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

Genome-wide Association Studies-GWAS01:11

Genome-wide Association Studies-GWAS

13.4K
Genome-wide association studies or GWAS are used to identify whether common SNPs are associated with certain diseases. Suppose specific SNPs are more frequently observed in individuals with a particular disease than those without the disease. In that case, those SNPs are said to be associated with the disease. Chi-square analysis is performed to check the probability of the allele likely to be associated with the disease.
GWAS does not require the identification of the target gene involved in...
13.4K
Polygenic Traits01:18

Polygenic Traits

65.9K
When more than one gene is responsible for a given phenotype, the trait is considered polygenic. Human height is a polygenic trait. Studies have uncovered hundreds of loci that influence height, and there are believed to be many more. Due to the high number of genes involved, as well as environmental and nutritional factors, height varies significantly within a given population. The distribution of height forms a bell-shaped curve, with relatively few individuals in the population at the...
65.9K
Parametric Survival Analysis: Weibull and Exponential Methods01:14

Parametric Survival Analysis: Weibull and Exponential Methods

440
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...
440
Comparing Copy Number Variations and SNPs02:26

Comparing Copy Number Variations and SNPs

17.7K
Sequencing of the human genome has opened up several best-kept secrets of the genome. Scientists have identified thousands of genome variations that exist within a population. These variations can be a single nucleotide or a larger chromosomal variation.
Copy number variations or CNVs are the structural variations that cover more than 1kb of DNA sequence. The single nucleotide polymorphism (SNP), on the other hand, is a single nucleotide change or a point mutation that is found in more than 1%...
17.7K
Expected Frequencies in Goodness-of-Fit Tests01:19

Expected Frequencies in Goodness-of-Fit Tests

2.5K
A goodness-of-fit test is conducted to determine whether the observed frequency values are statistically similar to the frequencies expected for the dataset. Suppose the expected frequencies for a dataset are equal such as when predicting the frequency of any number appearing when casting a die. In that case, the expected frequency is the ratio of the total number of observations (n)  to the number of categories (k).
2.5K
Truncation in Survival Analysis01:09

Truncation in Survival Analysis

209
Truncation in survival analysis refers to the exclusion of individuals or events from the dataset based on specific criteria related to the time of the event. This exclusion can happen in two primary forms: left truncation and right truncation.
Left truncation occurs when individuals who experienced the event of interest before a certain time are not included in the study. This is often due to a "delayed entry" into the study where only those who survive until a certain entry point are...
209

You might also read

Related Articles

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

Sort by
Same author

Spatial co-expression and cell-cell communication inference from spatially resolved transcriptomics with CONCISE.

bioRxiv : the preprint server for biology·2026
Same author

A unified framework for selecting and evaluating cell-type-specific gene co-expressions in single-cell data.

Briefings in bioinformatics·2026
Same author

MIXPRS enables multi-population and multi-method polygenic risk scores using summary statistics.

Nature genetics·2026
Same author

Identification of multi-omic pleiotropy factors for peripheral artery disease.

Human molecular genetics·2026
Same author

Multi-ancestry transcriptome-wide association studies uncover insights into breast cancer genetics and biology.

Nature communications·2026
Same author

Loss of Cyclin G-Associated Kinase (Gak) Leads to Lysosome Dysfunction and Immune Modulation in Podocytes.

Journal of the American Society of Nephrology : JASN·2026

Related Experiment Video

Updated: Jul 6, 2025

Large-Scale Multi-Omics Genome-Wide Association Studies Mo-GWAS: Guidelines for Sample Preparation and Normalization
08:27

Large-Scale Multi-Omics Genome-Wide Association Studies Mo-GWAS: Guidelines for Sample Preparation and Normalization

Published on: July 27, 2021

3.7K

Tuning parameters for polygenic risk score methods using GWAS summary statistics from training data.

Wei Jiang1, Ling Chen2, Matthew J Girgenti3

  • 1Department of Biostatistics, Yale School of Public Health, New Haven, CT, USA.

Nature Communications
|January 3, 2024
PubMed
Summary
This summary is machine-generated.

PRStuning tunes polygenic risk score (PRS) parameters using only training data summary statistics, enhancing privacy and accuracy for disease risk prediction. This method improves PRS performance without needing external individual-level data.

More Related Videos

A Pathway Association Study Tool for GWAS Analyses of Metabolic Pathway Information
05:01

A Pathway Association Study Tool for GWAS Analyses of Metabolic Pathway Information

Published on: July 1, 2020

3.3K
Mapping Alzheimer's Disease Variants to Their Target Genes Using Computational Analysis of Chromatin Configuration
04:41

Mapping Alzheimer's Disease Variants to Their Target Genes Using Computational Analysis of Chromatin Configuration

Published on: January 9, 2020

19.0K

Related Experiment Videos

Last Updated: Jul 6, 2025

Large-Scale Multi-Omics Genome-Wide Association Studies Mo-GWAS: Guidelines for Sample Preparation and Normalization
08:27

Large-Scale Multi-Omics Genome-Wide Association Studies Mo-GWAS: Guidelines for Sample Preparation and Normalization

Published on: July 27, 2021

3.7K
A Pathway Association Study Tool for GWAS Analyses of Metabolic Pathway Information
05:01

A Pathway Association Study Tool for GWAS Analyses of Metabolic Pathway Information

Published on: July 1, 2020

3.3K
Mapping Alzheimer's Disease Variants to Their Target Genes Using Computational Analysis of Chromatin Configuration
04:41

Mapping Alzheimer's Disease Variants to Their Target Genes Using Computational Analysis of Chromatin Configuration

Published on: January 9, 2020

19.0K

Area of Science:

  • Genetics
  • Biostatistics
  • Computational Biology

Background:

  • Polygenic risk score (PRS) methods predict disease risk by aggregating single nucleotide polymorphism (SNP) effects from genome-wide association studies (GWAS).
  • Current PRS parameter tuning often requires external individual-level GWAS data, raising privacy and security concerns.
  • Excluding data for tuning can compromise prediction accuracy.

Purpose of the Study:

  • To introduce PRStuning, a novel method for tuning PRS parameters.
  • To enable PRS parameter optimization using only training data summary statistics.
  • To address privacy concerns and improve prediction accuracy in PRS development.

Main Methods:

  • PRStuning utilizes GWAS summary statistics from training data to predict and select optimal PRS parameters.
  • An empirical Bayes approach is employed to adjust predicted performance, accounting for potential overestimation on testing data.
  • The method incorporates disease genetic architecture for performance shrinkage.

Main Results:

  • PRStuning demonstrates accurate parameter tuning across various PRS methods.
  • The method effectively predicts PRS performance for different parameter settings.
  • Simulations and real-world data confirm PRStuning's reliability and accuracy.

Conclusions:

  • PRStuning offers a privacy-preserving and accurate alternative for PRS parameter tuning.
  • The method enhances the utility of GWAS summary statistics for genetic risk prediction.
  • PRStuning improves the performance and applicability of polygenic risk scores for common diseases.