Genetic diversity and population structure of <i><scp>S</scp>orghum mosaic virus</i> infecting <i><scp>S</scp>accharum</i> spp. hybrids

Luo, Qian; Ahmad, Kashif; Fu, Hua‐Ying; Wang, J.-D.; Chen, R.-K.; Gao, San‐Ji

doi:10.1111/aab.12310

Cited by 25 publications

(25 citation statements)

References 39 publications

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“…Since then, Tajima’s D has been deployed in numerous studies as a neutrality test to detect selection [43–49]. This summary statistic captures the difference between two estimates of the diversity level , where is the mutation rate.…”

Section: Methods and Toolsmentioning

confidence: 99%

A survey of methods and tools to detect recent and strong positive selection

Pavlidis¹,

Alachiotis²

2017

J of Biol Res-Thessaloniki

109

104

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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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Section: Methods and Toolsmentioning

confidence: 99%

A survey of methods and tools to detect recent and strong positive selection

Pavlidis¹,

Alachiotis²

2017

J of Biol Res-Thessaloniki

109

104

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“…Relying on Tajima's D, Braverman et al [22] were able to detect genomic regions affected by recent and strong positive selection in simulated datasets, as well as to demonstrate that in regions of low genetic diversity and low recombination rate (e.g., around centromeres or at telomeres) a simple hitchhiking model is not a sufficient explanation for the observed DNA polymorphisms. Since then, Tajima's D has been deployed in numerous studies as a neutrality test to detect selection [12,19,20,58,67,79,94]. This summary statistic captures the difference between two estimates of the diversity level θ ¼ 4N e μ, where μ is the mutation rate.…”

Section: Summary Statisticsmentioning

confidence: 99%

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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“…According to reports, SCMV, SrMV, and SCSMV were the pathogens that caused SMD in Southern China; among them, SrMV was the dominant pathogen and the other viruses were secondary (Xu et al, 2014;Zhang et al, 2015;Jing et al, 2015). In Southern China, there were different degrees of two or more type of viruses that were co-infecting sugarcane in the fields, including the coinfection of SrMV and SCMV (Xu et al, 2014;Jing et al, 2015); the co-infection of SrMV and SCYLV (Xiong et al, 2011;Xu et al, 2014); the co-infection of SCMV and SCYLV (Xie et al, 2009;Xu et al, 2014); the co-infection of SrMV and SCSMV (Zhang et al, 2015;Luo et al, 2016); and the co-infection of SrMV, SCMV, and SCYLV (Xu et al, 2014). There were even three kinds of RNA viruses (SrMV, SCMV, and SCYLV) and one DNA virus (SCBV) complex infection; however, the incidence of coinfection is generally low in the field (Tang et al, 2015).…”

Section: Introductionmentioning

confidence: 99%

Molecular Detection of Viral Diseases in Chewing Cane (Saccharum officinarum) from Southern China

Wang¹

2018

IJAB

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One hundred and six samples of chewing cane (Saccharum officinarum L.) with suspected symptoms of mosaic disease were collected from Southern China in 2016. RT-PCR or PCR was used to determine the types of viral diseases and pathogenic viruses as well as the occurrence of the infections to provide a scientific basis for the use of viral detection to ensure virus-free seedling production of chewing cane. The results showed that sugarcane mosaic disease and sugarcane yellow leaf disease usually occurred in chewing cane planted in Southern China, and three types of viruses were detected in all the 106 samples of chewing cane, namely SrMV (97.2%), SCMV (98.1%), and SCYLV (95.3%). Among them, the co-infection rates of SrMV and SCMV; SrMV and SCYLV; SCMV and SCYLV; and SrMV, SCMV, and SCYLV were 95.3, 92.5, 93.4 and 90.6%, respectively. No positive samples of SCSMV, JGMV, MDMV, or SCBV were detected in any of the samples. The dominant pathogenic virus of mosaic disease in the chewing cane of Southern China was SCMV or the co-infection of SCMV and SrMV. This study indicates that it is necessary to carry out detoxification and detection of SCMV, SrMV, and SCYLV in chewing cane seedlings in Southern China. These findings will be helpful for the development of virus-free chewing cane seedlings in the future.

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Genetic diversity and population structure of Sorghum mosaic virus infecting Saccharum spp. hybrids

Cited by 25 publications

References 39 publications

A survey of methods and tools to detect recent and strong positive selection

A survey of methods and tools to detect recent and strong positive selection

Detecting Positive Selection in Populations Using Genetic Data

Molecular Detection of Viral Diseases in Chewing Cane (Saccharum officinarum) from Southern China

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