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

Prediction Intervals01:03

Prediction Intervals

2.5K
The interval estimate of any variable is known as the prediction interval. It helps decide if a point estimate is dependable.
However, the point estimate is most likely not the exact value of the population parameter, but close to it. After calculating point estimates, we construct interval estimates, called confidence intervals or prediction intervals. This prediction interval comprises a range of values unlike the point estimate and is a better predictor of the observed sample value, y. 
2.5K
Polygenic Traits01:18

Polygenic Traits

58.4K
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...
58.4K
Polygenic Traits01:18

Polygenic Traits

7.1K
7.1K
Receiver Operating Characteristic Plot01:15

Receiver Operating Characteristic Plot

584
A ROC (Receiver Operating Characteristic) plot is a graphical tool used to assess the performance of a binary classification model by illustrating the trade-off between sensitivity (true positive rate) and specificity (false positive rate). By plotting sensitivity against 1 - specificity across various threshold settings, the ROC curve shows how well the model distinguishes between classes, with a curve closer to the top-left corner indicating a more accurate model. The area under the ROC curve...
584
Pharmacogenomics: Identification of New Drug Targets01:29

Pharmacogenomics: Identification of New Drug Targets

121
Advances in genomics have profoundly influenced drug discovery by increasing both the speed and accuracy of pharmaceutical development. Pharmacogenomics, which examines how genetic variation influences drug response, facilitates the identification of novel therapeutic targets and enables patient stratification for personalized treatment. These strategies contribute to improved drug efficacy, minimized adverse effects, and more efficient clinical trial design.Mapping genetic differences...
121
Punnett Squares01:00

Punnett Squares

100.1K
Overview
100.1K

You might also read

Related Articles

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

Sort by
Same author

Quantitative trait loci mapping of gene expression and chromatin accessibility in primary fibroblasts reveals shared allelic effects between Latin American and European ancestries.

BMC genomics·2026
Same author

Leveraging tumor dynamics to discover mutations influencing progression and treatment response for precision oncology.

Genome medicine·2026
Same author

Genome-wide analysis implicates inner ear development in Ménière disease.

American journal of human genetics·2026
Same author

Integrative analyses elucidate transcriptional regulatory functions of risk alleles for metabolic liver disease.

Nature genetics·2026
Same author

Functionally informed cis and trans proteome-wide association studies prioritize disease-critical genes.

Research square·2026
Same author

Evaluation of the genome-informed risk assessment (GIRA) approach from eMERGE in an independent health system.

medRxiv : the preprint server for health sciences·2026
Same journal

From Chaos to Care: Personalized AI for Early Cardiac Arrhythmia Warning.

medRxiv : the preprint server for health sciences·2026
Same journal

Large distant deletion disrupts CDKN2A enhancer and predisposes to melanoma.

medRxiv : the preprint server for health sciences·2026
Same journal

Artificial Intelligence-Based Chatbots in Genetic Counseling Practice: Current Uptake, Utilization, and Perspectives.

medRxiv : the preprint server for health sciences·2026
Same journal

Longitudinal MAP-MRI-based Assessment of Tissue Microstructural Alterations in Acute mTBI.

medRxiv : the preprint server for health sciences·2026
Same journal

A class of deep intronic <i>IGHMBP2</i> variants activate a shared cryptic splice donor, enabling correction of select variants with a single antisense oligonucleotide.

medRxiv : the preprint server for health sciences·2026
Same journal

Global Socioeconomic Context and Brain Ageing in Epilepsy: an ENIGMA-Epilepsy study.

medRxiv : the preprint server for health sciences·2026
See all related articles

Related Experiment Video

Updated: May 5, 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

2.3K

CalPred yields calibrated intervals for polygenic risk prediction.

Zhuozheng Shi1,2, Zixuan Eleanor Zhang1,3, Ravi Mandla1,2

  • 1Department of Genetics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.

Medrxiv : the Preprint Server for Health Sciences
|May 4, 2026
PubMed
Summary
This summary is machine-generated.

CalPred provides accurate prediction intervals for polygenic scores (PGS), outperforming PredInterval. This ensures reliable risk stratification in genomic medicine by accounting for statistical uncertainty across diverse populations.

More Related Videos

Identification and Classification of Position-specific GABAA Receptor Subunit Missense Variants for Their Role In Hippocampal Pyramidal Neurons
08:04

Identification and Classification of Position-specific GABAA Receptor Subunit Missense Variants for Their Role In Hippocampal Pyramidal Neurons

Published on: June 6, 2025

1.5K
Candidate Gene Testing in Clinical Cohort Studies with Multiplexed Genotyping and Mass Spectrometry
05:53

Candidate Gene Testing in Clinical Cohort Studies with Multiplexed Genotyping and Mass Spectrometry

Published on: June 21, 2018

9.2K

Related Experiment Videos

Last Updated: May 5, 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

2.3K
Identification and Classification of Position-specific GABAA Receptor Subunit Missense Variants for Their Role In Hippocampal Pyramidal Neurons
08:04

Identification and Classification of Position-specific GABAA Receptor Subunit Missense Variants for Their Role In Hippocampal Pyramidal Neurons

Published on: June 6, 2025

1.5K
Candidate Gene Testing in Clinical Cohort Studies with Multiplexed Genotyping and Mass Spectrometry
05:53

Candidate Gene Testing in Clinical Cohort Studies with Multiplexed Genotyping and Mass Spectrometry

Published on: June 21, 2018

9.2K

Area of Science:

  • Genomic Medicine
  • Biomarker Development
  • Statistical Genetics

Background:

  • Polygenic scores (PGS) are crucial biomarkers for identifying high-risk individuals in genomic medicine.
  • Individual-level predictions from PGS require incorporating statistical uncertainty.
  • Prediction intervals are a principled method for quantifying this uncertainty.

Purpose of the Study:

  • To evaluate the calibration of prediction intervals generated by CalPred and PredInterval methods.
  • To compare the performance of CalPred and PredInterval across various demographic and contextual factors.
  • To determine the reliability of PGS prediction intervals for risk stratification.

Main Methods:

  • Comparative analysis of CalPred and PredInterval prediction interval calibration.
  • Assessment of calibration across diverse factors including ancestry, age, sex, and socio-economic status.
  • Evaluation of prediction intervals' ability to contain trait phenotypes at targeted confidence levels.

Main Results:

  • CalPred demonstrates well-calibrated prediction intervals that accurately contain trait phenotypes.
  • CalPred maintains calibration across varying contextual factors, unlike PredInterval.
  • PredInterval exhibits miscalibration, particularly when focusing on marginal calibration across all individuals.

Conclusions:

  • CalPred is a reliable method for generating calibrated prediction intervals for polygenic scores.
  • CalPred's robustness across diverse factors makes it suitable for equitable genomic medicine applications.
  • Accurate prediction intervals are essential for precise risk stratification using polygenic scores.