Speciation, ecological opportunity, and latitude
Dolph Schluter

Evolutionary hypotheses to explain the greater numbers of species in the tropics than the temperate zone include greater age and area, higher temperature and metabolic rates, and greater ecological opportunity. These ideas make contrasting predictions about the relationship between speciation processes and latitude, which I elaborate and evaluate. Available data suggest that per capita speciation rates are currently highest in the temperate zone, and that diversification rates (speciation minus extinction) are similar between latitudes. In contrast, clades whose oldest analyzed dates precede the Eocene thermal maximum, when the extent of the tropics was much greater than today, tend to show highest speciation and diversification rates in the tropics. These findings are consistent with age and area, which is alone among hypotheses in predicting a time trend. Higher recent speciation rates in the temperate zone than the tropics suggest an additional response to high ecological opportunity associated with low species diversity. These broad patterns are compelling but provide limited insights into underlying mechanisms, arguing that studies of speciation processes along the latitudinal gradient will be vital. Using threespine stickleback in depauperate northern lakes as an example, I show how high ecological opportunity can lead to rapid speciation. The results support a role for ecological opportunity in speciation, but its importance in the evolution of the latitudinal gradient remains uncertain. I conclude that per-capita evolutionary rates are no longer higher in the tropics than the temperate zone. Nevertheless, the vast numbers of species that have already accumulated in the tropics ensure that total rate of species production remains highest there. Thus, tropical evolutionary momentum helps to perpetuate the steep latitudinal biodiversity gradient.

Combining exome and gene expression datasets in one graphical model of disease to empower the discovery of disease mechanisms
Aziz M. Mezlini, Fabio Fuligni, Adam Shlien, Anna Goldenberg

Identifying genes associated with complex human diseases is one of the main challenges of human genetics and computational medicine. To answer this question, millions of genetic variants get screened to identify a few of importance. To increase the power of identifying genes associated with diseases and to account for other potential sources of protein function aberrations, we propose a novel factor-graph based model, where much of the biological knowledge is incorporated through factors and priors. Our extensive simulations show that our method has superior sensitivity and precision compared to variant-aggregating and differential expression methods. Our integrative approach was able to identify important genes in breast cancer, identifying genes that had coding aberrations in some patients and regulatory abnormalities in others, emphasizing the importance of data integration to explain the disease in a larger number of patients.

An in-host model of HIV incorporating latent infection and viral mutation
Stephen Pankavich, Deborah Shutt

We construct a seven-component model of the in-host dynamics of the Human Immunodeficiency Virus Type-1 (i.e, HIV) that accounts for latent infection and the propensity of viral mutation. A dynamical analysis is conducted and a theorem is presented which characterizes the long time behavior of the model. Finally, we study the effects of an antiretroviral drug and treatment implications.

Fundamental Properties of the Evolution of Mutational Robustness
Lee Altenberg

Evolution on neutral networks of genotypes has been found in models to concentrate on genotypes with high mutational robustness, to a degree determined by the topology of the network. Here analysis is generalized beyond neutral networks to arbitrary selection and parent-offspring transmission. In this larger realm, geometric features determine mutational robustness: the alignment of fitness with the orthogonalized eigenvectors of the mutation matrix weighted by their eigenvalues. “House of cards” mutation is found to preclude the evolution of mutational robustness. Genetic load is shown to increase with increasing mutation in arbitrary single and multiple locus fitness landscapes. The rate of decrease in population fitness can never grow as mutation rates get higher, showing that “error catastrophes” for genotype frequencies never cause precipitous losses of population fitness. The “inclusive inheritance” approach taken here naturally extends these results to a new concept of dispersal robustness.

Using Genetic Distance to Infer the Accuracy of Genomic Prediction
Marco Scutari, Ian Mackay, David Balding

The prediction of phenotypic traits using high-density genomic data has many applications such as the selection of plants and animals of commercial interest; and it is expected to play an increasing role in medical diagnostics. Statistical models used for this task are usually tested using cross-validation, which implicitly assumes that new individuals (whose phenotypes we would like to predict) originate from the same population the genomic prediction model is trained on.
In this paper we investigate the effect of increasing genetic distance between training and target populations when predicting quantitative traits. This is important for plant and animal genetics, where genomic selection programs rely on the precision of predictions in future rounds of breeding. Therefore, estimating how quickly predictive accuracy decays is important in deciding which training population to use and how often the model has to be recalibrated. We find that the correlation between true and predicted values decays approximately linearly with respect to either $\F$ or mean kinship between the training and the target populations. We illustrate this relationship using simulations and a collection of data sets from mice, wheat and human genetics.

Extreme Mitogenomic Variation Without Cryptic Speciation in Chaetognaths

Application of a dense genetic map for assessment of genomic responses to selection and inbreeding in Heliothis virescens.

Brain Transcriptional Profiles of Male Alternative Reproductive Tactics in Bluegill Sunfish

Bluegill sunfish are one of the classic systems for studying male alternative reproductive tactics (ARTs) in teleost fishes. In this species, there are two distinct life histories: parental and cuckolder, encompassing three reproductive tactics, parental, satellite, and sneaker. The parental tactic is fixed, whereas individuals who enter the cuckolder life history transition from the sneaker to the satellite tactic as they grow. For this study, we used RNAseq to characterize the brain transcriptome of the three male tactics during spawning to identify gene categories associated with each tactic and identify potential candidate genes influencing their different spawning behaviors. We found that sneaker males had higher levels of gene differentiation compared to the other two tactics, suggesting that life history does not exclusively drive differential gene expression. Sneaker males had high expression in ionotropic glutamate receptor genes, specifically AMPA receptors, which may be important for increased working spatial memory while attempting to cuckold nests in bluegill colonies. We also found significant expression differences in several candidate genes involved in ARTs that were previously identified in other species and suggest a previously undescribed role for cytosolic 5-nucleotidase II (nt5c2) in influencing parental male behavior during spawning.