Dataset Information

Developing and validating polygenic risk scores for colorectal cancer risk prediction in East Asians.

ABSTRACT: Several polygenic risk scores (PRSs) have been developed to predict the risk of colorectal cancer (CRC) in European descendants. We used genome-wide association study (GWAS) data from 22 702 cases and 212 486 controls of Asian ancestry to develop PRSs and validated them in two case-control studies (1454 Korean and 1736 Chinese). Eleven PRSs were derived using three approaches: GWAS-identified CRC risk SNPs, CRC risk variants identified through fine-mapping of known risk loci and genome-wide risk prediction algorithms. Logistic regression was used to estimate odds ratios (ORs) and area under the curve (AUC). PRS_115-EAS , a PRS with 115 GWAS-reported risk variants derived from East-Asian data, validated significantly better than PRS_115-EUR derived from European descendants. In the Korea validation set, OR per SD increase of PRS_115-EAS was 1.63 (95% CI = 1.46-1.82; AUC = 0.63), compared with OR of 1.44 (95% CI = 1.29-1.60, AUC = 0.60) for PRS_115-EUR . PRS_115-EAS/EUR derived using meta-analysis results of both populations slightly improved the AUC to 0.64. Similar but weaker associations were found in the China validation set. Individuals among the highest 5% of PRS_115-EAS/EUR have a 2.52-fold elevated CRC risk compared with the medium (41-60th) risk group and have a 12% to 20% risk of developing CRC by age 85. PRSs constructed using results from fine-mapping and genome-wide algorithms did not perform as well as PRS_115-EAS and PRS_115-EAS/EUR in risk prediction, possibly due to a small sample size. Our results indicate that CRC PRSs are promising in predicting CRC risk in East Asians and highlights the importance of using population-specific data to build CRC risk prediction models.

SUBMITTER: Ping J

PROVIDER: S-EPMC9509464 | biostudies-literature | 2022 Nov

REPOSITORIES: biostudies-literature

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Developing and validating polygenic risk scores for colorectal cancer risk prediction in East Asians.

Ping Jie J Yang Yaohua Y Wen Wanqing W Kweon Sun-Seog SS Matsuda Koichi K Jia Wei-Hua WH Shin Aesun A Gao Yu-Tang YT Matsuo Keitaro K Kim Jeongseon J Kim Dong-Hyun DH Jee Sun Ha SH Cai Qiuyin Q Chen Zhishan Z Tao Ran R Shin Min-Ho MH Tanikawa Chizu C Pan Zhi-Zhong ZZ Oh Jae Hwan JH Oze Isao I Ahn Yoon-Ok YO Jung Keum Ji KJ Ren Zefang Z Shu Xiao-Ou XO Long Jirong J Zheng Wei W

International journal of cancer 20220721 10

Several polygenic risk scores (PRSs) have been developed to predict the risk of colorectal cancer (CRC) in European descendants. We used genome-wide association study (GWAS) data from 22 702 cases and 212 486 controls of Asian ancestry to develop PRSs and validated them in two case-control studies (1454 Korean and 1736 Chinese). Eleven PRSs were derived using three approaches: GWAS-identified CRC risk SNPs, CRC risk variants identified through fine-mapping of known risk loci and genome-wide risk ...[more]

PMID: 35765848

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Project description:Objective:Genome-wide association studies (GWAS) have identified over 30 susceptibility loci associated with Alzheimer's disease (AD). Using AD GWAS data from the International Genomics of Alzheimer's Project (IGAP), Polygenic Risk Score (PRS) was successfully applied to predict life time risk of AD development. A recently introduced Polygenic Hazard Score (PHS) is able to quantify individuals with age-specific genetic risk for AD. The aim of this study was to quantify the age-specific genetic risk for AD with PRS and compare the results generated by PRS with those from PHS. Methods:Quantification of individual differences in age-specific genetic risk for AD identified by the PRS, was performed with Cox Regression on 9903 (2626 cases and 7277 controls) individuals from the Genetic and Environmental Risk in Alzheimer's Disease consortium (GERAD). Polygenic Hazard Scores were generated for the same individuals. The age-specific genetic risk for AD identified by the PRS was compared with that generated by the PHS. This was repeated using varying SNPs P-value thresholds for disease association. Results:Polygenic Risk Score significantly predicted the risk associated with age at AD onset when SNPs were preselected for association to AD at P ? 0.001. The strongest effect (B = 0.28, SE = 0.04, P = 2.5 × 10-12) was observed for PRS based upon genome-wide significant SNPs (P ? 5 × 10-8). The strength of association was weaker with less stringent SNP selection thresholds. Interpretation:Both PRS and PHS can be used to predict an age-specific risk for developing AD. The PHS approach uses SNP effect sizes derived with the Cox Proportional Hazard Regression model. When SNPs were selected based upon AD GWAS case/control P ? 10-3, we found no advantage of using SNP effects sizes calculated with the Cox Proportional Hazard Regression model in our study. When SNPs are selected for association with AD risk at P > 10-3, the age-specific risk prediction results are not significant for either PRS or PHS. However PHS could be more advantageous than PRS of age specific AD risk predictions when SNPs are prioritized for association with AD age at onset (i.e., powerful Cox Regression GWAS study).

Project description:BackgroundRisk 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.MethodsWe 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.ResultsWe 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.ConclusionsThough 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.

Dataset Information

Developing and validating polygenic risk scores for colorectal cancer risk prediction in East Asians.

Publications

Developing and validating polygenic risk scores for colorectal cancer risk prediction in East Asians.

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