A Penalized Regression Framework for Building Polygenic Risk Models Based on Summary Statistics From Genome-Wide Association Studies and Incorporating External Information

Chen, Ting Huei; Chatterjee, Nilanjan; Landi, Maria Teresa; Shi, Jianxin

doi:10.1080/01621459.2020.1764849

Cited by 21 publications

(28 citation statements)

References 38 publications

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“…1 Important examples include genetic or molecular data (e.g. Chen et al 2 ; Choi et al 3 ), but also in more classical clinical studies one often aims to obtain a relatively sparse model with good prediction accuracy including only the most relevant variables (e.g. Steyerberg and Vergouwe 4 ; Sauerbrei et al 5 ).…”

Section: Introductionmentioning

confidence: 99%

Deselection of base-learners for statistical boosting—with an application to distributional regression

Strömer

Staerk

Klein

et al. 2021

Stat Methods Med Res

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We present a new procedure for enhanced variable selection for component-wise gradient boosting. Statistical boosting is a computational approach that emerged from machine learning, which allows to fit regression models in the presence of high-dimensional data. Furthermore, the algorithm can lead to data-driven variable selection. In practice, however, the final models typically tend to include too many variables in some situations. This occurs particularly for low-dimensional data ([Formula: see text]), where we observe a slow overfitting behavior of boosting. As a result, more variables get included into the final model without altering the prediction accuracy. Many of these false positives are incorporated with a small coefficient and therefore have a small impact, but lead to a larger model. We try to overcome this issue by giving the algorithm the chance to deselect base-learners with minor importance. We analyze the impact of the new approach on variable selection and prediction performance in comparison to alternative methods including boosting with earlier stopping as well as twin boosting. We illustrate our approach with data of an ongoing cohort study for chronic kidney disease patients, where the most influential predictors for the health-related quality of life measure are selected in a distributional regression approach based on beta regression.

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Section: Introductionmentioning

confidence: 99%

Deselection of base-learners for statistical boosting—with an application to distributional regression

Strömer

Staerk

Klein

et al. 2021

Stat Methods Med Res

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show abstract

“…LASSO frameworks have been used to identify SNPs for polygenic risk scores of several phenotypes, including fracture risk 30 , type 2 diabetes 31 , and breast cancer 12 . In this work, we have extended the application of LASSO to select SNPs in a polygenic hazard model of prostate cancer from a list of candidates previously identified through logistic and time-to-event analysis.…”

Section: Discussionmentioning

confidence: 99%

Additional SNPs improve the performance of a polygenic hazard score for prostate cancer

Karunamuni¹,

Huynh‐Le²,

Fan³

et al. 2020

Preprint

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Background: Polygenic hazard scores (PHS) can identify individuals with increased risk of prostate cancer. We estimated the benefit of additional SNPs on performance of a previously validated PHS (PHS46). Materials and Method: 180 SNPs, shown to be previously associated with prostate cancer, were used to develop a PHS model in men with European ancestry. A machine-learning approach, LASSO-regularized Cox regression, was used to select SNPs and to estimate their coefficients in the training set (75,596 men). Performance of the resulting model was evaluated in the testing/validation set (6,411 men) with two metrics: (1) hazard ratios (HRs) and (2) positive predictive value (PPV) of prostate-specific antigen (PSA) testing. HRs were estimated between individuals with PHS in the top 5% to those in the middle 40% (HR95/50), top 20% to bottom 20% (HR80/20), and bottom 20% to middle 40% (HR20/50). PPV was calculated for the top 20% (PPV80) and top 5% (PPV95) of PHS as the fraction of individuals with elevated PSA that were diagnosed with clinically significant prostate cancer on biopsy. Results: 166 SNPs had non-zero coefficients in the Cox model (PHS166). All HR metrics showed significant improvements for PHS166 compared to PHS46: HR95/50 increased from 3.72 to 5.09, HR80/20 increased from 6.12 to 9.45, and HR20/50 decreased from 0.41 to 0.34. By contrast, no significant differences were observed in PPV of PSA testing for clinically significant prostate cancer. Conclusion: Incorporating 120 additional SNPs (PHS166 vs PHS46) significantly improved HRs for prostate cancer, while PPV of PSA testing remained the same.

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“…(MLE) is one of the important methods in the process of estimation, its properties and the most important of these properties is the property invariance [17]. In failure monitoring experiments, n of experimental units are placed under observation in a life-model test or product longevity at zero time (where time is a random variable that cannot be determined) and by specifying r of observations where r < n where data consists of observations t 1 ,t 2 ,……t r that represent ages Test units This means that there is no information on survival units (n-r) except for those whose useful life is greater than t r The test stops and the experiment ends when unit r fails [18].…”

Section: Maximum Likelihood Methods (Mle)mentioning

confidence: 99%

E- Bayesian Estimation of System Reliability (Series, Parallel) and Failure Rate Functions with Kumaraswamy Distribution based on Type II Censoring Data

Hussain

Akka²,

Hamza

2020

Int. J. Adv. Sci. Eng. Inf. Technol.

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In this paper, the failure rate function and the shape parameter for the kumaraswamy distribution and reliability function of a system with a number (m) of independent compounds associated with a system (serial, parallel) were estimated, by relying on observational data of the second type, knowing that the survival time of the compounds are independent. Based on the findings the graphical predictor of the failure rate and parameter-and the reliability function of the serial and parallel system is smaller than the Standard Bayesian estimator (MLE) in simulation and real data. Thus, a decreasing in AMPE with an increase in the sample size n and an increase in the size of the failure sample r as the physical prediction capabilities have a high efficiency. The using of the Bayesian prediction method to estimate the reliability of different production systems for other failure distributions such as the Burr family distributions and various other failure distributions. Based on the output he results are reasonably consistent with simulation and real data. The E-Bayesian method was used for estimating with three primary distribution functions for the above parameters and comparing them with the standard Bayesian method with a squared loss function and the maximum likelihood method where simulation experiments were employed to compare the estimation results and the results showed the advantage of the E-Bayesian method in estimating through comparison statistics (MAPE).

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A Penalized Regression Framework for Building Polygenic Risk Models Based on Summary Statistics From Genome-Wide Association Studies and Incorporating External Information

Cited by 21 publications

References 38 publications

Deselection of base-learners for statistical boosting—with an application to distributional regression

Deselection of base-learners for statistical boosting—with an application to distributional regression

Additional SNPs improve the performance of a polygenic hazard score for prostate cancer

E- Bayesian Estimation of System Reliability (Series, Parallel) and Failure Rate Functions with Kumaraswamy Distribution based on Type II Censoring Data

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