BACKGROUND: Colorectal cancer is a major global public health problem, with approximately 950,000 patients newly diagnosed each year. We report the first comprehensive field synopsis and creation of a parallel publicly available and regularly updated database (CRCgene) that catalogs all genetic association studies on colorectal cancer (http://www.chs.med.ed.ac.uk/CRCgene/). METHODS: We performed two independent systematic reviews, reviewing 10 145 titles, then collated and extracted data from 635 publications reporting on 445 polymorphisms in 110 different genes. We carried out meta-analyses to derive summary effect estimates for 92 polymorphisms in 64 different genes. For assessing the credibility of associations, we applied the Venice criteria and the Bayesian False Discovery Probability (BFDP) test. RESULTS: We consider 16 independent variants at 13 loci (MUTYH, MTHFR, SMAD7, and common variants tagging the loci 8q24, 8q23.3, 11q23.1, 14q22.2, 1q41, 20p12.3, 20q13.33, 3q26.2, 16q22.1, and 19q13.1) to have the most highly credible associations with colorectal cancer, with all variants except those in MUTYH and 19q13.1 reaching genome-wide statistical significance in at least one meta-analysis model. We identified less-credible (higher heterogeneity, lower statistical power, BFDP >0.2) associations with 23 more variants at 22 loci. The meta-analyses of a further 20 variants for which associations have previously been reported found no evidence to support these as true associations. CONCLUSION: The CRCgene database provides the context for genetic association data to be interpreted appropriately and helps inform future research direction.
BACKGROUND: Colorectal cancer is a major global public health problem, with approximately 950,000 patients newly diagnosed each year. We report the first comprehensive field synopsis and creation of a parallel publicly available and regularly updated database (CRCgene) that catalogs all genetic association studies on colorectal cancer (http://www.chs.med.ed.ac.uk/CRCgene/). METHODS: We performed two independent systematic reviews, reviewing 10 145 titles, then collated and extracted data from 635 publications reporting on 445 polymorphisms in 110 different genes. We carried out meta-analyses to derive summary effect estimates for 92 polymorphisms in 64 different genes. For assessing the credibility of associations, we applied the Venice criteria and the Bayesian False Discovery Probability (BFDP) test. RESULTS: We consider 16 independent variants at 13 loci (MUTYH, MTHFR, SMAD7, and common variants tagging the loci 8q24, 8q23.3, 11q23.1, 14q22.2, 1q41, 20p12.3, 20q13.33, 3q26.2, 16q22.1, and 19q13.1) to have the most highly credible associations with colorectal cancer, with all variants except those in MUTYH and 19q13.1 reaching genome-wide statistical significance in at least one meta-analysis model. We identified less-credible (higher heterogeneity, lower statistical power, BFDP >0.2) associations with 23 more variants at 22 loci. The meta-analyses of a further 20 variants for which associations have previously been reported found no evidence to support these as true associations. CONCLUSION: The CRCgene database provides the context for genetic association data to be interpreted appropriately and helps inform future research direction.
Authors: Karen W Makar; Elizabeth M Poole; Alexa J Resler; Brenna Seufert; Karen Curtin; Sarah E Kleinstein; David Duggan; Richard J Kulmacz; Li Hsu; John Whitton; Christopher S Carlson; Christine F Rimorin; Bette J Caan; John A Baron; John D Potter; Martha L Slattery; Cornelia M Ulrich Journal: Cancer Causes Control Date: 2013-12 Impact factor: 2.506
Authors: Cindy M Chang; Victoria M Chia; Marc J Gunter; Krista A Zanetti; Bríd M Ryan; Julie E Goodman; Curtis C Harris; Joel Weissfeld; Wen-Yi Huang; Stephen Chanock; Meredith Yeager; Richard B Hayes; Sonja I Berndt Journal: Carcinogenesis Date: 2013-06-26 Impact factor: 4.944
Authors: Ashwin N Ananthakrishnan; Mengmeng Du; Sonja I Berndt; Hermann Brenner; Bette J Caan; Graham Casey; Jenny Chang-Claude; David Duggan; Charles S Fuchs; Steven Gallinger; Edward L Giovannucci; Tabitha A Harrison; Richard B Hayes; Michael Hoffmeister; John L Hopper; Lifang Hou; Li Hsu; Mark A Jenkins; Peter Kraft; Jing Ma; Hongmei Nan; Polly A Newcomb; Shuji Ogino; John D Potter; Daniela Seminara; Martha L Slattery; Mark Thornquist; Emily White; Kana Wu; Ulrike Peters; Andrew T Chan Journal: Cancer Epidemiol Biomarkers Prev Date: 2014-10-23 Impact factor: 4.254
Authors: Hansong Wang; Darin Taverna; Daniel O Stram; Barbara K Fortini; Iona Cheng; Lynne R Wilkens; Terrilea Burnett; Karen W Makar; Noralane M Lindor; John L Hopper; Steve Gallinger; John A Baron; Robert Haile; Laurence N Kolonel; Brian E Henderson; Polly A Newcomb; Graham Casey; David Duggan; Cornelia M Ulrich; Loïc Le Marchand Journal: Cancer Epidemiol Biomarkers Prev Date: 2013-09-17 Impact factor: 4.254