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If I estimated the genetic additive σ2A, genetic dominance σ2D and environmental effect σ2E of a trait in a population, what will be the predicted value of the offsprings of two individuals with known phenotypes?

My understanding: given that the two individuals are not close siblings, and that the mean of the two parents is μparents, the offspring will have a mean of:

μoffspring = μparents + μparents x σ2D

and the variance will be the environmental variance, σ2E

How the additive effect σ2A plays a role in a single mating ? (assuming thousands of offsprings (like in some plants) )?

Chris
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Dror Hilman
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  • I was starting to make complicated calculations but actually to answer this question one does not need much quantitative genetics. The expected should just be the average between the parent phenotypes $\mu_{offspring} = \frac{1}{2}\left(\mu_{mother} + \mu_{father}\right)$, isn't it? Note btw that the covariance between any parent and the offspring will be $\frac{r \sigma^2_A}{\sigma^2_P}$ and the correlation between the mid-parent and the offspring should be $\frac{1}{\sqrt{2}}\frac{\sigma^2_A}{\sigma^2_P}$, where $\sigma^2_P = \sigma^2_A+\sigma^2_D+\sigma^2_E$ – Remi.b Jun 02 '16 at 03:11
  • Is the question for an advanced class in quantitative genetics on just an introductory class in evolutionary biology or genetics? If the question is only about the expected offspring phenotype, then I think it is as easy as $\mu_{offspring} = \frac{1}{2}\left(\mu_{mother} + \mu_{father}\right)$. – Remi.b Jun 02 '16 at 03:13
  • I see, but how the dominance effect changes the mean? and what will be the variance of the offspring population - is it just the environmental related variance? – Dror Hilman Jun 02 '16 at 03:27
  • The offspring population will have a variance that will be much greater than just the environmental variance. If the trait of interest in not fitness, then the expected variance in the offspring population will be the same if you assume infinite population size and no mutation. – Remi.b Jun 02 '16 at 16:08
  • However, you are not asking this question in your post. That gets me confused as to what the question is. Can please highlight (in bold or something) the one single question of your post? – Remi.b Jun 02 '16 at 16:09
  • I see you edited the question. Thanks. What do you mean "plays a role" in your highlighted question? Also, I don't quite see the parallel between the question in the title and the one highlighted in the post. I don't want to be pedantic, I am just trying to make sure I really understand what you're asking. – Remi.b Jun 02 '16 at 16:58
  • I would like to simulate a population in which I know the real heritability, dominance effects and environment effect, estimated from sibling ANOVA, parent-offspring regression and comparing different environments. Although I can understand the role of these effects on a large population, I am not sure how to apply this to the predicted parameters of each individual offspring of two individual parents. What will be the right formula to estimate the phenotype of each offspring in the simulation? – Dror Hilman Jun 03 '16 at 02:08
  • I don't understand. What do you mean by in which I know the real heritability. Do you mean you want to force heritability to remain constant? However, in the same time you say, you want to estimate it as if you don't want heritability to be constant. By simulate, I suppose you refer to numerical simulation of course, not analytical simulation/modelling. – Remi.b Jun 04 '16 at 15:49
  • Let say I have large phenotypic data, spanning many parents, children and environments. I estimated the broad sense Heritability from siblings ANOVA, the narrow sense heritability from parent-offs. regression (~σ2A). and the environment effect from comparing individuals across environments. So now, given a random parent values, The predicted children mean will be the parent mean + some X value, associated with the dominance effect, and variance coming from the enviro. effect. how to calculate this "X" factor ? This what puzzles me. - what will be the mean and var. of the population? – Dror Hilman Jun 05 '16 at 04:14
  • oohh...I think I got your question. when you say "children mean" and "parent mean", you imply "mean of the children population" and "mean of the parent population" (assuming discrete generations). I wrote an answer. let me know if it is what you expected. – Remi.b Jun 05 '16 at 06:03

1 Answers1

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What is the expected phenotypic trait of an offspring given the phenotypic trait of its parents?

The expected trait of the offspring is equal to the mean of the parents traits.

For this I assumed we don't have more information about the genetic of the trait (typically, without knowing the directionality of dominance and number).

What is the expected phenotypic trait of the offspring population given the phenotypic trait of the parent population?

As a consequence of the above, the expected mean phenotypic trait of the offspring population is equal to the mean of the parent population (in absence of selection).

What is the expected variance in the offspring population?

No drift, no selection

In absence of drift and selection (and environmental changes), the variance of the offspring population equals the variance of the parent population.

Directional selection only

In presence of directional selection, please have a look at How does Natural Selection shape Genetic Variation? and ask follow-up question if needed.

Drift only

In presence of drift only, the heterozygosity is reduced by a factor $\frac{1}{2N}$, where $N$ is the population size. The loss of genetic variance is then dependent on (1) the frequency, (2) the dominance and (3) the effect size of alleles at loci causing this genetic variance. There is therefore no simple answer. The only generalization that can be done is that (1) the expected variance of the offspring population is lower than the variance of the parent population and (2) the smaller the population size, the higher is the expected loss of genetic variance

General Note

My feeling about quantitative genetics (but I am not really an expert in that field) is that it is often frustrating to see that generalization is often impossible and general solution to recursive equations often don't exist implying that predictions to more than one generation must be calculated through iteration.

I suspect that an expert in quantitative genetics would be able to say much more than me in this post.

The book Introduction to Quantitative Genetics (by Falconer and Mackay) is probably still the best book to increase your knowledge in quantitative genetics.

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Remi.b
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