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 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.

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 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>

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]

scholarly journals Sibling validation of polygenic risk scores and complex trait prediction

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Louis Lello ◽  
Timothy G. Raben ◽  
Stephen D. H. Hsu
Keyword(s):  
Complex Trait ◽  
Risk Scores ◽  
Polygenic Risk ◽  
Trait Prediction

scholarly journals Transforming summary statistics from logistic regression to the liability scale: application to genetic and environmental risk scores

2018 ◽  
Author(s):  
Alexandra C. Gillett ◽  
Evangelos Vassos ◽  
Cathryn M. Lewis
Keyword(s):  
Risk Factors ◽  
Risk Score ◽  
Environmental Risk ◽  
Effect Size ◽  
Association Studies ◽  
Risk Scores ◽  
Model Parameters ◽  
Polygenic Risk Score ◽  
Polygenic Risk ◽  
Size Estimates

1.Abstract1.1.ObjectiveStratified medicine requires models of disease risk incorporating genetic and environmental factors. These may combine estimates from different studies and models must be easily updatable when new estimates become available. The logit scale is often used in genetic and environmental association studies however the liability scale is used for polygenic risk scores and measures of heritability, but combining parameters across studies requires a common scale for the estimates.1.2.MethodsWe present equations to approximate the relationship between univariate effect size estimates on the logit scale and the liability scale, allowing model parameters to be translated between scales.1.3.ResultsThese equations are used to build a risk score on the liability scale, using effect size estimates originally estimated on the logit scale. Such a score can then be used in a joint effects model to estimate the risk of disease, and this is demonstrated for schizophrenia using a polygenic risk score and environmental risk factors.1.4.ConclusionThis straightforward method allows conversion of model parameters between the logit and liability scales, and may be a key tool to integrate risk estimates into a comprehensive risk model, particularly for joint models with environmental and genetic risk factors.

scholarly journals Validation and Disease Risk Assessment of Previously Reported Genome-Wide Genetic Variants Associated With Brugada Syndrome

2020 ◽  
Vol 13 (4) ◽  
Author(s):  
Jyh-Ming Jimmy Juang ◽  
Yen-Bin Liu ◽  
Ching-Yu Julius Chen ◽  
Qi-You Yu ◽  
Amrita Chattopadhyay ◽  
...  
Keyword(s):  
Cardiac Arrest ◽  
Risk Score ◽  
Brugada Syndrome ◽  
Odds Ratio ◽  
Disease Risk ◽  
Sudden Cardiac Arrest ◽  
Polygenic Risk Score ◽  
Polygenic Risk ◽  
Risk Alleles ◽  
Genome Wide

Background: Brugada syndrome (BrS) is an oligogenic arrhythmic disease with increased risk of sudden cardiac arrest. Several BrS or ECG traits-related single-nucleotide polymorphisms (SNPs) were identified through previous genome-wide association studies in white patients. We aimed to validate these SNPs in BrS patients in the Taiwanese population, assessing the cumulative effect of risk alleles and the BrS-polygenic risk score in predicting cardiac events. Methods: We genotyped 190 unrelated BrS patients using the TWB Array, and Taiwan Biobank was used as controls. SNPs not included in the array were imputed by IMPUTE2. Cox proportional hazards model was used to evaluate the associations between each particular SNP, the collective BrS-polygenic risk score, and clinical outcomes. Results: Of the 88 previously reported SNPs, 22 were validated in Taiwanese BrS patients ( P <0.05). Of the 22 SNPs, 2 (rs10428132 and rs9388451) were linked with susceptibility to BrS, 10 were SNPs previously reaching genome-wide significance, and 10 were SNPs associated with ECG traits. For the 3 most commonly reported SNPs, disease risk increased consistently with the number of risk alleles (odds ratio, 3.54; P trend =1.38×10 −9 for 5 risk alleles versus 1). Similar patterns were observed in both SCN5A mutation+ (odds ratio, 3.66; P trend =0.049) and SCN5A mutation− (odds ratio, 3.75; P trend =8.54×10 −9 ) subgroups. Furthermore, BrS patients without SCN5A mutations had more risk alleles than BrS patients with SCN5A mutations regardless of the range of polygenic risk scores. Three SNPs (rs4687718, rs7784776, and rs2968863) showed significant associations with the composite outcome (sudden cardiac arrest plus syncope, hazard ratio, 2.13, 1.48, and 0.41; P =0.02, 0.006, and 0.008, respectively). Conclusions: Our findings suggested that some SNPs associated with BrS or ECG traits exist across multiple populations. The cumulative risk of the BrS-related SNPs is similar to that in white BrS patients, but it appears to correlate with the absence of SCN5A mutations.

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