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Therefore the coefficient of inbreeding of individual G is = (+) = + = %. If the parents of an individual are not inbred themselves, the coefficient of inbreeding of the individual is one-half the coefficient of relationship between the parents. This can be verified in the previous example, as 12.5% is one-half of 25%, the coefficient of ...
The coefficient of relationship is a measure of the degree of consanguinity (or biological relationship) between two individuals. The term coefficient of relationship was defined by Sewall Wright in 1922, and was derived from his definition of the coefficient of inbreeding of 1921. The measure is most commonly used in genetics and genealogy.
For two alleles, the chi-squared goodness of fit test for Hardy–Weinberg proportions is equivalent to the test for inbreeding, =. The inbreeding coefficient is unstable as the expected value approaches zero, and thus not useful for rare and very common alleles.
The coefficient of inbreeding, or the degree of inbreeding in an individual, is an estimate of the percent of homozygous alleles in the overall genome. [69] The more biologically related the parents are, the greater the coefficient of inbreeding, since their genomes have many similarities already.
F IT is the inbreeding coefficient of an individual (I) relative to the total (T) population, as above; F IS is the inbreeding coefficient of an individual (I) relative to the subpopulation (S), using the above for subpopulations and averaging them; and F ST is the effect of subpopulations (S) compared to the total population (T), and is ...
Alternatively, the effective population size may be defined by noting how the average inbreeding coefficient changes from one generation to the next, and then defining N e as the size of the idealized population that has the same change in average inbreeding coefficient as the population under consideration. The presentation follows Kempthorne ...
Wright was the inventor/discoverer of the inbreeding coefficient and F-statistics, standard tools in population genetics. He was the chief developer of the mathematical theory of genetic drift , [ 27 ] which is sometimes known as the Sewall Wright effect, [ 29 ] cumulative stochastic changes in gene frequencies that arise from random births ...
The crucial overview equation comes from Sewall Wright, [13]: 99, 130 [37] and is the outline of the inbred genotypic variance based on a weighted average of its extremes, the weights being quadratic with respect to the inbreeding coefficient. This equation is: