Academic literature on the topic 'Plant population genetics Statistical methods'
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Journal articles on the topic "Plant population genetics Statistical methods"
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Hoeschele, I., P. Uimari, F. E. Grignola, Q. Zhang, and K. M. Gage. "Advances in Statistical Methods to Map Quantitative Trait Loci in Outbred Populations." Genetics 147, no. 3 (November 1, 1997): 1445–57. http://dx.doi.org/10.1093/genetics/147.3.1445.
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Statistical methods to map quantitative trait loci (QTL) in outbred populations are reviewed, extensions and applications to human and plant genetic data are indicated, and areas for further research are identified. Simple and computationally inexpensive methods include (multiple) linear regression of phenotype on marker genotypes and regression of squared phenotypic differences among relative pairs on estimated proportions of identity-by-descent at a locus. These methods are less suited for genetic parameter estimation in outbred populations but allow the determination of test statistic distributions via simulation or data permutation; however, further inferences including confidence intervals of QTL location require the use of Monte Carlo or bootstrap sampling techniques. A method which is intermediate in computational requirements is residual maximum likelihood (REML) with a covariance matrix of random QTL effects conditional on information from multiple linked markers. Testing for the number of QTLs on a chromosome is difficult in a classical framework. The computationally most demanding methods are maximum likelihood and Bayesian analysis, which take account of the distribution of multilocus marker-QTL genotypes on a pedigree and permit investigators to fit different models of variation at the QTL. The Bayesian analysis includes the number of QTLS on a chromosome as an unknown.
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Mitchell-Olds, T., and J. Bergelson. "Statistical genetics of an annual plant, Impatiens capensis. I. Genetic basis of quantitative variation." Genetics 124, no. 2 (February 1, 1990): 407–15. http://dx.doi.org/10.1093/genetics/124.2.407.
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Abstract Analysis of quantitative genetics in natural populations has been hindered by computational and methodological problems in statistical analysis. We developed and validated a jackknife procedure to test for existence of broad sense heritabilities and dominance or maternal effects influencing quantitative characters in Impatiens capensis. Early life cycle characters showed evidence of dominance and/or maternal effects, while later characters exhibited predominantly environmental variation. Monte Carlo simulations demonstrate that these jackknife tests of variance components are extremely robust to heterogeneous error variances. Statistical methods from human genetics provide evidence for either a major locus influencing germination date, or genes that affect phenotypic variability per se. We urge explicit consideration of statistical behavior of estimation and testing procedures for proper biological interpretation of statistical results.
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Ward, Sarah M., and Marie Jasieniuk. "Review: Sampling Weedy and Invasive Plant Populations for Genetic Diversity Analysis." Weed Science 57, no. 6 (December 2009): 593–602. http://dx.doi.org/10.1614/ws-09-082.1.
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Recent advances in molecular methods and statistical analyses provide weed scientists with powerful tools for examining the genetic structure of weedy plant populations. The value of these studies depends on effective sampling protocols; however, there is little consensus on how to sample plant populations for genetic diversity analyses. In this review, we draw on published literature that incorporates sampling theory and spatial statistics in population genetic analyses to identify key factors to consider when designing a sampling strategy. We discuss how sampling design is affected by research objectives, biology of the study species, population structure, marker choice, and the genetic parameters to be investigated, and we offer suggestions on defining sampling units and developing sampling protocols.
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EPPERSON, B. K., Z. HUANG, and T. Q. LI. "Measures of spatial structure in samples of genotypes for multiallelic loci." Genetical Research 73, no. 3 (June 1999): 251–61. http://dx.doi.org/10.1017/s001667239900378x.
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Various spatial autocorrelation statistics have been widely used both in theoretical population genetics and to study the spatial distribution of diploid genotypes in many plant and animal populations. However, previous simulation studies have considered only diallelic loci. In this paper, we use a large number of space–time simulations to characterize for the first time the parametric and statistical values of Moran's I-statistics for converted individual genotypes as well as for join- count statistics. A wide range of levels of dispersal and numbers of alleles and allele frequencies are modelled and the results reveal the different general effects of each of these factors on these statistics. We also examine the range of appropriate sampling designs and sizes for which predicted values can be interpolated for specific sampling schemes for any given population genetic field survey. Numbers of alleles and allele frequencies each affect some statistics but not others. The results indicate generally low standard deviations. The results also develop precise and efficient methods of estimating gene dispersal, based on the various autocorrelation measures of standing spatial patterns of genetic variation within populations. The results also extend these methods to loci with multiple alleles, typical of those studied through modern molecular methods.
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Zhan, J., C. C. Mundt, and B. A. McDonald. "Estimation of Rates of Recombination and Migration in Populations of Plant Pathogens—A Reply." Phytopathology® 90, no. 4 (April 2000): 324–26. http://dx.doi.org/10.1094/phyto.2000.90.4.324.
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We find that the maximum likelihood method proposed by J. K. M. Brown has deficiencies that limit its usefulness for actual data sets. We propose two alternative statistical methods based on maximum likelihood that could be used to quantify rates of recombination and immigration in fungal populations. We also show that minor modification of our original method, which was based upon posterior probabilities, leads to a result that is identical to one of the maximum likelihood methods. Our previous estimates of the relative contributions of sexual reproduction, asexual reproduction, and immigration to the genetic structure of a Mycosphaerella graminicola population did not change significantly following reanalysis of our data with these new methods.
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STEFANINI, FEDERICO MATTIA, and ALESSANDRO CAMUSSI. "Information in molecular profile components evaluated by a Genetic Classifier System: a case study in Picea abies Karst." Genetical Research 70, no. 3 (December 1997): 205–13. http://dx.doi.org/10.1017/s001667239700298x.
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Individual records from the coding of molecular polymorphism (molecular profiles) are particularly useful for the identification of clones or cultivars, in pedigree analysis, in the estimation of genetic distances and relatedness, and as a tool in genome mapping and population genetics. A parametric statistical analysis of molecular profile components can be infeasible because of the huge number of observed markers, the presence of missing values and the high number of parameters required to evaluate the importance of interactions among markers. Moreover, new powerful molecular techniques make possible the analysis of numerous markers at one time; therefore parametric statistical methods could result in troublesome models with more parameters than data. The field of computer-based techniques offers new strategies to cope with the complexity of molecular profiles. We suggest the use of a Genetic Classifier System to evaluate the importance of profile components. The procedure is based on a Genetic Algorithm approach, a numerical technique that simulates some features of the natural selection process to solve problems. A set of isozyme data from a Norway spruce population is analysed in order to assess their ability to predict the individual plant response to the presence of abiotic stresses. The results, obtained by three different computer simulations, show that this computer-based approach is particularly effective for ranking profile components according to their relevance. Genetic Classifier Systems could also be used as a preliminary step to reduce the complexity of molecular data sets.
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Sokolovic, Dejan, Zoran Lugic, Jasmina Radovic, Tomislav Zivanovic, Snezana Babic, Aleksandar Simic, and Radojka Maletic. "Evaluation of morphological traits, dry matter yield and quality of Lolium perenne L. autochthonous populations from Serbia through multivariate analysis." Genetika 43, no. 1 (2011): 129–40. http://dx.doi.org/10.2298/gensr1101129s.
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Due to specific climatic conditions, perennial ryegrass breeding in Serbia is focused on resistance or tolerance to abiotic stress factors, especially to drought and high temperatures. These traits should be associated with high dry matter yield and quality. Therefore, most frequently used initial material is autochthonous populations and ecotypes adapted to local agro-ecological conditions, but knowledge about their variability of important traits for breeding is missing. Pre-selection evaluation of ten populations of perennial ryegrass originating from Serbia is presented in this paper. Twenty five traits were investigated during the two-year period and processed using analysis of variance and multivariate statistical methods (cluster and principal components analysis). The goal was to determine diversity and genetic distances of investigated populations by phenotyping and to define traits considerably affecting the variability and discrimination of populations. On cluster diagram two groups of population are observed, but geographic origin of populations (lowland, hilly, mountainous habitat) was not influence to clustering of collection. Factor analysis has clarified that first seven principal components (PC) described almost 95%. The traits which show high correlation coefficients with first principal component were plant height in first cut, leaf length and width, DM of generative tillers, spike and spikelet length and 1000 seed weight, and with second principal component time of heading, terminal internode length, DM of vegetative tillers, spikelet number and FSU. It can be concluded that variability between populations was high and that differences of population were mainly affected by most important traits for breeding, such as components of dry matter production and some seed yield components.
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Tokhtar, Valeriy K., Yulia K. Vinogradova, Alexander A. Notov, Аndrey Yu Kurskoy, and Elena S. Danilova. "Main directions of the study of plant invasions in Russia." Environmental & Socio-economic Studies 9, no. 4 (December 1, 2021): 45–56. http://dx.doi.org/10.2478/environ-2021-0024.
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Abstract This article is focused on the analysis of major approaches to plant invasion research used by Russian researchers. They fall within three main groups: 1. Conventional approaches to floristic analysis based on the Russian scientific tradition of floristic research, 2. Approaches focused on the study of the fraction of invasive flora, making blacklists and regional Black books, 3. New comprehensive approaches based on a synthesis of methods used in botany, geo-information technology and population genetics. Multivariate statistical methods allow for the visualization of various data, including those on alien species group structures in various regions. They make it possible to identify boundaries of ecological niches occupied by plants in respect to climate-and-environmental or ecological variables. An assessment of current statistical interdependence between alien plant characteristics and scores of factors limiting their dissemination facilitates the making of predictive models of plant invasion. Examples of multivariate statistical methods used in invasion biology were analyzed, along with different approaches to the study of the variability of alien species. Alien and invasive fractions of the flora of the Trans-Siberian Railway were analyzed not by administrative units but by natural biomes. This approach allowed us to assess the correlation between the number of invasive species with different natural-climatic and floristic characteristics of biomes. The publication of "Black Books" of various administrative subjects of Russia according to a unified methodology allowed us to make an inventory of invasive species over the vast territory of the country. The experience gained by Russian researchers may be further used for developing universal approaches to plant invasion research.
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Lucic, Aleksandar, Vasilije Isajev, Ljubinko Rakonjac, Milan Mataruga, Vojka Babic, Danijela Ristic, and Snezana Mladenovic-Drinic. "Application of various statistical methods to analyze genetic diversity of Austrian (Pinus nigra Arn.) and Scots pine (Pinus sylvestris L.) based on protein markers." Genetika 43, no. 3 (2011): 477–86. http://dx.doi.org/10.2298/gensr1103477l.
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This paper presents the results of studies on protein polymorphism in seeds of Scots pine (Pinus sylvestris L.) and Austrian pine (Pinus nigra Arn.) as the most important economic species of the genus Pinus in Serbia. Polymorphism of protein markers was determined in selected genotypes originating from seven populations (Scots pine) and six populations (Austrian pine). Analysis of protein markers was performed using two statistical methods, NTSYS and correspondence analysis. Both methods give the same arrangement of the analyzed populations, whereby, because of a different view of genetic distances, they can and should be combined, enabling easier and more precise understanding of mutual relationships of the observation units.
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Lu, Zhiqiang, Yongshuai Sun, Ying Li, Yongzhi Yang, Gaini Wang, and Jianquan Liu. "Species delimitation and hybridization history of a hazel species complex." Annals of Botany 127, no. 7 (February 10, 2021): 875–86. http://dx.doi.org/10.1093/aob/mcab015.
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Abstract Background and Aims Hybridization increases species adaptation and biodiversity but also obscures species boundaries. In this study, species delimitation and hybridization history were examined within one Chinese hazel species complex (Corylus chinensis–Corylus fargesii). Two species including four varieties have already been described for this complex, with overlapping distributions. Methods A total of 322 trees from 44 populations of these four varieties across their ranges were sampled for morphological and molecular analyses. Climatic datasets based on 108 geographical locations were used to evaluate their niche differentiations. Flowering phenology was also observed for two co-occurring species or varieties. Key Results Four statistically different phenotypic clusters were revealed, but these clusters were highly inconsistent with the traditional taxonomic groups. All the clusters showed statistically distinct niches, with complete or partial geographical isolation. Only two clusters displayed a distributional overlap, but they had distinct flowering phenologies at the site where they co-occurred. Population-level evidence based on the genotypes of ten simple sequence repeat loci supported four phenotypic clusters. In addition, one cluster was shown to have an admixed genetic composition derived from the other three clusters through repeated historical hybridizations. Conclusions Based on our new evidence, it is better to treat the four clusters identified here as four independent species. One of them was shown to have an admixed genetic composition derived from the other three through repeated historical hybridizations. This study highlights the importance of applying integrative and statistical methods to infer species delimitations and hybridization history. Such a protocol should be adopted widely for future taxonomic studies.
Dissertations / Theses on the topic "Plant population genetics Statistical methods"
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Csilléry, Katalin. "Statistical inference in population genetics using microsatellites." Thesis, University of Edinburgh, 2009. http://hdl.handle.net/1842/3865.
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Statistical inference from molecular population genetic data is currently a very active area of research for two main reasons. First, in the past two decades an enormous amount of molecular genetic data have been produced and the amount of data is expected to grow even more in the future. Second, drawing inferences about complex population genetics problems, for example understanding the demographic and genetic factors that shaped modern populations, poses a serious statistical challenge. Amongst the many different kinds of genetic data that have appeared in the past two decades, the highly polymorphic microsatellites have played an important role. Microsatellites revolutionized the population genetics of natural populations, and were the initial tool for linkage mapping in humans and other model organisms. Despite their important role, and extensive use, the evolutionary dynamics of microsatellites are still not fully understood, and their statistical methods are often underdeveloped and do not adequately model microsatellite evolution. In this thesis, I address some aspects of this problem by assessing the performance of existing statistical tools, and developing some new ones. My work encompasses a range of statistical methods from simple hypothesis testing to more recent, complex computational statistical tools. This thesis consists of four main topics. First, I review the statistical methods that have been developed for microsatellites in population genetics applications. I review the different models of the microsatellite mutation process, and ask which models are the most supported by data, and how models were incorporated into statistical methods. I also present estimates of mutation parameters for several species based on published data. Second, I evaluate the performance of estimators of genetic relatedness using real data from five vertebrate populations. I demonstrate that the overall performance of marker-based pairwise relatedness estimators mainly depends on the population relatedness composition and may only be improved by the marker data quality within the limits of the population relatedness composition. Third, I investigate the different null hypotheses that may be used to test for independence between loci. Using simulations I show that testing for statistical independence (i.e. zero linkage disequilibrium, LD) is difficult to interpret in most cases, and instead a null hypothesis should be tested, which accounts for the “background LD” due to finite population size. I investigate the utility of a novel approximate testing procedure to circumvent this problem, and illustrate its use on a real data set from red deer. Fourth, I explore the utility of Approximate Bayesian Computation, inference based on summary statistics, to estimate demographic parameters from admixed populations. Assuming a simple demographic model, I show that the choice of summary statistics greatly influences the quality of the estimation, and that different parameters are better estimated with different summary statistics. Most importantly, I show how the estimation of most admixture parameters can be considerably improved via the use of linkage disequilibrium statistics from microsatellite data.
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Shringarpure, Suyash. "Statistical Methods for studying Genetic Variation in Populations." Research Showcase @ CMU, 2012. http://repository.cmu.edu/dissertations/117.
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The study of genetic variation in populations is of great interest for the study of the evolutionary history of humans and other species. Improvement in sequencing technology has resulted in the availability of many large datasets of genetic data. Computational methods have therefore become quite important in analyzing these data. Two important problems that have been studied using genetic data are population stratification (modeling individual ancestry with respect to ancestral populations) and genetic association (finding genetic polymorphisms that affect a trait). In this thesis, we develop methods to improve our understanding of these two problems.
For the population stratification problem, we develop hierarchical Bayesian models that incorporate the evolutionary processes that are known to affect genetic variation. By developing mStruct, we show that modeling more evolutionary processes improves the accuracy of the recovered population structure. We demonstrate how nonparametric Bayesian processes can be used to address the question of choosing the optimal number of ancestral populations that describe the genetic diversity of a given sample of individuals. We also examine how sampling bias in genotyping study design can affect results of population structure analysis and propose a probabilistic framework for modeling and correcting sample selection bias.
Genome-wide association studies (GWAS) have vastly improved our understanding of many diseases. However, such studies have failed to uncover much of the variation responsible for a number of common multi-factorial diseases and complex traits. We show how artificial selection experiments on model organisms can be used to better understand the nature of genetic associations. We demonstrate using simulations that using data from artificial selection experiments improves the performance of conventional methods of performing association. We also validate our approach using semi-simulated data from an artificial selection experiment on Drosophila Melanogaster.
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Mayor, Lianne Rosalind. "Statistical methods in molecular and population genetics : clustering of similar genes and investigating relatedness of individuals." Thesis, Imperial College London, 2007. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.445322.
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Ahiska, Bartu. "Reference-free identification of genetic variation in metagenomic sequence data using a probabilistic model." Thesis, University of Oxford, 2012. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.561121.
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Microorganisms are an indispensable part of our ecosystem, yet the natural metabolic and ecological diversity of these organisms is poorly understood due to a historical reliance of microbiology on laboratory grown cultures. The awareness that this diversity cannot be studied by laboratory isolation, together with recent advances in low cost scalable sequencing technology, have enabled the foundation of culture-independent microbiology, or metagenomics. The study of environmental microbial samples with metagenomics has led to many advances, but a number of technological and methodological challenges still remain. A potentially diverse set of taxa may be represented in anyone environmental sample. Existing tools for representing the genetic composition of such samples sequenced with short-read data, and tools for identifying variation amongst them, are still in their infancy. This thesis makes the case that a new framework based on a joint-genome graph can constitute a powerful tool for representing and manipulating the joint genomes of population samples. I present the development of a collection of methods, called SCRAPS, to construct these efficient graphs in small communities without the availability or bias of a reference genome. A key novelty is that genetic variation is identified from the data structure using a probabilistic algorithm that can provide a measure of the confidence in each call. SCRAPS is first tested on simulated short read data for accuracy and efficiency. At least 95% of non-repetitive small-scale genetic variation with a minor allele read depth greater than 10x is correctly identified; the number false positives per conserved nucleotide is consistently better than 1 part in 333 x 103. SCRAPS is then applied to artificially pooled experimental datasets. As part of this study, SCRAPS is used to identify genetic variation in an epidemiological 11 sample Neisseria meningitidis dataset collected from the African meningitis belt". In total 14,000 sites of genetic variation are identified from 48 million Illumina/Solexa reads. The results clearly show the genetic differences between two waves of infection that has plagued northern Ghana and Burkina Faso.
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Coop, Graham M. "The likelihood of gene trees under selective models." Thesis, University of Oxford, 2004. http://ora.ox.ac.uk/objects/uuid:ba97d36c-61c1-40c8-a1f4-e7ddc8918d5b.
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The extent to which natural selection shapes diversity within populations is a key question for population genetics. Thus, there is considerable interest in quantifying the strength of selection. In this thesis a full likelihood approach for inference about selection at a single site within an otherwise neutral fully-linked sequence of sites is developed. Integral to many of the ideas introduced in this thesis is the reversibility of the diffusion process, and some past approaches to this concept are reviewed. A coalescent model of evolution is used to model the ancestry of a sample of DNA sequences which have the selected site segregating. A novel method for simulating the coalescent with selection, acting at a single biallelic site, is described. Selection is incorporated through modelling the frequency of the selected and neutral allelic classes stochastically back in time. The ancestry is then simulated using a subdivided population model considering the population frequencies through time as variable population sizes. The approach is general and can be used for any selection scheme at a biallelic locus. The mutation model, for the selected and neutral sites, is the infinitely-many-sites model where there is no back or parallel mutation at sites. This allows a unique perfect phylogeny, a gene tree, to be constructed from the configuration of mutations on the sample sequences. An importance sampling algorithm is described to explore over coalescent tree space consistent with this gene tree. The method is used to assess the evidence for selection in a number of data sets. These are as follows: a partial selective sweep in the G6PD gene (Verrelli et al., 2002); a recent full sweep in the Factor IX gene (Harris and Hey, 2001); and balancing selection in the DCP1 gene (Rieder et al., 1999). Little evidence of the action of selection is found in the data set of Verrelli et al. (2002) and the data set of Rieder et al. (1999) seems inconsistent with the model of balancing selection. The patterns of diversity in the data set of Harris and Hey (2001) offer support of the hypothesis of a full sweep.
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Cuthbertson, Charles. "Limits to the rate of adaptation." Thesis, University of Oxford, 2007. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.670176.
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McCaskie, Pamela Ann. "Multiple-imputation approaches to haplotypic analysis of population-based data with applications to cardiovascular disease." University of Western Australia. School of Population Health, 2008. http://theses.library.uwa.edu.au/adt-WU2008.0160.
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[Truncated abstract] This thesis investigates novel methods for the genetic association analysis of haplotype data in samples of unrelated individuals, and applies these methods to the analysis of coronary heart disease and related phenotypes. Determining the inheritance pattern of genetic variants in studies of unrelated individuals can be problematic because family members of the studied individuals are often not available. For the analysis of individual genetic loci, no problem arises because the unit of interest is the observed genotype. When the unit of interest is the linear combination of alleles along one chromosome, inherited together in a haplotype, it is not always possible to determine with certainty the inheritance pattern, and therefore statistical methods to infer these patterns must be adopted. Due to genotypic heterozygosity, mutliple possible haplotype configurations can often resolve an individual's genotype measures at multiple loci. When haplotypes are not known, but are inferred statistically, an element of uncertainty is thus inherent which, if not dealt with appropriately, can result in unreliable estimates of effect sizes in an association setting. The core aim of the research described in this thesis was to develop and implement a general method for haplotype-based association analysis using multiple imputation to appropriately deal with uncertainty haplotype assignment. Regression-based approaches to association analysis provide flexible methods to investigate the influence of a covariate on a response variable, adjusting for the effects of other variables including interaction terms. ... These methods are then applied to models accommodating binary, quantitative, longitudinal and survival data. The performance of the multiple imputation method implemented was assessed using simulated data under a range of haplotypic effect sizes and genetic inheritance patterns. The multiple imputation approach performed better, on average, than ignoring haplotypic uncertainty, and provided estimates that in most cases were similar to those observed when haplotypes were known. The haplotype association methods developed in this thesis were used to investigate the genetic epidemiology of cardiovascular disease, utilising data for the cholesteryl ester transfer protein gene (CETP), the hepatic lipase (LIPC) gene and the 15- lipoxygenase (ALOX15) gene on a total of 6,487 individuals from three Western Australian studies. Results of these analyses suggested single nucleotide polymorphisms (SNPs) and haplotypes in the CETP gene were associated with increased plasma high-density lipoprotein cholesterol (HDL-C). SNPs in the LIPC gene were also associated with increased HDL-C and haplotypes in the ALOX15 gene were associated with risk of carotid plaque among individuals with premature CHD. The research presented in this thesis is both novel and important as it provides methods for the analysis of haplotypic associations with a range of response types, while incorporating information about haplotype uncertainty inherent in populationbased studies. These methods are shown to perform well for a range of simulated and real data situations, and have been written into a statistical analysis package that has been freely released to the research community.
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Coelho, Alexandre Siqueira Guedes. "Abordagem Bayesiana na análise genética de populações utilizando dados de marcadores moleculares." Universidade de São Paulo, 2002. http://www.teses.usp.br/teses/disponiveis/11/11137/tde-30102002-163254/.
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Dentre os diversos aspectos geralmente observados na caracterização genética de populações naturais, a avaliação do grau de estruturação da variabilidade genética entre e dentro dos indivíduos e a obtenção de estimativas de parâmetros genéticos indicadores do sistema reprodutivo da espécie assumem grande importância. Os parâmetros de maior interesse neste caso são o índice de fixação intrapopulacional (f) e a taxa de fecundação cruzada (t). Pelo uso de simulações computacionais, este trabalho demonstra o caráter dinâmico do índice de fixação intrapopulacional em diferentes locos ao longo das gerações em decorrência do caráter finito da população e de variação nas taxas médias de fecundação cruzada entre gerações. Sugere-se que este caráter dinâmico representa uma explicação para a elevada variação, comumente reportada na literatura, das estimativas de f obtidas com locos diferentes avaliados em uma mesma população. Utilizando a abordagem Bayesiana, um modelo hierárquico de análise é proposto para a estimação de f, incorporando as informações obtidas de múltiplos locos não ligados, levando-se em conta a condicionalidade do processo de estimação ao polimorfismo dos locos utilizados. O modelo proposto incorpora o caráter dinâmico de f para diferentes locos e permite a estimação do número efetivo de indivíduos reprodutivamente ativos em uma população. Propõe-se ainda um modelo Bayesiano para a estimação da taxa de fecundação cruzada com base na informação de múltiplos locos, admitindo-se a possibilidade de ocorrência de apomixia. Os modelos propostos são avaliados por simulação e exemplos de aplicação a dados reais de marcadores moleculares codominantes são discutidos. Os resultados obtidos demonstram a aplicabilidade das metodologias propostas e o elevado potencial de aplicação da estatística Bayesiana em estudos de genética de populações.Among the various aspects generally considered in the genetic characterization of natural populations of plant species, the evaluation of the degree of genetic structure within and among individuals and the estimation of parameters related to the species mating system are of great importance. In general, considerable effort is focused on the estimation of the intrapopulation fixation index (f) and the outcrossing rate (t). Using computer simulated data, the dynamic nature of f for different loci along generations is illustrated. The dynamic nature of f is shown to result from the finite condition of populations and from the variation in the mean values of the outcrossing rates among generations. It is suggested that this dynamic behavior explains the inconsistency, commonly reported in the literature, of f estimates obtained for different loci in a given population. Using a Bayesian approach, we propose a hierarchical model for the estimation of f, incorporating information obtained from different unlinked loci and considering the conditionality of the estimation process to genetic polymorphism. The proposed model incorporates the dynamic nature of f values for different loci and allows the estimation of the effective number of reproductively active individuals in a given population. Using a similar approach, a Bayesian model is also proposed for estimating the outcrossing rate using multiple loci information and incorporating the possibility of apomixis. The models proposed are evaluated by computer simulations and examples using real data from codominant molecular markers are presented. Results obtained illustrate the applicability of the proposed methods and reveal the great potential of use of Bayesian statistics in population genetic studies.
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Gazal, Steven. "La consanguinité à l'ère du génome haut-débit : estimations et applications." Thesis, Paris 11, 2014. http://www.theses.fr/2014PA11T026/document.
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Un individu est dit consanguin si ses parents sont apparentés et s’il existe donc dans sa généalogie au moins une boucle de consanguinité aboutissant à un ancêtre commun. Le coefficient de consanguinité de l’individu est par définition la probabilité pour qu’à un point pris au hasard sur le génome, l’individu ait reçu deux allèles identiques par descendance qui proviennent d’un seul allèle présent chez un des ancêtres communs. Ce coefficient de consanguinité est un paramètre central de la génétique qui est utilisé en génétique des populations pour caractériser la structure des populations, mais également pour rechercher des facteurs génétiques impliqués dans les maladies. Le coefficient de consanguinité était classiquement estimé à partir des généalogies, mais des méthodes ont été développées pour s’affranchir des généalogies et l’estimer à partir de l’information apportée par des marqueurs génétiques répartis sur l’ensemble du génome.Grâce aux progrès des techniques de génotypage haut-débit, il est possible aujourd’hui d’obtenir les génotypes d’un individu sur des centaines de milliers de marqueurs et d’utiliser ces méthodes pour reconstruire les régions d’identité par descendance sur son génome et estimer un coefficient de consanguinité génomique. Il n’existe actuellement pas de consensus sur la meilleure stratégie à adopter sur ces cartes denses de marqueurs en particulier pour gérer les dépendances qui existent entre les allèles aux différents marqueurs (déséquilibre de liaison). Dans cette thèse, nous avons évalué les différentes méthodes disponibles à partir de simulations réalisées en utilisant de vraies données avec des schémas de déséquilibre de liaison réalistes. Nous avons montré qu’une approche intéressante consistait à générer plusieurs sous-cartes de marqueurs dans lesquelles le déséquilibre de liaison est minimal, d’estimer un coefficient de consanguinité sur chacune des sous-cartes par une méthode basée sur une chaîne de Markov cachée implémentée dans le logiciel FEstim et de prendre comme estimateur la médiane de ces différentes estimations. L’avantage de cette approche est qu’elle est utilisable sur n’importe quelle taille d’échantillon, voire sur un seul individu, puisqu’elle ne demande pas d’estimer les déséquilibres de liaison. L’estimateur donné par FEstim étant un estimateur du maximum de vraisemblance, il est également possible de tester si le coefficient de consanguinité est significativement différent de zéro et de déterminer la relation de parenté des parents la plus vraisemblable parmi un ensemble de relations. Enfin, en permettant l’identification de régions d’homozygoties communes à plusieurs malades consanguins, notre stratégie peut permettre l’identification des mutations récessives impliquées dans les maladies monogéniques ou multifactorielles.Pour que la méthode que nous proposons soit facilement utilisable, nous avons développé le pipeline, FSuite, permettant d’interpréter facilement les résultats d’études de génétique de populations et de génétique épidémiologique comme illustré sur le panel de référence HapMap III, et sur un jeu de données cas-témoins de la maladie d’AlzheimerAn individual is said to be inbred if his parents are related and if his genealogy contains at least one inbreeding loop leading to a common ancestor. The inbreeding coefficient of an individual is defined as the probability that the individual has received two alleles identical by descent, coming from a single allele present in a common ancestor, at a random marker on the genome. The inbreeding coefficient is a central parameter in genetics, and is used in population genetics to characterize the population structure, and also in genetic epidemiology to search for genetic factors involved in recessive diseases.The inbreeding coefficient was traditionally estimated from genealogies, but methods have been developed to avoid genealogies and to estimate this coefficient from the information provided by genetic markers distributed along the genome.With the advances in high-throughput genotyping techniques, it is now possible to genotype hundreds of thousands of markers for one individual, and to use these methods to reconstruct the regions of identity by descent on his genome and estimate a genomic inbreeding coefficient. There is currently no consensus on the best strategy to adopt with these dense marker maps, in particular to take into account dependencies between alleles at different markers (linkage disequilibrium).In this thesis, we evaluated the different available methods through simulations using real data with realistic patterns of linkage disequilibrium. We highlighted an interesting approach that consists in generating several submaps to minimize linkage disequilibrium, estimating an inbreeding coefficient of each of the submaps based on a hidden Markov method implemented in FEstim software, and taking as estimator the median of these different estimates. The advantage of this approach is that it can be used on any sample size, even on an individual, since it requires no linkage disequilibrium estimate. FEstim is a maximum likelihood estimator, which allows testing whether the inbreeding coefficient is significantly different from zero and determining the most probable mating type of the parents. Finally, through the identification of homozygous regions shared by several consanguineous patients, our strategy permits the identification of recessive mutations involved in monogenic and multifactorial diseases.To facilitate the use of our method, we developed the pipeline FSuite, to interpret results of population genetics and genetic epidemiology studies, as shown on the HapMap III reference panel, and on a case-control Alzheimer's disease data
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Rohrlach, Adam Benjamin. "Statistical Methods for Identifying Demographic Structure in DNA Sequence Alignments." Thesis, 2018. http://hdl.handle.net/2440/120353.
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All life on Earth, from viruses and bacteria, trees and flowers, to birds and human beings, can be traced back to a single common ancestor. However, the evolutionary history that led to this diversity of life is a complicated story that we do not yet fully understand. Since the discovery of the structure of deoxyribonucleic acid (DNA) in 1953, and the development of DNA sequencing technology, researchers have been using similarities and differences in the genomes of organisms to better understand the relationships between species. However, due to the complexity of the evolutionary history of life, simplifying assumptions must be made to make mathematical models tractable. It must then be of paramount importance for researchers to be able to identify when the simplifying assumptions of a specific model are unreasonable. In this thesis we present two projects, and although they are different in implementation, both attempt to investigate simplifying assumptions in the closely related fields of population genetics and phylogenetics. However, we also present applications of our projects where the results of our work are not used in assessing assumptions for further analyses, but are of standalone interest to researchers. Our first project is concerned with the development of a method for constructing coordinate representations for single-copy DNA, such as mitochondrial DNA (mtDNA) or Y-chromosomal DNA, analogous to the use of PCA for nuclear DNA. We construct a coordinate system such that, given p informative sites in an alignment of n individuals, returns p-dimensional coordinates for each n individuals. We order the dimensions by the proportion of variability each dimension captures in the overall genetic diversity. From these coordinates in \genetic space" researchers may perform a number of down stream analyses. It is possible to optimally visualise high-dimensional sequence data in two or three dimensions. One may use our method to identify closely related individuals, identify sites in the alignment that are closely linked, or to use the same coordinate space to
nd sites that are closely linked with groups of individuals. Finally, one may choose to test for significant relationships between the structure of the coordinates in genetic space, and metadata recorded on sequenced individuals, indicating demographic variables that are highly related to the evolutionary history of an alignment. This final application of our method, where one may test for demographic structure in sequence data, is of key importance to the theme of discovering when simplifying assumptions of analyses are not reasonable. Through the comparison of coordinates in gene space, and any demographic variables of interest, researchers may explore whether or not the individuals in the alignment indicate population substructure. For example, one may investigate if there appears to be a phylogeographic structure to the individuals forming distinct subpopulations, and if migration appears to occur between subpopulations. Through empirical data, we show that our method can readily recover tree-like structure, identify strong genetic groupings based on qualitative traits and show that we are able to recover phylogeographic signal given provenanced sampling information. We show that our method can even be used to suggest routes of migration based on mtDNA. Finally we apply our method to modern Aboriginal Australian mtDNA to show strong evidence for discrete geographic populations of Aboriginal Australian peoples that display permanence on the Australian landscape dating back to the original colonisation of Australia 50 thousand years before present (kya). Our second project is concerned with identifying departures from a tree-like evolutionary history at the species level. It is not uncommon for closely related species (Species A and C say) to still be capable of interbreeding, and producing viable \hybrid" offγspring (Species B say). Under these conditions, a phylogenetic tree cannot describe the evolutionary history of the hybrid species, and instead an admixture graph may be a better description. We begin by considering the evolutionary history of three species: a hybrid organism that has undergone some independent evolution (Species B), and two \parent" organisms, Species A and C. Relatively long, contiguous regions of the genome of Species B will have undergone no recombination since the admixture event. These regions will have been contributed by either Species A (and hence will be more closely related to Species A), or Species C. We aim to estimate the proportion of the genome contributed by Species A, and denote this by considering the proportion of informative site patterns that indicate evidence for the two possible ancestries. The mixing proportion is the parameter of interest in our analyses. However, due to the classical problem of the non-identifiability of mixing parameters in multinomial distributions, we describe two Bayesian methods for estimating γ. Our first method places prior distributions on the parameters of the model, and uses Approximate Bayesian Computation (ABC) to estimate the marginal posterior distribution of γ. Our second, closely related method, instead estimates the marginal posterior distribution of via numerical integration. We show via a simulation study that our methods can accurately estimate the true value of γ, and perform well under biologically reasonable scenarios. However, we also find that our methods suffer from a relatively small positive bias for small values of γ, i.e., when one species of the parent species contributes very little to the genome of the hybrid species. We compare the performance of our method to the popular method of the ratio of f4 statistics. We do this by estimating the proportion of Neanderthal ancestry in pre-ice age European human samples and comparing our results to the finding of Fu et al. [18]. We show that our method recovers extremely similar estimates of Neanderthal ancestry with no apparent systematic bias when compared to the results of Fu et al.. Finally we apply our method to the genomes of Late Pleistocene European bison (Bison bonasus) and Steppe Bison (Bison priscus) to understand the evolutionary history of bovid megafauna in Europe over the last seventy thousand years. It was thought that before 10 kya the only bovid present in Europe was the Steppe bison. However, from bone samples found dating from the present day, and back to approximately 70 kya, mtDNA indicated a second bison species was also roaming Europe before 10 kya, more closely related to modern cattle than the Steppe bison. After nuclear DNA was sequenced, we were able to show that this new species of bovid was actually a hybrid offspring of Aurochs (the ancestor of modern cattle) and Steppe bison, an event that occurred approximately 120 kya. We used our method, in concert with the ratio of f4 statistics, to show that the hybrid species contained approximately 10% Aurochs and 90% Steppe bison ancestry.Thesis (Ph.D.) -- University of Adelaide, School of Mathematical Sciences, 2018
Books on the topic "Plant population genetics Statistical methods"
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Statistical genetics. New York: Wiley, 1993.
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Prem, Narain. Statistical genetics. New York: Wiley, 1990.
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J, Balding D., Bishop M. J, and Cannings C. 1942-, eds. Handbook of statistical genetics. 3rd ed. Chichester, England: John Wiley & Sons, 2007.
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Genetic data analysis: Methods for discrete population genetic data. Sunderland, Mass: Sinauer Associates, 1990.
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1943-, Weir B. S., ed. Interpreting DNA evidence: Statistical genetics for forensic scientists. Sunderland, Mass: Sinauer Associates, 1998.
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Foulkes, Andrea S. Applied statistical genetics with R: For population-based association studies. New York: Springer Verlag, 2009.
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Applied statistical genetics with R: For population-based association studies. New York: Springer Verlag, 2009.
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Foulkes, Andrea S. Applied statistical genetics with R: For population-based association studies. New York: Springer Verlag, 2009.
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Weir, B. S. Genetic data analysis II: Methods for discrete population genetic data. Sunderland, Mass: Sinauer Associates, 1996.
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Potapov, V. A. Biometrii︠a︡ plodovykh kulʹtur. Michurinsk: Michurinskiĭ gos. agrarnyĭ universitet, 2004.
Find full textBook chapters on the topic "Plant population genetics Statistical methods"
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Lange, Kenneth. "Basic Principles of Population Genetics." In Mathematical and Statistical Methods for Genetic Analysis, 1–18. New York, NY: Springer New York, 1997. http://dx.doi.org/10.1007/978-1-4757-2739-5_1.
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Lange, Kenneth. "Basic Principles of Population Genetics." In Mathematical and Statistical Methods for Genetic Analysis, 1–20. New York, NY: Springer New York, 2002. http://dx.doi.org/10.1007/978-0-387-21750-5_1.
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Crespo-Herrera, Leonardo A., José Crossa, Mateo Vargas, and Hans-Joachim Braun. "Defining Target Wheat Breeding Environments." In Wheat Improvement, 31–45. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-90673-3_3.
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AbstractThe main objective of a plant breeding program is to deliver superior germplasm for farmers in a defined set of environments, or a target population of environments (TPE). Historically, CIMMYT has characterized the environments in which the developed germplasm will be grown. The main factors that determine when and where a wheat variety can be grown are flowering time, water availability and the incidence of pests and diseases. A TPE consists of many (population) environments and future years or seasons, that share common variation in the farmers’ fields, it can also be seen as a variable group of future production environments. TPEs can be characterized by climatic, soil and hydrological features, as well as socioeconomic aspects. Whereas the selection environments (SE) are the environments where the breeder does the selection of the lines. The SE are identified for predicting the performance in the TPE, but the SE may not belong to the TPE. The utilization of advanced statistical methods allows the identification of GEI to obtain higher precision when estimating the genetic effects. Multi-environmental testing (MET) is a fundamental strategy for CIMMYT to develop stable high grain yielding germplasm in countries with developing economies. An adequate MET strategy allows the evaluation of germplasm in stress hotspots and the identification of representative and correlated sites; thus, breeders can make better and targeted decisions in terms of crossing, selection and logistic operations.
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Fu, Yun-Xin, and Xiaoming Liu. "Statistical Methods for Detecting the Presence of Natural Selection in Bacterial Populations." In Bacterial Population Genetics in Infectious Disease, 87–101. Hoboken, NJ, USA: John Wiley & Sons, Inc., 2010. http://dx.doi.org/10.1002/9780470600122.ch5.
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dos Santos, Eduardo José Melo. "Statistical Approaches and Methods in Population Genetics Using Microsatellite Data." In DNA Profiling and DNA Fingerprinting, 215–28. Basel: Birkhäuser Basel, 1999. http://dx.doi.org/10.1007/978-3-0348-7582-0_14.
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Morota, Gota, Diego Jarquin, Malachy T. Campbell, and Hiroyoshi Iwata. "Statistical Methods for the Quantitative Genetic Analysis of High-Throughput Phenotyping Data." In Methods in Molecular Biology, 269–96. New York, NY: Springer US, 2022. http://dx.doi.org/10.1007/978-1-0716-2537-8_21.
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AbstractThe advent of plant phenomics, coupled with the wealth of genotypic data generated by next-generation sequencing technologies, provides exciting new resources for investigations into and improvement of complex traits. However, these new technologies also bring new challenges in quantitative genetics, namely, a need for the development of robust frameworks that can accommodate these high-dimensional data. In this chapter, we describe methods for the statistical analysis of high-throughput phenotyping (HTP) data with the goal of enhancing the prediction accuracy of genomic selection (GS). Following the Introduction in Sec. 1, Sec. 2 discusses field-based HTP, including the use of unoccupied aerial vehicles and light detection and ranging, as well as how we can achieve increased genetic gain by utilizing image data derived from HTP. Section 3 considers extending commonly used GS models to integrate HTP data as covariates associated with the principal trait response, such as yield. Particular focus is placed on single-trait, multi-trait, and genotype by environment interaction models. One unique aspect of HTP data is that phenomics platforms often produce large-scale data with high spatial and temporal resolution for capturing dynamic growth, development, and stress responses. Section 4 discusses the utility of a random regression model for performing longitudinal modeling. The chapter concludes with a discussion of some standing issues.
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Montesinos López, Osval Antonio, Abelardo Montesinos López, and Jose Crossa. "General Elements of Genomic Selection and Statistical Learning." In Multivariate Statistical Machine Learning Methods for Genomic Prediction, 1–34. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-89010-0_1.
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AbstractNowadays, huge data quantities are collected and analyzed for delivering deep insights into biological processes and human behavior. This chapter assesses the use of big data for prediction and estimation through statistical machine learning and its applications in agriculture and genetics in general, and specifically, for genome-based prediction and selection. First, we point out the importance of data and how the use of data is reshaping our way of living. We also provide the key elements of genomic selection and its potential for plant improvement. In addition, we analyze elements of modeling with machine learning methods applied to genomic selection and stress their importance as a predictive methodology. Two cultures of model building are analyzed and discussed: prediction and inference; by understanding modeling building, researchers will be able to select the best model/method for each circumstance. Within this context, we explain the differences between nonparametric models (predictors are constructed according to information derived from data) and parametric models (all the predictors take predetermined forms with the response) as well their type of effects: fixed, random, and mixed. Basic elements of linear algebra are provided to facilitate understanding the contents of the book. This chapter also contains examples of the different types of data using supervised, unsupervised, and semi-supervised learning methods.
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Jankowicz-Cieslak, Joanna, Florian Goessnitzer, Bradley J. Till, and Ivan L. Ingelbrecht. "Induced Mutagenesis and In Vitro Mutant Population Development in Musa spp." In Efficient Screening Techniques to Identify Mutants with TR4 Resistance in Banana, 3–20. Berlin, Heidelberg: Springer Berlin Heidelberg, 2022. http://dx.doi.org/10.1007/978-3-662-64915-2_1.
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AbstractMutagenesis of in vitro propagated bananas is an efficient method to introduce novel alleles and broaden genetic diversity. Mutations can be induced by treatment of plant cells with chemical mutagens or ionizing radiation. The FAO/IAEA Plant Breeding and Genetics Laboratory established efficient methods for mutation induction of in vitro shoot tips in banana using physical and chemical mutagens as well as methods for the efficient discovery of EMS-induced single nucleotide mutations in targeted genes or amplicons and identification of large genomic changes, including deletions and insertions. Mutagenesis of in vitro propagated tissues requires large populations serving as starting material, and a long process to dissolve genetic mosaics (chimeras) resulting from the mutagenesis of multicellular tissues. However, treating shoot apical meristems of tissue cultured bananas with a mutagen is a commonly used practice for banana mutation breeding programmes, and still the most effective. In our previous studies, we showed that chimeras, unique mutations accumulated in different cells of the plant propagule, could be rapidly removed via isolation of shoot apical meristems and subsequent longitudinal bisection. Further, induced mutations were maintained in mutant plants for several generations. We established such systems for inducing and maintaining both point mutations caused via EMS mutagenesis as well as insertions and deletions caused by gamma irradiation and describe hereafter methods for dose selection, gamma irradiation and chimera dissolution.
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Honor, Richard, and Robert I. Colautti. "EICA 2.0: a general model of enemy release and defence in plant and animal invasions." In Plant invasions: the role of biotic interactions, 192–207. Wallingford: CABI, 2020. http://dx.doi.org/10.1079/9781789242171.0192.
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Abstract Plants and animals have evolved a variety of strategies to limit the negative fitness consequences of natural enemies (i.e. herbivores, predators, parasites and pathogens). Demographic bottlenecks occurring during the invasion process reduce the number of co-introduced natural enemies, providing opportunities to study rapid evolution in environments with different or reduced enemy loads. Enemy release theory provides a set of hypotheses and predictions about the role of natural enemies in the proliferation of invasive species. This body of theory includes the Enemy Release Hypothesis (ERH) and the related Evolution of Increased Competitive Ability Hypothesis (EICA), but there is often confusion about these hypotheses and the data needed to test them. We introduce a simple, general model of enemy release to identify and clarify some of the key assumptions and predictions implicit in enemy release theory and its impacts on invasion. Although introduced populations likely benefit from a reduction in the direct fitness impacts of natural enemies in the early stages of invasion, an evolutionary shift in resource allocation from defence to growth and reproduction is much less likely and depends on a delicate balance between the fitness costs and benefits of defence and the fitness impacts of natural enemies in both the native and introduced ranges. Even when the abundance of natural enemies is lower in the introduced range, the majority of scenarios do not favour evolution of less defended genotypes that are more competitive or more fecund, contrary to predictions of EICA. Perhaps surprisingly, we find that the level of damage by natural enemies in field surveys is not generally a good parameter for testing enemy release theory. Instead, common garden experiments characterizing fitness reaction norms of multiple genotypes from the native and introduced range are crucial to estimate the historic rate of adaptive evolution or predict it into the future. Incorporating spatial autocorrelation and methods from population genetics can further improve our understanding of the role of enemy release and evolution in the proliferation of invasive species.
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Anderson, Eric C. "Statistical methods for identifying hybrids and groups." In Population Genetics for Animal Conservation, 25–41. Cambridge University Press, 2001. http://dx.doi.org/10.1017/cbo9780511626920.003.
Full textConference papers on the topic "Plant population genetics Statistical methods"
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Stingaci, Aurelia, and Leonid Volosciuc. "Particularitățile identificării VPN și VG a Hyphantria Cunea prin aplicarea microscopiei optice și electronice." In International symposium ”Functional ecology of animals” dedicated to the 70th anniversary from the birth of academician Ion Toderas. Institute of Zoology, Republic of Moldova, 2019. http://dx.doi.org/10.53937/9789975315975.61.
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Biopesticides are mass-produced, biologically based agents used for the control of plant pests. They are an important part of integrated pest management (IPM), which aims to use complementary methods to manage pest populations at low levels, rather than eliminate them entirely. Biopesticides are being used on increasing scales and there is considerable interest in their potential as alternatives to conventional pesticides. Biopesticides have also attracted great interest in the international research community, with a significant increase in the number of publications devoted to the subject. At Institute of Genetics, Physiology and Plant Protection are prepared the bioinsecticides for use in Republic Moldova, mostly for the control. In order to reduce the population of insect it is recommended utilization of inoffensive preparations baculoviruses highlypathogenic for the leaf-champing vermis of the Hyphantria cunea, were selected from the insect natural populations which is an efficient preparation for combating this pest in agricultural, onamental and forest biocenosis. This study aimed to highlight new agents for biological control of pest. The results of the present study revealed the larvicidal potential of baculovirusess isolates found in the larvae of H. cunea, local production of biopesticides, which will reduce the final cost of the product and will more accessible to farmer.
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Li, Yuhan, and Bo Kuang. "A Chinese 300MWe Two-Loop PWR NPP LBLOCA Analysis Based on the Deterministic Realistic Hybrid Methodology." In 2022 29th International Conference on Nuclear Engineering. American Society of Mechanical Engineers, 2022. http://dx.doi.org/10.1115/icone29-92431.
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Abstract Loss of coolant accident (LOCA) is among the important limiting design basis accidents for a PWR nuclear power plant (NPP). In China, a 300MWe two-loop PWR NPP, although facing the challenge of life extension, still adopted rather conservative tools and methods for safety analysis. This is supposed to have guaranteed sufficient margin for safe operation of the plant during the past years, yet, at the expense of plant economy and operation flexibility. To evaluate the safety margin more reasonably and realistically, the mixed methodology of DRHM (deterministic realistic hybrid methodology) is introduced for LBLOCA analysis of the Chinese 300MWe two-loop PWR NPP in the paper, with which conservative evaluation model plus best estimation analysis tool is applied, and effects of uncertainty of important plant state parameters are quantified. In the DRHM analysis of postulated LBLOCA caused by double ended-guillotine-cold-leg break for the 300MWe two-loop PWR NPP in this paper, the evaluation model RELAP5-APK (the conservative Appendix K physical models plus best-estimate system analysis code RELAP5/MOD3) is developed and verified. And during the transient analysis of the LBLOCA scenario, uncertainty of the effects of important plant state parameters are quantified through statistical sampling and corresponding calculation. Taking the cladding peak temperature (PCT) index for demonstration to measure the safety margin, the single-sided confidence upper limit including 95% PCT of the sampling population with 95% confidence level is acquired. The resultant shows that a greater PCT margin is achieved compared with that in the original FSAR. This provide a further confidence for life extension or power uprate of the plant.
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Ulrich, Thomas, Ronald Boring, and Roger Lew. "Studying Control Room Operations on a Shoestring Budget - Reflections on the Rancor Microworld." In 13th International Conference on Applied Human Factors and Ergonomics (AHFE 2022). AHFE International, 2022. http://dx.doi.org/10.54941/ahfe1001488.
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As the U.S. continues to develop and mature advanced reactor designs, the nuclear industry is becoming increasingly aware of the need for good human factors are to ensure safe, reliable, effective, and economical concept of operations. Advanced reactor designs aim to reduce staffing, and significant operational costs, by adopting high levels of automation. The highly automated control system designs must be informed with human factors and human reliability data. The proposed concepts of operations are unlike the current, largely manual, concept of operations found in operating nuclear power plants. Human performance data collection has proven difficult to obtain for existing nuclear power plants. Human factors researchers working on advanced reactor designs will encounter these same fundamental challenges and more. The novel concept of operations and accompanying human-system interfaces are novel and require human performance data for validation and licensing. Methods to evaluate novel concepts of operations for diverse advanced reactor designs must be identified to aid vendors in their system design activities. The Rancor microworld is a simulation platform that is currently used to support advanced reactor vendors in developing their control room concepts. The rationale and historical use of the Rancor microworld demonstrates a unique and complimentary approach to traditional full-scope simulator data collection methods that rely on expert licensed operators. The Rancor microworld is a reduced-order model of a small modular reactor conceived and developed to support human performance research on nuclear operations topics. The microworld represents the core elements of a nuclear power plant sans the complexity associated with full-scope simulators that are typically used to support human factors and human reliability research. The impetus for the microworld as an alternative method to acquire human performance data stems from the challenges in performing full-scope simulator studies. Full-scope simulators are expensive to build and maintain. Furthermore, they require extensive expertise to develop scenarios to support specific hypothesis testing. Operations data is historically difficult to obtain since even large research organizations that can afford a full-scope simulator facility encounter sample size issues. Licensed operators are expensive and fully time committed to their employing nuclear power plant. As such, it is very difficult to perform research on nuclear control room operations with sufficient sample sizes to approach statistical significance and draw generalizable conclusions applicable to different designs. Therefore, an alternative population using a simplified simulator offers an approach to evaluate human factors issues. Through numerous studies, the Rancor microworld has demonstrated an effective means to leverage inexpensive and ubiquitous student participants to expand the data collection capability and build a corpus of human performance data to inform advanced reactor control system designs and human reliability modeling. This paper provides an overview of the Rancor microworld studies and describes the benefits and disadvantages of using novice participants in simplified simulator environments in contrast with licensed operators in full-scope simulator environments.
Reports on the topic "Plant population genetics Statistical methods"
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Kistler, Harold Corby, and Talma Katan. Identification of DNA Unique to the Tomato Fusarium Wilt and Crown Rot Pathogens. United States Department of Agriculture, September 1995. http://dx.doi.org/10.32747/1995.7571359.bard.
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Wilt and crown rot are two important diseases of tomato caused by different strains ("formae speciales") of the fungus, Fusarium oxysporum. While both pathogens are members of the same fungal species, each differs genetically and resistance to the diseases is controlled by different genes in the plant. Additionally, the formae speciales differ in their ecology (e.g. optimal temperature of disease development) and epidemiology. Nevertheless, the distinction between these diseases based on symptoms alone may be unclear due to overlapping symptomatology. We have found in our research that the ambiguity of the pathogens is further confounded because strains causing tomato wilt or crown rot each may belong to several genetically and phylogenetically distinct lineages of F. oxysporum. Furthermore, individual lineages of the pathogen causing wilt or crown rot may themselves be very closely related. The diseases share the characteristic that the pathogen's inoculum may be aerially dispersed. This work has revealed a complex evolutionary relationship among lineages of the pathogens that makes development of molecular diagnostic methods more difficult than originally anticipated. However, the degree of diversity found in these soil-borne pathogens has allowed study of their population genetics and patterns of dispersal in agricultural settings.
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Jorgensen, Frieda, Andre Charlett, Craig Swift, Anais Painset, and Nicolae Corcionivoschi. A survey of the levels of Campylobacter spp. contamination and prevalence of selected antimicrobial resistance determinants in fresh whole UK-produced chilled chickens at retail sale (non-major retailers). Food Standards Agency, June 2021. http://dx.doi.org/10.46756/sci.fsa.xls618.
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Campylobacter spp. are the most common bacterial cause of foodborne illness in the UK, with chicken considered to be the most important vehicle for this organism. The UK Food Standards Agency (FSA) agreed with industry to reduce Campylobacter spp. contamination in raw chicken and issued a target to reduce the prevalence of the most contaminated chickens (those with more than 1000 cfu per g chicken neck skin) to below 10 % at the end of the slaughter process, initially by 2016. To help monitor progress, a series of UK-wide surveys were undertaken to determine the levels of Campylobacter spp. on whole UK-produced, fresh chicken at retail sale in the UK. The data obtained for the first four years was reported in FSA projects FS241044 (2014/15) and FS102121 (2015 to 2018). The FSA has indicated that the retail proxy target for the percentage of highly contaminated raw whole retail chickens should be less than 7% and while continued monitoring has demonstrated a sustained decline for chickens from major retailer stores, chicken on sale in other stores have yet to meet this target. This report presents results from testing chickens from non-major retailer stores (only) in a fifth survey year from 2018 to 2019. In line with previous practise, samples were collected from stores distributed throughout the UK (in proportion to the population size of each country). Testing was performed by two laboratories - a Public Health England (PHE) laboratory or the Agri-Food & Biosciences Institute (AFBI), Belfast. Enumeration of Campylobacter spp. was performed using the ISO 10272-2 standard enumeration method applied with a detection limit of 10 colony forming units (cfu) per gram (g) of neck skin. Antimicrobial resistance (AMR) to selected antimicrobials in accordance with those advised in the EU harmonised monitoring protocol was predicted from genome sequence data in Campylobacter jejuni and Campylobacter coli isolates The percentage (10.8%) of fresh, whole chicken at retail sale in stores of smaller chains (for example, Iceland, McColl’s, Budgens, Nisa, Costcutter, One Stop), independents and butchers (collectively referred to as non-major retailer stores in this report) in the UK that are highly contaminated (at more than 1000 cfu per g) with Campylobacter spp. has decreased since the previous survey year but is still higher than that found in samples from major retailers. 8 whole fresh raw chickens from non-major retailer stores were collected from August 2018 to July 2019 (n = 1009). Campylobacter spp. were detected in 55.8% of the chicken skin samples obtained from non-major retailer shops, and 10.8% of the samples had counts above 1000 cfu per g chicken skin. Comparison among production plant approval codes showed significant differences of the percentages of chicken samples with more than 1000 cfu per g, ranging from 0% to 28.1%. The percentage of samples with more than 1000 cfu of Campylobacter spp. per g was significantly higher in the period May, June and July than in the period November to April. The percentage of highly contaminated samples was significantly higher for samples taken from larger compared to smaller chickens. There was no statistical difference in the percentage of highly contaminated samples between those obtained from chicken reared with access to range (for example, free-range and organic birds) and those reared under standard regime (for example, no access to range) but the small sample size for organic and to a lesser extent free-range chickens, may have limited the ability to detect important differences should they exist. Campylobacter species was determined for isolates from 93.4% of the positive samples. C. jejuni was isolated from the majority (72.6%) of samples while C. coli was identified in 22.1% of samples. A combination of both species was found in 5.3% of samples. C. coli was more frequently isolated from samples obtained from chicken reared with access to range in comparison to those reared as standard birds. C. jejuni was less prevalent during the summer months of June, July and August compared to the remaining months of the year. Resistance to ciprofloxacin (fluoroquinolone), erythromycin (macrolide), tetracycline, (tetracyclines), gentamicin and streptomycin (aminoglycosides) was predicted from WGS data by the detection of known antimicrobial resistance determinants. Resistance to ciprofloxacin was detected in 185 (51.7%) isolates of C. jejuni and 49 (42.1%) isolates of C. coli; while 220 (61.1%) isolates of C. jejuni and 73 (62.9%) isolates of C. coli isolates were resistant to tetracycline. Three C. coli (2.6%) but none of the C. jejuni isolates harboured 23S mutations predicting reduced susceptibility to erythromycin. Multidrug resistance (MDR), defined as harbouring genetic determinants for resistance to at least three unrelated antimicrobial classes, was found in 10 (8.6%) C. coli isolates but not in any C. jejuni isolates. Co-resistance to ciprofloxacin and erythromycin was predicted in 1.7% of C. coli isolates. 9 Overall, the percentages of isolates with genetic AMR determinants found in this study were similar to those reported in the previous survey year (August 2016 to July 2017) where testing was based on phenotypic break-point testing. Multi-drug resistance was similar to that found in the previous survey years. It is recommended that trends in AMR in Campylobacter spp. isolates from retail chickens continue to be monitored to realise any increasing resistance of concern, particulary to erythromycin (macrolide). Considering that the percentage of fresh, whole chicken from non-major retailer stores in the UK that are highly contaminated (at more than 1000 cfu per g) with Campylobacter spp. continues to be above that in samples from major retailers more action including consideration of interventions such as improved biosecurity and slaughterhouse measures is needed to achieve better control of Campylobacter spp. for this section of the industry. The FSA has indicated that the retail proxy target for the percentage of highly contaminated retail chickens should be less than 7% and while continued monitoring has demonstrated a sustained decline for chickens from major retailer stores, chicken on sale in other stores have yet to meet this target.