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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...
Coefficient of Correlation01:12

Coefficient of Correlation

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 strength of the linear...
Comparing Copy Number Variations and SNPs02:26

Comparing Copy Number Variations and SNPs

Sequencing of the human genome has opened up several best-kept secrets of the genome. Scientists have identified thousands of genome variations that exist within a population. These variations can be a single nucleotide or a larger chromosomal variation.
Copy number variations or CNVs are the structural variations that cover more than 1kb of DNA sequence. The single nucleotide polymorphism (SNP), on the other hand, is a single nucleotide change or a point mutation that is found in more than 1%...
Spearman's Rank Correlation Test01:20

Spearman's Rank Correlation Test

Spearman's rank correlation test, also known as Spearman's rho, is a nonparametric method for assessing the strength and direction of association between two variables. This test is particularly valuable when the data distribution is unknown or when the assumption of normality does not hold. Named after the English psychologist and statistician Dr. Charles Edward Spearman, it serves as the nonparametric counterpart to Pearson's correlation coefficient.
Spearman's test calculates correlation by...
Correlation and Regression00:53

Correlation and Regression

In statistics, correlation describes the degree of association between two variables. In the subfield of linear regression, correlation is mathematically expressed by the correlation coefficient, which describes the strength and direction of the relationship between two variables. The coefficient is symbolically represented by 'r' and ranges from -1 to +1. A positive value indicates a positive correlation where the two variables move in the same direction. A negative value suggests a negative...
Calculating and Interpreting the Linear Correlation Coefficient01:11

Calculating and Interpreting the Linear Correlation Coefficient

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:

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Related Experiment Video

Updated: May 25, 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

A gene-based test of association using canonical correlation analysis.

Clara S Tang1, Manuel A R Ferreira

  • 1Queensland Institute of Medical Research, Brisbane, QLD 4029, Australia.

Bioinformatics (Oxford, England)
|February 3, 2012
PubMed
Summary

Canonical correlation analysis (CCA) offers a faster, powerful method for gene association testing. This approach enhances discovery of novel genetic loci, like SAFB, for complex traits.

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Last Updated: May 25, 2026

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

Published on: November 3, 2010

Area of Science:

  • Genetics
  • Statistical Genetics
  • Bioinformatics

Background:

  • Canonical correlation analysis (CCA) is a statistical method to measure associations between two sets of multidimensional variables.
  • Gene-based association testing is crucial for identifying genetic variants linked to complex traits.
  • Current methods often rely on computationally intensive permutation testing.

Purpose of the Study:

  • To evaluate the utility of CCA as an efficient and powerful tool for univariate and multivariate gene-based association tests.
  • To compare CCA's performance against traditional permutation-based approaches.

Main Methods:

  • Canonical correlation analysis (CCA) was applied for gene-based association testing.
  • Performance was assessed in terms of speed, type-I error rate, and statistical power.
  • CCA was applied to genome-wide association study (GWAS) data for leukocyte counts.

Main Results:

  • CCA demonstrated increased speed compared to permutation testing.
  • CCA maintained appropriate type-I error rates for quantitative traits with normal distributions.
  • CCA showed comparable power for small genes (<100 kb) and greater power for rare causal variants.
  • Reduced power was observed for larger genes (≥100 kb) with common causal variants.
  • Application identified SAFB and a histone gene cluster as novel loci associated with leukocyte counts.

Conclusions:

  • CCA provides an efficient and powerful alternative for gene-based association testing.
  • CCA can identify novel genetic loci and multiple independent variants regulating complex traits.
  • CCA's performance varies with gene size and variant frequency, necessitating careful consideration of its application.