The Friday Five #4

1) A Cost of Sexual Attractiveness to High-Fitness Females: An interesting read regarding sexual selection, if you are interested in this type of thing.

Adaptive mate choice by females is an important component of sexual selection in many species. The evolutionary consequences of male mate preferences, however, have received relatively little study, especially in the context of sexual conflict, where males often harm their mates. Here, we describe a new and counterintuitive cost of sexual selection in species with both male mate preference and sexual conflict via antagonistic male persistence: male mate choice for high-fecundity females leads to a diminished rate of adaptive evolution by reducing the advantage to females of expressing beneficial genetic variation. We then use a Drosophila melanogaster model system to experimentally test the key prediction of this theoretical cost: that antagonistic male persistence is directed toward, and harms, intrinsically higher-fitness females more than it does intrinsically lower-fitness females. This asymmetry in male persistence causes the tails of the population's fitness distribution to regress towards the mean, thereby reducing the efficacy of natural selection. We conclude that adaptive male mate choice can lead to an important, yet unappreciated, cost of sex and sexual selection.

2) Long-Branch Attraction Bias and Inconsistency in Bayesian Phylogenetics: Not that I big fan of evolutionary trees anyway but this is an interesting article as Bayesian types of analyzes become more and more pronounced in evolutionary biology.

Bayesian inference (BI) of phylogenetic relationships uses the same probabilistic models of evolution as its precursor maximum likelihood (ML), so BI has generally been assumed to share ML's desirable statistical properties, such as largely unbiased inference of topology given an accurate model and increasingly reliable inferences as the amount of data increases. Here we show that BI, unlike ML, is biased in favor of topologies that group long branches together, even when the true model and prior distributions of evolutionary parameters over a group of phylogenies are known. Using experimental simulation studies and numerical and mathematical analyses, we show that this bias becomes more severe as more data are analyzed, causing BI to infer an incorrect tree as the maximum a posteriori phylogeny with asymptotically high support as sequence length approaches infinity. BI's long branch attraction bias is relatively weak when the true model is simple but becomes pronounced when sequence sites evolve heterogeneously, even when this complexity is incorporated in the model. This bias—which is apparent under both controlled simulation conditions and in analyses of empirical sequence data—also makes BI less efficient and less robust to the use of an incorrect evolutionary model than ML. Surprisingly, BI's bias is caused by one of the method's stated advantages—that it incorporates uncertainty about branch lengths by integrating over a distribution of possible values instead of estimating them from the data, as ML does. Our findings suggest that trees inferred using BI should be interpreted with caution and that ML may be a more reliable framework for modern phylogenetic analysis.

3) Stable Patterns of Gene Expression Regulating Carbohydrate Metabolism Determined by Geographic Ancestry: I believe I've brought this up before () in regards to Diabetes but this indicates that differences in ancestry may impact how we metabolize food. Or perhaps I'm just thinking of this more since my wife instituted us on a high carb diet.

Background

Individuals of African descent in the United States suffer disproportionately from diseases with a metabolic etiology (obesity, metabolic syndrome, and diabetes), and from the pathological consequences of these disorders (hypertension and cardiovascular disease).

Methodology/Principal Findings

Using a combination of genetic/genomic and bioinformatics approaches, we identified a large number of genes that were both differentially expressed between American subjects self-identified to be of either African or European ancestry and that also contained single nucleotide polymorphisms that distinguish distantly related ancestral populations. Several of these genes control the metabolism of simple carbohydrates and are direct targets for the SREBP1, a metabolic transcription factor also differentially expressed between our study populations.

Conclusions/Significance

These data support the concept of stable patterns of gene transcription unique to a geographic ancestral lineage. Differences in expression of several carbohydrate metabolism genes suggest both genetic and transcriptional mechanisms contribute to these patterns and may play a role in exacerbating the disproportionate levels of obesity, diabetes, and cardiovascular disease observed in Americans with African ancestry.

4) My sister sent this to me and it is worth watching.




5) This however is not worth watching but the stupid song as been stuck in my head since S starting singing the other day. It is actually more horrific than watching the Checkov ear worm thing from Wrath of Khan

KHAN!!!