scholarly journals Capturing additional genetic risk from family history for improved polygenic risk prediction

2022 ◽  
Author(s):  
Tianyuan Lu ◽  
Vincenzo Forgetta ◽  
J Brent Richards ◽  
Celia MT Greenwood
Keyword(s):  
Family History ◽  
Risk Prediction ◽  
Risk Score ◽  
Genetic Risk ◽  
Adult Height ◽  
Prediction Models ◽  
European Ancestry ◽  
Polygenic Risk Score ◽  
Parental History ◽  
Polygenic Risk

Family history of complex traits may reflect transmitted rare pathogenic variants, intrafamilial shared exposures to environmental and lifestyle factors, as well as a common genetic predisposition. We developed a latent factor model to quantify trait heritability in excess of that captured by a common variant-based polygenic risk score, but inferable from family history. We applied our model to predict adult height for 941 children in the Avon Longitudinal Study of Parents and Children cohort as well as 11 complex diseases for ~400,000 European ancestry participants in the UK Biobank. Parental history brought consistent significant improvements in the predictive power of polygenic risk prediction. For instance, a joint predictor was able to explain ~55% of the total variance in sex-adjusted adult height z-scores, close to the estimated heritability. Our work showcases an innovative paradigm for risk calculation, and supports incorporation of family history into polygenic risk score-based genetic risk prediction models.

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 Development and Implementation of Polygenic Risk Score in Vietnamese Population

2019 ◽  
Vol 2019 (2) ◽  
pp. 75-83
Author(s):  
Nguyễn Trần Thế Hùng ◽  
Lê Đức Hậu
Keyword(s):  
Risk Score ◽  
Prediction Models ◽  
European Ancestry ◽  
Polygenic Risk Score ◽  
Clinical Settings ◽  
Nucleotide Polymorphisms ◽  
Single Nucleotide ◽  
Polygenic Risk ◽  
Vietnamese Population ◽  
Artery Disease

Recent technological advancements and availability of genetic databases have facilitated the integration of genetic factors into risk prediction models. A Polygenic Risk Score (PRS) combines the effect of many Single Nucleotide Polymorphisms (SNP) into a single score. This score has lately been shown to have a clinically predictive value in various common diseases. Some clinical interpretations of PRS are summarized in this review for coronary artery disease, breast cancer, prostate cancer, diabetes mellitus, and Alzheimer’s disease. While these findings gave support to the implementation of PRS in clinical settings, the populations of interest were derived mainly from European ancestry. Therefore, applying these findings to non-European ancestry (Vietnamese in this context) requires many efforts and cautions. This review aims to articulate the evidence supporting the clinical use of PRS, the concepts behind the validity of PRS, approach to implement PRS in Vietnamese population, and cautions in selecting methods and thresholds to develop an appropriate PRS.

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 A polygenic risk score to predict future adult short stature amongst children

2021 ◽  
Author(s):  
Tianyuan Lu ◽  
Vincenzo Forgetta ◽  
Haoyu Wu ◽  
John R B Perry ◽  
Ken K Ong ◽  
...  
Keyword(s):  
Short Stature ◽  
Risk Score ◽  
Clinical Utility ◽  
Adult Height ◽  
European Ancestry ◽  
Polygenic Risk Score ◽  
Uk Biobank ◽  
Polygenic Risk ◽  
Parental Height ◽  
The Uk

Abstract Context Adult height is highly heritable, yet no genetic predictor has demonstrated clinical utility compared to mid-parental height. Objective To develop a polygenic risk score for adult height and evaluate its clinical utility. Design A polygenic risk score was constructed based on meta-analysis of genome-wide association studies and evaluated on the Avon Longitudinal Study of Parents and Children (ALSPAC) cohort. Subjects Participants included 442,599 genotyped White British individuals in the UK Biobank, and 941 genotyped child-parent trios of European ancestry in the ALSPAC cohort. Interventions None. Main Outcome Measures Standing height was measured using stadiometer; Standing height two standard deviations below the sex-specific population average was considered as short stature. Results Combined with sex, a polygenic risk score captured 71.1% of the total variance in adult height in the UK Biobank. In the ALSPAC cohort, the polygenic risk score was able to identify children who developed adulthood short stature with an area under the receiver operating characteristic curve (AUROC) of 0.84, which is close to that of mid-parental height. Combining this polygenic risk score with mid-parental height, or only one of the child’s parent’s height, could improve the AUROC to at most 0.90. The polygenic risk score could also substitute mid-parental height in age-specific Khamis-Roche height predictors and achieve an equally strong discriminative power in identifying children with a short stature in adulthood. Conclusions A polygenic risk score could be considered as an alternative or adjunct to mid-parental height to improve screening for children at risk of developing short stature in adulthood in European ancestry populations.

scholarly journals Accurate Prediction Model - Polygenic Risk Score for High-Risk Individuals Predictive of Gastric Cancer

2021 ◽  
Author(s):  
Fujiao Duan ◽  
Chunhua Song ◽  
Peng Wang ◽  
Hua Ye ◽  
Liping Dai ◽  
...  
Keyword(s):  
Gastric Cancer ◽  
High Risk ◽  
Risk Score ◽  
Genetic Risk ◽  
Genetic Factors ◽  
Prediction Models ◽  
Information Criterion ◽  
Polygenic Risk Score ◽  
Polygenic Risk ◽  
H Pylori

Abstract Background The genetic variation of gastric cancer has not been fully identified. We aimed to screen and identify common variant single nucleotide polymorphisms (SNPs) and long noncoding RNA (lncRNA) related SNPs associated with the risk of gastric cancer, and construct and evaluate prediction models based on polygenic risk score (PRS). Methods Non-genetic factors such as H.pylori infection, environment, and genetic factors associated with gastric cancer were screened following meta-analysis and bioinformatics,verified by frequency matched case-control study. PRS and weighted genetic risk scores (wGRS) were derived from estimation of effect size. Net reclassification improvement (NRI), integrated discrimination improvement (IDI), akaike information criterion (AIC) and bayesian information criterion (BIC) were used to evaluate model. Results A risk gradient was observed across quantile of the PRS, the results showed that the risk of gastric cancer in the highest 10 quantile of PRS was 3.24 folds higher than that of the general population (OR=3.24,95%CI: 2.07, 5.06). The PRS with one or more risk factors (smoking, drinking and H. pylori infection) was superior to the single genetic risk model. For NRI and IDI, the PRS combinations were significantly improved compared to wGRS model combinations (P<0.001). The model of PRS combined with lncRNA SNPs, smoking, drinking and H. pylori infection was the best fitting model (AIC=117.23, BIC=122.31). Conclusion Our findings indicated that the model based on PRS combined with lncRNA SNPs, smoking, drinking, and H. pylori infection had the optimal predictive ability on the risk of gastric cancer, contributing to distinguish high-risk groups from population.

scholarly journals Development of a polygenic risk score to improve screening for fracture risk: A genetic risk prediction study

PLoS Medicine ◽  
2020 ◽  
Vol 17 (7) ◽  
pp. e1003152 ◽  
Author(s):  
Vincenzo Forgetta ◽  
Julyan Keller-Baruch ◽  
Marie Forest ◽  
Audrey Durand ◽  
Sahir Bhatnagar ◽  
...  
Keyword(s):  
Fracture Risk ◽  
Risk Prediction ◽  
Risk Score ◽  
Genetic Risk ◽  
Polygenic Risk Score ◽  
Polygenic Risk ◽  
Genetic Risk Prediction

scholarly journals Relative Contributions of Family History and a Polygenic Risk Score on COPD and Related Outcomes: COPDGene and ECLIPSE studies

2020 ◽  
Author(s):  
Matthew Moll ◽  
Sharon M Lutz ◽  
Auyon J Ghosh ◽  
Phuwanat Sakornsalkolpat ◽  
Craig Hersh ◽  
...  
Keyword(s):  
Family History ◽  
Risk Score ◽  
European Ancestry ◽  
Chronic Obstructive ◽  
Polygenic Risk Score ◽  
Positive Interaction ◽  
Predictive Capacity ◽  
Polygenic Risk ◽  
Mediation Analyses ◽  
The Impact

Introduction: Family history is a risk factor for chronic obstructive pulmonary disease (COPD). We previously developed a COPD risk score from genome-wide genetic markers (polygenic risk score, or PRS). Whether the PRS and family history provide complementary or redundant information for predicting COPD and related outcomes is unknown. Methods: We assessed the predictive capacity of family history and PRS on COPD and COPD-related outcomes in European ancestry subjects from the COPDGene and ECLIPSE studies. We also performed interaction and mediation analyses. Results: In COPDGene, family history and PRS were significantly associated with COPD in a single model (PFamHx = 1.6e-12; PPRS = 5.0e-92). Similar trends were seen in ECLIPSE. Area-under-the-receiver-operator-characteristic-curves (AUCs) for family history, PRS, and the combined predictors for COPD were 0.752, 0.798, and 0.803, respectively. The AUC for a model containing both family history and the PRS was significantly higher than models with PRS (p = 0.00035) or family history (p = 6.1e-29) alone. Both family history and PRS were significantly associated with BODE, SGRQ, and multiple measures of quantitative emphysema and airway thickness. There was a weakly positive interaction between family history and the PRS under the additive, but not the multiplicative scale (RERI = 0.48, p=0.04). Mediation analysis found 16.5% of the effect of family history on risk for COPD was mediated through the PRS [95% CI: 9.4%-24.3%]. Conclusion: Family history and the PRS provide complementary information for predicting COPD and related outcomes. Future studies can address the impact of obtaining both measures in clinical practice.

scholarly journals Parkinson’s disease determinants, prediction and gene-environment interactions in the UK Biobank

2020 ◽  
Author(s):  
Benjamin M. Jacobs ◽  
Daniel Belete ◽  
Jonathan P Bestwick ◽  
Cornelis Blauwendraat ◽  
Sara Bandres-Ciga ◽  
...  
Keyword(s):  
Risk Factors ◽  
Family History ◽  
Risk Score ◽  
Genetic Risk ◽  
Positive Family History ◽  
Polygenic Risk Score ◽  
Uk Biobank ◽  
Polygenic Risk ◽  
Gene Environment ◽  
History Of

AbstractObjectiveTo systematically investigate the association of environmental risk factors and prodromal features with incident Parkinson’s disease (PD) diagnosis and the interaction of genetic risk with these factors. To evaluate existing risk prediction algorithms and the impact of including addition genetic risk on the performance of prediction.MethodsWe identified individuals with incident PD diagnoses (n=1276) and unmatched controls (n=500,406) in UK Biobank. We determined the association of risk factors with incident PD using adjusted logistic regression models. A polygenic risk score (PRS) was constructed and used to examine gene-environment interactions. The PRS was also incorporated into a previously-developed prediction algorithm for finding incident cases.ResultsStrong evidence of association (Pcorr<0.05) was found between PD and a positive family history of PD, a positive family history of dementia, non-smoking, low alcohol consumption, depression, and daytime somnolence, and novel associations with epilepsy and earlier menarche. Individuals with the highest 10% of PRS scores had increased risk of PD (OR=3.30, 95% CI 2.57-4.24) compared to the lowest risk decile. Higher PRS scores were associated with earlier age at PD diagnosis and inclusion of the PRS in the PREDICT-PD algorithm improved model performance (Nagelkerke pseudo-R2 0.0053, p=6.87×10−14). We found evidence of interaction between the PRS and diabetes.InterpretationHere we used UK Biobank data to reproduce several well-known associations with PD, to demonstrate the validity and predictive power of a polygenic risk score, and to demonstrate a novel gene-environment interaction, whereby the effect of diabetes on PD risk appears to depend on prior genetic risk for PD.

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>

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