scholarly journals A multi-locus predictiveness curve and its summary assessment for genetic risk prediction

2019 ◽  
Vol 29 (1) ◽  
pp. 44-56
Author(s):  
Changshuai Wei ◽  
Ming Li ◽  
Yalu Wen ◽  
Chengyin Ye ◽  
Qing Lu
Keyword(s):  
Prediction Model ◽  
Risk Prediction ◽  
Genetic Variants ◽  
Genetic Risk ◽  
Prediction Models ◽  
Association Studies ◽  
Risk Prediction Model ◽  
Substantial Contribution ◽  
Great Promise ◽  
Genetic Risk Prediction

Genetic association studies using high-throughput genotyping and sequencing technologies have identified a large number of genetic variants associated with complex human diseases. These findings have provided an unprecedented opportunity to identify individuals in the population at high risk for disease who carry causal genetic mutations and hold great promise for early intervention and individualized medicine. While interest is high in building risk prediction models based on recent genetic findings, it is crucial to have appropriate statistical measurements to assess the performance of a genetic risk prediction model. Predictiveness curves were recently proposed as a graphic tool for evaluating a risk prediction model on the basis of a single continuous biomarker. The curve evaluates a risk prediction model for classification performance as well as its usefulness when applied to a population. In this article, we extend the predictiveness curve to measure the collective contribution of multiple genetic variants. We further propose a nonparametric, U-statistics-based measurement, referred to as the U-Index, to quantify the performance of a multi-locus predictiveness curve. In particular, a global U-Index and a partial U-Index can be used in the general population and a subpopulation of particular clinical interest, respectively. Through simulation studies, we demonstrate that the proposed U-Index has advantages over several existing summary statistics under various disease models. We also show that the partial U-Index can have its own uniqueness when rare variants have a substantial contribution to disease risk. Finally, we use the proposed predictiveness curve and its corresponding U-Index to evaluate the performance of a genetic risk prediction model for nicotine dependence.

Assessing the added value of breast tumor markers in genetic risk prediction model BRCAPRO

2012 ◽  
Vol 133 (1) ◽  
pp. 347-355 ◽  
Author(s):  
Swati Biswas ◽  
Neelam Tankhiwale ◽  
Amanda Blackford ◽  
Angelica M. Gutierrez Barrera ◽  
Kaylene Ready ◽  
...  
Keyword(s):  
Prediction Model ◽  
Tumor Markers ◽  
Risk Prediction ◽  
Genetic Risk ◽  
Breast Tumor ◽  
Risk Prediction Model ◽  
Added Value ◽  
Genetic Risk Prediction

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.

Reproducibility, performance, and clinical utility of a genetic risk prediction model for prostate cancer in Japanese patients.

2012 ◽  
Vol 30 (15_suppl) ◽  
pp. 10520-10520
Author(s):  
Shusuke Akamatsu ◽  
Ryo Takata ◽  
Atsushi Takahashi ◽  
Takahiro Inoue ◽  
Michiaki Kubo ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Prediction Model ◽  
Risk Prediction ◽  
Genetic Risk ◽  
Predictive Performance ◽  
Risk Prediction Model ◽  
Clinical Scenario ◽  
Gray Zone ◽  
Diagnostic Biomarker ◽  
Genetic Risk Prediction

10520 Background: Prostate specific antigen (PSA) is widely used as a diagnostic biomarker for prostate cancer (PC). However, due to its low predictive performance, many patients without PC suffer from the harms of unnecessary prostate needle biopsies. The present study aims to evaluate the reproducibility and performance of a genetic risk prediction model and estimate its utility as a diagnostic biomarker in a clinical scenario. Methods: We created a logistic regression model incorporating 16 SNPs that were significantly associated with PC in a genome-wide association study of the Japanese. The model was validated by two independent sets of samples comprising 3,294 cases and 6,281 controls. Various cut offs were evaluated to be used in a clinical scenario. Results: The area under a curve (AUC) of the model was 0.679, 0.655, and 0.661 for the samples used to create the model and those used for validation respectively. The AUC of the model was not significantly altered in samples with PSA 1-10 ng/ml. 24.2% and 9.7% of the patients had odds ratio <0.5 (low risk) or >2 (high risk) in the model, and assuming the overall positive rate of prostate needle biopsies to be 20% in PSA gray zone (PSA 2-10 ng/ml), the positive biopsy rates were 10.7% and 42.4% respectively for the two genetic risk groups. Conclusions: The genetic risk prediction model was highly reproducible, and its predictive performance was not influenced by PSA. The model could have a potential to affect clinical decision when it is applied to patients with gray-zone PSA, which should be confirmed in future clinical studies.

scholarly journals Simplifying clinical use of the genetic risk prediction model BRCAPRO

2013 ◽  
Vol 139 (2) ◽  
pp. 571-579 ◽  
Author(s):  
Swati Biswas ◽  
Philamer Atienza ◽  
Jonathan Chipman ◽  
Kevin Hughes ◽  
Angelica M. Gutierrez Barrera ◽  
...  
Keyword(s):  
Prediction Model ◽  
Risk Prediction ◽  
Genetic Risk ◽  
Risk Prediction Model ◽  
Clinical Use ◽  
Genetic Risk Prediction

Assessing the added value of breast tumor markers in breast cancer genetic risk prediction model BRCAPRO.

2011 ◽  
Vol 29 (27_suppl) ◽  
pp. 164-164
Author(s):  
B. Arun ◽  
S. Biswas ◽  
N. Tankhiwale ◽  
A. L. Blackford ◽  
A. M. Gutierrez-Barrera ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Prediction Model ◽  
Tumor Markers ◽  
Risk Prediction ◽  
Validation Study ◽  
Genetic Risk ◽  
Breast Tumor ◽  
Model Building ◽  
Risk Prediction Model ◽  
Genetic Risk Prediction

164 Background: BRCAPRO is a widely used genetic risk prediction model for estimating the carrier probabilities of mutations in BRCA1/2 genes. BRCAPRO has been enhanced to utilize information on molecular markers ER, PR, CK5/6, and CK14. However, no independent validation study on the utility of these markers in risk prediction exists to support using these in actual clinical settings. Further, an important predictive and prognostic marker for breast cancer, Her-2/neu (Her2) is not utilized in BRCAPRO. Therefore, the aim of our study was to: 1) incorporate Her2 in BRCAPRO; 2) conduct a validation study of the markers. Methods: Patients with breast cancer at the UT M. D. Anderson Cancer Center’s breast clinic who were referred for genetic evaluation were included. Separate sets of cohort were used for model building with Her2 and validation to avoid bias. This study was approved by the IRB. For the model building, we estimated the joint probabilities of ER and Her2 status for carriers and non-carriers of BRCA1/2 mutations. For the validation, BRCAPRO was run at two settings: 1) no marker data used and 2) ER/PR used. We calculated the Area Under the receiving operator characteristic Curve (AUC) using the probabilities of carrying any of BRCA1 or BRCA2 and conditional probabilities of carrying BRCA1 (CondBRCA1) and BRCA2 (CondBRCA2) given a proband is carrier. Results: The model-building set for Her2 was based on 409 probands and validation set on 796 probands wherein 23% of the probands were carriers. In the model-building step, we found that joint consideration of Her2 and ER/PR is useful in discriminating between carriers and non-carriers in some subgroups, e.g., a proband with ER-, Her2+ is much more likely to be a non-carrier than a carrier. In the validation step, the AUC for CondBRCA1 and CondBRCA2 improved substantially when ER/PR was used. We are in the process of coding Her2 in BRCAPRO and then validating its utility. Conclusions: Breast tumor markers are useful for prediction of BRCA1/2 mutation status in the BRCAPRO model. ER/PR helps discriminate between BRCA1 and BRCA2 mutation carriers. In our ongoing validation study Her2 is expected to improve discrimination between carriers and non-carriers in certain sub-groups.

scholarly journals Reproducibility, Performance, and Clinical Utility of a Genetic Risk Prediction Model for Prostate Cancer in Japanese

PLoS ONE ◽  
2012 ◽  
Vol 7 (10) ◽  
pp. e46454 ◽  
Author(s):  
Shusuke Akamatsu ◽  
Atsushi Takahashi ◽  
Ryo Takata ◽  
Michiaki Kubo ◽  
Takahiro Inoue ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Prediction Model ◽  
Risk Prediction ◽  
Genetic Risk ◽  
Clinical Utility ◽  
Risk Prediction Model ◽  
Genetic Risk Prediction

scholarly journals Polygenic risk prediction models for colorectal cancer: a systematic review

BMC Cancer ◽  
2022 ◽  
Vol 22 (1) ◽  
Author(s):  
Michele Sassano ◽  
Marco Mariani ◽  
Gianluigi Quaranta ◽  
Roberta Pastorino ◽  
Stefania Boccia
Keyword(s):  
Colorectal Cancer ◽  
Risk Factors ◽  
Systematic Review ◽  
Prediction Model ◽  
Risk Prediction ◽  
Genetic Variants ◽  
Prediction Models ◽  
Risk Prediction Models ◽  
Discriminatory Accuracy ◽  
Traditional Risk Factors

Abstract Background Risk prediction models incorporating single nucleotide polymorphisms (SNPs) could lead to individualized prevention of colorectal cancer (CRC). However, the added value of incorporating SNPs into models with only traditional risk factors is still not clear. Hence, our primary aim was to summarize literature on risk prediction models including genetic variants for CRC, while our secondary aim was to evaluate the improvement of discriminatory accuracy when adding SNPs to a prediction model with only traditional risk factors. Methods We conducted a systematic review on prediction models incorporating multiple SNPs for CRC risk prediction. We tested whether a significant trend in the increase of Area Under Curve (AUC) according to the number of SNPs could be observed, and estimated the correlation between AUC improvement and number of SNPs. We estimated pooled AUC improvement for SNP-enhanced models compared with non-SNP-enhanced models using random effects meta-analysis, and conducted meta-regression to investigate the association of specific factors with AUC improvement. Results We included 33 studies, 78.79% using genetic risk scores to combine genetic data. We found no significant trend in AUC improvement according to the number of SNPs (p for trend = 0.774), and no correlation between the number of SNPs and AUC improvement (p = 0.695). Pooled AUC improvement was 0.040 (95% CI: 0.035, 0.045), and the number of cases in the study and the AUC of the starting model were inversely associated with AUC improvement obtained when adding SNPs to a prediction model. In addition, models constructed in Asian individuals achieved better AUC improvement with the incorporation of SNPs compared with those developed among individuals of European ancestry. Conclusions Though not conclusive, our results provide insights on factors influencing discriminatory accuracy of SNP-enhanced models. Genetic variants might be useful to inform stratified CRC screening in the future, but further research is needed.

A Validated Risk Prediction Model for Breast Cancer in US Black Women

2021 ◽  
Author(s):  
Julie R. Palmer ◽  
Gary Zirpoli ◽  
Kimberly A. Bertrand ◽  
Tracy Battaglia ◽  
Leslie Bernstein ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Estrogen Receptor ◽  
Black Women ◽  
Prediction Model ◽  
Risk Prediction ◽  
Prediction Models ◽  
Risk Prediction Model ◽  
Risk Prediction Models ◽  
Relative Risks ◽  
Discriminatory Accuracy

PURPOSE Breast cancer risk prediction models are used to identify high-risk women for early detection, targeted interventions, and enrollment into prevention trials. We sought to develop and evaluate a risk prediction model for breast cancer in US Black women, suitable for use in primary care settings. METHODS Breast cancer relative risks and attributable risks were estimated using data from Black women in three US population-based case-control studies (3,468 breast cancer cases; 3,578 controls age 30-69 years) and combined with SEER age- and race-specific incidence rates, with incorporation of competing mortality, to develop an absolute risk model. The model was validated in prospective data among 51,798 participants of the Black Women's Health Study, including 1,515 who developed invasive breast cancer. A second risk prediction model was developed on the basis of estrogen receptor (ER)–specific relative risks and attributable risks. Model performance was assessed by calibration (expected/observed cases) and discriminatory accuracy (C-statistic). RESULTS The expected/observed ratio was 1.01 (95% CI, 0.95 to 1.07). Age-adjusted C-statistics were 0.58 (95% CI, 0.56 to 0.59) overall and 0.63 (95% CI, 0.58 to 0.68) among women younger than 40 years. These measures were almost identical in the model based on estrogen receptor–specific relative risks and attributable risks. CONCLUSION Discriminatory accuracy of the new model was similar to that of the most frequently used questionnaire-based breast cancer risk prediction models in White women, suggesting that effective risk stratification for Black women is now possible. This model may be especially valuable for risk stratification of young Black women, who are below the ages at which breast cancer screening is typically begun.

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.

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