Concept and benchmarks for assessing narrow‐sense validity of genetic risk score values

Yu, Hongjie; Shi, Zhuqing; Wu, Yishuo; Wang, Chi Hsiung; Lin, Xiaoling; Perschon, Chelsea; Isaacs, William B.; Helfand, Brian T.; Zheng, S. Lilly; Duggan, David; Mo, Zengnan; Lu, Daru; Xu, Jianfeng

doi:10.1002/pros.23821

Cited by 18 publications

(21 citation statements)

References 39 publications

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“…Narrow‐sense validity was then assessed using newly proposed baseline benchmark and calibration benchmark . For the baseline benchmark (mean PRS in a general population approximates to a value of 1.0), we estimated the mean scaled values of three PRS methods among men without a diagnosis of PCa.…”

Section: Methodsmentioning

confidence: 99%

“…Population‐standardized GRS was calculated based on 110 well‐established PCa risk‐associated SNPs, described previously . They were selected based on our evidence review of original papers that met the following criteria: (a) discovered from GWAS studies of Caucasian subjects, with at least 1000 cases and 1000 controls in the first stage; (b) confirmed in additional stages with combined P < 5 × 10 −8 ; (c) independent, LD measurement ( r 2 < .2) between any pair of SNPs; and (d) available in our study (genotyped or imputed).…”

Section: Methodsmentioning

confidence: 99%

“…Narrow-sense validity was then assessed using newly proposed baseline benchmark and calibration benchmark. 17 For the baseline benchmark (mean PRS in a general population approximates to a value of 1.0), we estimated the mean scaled values of three PRS methods among men without a diagnosis of PCa. For the calibration benchmark (agreement between observed and predicted risk), we plotted observed OR of PCa with mean estimated OR (mean scaled values of three PRS methods for subjects) in each PRS decile.…”

Section: Assessment Of Broad-and Narrow-sense Validity Of Prsmentioning

confidence: 99%

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Broad‐ and narrow‐sense validity performance of three polygenic risk score methods for prostate cancer risk assessment

Shi

Lin

et al. 2019

The Prostate

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Background Several polygenic risk score (PRS) methods are available for measuring the cumulative effect of multiple risk‐associated single nucleotide polymorphisms (SNPs). Their performance in predicting risk at the individual level has not been well studied. Methods We compared the performance of three PRS methods for prostate cancer risk assessment in a clinical trial cohort, including genetic risk score (GRS), pruning and thresholding (P + T), and linkage disequilibrium prediction (LDpred). Performance was evaluated for score deciles (broad‐sense validity) and score values (narrow‐sense validity). Results A training process was required to identify the best P + T model (397 SNPs) and LDpred model (3 011 362 SNPs). In contrast, GRS was directly calculated based on 110 established risk‐associated SNPs. For broad‐sense validity in the testing population, higher deciles were significantly associated with higher observed risk; Ptrend was 7.40 × 10−11, 7.64 × 10−13, and 7.51 × 10−10 for GRS, P + T, and LDpred, respectively. For narrow‐sense validity, the calibration slope (1 is best) was 1.03, 0.77, and 0.87, and mean bias score (0 is best) was 0.09, 0.21, and 0.10 for GRS, P + T, and LDpred, respectively. Conclusions The performance of GRS was better than P + T and LDpred. Fewer and well‐established SNPs of GRS also make it more feasible and interpretable for genetic testing at the individual level.

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Assessment Of Broad-and Narrow-sense Validity Of Prsmentioning

confidence: 99%

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Broad‐ and narrow‐sense validity performance of three polygenic risk score methods for prostate cancer risk assessment

Shi

Lin

et al. 2019

The Prostate

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“…Only independent SNPs derived from stepwise regression analysis from GWAS stages 1 and 2 were used to calculate the GRS. A GRS is an odds ratio (OR)-weighted and population-standardized polygenic risk score and is calculated as follows:

W i = f i 2 OR i 2 + 2 f i (1 – f i ) OR i + (1 – f i ) 2 where g i stands for the genotype of SNP i in an individual (zero, one, or two risk alleles), OR i stands for the allelic OR of SNP i , and f i stands for the risk allele frequency of SNP i in the population [22] . The OR estimates of each SNP from stages 1 and 2 and allele frequency from gnomAD were used.…”

Section: Methodsmentioning

confidence: 99%

Genetic Susceptibility for Low Testosterone in Men and Its Implications in Biology and Screening: Data from the UK Biobank

Fantus

Wei

et al. 2021

European Urology Open Science

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Background Despite strong evidence of heritability, few studies have attempted to unveil the genetic underpinnings of testosterone levels. Objective To identify testosterone-associated loci in a large study and assess their biological and clinical implications. Design, setting, and participants The participants were men from the UK Biobank. A two-stage genome-wide association study (GWAS) was first used to identify/validate loci for low testosterone (LowT, <8 nmol/l) in 80% of men ( N = 148 902). The cumulative effect of independent LowT risk loci was then evaluated in the remaining 20% of men. Outcome measurements and statistical analysis Associations of single nucleotide polymorphisms (SNPs) with LowT were tested using an additive model. Analyses of the expression quantitative trait loci (eQTLs) were performed to assess the associations between significant SNPs and expression of nearby genes (within 1 Mbp). A genetic risk score (GRS) was used to assess the cumulative effect of multiple independent SNPs on LowT risk. Results and limitations The two-stage GWAS found SNPs in 141 loci of 41 cytobands that were significantly associated with LowT ( p < 5 × 10 –8 ), including 94 novel loci from 38 cytobands. An eQTL analysis of these 141 loci revealed significant associations with RNA expression of 155 genes, including previously implicated ( SHBG and JMJD1C ) and novel ( LIN28B , LCMT2 , and ZBTB4 ) genes. Among the 141 loci, 42 were independently associated with LowT after a multivariable analysis. The GRS based on these 42 loci was significantly associated with LowT risk in independent individuals ( N = 37 225, p trend = 3.16 × 10 –162 ). The risk ratio for LowT between men in the top and those in the bottom GRS deciles was 4.98-fold. Results are limited in generalizability as only Caucasians were studied. Conclusions Identification of the genetic variants associated with LowT may improve our understanding of its etiology and identify high-risk men for LowT screening. Patient summary We identified 141 new genetic loci that can be incorporated into a genetic risk score that can potentially identify men with low testosterone.

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“…GRS was calculated by multiplying the per-allele odds ratio (OR) with number of risk alleles of each SNP and normalizing the risk by the average risk expected in the population. 26 As such, GRS value can be interpreted as relative risk to the general population regardless number of risk-associated SNPs used in GRS calculation.…”

Section: Snps and Polygenic Risk Scorementioning

confidence: 99%

Shared Inherited Genetics of Benign Prostatic Hyperplasia and Prostate Cancer

Glaser

Shi

Wei

et al. 2021

Preprint

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BackgroundThe association between benign prostatic hyperplasia (BPH) and prostate cancer (PCa) remains controversial, largely due to inherent detection bias in traditional observational studies. The objective of this study is to assess their association using inherited SNPs.MethodsSubjects were White men from the large population-based UK Biobank (UKB). Association between BPH and PCa was tested: 1) phenotypical correlation using chi-square test, 2) genetic correlation (rg) based on 1,126,841 polymorphic SNPs across the genome using linkage disequilibrium score regression (LDSR), and 3) cross-disease genetic associations based on known risk-associated SNPs (15 for BPH and 239 for PCa), individually and cumulatively as measured by genetic risk score (GRS).FindingsAmong 214,717 White men in the UKB, 24,623 (11.47%) and 14,311 (6.67%) had a diagnosis of BPH and PCa, respectively. Diagnoses of these two diseases were significantly correlated, χ2=1862.80, P<1E-299. A significant genetic correlation was found, rg (95% confidence interval (CI))=0.27 (0.15-0.39), P=9.17E-06. In addition, significant cross-disease genetic associations for established risk-associated SNPs were also found. Among the 250 established GWAS-significant SNPs of PCa or BPH, 51 were significantly associated with risk of the other disease at P<0.05, significantly more than expected by chance (N=12), P=3.04E-7 (χ2-test). Furthermore, significant cross-disease GRS associations were also found; GRSBPH was significantly associated with PCa risk (odds ratio (OR)=1.26 (1.18-1.36), P=1.62E-10), and GRSPCa was significantly associated with BPH risk (OR=1.03 (1.02-1.04), P=8.57E-06). Moreover, GRSBPH was significantly and inversely associated with lethal PCa risk in a PCa case-case analysis (OR=0.58 (0.41-0.81), P=1.57E-03). In contrast, GRSPCa was not significantly associated with lethal PCa (OR=0.99 (0.94-1.04), P=0.79).InterpretationBPH and PCa share common inherited genetics which suggests the phenotypical association of these two diseases in observational studies is not entirely caused by detection bias. This novel finding may have implications in disease etiology and risk stratification.FundingNone.

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Concept and benchmarks for assessing narrow‐sense validity of genetic risk score values

Cited by 18 publications

References 39 publications

Broad‐ and narrow‐sense validity performance of three polygenic risk score methods for prostate cancer risk assessment

Broad‐ and narrow‐sense validity performance of three polygenic risk score methods for prostate cancer risk assessment

Genetic Susceptibility for Low Testosterone in Men and Its Implications in Biology and Screening: Data from the UK Biobank

Shared Inherited Genetics of Benign Prostatic Hyperplasia and Prostate Cancer

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