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

12.4K
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...
12.4K
Estimating Population Mean with Unknown Standard Deviation01:22

Estimating Population Mean with Unknown Standard Deviation

7.6K
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...
7.6K
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
Genetic Variation01:25

Genetic Variation

258
Genetic variation is the diversity in DNA sequences found among individuals of the same species. This diversity is crucial for a species' survival because it helps organisms adapt to environmental changes. Genetic variation begins with fertilization, where an egg and sperm cell merge. Each of these cells carries 23 chromosomes, up to 46 in the fertilized egg. Chromosomes are long DNA strands that contain genes, the basic units of heredity.
Genes exist in different versions called alleles,...
258
Mutation, Gene Flow, and Genetic Drift01:09

Mutation, Gene Flow, and Genetic Drift

58.0K
In a population that is not at Hardy-Weinberg equilibrium, the frequency of alleles changes over time. Therefore, any deviations from the five conditions of Hardy-Weinberg equilibrium can alter the genetic variation of a given population. Conditions that change the genetic variability of a population include mutations, natural selection, non-random mating, gene flow, and genetic drift (small population size).
58.0K
Distributions to Estimate Population Parameter01:26

Distributions to Estimate Population Parameter

4.0K
The accurate values of population parameters such as population proportion, population mean, and population standard deviation (or variance) are usually unknown. These are fixed values that can only be estimated from the data collected from the samples. The estimates of each of these parameters are sample proportion, the sample mean, and sample standard deviation (or variance). To obtain the values of these sample statistics, data are required that have particular distribution and central...
4.0K

You might also read

Related Articles

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

Sort by
Same author

Shared Genetic Architecture Between Kidney Function and Alzheimer Disease Across Ancestries.

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

Combining xQTL and genome-wide association studies from diverse populations improves druggable gene discovery.

Nature communications·2026
Same author

Improving causal effect estimation in multi-ancestry multivariable Mendelian randomization with transfer learning.

bioRxiv : the preprint server for biology·2025
Same author

Uncovering causal gene-tissue pairs and variants through a multivariate TWAS controlling for infinitesimal effects.

Nature communications·2025
Same author

Multi-ancestry genome-wide association analyses incorporating SNP-by-psychosocial interactions identify novel loci for serum lipids.

Translational psychiatry·2025
Same author

Combining xQTL and genome-wide association studies from ethnically diverse populations improves druggable gene discovery.

Research square·2025

Related Experiment Video

Updated: Jun 5, 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

3.5K

Estimation of a genetic Gaussian network using GWAS summary data.

Yihe Yang1, Noah Lorincz-Comi1, Xiaofeng Zhu1

  • 1Department of Population and Quantitative Health Sciences, School of Medicine, Case Western Reserve University, 10900 Euclid Ave, Cleveland, OH 44106, United States.

Biometrics
|December 10, 2024
PubMed
Summary
This summary is machine-generated.

We developed a new method, Estimation of Genetic Graph (EGG), to accurately estimate genetic networks. EGG corrects biases in genetic correlation estimates, providing a clearer view of phenotype dependencies.

Keywords:
Mendelian randomizationgenetic networkgenome-wide association studiesprobabilistic graphical model

More Related Videos

Mapping Bacterial Functional Networks and Pathways in Escherichia Coli using Synthetic Genetic Arrays
14:06

Mapping Bacterial Functional Networks and Pathways in Escherichia Coli using Synthetic Genetic Arrays

Published on: November 12, 2012

46.4K
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

3.2K

Related Experiment Videos

Last Updated: Jun 5, 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

3.5K
Mapping Bacterial Functional Networks and Pathways in Escherichia Coli using Synthetic Genetic Arrays
14:06

Mapping Bacterial Functional Networks and Pathways in Escherichia Coli using Synthetic Genetic Arrays

Published on: November 12, 2012

46.4K
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

3.2K

Area of Science:

  • Genetics
  • Bioinformatics
  • Statistical Genetics

Background:

  • Genetic Gaussian networks reveal biological dependencies between phenotypes.
  • Estimating these networks is challenging due to biases in genetic correlation estimates from GWAS summary statistics, including estimation errors and pleiotropy.

Purpose of the Study:

  • To introduce a novel method, Estimation of Genetic Graph (EGG), for unbiased genetic network estimation.
  • To improve the understanding of biological dependencies between multiple phenotypes.

Main Methods:

  • Developed the Estimation of Genetic Graph (EGG) approach.
  • Utilized techniques from multivariable Mendelian randomization to eliminate estimation error and idiosyncratic pleiotropy biases.
  • Applied EGG to both simulated and real-world genetic data.

Main Results:

  • EGG successfully eliminates biases present in traditional genetic network estimation methods.
  • The genetic network estimated by EGG represents shared biological contributions between phenotypes, conditional on others.
  • Demonstrated superior efficacy of EGG compared to traditional estimators through simulations and real data analysis.

Conclusions:

  • EGG provides a robust and accurate method for estimating genetic networks from GWAS summary statistics.
  • The method enhances the interpretability of biological relationships between phenotypes.
  • EGG represents a significant advancement in the field of statistical genetics and network analysis.