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Introduction

Standard models in population genetics look up at the evolution of few loci which impact fitness. The variance in fitness is determined by the genetic variance and the environmental variance (and the co-variance between environment and genetics). In this question I am interested only about genetic variance and about what percentage of the total (additive or not) genetic variance in fitness do 'n' loci explain.

Question

In general, in natural populations, what percentage of the total genetic variance is explained by the 'n'- most important loci?

Here, by "most important loci" I mean loci which variance explain much of the total genetic variance.

In other words, the subquestions are of the kind:

  • how much of the fitness variance does the most important locus explain?
  • How much of the fitness variance do the 3 most important loci explain?
  • How much of the fitness variance do the 100 most important loci explain?

Of course, the answer depends on the population under consideration. Factors that might influence the answers are for example

  • species
  • population size
  • environment stability

Beside this question, I also welcome some insights concerning how different factors are likely to influence the answer.

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    $\begingroup$ Do you have at least one example of an empirical study of the type you are looking for ? It may be a helpful starting point for us in finding other ones. $\endgroup$
    – Barbara
    Commented Feb 10, 2014 at 13:16
  • $\begingroup$ No, unfortunately I don't know any empirical study that investigates my question. $\endgroup$
    – Remi.b
    Commented Feb 10, 2014 at 14:25
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    $\begingroup$ I think its exceptionally rare for the most important loci to collectively explain more than a small percentage of the total genetic variance, it's a fundamental problem with QTL - looking for something of large effect when by definition we expect most or all effects to be tiny. I'm looking forward to seeing an answer to this one! It's going on my favorites $\endgroup$
    – rg255
    Commented Nov 20, 2014 at 16:27
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    $\begingroup$ I don't think there's a great answer to this one, partly because fitness is a tough trait to measure. I think more is known for other traits like height, but even there, if we want accurate estimates of the variance associated with 100 alleles, we're limited to humans at best. $\endgroup$ Commented Sep 18, 2016 at 17:59
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    $\begingroup$ I agree with Daniel. Maybe you should open your question to other polygenic trait. A lot of money is being invested in genomic selection and most of the time the target is yield. here we have hundred of markers with effect lower than 1%. Fitness seems to be quite integrative and therefore very polygenic and environment dependent. Do you have study showing heritability of this trait? Another factor that could influence your variance is allele frequency. See GWAS model. Are you interested in study doing that on other trait? I may answer in that case! $\endgroup$
    – Untitpoi
    Commented Apr 14, 2018 at 8:37

1 Answer 1

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This should be taken less as a holistic answer than as a case study describing an extreme example of very strong selection in a particular environmental context, yielding a particular natural experiment. I suspect that this question may be very difficult to answer in a general case without more work!!

This answer should not be taken to show any general case or reflect anything about the distribution of variant fitness effects of a relatively stable population.

I hope that posting this very partial and contextual answer might inspire other answers to this very interesting question.

In sticklebacks, armor morphology is mostly controlled by a single large-effect variant locus at the Ectodysplasin (Eda) gene, ~85% of phenotypic variance is associated with this locus. It is known that there is a strong fitness relevance of armoring according to environment, wherein freshwater fish are under selection for low armor (due to metabolic cost), and saltwater fish are under selection for high armor (due to predation pressure).

This pattern is highly repeatable across events of colonization by saltwater fish of new freshwater habitats, wherein populations are swept by the low-armored allele of Eda as soon as they become resident freshwater populations.

Recently, Dolph Schluter and colleagues set out to directly quantify the fitness effects of this variant in a newly colonized population of sticklebacks that was undergoing the microevolutionary shift from high to low armor. In this population, the selection coefficient for the lower-armor allele was extremely large ($s = 0.5$).

Schluter and colleagues mapped QTLs for female reproductive fitness in an F2 cross, unsurprisingly yielding the Eda locus. They detected no other loci, though this should not be taken to mean that there are no other loci, due to power issues. They found that Eda explained 8% of the variation in fitness across their F2 panel.

In summary, in this particular case of very strong selection and quite high fitness variation of the population, one locus explains 8% of the fitness variation in an F2 cross (which is likely not completely representative of the population). No other loci have any detectable fitness effects compared to this.

It would be very interesting to see how this is repeated in other case studies, more stable populations, more polygenic cases, or measured at the whole-population (GWAS) level rather than the F2 intercross case.

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