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Single-Nucleotide Polymorphism-Based Genetic Risk Score and Patient Age at Prostate Cancer Diagnosis.

ABSTRACT: Importance:Few studies have evaluated the association between a single-nucleotide polymorphism-based genetic risk score (GRS) and patient age at prostate cancer (PCa) diagnosis. Objectives:To test the association between a GRS and patient age at PCa diagnosis and to compare the performance of a GRS with that of family history (FH) in PCa risk stratification. Design, Setting, and Participants:A cohort study of 3225 white men was conducted as a secondary analysis of the Reduction by Dutasteride of Prostate Cancer Events (REDUCE) chemoprevention trial, a 4-year, randomized, double-blind, placebo-controlled multicenter study conducted from March 2003 to April 2009 to evaluate the safety and efficacy of dutasteride in reducing PCa events. Participants were confirmed to be cancer free by prostate biopsy (6-12 cores) within 6 months prior to the study and underwent 10 core biopsies every 2 years per protocol. The dates for performing data analysis were from July 2016 to October 2019. Interventions:A well-established, population-standardized GRS was calculated for each participant based on 110 known PCa risk-associated single-nucleotide polymorphisms, which is a relative risk compared with the general population. Men were classified into 3 GRS risk groups based on predetermined cutoff values: low (<0.50), average (0.50-1.49), and high (?1.50). Main Outcomes and Measures:Prostate cancer diagnosis-free survival among men of different risk groups. Results:Among 3225 men (median age, 63 years [interquartile range, 58-67 years]) in the study, 683 (21%) were classified as low risk, 1937 (60%) as average risk, and 605 (19%) as high risk based on GRS alone. In comparison, 2789 (86%) were classified as low or average risk and 436 (14%) as high risk based on FH alone. Men in higher GRS risk groups had a PCa diagnosis-free survival rate that was worse than that of those in the lower GRS risk group (?2?=?53.3; P?80 years) for men at average genetic risk, and more than 80 years (95% CI, >80 to >80 years) for men at low genetic risk. In contrast, the median PCa diagnosis-free survival was 73 years (95% CI, 71-76 years) for men with a positive FH and 77 years (95% CI, 76-79 years) for men with a negative FH. Conclusions and Relevance:This study suggests that a GRS is significantly associated with patient age at PCa diagnosis. Combining FH and GRS may better stratify inherited risk than FH alone for developing personalized PCa screening strategies.

SUBMITTER: Na R

PROVIDER: S-EPMC6991229 | biostudies-literature | 2019 Dec

REPOSITORIES: biostudies-literature

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Single-Nucleotide Polymorphism-Based Genetic Risk Score and Patient Age at Prostate Cancer Diagnosis.

Na Rong R Labbate Craig C Yu Hongjie H Shi Zhuqing Z Fantus Richard J RJ Wang Chi-Hsiung CH Andriole Gerald L GL Isaacs William B WB Zheng S Lilly SL Helfand Brian T BT Xu Jianfeng J

JAMA network open 20191202 12

<h4>Importance</h4>Few studies have evaluated the association between a single-nucleotide polymorphism-based genetic risk score (GRS) and patient age at prostate cancer (PCa) diagnosis.<h4>Objectives</h4>To test the association between a GRS and patient age at PCa diagnosis and to compare the performance of a GRS with that of family history (FH) in PCa risk stratification.<h4>Design, setting, and participants</h4>A cohort study of 3225 white men was conducted as a secondary analysis of the Reduc ...[more]

PMID: 31880795

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Project description:BackgroundThe malaria risk prediction is currently limited to using advanced statistical methods, such as time series and cluster analysis on epidemiological data. Nevertheless, machine learning models have been explored to study the complexity of malaria through blood smear images and environmental data. However, to the best of our knowledge, no study analyses the contribution of Single Nucleotide Polymorphisms (SNPs) to malaria using a machine learning model. More specifically, this study aims to quantify an individual's susceptibility to the development of malaria by using risk scores obtained from the cumulative effects of SNPs, known as weighted genetic risk scores (wGRS).ResultsWe proposed an SNP-based feature extraction algorithm that incorporates the susceptibility information of an individual to malaria to generate the feature set. However, it can become computationally expensive for a machine learning model to learn from many SNPs. Therefore, we reduced the feature set by employing the Logistic Regression and Recursive Feature Elimination (LR-RFE) method to select SNPs that improve the efficacy of our model. Next, we calculated the wGRS of the selected feature set, which is used as the model's target variables. Moreover, to compare the performance of the wGRS-only model, we calculated and evaluated the combination of wGRS with genotype frequency (wGRS + GF). Finally, Light Gradient Boosting Machine (LightGBM), eXtreme Gradient Boosting (XGBoost), and Ridge regression algorithms are utilized to establish the machine learning models for malaria risk prediction.ConclusionsOur proposed approach identified SNP rs334 as the most contributing feature with an importance score of 6.224 compared to the baseline, with an importance score of 1.1314. This is an important result as prior studies have proven that rs334 is a major genetic risk factor for malaria. The analysis and comparison of the three machine learning models demonstrated that LightGBM achieves the highest model performance with a Mean Absolute Error (MAE) score of 0.0373. Furthermore, based on wGRS + GF, all models performed significantly better than wGRS alone, in which LightGBM obtained the best performance (0.0033 MAE score).

Project description:ImportanceGenetic variants associated with primary open-angle glaucoma (POAG) are known to influence disease risk. However, the clinical effect of associated variants individually or in aggregate is not known. Genetic risk scores (GRS) examine the cumulative genetic load by combining individual genetic variants into a single measure, which is assumed to have a larger effect and increased power to detect relevant disease-related associations.ObjectiveTo investigate if a GRS that comprised 12 POAG genetic risk variants is associated with age at disease diagnosis.Design, setting, and participantsA cross-sectional study included individuals with POAG and controls from the Glaucoma Genes and Environment (GLAUGEN) study and the National Eye Institute Glaucoma Human Genetics Collaboration (NEIGHBOR) study. A GRS was formulated using 12 variants known to be associated with POAG, and the alleles associated with increasing risk of POAG were aligned in the case-control sets. In case-only analyses, the association of the GRS with age at diagnosis was analyzed as an estimate of disease onset. Results from cohort-specific analyses were combined with meta-analysis. Data collection started in August 2012 for the NEIGHBOR cohort and in July 2008 for the GLAUGEN cohort and were analyzed starting in March 2018.Main outcomes and measuresAssociation of a 12 single-nucleotide polymorphism POAG GRS with age at diagnosis in individuals with POAG using linear regression.ResultsThe GLAUGEN study included 976 individuals with POAG and 1140 controls. The NEIGHBOR study included 2132 individuals with POAG and 2290 controls. For individuals with POAG, the mean (SD) age at diagnosis was 63.6 (9.8) years in the GLAUGEN cohort and 66.0 (13.7) years in the NEIGHBOR cohort. For controls, the mean (SD) age at enrollment was 65.5 (9.2) years in the GLAUGEN cohort and 68.9 (11.4) years in the NEIGHBOR cohort. All study participants were European white. The GRS was strongly associated with POAG risk in case-control analysis (odds ratio per 1-point increase in score = 1.24; 95% CI, 1.21-1.27; P = 3.4 × 10-66). In case-only analyses, each higher GRS unit was associated with a 0.36-year earlier age at diagnosis (β = -0.36; 95% CI, -0.56 to -0.16; P = 4.0 × 10-4). Individuals in the top 5% of the GRS had a mean (SD) age at diagnosis of 5.2 (12.8) years earlier than those in the bottom 5% GRS (61.4 [12.7] vs 66.6 [12.9] years; P = 5.0 × 10-4).Conclusions and relevanceA higher dose of POAG risk alleles was associated with an earlier age at glaucoma diagnosis. On average, individuals with POAG with the highest GRS had 5.2-year earlier age at diagnosis of disease. These results suggest that a GRS that comprised genetic variants associated with POAG could help identify patients with risk of earlier disease onset impacting screening and therapeutic strategies.

Dataset Information

Single-Nucleotide Polymorphism-Based Genetic Risk Score and Patient Age at Prostate Cancer Diagnosis.

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Single-Nucleotide Polymorphism-Based Genetic Risk Score and Patient Age at Prostate Cancer Diagnosis.

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