DNA motifs are not general predictors of recombination in two<i>Drosophila</i>sister species

Howie, James Malcolm; Mazzucco, Rupert; Taus, Thomas; Nolte, Viola; Schlötterer, Christian

doi:10.1101/453639

Cited by 10 publications

(12 citation statements)

References 83 publications

(38 reference statements)

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“…To account for linkage, we used 189 D. simulans haplotypes for the simulations (Howie et al 2019). Contributing alleles were randomly distributed across the entire chromosomes 2 and 3 and we used the recombination map of D. simulans (Howie et al 2019). Because D. simulans males do not recombine, we divided the recombination rate estimates by two.…”

Section: Methodsmentioning

confidence: 99%

Distance to trait optimum is a crucial factor determining the genomic signature of polygenic adaptation

Christodoulaki¹,

Barghi²,

Schlötterer³

2019

Preprint

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1Polygenic adaptation is frequently associated with small allele frequency changes of many loci. 2 Recent works suggest, that large allele frequency changes can be also expected. Laboratory 3 natural selection (LNS) experiments provide an excellent experimental framework to study the 4 adaptive architecture under controlled laboratory conditions: time series data in replicate 5 populations evolving independently to the same trait optimum can be used to identify selected 6 loci. Nevertheless, the choice of the new trait optimum in the laboratory is typically an ad hoc 7 decision without consideration of the distance of the starting population to the new optimum.8Here, we used forward-simulations to study the selection signatures of polygenic adaptation in 9 populations evolving to different trait optima. Mimicking LNS experiments we analyzed allele 10 frequencies of the selected alleles and population fitness at multiple time points. We 11 demonstrate that the inferred adaptive architecture strongly depends on the choice of the new 12 trait optimum in the laboratory and the significance cut-off used for identification of selected loci. 13Our results not only have a major impact on the design of future Evolve and Resequence (E&R) 14 studies, but also on the interpretation of current E&R data sets. 15 16 Polymorphic populations are subjected to different types of stressors (e.g. temperature, 20 3 desiccation, etc) and monitored for changes in the phenotype of one or several traits that are 21 usually controlled by a large number of loci (i.e polygenic traits). Recently, the analysis of 22 phenotypic change is combined with the analysis of allele frequency changes by contrasting the 23 evolved and ancestral populations (Evolve & Resequence, E&R). The primary goal of such 24 experiments is to uncover the adaptive architecture of the focal trait(s) in populations subjected 25 to certain conditions based on the allele frequency changes during the experiment. 26 The analysis of different LNS studies revealed contrasting genomic signatures. The most 27 apparent discrepancies between studies are related to the number of putative selection targets 28 and the extent of parallelism across replicates. While some studies detected only a small number 29 of selection targets (Magwire et al. 2012; Mallard et al. 2018), others suggested a polygenic 30 response (Barghi et al., 2019; Jha et al., 2015). While some E&R studies found highly parallel 31 selection response among replicates (Burke et al. 2010; Burke et al. 2016; Graves et al. 2017; 32 Fragata et al. 2018; Mallard et al. 2018; Rebolleda-Gómez and Travisano 2018; Michalak et al. 33 2019) in other studies selection signatures were much less concordant (Cohan and Hoffmann 34 1986; Teotónio et al. 2004; Simões et al. 2008; Griffin et al. 2017; Barghi et al. 2019). A particularly 35 striking difference has been observed for natural Drosophila simulans populations exposed to the 36 same hot environment. A Portuguese population showed parallel strong selection response at 37 few g...

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Section: Methodsmentioning

confidence: 99%

Distance to trait optimum is a crucial factor determining the genomic signature of polygenic adaptation

Christodoulaki¹,

Barghi²,

Schlötterer³

2019

Preprint

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“…Indeed, there are reasons to expect the opposite. For one, the mutational target size of all possible variations in the recombination landscape (e.g., insertion or deletion of recombination-associated motifs) is likely much, much larger than all possible mutations of global regulators of recombination (e.g., genes in the meiosis pathway, (Ratnappan et al ., 2012; Adrian et al ., 2016; Howie et al ., 2019). Given this, why did we observe largely global variation?…”

Section: Discussionmentioning

confidence: 99%

Natural selection shapes variation in genome-wide recombination rate inDrosophila pseudoobscura

Samuk

Manzano‐Winkler

Ritz

et al. 2019

Preprint

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AbstractWhile recombination is widely recognized to be a key modulator of numerous evolutionary phenomena, we have a poor understanding of how recombination rate itself varies and evolves within a species. Here, we performed a comprehensive study of recombination rate (rate of meiotic crossing over) in two natural populations of Drosophila pseudoobscura from Utah and Arizona, USA. We used an amplicon sequencing approach to obtain high-quality genotypes in approximately 8000 individual backcrossed offspring (17 mapping populations with roughly 530 individuals each), for which we then quantified crossovers. Interestingly, variation in recombination rate within and between populations largely manifested as differences in genome-wide recombination rate rather than remodeling of the local recombination landscape. Comparing populations, we discovered individuals from the Utah population displayed on average 8% higher crossover rates than the Arizona population, a statistically significant difference. Using a QST-FST analysis, we found that this difference in crossover rate was dramatically higher than expected under neutrality, indicating that this difference may have been driven by natural selection. Finally, using a combination of short and long read whole-genome sequencing, we found no significant association between crossover rate and structural variation at the 200-400kb scale. Our results demonstrate that (1) there is abundant variation in genome-wide crossover rate in natural populations (2) interpopulation differences in recombination rate may be the result of local adaptation, and (3) the observed variation among individuals in recombination rate is primarily driven by global regulators of crossover rate, with little detected variation in recombination rate among strains across specific tracts of individual chromosomes.

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“…SNPs matching the allele frequency were randomly chosen from the founder population. We used the D. simulans specific recombination map (Dsim_recombination_map_LOESS_100kb_1.txt, Howie et al (2019)). We generated "Pool-seq data" with 50x coverage and added sequencing noise by binomial sampling based on the allele frequencies.…”

Section: Simulationsmentioning

confidence: 99%

A generalised approach to detect selected haplotype blocks in Evolve and Resequence experiments

Otte¹,

Schlötterer²

2019

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Shifting from the analysis of single nucleotide polymorphisms to the reconstruction of selected haplotypes greatly facilitates the interpretation of Evolve and Resequence (E&R) experiments. Merging highly correlated hitchhiker SNPs into haplotype blocks reduces thousands of candidates to few selected regions. Current methods of haplotype reconstruction from Pool-Seq data need a variety of data-specific parameters that are typically defined ad hoc and require haplotype sequences for validation. Here, we introduce haplovalidate, a tool which detects selected haplotypes in a broad range of Pool-seq time series data without the need of sequenced haplotypes. Haplovalidate makes data-driven choices of two key parameters for the clustering procedure, the minimum correlation between SNPs constituting a cluster and the window size. Applying haplovalidate to simulated and experimental E&R data reliably detects selected haplotype blocks with low false discovery rates -independent if few or many selection targets are included. Our analyses identified an important restriction of the haplotype block-based approach to describe the genomic architecture of adaptation.We detected a substantial fraction of haplotypes containing multiple selection targets. These blocks were considered as one region of selection and therefore led to under-estimation of the number of selection targets. We demonstrate that the separate analysis of earlier time points can significantly increase the separation of selection targets into individual haplotype blocks. We conclude that the analysis of selected haplotype blocks has a large potential for the characterisation of the adaptive architecture with E&R experiments.

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DNA motifs are not general predictors of recombination in twoDrosophilasister species

Cited by 10 publications

References 83 publications

Distance to trait optimum is a crucial factor determining the genomic signature of polygenic adaptation

Distance to trait optimum is a crucial factor determining the genomic signature of polygenic adaptation

Natural selection shapes variation in genome-wide recombination rate inDrosophila pseudoobscura

A generalised approach to detect selected haplotype blocks in Evolve and Resequence experiments

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