Calibration of polygenic risk scores is required prior to clinical implementation: results of three common cancers in UKB

2020 ◽  
pp. jmedgenet-2020-107286
Author(s):  
Jun Wei ◽  
Zhuqing Shi ◽  
Rong Na ◽  
W Kyle Resurreccion ◽  
Chi-Hsiung Wang ◽  
...  
Keyword(s):  
Risk Score ◽  
Disease Risk ◽  
Genetic Risk Score ◽  
Training Data ◽  
Risk Scores ◽  
Polygenic Risk Score ◽  
Systematic Bias ◽  
Clinical Implementation ◽  
Data Set ◽  
Polygenic Risk

BackgroundSNP-based polygenic risk scores have recently been adopted in the clinic for risk assessment of some common diseases. Their validity is supported by a consistent trend between their percentile rank and disease risk in populations. However, for clinical use at the individual level, the reliability of score values is necessary considering they are directly used to calculate remaining lifetime risk.ObjectivesWe assessed the reliability of polygenic score values to estimate prostate cancer (PCa), breast cancer (BCa) and colorectal cancer (CRC) risk in three incident cohorts from the UK Biobank (n>500 000).MethodsCancer-specific Genetic Risk Score (GRS), a well-established population-standardised polygenic risk score, was calculated.ResultsA systematic bias was found between estimated risks (GRS values) and observed risks; β (95% CI) was 0.67 (0.58–0.76), 0.74 (0.65–0.84) and 0.82 (0.75–0.89), respectively, for PCa, BCa and CRC, all significantly lower than 1.00 (perfect calibration), p<0.001. After applying a correction factor derived from a training data set, the β for corrected GRS values in an independent testing data set were 1.09 (1.05–1.13), 1.00 (0.88–1.12) and 1.08 (0.96–1.21), respectively, for PCa, BCa and CRC.ConclusionAssessing the calibration of polygenic risk scores is necessary and feasible to ensure their reliability prior to clinical implementation.

scholarly journals Benchmarking the Accuracy of Polygenic Risk Scores and their Generative Methods

2020 ◽  
Author(s):  
Scott Kulm ◽  
Jason Mezey ◽  
Olivier Elemento
Keyword(s):  
Risk Score ◽  
Optimal Number ◽  
Training Data ◽  
Receiver Operating Curve ◽  
Risk Scores ◽  
Polygenic Risk Score ◽  
Data Set ◽  
Polygenic Risk ◽  
Logistic Regression Models ◽  
The Difference

ABSTRACTThe estimate of an individual’s genetic susceptibility to a disease can provide critical information when setting screening schedules, prescribing medication and making lifestyle change recommendations. The polygenic risk score is the predominant susceptibility metric, with many methods available to describe its construction. However, these methods have never been comprehensively compared or the predictive value of their outputs systematically assessed, leaving the clinical utility of polygenic risk scores uncertain. This study aims to resolve this uncertainty by deeply comparing the maximum possible, currently available, 15 polygenic risk scoring methods to 25 well-powered, UK Biobank derived, disease phenotypes. Our results show that simpler methods, which employ heuristics, bested complex, methods, which predominately model linkage disequilibrium. Accuracy was assessed with AUC improvement, the difference in area under the receiver operating curve generated by two logistic regression models, both of which share the covariates of age, sex, and principal components, while the second model also contains the polygenic risk score. To better determine the maximal utility of polygenic risk scores, straightforward score ensembles, which bested all methods across all traits in the training data-set, were evaluated in the withheld data-set. The score ensembles revealed that the accuracy gained by considering a polygenic risk score varied greatly, with AUC improvement greater than 0.05 for 9 traits. Many additional analyses revealed widespread pleiotropy across scores, large variations between assessment statistics, peculiar patterns amongst phenotype definitions, and wide ranges in the optimal number of variants used for scoring. If these many variable aspects of score creation can be well controlled and documented, simple methods can easily generate polygenic risk score that well predict an individual’s future liability of certain diseases.

scholarly journals Comparisons of Polyexposure, Polygenic, and Clinical Risk Scores in Risk Prediction of Type 2 Diabetes

2021 ◽  
Author(s):  
Yixuan He ◽  
Chirag M Lakhani ◽  
Danielle Rasooly ◽  
Arjun K Manrai ◽  
Ioanna Tzoulaki ◽  
...  
Keyword(s):  
Risk Factors ◽  
Risk Prediction ◽  
Risk Score ◽  
Prediction Models ◽  
Disease Risk ◽  
Clinical Risk Factors ◽  
Risk Scores ◽  
Polygenic Risk Score ◽  
Polygenic Risk ◽  
Clinical Risk

OBJECTIVE: <p>Establish a polyexposure score for T2D incorporating 12 non-genetic exposure and examine whether a polyexposure and/or a polygenic risk score improves diabetes prediction beyond traditional clinical risk factors.</p> <h2><a></a>RESEARCH DESIGN AND METHODS:</h2> <p>We identified 356,621 unrelated individuals from the UK Biobank of white British ancestry with no prior diagnosis of T2D and normal HbA1c levels. Using self-reported and hospital admission information, we deployed a machine learning procedure to select the most predictive and robust factors out of 111 non-genetically ascertained exposure and lifestyle variables for the polyexposure risk score (PXS) in prospective T2D. We computed the clinical risk score (CRS) and polygenic risk score (PGS) by taking a weighted sum of eight established clinical risk factors and over six million SNPs, respectively.</p> <h2><a></a>RESULTS:</h2> <p>In the study population, 7,513 had incident T2D. The C-statistics for the PGS, PXS, and CRS models were 0.709, 0.762, and 0.839, respectively. Hazard ratios (HR) associated with risk score values in the top 10% percentile versus the remaining population is 2.00, 5.90, and 9.97 for PGS, PXS, and CRS respectively. Addition of PGS and PXS to CRS improves T2D classification accuracy with a continuous net reclassification index of 15.2% and 30.1% for cases, respectively, and 7.3% and 16.9% for controls, respectively. </p> <h2><a></a>CONCLUSIONS:</h2> <p>For T2D, the PXS provides modest incremental predictive value over established clinical risk factors. The concept of PXS merits further consideration in T2D risk stratification and is likely to be useful in other chronic disease risk prediction models.</p>

scholarly journals Comparisons of Polyexposure, Polygenic, and Clinical Risk Scores in Risk Prediction of Type 2 Diabetes

2021 ◽  
Author(s):  
Yixuan He ◽  
Chirag M Lakhani ◽  
Danielle Rasooly ◽  
Arjun K Manrai ◽  
Ioanna Tzoulaki ◽  
...  
Keyword(s):  
Risk Factors ◽  
Risk Prediction ◽  
Risk Score ◽  
Prediction Models ◽  
Disease Risk ◽  
Clinical Risk Factors ◽  
Risk Scores ◽  
Polygenic Risk Score ◽  
Polygenic Risk ◽  
Clinical Risk

OBJECTIVE: <p>Establish a polyexposure score for T2D incorporating 12 non-genetic exposure and examine whether a polyexposure and/or a polygenic risk score improves diabetes prediction beyond traditional clinical risk factors.</p> <h2><a></a>RESEARCH DESIGN AND METHODS:</h2> <p>We identified 356,621 unrelated individuals from the UK Biobank of white British ancestry with no prior diagnosis of T2D and normal HbA1c levels. Using self-reported and hospital admission information, we deployed a machine learning procedure to select the most predictive and robust factors out of 111 non-genetically ascertained exposure and lifestyle variables for the polyexposure risk score (PXS) in prospective T2D. We computed the clinical risk score (CRS) and polygenic risk score (PGS) by taking a weighted sum of eight established clinical risk factors and over six million SNPs, respectively.</p> <h2><a></a>RESULTS:</h2> <p>In the study population, 7,513 had incident T2D. The C-statistics for the PGS, PXS, and CRS models were 0.709, 0.762, and 0.839, respectively. Hazard ratios (HR) associated with risk score values in the top 10% percentile versus the remaining population is 2.00, 5.90, and 9.97 for PGS, PXS, and CRS respectively. Addition of PGS and PXS to CRS improves T2D classification accuracy with a continuous net reclassification index of 15.2% and 30.1% for cases, respectively, and 7.3% and 16.9% for controls, respectively. </p> <h2><a></a>CONCLUSIONS:</h2> <p>For T2D, the PXS provides modest incremental predictive value over established clinical risk factors. The concept of PXS merits further consideration in T2D risk stratification and is likely to be useful in other chronic disease risk prediction models.</p>

scholarly journals Common variants in Alzheimer’s disease and risk stratification by polygenic risk scores

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Itziar de Rojas ◽  
Sonia Moreno-Grau ◽  
Niccolo Tesi ◽  
Benjamin Grenier-Boley ◽  
Victor Andrade ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Risk Stratification ◽  
Risk Score ◽  
Disease Risk ◽  
Age At Onset ◽  
Risk Scores ◽  
Polygenic Risk Score ◽  
Polygenic Risk ◽  
Study Results

AbstractGenetic discoveries of Alzheimer’s disease are the drivers of our understanding, and together with polygenetic risk stratification can contribute towards planning of feasible and efficient preventive and curative clinical trials. We first perform a large genetic association study by merging all available case-control datasets and by-proxy study results (discovery n = 409,435 and validation size n = 58,190). Here, we add six variants associated with Alzheimer’s disease risk (near APP, CHRNE, PRKD3/NDUFAF7, PLCG2 and two exonic variants in the SHARPIN gene). Assessment of the polygenic risk score and stratifying by APOE reveal a 4 to 5.5 years difference in median age at onset of Alzheimer’s disease patients in APOE ɛ4 carriers. Because of this study, the underlying mechanisms of APP can be studied to refine the amyloid cascade and the polygenic risk score provides a tool to select individuals at high risk of Alzheimer’s disease.

scholarly journals Within-Family Validation of Polygenic Risk Scores and Complex Trait Prediction

2020 ◽  
Author(s):  
Louis Lello ◽  
Timothy G. Raben ◽  
Stephen D.H. Hsu
Keyword(s):  
Risk Score ◽  
Disease Risk ◽  
Complex Trait ◽  
Risk Scores ◽  
Polygenic Risk Score ◽  
Polygenic Risk ◽  
Sibling Pairs ◽  
Score Threshold ◽  
Affected Sibling ◽  
Trait Prediction

AbstractWe test a variety of polygenic predictors using tens of thousands of genetic siblings for whom we have SNP genotypes, health status, and phenotype information in late adulthood. Siblings have typically experienced similar environments during childhood, and exhibit negligible population stratification relative to each other. Therefore, the ability to predict differences in disease risk or complex trait values between siblings is a strong test of genomic prediction in humans. We compare validation results obtained using non-sibling subjects to those obtained among siblings and find that typically most of the predictive power persists in within-family designs. In the case of disease risk we test the extent to which higher polygenic risk score (PRS) identifies the affected sibling, and also compute Relative Risk Reduction as a function of risk score threshold. For quantitative traits we examine between-sibling differences in trait values as a function of predicted differences, and compare to performance in non-sibling pairs. Example results: Given 1 sibling with normal-range PRS score (<84 percentile) and 1 sibling with high PRS score (top few percentiles), the predictors identify the affected sibling about 70-90% of the time across a variety of disease conditions, including Breast Cancer, Heart Attack, Diabetes, etc. For height, the predictor correctly identifies the taller sibling roughly 80 percent of the time when the (male) height difference is 2 inches or more.

scholarly journals A Web Portal for Communicating Polygenic Risk Score Results for Health Care Use—The P5 Study

2021 ◽  
Vol 12 ◽  
Author(s):  
Heidi Marjonen ◽  
Minttu Marttila ◽  
Teemu Paajanen ◽  
Marleena Vornanen ◽  
Minna Brunfeldt ◽  
...  
Keyword(s):  
Health Care ◽  
Risk Score ◽  
Health Care Professionals ◽  
Disease Risk ◽  
Absolute Risk ◽  
Health Care Use ◽  
Risk Scores ◽  
Polygenic Risk Score ◽  
Web Portal ◽  
Polygenic Risk

We present a method for communicating personalized genetic risk information to citizens and their physicians using a secure web portal. We apply the method for 3,177 Finnish individuals in the P5 Study where estimates of genetic and absolute risk, based on genetic and clinical risk factors, of future disease are reported to study participants, allowing individuals to participate in managing their own health. Our method facilitates using polygenic risk score as a personalized tool to estimate a person’s future disease risk while offering a way for health care professionals to utilize the polygenic risk scores as a preventive tool in patient care.

scholarly journals Comparisons of Polyexposure, Polygenic, and Clinical Risk Scores in Risk Prediction of Type 2 Diabetes

2021 ◽  
Author(s):  
Yixuan He ◽  
Chirag M Lakhani ◽  
Danielle Rasooly ◽  
Arjun K Manrai ◽  
Ioanna Tzoulaki ◽  
...  
Keyword(s):  
Risk Factors ◽  
Risk Prediction ◽  
Risk Score ◽  
Prediction Models ◽  
Disease Risk ◽  
Clinical Risk Factors ◽  
Risk Scores ◽  
Polygenic Risk Score ◽  
Polygenic Risk ◽  
Clinical Risk

OBJECTIVE: <p>Establish a polyexposure score for T2D incorporating 12 non-genetic exposure and examine whether a polyexposure and/or a polygenic risk score improves diabetes prediction beyond traditional clinical risk factors.</p> <h2><a></a>RESEARCH DESIGN AND METHODS:</h2> <p>We identified 356,621 unrelated individuals from the UK Biobank of white British ancestry with no prior diagnosis of T2D and normal HbA1c levels. Using self-reported and hospital admission information, we deployed a machine learning procedure to select the most predictive and robust factors out of 111 non-genetically ascertained exposure and lifestyle variables for the polyexposure risk score (PXS) in prospective T2D. We computed the clinical risk score (CRS) and polygenic risk score (PGS) by taking a weighted sum of eight established clinical risk factors and over six million SNPs, respectively.</p> <h2><a></a>RESULTS:</h2> <p>In the study population, 7,513 had incident T2D. The C-statistics for the PGS, PXS, and CRS models were 0.709, 0.762, and 0.839, respectively. Hazard ratios (HR) associated with risk score values in the top 10% percentile versus the remaining population is 2.00, 5.90, and 9.97 for PGS, PXS, and CRS respectively. Addition of PGS and PXS to CRS improves T2D classification accuracy with a continuous net reclassification index of 15.2% and 30.1% for cases, respectively, and 7.3% and 16.9% for controls, respectively. </p> <h2><a></a>CONCLUSIONS:</h2> <p>For T2D, the PXS provides modest incremental predictive value over established clinical risk factors. The concept of PXS merits further consideration in T2D risk stratification and is likely to be useful in other chronic disease risk prediction models.</p>

scholarly journals Identifying individuals with high risk of Alzheimer’s disease using polygenic risk scores

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Ganna Leonenko ◽  
Emily Baker ◽  
Joshua Stevenson-Hoare ◽  
Annerieke Sierksma ◽  
Mark Fiers ◽  
...  
Keyword(s):  
High Risk ◽  
Risk Score ◽  
Genetic Risk ◽  
Prediction Accuracy ◽  
Genetic Risk Score ◽  
Low Risk ◽  
Risk Scores ◽  
Sample Mean ◽  
Polygenic Risk ◽  
Reliable Identification

AbstractPolygenic Risk Scores (PRS) for AD offer unique possibilities for reliable identification of individuals at high and low risk of AD. However, there is little agreement in the field as to what approach should be used for genetic risk score calculations, how to model the effect of APOE, what the optimal p-value threshold (pT) for SNP selection is and how to compare scores between studies and methods. We show that the best prediction accuracy is achieved with a model with two predictors (APOE and PRS excluding APOE region) with pT<0.1 for SNP selection. Prediction accuracy in a sample across different PRS approaches is similar, but individuals’ scores and their associated ranking differ. We show that standardising PRS against the population mean, as opposed to the sample mean, makes the individuals’ scores comparable between studies. Our work highlights the best strategies for polygenic profiling when assessing individuals for AD risk.

scholarly journals P820 An easy and rapid targeted next generation sequencing-based genotyping assay for the validated IBD risk loci

2020 ◽  
Vol 14 (Supplement_1) ◽  
pp. S637-S638
Author(s):  
S Verstockt ◽  
L Hannes ◽  
S Deman ◽  
W J Wollants ◽  
E Souche ◽  
...  
Keyword(s):  
Risk Score ◽  
Performance Metrics ◽  
Genetic Risk Score ◽  
Cost Effective ◽  
Polygenic Risk Score ◽  
Call Rate ◽  
Susceptibility Loci ◽  
Polygenic Risk ◽  
Genotyping Assay ◽  
Genotyping Call Rate

Abstract Background Inflammatory bowel diseases (IBD) are complex genetic diseases for which 242 susceptibility loci have been identified thus far. For translational or functional follow-up studies it can be of interest to know the genotype of specific variants. For other studies a composite genetic risk score–the polygenic risk score–is of value. There currently is a gap in technology to genotype a few hundred variants in a flexible and cost-effective way. We therefore developed a genotyping assay for the 242 validated IBD susceptibility loci. Methods Using MIPgen v.1.1, we designed molecular inversion probes (MIPs) covering 269 independent variants from the 242 IBD loci. MIP libraries were prepared according to Neveling et al. (Clin Chem. 2017), followed by paired-end sequencing using a MiSeq® System (Illumina). In the pilot studies, 16 IBD patients were genotyped, and results were compared with available immunochip (ichip) data. Genotypes for the covered variants were obtained using an in-house developed pipeline, and performance metrics were assessed (incl. genotyping call rate, percentage off-target reads and concordance with ichip-based genotypes). After optimisation, we genotyped 279 individuals (168 IBD patients and 111 non-IBD controls). We also calculated a weighted IBD polygenic risk score (PRSice 2.0) for these. Results Despite a genotyping call rate of 94.3%, the first pilot run suffered from a high rate of off-target reads (52.5%). After redesigning poorly-performing MIPs, off-target reads dropped to 9.4%, and the genotyping call rate increased to 97.5%. Concordance with genotypes previously obtained from ichip was 99.3%. When applying the optimised design on a larger scale (i.e. on the 279 individuals), we obtained similar performance metrics, with 8.0% off-target reads and a genotyping call rate of 97.3%. Moreover, upscaling resulted in a turnaround time of 2.5 working days/96 samples and a cost of €14/sample. The calculated IBD polygenic risk scores showed higher scores in patients as compared with controls (5.5E−03 vs. 4.0E−03, p = 8.80E−10; R² IBD polygenic risk score = 0.15, p = 1.28E−07), however with a large overlap between both groups. Quartile analysis showed that individuals within the highest quartile had an 8.1-fold (95% CI: 3.7–17.5) increase in risk towards IBD compared with individuals in the first quartile. Conclusion We developed a cost-effective genotyping assay for currently known IBD risk loci, with an integrated bioinformatics pipeline from raw sequencing data to individual genotypes and calculation of a polygenic risk score. Furthermore, this assay enables genotyping of individuals on a large scale while remaining flexible to implement newly identified genetic variants.

Performance of polygenic risk scores for cancer prediction in an academic biobank.

2020 ◽  
Vol 38 (15_suppl) ◽  
pp. 1528-1528
Author(s):  
Heena Desai ◽  
Anh Le ◽  
Ryan Hausler ◽  
Shefali Verma ◽  
Anurag Verma ◽  
...  
Keyword(s):  
Risk Score ◽  
Genetic Variants ◽  
Association Studies ◽  
Risk Scores ◽  
Polygenic Risk Score ◽  
Genome Wide Association Studies ◽  
Polygenic Risk ◽  
European Americans ◽  
Genome Wide ◽  
Common Genetic Variants

1528 Background: The discovery of rare genetic variants associated with cancer have a tremendous impact on reducing cancer morbidity and mortality when identified; however, rare variants are found in less than 5% of cancer patients. Genome wide association studies (GWAS) have identified hundreds of common genetic variants significantly associated with a number of cancers, but the clinical utility of individual variants or a polygenic risk score (PRS) derived from multiple variants is still unclear. Methods: We tested the ability of polygenic risk score (PRS) models developed from genome-wide significant variants to differentiate cases versus controls in the Penn Medicine Biobank. Cases for 15 different cancers and cancer-free controls were identified using electronic health record billing codes for 11,524 European American and 5,994 African American individuals from the Penn Medicine Biobank. Results: The discriminatory ability of the 15 PRS models to distinguish their respective cancer cases versus controls ranged from 0.68-0.79 in European Americans and 0.74-0.93 in African Americans. Seven of the 15 cancer PRS trended towards an association with their cancer at a p<0.05 (Table), and PRS for prostate, thyroid and melanoma were significantly associated with their cancers at a bonferroni corrected p<0.003 with OR 1.3-1.6 in European Americans. Conclusions: Our data demonstrate that common variants with significant associations from GWAS studies can distinguish cancer cases versus controls for some cancers in an unselected biobank population. Given the small effects, future studies are needed to determine how best to incorporate PRS with other risk factors in the precision prediction of cancer risk. [Table: see text]

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