One of the grand challenges of evo-devo is to understand how mutations of genetic sequences affect concomitant phenotypic traits. Eighty-one years ago, Fisher (1930) proposed that every mutation may affect every trait, and the effect size of a gene on a trait is uniformly distributed: thus we should observe equal proportions of mutations causing large and small per-trait effects. As a logical consequence of Fisher’s hypothesis, more complex organisms (that is, with more traits) should evolutionarily adapt to their environment at a slower rate than less complex organisms because the presence of more traits implies a higher density of gene-trait relationships and thus incurs a “cost of complexity” (Orr 2000). However, it is widely accepted that organisms *do* evolve to be more complex, and populations of complex organisms successfully evolve towards fitness optima. This implies the “cost of complexity” hypothesis is incorrect, or the cost is counteracted by some unknown force.
In contrast to a Fisherian view, contemporary evo-devo research widely accepts the general principle that genes interact in hierarchical modules to produce morphological and physiological traits. A network-centric perspective of gene-trait interactions suggests that the effect of a particular mutation on downstream traits depends on the network location of the mutated gene: mutations in genes with high network centrality tend to be more pleiotropic because those genes affect many downstream traits, whereas mutations to peripheral genes are less pleiotropic. However, the extent of modularity and pleiotropy across genomes is unknown.
A recently-published PNAS paper (Wang et al., 2010) repudiates the Fisher-Orr “cost of complexity” hypothesis and confirms contemporary intuition regarding genetic modularity using empirical data and an extension of an exiting model of adaptation.
Wang et al. analyzed genome-wide patterns of pleiotropy in three eukaryotes—yeast, mice, and nematodes—and observed significant modularity in the gene-trait relationship graph and generally low levels of pleiotropy for most genes. This highly modular structure and generally low pleiotropy means that a mutation is more likely to be beneficial, as it is more likely to affect a small, related set of phenotypes in the same direction, as opposed to many phenotypes in random directions.
Moreover, the authors observed that pleiotropic mutations tend to have a larger per-gene effect than non-pleiotropic mutations. By extending Orr’s “complexity cost” equation to allow for variable levels of pleiotropy, Wang et al. observed a small non-zero degree of pleiotropy actually increases—rather that impairs—the rate of adaptation. This is because the positive correlation between pleiotropy and effect size increases the probability of fixation and fitness gain in more complex organisms, i.e., those with greater complexity. This result is important because it may explain the repeated evolution of complexity in many taxa.
Wang et al.’s analysis is based entirely on data mined from knock-out and RNAi experiments; their conclusions are consequently limited to the sequence space of null mutations that silence the function(s) of genes. In contrast, a less-explored region of sequence space contains mutations that merely affect the relative activity of a gene’s protein product without entirely silencing the gene. In non-null sequence space, the magnitude of a mutation’s effect is determined not only by the pleiotropy (a.k.a. the network centrality) of the mutated gene, but also the number of redundant pathways leading from that gene to a downstream phenotype. It is widely accepted that pathway redundancy buffers traits from upstream changes in enzyme activity or dosage [see Kacser and Burns, “The Molecular Basis on Dominance”, Genetics 1981]. Whereas the effects of null mutations are strongly predicted by the extent of pleiotropy (as presently shown by Wang et al.), we hypothesize that the effect of a non-null mutation is largely predicted by the number of interaction pathways between the mutated gene and a downstream phenotype. This counterhypothesis, however, has yet to be tested.
Read the paper by Wang et al., here:
Wang Z, Liao BY, & Zhang J (2010). Genomic patterns of pleiotropy and the evolution of complexity. Proceedings of the National Academy of Sciences of the United States of America, 107 (42), 18034-9 PMID: 20876104