Genomic evidence of rapid and stable adaptive oscillations over seasonal time scales in Drosophila

Genomic evidence of rapid and stable adaptive oscillations over seasonal time scales in Drosophila
Alan O. Bergland, Emily L. Behrman, Katherine R. O’Brien, Paul S. Schmidt, Dmitri A. Petrov
(Submitted on 20 Mar 2013)

In many species, genomic data have revealed pervasive adaptive evolution indicated by the near fixation of beneficial alleles. However, when selection pressures are highly variable along a species range or through time adaptive alleles may persist at intermediate frequencies for long periods. So called balanced polymorphisms have long been understood to be an important component of standing genetic variation yet direct evidence of the ubiquity of balancing selection has remained elusive. We hypothesized that environmental fluctuations between seasons in a North American orchard would impose temporally variable selection on Drosophila melanogaster and consequently maintain allelic variation at polymorphisms adaptively evolving in response climatic variation. We identified hundreds of polymorphisms whose frequency oscillates among seasons and argue that these loci are subject to strong, temporally variable selection. We show that adaptively oscillating polymorphisms are often millions of years old, predating the divergence between D. melanogaster and D. simulans and that a subset of these polymorphisms respond predictably to an acute frost event. Taken together, our results demonstrate that rapid temporal fluctuations in climate over generational scales is a predominant force that maintains adaptive alleles and promotes genetic diversity.


Membrane environment imposes unique selection pressures on transmembrane domains of G protein-coupled receptors

Membrane environment imposes unique selection pressures on transmembrane domains of G protein-coupled receptors
Stephanie J. Spielman, Claus O. Wilke
(Submitted on 25 Nov 2012)

We have investigated the influence of the plasma membrane environment on the molecular evolution of G protein-coupled receptors (GPCRs), the largest receptor family in Metazoa. In particular, we have analyzed the site-specific rate variation across the two primary structural partitions, transmembrane (TM) and extramembrane (EM), of these membrane proteins. We find that transmembrane domains evolve more slowly than do extramembrane domains, though TM domains display increased rate heterogeneity relative to their EM counterparts. Although the majority of residues across GPCRs experience strong to weak purifying selection, many GPCRs experience positive selection at both TM and EM residues, albeit with a slight bias towards the EM. Further, a subset of GPCRs, chemosensory receptors (including olfactory and taste receptors), exhibit increased rates of evolution relative to other GPCRs, an effect which is more pronounced in their TM spans. Although it has been previously suggested that the TM’s low evolutionary rate is caused by their high percentage of buried residues, we show that their attenuated rate seems to stem from the strong biophysical constraints of the membrane itself, or by functional requirements.

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.

Generative Probabilistic Model for Detecting Selection on Dispersed Genomic Elements from Polymorphism and Divergence

Generative Probabilistic Model for Detecting Selection on Dispersed Genomic Elements from Polymorphism and Divergence
Ilan Gronau, Leonardo Arbiza, Adam Siepel
(Submitted on 29 Sep 2011 (v1), last revised 13 Aug 2012 (this version, v3))

We present a new probabilistic method for measuring the influence of natural selection on a collection of short elements scattered across a genome based on observed patterns of polymorphism and divergence. This is a challenging task for various reasons, including variation across loci in mutation rates and genealogical backgrounds, and the influence of demography on patterns of polymorphism. In addition, accounting for the combined effects of different modes of selection is known to be a serious challenge for tests of selection that use patterns of polymorphism and divergence. Our method addresses these challenges by contrasting patterns of polymorphism and divergence in the elements of interest with those in flanking neutral sites. While this general approach is common to several existing tests of selection, our method improves substantially on these methods by making use of a full generative probabilistic model, directly accommodating weak negative selection, allowing information from many short elements to be combined in a statistically rigorous manner, and integrating phylogenetic information from multiple outgroup species with genome-wide population genetic data. Our model is able to account for of weak negative, strong negative, and strong positive selection, by making a small set of simple assumptions on their separate effects on polymorphism and divergence. We implemented an expectation maximization algorithm for inference under this model and applied it to simulated and real data. Using simulations, we show that our inference procedure effectively disentangles the different modes of selection and provides accurate estimates of the parameters of interest that are robust to demography. We demonstrate an application of our methods to real data by analyzing several collections of human transcription factor binding sites identified using recently generated genome-wide ChIP-seq data.