: 0.94-1.01) were not significantly associated with CRC risk. The sensitivity analyses yielded similar results. CONCLUSIONS: Our study does not support a causal relationship between genetic risk scores for age at menarche and age at menopause and CRC risk.

  • Open Access
    Authors: 
    Jian Gong; Carolyn M Hutter; Polly A Newcomb; Cornelia M Ulrich; Stephanie A Bien; Peter T Campbell; John A Baron; Sonja I Berndt; Stephane Bezieau; Hermann Brenner; +39 more
    Publisher: Public Library of Science (PLoS)
    Countries: United States, United Kingdom
    Project: NIH | The Colon Cancer Family R... (2U24CA097735-06), NIH | A Whole Genome Admixture ... (3R01CA063464-09S1), NIH | Accelerating Transdiscipl... (5R35CA197735-04), NIH | Colon Cancer Family Regis... (4UM1CA167551-04), NIH | Multiethnic Cohort Study ... (5R37CA054281-13), NIH | Genome Wide Association C... (5U01HG004446-04), NIH | Genomic Wide Association ... (7U01CA122839-03), NIH | The Colon Cancer Family R... (2U24CA074783-10), NIH | PROSPECTIVE STUDY OF DIET... (3P01CA055075-10S1), NIH | Prospective Study on Sele... (5R01CA120582-08),...

    Genome-wide association studies (GWAS) have identified many genetic susceptibility loci for colorectal cancer (CRC). However, variants in these loci explain only a small proportion of familial aggregation, and there are likely additional variants that are associated with CRC susceptibility. Genome-wide studies of gene-environment interactions may identify variants that are not detected in GWAS of marginal gene effects. To study this, we conducted a genome-wide analysis for interaction between genetic variants and alcohol consumption and cigarette smoking using data from the Colon Cancer Family Registry (CCFR) and the Genetics and Epidemiology of Colorectal Cancer Consortium (GECCO). Interactions were tested using logistic regression. We identified interaction between CRC risk and alcohol consumption and variants in the 9q22.32/HIATL1 (Pinteraction = 1.76×10−8; permuted p-value 3.51x10-8) region. Compared to non-/occasional drinking light to moderate alcohol consumption was associated with a lower risk of colorectal cancer among individuals with rs9409565 CT genotype (OR, 0.82 [95% CI, 0.74–0.91]; P = 2.1×10−4) and TT genotypes (OR,0.62 [95% CI, 0.51–0.75]; P = 1.3×10−6) but not associated among those with the CC genotype (p = 0.059). No genome-wide statistically significant interactions were observed for smoking. If replicated our suggestive finding of a genome-wide significant interaction between genetic variants and alcohol consumption might contribute to understanding colorectal cancer etiology and identifying subpopulations with differential susceptibility to the effect of alcohol on CRC risk. Author Summary Alcohol consumption and smoking are associated with CRC risk. We performed a genome-wide analysis for interaction between genetic variants and alcohol consumption and cigarette smoking to identify potential new genetic regions associated with CRC. About 8,000 CRC cases and 8,800 controls were included in alcohol-related analysis and over 11,000 cases and 11,000 controls were involved in smoking-related analysis. We identified interaction between variants at 9q22.32/HIATL1 and alcohol consumption in relation to CRC risk (Pinteraction = 1.76×10−8). If replicated our suggested finding of the interaction between genetic variants and alcohol consumption might contribute to understanding colorectal cancer etiology and identifying subpopulations with differential susceptible to the effect of alcohol on CRC risk.