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

15.9K
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...
15.9K
Hardy-Weinberg Principle01:49

Hardy-Weinberg Principle

76.8K
Diploid organisms have two alleles of each gene, one from each parent, in their somatic cells. Therefore, each individual contributes two alleles to the gene pool of the population. The gene pool of a population is the sum of every allele of all genes within that population and has some degree of variation. Genetic variation is typically expressed as a relative frequency, which is the percentage of the total population that has a given allele, genotype or phenotype.
76.8K
Multiple Allele Traits01:49

Multiple Allele Traits

38.3K
The Concept of Multiple Allelism
38.3K
Evolutionary Relationships through Genome Comparisons02:54

Evolutionary Relationships through Genome Comparisons

7.1K
Genome comparison is one of the excellent ways to interpret the evolutionary relationships between organisms. The basic principle of genome comparison is that if two species share a common feature, it is likely encoded by the DNA sequence conserved between both species. The advent of genome sequencing technologies in the late 20th century enabled scientists to understand the concept of conservation of domains between species and helped them to deduce evolutionary relationships across diverse...
7.1K
Coefficient of Correlation01:12

Coefficient of Correlation

8.8K
The correlation coefficient, r, developed by Karl Pearson in the early 1900s, is numerical and provides a measure of strength and direction of the linear association between the independent variable x and the dependent variable y.
If you suspect a linear relationship between x and y, then r can measure how strong the linear relationship is.
What the VALUE of r tells us:
The value of r is always between –1 and +1: –1 ≤ r ≤ 1.
The size of the correlation r indicates the...
8.8K
Calculating and Interpreting the Linear Correlation Coefficient01:11

Calculating and Interpreting the Linear Correlation Coefficient

8.3K
The correlation coefficient, r, developed by Karl Pearson in the early 1900s, is numerical and provides a measure of strength and direction of the linear association between the independent variable, x, and the dependent variable, y. Hence, it is also known as the Pearson product-moment correlation coefficient. It can be calculated using the following equation:
8.3K

You might also read

Related Articles

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

Sort by
Same author

The use of a genetic relationship matrix biases the best linear unbiased prediction.

Journal of genetics·2020
Same author

Classifying <i>Oryza sativa</i> accessions into <i>Indica</i> and <i>Japonica</i> using logistic regression model with phenotypic data.

PeerJ·2019
Same author

GWASpro: a high-performance genome-wide association analysis server.

Bioinformatics (Oxford, England)·2018
Same author

How to Reveal Magnitude of Gene Signals: Hierarchical Hypergeometric Complementary Cumulative Distribution Function.

Evolutionary bioinformatics online·2018
Same author

Numericware i: Identical by State Matrix Calculator.

Evolutionary bioinformatics online·2017
Same author

Numericware N: Numerator Relationship Matrix Calculator.

The Journal of heredity·2016

Related Experiment Video

Updated: Feb 23, 2026

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

4.9K

Hierarchical Association Coefficient Algorithm: New Method for Genome-Wide Association Study.

Bongsong Kim1

  • 1Department of Agronomy, Iowa State University, Ames, IA, USA.

Evolutionary Bioinformatics Online
|September 13, 2017
PubMed
Summary

The hierarchical association coefficient (HA-coefficient) algorithm quantifies category association. This robust algorithm reliably measures how closely observations align with expected category patterns.

Keywords:
GWASGenome-wide association studyHA-coefficientHierarchical Association CoefficientQTL

More Related Videos

Heuristic Mining of Hierarchical Genotypes and Accessory Genome Loci in Bacterial Populations
08:03

Heuristic Mining of Hierarchical Genotypes and Accessory Genome Loci in Bacterial Populations

Published on: December 7, 2021

2.8K
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

3.8K

Related Experiment Videos

Last Updated: Feb 23, 2026

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

4.9K
Heuristic Mining of Hierarchical Genotypes and Accessory Genome Loci in Bacterial Populations
08:03

Heuristic Mining of Hierarchical Genotypes and Accessory Genome Loci in Bacterial Populations

Published on: December 7, 2021

2.8K
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

3.8K

Area of Science:

  • Statistics
  • Data Analysis

Background:

  • Quantifying the association between observations and categories is crucial in data analysis.
  • Existing methods may lack a standardized measure for this relationship.

Purpose of the Study:

  • To introduce and validate the hierarchical association coefficient (HA-coefficient) algorithm.
  • To establish a reliable metric for the degree of association between observations and categories.

Main Methods:

  • The HA-coefficient algorithm calculates an association value between 0 and 1.
  • It operates on stratified ascending categories based on average observations.
  • The algorithm defines upper and lower limits based on observation ordering.

Main Results:

  • The HA-coefficient algorithm was applied to three simulated datasets with consistent association patterns.
  • Identical results were obtained across all datasets, demonstrating consistency.

Conclusions:

  • The HA-coefficient algorithm is robust and reliable for measuring observation-category association.
  • This metric provides a standardized approach to assessing categorization alignment.