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Related Concept Videos

Test for Homogeneity01:23

Test for Homogeneity

The goodness–of–fit test can be used to decide whether a population fits a given distribution, but it will not suffice to decide whether two populations follow the same unknown distribution. A different test, called the test for homogeneity, can be used to conclude whether two populations have the same distribution. To calculate the test statistic for a test for homogeneity, follow the same procedure as with the test of independence. The hypotheses for the test for homogeneity can be stated as...
Wald-Wolfowitz Runs Test II01:17

Wald-Wolfowitz Runs Test II

The Wald-Wolfowitz runs test, commonly referred to as the runs test, is a nonparametric test used to assess the randomness of ordered data. The test evaluates the number of runs, which are consecutive sequences of similar elements within the data. If the number of runs is significantly higher or lower than expected, the data is considered non-random, indicating a detectable pattern or structure.
For binary data, runs are identified using symbols such as + and −, or equivalently, 1s and 0s. In...
Hardy-Weinberg Principle01:49

Hardy-Weinberg Principle

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.In the early 20th century,...
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.
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Significance Testing: Overview01:04

Significance Testing: Overview

Significance testing is a set of statistical methods used to test whether a claim about a parameter is valid. In analytical chemistry, significance testing is used primarily to determine whether the difference between two values comes from determinate or random errors. The effect of a particular change in the measurement protocol, analyst, or sample itself can cause a deviation from the expected result. In the case of a suspected deviation/outlier, we need to be able to confirm mathematically...
Introduction to Test of Independence01:21

Introduction to Test of Independence

In statistics, the term independence means that one can directly obtain the probability of any event involving both variables by multiplying their individual probabilities. Tests of independence are chi-square tests involving the use of a contingency table of observed (data) values.
The test statistic for a test of independence is similar to that of a goodness-of-fit test:

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Related Experiment Video

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Combined Immunofluorescence and DNA FISH on 3D-preserved Interphase Nuclei to Study Changes in 3D Nuclear Organization
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An entropy test for single-locus genetic association analysis.

Manuel Ruiz-Marín1, Mariano Matilla-García, José Antonio García Cordoba

  • 1Department of Quantitative Methods, Technical University of Cartagena, Paseo Alfonso XIII, 50, 30203, Cartagena, Spain. manuel.ruiz@upct.es

BMC Genetics
|March 25, 2010
PubMed
Summary
This summary is machine-generated.

A new entropy-based genetic association test offers greater power for identifying disease-related genetic factors, especially rare variants. This powerful statistical method improves the detection of complex disease associations across various single nucleotide polymorphism (SNP) frequencies.

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Large-Scale Multi-Omics Genome-Wide Association Studies (Mo-GWAS): Guidelines for Sample Preparation and Normalization
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Large-Scale Multi-Omics Genome-Wide Association Studies (Mo-GWAS): Guidelines for Sample Preparation and Normalization

Published on: July 27, 2021

Area of Science:

  • Genetics
  • Statistical genetics
  • Bioinformatics

Background:

  • Complex diseases arise from numerous genetic and environmental factors, often with small individual effects.
  • Identifying these small-effect factors and rare variants remains a significant challenge in genetic association studies.
  • There is a need for more powerful statistical tests to detect genetic associations, particularly for low-frequency and rare variants.

Purpose of the Study:

  • To introduce a novel genetic association test utilizing symbolic dynamics and symbolic entropy.
  • To evaluate the performance of this new entropy test against conventional methods and the Fisher exact test.
  • To demonstrate the utility of the entropy test for identifying genetic associations, especially with low-frequency single nucleotide polymorphisms (SNPs).

Main Methods:

  • Development of a new genetic association test based on symbolic dynamics and symbolic entropy.
  • Application of the entropy test and a conventional test to simulated and real datasets.
  • Comparison of the entropy test with the Fisher exact test for assessing association with low-frequency SNPs.
  • Estimation of type I error rates and statistical power for the entropy test.

Main Results:

  • The entropy test demonstrates generally higher power compared to conventional genetic association tests.
  • The entropy test shows significantly improved power when applied to low allele-frequency markers.
  • Both the Fisher exact test and the entropy test are optimal for low-frequency SNPs (Minor Allele Frequency [MAF] ~1-5%) and conservative for very rare SNPs (MAF <1%).

Conclusions:

  • A new, powerful, and consistent test for genetic association of SNPs in case-control data has been developed using symbolic entropy.
  • The entropy test provides a standard asymptotic distribution and avoids nonparametric estimation issues.
  • The entropy-based test is computationally efficient, generally as powerful or more powerful than existing methods, and optimal for most SNPs (MAF 1-50%), facilitating the detection of rare genetic effects.