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

Prediction Model Using Readily Available Clinical Data for Colorectal Cancer in Chinese Population

BACKGROUND

In China, health screening has become common, although colonoscopy is not always available or acceptable. We sought to develop a prediction model of colorectal cancer (CRC) for health screening population based on readily available clinical data to reduce labor and economic costs.

METHODS

We conducted a cross-sectional study based on a health screening population in Karamay Central Hospital. By collecting clinical data and basic information from participants, we identified independent risk factors and established a prediction model of CRC. Internal and external validation, calibration plot, and decision curve analysis were employed to test discriminating ability, calibration ability, and clinical practicability.

RESULTS

Independent risk factors of CRC, which were readily available in basic public health institutions, included high-density lipoprotein cholesterol, male sex, total cholesterol, advanced age, and hemoglobin. These factors were successfully incorporated into the prediction model (AUC 0.740, 95% CI 0.713-0.767). The model demonstrated a high degree of discrimination and calibration, in addition to a high degree of clinical practicability in high-risk people.

CONCLUSIONS

The prediction model exhibits good discrimination and calibration and is pragmatic for CRC screening in rural areas and basic public health institutions.

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.

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.

Biological function integrated prediction of severe radiographic progression in rheumatoid arthritis: a nested case control study

Background

Radiographic progression is reported to be highly heritable in rheumatoid arthritis (RA). However, previous study using genetic loci showed an insufficient accuracy of prediction for radiographic progression. The aim of this study is to identify a biologically relevant prediction model of radiographic progression in patients with RA using a genome-wide association study (GWAS) combined with bioinformatics analysis.

Methods

We obtained genome-wide single nucleotide polymorphism (SNP) data for 374 Korean patients with RA using Illumina HumanOmni2.5Exome-8 arrays. Radiographic progression was measured using the yearly Sharp/van der Heijde modified score rate, and categorized in no or severe progression. Significant SNPs for severe radiographic progression from GWAS were mapped on the functional genes and reprioritized by post-GWAS analysis. For robust prediction of radiographic progression, tenfold cross-validation using a support vector machine (SVM) classifier was conducted. Accuracy was used for selection of optimal SNPs set in the Hanyang Bae RA cohort. The performance of our final model was compared with that of other models based on GWAS results and SPOT (one of the post-GWAS analyses) using receiver operating characteristic (ROC) curves. The reliability of our model was confirmed using GWAS data of Caucasian patients with RA.

Results

A total of 36,091 significant SNPs with a p value <0.05 from GWAS were reprioritized using post-GWAS analysis and approximately 2700 were identified as SNPs related to RA biological features. The best average accuracy of ten groups was 0.6015 with 85 SNPs, and this increased to 0.7481 when combined with clinical information. In comparisons of the performance of the model, the 0.7872 area under the curve (AUC) in our model was superior to that obtained with GWAS (AUC 0.6586, p value 8.97 × 10^-5) or SPOT (AUC 0.7449, p value 0.0423). Our model strategy also showed superior prediction accuracy in Caucasian patients with RA compared with GWAS (p value 0.0049) and SPOT (p value 0.0151).

Conclusions

Using various biological functions of SNPs and repeated machine learning, our model could predict severe radiographic progression relevantly and robustly in patients with RA compared with models using only GWAS results or other post-GWAS tools.

Electronic supplementary material

The online version of this article (doi:10.1186/s13075-017-1414-x) contains supplementary material, which is available to authorized users.

Risk Model for Colorectal Cancer in Spanish Population Using Environmental and Genetic Factors: Results from the MCC-Spain study

Colorectal cancer (CRC) screening of the average risk population is only indicated according to age. We aim to elaborate a model to stratify the risk of CRC by incorporating environmental data and single nucleotide polymorphisms (SNP). The MCC-Spain case-control study included 1336 CRC cases and 2744 controls. Subjects were interviewed on lifestyle factors, family and medical history. Twenty-one CRC susceptibility SNPs were genotyped. The environmental risk model, which included alcohol consumption, obesity, physical activity, red meat and vegetable consumption, and nonsteroidal anti-inflammatory drug use, contributed to CRC with an average per factor OR of 1.36 (95% CI 1.27 to 1.45). Family history of CRC contributed an OR of 2.25 (95% CI 1.87 to 2.72), and each additional SNP contributed an OR of 1.07 (95% CI 1.04 to 1.10). The risk of subjects with more than 25 risk alleles (5^th quintile) was 82% higher (OR 1.82, 95% CI 1.11 to 2.98) than subjects with less than 19 alleles (1^st quintile). This risk model, with an AUROC curve of 0.63 (95% CI 0.60 to 0.66), could be useful to stratify individuals. Environmental factors had more weight than the genetic score, which should be considered to encourage patients to achieve a healthier lifestyle.

Risk Prediction Models for Colorectal Cancer: A Systematic Review

Colorectal cancer is the second leading cause of cancer-related death in Europe and the United States. Survival is strongly related to stage at diagnosis and population-based screening reduces colorectal cancer incidence and mortality. Stratifying the population by risk offers the potential to improve the efficiency of screening. In this systematic review we searched Medline, EMBASE, and the Cochrane Library for primary research studies reporting or validating models to predict future risk of primary colorectal cancer for asymptomatic individuals. A total of 12,808 papers were identified from the literature search and nine through citation searching. Fifty-two risk models were included. Where reported (n = 37), half the models had acceptable-to-good discrimination (the area under the receiver operating characteristic curve, AUROC >0.7) in the derivation sample. Calibration was less commonly assessed (n = 21), but overall acceptable. In external validation studies, 10 models showed acceptable discrimination (AUROC 0.71-0.78). These include two with only three variables (age, gender, and BMI; age, gender, and family history of colorectal cancer). A small number of prediction models developed from case-control studies of genetic biomarkers also show some promise but require further external validation using population-based samples. Further research should focus on the feasibility and impact of incorporating such models into stratified screening programmes.

Pathway analysis of genome-wide association study on serum prostate-specific antigen levels

The wide application of prostate-specific antigen (PSA) has contributed to the early diagnosis and improved management of prostate cancer (PCa). Accumulating evidence has suggested the involvement of genetic components in regulating serum PSA levels, and several single nucleotide polymorphisms (SNPs) have been identified by genome-wide association studies (GWASs). However, the GWASs' results have the limited power to identify the causal variants and pathways. After the quality control filters, a total of 330,540 genotyped SNPs from one GWAS with 657 PCa-free Caucasian males were included for the identify candidate causal SNPs and pathways (ICSNPathway) analysis. In addition, the genotype-phenotype association analysis has been conducted with the data from HapMap database. Overall, a total of four SNPs in three genes and six pathways were identified by ICSNPathway analysis, which in total provided three hypothetical mechanisms. First, CYP26B1 rs2241057 polymorphism (nonsynonymous coding) which leads to a Leu-to-Ser amino acid shift at position 264, was implicated in the pathways including meiosis, proximal/distal pattern formation, and M phase of meiotic cell cycle. Second, CLIC5 rs3734207 and rs11752816 polymorphisms (regulatory region) to the 2 iron, 2 sulfur cluster binding pathway through regulating expression levels of CLIC5 mRNA. Third, rs4819522 polymorphism (nonsynonymous coding) leads to a Thr-to-Met transition at position 350 of TBX1 and involves in the pathways about gland and endocrine system development. In summary, our results demonstrated four candidate SNPs in three genes (CYP26B1 rs2241057, CISD1 rs2251039, rs2590370, and TBX1 rs4819522 polymorphisms), which were involved in six potential pathways to influence serum PSA levels.

Association analysis of STK39, MCCC1/LAMP3 and sporadic PD in the Chinese Han population

With the completion of the Human Genome Project, GWAS have been widely used in exploring the genetic studies of complex diseases. A meta-analysis of datasets from five Parkinson's disease GWAS from the USA and Europe found 11 loci that surpassed the threshold for genome-wide significance (p<5×10(-8)), and five were newly identified loci (ACMSD, STK39, MCCC1/LAMP3, SYT11 and CCDC62/HIP1R). Another GWAS of the Ashkenazi Jewish population also identified loci in STK39 and LAMP3. Because the association between the STK39 and MCCC1/LAMP3 genes and PD was confirmed in different populations, we conducted a case-control cohort to clarify the association between the four single nucleotide polymorphism (SNP) loci (rs2102808 and rs3754775 in the STK39; rs11711441 and rs12493050 in the MCCC1/LAMP3) and PD in the Chinese Han population. Polymerase chain reaction and direct DNA sequencing analyses were used to detect the four variations in a case-control cohort comprised of 993 ethnic Chinese subjects. We found that in the detection of the rs11711441, there was a significant difference between ungrouped populations, early-onset PD, late-onset PD, male PD, female PD and the corresponding control group in allele and genotype frequency (p<0.001, OR<1). In the detection of the rs2102808, rs3754775 and rs12493050, ungrouped populations, early-onset PD, late-onset PD, male PD or female PD with the corresponding control group showed no significant difference in allele and genotype frequency (p>0.0125). Our findings suggested that the allele G of rs11711441 of the MCCC1/LAMP3 gene can decrease the risk of PD in Chinese population. No statistically significant difference in genotype frequency between cases and controls was observed for the other three SNPs.

Genome-wide screening for understanding the role of DNA methylation in colorectal cancer

DNA methylation analysis methods have undergone an impressive revolution over the past 15 years. Regarding colorectal cancer (CRC), the localization and distribution of several differently methylated genes have been determined by genome-wide DNA methylation assays. These genes do not just influence the pathogenesis of CRC, but can be used further as diagnostic or prognostic markers. Moreover, the identified four DNA methylation-based subgroups of CRC have important clinical and therapeutic merit. Since genome-wide DNA methylation analyzes result in a large amount of data, there is a need for complex bioinformatic and pathway analysis. Future challenges in epigenetic alterations of CRC include the demand for comprehensive identification and experimental validation of gene abnormalities. By introduction of genome-wide DNA methylation profiling into clinical practice not only the patients' risk stratification but development of targeted therapies will also be possible.