Regulatory networks and phenotypic evolution

Kopp & McIntyre. 2012. Transcriptional network structure has little effect on the rate of regulatory evolution in yeast. MBE

It's expected that network position will affect regulatory evolution because evolutionary changes at nodes with fewer connections are less likely to have deleterious pleiotropic effects. The authors test this prediction by combining cis-regulated expression divergence between S. cerevisiae and S. paradoxus with a number of gene network data sets for S. cerevisiae. In particular, they looked at the number of transcription factors binding to each gene ('incoming connections') and the number of genes regulated by each transcription factor ('outgoing connections'), They found no overall relationship between the number of outgoing connections and cis-regulatory divergence. There was a significant correlation between the number of incoming connections and cis-regulatory expression divergence: genes regulated by many transcription factors have a higher cis-regulatory divergence than those regulated by few transcription factors. The authors claim that the magnitude of this effect is small but it's hard to tell based on the data they present. Their main explanation for this is that genes that are regulated by many different transcription factors are likely to have more binding sites and thus a larger mutational target. The authors also looked at five smaller data sets made up of condition-dependent subnetworks and found a significant relationship between incoming connection number and divergence in most subnetworks but only found a relationship between outgoing connections and divergence in the stress response subnetwork.

Method notes: The divergence data comes from allele-specific expression measured in F1 hybrids of S. cerevisiae and S. paradoxus in multiple conditions while the network data comes from various chromatin imunoprecipitation (ChIP) experiments. ChIP experiments quantify binding between candidate transcription factors and genomic regions by hybridizing transcription factors to tiling microarrays. Since TF binding can be condition specific, this method could miss some true binding sites while finding others which are not biologically significant. 

Em summarizes

Andres et al. 2009. Targets of balancing selection in the human genome. Molecular Biology and Evolution

While genome scans have been successfully used to find the signature of purifying selection, finding evidence of balancing selection is more difficult. Andres et al. approach this problem, scanning human coding seuqnece for evidence long-term balancing selection, which should leave narrow regions of excess polymorphism. They used sequence data from 13,400 genes in 39 humans (two populations) to construct a demographic model and then tested for balancing selection in the 4,877 genes which had 10+ polymorphic or divergent sites. They conducted a two part test: first they used a modified HKA test to detect genes that showed an excess of polymorphism relative to variation. Second, they looked at each gene's allele frequency spectrum and found genes with an excess of intermediate-frequency alleles. They identified 60 genes that deviated from the expectations set by the neutral demographic model in both tests. An MHC gene which was previously known to be under balancing selection was included in this set, validating their results. They also found that on average these 60 genes had higher LD than the rest of the genome, consistent with there being positive epistasis between sites. 

Em summarizes

Obbard et al. 2009 Quantifying adaptive evolution in the Drosophila immune system. PLoS Genetics.

Stephen said to read this a while ago, I did, and didn't think much of it. Now, after banging around calculating alpha myself, it seems a lot more interesting ...


Population genetics studies have found that a surprisingly large proportion of changes in Drosophila genomes were fixed by positive selection (this value is measure as α). Obbard and co. explore this result by investigating sequence of immune-related genes, which they expect to have higher rates of adaptive evolution. They resequenced 136 immune genes and 136 nearby non-immune related control genes in 6 populations of Drosophila melanogaster and 2 populations of D simulans, with 4 individuals pooled per population. They first found that, as expected, α is higher in immune genes (α = 0.65) compared to their controls (α = 0.41). Second, they looked at the distributions of α values and found that this difference is driven by a small subset of their immune-related genes. Third, they classified genes by various pathways and function and found that some of these groups have higher average α values than others (this presumably makes sense for people that understand Drosophila immune systems). Finally, they look at lineage specific divergence and show that a values are correlated in D. simulans and D. melanogaster, suggesting that similar selective pressures are operating in both species. 

Overall this paper suggests that, since α is higher in genes that are expected to be under strong positive selection, high α estimates in Drosophila represent reality, not artifacts of some other process. Also interesting, for me, is their calculations of the exact number of variants fixed by positive selection (a), which I'd like to do with my own data. Also, they appear to calculate lineage-specific divergence by using PAML to estimate the ancestral state and then calculate divergence between the inferred ancestor and their sequenced genes, something which the author of PAML says not to do.