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Inclusion of a Genetic Risk Score into a Validated Risk Prediction Model for Colorectal Cancer in Japanese Men Improves Performance.

ABSTRACT: We previously developed and validated a risk prediction model for colorectal cancer in Japanese men using modifiable risk factors. To further improve risk prediction, we evaluated the degree of improvement obtained by adding a genetic risk score (GRS) using genome-wide association study (GWAS)-identified risk variants to our validated model. We examined the association between 36 risk variants identified by GWAS and colorectal cancer risk using a weighted Cox proportional hazards model in a nested case-control study within the Japan Public Health Center-based Prospective Study. GRS was constructed using six variants associated with risk in this study of the 36 tested. We assessed three models: a nongenetic model that included the same variables used in our previously validated model; a genetic model that used GRS; and an inclusive model, which included both. The c-statistic, integrated discrimination improvement (IDI), and net reclassification improvement (NRI) were calculated by the 5-fold cross-validation method. We estimated 10-year absolute risks for developing colorectal cancer. A statistically significant association was observed between the weighted GRS and colorectal cancer risk. The mean c-statistic for the inclusive model (0.66) was slightly greater than that for the nongenetic model (0.60). Similarly, the mean IDI and NRI showed improvement when comparing the nongenetic and inclusive models. These models for colorectal cancer were well calibrated. The addition of GRS using GWAS-identified risk variants to our validated model for Japanese men improved the prediction of colorectal cancer risk. Cancer Prev Res; 10(9); 535-41. ©2017 AACR.

SUBMITTER: Iwasaki M

PROVIDER: S-EPMC6365161 | biostudies-literature | 2017 Sep

REPOSITORIES: biostudies-literature

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Inclusion of a Genetic Risk Score into a Validated Risk Prediction Model for Colorectal Cancer in Japanese Men Improves Performance.

Iwasaki Motoki M Tanaka-Mizuno Sachiko S Kuchiba Aya A Yamaji Taiki T Sawada Norie N Goto Atsushi A Shimazu Taichi T Sasazuki Shizuka S Wang Hansong H Marchand Loïc Le LL Tsugane Shoichiro S

Cancer prevention research (Philadelphia, Pa.) 20170720 9

We previously developed and validated a risk prediction model for colorectal cancer in Japanese men using modifiable risk factors. To further improve risk prediction, we evaluated the degree of improvement obtained by adding a genetic risk score (GRS) using genome-wide association study (GWAS)-identified risk variants to our validated model. We examined the association between 36 risk variants identified by GWAS and colorectal cancer risk using a weighted Cox proportional hazards model in a nest ...[more]

PMID: 28729251

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Project description:IntroductionStratification of Alzheimer's disease (AD) patients into risk subgroups using Polygenic Risk Scores (PRS) presents novel opportunities for the development of clinical trials and disease-modifying therapies. However, the heterogeneous nature of AD continues to pose significant challenges for the clinical broadscale use of PRS. PRS remains unfit in demonstrating sufficient accuracy in risk prediction, particularly for individuals with mild cognitive impairment (MCI), and in allowing feasible interpretation of specific genes or SNPs contributing to disease risk. We propose adORS, a novel oligogenic risk score for AD, to better predict risk of disease by using an optimized list of relevant genetic risk factors.MethodsUsing whole genome sequencing data from the Alzheimer's Disease Neuroimaging Initiative (ADNI) cohort (n = 1,545), we selected 20 genes that exhibited the strongest correlations with FDG-PET and AV45-PET, recognized neuroimaging biomarkers that detect functional brain changes in AD. This subset of genes was incorporated into adORS to assess, in comparison to PRS, the prediction accuracy of CN vs. AD classification and MCI conversion prediction, risk stratification of the ADNI cohort, and interpretability of the genetic information included in the scores.ResultsadORS improved AUC scores over PRS in both CN vs. AD classification and MCI conversion prediction. The oligogenic model also refined risk-based stratification, even without the assistance of APOE, thus reflecting the true prevalence rate of the ADNI cohort compared to PRS. Interpretation analysis shows that genes included in adORS, such as ATF6, EFCAB11, ING5, SIK3, and CD46, have been observed in similar neurodegenerative disorders and/or are supported by AD-related literature.DiscussionCompared to conventional PRS, adORS may prove to be a more appropriate choice of differentiating patients into high or low genetic risk of AD in clinical studies or settings. Additionally, the ability to interpret specific genetic information allows the focus to be shifted from general relative risk based on a given population to the information that adORS can provide for a single individual, thus permitting the possibility of personalized treatments for AD.

Dataset Information

Inclusion of a Genetic Risk Score into a Validated Risk Prediction Model for Colorectal Cancer in Japanese Men Improves Performance.

Publications

Inclusion of a Genetic Risk Score into a Validated Risk Prediction Model for Colorectal Cancer in Japanese Men Improves Performance.

Similar Datasets

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