Relevant bibliographies by topics / Genomic imprinting - Statistical methods

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  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters

Academic literature on the topic 'Genomic imprinting - Statistical methods'

Author: Grafiati
Published: 4 June 2021
Last updated: 1 February 2022

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Journal articles on the topic "Genomic imprinting - Statistical methods"

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Cui, Yuehua, James M. Cheverud, and Rongling Wu. "A statistical model for dissecting genomic imprinting through genetic mapping." Genetica 130, no. 3 (September 6, 2006): 227–39. http://dx.doi.org/10.1007/s10709-006-9101-x.

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Spencer, Hamish G. "The Correlation Between Relatives on the Supposition of Genomic Imprinting." Genetics 161, no. 1 (May 1, 2002): 411–17. http://dx.doi.org/10.1093/genetics/161.1.411.

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Abstract Standard genetic analyses assume that reciprocal heterozygotes are, on average, phenotypically identical. If a locus is subject to genomic imprinting, however, this assumption does not hold. We incorporate imprinting into the standard quantitative-genetic model for two alleles at a single locus, deriving expressions for the additive and dominance components of genetic variance, as well as measures of resemblance among relatives. We show that, in contrast to the case with Mendelian expression, the additive and dominance deviations are correlated. In principle, this correlation allows imprinting to be detected solely on the basis of different measures of familial resemblances, but in practice, the standard error of the estimate is likely to be too large for a test to have much statistical power. The effects of genomic imprinting will need to be incorporated into quantitative-genetic models of many traits, for example, those concerned with mammalian birthweight.
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Li, Yao, Yunqian Guo, Jianxin Wang, Wei Hou, Myron N. Chang, Duanping Liao, and Rongling Wu. "A Statistical Design for Testing Transgenerational Genomic Imprinting in Natural Human Populations." PLoS ONE 6, no. 2 (February 25, 2011): e16858. http://dx.doi.org/10.1371/journal.pone.0016858.

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Market, Brenna A., Liyue Zhang, Lauren S. Magri, Michael C. Golding, and Mellissa RW Mann. "INVESTIGATING THE MOLECULAR AND DEVELOPMENTAL EFFECTS OF VARIOUS CULTURE REGIMES IN A MOUSE MODEL SYSTEM." Clinical & Investigative Medicine 31, no. 4 (August 1, 2008): 16. http://dx.doi.org/10.25011/cim.v31i4.4814.

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Background/Purpose: Genomic imprinting is a specialized transcriptional mechanism that results in the unequal expression of alleles based on their parent-of-origin [1]. Many imprinted genes are critical for proper embryonic and fetaldevelopment [2] and disruption of genomic imprinting are associated with many development disorders [3]. Recently, increased frequencies of imprinting disorders have been correlated with the use of assisted reproductive technologies (ARTs)[2]. Rigorous and thorough testing of ARTs is required to determine their influence on genomic imprinting and development. I hypothesize that imprinting maintenance mechanisms are disrupted during early mouse development by the environmental insult of culture media used in human ARTs, and that loss of imprinting correlates with delayed embryonic development. Methods: The specific aims of my project are to develop a method to evaluate the methylation and expression patterns of 4 known imprinted genes in individual blastocysts. Results: We have successfully developed a novel method to evaluate both imprinted methylation and expression from a single mouse blastocyst. This method has been tested and results compared to methods used to evaluate imprinted methylation and expression separately; we have determined that results obtained with a combined protocol are equivalent to either alone. I will use this method to evaluate relationships between development rates in culture andgenomic imprinting, as well as the effects of various culture media used formouse and human embryo culture on genomic imprinting. Conclusion: This analysis allow for a more comprehensive study ofthe effects of environmental insult on genomic imprinting and preimplantation embryo development. References: 1. Reik W, Walter J. Genomic imprinting:parental influence on the genome. Nat Rev Genet 2001;2:21-32. 2. Rodenhiser D, Mann M. Epigenetics andhuman disease: translating basic biology into clinical applications. CMAJ. 2006;174:341-8. 3.Paoloni-Giacobino A. Epigenetics in reproductive medicine. Pediatr Res 2007;61:51R-57R.
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He, Tao, Jian Sa, Ping-Shou Zhong, and Yuehua Cui. "Statistical Dissection of Cyto-Nuclear Epistasis Subject to Genomic Imprinting in Line Crosses." PLoS ONE 9, no. 3 (March 18, 2014): e91702. http://dx.doi.org/10.1371/journal.pone.0091702.

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Rienecker, Kira DA, Matthew J. Hill, and Anthony R. Isles. "Methods of epigenome editing for probing the function of genomic imprinting." Epigenomics 8, no. 10 (October 2016): 1389–98. http://dx.doi.org/10.2217/epi-2016-0073.

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Elbracht, Miriam, Deborah Mackay, Matthias Begemann, Karl Oliver Kagan, and Thomas Eggermann. "Disturbed genomic imprinting and its relevance for human reproduction: causes and clinical consequences." Human Reproduction Update 26, no. 2 (February 18, 2020): 197–213. http://dx.doi.org/10.1093/humupd/dmz045.

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Abstract BACKGROUND Human reproductive issues affecting fetal and maternal health are caused by numerous exogenous and endogenous factors, of which the latter undoubtedly include genetic changes. Pathogenic variants in either maternal or offspring DNA are associated with effects on the offspring including clinical disorders and nonviable outcomes. Conversely, both fetal and maternal factors can affect maternal health during pregnancy. Recently, it has become evident that mammalian reproduction is influenced by genomic imprinting, an epigenetic phenomenon that regulates the expression of genes according to their parent from whom they are inherited. About 1% of human genes are normally expressed from only the maternally or paternally inherited gene copy. Since numerous imprinted genes are involved in (embryonic) growth and development, disturbance of their balanced expression can adversely affect these processes. OBJECTIVE AND RATIONALE This review summarises current our understanding of genomic imprinting in relation to human ontogenesis and pregnancy and its relevance for reproductive medicine. SEARCH METHODS Literature databases (Pubmed, Medline) were thoroughly searched for the role of imprinting in human reproductive failure. In particular, the terms ‘multilocus imprinting disturbances, SCMC, NLRP/NALP, imprinting and reproduction’ were used in various combinations. OUTCOMES A range of molecular changes to specific groups of imprinted genes are associated with imprinting disorders, i.e. syndromes with recognisable clinical features including distinctive prenatal features. Whereas the majority of affected individuals exhibit alterations at single imprinted loci, some have multi-locus imprinting disturbances (MLID) with less predictable clinical features. Imprinting disturbances are also seen in some nonviable pregnancy outcomes, such as (recurrent) hydatidiform moles, which can therefore be regarded as a severe form of imprinting disorders. There is growing evidence that MLID can be caused by variants in the maternal genome altering the imprinting status of the oocyte and the embryo, i.e. maternal effect mutations. Pregnancies of women carrying maternal affect mutations can have different courses, ranging from miscarriages to birth of children with clinical features of various imprinting disorders. WIDER IMPLICATIONS Increasing understanding of imprinting disturbances and their clinical consequences have significant impacts on diagnostics, counselling and management in the context of human reproduction. Defining criteria for identifying pregnancies complicated by imprinting disorders facilitates early diagnosis and personalised management of both the mother and offspring. Identifying the molecular lesions underlying imprinting disturbances (e.g. maternal effect mutations) allows targeted counselling of the family and focused medical care in further pregnancies.
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Varrault, Annie, Emeric Dubois, Anne Le Digarcher, and Tristan Bouschet. "Quantifying Genomic Imprinting at Tissue and Cell Resolution in the Brain." Epigenomes 4, no. 3 (September 4, 2020): 21. http://dx.doi.org/10.3390/epigenomes4030021.

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Imprinted genes are a group of ~150 genes that are preferentially expressed from one parental allele owing to epigenetic marks asymmetrically distributed on inherited maternal and paternal chromosomes. Altered imprinted gene expression causes human brain disorders such as Prader-Willi and Angelman syndromes and additional rare brain diseases. Research data principally obtained from the mouse model revealed how imprinted genes act in the normal and pathological brain. However, a better understanding of imprinted gene functions calls for building detailed maps of their parent-of-origin-dependent expression and of associated epigenetic signatures. Here we review current methods for quantifying genomic imprinting at tissue and cell resolutions, with a special emphasis on methods to detect parent-of-origin dependent expression and their applications to the brain. We first focus on bulk RNA-sequencing, the main method to detect parent-of-origin-dependent expression transcriptome-wide. We discuss the benefits and caveats of bulk RNA-sequencing and provide a guideline to use it on F1 hybrid mice. We then review methods for detecting parent-of-origin-dependent expression at cell resolution, including single-cell RNA-seq, genetic reporters, and molecular probes. Finally, we provide an overview of single-cell epigenomics technologies that profile additional features of genomic imprinting, including DNA methylation, histone modifications and chromatin conformation and their combination into sc-multimodal omics approaches, which are expected to yield important insights into genomic imprinting in individual brain cells.
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Wang, Yuedong. "Statistical methods for detecting genomic alterations through array-based comparative genomic hybridization (CGH)." Frontiers in Bioscience 9, no. 1-3 (2004): 540. http://dx.doi.org/10.2741/1186.

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Suzuki, Yoshiyuki. "Statistical methods for detecting natural selection from genomic data." Genes & Genetic Systems 85, no. 6 (2010): 359–76. http://dx.doi.org/10.1266/ggs.85.359.

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Dissertations / Theses on the topic "Genomic imprinting - Statistical methods"

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Zhou, Jiyuan, and 周基元. "Single-marker and haplotype analyses for detecting parent-of-origin effects using family and pedigree data." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2009. http://hub.hku.hk/bib/B4308543X.

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Xia, Fan, and 夏凡. "Some topics on statistical analysis of genetic imprinting data and microbiome compositional data." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2014. http://hdl.handle.net/10722/206673.

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Genetic association study is a useful tool to identify the genetic component that is responsible for a disease. The phenomenon that a certain gene expresses in a parent-of-origin manner is referred to as genomic imprinting. When a gene is imprinted, the performance of the disease-association study will be affected. This thesis presents statistical testing methods developed specially for nuclear family data centering around the genetic association studies incorporating imprinting effects. For qualitative diseases with binary outcomes, a class of TDTI* type tests was proposed in a general two-stage framework, where the imprinting effects were examined prior to association testing. On quantitative trait loci, a class of Q-TDTI(c) type tests and another class of Q-MAX(c) type tests were proposed. The proposed testing methods flexibly accommodate families with missing parental genotype and with multiple siblings. The performance of all the methods was verified by simulation studies. It was found that the proposed methods improve the testing power for detecting association in the presence of imprinting. The class of TDTI* tests was applied to a rheumatoid arthritis study data. Also, the class of Q-TDTI(c) tests was applied to analyze the Framingham Heart Study data. The human microbiome is the collection of the microbiota, together with their genomes and their habitats throughout the human body. The human microbiome comprises an inalienable part of our genetic landscape and contributes to our metabolic features. Also, current studies have suggested the variety of human microbiome in human diseases. With the high-throughput DNA sequencing, the human microbiome composition can be characterized based on bacterial taxa relative abundance and the phylogenetic constraint. Such taxa data are often high-dimensional overdispersed and contain excessive number of zeros. Taking into account of these characteristics in taxa data, this thesis presents statistical methods to identify associations between covariate/outcome and the human microbiome composition. To assess environmental/biological covariate effect to microbiome composition, an additive logistic normal multinomial regression model was proposed and a group l1 penalized likelihood estimation method was further developed to facilitate selection of covariates and estimation of parameters. To identify microbiome components associated with biological/clinical outcomes, a Bayesian hierarchical regression model with spike and slab prior for variable selection was proposed and a Markov chain Monte Carlo algorithm that combines stochastic variable selection procedure and random walk metropolis-hasting steps was developed for model estimation. Both of the methods were illustrated using simulations as well as a real human gut microbiome dataset from The Penn Gut Microbiome Project.
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Statistics and Actuarial Science
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Doctor of Philosophy
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He, Feng, and 贺峰. "Detection of parent-of-origin effects and association in relation to aquantitative trait." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2010. http://hub.hku.hk/bib/B44921408.

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Hu, Yueqing, and 胡躍清. "Some topics in the statistical analysis of forensic DNA and genetic family data." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2007. http://hub.hku.hk/bib/B38831491.

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Hu, Yueqing. "Some topics in the statistical analysis of forensic DNA and genetic family data." Click to view the E-thesis via HKUTO, 2007. http://sunzi.lib.hku.hk/hkuto/record/B38831491.

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Ming, Jingsi. "Statistical methods for integrative analysis of genomic data." HKBU Institutional Repository, 2018. https://repository.hkbu.edu.hk/etd_oa/545.

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Thousands of risk variants underlying complex phenotypes (quantitative traits and diseases) have been identified in genome-wide association studies (GWAS). However, there are still several challenges towards deepening our understanding of the genetic architectures of complex phenotypes. First, the majority of GWAS hits are in non-coding region and their biological interpretation is still unclear. Second, most complex traits are suggested to be highly polygenic, i.e., they are affected by a vast number of risk variants with individually small or moderate effects, whereas a large proportion of risk variants with small effects remain unknown. Third, accumulating evidence from GWAS suggests the pervasiveness of pleiotropy, a phenomenon that some genetic variants can be associated with multiple traits, but there is a lack of unified framework which is scalable to reveal relationship among a large number of traits and prioritize genetic variants simultaneously with functional annotations integrated. In this thesis, we propose two statistical methods to address these challenges using integrative analysis of summary statistics from GWASs and functional annotations. In the first part, we propose a latent sparse mixed model (LSMM) to integrate functional annotations with GWAS data. Not only does it increase the statistical power of identifying risk variants, but also offers more biological insights by detecting relevant functional annotations. To allow LSMM scalable to millions of variants and hundreds of functional annotations, we developed an efficient variational expectation-maximization (EM) algorithm for model parameter estimation and statistical inference. We first conducted comprehensive simulation studies to evaluate the performance of LSMM. Then we applied it to analyze 30 GWASs of complex phenotypes integrated with nine genic category annotations and 127 cell-type specific functional annotations from the Roadmap project. The results demonstrate that our method possesses more statistical power than conventional methods, and can help researchers achieve deeper understanding of genetic architecture of these complex phenotypes. In the second part, we propose a latent probit model (LPM) which combines summary statistics from multiple GWASs and functional annotations, to characterize relationship and increase statistical power to identify risk variants. LPM can also perform hypothesis testing for pleiotropy and annotations enrichment. To enable the scalability of LPM as the number of GWASs increases, we developed an efficient parameter-expanded EM (PX-EM) algorithm which can execute parallelly. We first validated the performance of LPM through comprehensive simulations, then applied it to analyze 44 GWASs with nine genic category annotations. The results demonstrate the benefits of LPM and can offer new insights of disease etiology.
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Campbell, Kieran. "Probabilistic modelling of genomic trajectories." Thesis, University of Oxford, 2017. https://ora.ox.ac.uk/objects/uuid:24e6704c-8a7f-4967-9fcd-95d6034eab39.

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The recent advancement of whole-transcriptome gene expression quantification technology - particularly at the single-cell level - has created a wealth of biological data. An increasingly popular unsupervised analysis is to find one dimensional manifolds or trajectories through such data that track the development of some biological process. Such methods may be necessary due to the lack of explicit time series measurements or due to asynchronicity of the biological process at a given time. This thesis aims to recast trajectory inference from high-dimensional "omics" data as a statistical latent variable problem. We begin by examining sources of uncertainty in current approaches and examine the consequences of propagating such uncertainty to downstream analyses. We also introduce a model of switch-like differentiation along trajectories. Next, we consider inferring such trajectories through parametric nonlinear factor analysis models and demonstrate that incorporating information about gene behaviour as informative Bayesian priors improves inference. We then consider the case of bifurcations in data and demonstrate the extent to which they may be modelled using a hierarchical mixture of factor analysers. Finally, we propose a novel type of latent variable model that performs inference of such trajectories in the presence of heterogeneous genetic and environmental backgrounds. We apply this to both single-cell and population-level cancer datasets and propose a nonparametric extension similar to Gaussian Process Latent Variable Models.
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Zhang, Fan. "Statistical Methods for Characterizing Genomic Heterogeneity in Mixed Samples." Digital WPI, 2016. https://digitalcommons.wpi.edu/etd-dissertations/419.

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"Recently, sequencing technologies have generated massive and heterogeneous data sets. However, interpretation of these data sets is a major barrier to understand genomic heterogeneity in complex diseases. In this dissertation, we develop a Bayesian statistical method for single nucleotide level analysis and a global optimization method for gene expression level analysis to characterize genomic heterogeneity in mixed samples. The detection of rare single nucleotide variants (SNVs) is important for understanding genetic heterogeneity using next-generation sequencing (NGS) data. Various computational algorithms have been proposed to detect variants at the single nucleotide level in mixed samples. Yet, the noise inherent in the biological processes involved in NGS technology necessitates the development of statistically accurate methods to identify true rare variants. At the single nucleotide level, we propose a Bayesian probabilistic model and a variational expectation maximization (EM) algorithm to estimate non-reference allele frequency (NRAF) and identify SNVs in heterogeneous cell populations. We demonstrate that our variational EM algorithm has comparable sensitivity and specificity compared with a Markov Chain Monte Carlo (MCMC) sampling inference algorithm, and is more computationally efficient on tests of relatively low coverage (27x and 298x) data. Furthermore, we show that our model with a variational EM inference algorithm has higher specificity than many state-of-the-art algorithms. In an analysis of a directed evolution longitudinal yeast data set, we are able to identify a time-series trend in non-reference allele frequency and detect novel variants that have not yet been reported. Our model also detects the emergence of a beneficial variant earlier than was previously shown, and a pair of concomitant variants. Characterization of heterogeneity in gene expression data is a critical challenge for personalized treatment and drug resistance due to intra-tumor heterogeneity. Mixed membership factorization has become popular for analyzing data sets that have within-sample heterogeneity. In recent years, several algorithms have been developed for mixed membership matrix factorization, but they only guarantee estimates from a local optimum. At the gene expression level, we derive a global optimization (GOP) algorithm that provides a guaranteed epsilon-global optimum for a sparse mixed membership matrix factorization problem for molecular subtype classification. We test the algorithm on simulated data and find the algorithm always bounds the global optimum across random initializations and explores multiple modes efficiently. The GOP algorithm is well-suited for parallel computations in the key optimization steps. "
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Yu, Xuesong. "Statistical methods for analyzing genomic data with consideration of spatial structures /." Thesis, Connect to this title online; UW restricted, 2007. http://hdl.handle.net/1773/9553.

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Guennel, Tobias. "Statistical Methods for Normalization and Analysis of High-Throughput Genomic Data." VCU Scholars Compass, 2012. http://scholarscompass.vcu.edu/etd/2647.

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High-throughput genomic datasets obtained from microarray or sequencing studies have revolutionized the field of molecular biology over the last decade. The complexity of these new technologies also poses new challenges to statisticians to separate biological relevant information from technical noise. Two methods are introduced that address important issues with normalization of array comparative genomic hybridization (aCGH) microarrays and the analysis of RNA sequencing (RNA-Seq) studies. Many studies investigating copy number aberrations at the DNA level for cancer and genetic studies use comparative genomic hybridization (CGH) on oligo arrays. However, aCGH data often suffer from low signal to noise ratios resulting in poor resolution of fine features. Bilke et al. showed that the commonly used running average noise reduction strategy performs poorly when errors are dominated by systematic components. A method called pcaCGH is proposed that significantly reduces noise using a non-parametric regression on technical covariates of probes to estimate systematic bias. Then a robust principal components analysis (PCA) estimates any remaining systematic bias not explained by technical covariates used in the preceding regression. The proposed algorithm is demonstrated on two CGH datasets measuring the NCI-60 cell lines utilizing NimbleGen and Agilent microarrays. The method achieves a nominal error variance reduction of 60%-65% as well as an 2-fold increase in signal to noise ratio on average, resulting in more detailed copy number estimates. Furthermore, correlations of signal intensity ratios of NimbleGen and Agilent arrays are increased by 40% on average, indicating a significant improvement in agreement between the technologies. A second algorithm called gamSeq is introduced to test for differential gene expression in RNA sequencing studies. Limitations of existing methods are outlined and the proposed algorithm is compared to these existing algorithms. Simulation studies and real data are used to show that gamSeq improves upon existing methods with regards to type I error control while maintaining similar or better power for a range of sample sizes for RNA-Seq studies. Furthermore, the proposed method is applied to detect differential 3' UTR usage.
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Books on the topic "Genomic imprinting - Statistical methods"

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Kasprzak, Marta. Combinatorial models and methods for reading genomic sequences. Poznań: Wydawn. Politechniki Poznańskiej, 2004.

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Genomic Imprinting Methods in Molecular Biology Hardcover. Humana Press, 2012.

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Genomic Imprinting: Methods and Protocols (Methods in Molecular Biology). Humana Press, 2002.

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Statistical Methods for the Analysis of Genomic Data. MDPI, 2020. http://dx.doi.org/10.3390/books978-3-03936-141-0.

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Stoddard Jr, Frederick J., David M. Benedek, Mohammed R. Milad, and Robert J. Ursano. Posttraumatic Stress Disorder. Edited by Frederick J. Stoddard, David M. Benedek, Mohammed R. Milad, and Robert J. Ursano. Oxford University Press, 2018. http://dx.doi.org/10.1093/med/9780190457136.003.0003.

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Posttraumatic stress disorder (PTSD) affects people of all ages and backgrounds and causes persistent suffering and impaired function, but its diagnosis offers the opportunity for early intervention. It is the subject of intensive developmental, epidemiological, genetic/genomic, translational, neurobiological, neuropsychological, and psychological research, and emerging computational methods with “big data,” statistical modeling, and machine learning are likely to accelerate this research. The findings from research on PTSD are changing education and the ways clinicians practice, offering the hope for improved care of those experiencing traumatic stress. Those at particular risk for PTSD include children and adolescents, women, soldiers, refugees and survivors of genocide, sexual orientation minorities, racial and ethnic minorities, patients with burns, injuries and medical trauma, and victims of rape, violence, accidents, and disasters. This chapter provides an overview of PTSD, covering Diagnostic and Statistical Manual of Mental Disorders (fifth edition) diagnostic criteria, epidemiology, neurochemistry and neurobiology, biological and psychological models, assessment, and treatment.
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Book chapters on the topic "Genomic imprinting - Statistical methods"

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Cheng, Yong, Dasari Amarnath, and Keith E. Latham. "Uniparental Embryos in the Study of Genomic Imprinting." In Methods in Molecular Biology, 3–19. Totowa, NJ: Humana Press, 2012. http://dx.doi.org/10.1007/978-1-62703-011-3_1.

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Ball, Roderick D. "Statistical Analysis of Genomic Data." In Methods in Molecular Biology, 171–92. Totowa, NJ: Humana Press, 2013. http://dx.doi.org/10.1007/978-1-62703-447-0_7.

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Chamberlain, Stormy J., Noelle D. Germain, Pin-Fang Chen, Jack S. Hsiao, and Heather Glatt-Deeley. "Modeling Genomic Imprinting Disorders Using Induced Pluripotent Stem Cells." In Methods in Molecular Biology, 45–64. New York, NY: Springer New York, 2014. http://dx.doi.org/10.1007/7651_2014_169.

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Koestler, Devin C. "Semisupervised Methods for Analyzing High-dimensional Genomic Data." In Statistical Diagnostics for Cancer, 93–106. Weinheim, Germany: Wiley-VCH Verlag GmbH & Co. KGaA, 2013. http://dx.doi.org/10.1002/9783527665471.ch6.

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McKeown, Peter C., Antoine Fort, and Charles Spillane. "Analysis of Genomic Imprinting by Quantitative Allele-Specific Expression by Pyrosequencing®." In Methods in Molecular Biology, 85–104. Totowa, NJ: Humana Press, 2014. http://dx.doi.org/10.1007/978-1-62703-773-0_6.

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Choi, Yoonha, and Jing Huang. "Validation of Genomic-Based Assay." In Statistical Methods in Biomarker and Early Clinical Development, 117–36. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-31503-0_7.

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Sen, Pranab Kumar. "Clinical Trials and the Genomic Evolution: Some Statistical Perspectives." In Statistical Models and Methods for Biomedical and Technical Systems, 537–51. Boston, MA: Birkhäuser Boston, 2008. http://dx.doi.org/10.1007/978-0-8176-4619-6_37.

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Reich, Jens G. "Pattern Recognition in Genomic and Protein Sequences: A Survey of Statistical Validation Problems." In Computational Methods in Genome Research, 137–52. Boston, MA: Springer US, 1994. http://dx.doi.org/10.1007/978-1-4615-2451-9_11.

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Ott, Jurg. "Statistical Methods to Discover Susceptibility Genes for Nervous System Diseases." In Methods in Genomic Neuroscience, 287–96. CRC Press, 2001. http://dx.doi.org/10.1201/9781420038477-17.

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Ott, Jurg. "Statistical Methods to Discover Susceptibility Genes for Nervous System Diseases." In Methods in Genomic Neuroscience. CRC Press, 2001. http://dx.doi.org/10.1201/9781420038477.sec5.

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