Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Biostatistics: Overview01:20

Biostatistics: Overview

Biostatistics plays a crucial role in understanding and analyzing data in healthcare and biology. Biostatisticians conduct experiments, gather evidence, and draw meaningful conclusions using statistical methods and techniques. Different variables form the foundation of biostatistical analysis, allowing researchers to understand and interpret data effectively. These variables are classified into different types, each serving a specific purpose in statistical analysis.
Discrete variables are...
Statistical Methods for Analyzing Epidemiological Data01:25

Statistical Methods for Analyzing Epidemiological Data

Epidemiological data primarily involves information on specific populations' occurrence, distribution, and determinants of health and diseases. This data is crucial for understanding disease patterns and impacts, aiding public health decision-making and disease prevention strategies. The analysis of epidemiological data employs various statistical methods to interpret health-related data effectively. Here are some commonly used methods:
Estimating Population Standard Deviation01:26

Estimating Population Standard Deviation

When the population standard deviation is unknown and the sample size is large, the sample standard deviation s is commonly used as a point estimate of σ. However, it can sometimes under or overestimate the population standard deviation. To overcome this drawback, confidence intervals are determined to estimate population parameters and eliminate any calculation bias accurately. However, this only applies to random samples from normally distributed populations. Knowing the sample mean and...
Statistical Analysis: Overview01:11

Statistical Analysis: Overview

When we take repeated measurements on the same or replicated samples, we will observe inconsistencies in the magnitude. These inconsistencies are called errors. To categorize and characterize these results and their errors, the researcher can use statistical analysis to determine the quality of the measurements and/or suitability of the methods.
One of the most commonly used statistical quantifiers is the mean, which is the ratio between the sum of the numerical values of all results and the...
Parametric Survival Analysis: Weibull and Exponential Methods01:14

Parametric Survival Analysis: Weibull and Exponential Methods

Parametric survival analysis models survival data by assuming a specific probability distribution for the time until an event occurs. The Weibull and exponential distributions are two of the most commonly used methods in this context, due to their versatility and relatively straightforward application.
Weibull Distribution
The Weibull distribution is a flexible model used in parametric survival analysis. It can handle both increasing and decreasing hazard rates, depending on its shape parameter...
Types of Biopharmaceutical Studies: Controlled and Non-Controlled Approaches01:23

Types of Biopharmaceutical Studies: Controlled and Non-Controlled Approaches

Biopharmaceutical studies constitute a vital field aiming to enhance drug delivery methods and refine therapeutic approaches, drawing upon diverse interdisciplinary knowledge. In research methodologies, the choice between controlled and non-controlled studies significantly influences the study's reliability and accuracy.
Non-controlled studies, commonly employed for initial exploration, lack a control group, rendering them susceptible to biases and external influences. In contrast, controlled...

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Semaglutide and Neovascular Age-Related Macular Degeneration Among Adults with Type 2 Diabetes: An OHDSI Network Study.

Ophthalmology·2026
Same author

Prone Positioning in a North American Cohort of Hypoxemic Patients on Mechanical Ventilation.

Critical care medicine·2026
Same author

Comparative Cardiovascular Effectiveness of Glucagon-Like Peptide 1 Receptor Agonists and Sodium-Glucose Cotransporter 2 Inhibitors in Diabetes Mellitus.

Journal of the American College of Cardiology·2026
Same author

Real-world evidence for comparative safety of second-line antihyperglycemic agents in older adults with type 2 diabetes.

Nature communications·2026
Same author

Prostate cancer molecular subtypes in systematic versus MRI-targeted biopsy cores at active surveillance: association of PTEN and ERG status with extreme grade reclassification.

Histopathology·2025
Same author

Semaglutide and diabetic retinopathy: an OHDSI network study.

BMJ open diabetes research & care·2025

Related Experiment Video

Updated: Jul 15, 2026

An R-Based Landscape Validation of a Competing Risk Model
05:37

An R-Based Landscape Validation of a Competing Risk Model

Published on: September 16, 2022

Constructing Genetic Risk Scores: Robust Bayesian Approach through Projected Summary Statistics and Flexible

Yuzheng Dun1, Nilanjan Chatterjee1,2, Jin Jin3

  • 1Department of Biostatistics, Bloomberg School of Public Health, Johns Hopkins University.

Journal of the American Statistical Association
|July 14, 2026
PubMed
Summary

New Bayesian methods improve polygenic risk scores (PRS) for disease risk stratification. A novel projection technique ensures data compatibility, while a flexible prior enhances performance, offering consistent and superior results in genetic risk prediction.

Keywords:
conjugate gradient samplerhigh-dimensional regressionpolygenic scoreshrinkage priorstatistical genetics

More Related Videos

Establishing a Competing Risk Regression Nomogram Model for Survival Data
04:57

Establishing a Competing Risk Regression Nomogram Model for Survival Data

Published on: October 23, 2020

Related Experiment Videos

Last Updated: Jul 15, 2026

An R-Based Landscape Validation of a Competing Risk Model
05:37

An R-Based Landscape Validation of a Competing Risk Model

Published on: September 16, 2022

Establishing a Competing Risk Regression Nomogram Model for Survival Data
04:57

Establishing a Competing Risk Regression Nomogram Model for Survival Data

Published on: October 23, 2020

Area of Science:

  • Genetics
  • Statistical genetics
  • Computational biology

Background:

  • Polygenic risk scores (PRS) leverage genome-wide association studies (GWAS) for disease risk stratification.
  • Bayesian methods are favored for PRS due to model regularization and external information integration.
  • Existing Bayesian PRS frameworks face potential issues with data source incompatibility.

Purpose of the Study:

  • To advance Bayesian methods for developing more robust and accurate polygenic risk scores.
  • To address the risk of posterior impropriety arising from integrating disparate GWAS and linkage disequilibrium (LD) data.
  • To introduce a novel Bayesian PRS method with enhanced flexibility in modeling effect-size distributions.

Main Methods:

  • Developed a projection method to ensure compatibility between GWAS summary statistics and LD data.
  • Introduced a new PRS method, PRS-Bridge, utilizing a Bayesian bridge prior for flexible sparsity modeling.
  • Conducted extensive benchmarking using synthetic and real datasets against alternative Bayesian PRS methods.

Main Results:

  • The proposed projection technique resolves posterior impropriety issues in Bayesian PRS.
  • PRS-Bridge demonstrates consistent and superior performance across diverse scenarios compared to existing methods.
  • Prior specification and LD estimation strategies significantly impact PRS performance.

Conclusions:

  • The novel projection technique and flexible Bayesian bridge prior offer a principled advancement for PRS development.
  • PRS-Bridge provides a robust and high-performing tool for genetic risk prediction.
  • These advancements have the potential to enhance clinical applications of PRS for disease risk stratification.