ReviewJournal of biological research (Thessalonike, Greece)2017
A survey of methods and tools to detect recent and strong positive selection.
Review in Journal of biological research (Thessalonike, Greece), 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 64 papers.
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Who cites it
64 citing papers in PubMed, 132 citations in OpenAlex.
- Enhancing detection of polygenic adaptation: a comparative study of machine learning and statistical approaches using simulated evolve-and-resequence data.BMC genomics · 2026Article
- Phenotypic Variation in Water-Use Efficiency, Heat Tolerance, and Carbon Isotope Discrimination Across Canadian Spring Wheat Cultivars Under Climate Stress.Plants (Basel, Switzerland) · 2026Article
- PolyAdapt: Characterizing Polygenic Adaptive Architectures in the Presence of Strong Linkage Disequilibrium.Genome biology and evolution · 2026Article
- Selection scans and downstream analysis with selscan.Human population genetics and genomics · 2026Article
- Comparative Population Genomics of Relictual Caribbean Island Gossypium hirsutum.Molecular ecology · 2026Article
- Genome-Wide, High-Density Genotyping Approaches for Plant Germplasm Characterisation (Methods and Applications).International journal of molecular sciences · 2025Review
- Selection scans and downstream analysis with selscan.bioRxiv : the preprint server for biology · 2025Article
- Longitudinal sequencing reveals polygenic and epistatic nature of genomic response to selection.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
- Causes of heterozygosity excess: The case of Mexican populations ofPlant diversity · 2025Article
- Developing a crop- wild-reservoir pathogen system to understand pathogen evolution and emergence.eLife · 2025Article
- Fast and accurate deep learning scans for signatures of natural selection in genomes using FASTER-NN.Communications biology · 2025Article
- Repeated global adaptation across plant species.Proceedings of the National Academy of Sciences of the United States of America · 2024Article
- Genome-Wide Analysis of Genetic Diversity and Selection Signatures in Zaobei Beef Cattle.Animals : an open access journal from MDPI · 2024Article
- Temporal challenges in detecting balancing selection from population genomic data.G3 (Bethesda, Md.) · 2024Article
- Genome-wide analysis reveals genomic diversity and signatures of selection in Qinchuan beef cattle.BMC genomics · 2024Article
- A Novel Approach Utilizing Domain Adversarial Neural Networks for the Detection and Classification of Selective Sweeps.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2024Article
- Landscape genomics reveals regions associated with adaptive phenotypic and genetic variation in Ethiopian indigenous chickens.BMC genomics · 2024Article
- Evaluating power to detect recurrent selective sweeps under increasingly realistic evolutionary null models.Evolution; international journal of organic evolution · 2023Article
- Power of neutrality tests for detecting natural selection.G3 (Bethesda, Md.) · 2023Article
- De novo assembly and annotation of the singing mouse genome.BMC genomics · 2023Article
4 more citing papers are in PubMed but not listed here.
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Authors and funding
2 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Positive 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 the neighboring linked variation diminishes, creating so-called selective sweeps. 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 LD patterns in the region. A variety of methods and tools can be used for detecting sweeps, ranging from simple implementations that compute summary statistics such as Tajima's D, to more advanced statistical approaches that use combinations of statistics, maximum likelihood, machine learning etc. In this survey, we present and discuss summary statistics and software tools, and classify them based on the selective sweep signature they detect, i.e., SFS-based vs. LD-based, as well as their capacity to analyze whole genomes or just subgenomic regions. Additionally, we summarize the results of comparisons among four open-source software releases (SweeD, SweepFinder, SweepFinder2, and OmegaPlus) regarding sensitivity, specificity, and execution times. In equilibrium neutral models or mild bottlenecks, both SFS- and LD-based methods are able to detect selective sweeps accurately. Methods and tools that rely on LD exhibit higher true positive rates than SFS-based ones 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 represent the null hypothesis. When the correct (or similar to the correct) demographic model is used instead, the false positive rates are considerably reduced. The accuracy of detecting the true target of selection is decreased in bottleneck scenarios. In terms of execution time, LD-based methods are typically faster than SFS-based methods, due to the nature of required arithmetic.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.