diploS/HIC: an updated approach to classifying selective sweeps

Kern, Andrew D.; Schrider, Daniel R.

doi:10.1101/267229

Cited by 33 publications

(89 citation statements)

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“…As presented in the Introduction, bottlenecks can mimic signals of selection as they also lead to the fixation of long haplotypes. To alleviate this issue, we applied diploS/HIC, a recently developed machine‐learning approach to detect selective sweeps and classify them as hard or soft (Schrider and Kern, ; Kern and Schrider, ) while integrating the demographic history of the eastern and western population previously inferred by Stritt et al . ().…”

Section: Resultsmentioning

confidence: 99%

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Genome‐wide scans of selection highlight the impact of biotic and abiotic constraints in natural populations of the model grass Brachypodium distachyon

et al. 2018

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Grasses are essential plants for ecosystem functioning. Quantifying the selective pressures that act on natural variation in grass species is therefore essential regarding biodiversity maintenance. In this study, we investigate the selection pressures that act on two distinct populations of the grass model Brachypodium distachyon without prior knowledge about the traits under selection. We took advantage of whole-genome sequencing data produced for 44 natural accessions of B. distachyon and used complementary genome-wide selection scans (GWSS) methods to detect genomic regions under balancing and positive selection. We show that selection is shaping genetic diversity at multiple temporal and spatial scales in this species, and affects different genomic regions across the two populations. Gene ontology annotation of candidate genes reveals that pathogens may constitute important factors of positive and balancing selection in B. distachyon. We eventually cross-validated our results with quantitative trait locus data available for leaf-rust resistance in this species and demonstrate that, when paired with classical trait mapping, GWSS can help pinpointing candidate genes for further molecular validation. Thanks to a near base-perfect reference genome and the large collection of freely available natural accessions collected across its natural range, B. distachyon appears as a prime system for studies in ecology, population genomics and evolutionary biology.

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Section: Resultsmentioning

confidence: 99%

“…(). One of the major advantages of diploS/HIC is that the algorithm is robust to demographic model misspecification (Kern and Schrider, ). By providing a confusion matrix, it also estimates false positive and false negative rates and allows a better assessment of the confidence levels expected under different scenarios of selection.…”

Section: Resultsmentioning

confidence: 99%

Genome‐wide scans of selection highlight the impact of biotic and abiotic constraints in natural populations of the model grass Brachypodium distachyon

et al. 2018

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Section: Methodsmentioning

confidence: 99%

“…We used diploS/HIC (Kern & Schrider, ), a recently developed machine‐learning algorithm, to classify genomic windows as selected or not in northern clades. Simulated data sets are split into subwindows that are described by a set of 12 summary statistics (Kern & Schrider, ) recapitulating the allele frequency spectrum or linkage disequilibrium. In the case of selection, simulated windows where the selected site lies in the central subwindows are considered hard or soft sweep examples, while other windows are considered linked‐hard or linked‐soft examples.…”

Section: Methodsmentioning

confidence: 99%

“…From an analytical perspective, reduced representation rarely allows to investigate divergence at the haplotype level, which limits most studies to the comparison of allele frequencies between populations. On the other hand, whole‐genome resequencing (WGS) provides dense genotyping information that can be used to investigate the variation in diversity and linkage along chromosomes, facilitating the identification of selective sweeps (Haasl & Payseur, ; Kern & Schrider, ; Sabeti et al, ).…”

Section: Introductionmentioning

confidence: 99%

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Selection at behavioural, developmental and metabolic genes is associated with the northward expansion of a successful tropical colonizer

Bourgeois

Boissinot

2019

Molecular Ecology

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What makes a species able to colonize novel environments? This question is key to understand the dynamics of adaptive radiations and ecological niche shifts, but the mechanisms that underlie expansion into novel habitats remain poorly understood at a genomic scale. Lizards from the genus Anolis are typically tropical, and the green anole (Anolis carolinensis) constitutes an exception since it expanded into temperate North America from subtropical Florida. Thus, we used the green anole as a model to investigate signatures of selection associated with colonization of a new environment, namely temperate North America. To this end, we analysed 29 whole‐genome sequences, covering the entire native range of the species. We used a combination of recent methods to quantify both positive and balancing selection in northern populations, including FST outlier methods, machine learning and ancestral recombination graphs. We naively scanned for genes of interest and assessed the overlap between multiple tests. Strikingly, we identified many genes involved in behaviour, suggesting that the recent successful colonization of northern environments may have been linked to behavioural shifts as well as physiological adaptation. Using a candidate genes strategy, we determined that genes involved in response to cold or behaviour displayed more frequently signals of selection, while controlling for local recombination rate, gene clustering and gene length. In addition, we found signatures of balancing selection at immune genes in all investigated genetic groups, but also at genes involved in neuronal and anatomical development.

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Detecting Positive Selection in Populations Using Genetic Data

Koropoulis

Alachiotis

Pavlidis

2020

Methods in Molecular Biology

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High-throughput genomic sequencing allows to disentangle the evolutionary forces acting in populations. Among evolutionary forces, positive selection has received a lot of attention because it is related to the adaptation of populations in their environments, both biotic and abiotic. Positive selection, also known as Darwinian selection, occurs when an allele is favored by natural selection. The frequency of the favored allele increases in the population and, due to genetic hitchhiking, neighboring linked variation diminishes, creating so-called selective sweeps. Such a process leaves traces in genomes that can be detected in a future time point. Detecting traces of positive selection in genomes is achieved by searching for signatures introduced by selective sweeps, such as regions of reduced variation, a specific shift of the site frequency spectrum, and particular linkage disequilibrium (LD) patterns in the region. A variety of approaches can be used for detecting selective sweeps, ranging from simple implementations that compute summary statistics to more advanced statistical approaches, e.g., Bayesian approaches, maximum-likelihood-based methods, and machine learning methods. In this chapter, we discuss selective sweep detection methodologies on the basis of their capacity to analyze whole genomes or just subgenomic regions, and on the specific polymorphism patterns they exploit as selective sweep signatures. We also summarize the results of comparisons among five open-source software releases (SweeD, SweepFinder, SweepFinder2, OmegaPlus, and RAiSD) regarding sensitivity, specificity, and execution times. Furthermore, we test and discuss machine learning methods and present a thorough performance analysis. In equilibrium neutral models or mild bottlenecks, most methods are able to detect selective sweeps accurately. Methods and tools that rely on linkage disequilibrium (LD) rather than single SNPs exhibit higher true positive rates than the site frequency spectrum (SFS)based methods under the model of a single sweep or recurrent hitchhiking. However, their false positive rate is elevated when a misspecified demographic model is used to build the distribution of the statistic under the null hypothesis. Both LD and SFS-based approaches suffer from decreased accuracy on localizing the true target of selection in bottleneck scenarios. Furthermore, we present an extensive analysis of the effects of gene flow on selective sweep detection, a problem that has been understudied in selective sweep literature.

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diploS/HIC: an updated approach to classifying selective sweeps

Cited by 33 publications

References 36 publications

Genome‐wide scans of selection highlight the impact of biotic and abiotic constraints in natural populations of the model grass Brachypodium distachyon

Genome‐wide scans of selection highlight the impact of biotic and abiotic constraints in natural populations of the model grass Brachypodium distachyon

Selection at behavioural, developmental and metabolic genes is associated with the northward expansion of a successful tropical colonizer

Detecting Positive Selection in Populations Using Genetic Data

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