Price equation

Price equation

The Price equation (also known as Price's equation) is a covariance equation which is a mathematical description of evolution and natural selection. The Price equation was derived by George R. Price, working in London to rederive W.D. Hamilton's work on
kin selection.

Suppose there is a population of n individuals over which the amount of a particular characteristic varies. Those n individuals can be grouped by the amount of the characteristic that each displays. In this case, at most there will be n groups of n distinct values of the characteristic, and at least there will be 1 group of a single shared value of the characteristic. Index each group with i so that the number of members in the group is n_i and the value of the characteristic shared among all members of the group is z_i. Now assume that having z_i of the characteristic is associated with having a fitness w_i where the product w_i n_i represents the number of offspring in the next generation. Denote this number of offspring from group i by n_i' so that w_i=n_i'/n_i. Let z_i' be the amount of the characteristic displayed by the offspring from group i. Denote the amount of change in characteristic in group i by Delta z_i defined by

:Delta{z_i} stackrel{mathrm{def{=} z_i' - z_i

Now take z to be the average characteristic value in this population and z' to be the average characteristic value in the next generation. Define the change in average characteristic by Delta{z}. That is,

:Delta{z} stackrel{mathrm{def{=} z' - z

Note that this is "not" the average value of Delta{z_i}. Also take w to be the average fitness of this population. The Price equation states:

:w,Delta{z}=operatorname{cov}(w_i,z_i)+operatorname{E}(w_i,Delta z_i) , ,!

where the functions operatorname{E} and operatorname{cov} are respectively defined in Equations (1) and (2) below but are "like" the sample versions of the expected value and covariance operators from probability (see Sample mean and covariance). Note that this is really a difference equation relating the average value of a characteristic in one generation to the average value of the characteristic in the very next generation. In fact, assuming that w is not zero, it is often useful to write it as

:Delta{z}=frac{operatorname{cov}(w_i,z_i)}{w}+frac{operatorname{E}(w_i,Delta z_i)}{w},

In the specific case that characteristic z_i = w_i (i.e., the fitness is the characteristic of interest), then Price's equation reformulates Fisher's fundamental theorem of natural selection.

Price's equation is, importantly, a tautology. It is a statement of mathematical fact between certain variables, and its value lies in theinsight gained by assigning certain values encountered in evolutionary genetics tothe variables. For example, the statement "if every pair of birds has twooffspring, then among ten pairs of birds there will be twenty offspring" is a tautology. Itdoesn't really impart any new information about birds so much as it organizes ourconcepts about birds and their offspring. The Price equation is much moresophisticated than the above statement, but at its core, it too is a mathematically provable tautology.

The Price equation also has applications in economics.

Proof of the Price equation

To prove the Price equation, the following definitions are needed. If n_i is the number of occurrences of a pair of real numbers x_i and y_i, then:

* The average or expected value of the x_i values is:

Now suppose that the population reproduces, all parents are eliminated, and then there is a selection process on the children, by which less fit children are removed from the reproducing population. After reproduction and selection, the population numbers for the child groups will change to "n"′"i". Primes will be used to denote child parameters, unprimed variables denote parent parameters.

The total number of children is "n' " where:

:n' = sum_i n'_i,

The fitness of group "i" will be defined to be the ratio of children to parents:

where "z"′"i" are the (possibly new) values of the characteristic in the child population. Equation (2) shows that:

but from Equation (1) gives:

:operatorname{E}(w_iz'_i)=frac{sum_i w_iz'_in_i}{n}

and from Equation (4) gives:

We would like to know how much average visual acuity has increased or decreased in the population. From Equation (3), the average sightedness of the parent population is "z" = 5/3. The average sightedness of the child population is "z"' = 2, so that the change in average sightedness is:

:Delta z stackrel{mathrm{def{=} z'-z = 1/3 ,!

which indicates that the trait of sightedness isincreasing in the population. Applying the Price equation wehave (since "z"′"i"= "z""i"):

:Delta z = operatorname{cov}left(w_i,z_i ight)/w = 1/3

Dynamical sufficiency and the simple Price equation

Sometimes the genetic model being used encodes enough information into the parameters used by the Price equation to allow the calculation of the parameters for all subsequent generations. This property is referred to as dynamical sufficiency. For simplicity, the following looks at dynamical sufficiency for the simple Price equation, but is also valid for the full Price equation.

Referring to the definition in Equation (2), the simple Price equation for the character "z" can be written:

:w(z'-z)=langle w_i z_i angle - wz

For the second generation:

:w'(z"-z')=langle w'_i z'_i angle - w'z'

The simple Price equation for "z" only gives us the value of "z" ' for the first generation, but does not give us the value of "w" ' and 〈"w" 'i "z" 'i 〉 which needs to be calculated "z" ' ' for the second generation. "w" ' and 〈"w" 'i "z" 'i 〉 can both be thought of characteristics of the first generation, so the Price equation can be used to calculate them as well:

:w(w'-w)=langle w_i^2 angle - w^2:w(langle w'_i z'_i angle-langle w_i z_i angle)=langle w_i ^2z_i angle - wlangle w_i z_i angle

The five 0-generation variables "w", "z", 〈"w""i" "z""i" 〉, 〈"w"2"i" 〉, and 〈"w"2"i" "z""i" 〉, which must be known before proceeding to calculate the three first generation variables "w" ', "z" ', and 〈"w" '"i" "z" '"i" 〉, which are needed to calculate "z" ' for the second generation. It can be seen that in general the Price equation cannot be used to propagate forward in time unless there is a way of calculating the higher moments (〈"w""n""i" 〉 and 〈"w""n""i" "z""i" 〉) from the lower moments in a way that is independent of the generation. Dynamical sufficiency means that such equations can be found in the genetic model, allowing the Price equation to be used alone as a propagator of the dynamics of the model forward in time.

Example: Evolution of sickle cell anemia

300px|thumb|An example of autosomal recessive inheritance. In the sickle cell case, the two parents are "carriers" who are resistant to malaria. Their children have one chance in four of inheriting both sickle cell genes and suffering sickle cell anemia, two chances in four of being a carrier themselves, and being resistant to malaria like their parents, and one chance in four of not inheriting the gene from either parent, and being susceptible to malaria.

As an example of dynamical sufficiency, consider the case of sickle cell anemia.Each person has two sets of genes, one set inherited from the father, one from the mother. Sickle cell anemia is a blood disorder which occurs when a particular pair of genes both carry the 'sickle-cell trait'. The reason that the sickle-cell gene has not been eliminated from the human population by selection is because when there is only one of the pair of genes carrying the sickle-cell trait, that individual (a "carrier") is highly resistant to malaria, while a person who has neither gene carrying the sickle-cell trait will be susceptible to malaria. Let's see what the Price equation has to say about this.

Let "z""i"="i" be the number of sickle-cell genes that organisms of type"i" have so that "z""i" = 0 or 1 or 2. Assume the population sexuallyreproduces and matings are random between type 0 and 1, so that the number of0–1 matings is "n"0"n"1/("n"0+"n"1) and thenumber of "i"–"i" matings is "n"2"i"/ [2("n"0+"n"1)] where "i" = 0or 1. Suppose also that each gene has a 1/2 chance of being passed to any givenchild and that the initial population is"n""i"= ["n"0,"n"1,"n"2] . If "b" is thebirth rate, then after reproduction there will be

:bleft(frac{n_0^2/2+n_0n_1/2+n_1^2/8}{n_0+n_1} ight) type 0 children (unaffected):bleft(frac{n_0n_1/2+n_1^2/4}{n_0+n_1} ight) type 1 children (carriers)

:bleft(frac{n_1^2/8}{n_0+n_1} ight) type 2 children (affected)

Suppose a fraction "a" of type 0 reproduce, the loss being due to malaria. Suppose all of type 1 reproduce, since they are resistant to malaria, while none of the type 2 reproduce, since they all have sickle-cell anemia. The fitness coefficients are then:

:w_0=ableft(frac{n_0^2/2+n_0n_1/2+n_1^2/8}{n_0(n_0+n_1)} ight):w_1=bleft(frac{n_0n_1/2+n_1^2/4}{n_1(n_0+n_1)} ight):w_2=0,

To find the concentration "n"1 of carriers in the population at equilibrium, with the equilibrium condition of Δ "z"=0, the simple Price equation is used:

: 0=operatorname{cov}(w_i/w,z_i) = frac{f(2-2a-af)}{(1+f)(2a+2f+af)}

where "f"="n"1/"n"0. At equilibrium then, assuming "f" isnot zero:

: f=frac{n_1}{n_0}=frac{2(1-a)}{a}

In other words the ratio of carriers to non-carriers will be equal to the above constant non-zero value. In the absence of malaria, "a"=1 and "f"=0 so that the sickle-cell gene is eliminated from the gene pool. For any presence of malaria, "a" will be smaller than unity and the sickle-cell gene will persist.

The situation has been effectively determined for the infinite (equilibrium) generation. This means that there is dynamical sufficiency with respect to the Price equation, and that there is an equation relating higher moments to lower moments. For example, for the second moments:

: langle w_i^2z_i angle = frac{langle w_i z_i angle}{z}: langle w_i^2 angle = frac{-b z^2 w^2 + 2 b z^2 w langle w_i z_i angle + b z langle w_i z_i angle^2 - b z^2 langle w_i z_i angle^2 - 4 langle w_i z_i angle^3} { b z^2 - 4 z langle w_i z_i angle}

Example: sex ratios

In a 2-sex species or deme with sexes 1 and 2 where z_1= 1,z_2 = 0, z is the relative frequency of sex 1. Since all individuals have one parent of each sex, the fitness of each sex is proportional to the other sex's size. Consider proportionality constants a and b such that w_1 = a(1 - z) and w_2 = bz. This gives w = (a + b)z(1 - z) and cov(w_i, z_i) + wz = E(w_i,z_i) = az(1 - z), so cov(w_i, z_i) = az(1 - z) - (a + b)z^2(1 - z) = z(1 - z)(a - (a + b)z) . Hence, Delta z = a/(a + b) - z so that z' = a/(a + b) .

Full Price equation

The simple Price equation was based on the assumption that the characters "z""i" do not change over one generation. If it is assumed that they do change, with "z""i" being the value of the character in the child population, then the full Price equation must be used. A change in character can come about in a number of ways. The following two examples illustrate two such possibilities, each of which introduces new insight into the Price equation.


Evolution of altruism

To study the evolution of a genetic predisposition to altruism, altruism will be defined as the genetic predisposition to behavior which decreases individual fitness while increasing the average fitness of the group to which the individual belongs. First specifying a simple model, which will only require the simple Price equation. Specify a fitness "w""i" by a model equation:

: w_i = frac{n'_i}{n_i} = k - a z_i + b z

where "z""i" is a measure of altruism, the "az""i" term is the decrease in fitness of an individual due to altruism towards the group and "bz" is the increase in fitness of an individual due to the altruism of the group towards an individual.Assume that "a" and "b" are both greater than zero. From the Price equation:

: w,Delta z = -a~operatorname{var}left(z_i ight)

where var("z""i") is the variance of "z""i" which is just the covariance of "z""i" with itself:

:operatorname{var}(z_i) stackrel{mathrm{def{=} operatorname{E}(z_i^2)-operatorname{E}(z_i)^2

It can be seen that, by this model, in order for altruism to persist it must be uniformthroughout the group. If there are two altruist types the average altruism of the group will decrease, the more altruistic will lose out to the less altruistic.

Now assuming a hierarchy of groups which will require the full Price equation. The population will be divided into groups, labelled with index "i" and then each group will have a set of subgroups labelled by index "j". Individuals will thus be identified by two indices,"i" and "j", specifying which group and subgroup they belong to. "n""ij" willspecify the number of individuals of type "ij". Let "z""ij" be the degree ofaltruism expressed by individual "j" of group "i" towards the members of group "i". Let's specify the fitness "w""ij" by a model equation:

: w_{ij} = frac{n'_{ij{n_{ij = k - a z_{ij} + b z_i

The "a z""ij" term is the fitness the organism loses by being altruistic and isproportional to the degree of altruism "z""ij" that it expresses towards membersof its own group. The "b z""i" term is the fitness that the organism gains from the altruism of the members of its group, and is proportional to the average altruism "z""i" expressed by the group towards its members. Again, in studying study altruistic (rather than spiteful) behavior, it is expected that "a" and "b" are positive numbers. Note that the above behavior is altruistic only when "az""ij" >"bz""i". Defining the group averages:

:n_i = sum_j n_{ij},

:z_i = frac{sum_j z_{ij}n_{ij{n_i}

:w_i = frac{sum_j w_{ij}n_{ij{n_i}=k+(b-a)z_i

:n_i'= sum_j n_{ij}'=n_i(k+(b-a)z_i),

:z_i'= frac{sum_j z_{ij}n_{ij}'}{n_i'}

and global averages:

:n = sum_{ij} n_{ij} = sum_i n_i,

:z = frac{sum_{ij} z_{ij}n_{ij{n} = frac{sum_i z_in_i}{n}

:w = frac{sum_{ij} w_{ij}n_{ij{n} = frac{sum_i w_in_i}{n}

:n'= sum_{ij} n_{ij}' = sum_i n_i',

:z'= frac{sum_{ij} z_{ij}n_{ij}'}{n'} = frac{sum_i z_i'n_i'}{n'}

It can be seen that since the "z""i" and "z""i" are now averages over a particular group, and since these groups are subject to selection, the value of Δ"z""i" = "z"′"i"−"z""i" will not necessarily be zero, and the full Price equation will be needed.

: Delta z = operatorname{cov}(w_i/w,z_i)+operatorname{E}(w_i,Delta z_i/w),

In this case, the first term isolates the advantage to each group conferred by having altruistic members. The second term isolates the loss of altruistic members from their group due to their altruistic behavior. The second term will be negative. In other words there will be an average loss of altruism due to the in-group loss of altruists, assuming that the altruism is not uniform across the group. The first term is:

: operatorname{cov}(w_i/w,z_i)=left(b-a ight)operatorname{var}(z_i)

In other words, for "b">"a" there may be a positive contribution to the average altruism as a result of a group growing due to its high number of altruists and this growth can offset in-group losses, especially if the variance of the in-group altruism is low. In order for this effect to be significant, there must be a spread in the average altruism of the groups.

Evolution of mutability

Suppose there is an environment containing two kinds of food. Let α be the amount of the first kind of food and β be the amount of the second kind. Suppose an organism has a single allele which allows it to utilize a particular food. The allele has four gene forms: "A"0, "A""m", "B"0, and "B""m". If an organism's single food gene is of the "A" type, then the organism can utilize "A"-food only, and its survival is proportional to α. Likewise, if an organism's single food gene is of the "B" type, then the organism can utilize "B"-food only, and its survival is proportional to β. "A"0 and "A""m" are both "A"-alleles, but organisms with the "A"0 gene produce offspring with "A"0-genesonly, while organisms with the "A""m" gene produce (1−3"m") offspring with the "A""m" gene, and "m" organisms of the remaining three gene types. Likewise, "B"0 and "B""m" are both "B"-alleles, but organisms with the "B"0 gene produce offspring with "B"0-genes only, whileorganisms with the "B""m" gene produce (1−3"m") offspring with the "B""m" gene, and "m" organisms of the remaining three gene types.

Let "i"=0,1,2,3 be the indices associated with the "A"0, "A""m", "B"0, and "B""m" genes respectively. Let "w""ij" be the number of viable type-"j" organisms produced per type-"i" organism. The "w""ij" matrix is: (with "i" denoting rows and "j" denoting columns)


:mathbf{w}=egin{bmatrix}alpha & 0 &0 & 0 \malpha & (1-3m)alpha & meta & meta \0 & 0 & eta & 0 \malpha & malpha & meta & (1-3m)etaend{bmatrix}

Mutators are at a disadvantage when the food supplies α and β are constant. They lose every generation compared to the non-mutating genes. But when the food supply varies, even though the mutators lose relative to an "A" or "B" non-mutator, they may lose less than them over the long run because, for example, an "A" type loses a lot when α is low. In this way, "purposeful" mutation may be selected for. This may explain the redundancy in the genetic code, in which some amino acids are encoded by more than one codon in the DNA. Although the codons produce the same amino acids, they have an effect on the mutability of the DNA, which may be selected for or against under certain conditions.

With the introduction of mutability, the question of identity versus lineage arises. Is fitness measured by the number of children an individual has, regardless of the children's genetic makeup, or is fitness the child/parent ratio of a particular genotype?. Fitness is itself a characteristic, and as a result, the Price equation will handle both.

Suppose we want to examine the evolution of mutator genes. Define the "z"-score as:

:z_i = left [0,1,0,1 ight]

in other words, 0 for non-mutator genes, 1 for mutator genes. There are two cases:

Genotype fitness

Lets focus on the idea of the fitness of the genotype. The index "i" indicates the genotype and the number of type "i" genotypes in the child population is:: n'_i = sum_j w_{ji}n_j,which gives fitness:: w_i=frac{n'_i}{n_i}Since the individual mutability "z""i" does not change, the average mutabilities will be:

:z = frac{sum_i z_i n_i}{n}:z' = frac{sum_i z_i n'_i}{n'}

with these definitions, the simple Price equation now applies.

Lineage fitness

In this case we want to look at the idea that fitness is measured by the number of children an organism has, regardless of their genotype. Note that we now have two methods of grouping, by lineage, and by genotype. It is this complication that will introduce the need for the full Price equation. The number of children an "i"-type organism has is:: n'_i = n_isum_j w_{ij},which gives fitness:

: w_i=frac{n'_i}{n_i} = sum_j w_{ij}

We now have characters in the child population which are the average character of the "i"-th parent.: z'_j = frac{sum_i n_i z_i w_{ij} }{sum_i n_i w_{ijwith global characters:

:z = frac{sum_i z_i n_i}{n}:z' = frac{sum_i z_i n'_i}{n'}

with these definitions, the full Price equation now applies.

Cultural References

Price's equation features in the plot and title of the 2008 thriller film "WΔZ".


* cite journal
title=George Price's contributions to Evolutionary Genetics
journal=Journal of Theoretical Biology

* cite journal
title=The Price Equation, Fisher's Fundamental Theorem, Kin Selection, and Causal Analysis | journal=Evolution

* cite journal
title=The Price equation
journal=Curr. Biol.

* cite journal
title=Developments of the Price equation and natural selection under uncertainty
journal=Proc. R. Soc. London B

* cite journal
author=Langdon, W. B.
title=Evolution of GP Populations: Price's Selection and Covariance Theorem
journal=Genetic Programming and Data Structures

* cite journal
author=Price, G.R.
authorlink = George R. Price
title=Selection and covariance

* cite journal
author=Price, G.R.
authorlink = George R. Price
title=Fisher's "fundamental theorem" made clear
journal=Annals of Human Genetics

* cite journal
author=Van Veelen, M.
title=On the use of the Price equation
journal=Journal of Theoretical Biology

* cite journal
title=Insights from Price's equation into evolutionnary epidemiology
journal=DIMACS Series in Discrete Mathematics and Theoretical Computer Science

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