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

Polygenic Traits01:18

Polygenic Traits

66.0K
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...
66.0K
Regression Toward the Mean01:52

Regression Toward the Mean

6.3K
Regression toward the mean (“RTM”) is a phenomenon in which extremely high or low values—for example, and individual’s blood pressure at a particular moment—appear closer to a group’s average upon remeasuring. Although this statistical peculiarity is the result of random error and chance, it has been problematic across various medical, scientific, financial and psychological applications. In particular, RTM, if not taken into account, can interfere when...
6.3K
Testing a Claim about Population Proportion01:24

Testing a Claim about Population Proportion

3.4K
A complete procedure for testing a claim about a population proportion is provided here.
There are two methods of testing a claim about a population proportion: (1) Using the sample proportion from the data where a binomial distribution is approximated to the normal distribution and (2) Using the binomial probabilities calculated from the data.
The first method uses normal distribution as an approximation to the binomial distribution. The requirements are as follows: sample size is large...
3.4K
Prediction Intervals01:03

Prediction Intervals

2.3K
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. 
2.3K
Estimating Population Standard Deviation01:26

Estimating Population Standard Deviation

3.0K
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...
3.0K
Estimating Population Mean with Unknown Standard Deviation01:22

Estimating Population Mean with Unknown Standard Deviation

8.0K
In practice, we rarely know the population standard deviation. In the past, when the sample size was large, this did not present a problem to statisticians. They used the sample standard deviation s as an estimate for σ and proceeded as before to calculate a confidence interval with close enough results. However, statisticians ran into problems when the sample size was small. A small sample size caused inaccuracies in the confidence interval.
William S. Gosset (1876–1937) of the...
8.0K

You might also read

Related Articles

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

Sort by
Same author

Promoting Gas Sensitivity of Graphitic Carbon Nitride via Incorporation of Platinum Nanoparticles under Blue-Light Irradiation.

ACS sensors·2026
Same author

Who may benefit from robot-assisted gait training with an exoskeleton in subacute stroke patients? A prespecified analysis.

Journal of rehabilitation medicine·2026
Same author

Incidence and preoperative risk factors for failed back surgery syndrome: a nationwide population-based cohort study.

Scientific reports·2026
Same author

Clonal relationship in a rare pulmonary mixed tumor: shared ALK fusion in atypical carcinoid and adenocarcinoma-a case report.

Translational lung cancer research·2026
Same author

Learning brain dynamics across distinct scaling regimes reveals psychiatric signatures.

Communications biology·2026
Same author

Toward trustworthy clinical AI for obsessive-compulsive disorder: reliability, generalizability, and interpretability of a transformer model across the ENIGMA-OCD consortium.

medRxiv : the preprint server for health sciences·2026

Related Experiment Video

Updated: Jul 16, 2025

Navigating MARRVEL, a Web-Based Tool that Integrates Human Genomics and Model Organism Genetics Information
09:37

Navigating MARRVEL, a Web-Based Tool that Integrates Human Genomics and Model Organism Genetics Information

Published on: August 15, 2019

9.8K

Overestimated prediction using polygenic prediction derived from summary statistics.

David Keetae Park1, Mingshen Chen2, Seungsoo Kim3

  • 1Department of Biomedical Engineering, Columbia University, New York, USA.

BMC Genomic Data
|September 14, 2023
PubMed
Summary
This summary is machine-generated.

Polygenic risk score (PRS) studies using summary statistics may inflate results due to unmonitored sample overlap. Comparing raw data PRS (rPRS) and summary statistics PRS (sPRS) revealed inflated sPRS for traits like hypertension and height.

Keywords:
Alzheimer’s diseaseComplex genetic diseaseOverestimation biasPolygenic risk score

More Related Videos

A Protocol for Using Gene Set Enrichment Analysis to Identify the Appropriate Animal Model for Translational Research
09:35

A Protocol for Using Gene Set Enrichment Analysis to Identify the Appropriate Animal Model for Translational Research

Published on: August 16, 2017

17.9K
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

Related Experiment Videos

Last Updated: Jul 16, 2025

Navigating MARRVEL, a Web-Based Tool that Integrates Human Genomics and Model Organism Genetics Information
09:37

Navigating MARRVEL, a Web-Based Tool that Integrates Human Genomics and Model Organism Genetics Information

Published on: August 15, 2019

9.8K
A Protocol for Using Gene Set Enrichment Analysis to Identify the Appropriate Animal Model for Translational Research
09:35

A Protocol for Using Gene Set Enrichment Analysis to Identify the Appropriate Animal Model for Translational Research

Published on: August 16, 2017

17.9K
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

Area of Science:

  • Genetics
  • Biostatistics
  • Bioinformatics

Background:

  • Polygenic risk score (PRS) studies are crucial for predicting disease risk.
  • Deriving PRS from summary statistics (sPRS) prevents monitoring of independence between discovery and test sets.
  • This lack of independence can lead to inflated results, particularly for highly heritable traits.

Purpose of the Study:

  • To compare the performance of PRS derived from raw genetic data (rPRS) versus summary statistics (sPRS).
  • To assess the potential inflation of sPRS results when independence between datasets cannot be guaranteed.
  • To evaluate sPRS performance using summary statistics from the International Genomics of Alzheimer's Project (IGAP) and compare it with rPRS on UK Biobank data for hypertension and height.

Main Methods:

  • Comparison of rPRS and sPRS methodologies.
  • Utilized summary statistics from IGAP for sPRS, excluding APOE.
  • Applied rPRS to UK Biobank data for hypertension and height, ensuring similar discovery and test set sizes.

Main Results:

  • sPRS derived from IGAP showed inflated performance metrics (ΔAUC and ΔR²) for Alzheimer's disease compared to expectations.
  • On UK Biobank data, rPRS for hypertension yielded ΔAUC of 0.0036 ± 0.0027 and ΔR² of 0.0032 ± 0.0028.
  • rPRS for height on UK Biobank yielded ΔR² of 0.029 ± 0.0037.

Conclusions:

  • sPRS results, particularly for highly heritable traits like hypertension and height, are likely inflated when derived from summary statistics due to potential sample overlap.
  • The inability to monitor dataset independence in sPRS studies necessitates careful consideration of potential duplications within ethnic groups.
  • Ensuring dataset independence is a fundamental requirement for reliable PRS studies, highlighting the limitations of sPRS when this cannot be verified.