Evolution of male life histories and age-dependent sexual signals under female choice

Evolution of male life histories and age-dependent sexual signals under female choice
Joel James Adamson
(Submitted on 16 Nov 2012)

Strategic models have predicted that males could benefit from age-dependent sexual advertisement following evolution of increased lifespan. Dynamical considerations may play a crucial role in the origin of age-dependent sexual signals, despite strategic advantages in populations with established signals and preferences. I investigated the problem that rare trait-bearing males may suffer low viability due to small young-age signals, restricting the favorable conditions for age-dependent trait evolution. I also ask when age-dependence will prevail during trait evolution if males bearing age-dependent traits co-occur with males carrying age-independent traits. I used numerical simulations to analyze the evolution of an age-structured haploid population with no genetic drift. Age-dependence limits the evolution of male traits to cases of relatively weak selection against the trait, but the trait fixes at smaller sizes when age-dependent than when age-independent. When mode of expression (age-dependence versus age-independence) evolved along with the trait, age-independence prevailed over much of parameter space, although mode of expression remained polymorphic at small trait sizes under weak selection. The ubiquity of age-dependent traits in nature shows that many species’ life-histories satisfy the conditions for age-dependent trait evolution. My results suggest that high adult male survival facilitates sexual selection by favoring the evolution of age-dependent sexual signals under fairly broad conditions.


Bacterial diversity associated with Drosophila in the laboratory and in the natural environment

Bacterial diversity associated with Drosophila in the laboratory and in the natural environment
Fabian Staubach, John F. Baines, Sven Kuenzel, Elisabeth M. Bik, Dmitri A. Petrov
(Submitted on 14 Nov 2012)

All higher organisms are associated with bacterial communities. Bacteria have a range of effects on their metazoan hosts from being indispensable for survival to being lethal pathogens. Because bacteria have phenotypic effects on their hosts, they can also be involved in host adaptation to the environment. The fruit fly Drosophila is a classic model organism to study adaptation as well as the relationship between genetic variation and phenotypes. Recently, Drosophila has received attention in immunology and studies of host-microbe interaction. Although bacterial communities associated with Drosophila might be important for many aspects of Drosophila biology, little is known about their diversity and composition or the factors shaping these communities. We used 454-based sequencing of a variable region of the bacterial 16S ribosomal gene to characterize the bacterial communities associated with wild and laboratory Drosophila isolates. In order to specifically investigate effects of food source and host species on bacterial communities, we analyzed samples from wild Drosophila melanogaster and D. simulans flies collected from a variety of natural substrates, as well as from adults and larvae of nine laboratory-reared Drosophila species. We find substantial variation of bacterial communities within and between laboratories that could interfere with phenotype studies. We show that bacterial communities associated with wild-caught Drosophila contain more bacterial species than laboratory-raised flies, but that they are on average less diverse than vertebrate communities. The natural Drosophila-associated microbiota appears to be predominantly shaped by food substrate with an additional but smaller effect of host species identity.

The genetic architecture of adaptations to high altitude in Ethiopia

The genetic architecture of adaptations to high altitude in Ethiopia

Gorka Alkorta-Aranburu, Cynthia M. Beall, David B. Witonsky, Amha Gebremedhin, Jonathan K. Pritchard, Anna Di Rienzo
(Submitted on 13 Nov 2012)

Although hypoxia is a major stress on physiological processes, several human populations have survived for millennia at high altitudes, suggesting that they have adapted to hypoxic conditions. This hypothesis was recently corroborated by studies of Tibetan highlanders, which showed that polymorphisms in candidate genes show signatures of natural selection as well as well-replicated association signals for variation in hemoglobin levels. We extended genomic analysis to two Ethiopian ethnic groups: Amhara and Oromo. For each ethnic group, we sampled low and high altitude residents, thus allowing genetic and phenotypic comparisons across altitudes and across ethnic groups. Genome-wide SNP genotype data were collected in these samples by using Illumina arrays. We find that variants associated with hemoglobin variation among Tibetans or other variants at the same loci do not influence the trait in Ethiopians. However, in the Amhara, SNP rs10803083 is associated with hemoglobin levels at genome-wide levels of significance. No significant genotype association was observed for oxygen saturation levels in either ethnic group. Approaches based on allele frequency divergence did not detect outliers in candidate hypoxia genes, but the most differentiated variants between high- and lowlanders have a clear role in pathogen defense. Interestingly, a significant excess of allele frequency divergence was consistently detected for genes involved in cell cycle control, DNA damage and repair, thus pointing to new pathways for high altitude adaptations. Finally, a comparison of CpG methylation levels between high- and lowlanders found several significant signals at individual genes in the Oromo.

Defensive complexity and the phylogenetic conservation of immune control

Defensive complexity and the phylogenetic conservation of immune control

Erick Chastain, Rustom Antia, Carl T. Bergstrom
(Submitted on 13 Nov 2012)

One strategy for winning a coevolutionary struggle is to evolve rapidly. Most of the literature on host-pathogen coevolution focuses on this phenomenon, and looks for consequent evidence of coevolutionary arms races. An alternative strategy, less often considered in the literature, is to deter rapid evolutionary change by the opponent. To study how this can be done, we construct an evolutionary game between a controller that must process information, and an adversary that can tamper with this information processing. In this game, a species can foil its antagonist by processing information in a way that is hard for the antagonist to manipulate. We show that the structure of the information processing system induces a fitness landscape on which the adversary population evolves. Complex processing logic can carve long, deep fitness valleys that slow adaptive evolution in the adversary population. We suggest that this type of defensive complexity on the part of the vertebrate adaptive immune system may be an important element of coevolutionary dynamics between pathogens and their vertebrate hosts. Furthermore, we cite evidence that the immune control logic is phylogenetically conserved in mammalian lineages. Thus our model of defensive complexity suggests a new hypothesis for the lower rates of evolution for immune control logic compared to other immune structures.

BayesHammer: Bayesian clustering for error correction in single-cell sequencing

BayesHammer: Bayesian clustering for error correction in single-cell sequencing

Sergey I. Nikolenko, Anton I. Korobeynikov, Max A. Alekseyev
(Submitted on 12 Nov 2012)

Error correction of sequenced reads remains a difficult task, especially in single-cell sequencing projects with extremely non-uniform coverage. While existing error correction tools designed for standard (multi-cell) sequencing data usually come up short in single-cell sequencing projects, algorithms actually used for single-cell error correction have been so far very simplistic.
We introduce several novel algorithms based on Hamming graphs and Bayesian subclustering in our new error correction tool BayesHammer. While BayesHammer was designed for single-cell sequencing, we demonstrate that it also improves on existing error correction tools for multi-cell sequencing data while working much faster on real-life datasets. We benchmark BayesHammer on both $k$-mer counts and actual assembly results with the SPAdes genome assembler.

Exact results for fixation probability of bithermal evolutionary graphs

Exact results for fixation probability of bithermal evolutionary graphs

Bahram Houchmandzadeh (LIPhy), Marcel Vallade (LIPhy)
(Submitted on 12 Nov 2012)

One of the most fundamental concepts of evolutionary dynamics is the “fixation” probability, i.e. the probability that a mutant spreads through the whole population. Most natural communities are geographically structured into habitats exchanging individuals among each other and can be modeled by an evolutionary graph (EG), where directed links weight the probability for the offspring of one individual to replace another individual in the community. Very few exact analytical results are known for EGs. We show here how by using the techniques of the fixed point of Probability Generating Function, we can uncover a large class of of graphs, which we term bithermal, for which the exact fixation probability can be simply computed.

Improved haplotyping of rare variants using next-generation sequence data

Improved haplotyping of rare variants using next-generation sequence data
Fouad Zakharia, Carlos Bustamante
(Submitted on 9 Nov 2012)

Accurate identification of haplotypes in sequenced human genomes can provide invaluable information about population demography and fine-scale correlations along the genome, thus empowering both population genomic and medical association studies. Yet phasing unrelated individuals remains a challenging problem. Incorporating available data from high throughput sequencing into traditional statistical phasing approaches is a promising avenue to alleviate these issues. We present a novel statistical method that expands on an existing graphical haplotype reconstruction method (shapeIT) to incorporate phasing information from paired-end read data. The algorithm harnesses the haplotype graph information estimated by shapeIT from genotypes across the population and refines haplotype likelihoods for a given individual to be compatible with the sequencing data. Applying the method to HapMap individuals genotyped on the Affymetrix Axiom chip at 7,745,081 SNPs and on a trio sequenced by Complete Genomics, we found that the inclusion of paired end read data significantly improved phasing, with reductions in switch error on the order of 4-15% against shapeIT across all panels. As expected, the improvements were found to be most significant at sites harboring rare variants; furthermore, we found that longer read sizes and higher throughput translated to greater decreases in switching error, as did higher variance in the size of the insert separating the two reads–suggesting that multi-platform next generation sequencing may be exploited to yield particularly accurate haplotypes. Overall, the phasing improvements afforded by this new method highlight the power of integrating sequencing read information and population genotype data for reconstructing haplotypes in unrelated individuals.