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

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...
Statgraphics01:10

Statgraphics

Statgraphics is a comprehensive statistical software suite designed for both basic and advanced data analysis. Originating in 1980 at Princeton University under Dr. Neil W. Polhemus, it was one of the pioneering tools for statistical computing on personal computers, with its public release in 1982 marking an early milestone in data science software. Over the years, it has evolved into a robust platform for data science, offering tools for regression analysis, ANOVA, multivariate statistics,...
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 Inference Techniques in Hypothesis Testing: Parametric Versus Nonparametric Data01:16

Statistical Inference Techniques in Hypothesis Testing: Parametric Versus Nonparametric Data

Statistical inference techniques, paramount in hypothesis testing, differentiate into two broad categories: parametric and nonparametric statistics.
Parametric statistics, as the name suggests, assumes that data follow a specific distribution, often a normal distribution. This assumption enables robust hypothesis testing and estimation. Parametric methods, like the Student's t-test or Goodness-of-fit test, are frequently employed in biostatistics due to their robustness. For instance, comparing...
Statistical Software for Data Analysis and Clinical Trials01:12

Statistical Software for Data Analysis and Clinical Trials

Statistical software is pivotal in data analysis and clinical trials by providing tools to analyze data, draw conclusions, and make predictions. These software packages range from simple data management applications to complex analytical platforms, supporting various statistical tests, models, and simulation techniques. Their significance lies in their ability to handle vast amounts of data with precision and efficiency, enabling researchers to validate hypotheses, identify trends, and make...
Statistical Hypothesis Testing01:16

Statistical Hypothesis Testing

Hypothesis testing is a critical statistical procedure facilitating informed, evidence-based decisions. It begins with a hypothesis, which is a tentative explanation, or a prediction about a population parameter. This hypothesis can be either a null hypothesis (H0), indicating no effect or difference, or an alternative hypothesis (Ha), suggesting an effect or difference.
Statistical significance measures the probability that an observed result occurred by chance. If this probability, known as...

You might also read

Related Articles

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

Sort by
Same author

Simultaneous Immunofluorescence-Based In Situ mRNA Expression and Protein Detection in Bone Marrow Biopsy Samples.

Bio-protocol·2026
Same author

Robust causal gene network estimation for large-scale single-cell perturbation screens using reduced control function.

bioRxiv : the preprint server for biology·2026
Same author

Development of an automated, imaging-based preoperative screening model for early identification of malnutrition in an abdominal surgery cohort.

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

Factors Associated with Adherence to Recommended Colorectal Surveillance Intervals in Lynch Syndrome.

Cancers·2026
Same author

Rejoinder to the discussion on "INTACT: A method for integration of longitudinal physical activity data from multiple sources".

Biometrics·2026
Same author

INTACT: a method for integration of longitudinal physical activity data from multiple sources.

Biometrics·2026

Related Experiment Video

Updated: May 22, 2026

An Allele-specific Gene Expression Assay to Test the Functional Basis of Genetic Associations
10:17

An Allele-specific Gene Expression Assay to Test the Functional Basis of Genetic Associations

Published on: November 3, 2010

U-statistics in genetic association studies.

Hongzhe Li1

  • 1Department of Biostatistics and Epidemiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA. hongzhe@upenn.edu

Human Genetics
|May 22, 2012
PubMed
Summary
This summary is machine-generated.

U-statistics offer a powerful approach for analyzing complex human diseases by examining genomic similarity. These methods enhance genome-wide association studies (GWAS) by effectively identifying genetic variants linked to complex traits.

More Related Videos

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

Screening for Functional Non-coding Genetic Variants Using Electrophoretic Mobility Shift Assay (EMSA) and DNA-affinity Precipitation Assay (DAPA)
11:35

Screening for Functional Non-coding Genetic Variants Using Electrophoretic Mobility Shift Assay (EMSA) and DNA-affinity Precipitation Assay (DAPA)

Published on: August 21, 2016

Related Experiment Videos

Last Updated: May 22, 2026

An Allele-specific Gene Expression Assay to Test the Functional Basis of Genetic Associations
10:17

An Allele-specific Gene Expression Assay to Test the Functional Basis of Genetic Associations

Published on: November 3, 2010

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

Screening for Functional Non-coding Genetic Variants Using Electrophoretic Mobility Shift Assay (EMSA) and DNA-affinity Precipitation Assay (DAPA)
11:35

Screening for Functional Non-coding Genetic Variants Using Electrophoretic Mobility Shift Assay (EMSA) and DNA-affinity Precipitation Assay (DAPA)

Published on: August 21, 2016

Area of Science:

  • Genetics and Genomics
  • Statistical Genetics
  • Bioinformatics

Background:

  • Complex human diseases exhibit heterogeneity, involving multiple genetic loci and variants.
  • Genome-wide association studies (GWAS) identify genetic variants but explain small heritability percentages.
  • Existing single-marker tests are limited in accounting for locus and allelic heterogeneity.

Purpose of the Study:

  • To review U-statistics formulations for genetic association studies.
  • To highlight the utility of U-statistics in analyzing complex phenotypes and heterogeneity.
  • To explore the application of U-statistics in next-generation sequencing and rare variant studies.

Main Methods:

  • U-statistics are employed to summarize genomic similarity between individuals.
  • These statistics link genomic similarity to phenotype similarity for association testing.
  • Comparison of U-statistics-based methods with single-marker analyses.

Main Results:

  • U-statistics are effective for testing associations between sets of single nucleotide polymorphisms and phenotypes.
  • U-statistics-based methods show advantages over single-marker analysis, especially with large marker sets.
  • U-statistics demonstrate links with other similarity-based genetic association tests.

Conclusions:

  • U-statistics provide a robust framework for dissecting complex disease genetics.
  • These methods are valuable for gene-based and pathway-based analyses of GWAS data.
  • Future applications include analyzing next-generation sequencing data and rare variants.