Ability of known susceptibility SNPs to predict colorectal cancer risk for persons with and without a family history

Personalised medicine based on host genetics and microbiota applied to colorectal cancer

Colorectal cancer (CRC) ranks second in incidence and third in cancer mortality worldwide. This situation, together with the understanding of the heterogeneity of the disease, has highlighted the need to develop a more individualised approach to its prevention, diagnosis and treatment through personalised medicine. This approach aims to stratify patients according to risk, predict disease progression and determine the most appropriate treatment. It is essential to identify patients who may respond adequately to treatment and those who may be resistant to treatment to avoid unnecessary therapies and minimise adverse side effects. Current research is focused on identifying biomarkers such as specific mutated genes, the type of mutations and molecular profiles critical for the individualisation of CRC diagnosis, prognosis and treatment guidance. In addition, the study of the intestinal microbiota as biomarkers is being incorporated due to the growing scientific evidence supporting its influence on this disease. This article comprehensively addresses the use of current and emerging diagnostic, prognostic and predictive biomarkers in precision medicine against CRC. The effects of host genetics and gut microbiota composition on new approaches to treating this disease are discussed. How the gut microbiota could mitigate the side effects of treatment is reviewed. In addition, strategies to modulate the gut microbiota, such as dietary interventions, antibiotics, and transplantation of faecal microbiota and phages, are discussed to improve CRC prevention and treatment. These findings provide a solid foundation for future research and improving the care of CRC patients.

Clinically relevant combined effect of polygenic background, rare pathogenic germline variants, and family history on colorectal cancer incidence

BACKGROUND AND AIMS

Summarised in polygenic risk scores (PRS), the effect of common, low penetrant genetic variants associated with colorectal cancer (CRC), can be used for risk stratification.

METHODS

To assess the combined impact of the PRS and other main factors on CRC risk, 163,516 individuals from the UK Biobank were stratified as follows: 1. carriers status for germline pathogenic variants (PV) in CRC susceptibility genes (APC, MLH1, MSH2, MSH6, PMS2), 2. low (< 20%), intermediate (20-80%), or high PRS (> 80%), and 3. family history (FH) of CRC. Multivariable logistic regression and Cox proportional hazards models were applied to compare odds ratios and to compute the lifetime incidence, respectively.

RESULTS

Depending on the PRS, the CRC lifetime incidence for non-carriers ranges between 6 and 22%, compared to 40% and 74% for carriers. A suspicious FH is associated with a further increase of the cumulative incidence reaching 26% for non-carriers and 98% for carriers. In non-carriers without FH, but high PRS, the CRC risk is doubled, whereas a low PRS even in the context of a FH results in a decreased risk. The full model including PRS, carrier status, and FH improved the area under the curve in risk prediction (0.704).

CONCLUSION

The findings demonstrate that CRC risks are strongly influenced by the PRS for both a sporadic and monogenic background. FH, PV, and common variants complementary contribute to CRC risk. The implementation of PRS in routine care will likely improve personalized risk stratification, which will in turn guide tailored preventive surveillance strategies in high, intermediate, and low risk groups.

Predicting the predisposition to colorectal cancer based on SNP profiles of immune phenotypes using supervised learning models

This study explores the machine learning-based assessment of predisposition to colorectal cancer based on single nucleotide polymorphisms (SNP). Such a computational approach may be used as a risk indicator and an auxiliary diagnosis method that complements the traditional methods such as biopsy and CT scan. Moreover, it may be used to develop a low-cost screening test for the early detection of colorectal cancers to improve public health. We employ several supervised classification algorithms. Besides, we apply data imputation to fill in the missing genotype values. The employed dataset includes SNPs observed in particular colorectal cancer-associated genomic loci that are located within DNA regions of 11 selected genes obtained from 115 individuals. We make the following observations: (i) random forest-based classifier using one-hot encoding and K-nearest neighbor (KNN)-based imputation performs the best among the studied classifiers with an F1 score of 89% and area under the curve (AUC) score of 0.96. (ii) One-hot encoding together with K-nearest neighbor-based data imputation increases the F1 scores by around 26% in comparison to the baseline approach which does not employ them. (iii) The proposed model outperforms a commonly employed state-of-the-art approach, ColonFlag, under all evaluated settings by up to 24% in terms of the AUC score. Based on the high accuracy of the constructed predictive models, the studied 11 genes may be considered a gene panel candidate for colon cancer risk screening.

A scoping review of risk-stratified bowel screening: current evidence, future directions

PURPOSE

In this scoping review, we examined the international literature on risk-stratified bowel screening to develop recommendations for future research, practice and policy.

METHODS

Six electronic databases were searched from inception to 18 October 2021: Medline, Embase, PsycINFO, CINAHL, Cochrane Database of Systematic Reviews and Cochrane Central Register of Controlled Trials. Forward and backwards citation searches were also undertaken. All relevant literature were included.

RESULTS

After de-deduplication, 3,629 records remained. 3,416 were excluded at the title/abstract screening stage. A further 111 were excluded at full-text screening stage. In total, 102 unique studies were included. Results showed that risk-stratified bowel screening programmes can potentially improve diagnostic performance, but there is a lack of information on longer-term outcomes. Risk models do appear to show promise in refining existing risk stratification guidelines but most were not externally validated and less than half achieved good discriminatory power. Risk assessment tools in primary care have the potential for high levels of acceptability and uptake, and therefore, could form an important component of future risk-stratified bowel screening programmes, but sometimes the screening recommendations were not adhered to by the patient or healthcare provider. The review identified important knowledge gaps, most notably in the area of organisation of screening services due to few pilots, and what risk stratification might mean for inequalities.

CONCLUSION

We recommend that future research focuses on what organisational challenges risk-stratified bowel screening may face and a consideration of inequalities in any changes to organised bowel screening programmes.

Risk Stratification for Early-Onset Colorectal Cancer Using a Combination of Genetic and Environmental Risk Scores: An International Multi-Center Study

BACKGROUND

The incidence of colorectal cancer (CRC) among individuals aged younger than 50 years has been increasing. As screening guidelines lower the recommended age of screening initiation, concerns including the burden on screening capacity and costs have been recognized, suggesting that an individualized approach may be warranted. We developed risk prediction models for early-onset CRC that incorporate an environmental risk score (ERS), including 16 lifestyle and environmental factors, and a polygenic risk score (PRS) of 141 variants.

METHODS

Relying on risk score weights for ERS and PRS derived from studies of CRC at all ages, we evaluated risks for early-onset CRC in 3486 cases and 3890 controls aged younger than 50 years. Relative and absolute risks for early-onset CRC were assessed according to values of the ERS and PRS. The discriminatory performance of these scores was estimated using the covariate-adjusted area under the receiver operating characteristic curve.

RESULTS

Increasing values of ERS and PRS were associated with increasing relative risks for early-onset CRC (odds ratio per SD of ERS = 1.14, 95% confidence interval [CI] = 1.08 to 1.20; odds ratio per SD of PRS = 1.59, 95% CI = 1.51 to 1.68), both contributing to case-control discrimination (area under the curve = 0.631, 95% CI = 0.615 to 0.647). Based on absolute risks, we can expect 26 excess cases per 10 000 men and 21 per 10 000 women among those scoring at the 90th percentile for both risk scores.

CONCLUSIONS

Personal risk scores have the potential to identify individuals at differential relative and absolute risk for early-onset CRC. Improved discrimination may aid in targeted CRC screening of younger, high-risk individuals, potentially improving outcomes.

Polygenic risk prediction models for colorectal cancer: a systematic review

BACKGROUND

Risk prediction models incorporating single nucleotide polymorphisms (SNPs) could lead to individualized prevention of colorectal cancer (CRC). However, the added value of incorporating SNPs into models with only traditional risk factors is still not clear. Hence, our primary aim was to summarize literature on risk prediction models including genetic variants for CRC, while our secondary aim was to evaluate the improvement of discriminatory accuracy when adding SNPs to a prediction model with only traditional risk factors.

METHODS

We conducted a systematic review on prediction models incorporating multiple SNPs for CRC risk prediction. We tested whether a significant trend in the increase of Area Under Curve (AUC) according to the number of SNPs could be observed, and estimated the correlation between AUC improvement and number of SNPs. We estimated pooled AUC improvement for SNP-enhanced models compared with non-SNP-enhanced models using random effects meta-analysis, and conducted meta-regression to investigate the association of specific factors with AUC improvement.

RESULTS

We included 33 studies, 78.79% using genetic risk scores to combine genetic data. We found no significant trend in AUC improvement according to the number of SNPs (p for trend = 0.774), and no correlation between the number of SNPs and AUC improvement (p = 0.695). Pooled AUC improvement was 0.040 (95% CI: 0.035, 0.045), and the number of cases in the study and the AUC of the starting model were inversely associated with AUC improvement obtained when adding SNPs to a prediction model. In addition, models constructed in Asian individuals achieved better AUC improvement with the incorporation of SNPs compared with those developed among individuals of European ancestry.

CONCLUSIONS

Though not conclusive, our results provide insights on factors influencing discriminatory accuracy of SNP-enhanced models. Genetic variants might be useful to inform stratified CRC screening in the future, but further research is needed.

A Comparison of Ten Polygenic Score Methods for Psychiatric Disorders Applied Across Multiple Cohorts

BACKGROUND

Polygenic scores (PGSs), which assess the genetic risk of individuals for a disease, are calculated as a weighted count of risk alleles identified in genome-wide association studies. PGS methods differ in which DNA variants are included and the weights assigned to them; some require an independent tuning sample to help inform these choices. PGSs are evaluated in independent target cohorts with known disease status. Variability between target cohorts is observed in applications to real data sets, which could reflect a number of factors, e.g., phenotype definition or technical factors.

METHODS

The Psychiatric Genomics Consortium Working Groups for schizophrenia and major depressive disorder bring together many independently collected case-control cohorts. We used these resources (31,328 schizophrenia cases, 41,191 controls; 248,750 major depressive disorder cases, 563,184 controls) in repeated application of leave-one-cohort-out meta-analyses, each used to calculate and evaluate PGS in the left-out (target) cohort. Ten PGS methods (the baseline PC+T method and 9 methods that model genetic architecture more formally: SBLUP, LDpred2-Inf, LDpred-funct, LDpred2, Lassosum, PRS-CS, PRS-CS-auto, SBayesR, MegaPRS) were compared.

RESULTS

Compared with PC+T, the other 9 methods gave higher prediction statistics, MegaPRS, LDPred2, and SBayesR significantly so, explaining up to 9.2% variance in liability for schizophrenia across 30 target cohorts, an increase of 44%. For major depressive disorder across 26 target cohorts, these statistics were 3.5% and 59%, respectively.

CONCLUSIONS

Although the methods that more formally model genetic architecture have similar performance, MegaPRS, LDpred2, and SBayesR rank highest in most comparisons and are recommended in applications to psychiatric disorders.

Ability of known colorectal cancer susceptibility SNPs to predict colorectal cancer risk: A cohort study within the UK Biobank

Colorectal cancer risk stratification is crucial to improve screening and risk-reducing recommendations, and consequently do better than a one-size-fits-all screening regimen. Current screening guidelines in the UK, USA and Australia focus solely on family history and age for risk prediction, even though the vast majority of the population do not have any family history. We investigated adding a polygenic risk score based on 45 single-nucleotide polymorphisms to a family history model (combined model) to quantify how it improves the stratification and discriminatory performance of 10-year risk and full lifetime risk using a prospective population-based cohort within the UK Biobank. For both 10-year and full lifetime risk, the combined model had a wider risk distribution compared with family history alone, resulting in improved risk stratification of nearly 2-fold between the top and bottom risk quintiles of the full lifetime risk model. Importantly, the combined model can identify people (n = 72,019) who do not have family history of colorectal cancer but have a predicted risk that is equivalent to having at least one affected first-degree relative (n = 44,950). We also confirmed previous findings by showing that the combined full lifetime risk model significantly improves discriminatory accuracy compared with a simple family history model 0.673 (95% CI 0.664–0.682) versus 0.666 (95% CI 0.657–0.675), p = 0.0065. Therefore, a combined polygenic risk score and first-degree family history model could be used to improve risk stratified population screening programs.

Immune-related genes STIM1, ITPKC and PELI1 polymorphisms are associated with risk of colorectal cancer

OBJECTIVES

STIM1, ITPKC and PELI1 are all immune-related genes that take part in the T cell activation, toll-like receptor and IL1 receptor pathways. The goal of this study was to evaluate the associations between STIM1, ITPKC and PELI1 polymorphisms and colorectal cancer (CRC) risk.

METHODS

Six single nucleotide polymorphisms (SNPs) in STIM1, ITPKC and PELI1 were genotyped using a MassARRAY platform in a discovery cohort including 480 CRC cases and 480 healthy individuals and validated in a replication cohort including 505 CRC cases and 510 controls.

RESULTS

The minor alleles of rs3794050, rs3750996 and rs2607420 were associated with an increased CRC risk (P < 0.05). In contrast, the minor allele of rs329497 was correlated with reduced disease risk (P = 0.025). Genetic model analysis showed that rs3794050 was related to an increased risk of disease in recessive and log-additive models (P < 0.05); rs3750996 had a strong correlation with CRC risk under all genetic models (P < 0.02); rs2607420 was correlated with an increased risk of disease in dominant and log-additive models (P < 0.01); whereas the protective effect of rs329497 on CRC risk was observed in dominant and log-additive models (P < 0.05). Finally, the association between the above SNPs and CRC risk was validated in a replication cohort (P < 0.05).

CONCLUSIONS

Our results could be helpful for the early screening of individuals with high CRC risk.

Smoking, Genetic Predisposition, and Colorectal Cancer Risk

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.

From Basic Science to Clinical Application of Polygenic Risk Scores: A Primer

IMPORTANCE

Polygenic risk scores (PRS) are predictors of the genetic susceptibilities of individuals to diseases. All individuals have DNA risk variants for all common diseases, but genetic susceptibility differences between people reflect the cumulative burden of these. Polygenic risk scores for an individual are calculated as weighted counts of thousands of risk variants that they carry, where the risk variants and their weights have been identified in genome-wide association studies. Here, we review the underlying basic science of PRS, providing a foundation for understanding the potential clinical utility and limitations of PRS.

OBSERVATIONS

Polygenic risk scores can be calculated for a wide range of diseases from a saliva or blood sample using genotyping technologies that are inexpensive. While genotyping only needs to be done once for each individual in their lifetime, the PRS can be recalculated as identification of risk variants improves. On their own, PRS will never be able to establish or definitively predict future diagnoses of common complex conditions because genetic factors only contribute part of the risk, and PRS will only ever capture part of the genetic contributions. Nonetheless, just as clinical medicine uses a multitude of other predictive measures, PRS either on their own or as part of multivariable predictive algorithms could play a role.

CONCLUSIONS AND RELEVANCE

Utility of PRS in clinical medicine and ethical issues related to their use should be evaluated in the context of realistic expectations of what PRS can and cannot deliver. For different diseases, PRS could have utility in community settings (stratification to better triage people into established screening programs) or could contribute to clinical decision-making for those presenting with symptoms but where formal diagnosis is unclear. In principle, PRS could contribute to treatment choices, but more data are needed to allow development of PRS in this context.

AKT1 Polymorphism (rs10138227) and Risk of Colorectal Cancer in Moroccan Population: A Case Control Study

BACKGROUND

LMTK3 and AKT1 each have a role in carcinogenesis and tumor progression. The analysis of single nucleotide polymorphisms of AKT1 and LMTK3 could lead to more complete and accurate risk estimates for colorectal cancer.

AIM

We evaluated the association between single nucleotide polymorphisms (SNPs) of AKT1 and LMTK3 and the risk of colorectal cancer in a case-control study in Moroccan population.

METHODS

Genomic DNA from 70 colorectal cancer patients and 50 healthy control subjects was extracted from whole blood. Genotyping was performed by direct sequencing after polymerase chain reactions for the 7 SNPs (AKT1rs1130214G/T, AKT1rs10138227C/T, AKT1rs3730358C/T, AKT1rs1000559097G/A, AKT1rs2494737A/T, LMTK3rs8108419G/A, and LMTK3rs9989661A/G.). Study subjects provided detailed information during the collection. All P values come from bilateral tests.

RESULTS

In the logistic regression analysis, a significantly high risk of colorectal cancer was associated with TC/TT genotypes of rs10138227 with adjusted odds ratio [OR] equal to 2.82 and 95% confidence interval [CI] of 1.15 to 6.91.

CONCLUSION

Our results suggest that the SNP AKT1rs10138227 could affect susceptibility to CRC, probably by modulating the transcriptional activity of AKT1. However, larger independent studies are needed to validate our results.

The Impact of a Comprehensive Risk Prediction Model for Colorectal Cancer on a Population Screening Program

BACKGROUND

In many countries, population colorectal cancer (CRC) screening is based on age and family history, though more precise risk prediction could better target screening. We examined the impact of a CRC risk prediction model (incorporating age, sex, lifestyle, genomic, and family history factors) to target screening under several feasible screening scenarios.

METHODS

We estimated the model's predicted CRC risk distribution in the Australian population. Predicted CRC risks were categorized into screening recommendations under 3 proposed scenarios to compare with current recommendations: 1) highly tailored, 2) 3 risk categories, and 3) 4 sex-specific risk categories. Under each scenario, for 35- to 74-year-olds, we calculated the number of CRC screens by immunochemical fecal occult blood testing (iFOBT) and colonoscopy and the proportion of predicted CRCs over 10 years in each screening group.

RESULTS

Currently, 1.1% of 35- to 74-year-olds are recommended screening colonoscopy and 56.2% iFOBT, and 5.7% and 83.2% of CRCs over 10 years were predicted to occur in these groups, respectively. For the scenarios, 1) colonoscopy was recommended to 8.1% and iFOBT to 37.5%, with 36.1% and 50.1% of CRCs in each group; 2) colonoscopy was recommended to 2.4% and iFOBT to 56.0%, with 13.2% and 76.9% of cancers in each group; and 3) colonoscopy was recommended to 5.0% and iFOBT to 54.2%, with 24.5% and 66.5% of cancers in each group.

CONCLUSIONS

A highly tailored CRC screening scenario results in many fewer screens but more cancers in those unscreened. Category-based scenarios may provide a good balance between number of screens and cancers detected and are simpler to implement.

A Genomic Test for Colorectal Cancer Risk: Is This Acceptable and Feasible in Primary Care?

INTRODUCTION

Genomic tests can predict risk and tailor screening recommendations for colorectal cancer (CRC). Primary care could be suitable for their widespread implementation.

OBJECTIVE

We aimed to assess the feasibility and acceptability of administering a CRC genomic test in primary care.

METHODS

Participants aged 45-74 years recruited from 4 Australian general practices were offered a genomic CRC risk test. Participants received brief verbal information about the test comprising 45 CRC-associated single-nucleotide polymorphisms, before choosing whether to undertake the test. Personalized risks were given to testers. Uptake and knowledge of the genomic test, cancer-specific anxiety (Cancer Worry Scale), psychosocial impact (Multidimensional Impact of Cancer Risk Assessment [MICRA] score), and impact on CRC screening behaviour within 6 months were measured.

RESULTS

In 150 participants, test uptake was high (126, 84%), with 125 (83%) having good knowledge of the genomic test. Moderate risk participants were impacted more by the test (MICRA mean: 15.9) than average risk participants (mean: 9.5, difference in means: 6.4, 95% confidence interval (CI): 1.5, 11.2, p = 0.01), but all scores were low. Average risk participants' cancer-specific anxiety decreased (mean differences from baseline: 1 month -0.5, 95% CI: -1.0, -0.1, p = 0.03; 6 months -0.6, 95% CI: -1.0, -0.2, p = 0.01). We found limited evidence for genomic testers being more likely to complete the risk-appropriate CRC screening than non-testers (41 vs. 17%, odds ratio = 3.4, 95% CI: 0.6, 34.8, p = 0.19), but some mediators of screening behaviour were altered in genomic testers.

CONCLUSIONS

Genomic testing for CRC risk in primary care is acceptable and likely feasible. Further development of the risk assessment intervention could strengthen the impact on screening behaviour.

The emerging field of polygenic risk scores and perspective for use in clinical care

Genetic testing is used widely for diagnostic, carrier and predictive testing in monogenic diseases. Until recently, there were no genetic testing options available for multifactorial complex diseases like heart disease, diabetes and cancer. Genome-wide association studies (GWAS) have been invaluable in identifying single-nucleotide polymorphisms (SNPs) associated with increased or decreased risk for hundreds of complex disorders. For a given disease, SNPs can be combined to generate a cumulative estimation of risk known as a polygenic risk score (PRS). After years of research, PRSs are increasingly used in clinical settings. In this article, we will review the literature on how both genome-wide and restricted PRSs are developed and the relative merit of each. The validation and evaluation of PRSs will also be discussed, including the recognition that PRS validity is intrinsically linked to the methodological and analytical approach of the foundation GWAS together with the ethnic characteristics of that cohort. Specifically, population differences may affect imputation accuracy, risk magnitude and direction. Even as PRSs are being introduced into clinical practice, there is a push to combine them with clinical and demographic risk factors to develop a holistic disease risk. The existing evidence regarding the clinical utility of PRSs is considered across four different domains: informing population screening programs, guiding therapeutic interventions, refining risk for families at high risk, and facilitating diagnosis and predicting prognostic outcomes. The evidence for clinical utility in relation to five well-studied disorders is summarized. The potential ethical, legal and social implications are also highlighted.

External Validation of Risk Prediction Models Incorporating Common Genetic Variants for Incident Colorectal Cancer Using UK Biobank

The aim of this study was to compare and externally validate risk scores developed to predict incident colorectal cancer that include common genetic variants (SNPs), with or without established lifestyle/environmental (questionnaire-based/classical/phenotypic) risk factors. We externally validated 23 risk models from a previous systematic review in 443,888 participants ages 37 to 73 from the UK Biobank cohort who had 6-year prospective follow-up, no prior history of colorectal cancer, and data for incidence of colorectal cancer through linkage to national cancer registries. There were 2,679 (0.6%) cases of incident colorectal cancer. We assessed model discrimination using the area under the operating characteristic curve (AUC) and relative risk calibration. The AUC of models including only SNPs increased with the number of included SNPs and was similar in men and women: the model by Huyghe with 120 SNPs had the highest AUC of 0.62 [95% confidence interval (CI), 0.59-0.64] in women and 0.64 (95% CI, 0.61-0.66) in men. Adding phenotypic risk factors without age improved discrimination in men but not in women. Adding phenotypic risk factors and age increased discrimination in all cases (P < 0.05), with the best performing models including SNPs, phenotypic risk factors, and age having AUCs between 0.64 and 0.67 in women and 0.67 and 0.71 in men. Relative risk calibration varied substantially across the models. Among middle-aged people in the UK, existing polygenic risk scores discriminate moderately well between those who do and do not develop colorectal cancer over 6 years. Consideration should be given to exploring the feasibility of incorporating genetic and lifestyle/environmental information in any future stratified colorectal cancer screening program.

Risk Prediction Models for Colorectal Cancer Incorporating Common Genetic Variants: A Systematic Review

Colorectal cancer screening reduces colorectal cancer incidence and mortality. Risk models based on phenotypic variables have relatively good discrimination in external validation and may improve efficiency of screening. Models incorporating genetic variables may perform better. In this review, we updated our previous review by searching Medline and EMBASE from the end date of that review (January 2014) to February 2019 to identify models incorporating at least one SNP and applicable to asymptomatic individuals in the general population. We identified 23 new models, giving a total of 29. Of those in which the SNP selection was on the basis of published genome-wide association studies, in external or split-sample validation the AUROC was 0.56 to 0.57 for models that included SNPs alone, 0.61 to 0.63 for SNPs in combination with other risk factors, and 0.56 to 0.70 when age was included. Calibration was only reported for four. The addition of SNPs to other risk factors increases discrimination by 0.01 to 0.06. Public health modeling studies suggest that, if determined by risk models, the range of starting ages for screening would be several years greater than using family history alone. Further validation and calibration studies are needed alongside modeling studies to assess the population-level impact of introducing genetic risk-based screening programs.