Genome-wide Modeling of Polygenic Risk Score in Colorectal Cancer Risk

Thomas, Minta; Sakoda, Lori C.; Hoffmeister, Michael; Rosenthal, Elisabeth; Lee, Jeffrey K.; Duijnhoven, Fränzel J.B. van; Platz, Elizabeth A.; Wu, Anna H.; Dampier, Christopher H.; Chapelle, Albert de la; Wolk, Alicja; Joshi, Amit D.; Burnett‐Hartman, Andrea N.; Gsur, Andrea; Lindblom, Annika; Castells, Antoni; Win, Aung Ko; Namjou, Bahram; Guelpen, Bethany Van; Tangen, Catherine M.; He, Qianchuan; Li, Christopher I.; Schafmayer, Clemens; Joshu, Corinne E.; Ulrich, Cornelia M.; Bishop, D. Timothy; Buchanan, Daniel D.; Schaid, Daniel J.; Drew, David A.; Muller, David C.; Duggan, David; Crosslin, David R.; Albanês, Demetrius; Giovannucci, Edward; Larson, Eric B.; Qu, Flora; Mentch, Frank; Giles, Graham G.; Hákonarson, Hákon; Hampel, Heather; Stanaway, Ian B.; Figueiredo, Jane C.; Huyghe, Jeroen R.; Minnier, Jessica; Chang‐Claude, Jenny; Hampe, Jochen; Harley, John B.; Visvanathan, Kala; Curtis, Keith R.; Offit, Kenneth; Li, Li; Marchand, Loı̈c Le; Vodičková, Ľudmila; Gunter, Marc J.; Jenkins, Mark A.; Slattery, Martha L.; Lemire, Mathieu; Woods, Michael O.; Song, Mingyang; Murphy, Neil; Lindor, Noralane M.; Dikilitas, Ozan; Pharoah, Paul D.P.; Campbell, Peter T.; Newcomb, Polly A.; Milne, Roger L.; MacInnis, Robert J.; Castellví–Bel, Sergi; Ogino, Shuji; Berndt, Sonja I.; Bézieau, Stéphane; Thibodeau, Stephen N.; Gallinger, Steven; Zaidi, Syed Hassan Ejaz; Harrison, Tabitha A.; Keku, Temitope O.; Hudson, Thomas J.; Vymetalková, Veronika; Moreno, Vı́ctor; Martín, Vicente; Arndt, Volker; Wei, Wei Qi; Chung, Wendy K.; Su, Yu Ru; Hayes, Richard B.; White, Emily; Vodička, Pavel; Casey, Graham; Gruber, Stephen B.; Schoen, Robert E.; Chan, Andrew T.; Potter, John D.; Brenner, Hermann; Jarvik, Gail P.; Corley, Douglas A.; Peters, Ulrike; Hsu, Li

doi:10.1016/j.ajhg.2020.07.006

Cited by 130 publications

(158 citation statements)

References 47 publications

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“…From the authors' point of view, the results obtained are coherent with previous results to be found in existing literature. Regarding the AUC values, it must be stated that although they would seem to be low, they are in line with other research [43]. In the case of the AMPK signaling pathway, previous studies found that AMPK promotes the survival of colorectal cancer stem cells [44].…”

Section: Discussionsupporting

confidence: 85%

GASVeM: A New Machine Learning Methodology for Multi-SNP Analysis of GWAS Data Based on Genetic Algorithms and Support Vector Machines

Díaz¹,

Lasheras

Moratalla-Navarro

et al. 2021

Mathematics

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Genome-wide association studies (GWAS) are observational studies of a large set of genetic variants in an individual’s sample in order to find if any of these variants are linked to a particular trait. In the last two decades, GWAS have contributed to several new discoveries in the field of genetics. This research presents a novel methodology to which GWAS can be applied to. It is mainly based on two machine learning methodologies, genetic algorithms and support vector machines. The database employed for the study consisted of information about 370,750 single-nucleotide polymorphisms belonging to 1076 cases of colorectal cancer and 973 controls. Ten pathways with different degrees of relationship with the trait under study were tested. The results obtained showed how the proposed methodology is able to detect relevant pathways for a certain trait: in this case, colorectal cancer.

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Section: Discussionsupporting

confidence: 85%

GASVeM: A New Machine Learning Methodology for Multi-SNP Analysis of GWAS Data Based on Genetic Algorithms and Support Vector Machines

Díaz¹,

Lasheras

Moratalla-Navarro

et al. 2021

Mathematics

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“…Details regarding genotyping and imputation are provided in Supplementary Table S1 (see Supplementary Digital Content 1, http://links.lww.com/CTG/A517 ). A total of 140 single nucleotide polymorphisms (SNPs) identified to be associated with CRC risk in populations of European descent in a recent GWAS ( 16 ) were extracted from our data set. We calculated the PRS as the sum of risk alleles of the respective variants (0, 1, or 2 copies of the risk allele for genotyped SNPs; imputed dosages for imputed SNPs), and categorized based on the distribution of the PRS among controls (cutoffs at the 10th, 25th, 75th, and 90th percentile, respectively).…”

Section: Methodsmentioning

confidence: 99%

“…It is unclear, however, to what extent it interacts with genetic predisposition in its impact on CRC risk. Although previous studies have explored interactions of smoking with specific single CRC susceptibility loci ( 9 – 12 ), data are lacking on the individual and joint contribution of smoking and overall genetic risk, as summarized by a polygenic risk score (PRS) based on large numbers of CRC susceptibility loci that have been identified by genomewide association studies (GWAS) in the past 2 decades ( 13 – 16 ). Detailed knowledge of such individual and joint contributions would be of high relevance for enhanced risk stratification and targeted efforts of prevention.…”

Section: Introductionmentioning

confidence: 99%

Smoking, Genetic Predisposition, and Colorectal Cancer Risk

Chen

Jansen

Guo

et al. 2021

Clinical and Translational Gastroenterology

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INTRODUCTION: Smoking and genetic predisposition are established risk factors for colorectal cancer (CRC). We aimed to assess and compare their individual and joint impact on CRC risk using the novel approach of genetic risk equivalent (GRE). METHODS: Data were extracted from the Darmkrebs: Chancen der Verhütung durch Screening study, a large population-based case-control study in Germany. A polygenic risk score (PRS) based on 140 CRC-related single nucleotide polymorphisms was derived to quantify genetic risk. Multiple logistic regression was used to estimate the individual and joint impact of smoking and PRS on CRC risk, and to quantify the smoking effect in terms of GRE, the corresponding effect conveyed by a defined difference in PRS percentiles. RESULTS: There were 5,086 patients with CRC and 4,120 controls included. Current smokers had a 48% higher risk of CRC than never smokers (adjusted odds ratio 1.48, 95% confidence interval 1.27–1.72). A PRS above the 90th percentile was significantly associated with a 3.6-, 4.3-, and 6.4-fold increased risk of CRC in never, former, and current smokers, respectively, when compared with a PRS below the 10th percentile in never smokers. The interaction between smoking and PRS on CRC risk did not reach statistical significance (P = 0.53). The effect of smoking was equivalent to the effect of having a 30 percentile higher level of PRS (GRE 30, 95% confidence interval 18–42). DISCUSSION: Both smoking and the PRS carry essentially independent CRC risk information, and their joint consideration provides powerful risk stratification. Abstinence from smoking can compensate for a substantial proportion of genetically determined CRC risk.

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“…A validated set of 140 CRC-related SNPs that had been identified by GWASs among populations of European descent was used to construct the PRS ( Supplementary Table 2 , available online) ( 8 ). We calculated the PRS by summing the product of reported regression coefficients and number of risk alleles (0, 1, and 2) across the 140 SNPs for all study participants.…”

Section: Methodsmentioning

confidence: 99%

Colorectal Cancer Risk by Genetic Variants in Populations With and Without Colonoscopy History

Guo

Chen

Chang‐Claude

et al. 2021

JNCI Cancer Spectrum

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Background Polygenic risk scores (PRS), which are derived from results of large genome-wide association studies (GWAS), are increasingly propagated for colorectal cancer (CRC) risk stratification. The majority of studies included in the large GWAS consortia were conducted in the United States and Germany, where colonoscopy with detection and removal of polyps has been widely practiced over the last decades. We aimed to assess if and to what extent the history of colonoscopy with polypectomy may alter metrics of the predictive ability of PRS for CRC risk. Methods A PRS based on 140 single nucleotide polymorphisms was compared between 4939 CRC cases and 3797 controls of the DACHS study, a population-based case-control study conducted in Germany. Risk discrimination was quantified according to the history of colonoscopy and polypectomy by areas under the curves (AUCs) and their 95% confidence intervals (CIs). All statistical tests were two-sided. Results AUCs (95% CIs) were higher among subjects without previous colonoscopy (0.622 [0.606–0.639]) than among those with previous colonoscopy and polypectomy (0.568 [0.536–0.601], difference [Δ AUC] = 0.054, p = .004). Such differences were consistently seen in sex-specific groups (women: Δ AUC = 0.073, p = .02; men: Δ AUC = 0.046, p = .048) and age-specific groups (<70 years: Δ AUC = 0.052, p = .07; ≥70 years: Δ AUC= 0.049, p = .045). Conclusions Predictive performance of PRS may be underestimated in populations with widespread use of colonoscopy. Future studies using PRS to develop CRC prediction models should carefully consider colonoscopy history in order to provide more accurate estimates.

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Genome-wide Modeling of Polygenic Risk Score in Colorectal Cancer Risk

Cited by 130 publications

References 47 publications

GASVeM: A New Machine Learning Methodology for Multi-SNP Analysis of GWAS Data Based on Genetic Algorithms and Support Vector Machines

GASVeM: A New Machine Learning Methodology for Multi-SNP Analysis of GWAS Data Based on Genetic Algorithms and Support Vector Machines

Smoking, Genetic Predisposition, and Colorectal Cancer Risk

Colorectal Cancer Risk by Genetic Variants in Populations With and Without Colonoscopy History

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