Species-specific regulation of gene expression contributes to the development and maintenance of reproductive isolation and to species differences in ecologically important traits. A better understanding of the evolutionary forces that shape regulatory variation and divergence can be developed by comparing expression differences among species and interspecific hybrids. Once expression differences are identified, the underlying genetics of regulatory variation or divergence can be explored. With the goal of associating cis and/or trans components of regulatory divergence with differences in gene expression, overall and allele-specific expression levels were assayed genomewide in female adult heads of Drosophila melanogaster, D. simulans, and their F 1 hybrids. A greater proportion of cis differences than trans differences were identified for genes expressed in heads and, in accordance with previous studies, cis differences also explained a larger number of species differences in overall expression level. Regulatory divergence was found to be prevalent among genes associated with defense, olfaction, and among genes downstream of the Drosophila sex determination hierarchy. In addition, two genes, with critical roles in sex determination and micro RNA processing, Sxl and loqs, were identified as misexpressed in hybrid female heads, potentially contributing to hybrid incompatibility.

Unraveling how regulatory divergence contributes to species differences and adaptation requires identifying functional variants from among millions of genetic differences. Analysis of allelic imbalance (AI) reveals functional genetic differences in cis regulation and has demonstrated differences in cis regulation within and between species. Regulatory mechanisms are often highly conserved, yet differences between species in gene expression are extensive. What evolutionary forces explain widespread divergence in cis regulation? AI was assessed in Drosophila melanogaster-Drosophila simulans hybrid female heads using RNA-seq technology. Mapping bias was virtually eliminated by using genotype-specific references. Allele representation in DNA sequencing was used as a prior in a novel Bayesian model for the estimation of AI in RNA. Cis regulatory divergence was common in the organs and tissues of the head with 41% of genes analyzed showing significant AI. Using existing population genomic data, the relationship between AI and patterns of sequence evolution was examined. Evidence of positive selection was found in 30% of cis regulatory divergent genes. Genes involved in defense, RNAi/RISC complex genes, and those that are sex regulated are enriched among adaptively evolving cis regulatory divergent genes. For genes in these groups, adaptive evolution may play a role in regulatory divergence between species. However, there is no evidence that adaptive evolution drives most of the cis regulatory divergence that is observed. The majority of genes showed patterns consistent with stabilizing selection and neutral evolutionary processes.

Background: One method of identifying cis regulatory differences is to analyze allele-specific expression (ASE) and identify cases of allelic imbalance (AI). RNA-seq is the most common way to measure ASE and a binomial test is often applied to determine statistical significance of AI. This implicitly assumes that there is no bias in estimation of AI. However, bias has been found to result from multiple factors including: genome ambiguity, reference quality, the mapping algorithm, and biases in the sequencing process. Two alternative approaches have been developed to handle bias: adjusting for bias using a statistical model and filtering regions of the genome suspected of harboring bias. Existing statistical models which account for bias rely on information from DNA controls, which can be cost prohibitive for large intraspecific studies. In contrast, data filtering is inexpensive and straightforward, but necessarily involves sacrificing a portion of the data. Results:Here we propose a flexible Bayesian model for analysis of AI, which accounts for bias and can be implemented without DNA controls. In lieu of DNA controls, this Poisson-Gamma (PG) model uses an estimate of bias from simulations. The proposed model always has a lower type I error rate compared to the binomial test. Consistent with prior studies, bias dramatically affects the type I error rate. All of the tested models are sensitive to misspecification of bias. The closer the estimate of bias is to the true underlying bias, the lower the type I error rate. Correct estimates of bias result in a level alpha test. Conclusions:To improve the assessment of AI, some forms of systematic error (e.g., map bias) can be identified using simulation. The resulting estimates of bias can be used to correct for bias in the PG model, without data filtering. Other sources of bias (e.g., unidentified variant calls) can be easily captured by DNA controls, but are missed by common filtering approaches. Consequently, as variant identification improves, the need for DNA controls will be reduced. Filtering does not significantly improve performance and is not recommended, as information is sacrificed without a measurable gain. The PG model developed here performs well when bias is known, or slightly misspecified. The model is flexible and can accommodate differences in experimental design and bias estimation.

For over a century, scientists have known that meiotic recombination rates can vary considerably among individuals, and that environmental conditions can modify recombination rates relative to the background. A variety of external and intrinsic factors such as temperature, age, sex and starvation can elicit ‘plastic’ responses in recombination rate. The influence of recombination rate plasticity on genetic diversity of the next generation has interesting and important implications for how populations evolve. Further, many questions remain regarding the mechanisms and molecular processes that contribute to recombination rate plasticity. Here, we review 100 years of experimental work on recombination rate plasticity conducted in Drosophila melanogaster. We categorize this work into four major classes of experimental designs, which we describe via classic studies in D. melanogaster. Based on these studies, we highlight molecular mechanisms that are supported by experimental results and relate these findings to studies in other systems. We synthesize lessons learned from this model system into experimental guidelines for using recent advances in genotyping technologies, to study recombination rate plasticity in non-model organisms. Specifically, we recommend (1) using fine-scale genome-wide markers, (2) collecting time-course data, (3) including crossover distribution measurements, and (4) using mixed effects models to analyse results. To illustrate this approach, we present an application adhering to these guidelines from empirical work we conducted in Drosophila pseudoobscura.This article is part of the themed issue ‘Evolutionary causes and consequences of recombination rate variation in sexual organisms’.

BackgroundAllele-specific expression (ASE) assays can be used to identify cis, trans, and cis-by-trans regulatory variation. Understanding the source of expression variation has important implications for disease susceptibility, phenotypic diversity, and adaptation. While ASE is commonly measured via relative fluorescence at a SNP, next generation sequencing provides an opportunity to measure ASE in an accurate and high-throughput manner using read counts.ResultsWe introduce a Solexa-based method to perform large numbers of ASE assays using only a single lane of a Solexa flowcell. In brief, transcripts of interest, which contain a known SNP, are PCR enriched and barcoded to enable multiplexing. Then high-throughput sequencing is used to estimate allele-specific expression using sequencing counts. To validate this method, we measured the allelic bias in a dilution series and found high correlations between measured and expected values (r>0.9, p < 0.001). We applied this method to a set of 5 genes in a Drosophila simulans parental mix, F1 and introgression and found that for these genes the majority of expression divergence can be explained by cis-regulatory variation.ConclusionWe present a new method with the capacity to measure ASE for large numbers of assays using as little as one lane of a Solexa flowcell. This will be a valuable technique for molecular and population genetic studies, as well as for verification of genome-wide data sets.