Adaptive divergence of a transcriptional enhancer between populations of<i>Drosophila melanogaster</i>

Glaser-Schmitt, Amanda; Catalán, Ana; Parsch, John

doi:10.1098/rstb.2013.0024

Cited by 16 publications

(33 citation statements)

References 74 publications

(137 reference statements)

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“…Our history is close to the PSMC result, although more resolution would be needed for a proper comparison. The expansion after the bottleneck is roughly consistent with previous results citing the range expansion of Drosophila melanogaster (out of sub-Saharan Africa) as beginning around 15,000 years ago [18]. This range expansion likely led to an effective population size increase like the one we infer.…”

Section: Results On Real Drosophila Melanogaster Datasupporting

confidence: 91%

Deep learning for population genetic inference

Sheehan

Song

2015

Preprint

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Given genomic variation data from multiple individuals, computing the likelihood of complex population genetic models is often infeasible. To circumvent this problem, we introduce here a novel likelihood-free inference framework by applying deep learning, a powerful modern technique in machine learning. In contrast to Approximate Bayesian Computation, another likelihood-free approach widely used in population genetics and other fields, deep learning does not require a distance function on summary statistics or a rejection step, and it is robust to the addition of uninformative statistics. To demonstrate that deep learning can be effectively employed to estimate population genetic parameters and learn informative features of data, we focus on the challenging problem of jointly inferring natural selection and demography (in the form of a population size change history). Our method is able to separate the global nature of demography from the local nature of selection, without sequential steps for these two factors. Studying demography and selection jointly is motivated by Drosophila, where pervasive selection confounds demographic analysis. We apply our method to 197 African Drosophila melanogaster genomes from Zambia to infer both their overall demography, and regions of their genome under selection. We find many regions of the genome that have experienced hard sweeps, and fewer under selection on standing variation (soft sweep) or balancing selection. Interestingly, we find that soft sweeps and balancing selection occur more frequently closer to the centromere of each chromosome. In addition, our demographic inference suggests that previously estimated bottlenecks for African Drosophila melanogaster are too extreme, likely due in part to the unaccounted impact of selection.

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Section: Results On Real Drosophila Melanogaster Datasupporting

confidence: 91%

Deep learning for population genetic inference

Sheehan

Song

2015

Preprint

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“…Several of the genes that were previously identified as showing expression divergence between populations in whole-fly studies were also detected as differing in expression between populations in the Malpighian tubule. These include Cyp6g1 (2.1-fold overexpression in Europe), CG10560 (2.4-fold overexpression in Africa), and CG9509 (1.9-fold overexpression in Europe), all of which show functional and population genetic evidence for recent adaptive evolution ( Daborn et al 2002 ; Catania et al 2004 ; Aminetzach et al 2005 ; Magwire et al 2011 ; Catalán et al 2012 ; Saminadin-Peter et al 2012 ; Glaser-Schmitt et al 2013 ). This suggests that some of the other population-biased genes also may have undergone adaptive regulatory evolution.…”

Section: Discussionmentioning

confidence: 99%

“…Similarly, the choline kinase gene CG10560 , which is part of the four-gene CHKov1 cluster that differs in expression between European and African flies and also has been implicated in insecticide resistance ( Aminetzach et al 2005 ; Catalán et al 2012 ), has highly enriched expression in the Malpighian tubules ( Chintapalli et al 2007 ). Finally, the choline dehydrogenase gene CG9505 , which shows evidence for an adaptive increase in expression in populations outside of sub-Saharan Africa ( Saminadin-Peter et al 2012 ; Glaser-Schmitt et al 2013 ), has its greatest expression levels in the Malpighian tubules ( Chintapalli et al 2007 ).…”

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confidence: 99%

Population- and Sex-Biased Gene Expression in the Excretion Organs ofDrosophila melanogaster

Huylmans¹,

Parsch²

2014

G3 Genes|Genomes|Genetics

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Within species, levels of gene expression typically vary greatly between tissues, sexes, individuals, and populations. To investigate gene expression variation between sexes and populations in a single somatic tissue, we performed a quantitative analysis of the Malpighian tubule transcriptome in adult males and females of Drosophila melanogaster derived from two distinct populations (one from sub-Saharan Africa and one from northern Europe). We identified 2308 genes that differed in expression between the sexes and 2474 genes that differed in expression between populations at a false discovery rate of 5%. We also identified more than 1000 genes that showed a sex-by-population interaction in their expression. The genes that differed in expression between sexes showed enrichment for a wide variety of functions, although only 55% of them overlapped with sex-biased genes identified in whole-fly studies. The genes expressed differentially between populations included several that were previously implicated in adaptive regulatory evolution, an excess of cytochrome P450 genes, and many genes that were not detected in previous studies of whole flies. Our results demonstrate that there is abundant intraspecific gene expression variation within in a single, somatic tissue and uncover new candidates for adaptive regulatory evolution between populations.

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“…Mapping may also detect genomic regions with signs of a selective sweep, an indication that natural selection is fixing a variant in the population. In this issue, Glaser-Schmitt et al [139] describe a selective sweep around an enhancer of the CG9509 gene (encoding an enzyme of unknown function) that is responsible for a consistently higher expression of this gene in European versus sub-Saharan African populations of D. melanogaster. Increased expression of the enzyme outside sub-Saharan Africa likely indicates that this phenotype has fitness value, perhaps because the enzyme may have detoxifying functions [139].…”

Section: (C) Acquisition Of New Enhancers and Expression Territoriesmentioning

confidence: 99%

Evolution of transcriptional enhancers and animal diversity

Rubinstein

Souza

2013

Phil. Trans. R. Soc. B

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Deciphering the genetic bases that drive animal diversity is one of the major challenges of modern biology. Although four decades ago it was proposed that animal evolution was mainly driven by changes in cis -regulatory DNA elements controlling gene expression rather than in protein-coding sequences, only now are powerful bioinformatics and experimental approaches available to accelerate studies into how the evolution of transcriptional enhancers contributes to novel forms and functions. In the introduction to this Theme Issue, we start by defining the general properties of transcriptional enhancers, such as modularity and the coexistence of tight sequence conservation with transcription factor-binding site shuffling as different mechanisms that maintain the enhancer grammar over evolutionary time. We discuss past and current methods used to identify cell-type-specific enhancers and provide examples of how enhancers originate de novo , change and are lost in particular lineages. We then focus in the central part of this Theme Issue on analysing examples of how the molecular evolution of enhancers may change form and function. Throughout this introduction, we present the main findings of the articles, reviews and perspectives contributed to this Theme Issue that together illustrate some of the great advances and current frontiers in the field.

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Adaptive divergence of a transcriptional enhancer between populations ofDrosophila melanogaster

Cited by 16 publications

References 74 publications

Deep learning for population genetic inference

Deep learning for population genetic inference

Population- and Sex-Biased Gene Expression in the Excretion Organs ofDrosophila melanogaster

Evolution of transcriptional enhancers and animal diversity

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