Relevant bibliographies by topics / Population Genetic Inference

Academic literature on the topic 'Population Genetic Inference'

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Published: 10 December 2022
Last updated: 29 January 2023

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Journal articles on the topic "Population Genetic Inference"

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Jiang, Rong, Simon Tavaré, and Paul Marjoram. "Population Genetic Inference From Resequencing Data." Genetics 181, no. 1 (November 3, 2008): 187–97. http://dx.doi.org/10.1534/genetics.107.080630.

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Sheehan, Sara, and Yun S. Song. "Deep Learning for Population Genetic Inference." PLOS Computational Biology 12, no. 3 (March 28, 2016): e1004845. http://dx.doi.org/10.1371/journal.pcbi.1004845.

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Pool, J. E., I. Hellmann, J. D. Jensen, and R. Nielsen. "Population genetic inference from genomic sequence variation." Genome Research 20, no. 3 (January 12, 2010): 291–300. http://dx.doi.org/10.1101/gr.079509.108.

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Lynch, Michael, Darius Bost, Sade Wilson, Takahiro Maruki, and Scott Harrison. "Population-Genetic Inference from Pooled-Sequencing Data." Genome Biology and Evolution 6, no. 5 (April 30, 2014): 1210–18. http://dx.doi.org/10.1093/gbe/evu085.

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Battey, C. J., Peter L. Ralph, and Andrew D. Kern. "Space is the Place: Effects of Continuous Spatial Structure on Analysis of Population Genetic Data." Genetics 215, no. 1 (March 24, 2020): 193–214. http://dx.doi.org/10.1534/genetics.120.303143.

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Real geography is continuous, but standard models in population genetics are based on discrete, well-mixed populations. As a result, many methods of analyzing genetic data assume that samples are a random draw from a well-mixed population, but are applied to clustered samples from populations that are structured clinally over space. Here, we use simulations of populations living in continuous geography to study the impacts of dispersal and sampling strategy on population genetic summary statistics, demographic inference, and genome-wide association studies (GWAS). We find that most common summary statistics have distributions that differ substantially from those seen in well-mixed populations, especially when Wright’s neighborhood size is < 100 and sampling is spatially clustered. “Stepping-stone” models reproduce some of these effects, but discretizing the landscape introduces artifacts that in some cases are exacerbated at higher resolutions. The combination of low dispersal and clustered sampling causes demographic inference from the site frequency spectrum to infer more turbulent demographic histories, but averaged results across multiple simulations revealed surprisingly little systematic bias. We also show that the combination of spatially autocorrelated environments and limited dispersal causes GWAS to identify spurious signals of genetic association with purely environmentally determined phenotypes, and that this bias is only partially corrected by regressing out principal components of ancestry. Last, we discuss the relevance of our simulation results for inference from genetic variation in real organisms.
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Tvedebrink, Torben. "Review of the Forensic Applicability of Biostatistical Methods for Inferring Ancestry from Autosomal Genetic Markers." Genes 13, no. 1 (January 14, 2022): 141. http://dx.doi.org/10.3390/genes13010141.

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The inference of ancestry has become a part of the services many forensic genetic laboratories provide. Interest in ancestry may be to provide investigative leads or identify the region of origin in cases of unidentified missing persons. There exist many biostatistical methods developed for the study of population structure in the area of population genetics. However, the challenges and questions are slightly different in the context of forensic genetics, where the origin of a specific sample is of interest compared to the understanding of population histories and genealogies. In this paper, the methodologies for modelling population admixture and inferring ancestral populations are reviewed with a focus on their strengths and weaknesses in relation to ancestry inference in the forensic context.
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Pritchard, Jonathan K., Matthew Stephens, and Peter Donnelly. "Inference of Population Structure Using Multilocus Genotype Data." Genetics 155, no. 2 (June 1, 2000): 945–59. http://dx.doi.org/10.1093/genetics/155.2.945.

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Abstract We describe a model-based clustering method for using multilocus genotype data to infer population structure and assign individuals to populations. We assume a model in which there are K populations (where K may be unknown), each of which is characterized by a set of allele frequencies at each locus. Individuals in the sample are assigned (probabilistically) to populations, or jointly to two or more populations if their genotypes indicate that they are admixed. Our model does not assume a particular mutation process, and it can be applied to most of the commonly used genetic markers, provided that they are not closely linked. Applications of our method include demonstrating the presence of population structure, assigning individuals to populations, studying hybrid zones, and identifying migrants and admixed individuals. We show that the method can produce highly accurate assignments using modest numbers of loci—e.g., seven microsatellite loci in an example using genotype data from an endangered bird species. The software used for this article is available from http://www.stats.ox.ac.uk/~pritch/home.html.
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Crisci, Jessica L., Yu-Ping Poh, Angela Bean, Alfred Simkin, and Jeffrey D. Jensen. "Recent Progress in Polymorphism-Based Population Genetic Inference." Journal of Heredity 103, no. 2 (January 12, 2012): 287–96. http://dx.doi.org/10.1093/jhered/esr128.

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Loog, Liisa. "Sometimes hidden but always there: the assumptions underlying genetic inference of demographic histories." Philosophical Transactions of the Royal Society B: Biological Sciences 376, no. 1816 (November 30, 2020): 20190719. http://dx.doi.org/10.1098/rstb.2019.0719.

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Demographic processes directly affect patterns of genetic variation within contemporary populations as well as future generations, allowing for demographic inference from patterns of both present-day and past genetic variation. Advances in laboratory procedures, sequencing and genotyping technologies in the past decades have resulted in massive increases in high-quality genome-wide genetic data from present-day populations and allowed retrieval of genetic data from archaeological material, also known as ancient DNA. This has resulted in an explosion of work exploring past changes in population size, structure, continuity and movement. However, as genetic processes are highly stochastic, patterns of genetic variation only indirectly reflect demographic histories. As a result, past demographic processes need to be reconstructed using an inferential approach. This usually involves comparing observed patterns of variation with model expectations from theoretical population genetics. A large number of approaches have been developed based on different population genetic models that each come with assumptions about the data and underlying demography. In this article I review some of the key models and assumptions underlying the most commonly used approaches for past demographic inference and their consequences for our ability to link the inferred demographic processes to the archaeological and climate records. This article is part of the theme issue ‘Cross-disciplinary approaches to prehistoric demography’.
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Wu, Yufeng. "Inference of population admixture network from local gene genealogies: a coalescent-based maximum likelihood approach." Bioinformatics 36, Supplement_1 (July 1, 2020): i326—i334. http://dx.doi.org/10.1093/bioinformatics/btaa465.

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Abstract Motivation Population admixture is an important subject in population genetics. Inferring population demographic history with admixture under the so-called admixture network model from population genetic data is an established problem in genetics. Existing admixture network inference approaches work with single genetic polymorphisms. While these methods are usually very fast, they do not fully utilize the information [e.g. linkage disequilibrium (LD)] contained in population genetic data. Results In this article, we develop a new admixture network inference method called GTmix. Different from existing methods, GTmix works with local gene genealogies that can be inferred from population haplotypes. Local gene genealogies represent the evolutionary history of sampled haplotypes and contain the LD information. GTmix performs coalescent-based maximum likelihood inference of admixture networks with inferred local genealogies based on the well-known multispecies coalescent (MSC) model. GTmix utilizes various techniques to speed up the likelihood computation on the MSC model and the optimal network search. Our simulations show that GTmix can infer more accurate admixture networks with much smaller data than existing methods, even when these existing methods are given much larger data. GTmix is reasonably efficient and can analyze population genetic datasets of current interests. Availability and implementation The program GTmix is available for download at: https://github.com/yufengwudcs/GTmix. Supplementary information Supplementary data are available at Bioinformatics online.
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Dissertations / Theses on the topic "Population Genetic Inference"

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Leslie, Stephen. "Inference of Population Stratification Using Population Genetic Data." Thesis, University of Oxford, 2008. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.504423.

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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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Anderson, Eric C. "Monte Carlo methods for inference in population genetic models /." Thesis, Connect to this title online; UW restricted, 2001. http://hdl.handle.net/1773/6368.

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Li, Xin. "Haplotype Inference from Pedigree Data and Population Data." Cleveland, Ohio : Case Western Reserve University, 2010. http://rave.ohiolink.edu/etdc/view?acc_num=case1259867573.

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Thesis(Ph.D.)--Case Western Reserve University, 2010
Title from PDF (viewed on 2009-12-30) Department of Electrical Engineering and Computer Science Includes abstract Includes bibliographical references and appendices Available online via the OhioLINK ETD Center
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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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Nunziata, Schyler O. "USING GENOMICS TO UNDERSTAND POPULATION DEMOGRAPHICS IN THE CONTEXT OF AMPHIBIAN CONSERVATION." UKnowledge, 2017. http://uknowledge.uky.edu/biology_etds/49.

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Understanding the demography of species over recent history (e.g., < 100 years) is critical in studies of ecology and evolution, but records of population history are rarely available. Large single nucleotide polymorphism datasets generated with restriction-site associated DNA sequencing (RADseq), in combination with demographic inference methods, are improving our ability to gain insights into the population history of both model and non-model species. However, to assess the performance of genetic methods it is important to compare their estimates of population history to known demography, in both simulation and empirical settings. Here, I used a simulation approach to examine the potential for RADseq datasets to accurately estimate effective population size (Ne) in Wright-Fisher populations over the course of stable and declining population trends, and distinguish stable from steadily declining populations over a contemporary time scale (20 generations). Overall, my results reveal that demographic inference using genome-wide data can be successfully applied to estimate Ne, and the detection of population-size declines. Next, I assess these methods in an empirical study from a wetland with 37 years of amphibian mark-recapture data to study the utility of genetically-based demographic inference on salamander species with documented population declines (Ambystoma talpoideum) and expansions (A. opacum). For both species, demographic model inference supported population size changes that corroborated mark-recapture data. To further validate these findings, I used individual-based population models of the pond-breeding salamander, Ambystoma opacum, with life-history parameters estimated from a long-term dataset, over a 50 year projection. My results demonstrate that genetically estimated Ne is positively correlated with census size in isolated and subdivided A. opacum populations. Finally, I investigated metapopulation patterns of genomic diversity in A. opacum and A. talpoideum and how migration may impact Ne estimation. I found strong patterns of subpopulation structuring, signatures of migration between subpopulations, and differences in Ne at the subpopulation level in both species. Overall, my findings suggest the ability of genomic data to reconstruct recent demographic changes, which can have important applications to conservation biology, and ultimately can help us elucidate the effects of environmental disturbances in the demography of endangered or declining species.
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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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Wallace, Lisa Ellen. "Systematic and Population Genetic Analyses of Northern Vs Southern Yellow Lady's Slippers (Cypripedium parviflorum Vars parviflorum, pubescens, and makasin): Inference from Isozyme and Morphological Data." W&M ScholarWorks, 1997. https://scholarworks.wm.edu/etd/1539626099.

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Maritz, E. L. "Computational inference with the coalescent in molecular population genetics." Thesis, University of Oxford, 2003. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.270209.

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Jones, Mary Beatrix. "Likelihood inference for parametric models of dispersal /." Thesis, Connect to this title online; UW restricted, 2000. http://hdl.handle.net/1773/8934.

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Books on the topic "Population Genetic Inference"

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The statistics of natural selection on animal populations. London: Chapman and Hall, 1985.

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Marko, Peter B., and Michael W. Hart, eds. Genetic Analysis of Larval Dispersal, Gene Flow, and Connectivity. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780198786962.003.0012.

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Does the dispersal of planktonic larvae promote strong connections between marine populations? Here we describe some of the most commonly used population- and individual-based genetic methods that have enhanced our understanding of larval dispersal and marine connectivity. Both approaches have strengths and weaknesses. Choosing between them depends on whether researchers want to know about average effective rates of connectivity over long timescales (over hundreds to thousands of generations) or recent patterns of connectivity on shorter timescales (one to two generations). The use of both approaches has improved our understanding of larval dispersal distances, the relationship between realized dispersal (from genetics) and dispersal potential (from planktonic larval duration), and the crucial distinction between genetic and demographic connectivity. Although rarely used together, combining population- and individual-based inferences from genetic data will likely further enrich our understanding of the scope and scale of larval dispersal in marine systems.
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Winther, Rasmus Grønfeldt. Phylogenetic Inference, Selection Theory, and History of Science: Selected Papers of A. W. F. Edwards with Commentaries. Cambridge University Press, 2018.

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Winther, Rasmus Grønfeldt. Phylogenetic Inference, Selection Theory, and History of Science: Selected Papers of A. W. F. Edwards with Commentaries. Cambridge University Press, 2018.

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Winther, Rasmus Grønfeldt. Phylogenetic Inference, Selection Theory, and History of Science: Selected Papers of A. W. F. Edwards with Commentaries. Cambridge University Press, 2018.

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The statistics of natural selection on animal populations. Chapman and Hall, 1987.

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Thun, Michael J., Martha S. Linet, James R. Cerhan, Christopher A. Haiman, and David Schottenfeld. Introduction. Oxford University Press, 2017. http://dx.doi.org/10.1093/oso/9780190238667.003.0001.

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This Introduction provides a broad overview of the scientific advances and crosscutting developments that increasingly influence epidemiologic research on the causes and prevention of cancer. High-throughput technologies have identified the molecular “driver” events in tumor tissue that underlie the multistage development of many types of cancer. These somatic (largely acquired) alterations disrupt normal genetic and epigenetic control over cell maintenance, division and survival. Tumor classification is also changing to reflect the genetic and molecular alterations in tumor tissue, as well as the anatomic, morphologic, and histologic phenotype of the cancer. Genome-wide association studies (GWAS) have identified more than 700 germline (inherited) genetic loci associated with susceptibility to various forms of cancer, although the risk estimates for almost all of these are small to modest and their exact location and function remain to identified. Advances in genomic and other “OMIC” technologies are identifying biomarkers that reflect internal exposures, biological processes and intermediate outcomes in large population studies. While research in many of these areas is still in its infancy, mechanistic and molecular assays are increasingly incorporated into etiologic studies and inferences about causation. Other sections of the book discuss the global public health impact of cancer, the growing list of exposures known to affect cancer risk, the epidemiology of over 30 types of cancer by tissue of origin, and preventive interventions that have dramatically reduced the incidence rates of several major cancers.
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Book chapters on the topic "Population Genetic Inference"

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Bromek, Tadeusz, and Elżbieta Pleszczyńska. "Statistical problems of population genetics." In Statistical Inference, 137–59. Dordrecht: Springer Netherlands, 1991. http://dx.doi.org/10.1007/978-94-009-0575-7_7.

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Rousset, F. "Inferences from Spatial Population Genetics." In Handbook of Statistical Genetics, 945–79. Chichester, UK: John Wiley & Sons, Ltd, 2008. http://dx.doi.org/10.1002/9780470061619.ch28.

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O’Connell, Neil. "Branching and Inference in Population Genetics." In Progress in Population Genetics and Human Evolution, 97–106. New York, NY: Springer New York, 1997. http://dx.doi.org/10.1007/978-1-4757-2609-1_6.

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Platt, Daniel E., Filippo Utro, Marc Pybus, and Laxmi Parida. "Genetic History of Populations: Limits to Inference." In Models and Algorithms for Genome Evolution, 309–23. London: Springer London, 2013. http://dx.doi.org/10.1007/978-1-4471-5298-9_14.

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Tavaré, Simon. "Part I: Ancestral Inference in Population Genetics." In Lecture Notes in Mathematics, 1–188. Berlin, Heidelberg: Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-540-39874-5_1.

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Stamatakis, Alexandros. "Population and Evolutionary Genetic Inferences in the Whole-Genome Era: Software Challenges." In Population Genomics, 161–75. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/13836_2018_42.

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Templeton, Alan R. "Using haplotype trees for phylogeographic and species inference in fish populations." In Genetics of Subpolar Fish and Invertebrates, 7–20. Dordrecht: Springer Netherlands, 2004. http://dx.doi.org/10.1007/978-94-007-0983-6_2.

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Fernando, R. L., and D. Gianola. "Statistical Inferences in Populations Undergoing Selection or Non-Random Mating." In Advances in Statistical Methods for Genetic Improvement of Livestock, 437–53. Berlin, Heidelberg: Springer Berlin Heidelberg, 1990. http://dx.doi.org/10.1007/978-3-642-74487-7_19.

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Stephan, Wolfgang, and Thomas Städler. "Population Genetics of Speciation and Demographic Inference Under Population Subdivision: Insights from Studies on Wild Tomatoes (Solanum sect. Lycopersicon)." In Evolution in Action, 119–35. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-12425-9_7.

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Lawson, Daniel John. "Populations in Statistical Genetic Modelling and Inference." In Population in the Human Sciences, 108–30. Oxford University Press, 2015. http://dx.doi.org/10.1093/acprof:oso/9780199688203.003.0004.

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Conference papers on the topic "Population Genetic Inference"

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Nouri, Javad, and Roman Yangarber. "From alignment of etymological data to phylogenetic inference via population genetics." In Proceedings of the 7th Workshop on Cognitive Aspects of Computational Language Learning. Stroudsburg, PA, USA: Association for Computational Linguistics, 2016. http://dx.doi.org/10.18653/v1/w16-1905.

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BuKhamseen, Nader, Ali Saffar, and Marko Maucec. "Rigorous Performance Evaluation of Stochastic Optimization for Water Injection Strategies." In SPE Middle East Oil & Gas Show and Conference. SPE, 2021. http://dx.doi.org/10.2118/204749-ms.

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Abstract This paper presents an approach to optimize field water injection strategies using stochastic methods under uncertainty. For many fields, voidage replacement was the dictating factor of setting injection strategies. Determining the optimum injection-production ratio (IPR) requires extensive experience taking into consideration all the operational facility constraints. We present the outcome of a study, in which several optimization techniques were used to find the optimum field IPR values and then elaborate on the techniques? strengths and weaknesses. The synthetic reservoir simulation model, with millions of grid blocks and significant numbers of producers and injectors, was divided into seven IPR regions based on a streamline study. Each region was assigned an IPR value with an associated uncertainty interval. An ensemble of fifty probabilistic scenarios was generated by experimental design, using Latin Hypercube sampling of IPR values within tolerance limits. Scenarios were used as the main sampling domain to evaluate a family of optimization engines: population-based methods of artificial intelligence (AI), such as Genetic algorithms and Evolutionary strategies, Bayesian inference using sequential or Markov chain Monte Carlo, and proxy-based optimization. The optimizers were evaluated based on the recommended IPR values that meet the objective of minimizing the water cut by maximizing oil production and minimizing water production. The speed of convergence of the optimization process was also a subject of evaluation. To ensure unbiased sampling of IPR values and to prevent oversampling of boundary extremes, a uniform triangular distribution was designed. The results of the study show a clear improvement of the objective function, compared to the initial sampled cases. As a direct search method, the Evolutionary strategies with covariance matrix adaptation (ES-CMA) yielded the optimum IPR value per region. While examining the effect of applying these IPR values in the reservoir simulation model, a significant reduction of water production from the initial cases without an impact on the oil production was observed. Compared to ESCMA, other optimization methods have dem
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Reports on the topic "Population Genetic Inference"

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McCarthy, Noel, Eileen Taylor, Martin Maiden, Alison Cody, Melissa Jansen van Rensburg, Margaret Varga, Sophie Hedges, et al. Enhanced molecular-based (MLST/whole genome) surveillance and source attribution of Campylobacter infections in the UK. Food Standards Agency, July 2021. http://dx.doi.org/10.46756/sci.fsa.ksj135.

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This human campylobacteriosis sentinel surveillance project was based at two sites in Oxfordshire and North East England chosen (i) to be representative of the English population on the Office for National Statistics urban-rural classification and (ii) to provide continuity with genetic surveillance started in Oxfordshire in October 2003. Between October 2015 and September 2018 epidemiological questionnaires and genome sequencing of isolates from human cases was accompanied by sampling and genome sequencing of isolates from possible food animal sources. The principal aim was to estimate the contributions of the main sources of human infection and to identify any changes over time. An extension to the project focussed on antimicrobial resistance in study isolates and older archived isolates. These older isolates were from earlier years at the Oxfordshire site and the earliest available coherent set of isolates from the national archive at Public Health England (1997/8). The aim of this additional work was to analyse the emergence of the antimicrobial resistance that is now present among human isolates and to describe and compare antimicrobial resistance in recent food animal isolates. Having identified the presence of bias in population genetic attribution, and that this was not addressed in the published literature, this study developed an approach to adjust for bias in population genetic attribution, and an alternative approach to attribution using sentinel types. Using these approaches the study estimated that approximately 70% of Campylobacter jejuni and just under 50% of C. coli infection in our sample was linked to the chicken source and that this was relatively stable over time. Ruminants were identified as the second most common source for C. jejuni and the most common for C. coli where there was also some evidence for pig as a source although less common than ruminant or chicken. These genomic attributions of themselves make no inference on routes of transmission. However, those infected with isolates genetically typical of chicken origin were substantially more likely to have eaten chicken than those infected with ruminant types. Consumption of lamb’s liver was very strongly associated with infection by a strain genetically typical of a ruminant source. These findings support consumption of these foods as being important in the transmission of these infections and highlight a potentially important role for lamb’s liver consumption as a source of Campylobacter infection. Antimicrobial resistance was predicted from genomic data using a pipeline validated by Public Health England and using BIGSdb software. In C. jejuni this showed a nine-fold increase in resistance to fluoroquinolones from 1997 to 2018. Tetracycline resistance was also common, with higher initial resistance (1997) and less substantial change over time. Resistance to aminoglycosides or macrolides remained low in human cases across all time periods. Among C. jejuni food animal isolates, fluoroquinolone resistance was common among isolates from chicken and substantially less common among ruminants, ducks or pigs. Tetracycline resistance was common across chicken, duck and pig but lower among ruminant origin isolates. In C. coli resistance to all four antimicrobial classes rose from low levels in 1997. The fluoroquinolone rise appears to have levelled off earlier and among animals, levels are high in duck as well as chicken isolates, although based on small sample sizes, macrolide and aminoglycoside resistance, was substantially higher than for C. jejuni among humans and highest among pig origin isolates. Tetracycline resistance is high in isolates from pigs and the very small sample from ducks. Antibiotic use following diagnosis was relatively high (43.4%) among respondents in the human surveillance study. Moreover, it varied substantially across sites and was highest among non-elderly adults compared to older adults or children suggesting opportunities for improved antimicrobial stewardship. The study also found evidence for stable lineages over time across human and source animal species as well as some tighter genomic clusters that may represent outbreaks. The genomic dataset will allow extensive further work beyond the specific goals of the study. This has been made accessible on the web, with access supported by data visualisation tools.
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Rajarajan, Kunasekaran, Alka Bharati, Hirdayesh Anuragi, Arun Kumar Handa, Kishor Gaikwad, Nagendra Kumar Singh, Kamal Prasad Mohapatra, et al. Status of perennial tree germplasm resources in India and their utilization in the context of global genome sequencing efforts. World Agroforestry, 2020. http://dx.doi.org/10.5716/wp20050.pdf.

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Tree species are characterized by their perennial growth habit, woody morphology, long juvenile period phase, mostly outcrossing behaviour, highly heterozygosity genetic makeup, and relatively high genetic diversity. The economically important trees have been an integral part of the human life system due to their provision of timber, fruit, fodder, and medicinal and/or health benefits. Despite its widespread application in agriculture, industrial and medicinal values, the molecular aspects of key economic traits of many tree species remain largely unexplored. Over the past two decades, research on forest tree genomics has generally lagged behind that of other agronomic crops. Genomic research on trees is motivated by the need to support genetic improvement programmes mostly for food trees and timber, and develop diagnostic tools to assist in recommendation for optimum conservation, restoration and management of natural populations. Research on long-lived woody perennials is extending our molecular knowledge and understanding of complex life histories and adaptations to the environment, enriching a field that has traditionally drawn its biological inference from a few short-lived herbaceous species. These concerns have fostered research aimed at deciphering the genomic basis of complex traits that are related to the adaptive value of trees. This review summarizes the highlights of tree genomics and offers some priorities for accelerating progress in the next decade.
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