Selection Threshold Severely Constrains Capture of Beneficial Mutations

2013 ◽  
Author(s):  
John C. Sanford ◽  
John R. Baumgardner ◽  
Wesley H. Brewer
Keyword(s):  
Beneficial Mutations ◽  
Selection Threshold

scholarly journals Selfish Genes, Pleiotropy and the Origin of Recombination

Genetics ◽  
1998 ◽  
Vol 149 (4) ◽  
pp. 2089-2097 ◽  
Author(s):  
Jody Hey
Keyword(s):  
Natural Selection ◽  
Simple Model ◽  
Computer Simulations ◽  
Recombination Rate ◽  
Evolutionary Origin ◽  
Deleterious Mutations ◽  
Beneficial Mutations ◽  
Selfish Genes ◽  
Simple Interaction

Abstract If multiple linked polymorphisms are under natural selection, then conflicts arise and the efficiency of natural selection is hindered relative to the case of no linkage. This simple interaction between linkage and natural selection creates an opportunity for mutations that raise the level of recombination to increase in frequency and have an enhanced chance of fixation. This important finding by S. Otto and N. Barton means that mutations that raise the recombination rate, but are otherwise neutral, will be selectively favored under fairly general circumstances of multilocus selection and linkage. The effect described by Otto and Barton, which was limited to neutral modifiers, can also be extended to include all modifiers of recombination, both beneficial and deleterious. Computer simulations show that beneficial mutations that also increase recombination have an increased chance of fixation. Similarly, deleterious mutations that also decrease recombination have an increased chance of fixation. The results suggest that a simple model of recombination modifiers, including both neutral and pleiotropic modifiers, is a necessary explanation for the evolutionary origin of recombination.

scholarly journals The Probability of Fixation in Populations of Changing Size

Genetics ◽  
1997 ◽  
Vol 146 (2) ◽  
pp. 723-733 ◽  
Author(s):  
Sarah P Otto ◽  
Michael C Whitlock
Keyword(s):  
Diffusion Equation ◽  
Adaptive Evolution ◽  
Process Model ◽  
Branching Process ◽  
Approximate Solutions ◽  
Logistic Growth ◽  
Beneficial Mutations ◽  
Demographic Models ◽  
Deleterious Alleles ◽  
Probability Of Fixation

The rate of adaptive evolution of a population ultimately depends on the rate of incorporation of beneficial mutations. Even beneficial mutations may, however, be lost from a population since mutant individuals may, by chance, fail to reproduce. In this paper, we calculate the probability of fixation of beneficial mutations that occur in populations of changing size. We examine a number of demographic models, including a population whose size changes once, a population experiencing exponential growth or decline, one that is experiencing logistic growth or decline, and a population that fluctuates in size. The results are based on a branching process model but are shown to be approximate solutions to the diffusion equation describing changes in the probability of fixation over time. Using the diffusion equation, the probability of fixation of deleterious alleles can also be determined for populations that are changing in size. The results developed in this paper can be used to estimate the fixation flux, defined as the rate at which beneficial alleles fix within a population. The fixation flux measures the rate of adaptive evolution of a population and, as we shall see, depends strongly on changes that occur in population size.

scholarly journals The Advantages of Segregation and the Evolution of Sex

Genetics ◽  
2003 ◽  
Vol 164 (3) ◽  
pp. 1099-1118 ◽  
Author(s):  
Sarah P Otto
Keyword(s):  
Sexual Reproduction ◽  
Asexual Reproduction ◽  
Deleterious Mutations ◽  
Evolution Of Sex ◽  
Beneficial Mutations ◽  
Selection Regime ◽  
Strong Selection ◽  
Genome Wide ◽  
Low Levels

AbstractIn diploids, sexual reproduction promotes both the segregation of alleles at the same locus and the recombination of alleles at different loci. This article is the first to investigate the possibility that sex might have evolved and been maintained to promote segregation, using a model that incorporates both a general selection regime and modifier alleles that alter an individual’s allocation to sexual vs. asexual reproduction. The fate of different modifier alleles was found to depend strongly on the strength of selection at fitness loci and on the presence of inbreeding among individuals undergoing sexual reproduction. When selection is weak and mating occurs randomly among sexually produced gametes, reductions in the occurrence of sex are favored, but the genome-wide strength of selection is extremely small. In contrast, when selection is weak and some inbreeding occurs among gametes, increased allocation to sexual reproduction is expected as long as deleterious mutations are partially recessive and/or beneficial mutations are partially dominant. Under strong selection, the conditions under which increased allocation to sex evolves are reversed. Because deleterious mutations are typically considered to be partially recessive and weakly selected and because most populations exhibit some degree of inbreeding, this model predicts that higher frequencies of sex would evolve and be maintained as a consequence of the effects of segregation. Even with low levels of inbreeding, selection is stronger on a modifier that promotes segregation than on a modifier that promotes recombination, suggesting that the benefits of segregation are more likely than the benefits of recombination to have driven the evolution of sexual reproduction in diploids.

Abstract 041: Cardiovascular Disease, Hypertension, Diabetes And Cystatin C Jointly Predict Covid-19 Mortality Alongside Age, Male Sex And Black Ethnicity

Circulation ◽  
2021 ◽  
Vol 143 (Suppl_1) ◽  
Author(s):  
Joshua Elliott ◽  
Barbara Bodinier ◽  
Matthew Whitaker ◽  
Ioanna Tzoulaki ◽  
Paul Elliott ◽  
...  
Keyword(s):  
Risk Factors ◽  
Cardiovascular Disease ◽  
Stability Analysis ◽  
Cystatin C ◽  
Penalized Regression ◽  
Pressure Control ◽  
Stability Selection ◽  
Selection Threshold ◽  
Male Sex ◽  
Sparse Set

Introduction: Studies of risk factors for severe/fatal COVID-19 to date may not have identified the optimal set of informative predictors. Hypothesis: Use of penalized regression with stability analysis may identify new, sparse sets of risk factors jointly associated with COVID-19 mortality. Methods: We investigated demographic, social, lifestyle, biological (lipids, cystatin C, vitamin D), medical (comorbidities, medications) and air pollution data from UK Biobank (N=473,574) in relation to linked COVID-19 mortality, and compared with non-COVID-19 mortality. We used penalized regression models (LASSO) with stability analysis (80% selection threshold from 1,000 models with 80% subsampling) to identify a sparse set of variables associated with COVID-19 mortality. Results: Among 43 variables considered by LASSO stability selection, cardiovascular disease, hypertension, diabetes, cystatin C, age, male sex and Black ethnicity were jointly predictive of COVID-19 mortality risk at 80% selection threshold (Figure). Of these, Black ethnicity and hypertension contributed to COVID-19 but not non-COVID-19 mortality. Conclusions: Use of LASSO stability selection identified a sparse set of predictors for COVID-19 mortality including cardiovascular disease, hypertension, diabetes and cystatin C, a marker of renal function that has also been implicated in atherogenesis and inflammation. These results indicate the importance of cardiometabolic comorbidities as predisposing factors for COVID-19 mortality. Hypertension was differentially highly selected for risk of COVID-19 mortality, suggesting the need for continued vigilance with good blood pressure control during the pandemic.

scholarly journals Inference of distribution of fitness effects and proportion of adaptive substitutions from polymorphism data

2016 ◽  
Author(s):  
Paula Tataru ◽  
Maéva Mollion ◽  
Sylvain Glemin ◽  
Thomas Bataillon
Keyword(s):  
Molecular Evolution ◽  
Evolutionary Genetics ◽  
Deleterious Mutations ◽  
Probabilistic Framework ◽  
Evolutionary Trajectory ◽  
Beneficial Mutations ◽  
Fitness Effects ◽  
Polymorphism Data ◽  
Inference Methods ◽  
Biased Estimates

ABSTRACTThe distribution of fitness effects (DFE) encompasses deleterious, neutral and beneficial mutations. It conditions the evolutionary trajectory of populations, as well as the rate of adaptive molecular evolution (α). Inference of DFE and α from patterns of polymorphism (SFS) and divergence data has been a longstanding goal of evolutionary genetics. A widespread assumption shared by numerous methods developed so far to infer DFE and α from such data is that beneficial mutations contribute only negligibly to the polymorphism data. Hence, a DFE comprising only deleterious mutations tends to be estimated from SFS data, and α is only predicted by contrasting the SFS with divergence data from an outgroup. Here, we develop a hierarchical probabilistic framework that extends on previous methods and also can infer DFE and α from polymorphism data alone. We use extensive simulations to examine the performance of our method. We show that both a full DFE, comprising both deleterious and beneficial mutations, and α can be inferred without resorting to divergence data. We demonstrate that inference of DFE from polymorphism data alone can in fact provide more reliable estimates, as it does not rely on strong assumptions about a shared DFE between the outgroup and ingroup species used to obtain the SFS and divergence data. We also show that not accounting for the contribution of beneficial mutations to polymorphism data leads to substantially biased estimates of the DFE and α. We illustrate these points using our newly developed framework, while also comparing to one of the most widely used inference methods available.

Rapid increase in viability due to new beneficial mutations in Drosophila melanogaster

Genetica ◽  
2009 ◽  
Vol 138 (2) ◽  
pp. 251-263 ◽  
Author(s):  
Priti Azad ◽  
Mingchai Zhang ◽  
R. C. Woodruff
Keyword(s):  
Drosophila Melanogaster ◽  
Beneficial Mutations

scholarly journals Determination of level and time thresholds for detectors with normalized indicator process

2020 ◽  
Author(s):  
С.Б. Егоров ◽  
Р.И. Горбачев
Keyword(s):  
False Alarm ◽  
Average Rate ◽  
Probability Model ◽  
False Alarm Probability ◽  
Statistical Characteristics ◽  
False Alarms ◽  
Time Interval ◽  
Number Of False Alarms ◽  
Time Threshold ◽  
Selection Threshold

«Выбросовая» вероятностная модель работы обнаружителя в режиме ожидания сигнала, предложенная авторами в [1], использована для оценки влияния селекции выбросов по длительности на вероятность ложной тревоги. Флюктуационные выбросы помехового индикаторного процесса, превысившие пороги селекции по уровню и длительности, трактуются как редкие события на интервале ожидания сигнала, подчиняющиеся вероятностному закону Пуассона. При условии, что средний период следования ложных выбросов превышает интервал корреляции индикаторного процесса, получено соотношение между средним числом выбросов любой длительности и средним числом выбросов, превысивших пороговую длительность. На основании известных числовых и вероятностных характеристик выбросов нормального стационарного случайного процесса получен уравнения, связывающие относительные пороги селекции по уровню и длительности с вероятностью ложной тревоги на интервале ожидания сигнала. Предложена методика определения порога селекции по длительности для снижения порога селекции по уровню до заданной величины. «Emissional» probability model of the detector in stand-by mode proposed by the authors in [1], is intended for estimation of false alarm rate dependence from the value of time-selection threshold. Fluctuation emissions of the noise indicator process are interpreted as rare events correspond to Poisson distribution. Assuming that average rate of false alarms exceeds the correlation interval of indicator process, obtained equation between average number of false alarms of any duration and average number of false alarms exceed the time threshold. Based on known numerical and statistical characteristics of emissions of normal stationary random process obtained equations, relating time and level thresholds with false alarm probability on stand-by mode time interval. Also suggested a method of determining time threshold intended to reduce level threshold.

Distributed Compressed Sensing Based Channel Parameter Estimation for MIMO-FBMC Communications

2021 ◽  
Author(s):  
Han Wang ◽  
Xianpeng Wang
Keyword(s):  
Channel Estimation ◽  
Compressed Sensing ◽  
Signal Reconstruction ◽  
Estimation Method ◽  
Sparse Signal ◽  
Weak Selection ◽  
Sparse Signal Reconstruction ◽  
Distributed Compressed Sensing ◽  
Time Frequency ◽  
Selection Threshold

Abstract For the sparse correlation between channels in multiple input multiple output filter bank multicarrier with offset quadrature amplitude modulation (MIMO-FBMC/OQAM) systems, the distributed compressed sensing (DCS)-based channel estimation approach is studied. A sparse adaptive distributed sparse channel estimation method based on weak selection threshold is proposed. Firstly, the correlation between MIMO channels is utilized to represent a joint sparse model, and channel estimation is transformed into a joint sparse signal reconstruction problem. Then, the number of correlation atoms for inner product operation is optimized by weak selection threshold, and sparse signal reconstruction is realized by sparse adaptation. The experiment results show that proposed DCS-based method not only estimates the multipath channel components accurately but also achieves higher channel estimation performance than classical orthogonal matching pursuit (OMP) method and other traditional DCS methods in the time-frequency dual selective channels.

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