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Hardy-Weinberg Principle

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.

Mutation, Gene Flow, and Genetic Drift

Mutation, Gene Flow, and Genetic Drift

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).

Test for Homogeneity

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...

Chi-square Analysis

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...

Expected Frequencies in Goodness-of-Fit Tests

Expected Frequencies in Goodness-of-Fit Tests

A goodness-of-fit test is conducted to determine whether the observed frequency values are statistically similar to the frequencies expected for the dataset. Suppose the expected frequencies for a dataset are equal such as when predicting the frequency of any number appearing when casting a die. In that case, the expected frequency is the ratio of the total number of observations (n) to the number of categories (k).

Genetic Drift

Genetic Drift

Natural selection—probably the most well-known evolutionary mechanism—increases the prevalence of traits that enhance survival and reproduction. However, evolution does not merely propagate favorable traits, nor does it always benefit populations.

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A Protocol for Functional Assessment of Whole-Protein Saturation Mutagenesis Libraries Utilizing High-Throughput Sequencing

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Testing for Hardy-Weinberg proportions: have we lost the plot?

Robin S Waples¹

¹From the Northwest Fisheries Science Center, National Marine Fisheries Service, National Oceanic and Atmospheric Administration, 2725 Montlake Blvd. East, Seattle, WA 98112. robin.waples@noaa.gov.

The Journal of Heredity

|November 27, 2014

Summary

Hardy-Weinberg proportions (HWP) testing is crucial in population genetics. This guide clarifies HWP test interpretation, common pitfalls, and distinguishing factors for genetic data analysis in nonmodel species.

Keywords:

Hardy–Weinberg equilibrium genotypic ratios heterozygotes linkage disequilibrium multiple testing statistical tests

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Area of Science:

Population genetics
Molecular ecology
Evolutionary biology

Background:

Hardy-Weinberg proportions (HWP) testing is a standard procedure in population genetics.
Many researchers encounter challenges in understanding HWP test purposes and result interpretation.
Issues include statistical power, significance vs. biological relevance, multiple testing, and causes of deviation.

Purpose of the Study:

To clarify the interpretation of Hardy-Weinberg proportions tests in population genetics.
To review factors causing deviations from HWP and linkage disequilibrium (LD).
To guide researchers in analyzing genetic data from nonmodel species, especially with large datasets.

Main Methods:

Review of factors influencing HWP at loci and LD at pairs of loci.
Discussion of common HWP and LD tests, focusing on multiple-testing considerations.
Guidance on distinguishing causes of HWP departures and steps for significant results.

Main Results:

Identifies common misunderstandings in HWP testing and interpretation.
Explains factors leading to statistically significant departures from HWP.
Provides a framework for interpreting deviations and addressing multiple-testing issues.

Conclusions:

Accurate interpretation of HWP tests is vital for robust population genetics studies.
Understanding statistical power and biological significance is key.
This work aids researchers in analyzing genetic data from nonmodel organisms, particularly in the genomics era.