Genetic redundancy fuels polygenic adaptation in Drosophila.

The genetic architecture of adaptive traits is of key importance to predict evolutionary responses. Most adaptive traits are polygenic-i.e., result from selection on a large number of genetic loci-but most molecularly characterized traits have a simple genetic basis. This discrepancy is best explained by the difficulty in detecting small allele frequency changes (AFCs) across many contributing loci. To resolve this, we use laboratory natural selection to detect signatures for selective sweeps and polygenic adaptation. We exposed 10 replicates of a Drosophila simulans population to a new temperature regime and uncovered a polygenic architecture of an adaptive trait with high genetic redundancy among beneficial alleles. We observed convergent responses for several phenotypes-e.g., fitness, metabolic rate, and fat content-and a strong polygenic response (99 selected alleles; mean s = 0.059). However, each of these selected alleles increased in frequency only in a subset of the evolving replicates. We discerned different evolutionary paradigms based on the heterogeneous genomic patterns among replicates. Redundancy and quantitative trait (QT) paradigms fitted the experimental data better than simulations assuming independent selective sweeps. Our results show that natural D. simulans populations harbor a vast reservoir of adaptive variation facilitating rapid evolutionary responses using multiple alternative genetic pathways converging at a new phenotypic optimum. This key property of beneficial alleles requires the modification of testing strategies in natural populations beyond the search for convergence on the molecular level.

Neuronal Function and Dopamine Signaling Evolve at High Temperature in Drosophila.

Neuronal activity is temperature sensitive and affects behavioral traits important for individual fitness, such as locomotion and courtship. Yet, we do not know enough about the evolutionary response of neuronal phenotypes in new temperature environments. Here, we use long-term experimental evolution of Drosophila simulans populations exposed to novel temperature regimes. Here, we demonstrate a direct relationship between thermal selective pressure and the evolution of neuronally expressed molecular and behavioral phenotypes. Several essential neuronal genes evolve lower expression at high temperatures and higher expression at low temperatures, with dopaminergic neurons standing out by displaying the most consistent expression change across independent replicates. We functionally validate the link between evolved gene expression and behavioral changes by pharmacological intervention in the experimentally evolved D. simulans populations as well as by genetically triggered expression changes of key genes in D. melanogaster. As natural temperature clines confirm our results for Drosophila and Anopheles populations, we conclude that neuronal dopamine evolution is a key factor for temperature adaptation.

Suitability of Different Mapping Algorithms for Genome-Wide Polymorphism Scans with Pool-Seq Data.

The cost-effectiveness of sequencing pools of individuals (Pool-Seq) provides the basis for the popularity and widespread use of this method for many research questions, ranging from unraveling the genetic basis of complex traits, to the clonal evolution of cancer cells. Because the accuracy of Pool-Seq could be affected by many potential sources of error, several studies have determined, for example, the influence of sequencing technology, the library preparation protocol, and mapping parameters. Nevertheless, the impact of the mapping tools has not yet been evaluated. Using simulated and real Pool-Seq data, we demonstrate a substantial impact of the mapping tools, leading to characteristic false positives in genome-wide scans. The problem of false positives was particularly pronounced when data with different read lengths and insert sizes were compared. Out of 14 evaluated algorithms novoalign, bwa mem and clc4 are most suitable for mapping Pool-Seq data. Nevertheless, no single algorithm is sufficient for avoiding all false positives. We show that the intersection of the results of two mapping algorithms provides a simple, yet effective, strategy to eliminate false positives. We propose that the implementation of a consistent Pool-Seq bioinformatics pipeline, building on the recommendations of this study, can substantially increase the reliability of Pool-Seq results, in particular when libraries generated with different protocols are being compared.