Ecology and genomic background shape the probability of parallel adaptation to climate

Evolution can repeat itself, resulting in parallel adaptations in independent lineages occupying similar environments. Moreover, parallel evolution sometimes, but not always, uses the same genes. Two main hypotheses have been put forth to explain the probability and extent of parallel evolution. First, parallel evolution is more likely when shared ecologies result in similar patterns of natural selection in different taxa. Second, parallelism is more likely when genomes are similar, because of shared standing variation and similar mutational effects in closely related genomes. Here we combine ecological, genomic, experimental, and phenotypic data with randomization tests and Bayesian modeling to quantify the degree of parallelism and study its relationship with ecology and genetics. Our results show that the probability of parallel adaptation to climate among species of Timema stick insects is shaped collectively by shared ecology and genomic background. Specifically, the probability of genetic parallelism decays with divergence in climatic (i.e., ecological) conditions and genomic similarity. Moreover, we find that climate-associated loci are likely subject to selection in a field experiment, overlap with genetic regions associated with cuticular hydrocarbon traits, and are not strongly shaped by introgression between species. Our findings shed light on when evolution is most expected to repeat itself.

Emergence of co-expression in gene regulatory networks

Transcriptomes are known to organize themselves into gene co-expression clusters or modules where groups of genes display distinct patterns of coordinated or synchronous expression across independent biological samples. The functional significance of these co-expression clusters is suggested by the fact that highly coexpressed groups of genes tend to be enriched in genes involved in common functions and biological processes. While gene co-expression is widely assumed to reflect close regulatory proximity, the validity of this assumption remains unclear. Here we use a simple synthetic gene regulatory network (GRN) model and contrast the resulting co-expression structure produced by these networks with their known regulatory architecture and with the co-expression structure measured in available human expression data. Using randomization tests, we found that the levels of co-expression observed in simulated expression data were, just as with empirical data, significantly higher than expected by chance. When examining the source of correlated expression, we found that individual regulators, both in simulated and experimental data, fail, on average, to display correlated expression with their immediate targets. However, highly correlated gene pairs tend to share at least one common regulator, while most gene pairs sharing common regulators do not necessarily display correlated expression. Our results demonstrate that widespread co-expression naturally emerges in regulatory networks, and that it is a reliable and direct indicator of active co-regulation in a given cellular context.

Evaluating Results

This chapter explores the tools used to evaluate the quality and plausibility of results of phylogenetic analyses. The term “fit” has been widely used in the phylogenetic literature to indicate the degree to which data conform to (or are explained by) a cladogram. The most commonly used measure of fit applied to discrete character data is the consistency index, or ci. Meanwhile, measures of synapomorphy are less frequently reported than the consistency index. The chapter then considers the resolution of branches; multiple equally parsimonious cladograms; successive approximations weighing; and data decisiveness. It also differentiates between total evidence and consensus, before describing supertrees. Finally, the chapter looks at approaches for evaluating support or stability of phylogenetic results, including branch support, jackknifing, bootstrapping, randomization tests, and sensitivity analysis.

Inference under covariate-adaptive randomization: A simulation study

In clinical trials, several covariate-adaptive designs have been proposed to balance treatment arms with respect to key covariates. Although some argue that conventional asymptotic tests are still appropriate when covariate-adaptive randomization is used, others think that re-randomization tests should be used. In this manuscript, we compare by simulation the performance of asymptotic and re-randomization tests under covariate-adaptive randomization. Our simulation study confirms results expected by the existing theory (e.g. asymptotic tests do not control type I error when the model is miss-specified). Furthermore, it shows that (i) re-randomization tests are as powerful as the asymptotic tests if the model is correct; (ii) re-randomization tests are more powerful when adjusting for covariates; (iii) minimization and permuted blocks provide similar results.

Precolonial States and Development: Evidence from Agriculture in Africa

Low agricultural productivity is a major source of poverty in Africa, where much of the population works in agriculture, yet subsistence production and food insecurity are widespread. However, some pockets of agriculture in Africa are highly productive. In this paper, we assemble a geospatial dataset of all pre-colonial African states in existence between 1500 and 1850, and utilize remote-sensing data based on satellite imagery to show that areas (pixels) in proximity to the location of pre-colonial state capitals display higher levels of contemporary agricultural output. This relationship exists across and within countries, agro-ecological zones, and river basins. We rule out spurious correlation with spatial randomization tests. We argue that via path-dependence and spatial agglomeration effects, pre-colonial states transmitted the territorial reach that was critical for state-led agricultural modernization in the twentieth century. The findings support a growing literature linking contemporary economic development to state capacity transmitted from pre-colonial political institutions.