scholarly journals Integration of Genomic Risk Scores to Improve the Prediction of Childhood Asthma Diagnosis

2022 ◽  
Vol 12 (1) ◽  
pp. 75
Author(s):  
Dilini M. Kothalawala ◽  
Latha Kadalayil ◽  
John A. Curtin ◽  
Clare S. Murray ◽  
Angela Simpson ◽  
...  
Keyword(s):  
Risk Score ◽  
Childhood Asthma ◽  
Prediction Models ◽  
Environmental Data ◽  
Risk Scores ◽  
Polygenic Risk Score ◽  
Asthma Diagnosis ◽  
Discriminative Performance ◽  
Genome Wide ◽  
Genomic Risk

Genome-wide and epigenome-wide association studies have identified genetic variants and differentially methylated nucleotides associated with childhood asthma. Incorporation of such genomic data may improve performance of childhood asthma prediction models which use phenotypic and environmental data. Using genome-wide genotype and methylation data at birth from the Isle of Wight Birth Cohort (n = 1456), a polygenic risk score (PRS), and newborn (nMRS) and childhood (cMRS) methylation risk scores, were developed to predict childhood asthma diagnosis. Each risk score was integrated with two previously published childhood asthma prediction models (CAPE and CAPP) and were validated in the Manchester Asthma and Allergy Study. Individually, the genomic risk scores demonstrated modest-to-moderate discriminative performance (area under the receiver operating characteristic curve, AUC: PRS = 0.64, nMRS = 0.55, cMRS = 0.54), and their integration only marginally improved the performance of the CAPE (AUC: 0.75 vs. 0.71) and CAPP models (AUC: 0.84 vs. 0.82). The limited predictive performance of each genomic risk score individually and their inability to substantially improve upon the performance of the CAPE and CAPP models suggests that genetic and epigenetic predictors of the broad phenotype of asthma are unlikely to have clinical utility. Hence, further studies predicting specific asthma endotypes are warranted.

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 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 An analysis on Dual Diagnosis Revealed a Possible deviation of Schizophrenia Polygenic Risk Score by Mexican Amerindian Ancestry

2018 ◽  
Author(s):  
Jose Jaime Martínez-Magaña ◽  
Thelma Beatriz Gonzalez-Castro ◽  
Alma Delia Genis-Mendoza ◽  
Carlos Alfonso Tovilla-Zarate ◽  
Isela Juarez-Rojop ◽  
...  
Keyword(s):  
Dual Diagnosis ◽  
Risk Score ◽  
Risk Scores ◽  
Polygenic Risk Score ◽  
Psychiatric Diseases ◽  
Polygenic Risk ◽  
Asian Populations ◽  
Genome Wide ◽  
Highly Correlated ◽  
Larger Sample

In order to summarized the polygenic background of psychiatric diseases, polygenic risk scores (PRS) have been developed. Recently, PRS have been use to predict patients with higher comorbidities in psychiatric diseases, like dual diagnosis. PRS are principally derived in analysis of Caucasian and Asian populations, we are not aware of how this PRS could be applied in populations with high admixture. In order to explored this, the present work has the aim to analyzed if previous calculated PRS for psychiatric diseases could predict dual diagnosis in Mexican population, and also, if PRS calculation could be influenced by Mexican Amerindian (MA) global ancestry. We performed PRS calculation, using PRSice, with summary genome-wide association statistics previously published for psychiatric diseases, and also, performed Nagelkerke correlation test in order to established if PRS are correlated with dual diagnosis. We found that dual diagnosis could be predicted by major depressive disorder polygenic risk score. Nevertheless, schizophrenia polygenic risk score is highly correlated with global MA ancestry, independently of the schizophrenia diagnosis. Our results reinforced the notion that PRS calculation could be deviated by the MA global ancestry, nevertheless analysis on larger sample sizes are required in order to clarified this issue.

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 Joint association between education and polygenic risk score for incident coronary heart disease events: a longitudinal population-based study of 26 203 men and women

2021 ◽  
pp. jech-2020-214358
Author(s):  
Pekka Martikainen ◽  
Kaarina Korhonen ◽  
Aline Jelenkovic ◽  
Hannu Lahtinen ◽  
Aki Havulinna ◽  
...  
Keyword(s):  
Coronary Heart Disease ◽  
Heart Disease ◽  
Risk Score ◽  
Density Lipoprotein ◽  
Population Based ◽  
Polygenic Risk Score ◽  
Genome Wide ◽  
Increased Risk ◽  
Region Of Residence

BackgroundGenetic vulnerability to coronary heart disease (CHD) is well established, but little is known whether these effects are mediated or modified by equally well-established social determinants of CHD. We estimate the joint associations of the polygenetic risk score (PRS) for CHD and education on CHD events.MethodsThe data are from the 1992, 1997, 2002, 2007 and 2012 surveys of the population-based FINRISK Study including measures of social, behavioural and metabolic factors and genome-wide genotypes (N=26 203). Follow-up of fatal and non-fatal incident CHD events (N=2063) was based on nationwide registers.ResultsAllowing for age, sex, study year, region of residence, study batch and principal components, those in the highest quartile of PRS for CHD had strongly increased risk of CHD events compared with the lowest quartile (HR=2.26; 95% CI: 1.97 to 2.59); associations were also observed for low education (HR=1.58; 95% CI: 1.32 to 1.89). These effects were largely independent of each other. Adjustment for baseline smoking, alcohol use, body mass index, igh-density lipoprotein (HDL) and total cholesterol, blood pressure and diabetes attenuated the PRS associations by 10% and the education associations by 50%. We do not find strong evidence of interactions between PRS and education.ConclusionsPRS and education predict CHD events, and these associations are independent of each other. Both can improve CHD prediction beyond behavioural risks. The results imply that observational studies that do not have information on genetic risk factors for CHD do not provide confounded estimates for the association between education and CHD.

scholarly journals Genomic Risk Prediction for Breast Cancer in Older Women

Cancers ◽  
2021 ◽  
Vol 13 (14) ◽  
pp. 3533
Author(s):  
Paul Lacaze ◽  
Andrew Bakshi ◽  
Moeen Riaz ◽  
Suzanne G. Orchard ◽  
Jane Tiller ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Older Women ◽  
Risk Prediction ◽  
Prediction Models ◽  
Cox Regression ◽  
Susceptibility Genes ◽  
Controlled Clinical Trial ◽  
Polygenic Risk Score ◽  
Double Blind ◽  
Genomic Risk

Genomic risk prediction models for breast cancer (BC) have been predominantly developed with data from women aged 40–69 years. Prospective studies of older women aged ≥70 years have been limited. We assessed the effect of a 313-variant polygenic risk score (PRS) for BC in 6339 older women aged ≥70 years (mean age 75 years) enrolled into the ASPREE trial, a randomized double-blind placebo-controlled clinical trial investigating the effect of daily 100 mg aspirin on disability-free survival. We evaluated incident BC diagnoses over a median follow-up time of 4.7 years. A multivariable Cox regression model including conventional BC risk factors was applied to prospective data, and re-evaluated after adding the PRS. We also assessed the association of rare pathogenic variants (PVs) in BC susceptibility genes (BRCA1/BRCA2/PALB2/CHEK2/ATM). The PRS, as a continuous variable, was an independent predictor of incident BC (hazard ratio (HR) per standard deviation (SD) = 1.4, 95% confidence interval (CI) 1.3–1.6) and hormone receptor (ER/PR)-positive disease (HR = 1.5 (CI 1.2–1.9)). Women in the top quintile of the PRS distribution had over two-fold higher risk of BC than women in the lowest quintile (HR = 2.2 (CI 1.2–3.9)). The concordance index of the model without the PRS was 0.62 (95% CI 0.56–0.68), which improved after addition of the PRS to 0.65 (95% CI 0.59–0.71). Among 41 (0.6%) carriers of PVs in BC susceptibility genes, we observed no incident BC diagnoses. Our study demonstrates that a PRS predicts incident BC risk in women aged 70 years and older, suggesting potential clinical utility extends to this older age group.

Abstract 881: Benchmarking genome-wide polygenic risk score development techniques in colorectal cancer risk prediction

2021 ◽  
Author(s):  
Minta Thomas ◽  
Lori C Sakoda ◽  
Jeffrey K Lee ◽  
Mark A Jenkins ◽  
Andrea Burnett-Hartman ◽  
...  
Keyword(s):  
Colorectal Cancer ◽  
Cancer Risk ◽  
Risk Prediction ◽  
Risk Score ◽  
Colorectal Cancer Risk ◽  
Polygenic Risk Score ◽  
Polygenic Risk ◽  
Genome Wide

scholarly journals Polygenic risk score, genome-wide association, and gene set analyses of cognitive domain deficits in schizophrenia

2018 ◽  
Vol 201 ◽  
pp. 393-399 ◽  
Author(s):  
Soichiro Nakahara ◽  
Sarah Medland ◽  
Jessica A. Turner ◽  
Vince D. Calhoun ◽  
Kelvin O. Lim ◽  
...  
Keyword(s):  
Risk Score ◽  
Genome Wide Association ◽  
Cognitive Domain ◽  
Polygenic Risk Score ◽  
Polygenic Risk ◽  
Gene Set ◽  
Genome Wide

Abstract PO-163: Genome-wide polygenic risk score of prostate cancer in African and European ancestry men

2022 ◽  
Author(s):  
Burcu F. Darst ◽  
Ravi K Madduri ◽  
Alexis A. Rodriguez ◽  
Xin Sheng ◽  
Rosalind A. Eeles ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Risk Score ◽  
European Ancestry ◽  
Polygenic Risk Score ◽  
Polygenic Risk ◽  
Genome Wide
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