INVITED ORIGINAL ARTICLE
Year : 2016  |  Volume : 18  |  Issue : 4  |  Page : 520-524

Population-standardized genetic risk score: the SNP-based method of choice for inherited risk assessment of prostate cancer


1 NorthShore University HealthSystem, Program for Personalized Cancer Care, 1001 University Place, Evanston, IL 60201, USA
2 NorthShore University HealthSystem, Program for Personalized Cancer Care, 1001 University Place, Evanston, IL 60201, USA; Fudan Institute of Urology, Huashan Hospital, Fudan University, 12 Mid-Wulumuqi Road, Shanghai 200040, P.R. China
3 Center for Genomic Translational Medicine and Prevention, School of Public Health, Fudan University, 138 Yixueyuan Road, Shanghai 200032, P.R. China
4 Fudan Institute of Urology, Huashan Hospital, Fudan University, 12 Mid-Wulumuqi Road, Shanghai 200040, P.R. China
5 NorthShore University HealthSystem, Program for Personalized Cancer Care, 1001 University Place, Evanston, IL 60201, USA; Fudan Institute of Urology, Huashan Hospital, Fudan University, 12 Mid-Wulumuqi Road, Shanghai 200040, P.R. China; Center for Genomic Translational Medicine and Prevention, School of Public Health, Fudan University, 138 Yixueyuan Road, Shanghai 200032, P.R. China

Correspondence Address:
Dr. Rong Na
NorthShore University HealthSystem, Program for Personalized Cancer Care, 1001 University Place, Evanston, IL 60201, USA; Fudan Institute of Urology, Huashan Hospital, Fudan University, 12 Mid-Wulumuqi Road, Shanghai 200040, P.R. China

Login to access the Email id

Source of Support: None, Conflict of Interest: None


DOI: 10.4103/1008-682X.179527

Rights and Permissions

Several different approaches are available to clinicians for determining prostate cancer (PCa) risk. The clinical validity of various PCa risk assessment methods utilizing single nucleotide polymorphisms (SNPs) has been established; however, these SNP-based methods have not been compared. The objective of this study was to compare the three most commonly used SNP-based methods for PCa risk assessment. Participants were men (n = 1654) enrolled in a prospective study of PCa development. Genotypes of 59 PCa risk-associated SNPs were available in this cohort. Three methods of calculating SNP-based genetic risk scores (GRSs) were used for the evaluation of individual disease risk such as risk allele count (GRS-RAC), weighted risk allele count (GRS-wRAC), and population-standardized genetic risk score (GRS-PS). Mean GRSs were calculated, and performances were compared using area under the receiver operating characteristic curve (AUC) and positive predictive value (PPV). All SNP-based methods were found to be independently associated with PCa (all P < 0.05; hence their clinical validity). The mean GRSs in men with or without PCa using GRS-RAC were 55.15 and 53.46, respectively, using GRS-wRAC were 7.42 and 6.97, respectively, and using GRS-PS were 1.12 and 0.84, respectively (all P < 0.05 for differences between patients with or without PCa). All three SNP-based methods performed similarly in discriminating PCa from non-PCa based on AUC and in predicting PCa risk based on PPV (all P > 0.05 for comparisons between the three methods), and all three SNP-based methods had a significantly higher AUC than family history (all P < 0.05). Results from this study suggest that while the three most commonly used SNP-based methods performed similarly in discriminating PCa from non-PCa at the population level, GRS-PS is the method of choice for risk assessment at the individual level because its value (where 1.0 represents average population risk) can be easily interpreted regardless of the number of risk-associated SNPs used in the calculation.


[FULL TEXT] [PDF]*
Print this article     Email this article
 Next article
 Previous article
 Table of Contents

 Similar in PUBMED
   Search Pubmed for
   Search in Google Scholar for
 Related articles
 Citation Manager
 Access Statistics
 Reader Comments
 Email Alert *
 Add to My List *
 * Requires registration (Free)
 

 Article Access Statistics
    Viewed2390    
    Printed96    
    Emailed0    
    PDF Downloaded447    
    Comments [Add]    
    Cited by others 14    

Recommend this journal