Meta-analysis of three genome-wide association studies identifies susceptibility loci for colorectal cancer at 1q41, 3q26.2, 12q13.13 and 20q13.33

Houlston, Richard S.; Cheadle, Jeremy P.; Dobbins, Sara E.; Tenesa, Albert; Jones, Alan M.; Howarth, Kimberley; Spain, Sarah L.; Broderick, Peter; Domingo, Enric; Farrington, Susan M.; Prendergast, James; Pittman, Alan; Τheodoratou, Evropi; Smith, Chris; Olver, Bianca; Walther, Axel; Barnetson, Rebecca A.; Churchman, Michael; Jaeger, Emma; Penegar, Steven; Barclay, Ella; Martin, Lynn; Gorman, Maggie; Mager, R.; Johnstone, Elaine; Midgley, Rachel; Niittymäki, Iina; Tuupanen, Sari; Colley, James; Idziaszczyk, Shelley; Thomas, Huw; Lucassen, Anneke; Evans, D. Gareth; Maher, Eamonn R.; Maughan, T; Dimas, Antigone S.; Dermitzakis, Emmanouil T.; Cazier, Jean‐Baptiste; Aaltonen, Lauri A.; Pharoah, Paul D.P.; Kerr, David J.; Carvajal‐Carmona, Luis G.; Campbell, Harry; Dunlop, Malcolm G.; Tomlinson, Ian

doi:10.1038/ng.670

Cited by 338 publications

(315 citation statements)

References 15 publications

(18 reference statements)

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“…34 In our data, the induced networks and several individual CpG sites, annotated to specific genes, suggest a mechanism related to tumor suppressor release (RB1), or oncogene activation (SOX2), which may explain part of the THM mechanism in humans. Moreover, some of the differentially methylated sites and regions are located in genes related to both or, specifically, to either bladder cancer 16,20 or colorectal cancer 21,35 (MYNN, SOX2, RB1, and SMAD3), which supports this hypothesis. In addition, the cancer-related KeGG pathway Genes were sorted by false discovery rate-FDR from the regression models adjusted for age, sex, the first principal component and the estimated white blood cell proportion.…”

Section: Discussionsupporting

confidence: 68%

DNA methylation levels and long-term trihalomethane exposure in drinking water: an epigenome-wide association study

et al. 2015

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Trihalomethanes (THM) are undesired disinfection byproducts (DBPs) formed during water treatment. Mice exposed to DBPs showed global DNA hypomethylation and c-myc and c-jun gene-specific hypomethylation, while evidence of epigenetic effects in humans is scarce. We explored the association between lifetime THM exposure and DNA methylation through an epigenome-wide association study. We selected 138 population-based controls from a casecontrol study of colorectal cancer conducted in Barcelona, Spain, exposed to average lifetime THM levels 85 mg/L vs. >85 mg/L (N D 68 and N D 70, respectively). Mean age of participants was 70 years, and 54% were male. Average lifetime THM level in the exposure groups was 64 and 130 mg/L, respectively. DNA was extracted from whole blood and was bisulphite converted to measure DNA methylation levels using the Illumina HumanMethylation450 BeadChip. Data preprocessing was performed using RnBeads. Methylation was compared between exposure groups using empirical Bayes moderated linear regression for CpG sites and Gaussian kernel for CpG regions. ConsensusPathDB was used for gene set enrichment. Statistically significant differences in methylation between exposure groups was found in 140 CpG sites and 30 gene-related regions, after false discovery rate <0.05 and adjustment for age, sex, methylation first principal component, and blood cell proportion. The annotated genes were localized to several cancer pathways. Among them, 29 CpGs had methylation levels associated with THM levels (|Db| 0.05) located in 11 genes associated with cancer in other studies. Our results suggest that THM exposure may affect DNA methylation in genes related to tumors, including colorectal and bladder cancers. Future confirmation studies are required.

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Section: Discussionsupporting

confidence: 68%

DNA methylation levels and long-term trihalomethane exposure in drinking water: an epigenome-wide association study

et al. 2015

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“…However, increasing sample size is difficult in the context of animal production due to difficulties in having populations with comparable phenotypes and the limited availability of samples from commercial production systems (Houlston et al . 2010). Furthermore, animal populations contain stratification that, if ignored, can result in spurious associations between markers and traits of interest (Rabinowitz 1997; Hirschhorn & Daly 2005).…”

Section: Introductionmentioning

confidence: 99%

Meta‐analysis of genome‐wide association from genomic prediction models

Rubio

Duarte²,

Bates³

et al. 2015

Animal Genetics

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SummaryGenome‐wide association (GWA) studies based on GBLUP models are a common practice in animal breeding. However, effect sizes of GWA tests are small, requiring larger sample sizes to enhance power of detection of rare variants. Because of difficulties in increasing sample size in animal populations, one alternative is to implement a meta‐analysis (MA), combining information and results from independent GWA studies. Although this methodology has been used widely in human genetics, implementation in animal breeding has been limited. Thus, we present methods to implement a MA of GWA, describing the proper approach to compute weights derived from multiple genomic evaluations based on animal‐centric GBLUP models. Application to real datasets shows that MA increases power of detection of associations in comparison with population‐level GWA, allowing for population structure and heterogeneity of variance components across populations to be accounted for. Another advantage of MA is that it does not require access to genotype data that is required for a joint analysis. Scripts related to the implementation of this approach, which consider the strength of association as well as the sign, are distributed and thus account for heterogeneity in association phase between QTL and SNPs. Thus, MA of GWA is an attractive alternative to summarizing results from multiple genomic studies, avoiding restrictions with genotype data sharing, definition of fixed effects and different scales of measurement of evaluated traits.

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“…In a recent meta-analysis, three genome wide association studies from the UK were combined to include 3,334 affected individuals (cases) and 4,628 controls, to be followed by multiple validation analyses including a 18,095 cases and 20,197 controls [12]. This meta-analysis identified associations at four new CRC risk loci: 1q41 (rs6691170 and rs6687758), 3q26.2 (rs10936599) and 12q13.13 (rs11169552 rs7136702) and 20q13.33 (rs4925386) [12]. However, more studies are needed to clarify the role of gene environmental interactions in the development of CRC.…”

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confidence: 99%