diff --git a/chapter3_bmintro.html b/chapter3_bmintro.html index c5a1afe06e5..3312583f3a9 100644 --- a/chapter3_bmintro.html +++ b/chapter3_bmintro.html @@ -83,7 +83,7 @@

Section 3.3a: Brownian

Note that this equation already matches the first property of Brownian motion.

Next, we need to also consider the variance of these mean phenotypes, which we will call the between-population phenotypic variance (σB2). Importantly, σB2 is the same quantity we earlier described as the “variance” of traits over time – that is, the variance of mean trait values across many independent “runs” of evolutionary change over a certain time period.

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To calculate σB2, we need to consider variation within our model populations. Because of our simplifying assumptions, we can focus solely on additive genetic variance within each population at some time t, which we can denote as σa2. Additive genetic variance measures the total amount of genetic variation that acts additively (i.e. the contributions of each allele add together to predict the final phenotype). This excludes genetic variation involving interacions between alleles, such as dominance and epistasis (see Lynch and Walsh 1998 for a more detailed discussion). Additive genetic variance in a population will change over time due to genetic drift (which tends to decrease σa2) and mutational input (which tends to increase σa2). We can model the expected value of σa2 from one generation to the next as (Clayton and Robertson 1955; Lande 1979, 1980):

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To calculate σB2, we need to consider variation within our model populations. Because of our simplifying assumptions, we can focus solely on additive genetic variance within each population at some time t, which we can denote as σa2. Additive genetic variance measures the total amount of genetic variation that acts additively (i.e. the contributions of each allele add together to predict the final phenotype). This excludes genetic variation involving interactions between alleles, such as dominance and epistasis (see Lynch and Walsh 1998 for a more detailed discussion). Additive genetic variance in a population will change over time due to genetic drift (which tends to decrease σa2) and mutational input (which tends to increase σa2). We can model the expected value of σa2 from one generation to the next as (Clayton and Robertson 1955; Lande 1979, 1980):

(eq. 3.2)


$$