scholarly journals Weak epistasis may drive adaptation in recombining bacteria

2017 ◽  
Author(s):  
Brian J Arnold ◽  
Michael Gutmann ◽  
Yonatan Grad ◽  
Sam K Sheppard ◽  
Jukka Corander ◽  
...  
Keyword(s):  
Homologous Recombination ◽  
Population Genetic ◽  
Cumulative Effects ◽  
Strong Interactions ◽  
Parameter Estimates ◽  
Metapopulation Dynamics ◽  
Genetic Theory ◽  
Multilocus Model ◽  
Heritable Effects ◽  
The Impact

The impact of epistasis on the evolution of multilocus traits depends on recombination. Population genetic theory has been largely developed for eukaryotes, many of which recombine so frequently that epistasis between polymorphisms has not been considered to play a large role in adaptation and has been compared to the fleeting influence of non-heritable effects. Many bacteria also recombine, some to the degree that their populations are described as 'panmictic' or 'freely recombining'. However, whether this recombination is sufficient to limit the ability of selection to act on epistatic contributions to fitness is unknown. We create a sensitive method to quantify homologous recombination in five bacterial pathogens and use these parameter estimates in a multilocus model of bacterial evolution with additive and epistatic effects. We find that even for highly recombining species (e.g. Streptococcus pneumoniae or Helicobacter pylori), selection may act on the cumulative effects of weak (as well as strong) interactions between distant mutations since homologous recombination typically transfers only short segments. Furthermore, whether selection acts more efficiently on physically proximal loci depends on the average recombination tract length. Epistasis may thus play an important role in the adaptive evolution of bacteria and, unlike in eukaryotes, does not need to be strong, involve near loci, or require specific metapopulation dynamics.

scholarly journals KoT: an automatic implementation of the K/θ method for species delimitation

2021 ◽  
Author(s):  
Yann Spöri ◽  
Fabio Stoch ◽  
Simon Dellicour ◽  
C. William Birky ◽  
Jean-François Flot
Keyword(s):  
Genetic Diversity ◽  
Species Delimitation ◽  
Population Genetic ◽  
Average Distance ◽  
Species Level ◽  
Species Boundaries ◽  
Fasta File ◽  
Genetic Theory ◽  
Putative Species ◽  
The Impact

K/θ is a method to delineate species that rests on the calculation of the ratio between the average distance K separating two putative species-level clades and the genetic diversity θ of these clades. Although this method is explicitly rooted in population genetic theory, it was never benchmarked due to the absence of a program allowing automated analyses. For the same reason, its application by hand was limited to small datasets of a few tens of sequences. We present an automatic implementation of the K/θ method, dubbed KoT (short for "K over Theta"), that takes as input a FASTA file, builds a neighbour-joining tree, and returns putative species boundaries based on a user-specified K/θ threshold. This automatic implementation avoids errors and makes it possible to apply the method to datasets comprising many sequences, as well as to test easily the impact of choosing different K/θ threshold ratios. KoT is implemented in Haxe, with a javascript webserver interface freely available at https://eeg-ebe.github.io/KoT/ .

The Impact of Missing and Error-Prone Auxiliary Information on Sparse-Matrix Sub-Population Parameter Estimates

Methodology ◽  
2015 ◽  
Vol 11 (3) ◽  
pp. 89-99 ◽  
Author(s):  
Leslie Rutkowski ◽  
Yan Zhou
Keyword(s):  
Sparse Matrix ◽  
Small Body ◽  
Auxiliary Information ◽  
Poor Quality ◽  
Quality Data ◽  
Estimation Methods ◽  
Parameter Estimates ◽  
Population Parameter ◽  
Conditioning Model ◽  
The Impact

Abstract. Given a consistent interest in comparing achievement across sub-populations in international assessments such as TIMSS, PIRLS, and PISA, it is critical that sub-population achievement is estimated reliably and with sufficient precision. As such, we systematically examine the limitations to current estimation methods used by these programs. Using a simulation study along with empirical results from the 2007 cycle of TIMSS, we show that a combination of missing and misclassified data in the conditioning model induces biases in sub-population achievement estimates, the magnitude and degree to which can be readily explained by data quality. Importantly, estimated biases in sub-population achievement are limited to the conditioning variable with poor-quality data while other sub-population achievement estimates are unaffected. Findings are generally in line with theory on missing and error-prone covariates. The current research adds to a small body of literature that has noted some of the limitations to sub-population estimation.

Systematic benchmark of ancient DNA read mapping

2021 ◽  
Author(s):  
Adrien Oliva ◽  
Raymond Tobler ◽  
Alan Cooper ◽  
Bastien Llamas ◽  
Yassine Souilmi
Keyword(s):  
Ancient Dna ◽  
Dna Sequences ◽  
Population Genetic ◽  
Reference Genome ◽  
Population Data ◽  
Human Populations ◽  
Current Standard ◽  
Read Mapping ◽  
Reference Bias ◽  
The Impact

Abstract The current standard practice for assembling individual genomes involves mapping millions of short DNA sequences (also known as DNA ‘reads’) against a pre-constructed reference genome. Mapping vast amounts of short reads in a timely manner is a computationally challenging task that inevitably produces artefacts, including biases against alleles not found in the reference genome. This reference bias and other mapping artefacts are expected to be exacerbated in ancient DNA (aDNA) studies, which rely on the analysis of low quantities of damaged and very short DNA fragments (~30–80 bp). Nevertheless, the current gold-standard mapping strategies for aDNA studies have effectively remained unchanged for nearly a decade, during which time new software has emerged. In this study, we used simulated aDNA reads from three different human populations to benchmark the performance of 30 distinct mapping strategies implemented across four different read mapping software—BWA-aln, BWA-mem, NovoAlign and Bowtie2—and quantified the impact of reference bias in downstream population genetic analyses. We show that specific NovoAlign, BWA-aln and BWA-mem parameterizations achieve high mapping precision with low levels of reference bias, particularly after filtering out reads with low mapping qualities. However, unbiased NovoAlign results required the use of an IUPAC reference genome. While relevant only to aDNA projects where reference population data are available, the benefit of using an IUPAC reference demonstrates the value of incorporating population genetic information into the aDNA mapping process, echoing recent results based on graph genome representations.

Examining Nonnormal Latent Variable Distributions for Non-Ignorable Missing Data

2021 ◽  
Vol 45 (3) ◽  
pp. 159-177
Author(s):  
Chen-Wei Liu
Keyword(s):  
Missing Data ◽  
Latent Variable ◽  
Bivariate Normal Distribution ◽  
Parameter Estimates ◽  
Missing Responses ◽  
Variable Distribution ◽  
Item Responses ◽  
Nonnormal Distribution ◽  
The Impact ◽  
Biased Estimates

Missing not at random (MNAR) modeling for non-ignorable missing responses usually assumes that the latent variable distribution is a bivariate normal distribution. Such an assumption is rarely verified and often employed as a standard in practice. Recent studies for “complete” item responses (i.e., no missing data) have shown that ignoring the nonnormal distribution of a unidimensional latent variable, especially skewed or bimodal, can yield biased estimates and misleading conclusion. However, dealing with the bivariate nonnormal latent variable distribution with present MNAR data has not been looked into. This article proposes to extend unidimensional empirical histogram and Davidian curve methods to simultaneously deal with nonnormal latent variable distribution and MNAR data. A simulation study is carried out to demonstrate the consequence of ignoring bivariate nonnormal distribution on parameter estimates, followed by an empirical analysis of “don’t know” item responses. The results presented in this article show that examining the assumption of bivariate nonnormal latent variable distribution should be considered as a routine for MNAR data to minimize the impact of nonnormality on parameter estimates.

Examining the Impact of and Sensitivity of Fit Indices to Omitting Covariates Interaction Effect in Multilevel Multiple-Indicator Multiple-Cause Models

2021 ◽  
pp. 001316442199240
Author(s):  
Chunhua Cao ◽  
Eun Sook Kim ◽  
Yi-Hsin Chen ◽  
John Ferron
Keyword(s):  
Root Mean Square ◽  
Interaction Effect ◽  
Intraclass Correlation ◽  
Information Criteria ◽  
Parameter Estimates ◽  
Mean Square ◽  
Fit Indices ◽  
Cluster Number ◽  
Multiple Indicator ◽  
The Impact

This study examined the impact of omitting covariates interaction effect on parameter estimates in multilevel multiple-indicator multiple-cause models as well as the sensitivity of fit indices to model misspecification when the between-level, within-level, or cross-level interaction effect was left out in the models. The parameter estimates produced in the correct and the misspecified models were compared under varying conditions of cluster number, cluster size, intraclass correlation, and the magnitude of the interaction effect in the population model. Results showed that the two main effects were overestimated by approximately half of the size of the interaction effect, and the between-level factor mean was underestimated. None of comparative fit index, Tucker–Lewis index, root mean square error of approximation, and standardized root mean square residual was sensitive to the omission of the interaction effect. The sensitivity of information criteria varied depending majorly on the magnitude of the omitted interaction, as well as the location of the interaction (i.e., at the between level, within level, or cross level). Implications and recommendations based on the findings were discussed.

Population genetic structure and dispersal of Pinus occidentalis in the Dominican Republic by chloroplastic SSR with implications for its conservation, management, and reforestation

2021 ◽  
Author(s):  
Luis E. Rodríguez de Francisco ◽  
Rosanna Carreras-De León ◽  
Rafael M. Navarro Cerrillo ◽  
Liz A. Paulino-Gervacio ◽  
María-Dolores Rey ◽  
...  
Keyword(s):  
Genetic Structure ◽  
Dominican Republic ◽  
Population Genetic Structure ◽  
Population Genetic ◽  
Conservation Management ◽  
Natural Populations ◽  
Limited Information ◽  
High Genetic Diversity ◽  
The Impact ◽  
Pinus Occidentalis

<i>Pinus occidentalis</i> is the dominant species of forest ecosystems in the Dominican Republic, located between 200 and 3000 meters above sea level, with extensive and overexploited natural populations. However, over the years, various restoration plans have been performed, which could affect the genetic structure of <i>P. occidentalis</i>. Despite being the species with the highest occurrence in the Dominican forests, there is no existing information on genetic structure and molecular characterization among natural populations with limited information on both phenological and productive characterization. In this study, the genetic structure, diversity, and gene flow of the five <i>P. occidentalis</i> natural populations of the Dominican Republic were determined using microsatellite markers. A total of 145 individuals were genotyped with eight polymorphic chloroplastic microsatellites, producing an average of 41 haplotypes with high genetic diversity across populations (H<sub>E</sub> = 0.90). Significant population genetic structure was found between populations (F<sub>ST</sub> = 0.123). These results reflect the impact of reforestation programs on natural populations and diluting the natural genetic signature. Analysis of population genetic data is, therefore, crucial for the breeding and conservation programs of <i>P. occidentalis</i> in the country.

Applicability of an extant batch respirometric assay in describing dynamics of ammonia and nitrite oxidation in a nitrifying bioreactor

2005 ◽  
Vol 52 (10-11) ◽  
pp. 503-508 ◽  
Author(s):  
K. Chandran ◽  
Z. Hu ◽  
B.F. Smets
Keyword(s):  
Kinetic Parameter ◽  
Sensitivity Analyses ◽  
Continuous Reactor ◽  
Parameter Estimates ◽  
Additional Parameter ◽  
Reactor Performance ◽  
Estimation Techniques ◽  
The Difference ◽  
The Impact ◽  
Best Fit

Several techniques have been proposed for biokinetic estimation of nitrification. Recently, an extant respirometric assay has been presented that yields kinetic parameters for both nitrification steps with minimal physiological change to the microorganisms during the assay. Herein, the ability of biokinetic parameter estimates from the extant respirometric assay to adequately describe concurrently obtained NH4+-N and NO2−-N substrate depletion profiles is evaluated. Based on our results, in general, the substrate depletion profiles resulted in a higher estimate of the maximum specific growth rate coefficient, μmax for both NH4+-N to NO2−-N oxidation and NO2−-N to NO3−-N oxidation compared to estimates from the extant respirograms. The trends in the kinetic parameter estimates from the different biokinetic estimation techniques are paralleled in the nature of substrate depletion profiles obtained from best-fit parameters. Based on a visual inspection, in general, best-fit parameters from optimally designed complete respirograms provided a better description of the substrate depletion profiles than estimates from isolated respirograms. Nevertheless, the sum of the squared errors for the best-fit respirometry based parameters was outside the 95% joint confidence interval computed for the best-fit substrate depletion based parameters. Notwithstanding the difference in kinetic parameter estimates determined in this study, the different biokinetic estimation techniques still are close to estimates reported in literature. Additional parameter identifiability and sensitivity analysis of parameters from substrate depletion assays revealed high precision of parameters and high parameter correlation. Although biokinetic estimation via automated extant respirometry is far more facile than via manual substrate depletion measurements, additional sensitivity analyses are needed to test the impact of differences in the resulting parameter values on continuous reactor performance.

scholarly journals Intraspecific divergences and phylogeography of Panzerina lanata (Lamiaceae) in Northwest China

2018 ◽  
Author(s):  
Yanfen Zhao ◽  
Hongxiang Zhang ◽  
Borong Pan ◽  
Mingli Zhang
Keyword(s):  
Genetic Diversity ◽  
Genetic Structure ◽  
Population Genetic ◽  
Divergence Time ◽  
Northwest China ◽  
Northwestern China ◽  
Intraspecific Diversity ◽  
Early Pleistocene ◽  
Helan Mountains ◽  
The Impact

Climactic fluctuations during the Quaternary played a crucial role in genetic diversity and population genetic structure of many plant species in northwestern China. In order to understand the impact of climate change on herbaceous plants, we studied Panzerina lanata (Lamiaceae), a widely distributed species. Two chloroplast DNA intergenic spacers (trnH-psbA and rpoB-trnC) were used to sequence 269 individuals from 27 populations and seven haplotypes were identified. Genetic structure and demographic characteristics were estimated using AMOVA, neutrality tests, and mismatch distribution analyses. The divergence times between the seven haplotypes were estimated using Beast. Our results revealed high levels of total genetic diversity (HT = 0.673±0.0869) and low levels of average within-population genetic diversity (HS = 0.033±0.0214). The analysis of molecular variance indicated major genetic differentiation among the three groups: northern, central, and eastern group. The species distribution modeling and demographic analysis indicated that P. lanata has not experience a recent range expansion. The divergence time within P. lanata occurred between the early Pleistocene and the late Pleistocene, which coincides with aridification and the expansion of the deserts in northwestern China that resulted in species diversification and habitat fragmentation. In addition, we speculate that the deserts and the Helan Mountains acted as effective geographic barriers that led to intraspecific diversity.

scholarly journals Reassessing the evidence for universal school-age BCG vaccination in England and Wales: re-evaluating and updating a modelling study

BMJ Open ◽  
2022 ◽  
Vol 12 (1) ◽  
pp. e031573
Author(s):  
Sam Abbott ◽  
Hannah Christensen ◽  
Ellen Brooks-Pollock
Keyword(s):  
Policy Change ◽  
Parameter Uncertainty ◽  
Incidence Rates ◽  
Parameter Estimates ◽  
School Age ◽  
England And Wales ◽  
Bcg Vaccination ◽  
Methodological Flaw ◽  
Notification Data ◽  
The Impact

ObjectivesIn 2005, England and Wales switched from universal BCG vaccination against tuberculosis (TB) disease for school-age children to targeted vaccination of neonates. We aimed to recreate and re-evaluate a previously published model, the results of which informed this policy change.DesignWe recreated an approach for estimating the impact of ending the BCG schools scheme, correcting a methodological flaw in the model, updating the model with parameter uncertainty and improving parameter estimates where possible. We investigated scenarios for the assumed annual decrease in TB incidence rates considered by the UK’s Joint Committee on Vaccination and Immunisation and explored alternative scenarios using notification data.SettingEngland and Wales.Outcome measuresThe number of vaccines needed to prevent a single notification and the average annual additional notifications caused by ending the policy change.ResultsThe previously published model was found to contain a methodological flaw and to be spuriously precise. It greatly underestimated the impact of ending school-age vaccination compared with our updated, corrected model. The updated model produced predictions with wide CIs when parameter uncertainty was included. Model estimates based on an assumption of an annual decrease in TB incidence rates of 1.9% were closest to those estimated using notification data. Using this assumption, we estimate that 1600 (2.5; 97.5% quantiles: 1300, 2000) vaccines would have been required to prevent a single notification in 2004.ConclusionsThe impact of ending the BCG schools scheme was found to be greater than previously thought when notification data were used. Our results highlight the importance of independent evaluations of modelling evidence, including uncertainty, and evaluating multiple scenarios when forecasting the impact of changes in vaccination policy.

Estimating a model of sportfishing trip expenditures using a quasi-maximum likelihood approach

2016 ◽  
Vol 23 (2) ◽  
pp. 448-459 ◽  
Author(s):  
Richard T. Melstrom
Keyword(s):  
Maximum Likelihood ◽  
Exponential Model ◽  
Parameter Estimates ◽  
Maximum Likelihood Approach ◽  
Likelihood Approach ◽  
Quasi Maximum Likelihood ◽  
Species Preferences ◽  
The Impact ◽  
Consistent Parameter

This article presents an exponential model of tourist expenditures estimated by a quasi-maximum likelihood (QML) technique. The advantage of this approach is that, unlike conventional OLS and Tobit estimators, it produces consistent parameter estimates under conditions of a corner solution at zero and heteroscedasticity. An application to sportfishing evaluates the role of socioeconomic demographics and species preferences on trip spending. The bias from an inappropriate estimator is illustrated by comparing the results from QML and OLS estimation, which shows that OLS significantly overstates the impact of trip duration on trip expenditures compared with the QML estimator. Both sets of estimates imply that trout and bass anglers spend significantly more on their fishing trips compared with other anglers.

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