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 Gastric Cancer Risk Prediction Using an Epidemiological Risk Assessment Model and Polygenic Risk Score

Cancers ◽  
2021 ◽  
Vol 13 (4) ◽  
pp. 876
Author(s):  
Boyoung Park ◽  
Sarah Yang ◽  
Jeonghee Lee ◽  
Il Ju Choi ◽  
Young-Il Kim ◽  
...  
Keyword(s):  
Gastric Cancer ◽  
Risk Assessment ◽  
Risk Score ◽  
Assessment Model ◽  
Polygenic Risk Score ◽  
Genome Wide Association Studies ◽  
Risk Assessment Model ◽  
Infection Status ◽  
Polygenic Risk ◽  
H Pylori

We investigated the performance of a gastric cancer (GC) risk assessment model in combination with single-nucleotide polymorphisms (SNPs) as a polygenic risk score (PRS) in consideration of Helicobacter pylori (H. pylori) infection status. Six SNPs identified from genome-wide association studies and a marginal association with GC in the study population were included in the PRS. Discrimination of the GC risk assessment model, PRS, and the combination of the two (PRS-GCS) were examined regarding incremental risk and the area under the receiver operating characteristic curve (AUC), with grouping according to H. pylori infection status. The GC risk assessment model score showed an association with GC, irrespective of H. pylori infection. Conversely, the PRS exhibited an association only for those with H. pylori infection. The PRS did not discriminate GC in those without H. pylori infection, whereas the GC risk assessment model showed a modest discrimination. Among individuals with H. pylori infection, discrimination by the GC risk assessment model and the PRS were comparable, with the PRS-GCS combination resulting in an increase in the AUC of 3%. In addition, the PRS-GCS classified more patients and fewer controls at the highest score quintile in those with H. pylori infection. Overall, the PRS-GCS improved the identification of a GC-susceptible population of people with H. pylori infection. In those without H. pylori infection, the GC risk assessment model was better at identifying the high-risk group.

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 Polygenic risk score and risk of monoclonal B-cell lymphocytosis in caucasians and risk of chronic lymphocytic leukemia (CLL) in African Americans

Leukemia ◽  
2021 ◽  
Author(s):  
Geffen Kleinstern ◽  
J. Brice Weinberg ◽  
Sameer A. Parikh ◽  
Esteban Braggio ◽  
Sara J. Achenbach ◽  
...  
Keyword(s):  
Environmental Factors ◽  
B Cell ◽  
Risk Score ◽  
Genetic Factors ◽  
Strong Predictor ◽  
Lymphocytic Leukemia ◽  
European Ancestry ◽  
Polygenic Risk Score ◽  
Polygenic Risk ◽  
C Statistic

AbstractMonoclonal B-cell lymphocytosis (MBL) is a precursor to CLL. Other than age, sex, and CLL family-history, little is known about factors associated with MBL risk. A polygenic-risk-score (PRS) of 41 CLL-susceptibility variants has been found to be associated with CLL risk among individuals of European-ancestry(EA). Here, we evaluate these variants, the PRS, and environmental factors for MBL risk. We also evaluate these variants and the CLL-PRS among African-American (AA) and EA-CLL cases and controls. Our study included 560 EA MBLs, 869 CLLs (696 EA/173 AA), and 2866 controls (2631 EA/235 AA). We used logistic regression, adjusting for age and sex, to estimate odds ratios (OR) and 95% confidence intervals within each race. We found significant associations with MBL risk among 21 of 41 variants and with the CLL-PRS (OR = 1.86, P = 1.9 × 10−29, c-statistic = 0.72). Little evidence of any association between MBL risk and environmental factors was observed. We observed significant associations of the CLL-PRS with EA-CLL risk (OR = 2.53, P = 4.0 × 10−63, c-statistic = 0.77) and AA-CLL risk (OR = 1.76, P = 5.1 × 10−5, c-statistic = 0.62). Inherited genetic factors and not environmental are associated with MBL risk. In particular, the CLL-PRS is a strong predictor for both risk of MBL and EA-CLL, but less so for AA-CLL supporting the need for further work in this population.

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 Polygenic risk for schizophrenia, transition and cortical gyrification: a high-risk study

2017 ◽  
Vol 48 (9) ◽  
pp. 1532-1539 ◽  
Author(s):  
E. Neilson ◽  
C. Bois ◽  
T.-K. Clarke ◽  
L. Hall ◽  
E. C. Johnstone ◽  
...  
Keyword(s):  
High Risk ◽  
Risk Score ◽  
Familial Risk ◽  
Positive Association ◽  
Polygenic Risk Score ◽  
Polygenic Risk ◽  
Score Analysis ◽  
Increased Risk ◽  
Heritable Disorder ◽  
Diagnostic Symptoms

AbstractBackgroundSchizophrenia is a highly heritable disorder, linked to several structural abnormalities of the brain. More specifically, previous findings have suggested that increased gyrification in frontal and temporal regions are implicated in the pathogenesis of schizophrenia.MethodsThe current study included participants at high familial risk of schizophrenia who remained well (n= 31), who developed sub-diagnostic symptoms (n= 28) and who developed schizophrenia (n= 9) as well as healthy controls (HC) (n= 16). We first tested whether individuals at high familial risk of schizophrenia carried an increased burden of trait-associated alleles using polygenic risk score analysis. We then assessed the extent to which polygenic risk was associated with gyral folding in the frontal and temporal lobes.ResultsWe found that individuals at high familial risk of schizophrenia who developed schizophrenia carried a significantly greater burden of risk-conferring variants for the disorder compared to those at high risk (HR) who developed sub-diagnostic symptoms or remained well and HC. Furthermore, within the HR cohort, there was a significant and positive association between schizophrenia polygenic risk score and bilateral frontal gyrification.ConclusionsThese results suggest that polygenic risk for schizophrenia impacts upon early neurodevelopment to confer greater gyral folding in adulthood and an increased risk of developing the disorder.

Effects of genetic risk for alcohol dependence and onset of regular drinking on the progression to alcohol dependence: A polygenic risk score approach

2021 ◽  
pp. 109117
Author(s):  
Ellen W. Yeung ◽  
Kellyn M. Spychala ◽  
Alex P. Miller ◽  
Jacqueline M. Otto ◽  
Joseph D. Deak ◽  
...  
Keyword(s):  
Alcohol Dependence ◽  
Risk Score ◽  
Genetic Risk ◽  
Polygenic Risk Score ◽  
Polygenic Risk

Polygenic risk score for breast cancer in high-risk women.

2018 ◽  
Vol 36 (15_suppl) ◽  
pp. 1508-1508 ◽  
Author(s):  
Mary Helen Black ◽  
Shuwei Li ◽  
Holly LaDuca ◽  
Jefferey Chen ◽  
Robert Hoiness ◽  
...  
Keyword(s):  
Breast Cancer ◽  
High Risk ◽  
Risk Score ◽  
Polygenic Risk Score ◽  
Polygenic Risk ◽  
High Risk Women

scholarly journals Prediction of primary venous thromboembolism based on clinical and genetic factors within the U.K. Biobank

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
David A. Kolin ◽  
Scott Kulm ◽  
Olivier Elemento
Keyword(s):  
Risk Factors ◽  
Venous Thromboembolism ◽  
Risk Score ◽  
Genetic Risk ◽  
Genetic Factors ◽  
External Validation ◽  
Predictive Score ◽  
Polygenic Risk Score ◽  
Genetic Components ◽  
Increased Risk

AbstractBoth clinical and genetic factors drive the risk of venous thromboembolism. However, whether clinically recorded risk factors and genetic variants can be combined into a clinically applicable predictive score remains unknown. Using Cox proportional-hazard models, we analyzed the association of risk factors with the likelihood of venous thromboembolism in U.K. Biobank, a large prospective cohort. We then created a polygenic risk score of 36 single nucleotide polymorphisms and a clinical score determined by age, sex, body mass index, previous cancer diagnosis, smoking status, and fracture in the last 5 years. Participants were at significantly increased risk of venous thromboembolism if they were at high clinical risk (subhazard ratio, 4.37 [95% CI, 3.85–4.97]) or high genetic risk (subhazard ratio, 3.02 [95% CI, 2.63–3.47]) relative to participants at low clinical or genetic risk, respectively. The combined model, consisting of clinical and genetic components, was significantly better than either the clinical or the genetic model alone (P < 0.001). Participants at high risk in the combined score had nearly an eightfold increased risk of venous thromboembolism relative to participants at low risk (subhazard ratio, 7.51 [95% CI, 6.28–8.98]). This risk score can be used to guide decisions regarding venous thromboembolism prophylaxis, although external validation is needed.

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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