diSTruct v1.0: generating biomolecular structures from distance constraints

Taubert, Oskar; Reinartz, Ines; Meyerhenke, Henning; Schug, Alexander

doi:10.1093/bioinformatics/btz578

Cited by 4 publications

(4 citation statements)

References 11 publications

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“…CLUMPP 1.1 [ 23 ] was used to average ancestry coefficients of snail individuals across independent runs. Distruct 1.1 was used to visualize clusters inferred [ 24 ]. A principal coordinate analysis was performed with GeneAlex 6.5 to investigate the patterns of genetic relationships between snail individuals.…”

Section: Methodsmentioning

confidence: 99%

“…With DIYABC analysis the posterior probability for each of the assumed 33 scenarios was calculated and presented in Supplementary Table S3. From the first round of the selection process, the five best scenarios (i.e., Scenario 5,12,24,29 and 33) were obtained, from which Scenario 24 with the highest posterior probability was selected after the second round. See Figure 5 and Table 6.…”

Section: Population Divergence Historymentioning

confidence: 99%

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Population Genetics of Oncomelania hupensis Snails from New-Emerging Snail Habitats in a Currently Schistosoma japonicum Non-Endemic Area

et al. 2023

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Schistosomiasis is still one of the most significant neglected tropical diseases worldwide, and China is endemic for Schistosoma japonicum. With its great achievement in schistosomiasis control, the government of China has set the goal to eliminate the parasitic disease at the country level by 2030. However, one major challenge is the remaining huge areas of habitats for the intermediate host Oncomelania hupensis. This is further exacerbated by an increasing number of new emerging snail habitats reported each year. Therefore, population genetics on snails in such areas will be useful in evaluation of snail control effect and/or dispersal. We then sampled snails from new emerging habitats in Taicang of Jiangsu, China, a currently S. japonicum non-endemic area from 2014 to 2017, and performed population genetic analyses based on nine microsatellites. Results showed that all snail populations had low genetic diversity, and most genetic variations originated from within snail populations. The estimated effective population size for the 2015 population was infinitive. All snails could be separated into two clusters, and further DIYABC analysis revealed that both the 2016 and the 2017 populations may derive from the 2015, indicating that the 2017 population must have been missed in the field survey performed in 2016. These findings may have implications in development of more practical guidelines for snail monitoring and control.

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Section: Methodsmentioning

confidence: 99%

Section: Population Divergence Historymentioning

confidence: 99%

Population Genetics of Oncomelania hupensis Snails from New-Emerging Snail Habitats in a Currently Schistosoma japonicum Non-Endemic Area

et al. 2023

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“…The results were submitted to the online software Structure Harvester v0.6.94 (Earl & vonHoldt, 2012 ) to determine the optimal K value by a Delta K method. Membership coefficient matrices (Q‐matrices) associated with the optimal K were processed using CLUMPP v1.12 (Jakobsson & Rosenberg, 2007 ), and then visualized using DISTRUCT v1.1 (Taubert et al, 2019 ).…”

Section: Methodsmentioning

confidence: 99%

Population differentiation and intraspecific genetic admixture in twoEucryptorrhynchusweevils (Coleoptera: Curculionidae) across northern China

Zhang

Song

Cao

et al. 2022

Ecology and Evolution

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Increasing damage of pests in agriculture and forestry can arise both as a consequence of changes in local species and through the introduction of alien species. In this study, we used population genetics approaches to examine population processes of two pests of the tree‐of‐heaven trunk weevil (TTW), Eucryptorrhynchus brandti (Harold) and the tree‐of‐heaven root weevil (TRW), E . scrobiculatus (Motschulsky) on the tree‐of‐heaven across their native range of China. We analyzed the population genetics of the two weevils based on ten highly polymorphic microsatellite markers. Population genetic diversity analysis showed strong population differentiation among populations of each species, with F ST ranges from 0.0197 to 0.6650 and from −0.0724 to 0.6845, respectively. Populations from the same geographic areas can be divided into different genetic clusters, and the same genetic cluster contained populations from different geographic populations, pointing to dispersal of the weevils possibly being human‐mediated. Redundancy analysis showed that the independent effects of environment and geography could account for 93.94% and 29.70% of the explained genetic variance in TTW, and 41.90% and 55.73% of the explained genetic variance in TRW, respectively, indicating possible impacts of local climates on population genetic differentiation. Our study helps to uncover population genetic processes of these local pest species with relevance to control methods.

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“…3 The K-value with the highest K represents the number of potential genetic clusters in the population. Membership coefficient matrices (Q-matrices) associated with the optimal K were processed using CLUMPP v1.12 (Jakobsson and Rosenberg, 2007) and then visualized using the DISTRUCT v1.1 (Taubert et al, 2019). Finally, we used discriminant analysis of principal component (DAPC) to analyze population genetic structure under default settings to complement the STRUCTURE analysis.…”

Section: Phylogenetic and Population Genetic Structure Analysismentioning

confidence: 99%

Lack of Genetic Structure Among Populations of Striped Flea Beetle Phyllotreta striolata (Coleoptera: Chrysomelidae) Across Southern China

Yongli³

et al. 2022

Front. Ecol. Evol.

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The striped flea beetle (SFB) Phyllotreta striolata (Fabricius) (Coleoptera: Chrysomelidae) is a major pest of cruciferous vegetables in southern China. The population diversity and genetic structure of SFB are unknown. Here, we assembled a draft genome for the SFB and characterized the distribution of microsatellites. Then, we developed 12 novel microsatellite markers across the genome. We used a segment of the cox1 gene and newly developed microsatellite markers to genotype the genetic diversity of SFB across southern China. There were 44 mitochondrial haplotypes in the SFB populations, with haplotype 2 as the most widespread. The population genetic differentiation was very low, indicated by FST-values (<0.05 except for Guangxi population with other populations based on cox1), high gene flow (4.10 and 44.88 of cox1 and microsatellite, respectively) and Principal Coordinate Analysis across all populations. Mantel test showed genetic distance in SFB was significantly associated with geographic distance based on microsatellites (R2 = 0.2373, P = 0.014) while result based on cox1 (R2 = 0.0365, P = 0.155) showed no significant difference. The phylogenetic analysis did not find any geographically related clades among all haplotypes. Analyses based on microsatellites showed a lack of population genetic structure among all populations. Our study provides a foundation for the future understanding of the ecology and evolution of SFB and its management.

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diSTruct v1.0: generating biomolecular structures from distance constraints

Cited by 4 publications

References 11 publications

Population Genetics of Oncomelania hupensis Snails from New-Emerging Snail Habitats in a Currently Schistosoma japonicum Non-Endemic Area

Population Genetics of Oncomelania hupensis Snails from New-Emerging Snail Habitats in a Currently Schistosoma japonicum Non-Endemic Area

Population differentiation and intraspecific genetic admixture in twoEucryptorrhynchusweevils (Coleoptera: Curculionidae) across northern China

Lack of Genetic Structure Among Populations of Striped Flea Beetle Phyllotreta striolata (Coleoptera: Chrysomelidae) Across Southern China

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