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Related Concept Videos

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.
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...
Wilcoxon Signed-Ranks Test for Median of Single Population01:14

Wilcoxon Signed-Ranks Test for Median of Single Population

The Wilcoxon signed-rank test for the median of a single population is a nonparametric test used to evaluate whether the median of a population differs from a specified value. Unlike parametric tests, it does not require data to follow a normal distribution, making it suitable for non-normal or small samples. The test begins by calculating the difference (d) between each observation and the hypothesized median. The absolute values of these differences are ranked in ascending order, with ties...
Chi-square Analysis02:46

Chi-square Analysis

The chi-square test is a statistical hypothesis test. It is used to check whether there is a significant difference between an expected value and an observed value. In the context of genetics, it enables us to either accept or reject a hypothesis, based on how much the observed values deviate from the expected values.
The chi-square test was developed by Pearson in 1990.
The first step of performing a Chi-square analysis is to establish a null hypothesis, which assumes that there is no real...
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...
Testing a Claim about Mean: Unknown Population SD01:21

Testing a Claim about Mean: Unknown Population SD

A complete procedure of testing a hypothesis about a population mean when the population standard deviation is unknown is explained here.
Estimating a population mean requires the samples to be approximately normally distributed. The data should be collected from the randomly selected samples having no sampling bias. There is no specific requirement for sample size. But if the sample size is less than 30, and we don't know the population standard deviation, a different approach is used; instead...

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A Protocol for Functional Assessment of Whole-Protein Saturation Mutagenesis Libraries Utilizing High-Throughput Sequencing
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Testing Hardy-Weinberg equilibrium with a simple root-mean-square statistic.

Rachel Ward1, Raymond J Carroll

  • 1Department of Mathematics, RLM 10.144, University of Texas at Austin, 2515 Speedway, Austin, TX 78712, USA.

Biostatistics (Oxford, England)
|August 27, 2013
PubMed
Summary
This summary is machine-generated.

A novel root-mean-square test offers greater power for detecting deviations from Hardy-Weinberg equilibrium compared to existing methods. This statistical approach excels by focusing on absolute, rather than relative, genotypic frequency discrepancies.

Keywords:
Absolute discrepanciesHardy–Weinberg equilibriumRelative discrepanciesRoot mean square

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Area of Science:

  • Population genetics
  • Statistical genetics

Background:

  • Hardy-Weinberg equilibrium is a fundamental principle in population genetics.
  • Detecting deviations from Hardy-Weinberg equilibrium is crucial for understanding evolutionary processes.
  • Current statistical tests have limitations in detecting certain types of genotypic frequency deviations.

Purpose of the Study:

  • To introduce and evaluate a root-mean-square (RMS) goodness-of-fit test.
  • To compare the statistical power of the RMS test against established methods for detecting Hardy-Weinberg disequilibrium.
  • To demonstrate the conditions under which the RMS test outperforms existing approaches.

Main Methods:

  • The study employed benchmark datasets and simulations to assess test performance.
  • Asymptotic analysis was used to theoretically evaluate the statistical properties of the RMS test.
  • The RMS test's sensitivity to absolute discrepancies in genotypic frequencies was contrasted with other tests' sensitivity to relative discrepancies.

Main Results:

  • The root-mean-square test demonstrated significantly higher statistical power in specific scenarios.
  • This increased power was observed in detecting deviations from Hardy-Weinberg equilibrium.
  • The RMS test's sensitivity to absolute discrepancies proved advantageous over relative discrepancies.

Conclusions:

  • The root-mean-square test is a powerful alternative for detecting Hardy-Weinberg equilibrium deviations.
  • Its sensitivity to absolute discrepancies enhances its utility in specific population genetics analyses.
  • This method offers improved statistical power over traditional tests like Pearson's chi-squared and likelihood-ratio tests.