scholarly journals Improving the computation efficiency of polygenic risk score modeling: Faster in Julia

2021 ◽  
Author(s):  
Annika Faucon ◽  
Julian Samaroo ◽  
Tian Ge ◽  
Lea K Davis ◽  
Ran Tao ◽  
...  
Keyword(s):  
Risk Score ◽  
Large Scale ◽  
Work Flow ◽  
Risk Scores ◽  
Polygenic Risk Score ◽  
Computationally Efficient ◽  
Efficient Manner ◽  
Polygenic Risk ◽  
Computation Efficiency ◽  
Accuracy Of Prediction

To enable large-scale application of polygenic risk scores in a computationally efficient manner we translate a widely used polygenic risk score construction method, Polygenic Risk Score – Continuous Shrinkage (PRS-CS), to the Julia programing language, PRS.jl. On nine different traits with varying genetic architectures, we demonstrate that PRS.jl maintains accuracy of prediction while decreasing the average run time by 5.5x. Additional programmatic modifications improve usability and robustness. This freely available software substantially improves work flow and democratizes utilization of polygenic risk scores by lowering the computational burden of the PRS-CS method.

scholarly journals An atlas of polygenic risk score associations to highlight putative causal relationships across the human phenome

2018 ◽  
Author(s):  
Tom G. Richardson ◽  
Sean Harrison ◽  
Gibran Hemani ◽  
George Davey Smith
Keyword(s):  
Web Application ◽  
Large Scale ◽  
Complex Disease ◽  
Association Studies ◽  
Risk Scores ◽  
Polygenic Risk Score ◽  
Genome Wide Association Studies ◽  
Genetic Liability ◽  
Polygenic Risk ◽  
The Uk

AbstractThe age of large-scale genome-wide association studies (GWAS) has provided us with an unprecedented opportunity to evaluate the genetic liability of complex disease using polygenic risk scores (PRS). In this study, we have analysed 162 PRS (P<5×l0 05) derived from GWAS and 551 heritable traits from the UK Biobank study (N=334,398). Findings can be investigated using a web application (http://mrcieu.mrsoftware.org/PRS_atlas/), which we envisage will help uncover both known and novel mechanisms which contribute towards disease susceptibility.To demonstrate this, we have investigated the results from a phenome-wide evaluation of schizophrenia genetic liability. Amongst findings were inverse associations with measures of cognitive function which extensive follow-up analyses using Mendelian randomization (MR) provided evidence of a causal relationship. We have also investigated the effect of multiple risk factors on disease using mediation and multivariable MR frameworks. Our atlas provides a resource for future endeavours seeking to unravel the causal determinants of complex disease.

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 Genomic Prediction of Depression Risk and Resilience Under Stress

2019 ◽  
Author(s):  
Yu Fang ◽  
Laura Scott ◽  
Peter Song ◽  
Margit Burmeister ◽  
Srijan Sen
Keyword(s):  
Risk Score ◽  
Large Scale ◽  
Association Studies ◽  
Health Study ◽  
Polygenic Risk Score ◽  
Genome Wide Association Studies ◽  
Risk And Resilience ◽  
Polygenic Risk ◽  
Response To Stress ◽  
Training Stress

AbstractAdvancing our ability to predict who is likely to develop depression in response to stress holds great potential in reducing the burden of the disorder. Large-scale genome-wide association studies (GWAS) of depression have, for the first time, provided a basis for meaningful depression polygenic risk score construction (MDD-PRS). The Intern Health Study utilizes the predictable and large increase in depression with physician training stress to identify predictors of depression. Applying the MDD-PRS derived from the PGC2/23andMe GWAS to 5,227 training physicians, we found that MDD-PRS predicted depression under training stress (beta=0.082, p=2.1×10−12) and that MDD-PRS was significantly more strongly associated with depression under stress than at baseline (MDD-PRS × stress interaction - beta=0.029, p=0.02). While known risk factors accounted for 85.6% of the association between MDD-PRS and depression at baseline, they only accounted for 55.4% of the association between MDD-PRS and depression under stress, suggesting that MDD-PRS can add unique predictive power to existing models of depression under stress. Further, we found that low MDD-PRS may have particular utility in identifying individuals with high resilience. Together, these findings suggest that polygenic risk score holds promise in furthering our ability to predict vulnerability and resilience under stress.

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 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 Serum Urate Polygenic Risk Score Can Improve Gout Risk Prediction: A Large-Scale Cohort Study

2021 ◽  
Vol 11 ◽  
Author(s):  
Yanfei Zhang ◽  
Ming Ta Michael Lee
Keyword(s):  
High Risk ◽  
Risk Score ◽  
Large Scale ◽  
Characteristic Curve ◽  
Model Performance ◽  
Serum Urate ◽  
Lifestyle Changes ◽  
Polygenic Risk Score ◽  
Polygenic Risk ◽  
Risk Patients

Gout is a painful inflammatory arthritis affecting more than 8 million Americans. Identifying high-risk patients in early life could potentially encourage people to adopt lifestyle changes to prevent gout. Polygenic risk score (PRS) provides an overall estimate of an individual's genetic liability to develop a disease and can be used for early identification of high-risk individuals. In this study, we validated a previously reported PRS in an independent cohort. The urate-PRS was constructed from 110 significant urate-associated variants identified in Europeans. Phenome-wide and PRS-wide association study showed the urate-PRS is highly specifically associated with gout (phecode: 274.10; beta = 1.495 [1.372, 1.619], p = 4.37e-124). Urate-PRS alone did not performed in the gout prediction (area under the receiver operating characteristic curve, AUROC = 0.640); however, the addition of PRS upon demographics significantly improved the model performance, yielding an AUROC of 0.804 from 0.777 (DeLong test p = 3.66e−9). Trans-ethnic PRS and European-specific PRS showed similar prediction performance. We observed increasing gout prevalence and odds ratio (OR) across the PRS quintiles. Our study showed 8.2% of the cohort had more than 2.5 odds for gout than remainders, indicating that urate-PRS may be a better marker than age and sex to stratify patient risk. With the rapid growth of large biorepositories, such as All of Us, urate-PRS can be applied quickly and widely in population to estimate individual's risk, providing a powerful tool for gout preventive purpose in population health.

scholarly journals Validation of a Trans-Ancestry Polygenic Risk Score for Type 2 Diabetes in Diverse Populations

2021 ◽  
Author(s):  
Tian Ge ◽  
Amit Patki ◽  
Vinodh Srinivasasainagendra ◽  
Yen-Feng Lin ◽  
Marguerite Ryan Irvin ◽  
...  
Keyword(s):  
Risk Factors ◽  
Type 2 Diabetes ◽  
African American ◽  
Large Scale ◽  
Association Studies ◽  
Risk Scores ◽  
Polygenic Risk Score ◽  
Genome Wide Association Studies ◽  
Polygenic Risk

ABSTRACTType 2 diabetes (T2D) is a worldwide scourge caused by both genetic and environmental risk factors that disproportionately afflicts communities of color. Leveraging existing large-scale genome-wide association studies (GWAS), polygenic risk scores (PRS) have shown promise to complement established clinical risk factors and intervention paradigms, and improve early diagnosis and prevention of T2D. However, to date, T2D PRS have been most widely developed and validated in individuals of European descent. Comprehensive assessment of T2D PRS in non-European populations is critical for an equitable deployment of PRS to clinical practice that benefits global populations. Here we integrate T2D GWAS in European, African American and East Asian populations to construct a trans-ancestry T2D PRS using a newly developed Bayesian polygenic modeling method, and evaluate the PRS in the multi-ethnic eMERGE study, four African American cohorts, and the Taiwan Biobank. The trans-ancestry PRS was significantly associated with T2D status across the ancestral groups examined, and the top 2% of the PRS distribution can identify individuals with an approximately 2.5-4.5 fold of increase in T2D risk, suggesting the potential of using the trans-ancestry PRS as a meaningful index of risk among diverse patients in clinical settings. Our efforts represent the first step towards the implementation of the T2D PRS into routine healthcare.

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 An atlas of polygenic risk score associations to highlight putative causal relationships across the human phenome

eLife ◽  
2019 ◽  
Vol 8 ◽  
Author(s):  
Tom G Richardson ◽  
Sean Harrison ◽  
Gibran Hemani ◽  
George Davey Smith
Keyword(s):  
Web Application ◽  
Large Scale ◽  
Complex Disease ◽  
Association Studies ◽  
Risk Scores ◽  
Polygenic Risk Score ◽  
Genome Wide Association Studies ◽  
Genetic Liability ◽  
Polygenic Risk ◽  
The Uk

The age of large-scale genome-wide association studies (GWAS) has provided us with an unprecedented opportunity to evaluate the genetic liability of complex disease using polygenic risk scores (PRS). In this study, we have analysed 162 PRS (p<5×10−05) derived from GWAS and 551 heritable traits from the UK Biobank study (N = 334,398). Findings can be investigated using a web application (http:‌//‌mrcieu.‌mrsoftware.org/‌PRS‌_atlas/), which we envisage will help uncover both known and novel mechanisms which contribute towards disease susceptibility. To demonstrate this, we have investigated the results from a phenome-wide evaluation of schizophrenia genetic liability. Amongst findings were inverse associations with measures of cognitive function which extensive follow-up analyses using Mendelian randomization (MR) provided evidence of a causal relationship. We have also investigated the effect of multiple risk factors on disease using mediation and multivariable MR frameworks. Our atlas provides a resource for future endeavours seeking to unravel the causal determinants of complex disease.

Sign in / Sign up

Export Citation Format

Share Document