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F-statistics

In population genetics, F-statistics (also known as fixation indices) describe the level of heterozygosity in a population; more specifically the degree of (usually) a reduction in homozygosity when compared to Hardy-Weinberg expectation. F-statistics can also be thought of as a measure of the correlation between genes drawn at different levels of a (hierarchically) subdivided population. This correlation is influenced by several evolutionary processes, such as mutation, migration, inbreeding, natural selection, or the Whalund effect, but it was originally designed to measure the amount of allelic fixation owing to genetic drift.

The concept of F-statistics was developed during the 1920s by the American geneticist Sewall Wright, who was interested in inbreeding in cattle. However, because complete dominance causes the phenotypes of homozygote dominants and heterozygotes to be the same, it was not until the advent of molecular genetics from the 1960s onwards that heterozygosity in populations could be measured.

Additional recommended knowledge

Safe Weighing Range Ensures Accurate Results

Essential Laboratory Skills Guide

Definitions and equations

The measures F_IS, F_st, and F_IT are related to the amounts of heterozygosity at various levels of population structure. Together, they are called F-statistics, and are derived from F, the inbreeding coefficient. In a simple two-allele system with inbreeding, the genotypic frequencies are:

p 2 + F p q

for AA;

2 p q (1 - F)

for Aa; and

q 2 + F p q

for aa.

The value for F is found by solving the equation for F using heterozygotes in the above inbred population. This becomes one minus the observed number of heterozygotes in a population divided by its expected number of heterozygotes at Hardy–Weinberg equilibrium:

$F = 1- \frac{\operatorname{O}(f(\mathbf{Aa}))} {\operatorname{E}(f(\mathbf{Aa}))} = 1- \frac{\operatorname{ObservedNumber}(\mathbf{Aa})} {n \operatorname{E}(f(\mathbf{Aa}))}, \!$

where the expected value at Hardy–Weinberg equilibrium is given by

$\operatorname{E}(f(\mathbf{Aa})) = 2\, p\, q\, \!$

where p and q are the allele frequencies of A and a, respectively. It is also the probability that at any locus, two alleles from the population are identical by descent.

For example, consider the data from E.B. Ford (1971) on a single population of the scarlet tiger moth:

**Table 1:**
Genotype	White-spotted (AA)	Intermediate (Aa)	Little spotting (aa)	Total
Number	1469	138	5	1612

From this, the allele frequencies can be calculated, and the expectation of f(AA) derived:

$p = {2 \times obs(AA) + obs(Aa) \over 2 \times (obs(AA) + obs (Aa) + obs(aa))} = 0.954$

$q = 1 - p = 0.046$

$F = 1- \frac{ obs(Aa) } { n*2pq } = 1- {138 \over 1612*2(0.954)(0.046)} = 0.023$

The different F-statistics look at different levels of population structure. 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 calculated by solving the equation:

(1 - F I S)(1 - F S T) = (1 - F I T),

as shown in the next section.

Partition due to population structure

Consider a population that has a population structure of two levels; one from the individual (I) to the subpopulation (S) and one from the subpopulation to the total (T). Then the total F, known here as F_IT, can be partitioned into F_IS (or f) and F_ST (or θ):

$1 - F_{IT} = (1 - F_{IS})\,(1 - F_{ST}). \!$

This may be further partitioned for population substructure, and it expands according to the rules of binomial expansion, so that for I partitions:

$1 - F = \prod_{i=0}^{i=I} (1 - F_{i,i+1}) \!$

Fst

A reformulation of the definition of F would be the ratio of the average number of differences between pairs of chromosomes sampled within diploid individuals with the average number obtained when sampling chromosomes randomly from the population (excluding the grouping per individual). One can modify this definition and consider a grouping per sub-population instead of per individual. Population geneticists have used that idea to measure the degree of structure in a population.

Unfortunately, there is a large number of definitions for Fst, causing some confusion in the scientific literature. A common definition is the following:

$F_{ST} = \frac{\operatorname{var}(p)}{p\,(1 - p)} \!$

where the variance of $p$ is computed across sub-populations.

Effective population size

F can be used to define effective population size.

Path coefficients

^{This short section requires expansion.}

F-statistics

Additional recommended knowledge

Safe Weighing Range Ensures Accurate Results

Essential Laboratory Skills Guide

Weighing the right way

Contents

Definitions and equations

Partition due to population structure

Fst

Effective population size

Path coefficients

References

See also

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