Saturday, June 11, 2016

Keystone Genes?

(The title of this post is new as of June 15, 2016, in honor of Bob Paine, who just passed away.)

While writing my book on Eco-Evolutionary Dymamics, I wanted a chapter on the genetic/genomic underpinnings of the interactions between ecology and evolution. About the time I finished the chapter, I received an invitation to submit a paper to Heredity, and so I converted the book chapter into a paper (Hendry 2013).


In the paper, I suggested that “The genetics and genomics of eco-evolutionary dynamics will be – to a large extent – the genetics and genomics of phenotypic traits” (more about this below) and then concluded (from the abstract):

(1) Considerable additive genetic variance is present for most traits in most populations.

(2) Trait correlations do not consistently oppose selection.

(3) Adaptive differences between populations often involve dominance and epistasis.

(4) Most adaptation is the result of genes of small-to-modest effect,

although (5) some genes certainly have larger effects than the others.

(6) Adaptation by independent lineages to similar environments is mostly driven by different alleles/genes.

(7) Adaptation to new environments is mostly driven by standing genetic variation, although new mutations can be important in some instances.

(8) Adaptation is driven by both structural and regulatory genetic variation, with recent studies emphasizing the latter.

(9) The ecological effects of organisms, considered as extended phenotypes, are often heritable.

Research in the past three years seems only to have bolstered these conclusions, but I can now see some important nuances.

Last week, I was at Monte Verita in Ascona, southern Switzerland, for a meeting titled The Genetics and Genomics of Eco-Evolutionary Dynamics, organized by a number of postdocs at the Adaptation to a Changing Environment (ACE) centre . The meeting brought together people studying the genomics of adaptation and people studying eco-evolutionary dynamics (two largely non-overlapping groups) to see if some progress could be made toward integrating the two areas of research.

The last light of day from my room at Monte Verita, Ascona, Switzerland.
For some time, it wasn’t clear that such integration was possible or profitable. In particular, because all eco-evolutionary dynamics are driven by phenotypes, the genomics of eco-evolutionary dynamics should simply be the genomics of phenotypic traits – a point that I argued in my 2013 paper (as noted above). However, a problem arises in the case of eco-evolutionary dynamics because the correlation between genes (i.e., genetic variation and evolutionary change) and ecological function is expected to be product of two correlations: that between genes and traits and that between traits and ecological function. Given that correlations are between 0 and 1, this product should be weaker than either of the two correlations. We already know from many studies that each of these two correlations is relative weak, probably nearly always less than 0.5, and so the correlation between genes and ecological function should be VERY weak. In short, the initial perspective of the group was that the genetics and genomics of eco-evolutionary dynamics would be considerably more difficult to study than the genetics and genomics of phenotypic traits (which is already quite hard).

This prospect made most of the genomics people in the room rather less than excited as it seemed to suggest that genomic mapping of ecological function – and the search for candidate genes and causal variants – would be hopeless. This same skepticism was – to some extent – the point I made in my 2013 paper where I argued that the genomics of eco-evolutionary dynamics would be very polygenic and so better served by quantitative genetics. However, further discussion brought up several important points that suggest the tools of genomics might be profitably turned to the exploration of ecological function.

These points can be illustrated by reference to a path model, where a set of genes influence a set of traits which influence a set of ecological functions. In these models, the correlations along each casual pathway are multiplied to get the final correlation between the start (a gene) and end (an ecological function) of that pathway as noted above. However, when multiple pathways link genes and ecological functions, those final correlations are summed across the pathways to get the total effect. Thinking in this manner yields several insights:

The Bailey et al. (2009) version of the relevant path model, which resulted in part from discussion we had when I edited their paper for a PTRSB special issue on Eco-Evolutionary Dynamics.

1. The effects of a given gene on a given ecological function could be greater than the effects of that gene on any one phenotypic trait. This situation could arise when one gene influences multiple traits that each influence the same ecological function. It could also arise if a given gene influences both a trait with a key ecological function and also organismal fitness, with fitness then also influencing ecological function. And the situation is even more promising if more than one aspect of ecological function is influenced by traits and (most obviously) by organismal fitness, such that the total ecological effect could be considerably greater than any single ecological effect.

2. The total effect of all genes on a given ecological function (or on total ecological function) could be large if multiple genes influence one trait that has a strong effect on ecological function(s) or if multiple genes influence multiple traits that together to have large effects on ecological function(s). Further, if we consider a multi-species context, a given ecological function could be influenced by genetic variation in multiple species – and so the total genetic effects of all species on a given ecological function could be large. This last possibility suggests the potential value of analyzing GxG effects on ecological function – as has already been done in several studies where the Gs are different clones. Of particular interest, GxG interactions suggest that the effects of genes in a given species might be most easily revealed if they are assessed on several genomic backgrounds of the other interacting species.  

Seth Rudman gives a summary of the working group. If you look closely, you can see a scribbled version of the path model on the board at left.
If I had the chance, I would now modify the statements in my 2013 paper to make the above points. I would then reiterate that we can treat the ecological effects of individuals as extended phenotypes, and so attempt all of the same genomic work done for more traditional traits (and sometimes fitness): QTL mapping, genome scans (comparing groups of individuals with different ecological functions), genome-wide association studies, candidate gene discovery, and searches causal variants (depending on the specific points of interest). Of course, these methods will need to be combined with quantitative genetic analyses, given that much ecological function will surely be polygenic.

Sadly, I can’t modify the 2013 paper. Nor can I modify the corresponding book chapter given that I will receive the proofs tomorrow and the publisher won’t want me making large changes. However, you can stay tuned for the paper resulting from the discussions in Switzerland, which is being led by Seth Rudman. Go Seth go!


The Genomics of Eco-Evolutionary Dynamics group.

A few days after writing this post, Bob Paine, the originator of the keystone species concept passed away. I took a class from Bob Paine when I was a graduate student and have certainly referred to keystone species multiple times in my writing. In particular, I have argued that eco-evolutionary dynamics are most likely when the evolving focal species is a keystone species (or a foundation species or a ecosystem engineer and so on). Thus, if we are to search for particular genes of large effect, we would probably want to look in these species. And we might then call these large ecological effect genes "keystone genes."

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