scholarly journals Comparison of genetic risk prediction models to improve prediction of coronary heart disease in two large cohorts of the MONICA/KORA study

2021 ◽  
Author(s):  
Alina Bauer ◽  
Astrid Zierer ◽  
Christian Gieger ◽  
Mustafa Büyüközkan ◽  
Martina Müller‐Nurasyid ◽  
...  
Keyword(s):  
Coronary Heart Disease ◽  
Heart Disease ◽  
Risk Prediction ◽  
Genetic Risk ◽  
Prediction Models ◽  
Risk Prediction Models ◽  
Genetic Risk Prediction

scholarly journals Development of Appropriate Coronary Heart Disease Risk Prediction Models in HIV-Infected Patients

Circulation ◽  
2008 ◽  
Vol 118 (2) ◽  
Author(s):  
Morris Schambelan ◽  
Peter W.F. Wilson ◽  
Kevin E. Yarasheski ◽  
W. Todd Cade ◽  
Victor G. Dávila-Román ◽  
...  
Keyword(s):  
Coronary Heart Disease ◽  
Heart Disease ◽  
Risk Prediction ◽  
Coronary Heart Disease Risk ◽  
Prediction Models ◽  
Disease Risk ◽  
Risk Prediction Models ◽  
Heart Disease Risk

scholarly journals Prediction of Fetal Hemoglobin in Sickle Cell Anemia Using an Ensemble of Genetic Risk Prediction Models

2014 ◽  
Vol 7 (2) ◽  
pp. 110-115 ◽  
Author(s):  
Jacqueline N. Milton ◽  
Victor R. Gordeuk ◽  
James G. Taylor ◽  
Mark T. Gladwin ◽  
Martin H. Steinberg ◽  
...  
Keyword(s):  
Risk Prediction ◽  
Sickle Cell ◽  
Sickle Cell Anemia ◽  
Genetic Risk ◽  
Prediction Models ◽  
Fetal Hemoglobin ◽  
Risk Prediction Models ◽  
Genetic Risk Prediction

scholarly journals Genetic Risk Prediction and a 2-Stage Risk Screening Strategy for Coronary Heart Disease

2013 ◽  
Vol 33 (9) ◽  
pp. 2261-2266 ◽  
Author(s):  
Emmi Tikkanen ◽  
Aki S. Havulinna ◽  
Aarno Palotie ◽  
Veikko Salomaa ◽  
Samuli Ripatti
Keyword(s):  
Coronary Heart Disease ◽  
Heart Disease ◽  
Risk Prediction ◽  
Genetic Risk ◽  
Screening Strategy ◽  
Risk Screening ◽  
Genetic Risk Prediction

Risk prediction models for heart failure admissions in adults with congenital heart disease

2021 ◽  
Vol 322 ◽  
pp. 149-157 ◽  
Author(s):  
Sarah Cohen ◽  
Aihua Liu ◽  
Fei Wang ◽  
Liming Guo ◽  
James M. Brophy ◽  
...  
Keyword(s):  
Heart Failure ◽  
Congenital Heart Disease ◽  
Heart Disease ◽  
Risk Prediction ◽  
Congenital Heart ◽  
Prediction Models ◽  
Risk Prediction Models

scholarly journals Clinical and Genetic Risk Prediction of Cognitive Impairment After Blood or Marrow Transplantation for Hematologic Malignancy

2020 ◽  
Vol 38 (12) ◽  
pp. 1312-1321
Author(s):  
Noha Sharafeldin ◽  
Joshua Richman ◽  
Alysia Bosworth ◽  
Yanjun Chen ◽  
Purnima Singh ◽  
...  
Keyword(s):  
Cognitive Impairment ◽  
Risk Prediction ◽  
Genetic Risk ◽  
Marrow Transplantation ◽  
Prediction Models ◽  
Hematologic Malignancy ◽  
Self Report ◽  
Candidate Gene Approach ◽  
Discovery Cohort ◽  
Genetic Risk Prediction

PURPOSE Using a candidate gene approach, we tested the hypothesis that individual single nucleotide polymorphisms (SNPs) and gene-level variants are associated with cognitive impairment in patients with hematologic malignancies treated with blood or marrow transplantation (BMT) and that inclusion of these SNPs improves risk prediction beyond that offered by clinical and demographic characteristics. PATIENTS AND METHODS In the discovery cohort, BMT recipients underwent a standardized battery of neuropsychological tests pre-BMT and at 6 months, 1 year, 2 years, and 3 years post-BMT. Associations between 68 candidate genes and cognitive impairment were assessed using generalized estimating equation models. Elastic-Net regression was used to build Base (sociodemographic), Clinical, and Combined (Base plus Clinical plus genetic) risk prediction models of post-BMT impairment. An independent nonoverlapping cohort from the BMT Survivor Study with self-report of learning/memory problems (as identified by their health care provider) was used for model replication. RESULTS The discovery cohort included 277 participants (58.5% males; 68.6% non-Hispanic whites; and 46.6% allogeneic BMT recipients). Adjusting for BMT type, age at BMT, sex, race/ethnicity, and cognitive reserve, SNPs in the blood-brain barrier, telomere homeostasis, and DNA repair genes were significantly associated with cognitive impairment. Compared with the Clinical Model, the Combined Model had higher predictive power in both the discovery cohort (mean area under the receiver operating characteristic curve [AUC], 0.89; 95% CI, 0.85 to 0.93 v 0.77; 95% CI, 0.71 to 0.83; P = 1.24 × 10−9) and the replication cohort (AUC, 0.71; 95% CI, 0.66 to 0.76 v 0.63; 95% CI, 0.57 to 0.68; P = .004). CONCLUSION Inclusion of candidate genetic variants enhanced the prediction of risk of post-BMT cognitive impairment beyond that offered by demographic/clinical characteristics and represents a step toward a personalized approach to managing patients at high risk for cognitive impairment after BMT.

scholarly journals Leveraging Functional Annotations in Genetic Risk Prediction for Human Complex Diseases

2016 ◽  
Author(s):  
Yiming Hu ◽  
Qiongshi Lu ◽  
Ryan Powles ◽  
Xinwei Yao ◽  
Fang Fang ◽  
...  
Keyword(s):  
Risk Prediction ◽  
Genetic Risk ◽  
Prediction Accuracy ◽  
Prediction Models ◽  
Association Studies ◽  
Treatment Strategies ◽  
Complex Diseases ◽  
Genome Wide Association Studies ◽  
Genetic Risk Prediction ◽  
Human Complex

AbstractGenome wide association studies have identified numerous regions in the genome associated with hundreds of human diseases. Building accurate genetic risk prediction models from these data will have great impacts on disease prevention and treatment strategies. However, prediction accuracy remains moderate for most diseases, which is largely due to the challenges in identifying all the disease-associated variants and accurately estimating their effect sizes. We introduce AnnoPred, a principled framework that incorporates diverse functional annotation data to improve risk prediction accuracy, and demonstrate its performance on multiple human complex diseases.

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