A new method for post Genome-Wide Association Study (GWAS) analysis of colorectal cancer in Taiwan

Wang, Hwei Ming; Chang, Tzu Hao; Lin, Feng; Chao, Te Hsin; Huang, Wei; Liang, Chao; Chu, Chao Fang; Chiu, Chih Min; Wu, Wei; Chen, Ming Cheng; Weng, Chen; Weng, Shun Long; Chiang, Feng Fan; Huang, Hsien Da

doi:10.1016/j.gene.2012.11.067

Cited by 15 publications

(14 citation statements)

References 68 publications

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“…Three models have been developed in China [16,23,36] and Japan [40] while, Netherlands [25,26], Germany [21], Taiwan [32,46] and United Kingdom [31,34] have developed two models each. Many other countries, such as Australia [19], Poland [24], Hong Kong [28], Italy [29], Qatar [41], Saudi Arabia [43], Spain [52] and Sri Lanka [15] have also developed country-specific risk prediction models for colorectal cancer.…”

Section: Questionnairementioning

confidence: 99%

“…Sixteen models were developed using cohort designs where most of the cases were identified through cancer registries over a period of follow-up. Four risk prediction models were developed from reviewing published literature [30,31,32,49], while one model was developed using a consensus procedure [54].…”

Section: Development Of the Modelsmentioning

confidence: 99%

“…The questionnaires range from those with only one or two simple questions to those with detailed questions on factors such as diet, physical activity, alcohol and smoking habits and past medical facts. Three purely genetic models have used only a blood test for genetic biomarkers [32,34,39], while thirteen models have used data from a questionnaire and the results of blood tests for genetic biomarkers and other biochemical tests [16,17,20,27,31,35,47]. One model has obtained data both from a questionnaire and medical records [15].…”

Section: Development Of the Modelsmentioning

confidence: 99%

“…One model [30] was developed from meta-analysis of various studies, one [31] used risk modelling software in a simulated population and one [26] used pure bivariate analysis. Several other statistical methods were used in the development of models such as jackknife feature selection and ANOVA testing [32], multivariate non-linear Poisson regression [42], recursive portioning analysis [44] and classification tree analysis [53]. However, four models were identified as developed from non-statistical methods such as consensus method [25,51], extracting data from previously published validated models [49] or from previous studies [54].…”

Section: Development Of the Modelsmentioning

confidence: 99%

“…Among the models four were purely genetic [17,32,34,47], seven models have both components [16,27,31,35,39] while the rest of the models are non-genetic (n=48). The authors of the 58 risk prediction models have considered 77 different risk factors (excluding genetic factors) as shown in Table 3.…”

Section: Risk Predictors In the Developed Modelsmentioning

confidence: 99%

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Risk prediction models for colorectal cancer: A Scoping review

Samarakoon¹,

Pathmeswaran

Gunawardena³

2018

J. Postgrad. Inst. Med.

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Background and objectives Many risk prediction models have been developed globally to identify specific populations at high risk for colorectal cancer in specific settings. Documentation of available evidence from existing studies will serve as a useful information base. We performed a scoping review, to review and analyse published risk prediction models for colorectal cancer the world over. Methods A scoping review was undertaken to address the following question 'what are the existing risk prediction models to identify the risk of developing colorectal cancer among individuals in different countries and settings?' using the framework developed by Arksey and O'Malley for scoping reviews. Forty-one articles were included in this review from database searches and from additional searches. The titles and abstracts were reviewed using predetermined screening criteria. We limited our search to existing literature in English language and included both observational and interventional studies. Results Out of the 58 risk prediction models identified, most were developed for colorectal cancer followed by advanced colorectal cancer. Most of the articles reviewed were cross sectional studies or cohort studies. Statistical methods such as multiple logistic regression was used by a majority, while few have incorporated non-statistical methods such as consensus method and extracting data from published literature. The authors of the 58 risk prediction models have considered 77 different risk factors excluding the genetic variants. Conclusions This comprehensive scoping review demonstrates the capacity of the existing risk models to stratify the general population into risk categories, detailing the studies conducted, location, study design, outcome, overview of the methods, data source and the identified risk predictors. While striving to build on existing knowledge, the review also identifies the research gaps and the need for further improvement.

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