Download or read book Modeling Biological Processes in Genome wide Association Studies Using Regularized Regression written by Gabriel Hoffman and published by . This book was released on 2013 with total page 336 pages. Available in PDF, EPUB and Kindle. Book excerpt: Genome-wide association studies (GWAS) have become a a widely adopted approach to identify genetic variation that produces variation in complex phenotype. Standard statistical methods are able to identify strong associations in these datasets, but more sophisticated statistical methods that model complex aspects of the biological data can identify weaker associations and further elucidate the underlying molecular biology. We develop and apply statistical methods that explicitly model two aspects of GWAS data using two complementary forms of regularized regression. First, we model the polygenic architecture of complex phenotypes using feature selection methods in a penalized regression framework. We propose novel algorithmic, computational and heuristic approaches in order to produce a method that scales to high dimensional GWAS data and increases power to detect weak associations that are not detectable by standard tests. Second, we model the covariance between individuals due to kinship and population structure using a linear mixed model that regularizes the statistical contribution of a metric of ancestry. Linear mixed models have been widely adopted for analysis of GWAS data, but their theoretical properties have not been examined in this context. We formalize the statistical properties of the linear mixed model, develop a novel interpretation in relation to population genetics, and propose a novel low rank linear mixed model that learns the dimensionality of the correction for kinship and population structure from the data. Finally, we combine these two complementary regularized regression models into a penalized linear mixed model. We develop a unified model incorporating a novel algorithm with novel approaches to tuning nonconvex penalties and determining the optimal stopping point in the regularization path. Leveraging recent work on assessing significance of selected features, we produce a well-principled and scalable statistical method applicable to feature selection, hypothesis testing and prediction in many contexts.

Download or read book Phenotypes and Genotypes written by Florian Frommlet and published by Springer. This book was released on 2016-02-12 with total page 232 pages. Available in PDF, EPUB and Kindle. Book excerpt: This timely text presents a comprehensive guide to genetic association, a new and rapidly expanding field that aims to elucidate how our genetic code (genotypes) influences the traits we possess (phenotypes). The book provides a detailed review of methods of gene mapping used in association with experimental crosses, as well as genome-wide association studies. Emphasis is placed on model selection procedures for analyzing data from large-scale genome scans based on specifically designed modifications of the Bayesian information criterion. Features: presents a thorough introduction to the theoretical background to studies of genetic association (both genetic and statistical); reviews the latest advances in the field; illustrates the properties of methods for mapping quantitative trait loci using computer simulations and the analysis of real data; discusses open challenges; includes an extensive statistical appendix as a reference for those who are not totally familiar with the fundamentals of statistics.

Download or read book Frontiers in Computational and Systems Biology written by Jianfeng Feng and published by Springer Science & Business Media. This book was released on 2010-06-14 with total page 411 pages. Available in PDF, EPUB and Kindle. Book excerpt: Biological and biomedical studies have entered a new era over the past two decades thanks to the wide use of mathematical models and computational approaches. A booming of computational biology, which sheerly was a theoretician’s fantasy twenty years ago, has become a reality. Obsession with computational biology and theoretical approaches is evidenced in articles hailing the arrival of what are va- ously called quantitative biology, bioinformatics, theoretical biology, and systems biology. New technologies and data resources in genetics, such as the International HapMap project, enable large-scale studies, such as genome-wide association st- ies, which could potentially identify most common genetic variants as well as rare variants of the human DNA that may alter individual’s susceptibility to disease and the response to medical treatment. Meanwhile the multi-electrode recording from behaving animals makes it feasible to control the animal mental activity, which could potentially lead to the development of useful brain–machine interfaces. - bracing the sheer volume of genetic, genomic, and other type of data, an essential approach is, ?rst of all, to avoid drowning the true signal in the data. It has been witnessed that theoretical approach to biology has emerged as a powerful and st- ulating research paradigm in biological studies, which in turn leads to a new - search paradigm in mathematics, physics, and computer science and moves forward with the interplays among experimental studies and outcomes, simulation studies, and theoretical investigations.

Download or read book Integration and Development of Machine Learning Methodologies to Improve the Power of Genome wide Association Studies written by Jing Li and published by . This book was released on 2016 with total page 250 pages. Available in PDF, EPUB and Kindle. Book excerpt: Genome-wide association studies (GWAS) have led to a great number of new findings in human genetics and genetic epidemiology. GWAS identifies DNA sequence variations using human genome data and identifies the genetic risk factors for common diseases. There are many challenges that remain when mapping the complex underlying relationships between genotypes and phenotypes in GWAS. Here, we attempt to improve the power to detect correct mapping in GWAS for disease prevention and treatment. We examine a number of assumptions in GWAS that have been made over the past decade, which need to be updated and discussed in light of recent GWAS algorithm development. To achieve this goal, we discuss some of the current assumptions of GWAS and all possible factors that could affect predictive power. Using simulation studies, we show statistical evidence of how different factors, including sample size, heritability, model misspecification, and measurement error, affect the power to detect correct genetic associations. These data have the potential to improve the design of GWAS. As epistasis is the key to studying GWAS, we specifically studied epistasis, which is believed to account for part of the missing heritability. To detect interactions, we developed permuted Random Forest (pRF), a scale-free method, which is based on the traditional machine learning method Random Forest (RF). This method accurately detects single nucleotide polymorphism (SNP)-SNP interactions and top interacting SNP pairs by estimating how much the power of a random forest classification model is influenced by removing pairwise interactions. We systematically tested this approach on a simulation study with datasets possessing various genetic constraints including heritability, number of SNPs, and sample size. Our methodology shows high success rates for detecting interacting SNP pairs. We also applied our approach to two bladder cancer datasets, which shows results consistent with well-studied methodologies and we built permuted Random Forest networks (PRFN), in which we used nodes to represent SNPs and edges to indicate interactions. Data suggest the pRF method could improve detection of pure gene-gene interactions. Classic methods used to detect genetic association in GWAS involved separating biological knowledge from genetic information, thus wasting useful biological information when modeling associations between genotypes and phenotypes. We therefore further developed a biological information guided machine learning methodology, based on Encyclopedia of DNA Elements (ENCODE), called ENCODE information guided synthetic feature Random Forest (E-SFRF). Instead of studying biological associations at the SNP level, we separated SNPs based on ENCODE information and grouped them into a particular gene or enhancer to calculate the synthetic feature (SF) on a higher level. In our study, we focused on genes or enhancers from the AHR pathway, which is involved in cancer development. This work showed that the E-SFRF method could identify consistent main effect models based on SFs from two independent bladder cancer studies. We further studied the SNP-SNP interactions inside the top main effect SFs and discovered interesting SNP-SNP interactions that may lead to strong main effects. We believe our method could increase the possibility of replicating results across different GWAS datasets by increasing both the consistency and accuracy in genetic studies. Overall, we have found that studying interactions among SNPs is essential to increasing the power to uncover genetic architectures. By developing different machine learning methods, pRF, and further incorporating biological information to develop E-SFRF, we were able to detect pure gene-gene interactions in a scale-free and non-parametric way, helping to increase repeatability and reliability of GWAS using biological knowledge.

Download or read book Genome Wide Association Studies written by Davoud Torkamaneh and published by Humana. This book was released on 2023-06-15 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: This detailed collection explores genome-wide association studies (GWAS), which have revolutionized the investigation of complex traits over the past decade and have unveiled numerous useful genotype–phenotype associations in plants. The book describes the key concepts and methods underlying GWAS, including the genetic architecture underlying variation for phenotypic traits, the structure of genetic variation in plants, technologies for capturing genetic information, study designs, and the statistical models and bioinformatics tools used for data analysis. Written for the highly successful Methods in Molecular Biology series, chapters include the kind of invaluable implementation advice that leads to the most fruitful research results. Authoritative and practical, Genome-Wide Association Studies serves as an extremely valuable resource for the plant research community by rendering GWAS analysis less challenging and more accessible to a broader group of users.

Download or read book Integrative Modeling for Genome wide Regulation of Gene Expression written by Zhengqing Ouyang and published by . This book was released on 2010 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: High-throughput genomics has been increasingly generating the massive amount of genome-wide data. With proper modeling methodologies, we can expect to archive a more comprehensive understanding of the regulatory mechanisms of biological systems. This work presents integrative approaches for the modeling and analysis of gene regulatory systems. In mammals, gene expression regulation is combinatorial in nature, with diverse roles of regulators on target genes. Microarrays (such as Exon Arrays) and RNA-Seq can be used to quantify the whole spectrum of RNA transcripts. ChIP-Seq is being used for the identification of transcription factor (TF) binding sites and histone modification marks. RNA interference (RNAi), coupled with gene expression profiles, allow perturbations of gene regulatory systems. Our approaches extract useful information from those genome-wide measurements for effectively modeling the logic of gene expression regulation. We present a predictive model for the prediction of gene expression from ChIP-Seq signals, based on quantitative modeling of regulator-gene association strength, principal component analysis, and regression-based model selection. We demonstrate the combinatorial regulation of TFs, and their power for explaining genome-wide gene expression variation. We also illustrate the roles of covalent histone modification marks on predicting gene expression and their regulation by TFs. We present a dynamical model of gene expression profiling, and derive the perturbed behaviors of the ordinary differential equation (ODE) system. Based on that, we present a regularized multivariate regression method for inferring the gene regulatory network of a stable cell type. We model the sparsity and stability of the network by a regularization approach. We applied the approaches to both a simulation data set and the RNAi perturbation data in mouse embryonic stem cells.

Download or read book Machine Learning in Genome Wide Association Studies written by Ting Hu and published by Frontiers Media SA. This book was released on 2020-12-15 with total page 74 pages. Available in PDF, EPUB and Kindle. Book excerpt: This eBook is a collection of articles from a Frontiers Research Topic. Frontiers Research Topics are very popular trademarks of the Frontiers Journals Series: they are collections of at least ten articles, all centered on a particular subject. With their unique mix of varied contributions from Original Research to Review Articles, Frontiers Research Topics unify the most influential researchers, the latest key findings and historical advances in a hot research area! Find out more on how to host your own Frontiers Research Topic or contribute to one as an author by contacting the Frontiers Editorial Office: frontiersin.org/about/contact.

Download or read book High Dimensional Methods to Model Biological Signal in Genome Wide Studies written by Andrew J. Bass and published by . This book was released on 2021 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Recent advancements in sequencing technology have substantially increased the quality and quantity of data in genomics, presenting novel analytical challenges for biological discovery. In particular, foundational ideas developed in statistics over the past century are not easily extended to these high-dimensional datasets. Therefore, creating novel methodologies to analyze this data is a key challenge faced in statistics, and more generally, biology and computational science.Here I focus on building statistical methods for genome-wide analysis that are statistically rigorous, computationally fast, and easy to implement. In particular, I develop four methods that improve statistical inference of high-dimensional biological data. The first focuses on differential expression analysis where I extend the optimal discovery procedure (ODP) to complex study designs and RNA-seq studies. I find that the extended ODP leverages shared biological signal to substantially improve the statistical power compared to other commonly used testing procedures. The second aims to model the functional relationship between sequencing depth and statistical power in RNA-seq differential expression studies. The resulting model, superSeq, accurately predicts the improvement in statistical power when sequencing additional reads in a completed study. Thus superSeq can guide researchers in choosing a sufficient sequencing depth to maximize statistical power while avoiding unnecessary sequencing costs.The third method estimates the posterior distribution of false discovery rate (FDR) quantities, such as local FDRs and q-values, using a Bayesian nonparametric approach. Specifically, I implement an approximation to these posterior distributions that is scalable to genome-wide datasets using variational inference. These estimated posterior distributions are informative in a significance analysis as they capture the uncertainty of FDR quantities in reported results.Finally, I develop a likelihood-based approach to estimating unobserved population structure on the canonical parameter scale. I demonstrate that this framework can flexibly capture arbitrary structure and provide accurate allele frequency estimates while being computationally fast for large population genetic studies. Therefore, this framework is useful for many applications in population genetics, such as accounting for structure in the genome-wide association testing procedure GCATest.Collectively, these four methods address problems typically encountered in a biological analysis and can thus help improve downstream inferences in high-dimensional settings.

Download or read book Model Selection Strategies in Genome wide Association Studies written by Sarah Keildson and published by . This book was released on 2011 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Unravelling the genetic architecture of common diseases is a continuing challenge in human genetics. While genome-wide association studies (GWAS) have proven to be successful in identifying many new disease susceptibility loci, the extension of these studies beyond single-SNP methods of analysis has been limited. The incorporation of multi-locus methods of analysis may, however, increase the power of GWAS to detect genes of smaller effect size, as well as genes that interact with each other and the environment. This investigation carried out large-scale simulations of four multi-locus model selection techniques; namely forward and backward selection, Bayesian model averaging (BMA) and least angle regression with a lasso modification (lasso), in order to compare the type I error rates and power of each method. At a type I error rate of ~5%, lasso showed the highest power across varied effect sizes, disease frequencies and genetic models. Lasso penalized regression was then used to perform three different types of analysis on GWAS data. Firstly, lasso was applied to the Wellcome Trust Case Control Consortium (WTCCC) data and identified many of the WTCCC SNPs that had a moderate-strong association (p

Download or read book A Bayesian Large Scale Multiple Regression Model for Genome Wide Association Summary Statistics written by Xiang Zhu and published by . This book was released on 2017 with total page 296 pages. Available in PDF, EPUB and Kindle. Book excerpt: We apply RSS methods to analyze published GWAS summary statistics of 1.1 millions common variants from 31 human phenotypes, 3,913 biological pathways retrieved from nine public databases, and 113 tissue-associated gene sets derived from gene expression profiles of 53 human tissues. We identify many previously-unreported genes, pathways and tissues that show strong evidence for association with complex traits in our large-scale integrated analyses. Software is available at https://github.com/stephenslab/rss.

Download or read book Machine Learning in Radiation Oncology written by Issam El Naqa and published by Springer. This book was released on 2015-06-19 with total page 336 pages. Available in PDF, EPUB and Kindle. Book excerpt: ​This book provides a complete overview of the role of machine learning in radiation oncology and medical physics, covering basic theory, methods, and a variety of applications in medical physics and radiotherapy. An introductory section explains machine learning, reviews supervised and unsupervised learning methods, discusses performance evaluation, and summarizes potential applications in radiation oncology. Detailed individual sections are then devoted to the use of machine learning in quality assurance; computer-aided detection, including treatment planning and contouring; image-guided radiotherapy; respiratory motion management; and treatment response modeling and outcome prediction. The book will be invaluable for students and residents in medical physics and radiation oncology and will also appeal to more experienced practitioners and researchers and members of applied machine learning communities.

Download or read book Statistical Methods for High dimensional Genomic Data written by Michael Chiao-An Wu and published by . This book was released on 2009 with total page 200 pages. Available in PDF, EPUB and Kindle. Book excerpt: High-throughput genomic studies hold great promise for providing insight into key biological and medical problems, but the high-dimensionality of the data from these studies constitutes a great challenge for researchers. This thesis seeks to address some of the methodological challenges posed by high-dimensional genomic data. First, the need to develop accurate classifiers based on genomic markers motivated the development of sparse linear discriminant analysis (sLDA), a regularized form of linear discriminant analysis, which performs simultaneous classification and variable selection. The second and third chapters of this thesis are concerned with multifeature testing. In the gene expression setting, we apply sLDA to test for differential expression of gene pathways by using the sLDA weights to reduce each pathway to a univariate score which may be evaluated via permutation. Then for genome wide association studies, we consider using the logistic kernel machine based testing framework to evaluate the significance of SNPs grouped on the basis of proximity to known genomic features. Finally, in the last chapter we study the use of sparse regularized regression for making inference in high dimensional data. Specifically, we develop a parametric permutation test based on the LASSO estimator for testing the effect of individual markers in "omics" settings.

Download or read book Ecological Genomics written by Christian R. Landry and published by Springer Science & Business Media. This book was released on 2013-11-25 with total page 358 pages. Available in PDF, EPUB and Kindle. Book excerpt: Researchers in the field of ecological genomics aim to determine how a genome or a population of genomes interacts with its environment across ecological and evolutionary timescales. Ecological genomics is trans-disciplinary by nature. Ecologists have turned to genomics to be able to elucidate the mechanistic bases of the biodiversity their research tries to understand. Genomicists have turned to ecology in order to better explain the functional cellular and molecular variation they observed in their model organisms. We provide an advanced-level book that covers this recent research and proposes future development for this field. A synthesis of the field of ecological genomics emerges from this volume. Ecological Genomics covers a wide array of organisms (microbes, plants and animals) in order to be able to identify central concepts that motivate and derive from recent investigations in different branches of the tree of life. Ecological Genomics covers 3 fields of research that have most benefited from the recent technological and conceptual developments in the field of ecological genomics: the study of life-history evolution and its impact of genome architectures; the study of the genomic bases of phenotypic plasticity and the study of the genomic bases of adaptation and speciation.

Download or read book Regularization Optimization Kernels and Support Vector Machines written by Johan A.K. Suykens and published by CRC Press. This book was released on 2014-10-23 with total page 522 pages. Available in PDF, EPUB and Kindle. Book excerpt: Regularization, Optimization, Kernels, and Support Vector Machines offers a snapshot of the current state of the art of large-scale machine learning, providing a single multidisciplinary source for the latest research and advances in regularization, sparsity, compressed sensing, convex and large-scale optimization, kernel methods, and support vecto

Download or read book Encyclopedia of Bioinformatics and Computational Biology written by and published by Elsevier. This book was released on 2018-08-21 with total page 3421 pages. Available in PDF, EPUB and Kindle. Book excerpt: Encyclopedia of Bioinformatics and Computational Biology: ABC of Bioinformatics, Three Volume Set combines elements of computer science, information technology, mathematics, statistics and biotechnology, providing the methodology and in silico solutions to mine biological data and processes. The book covers Theory, Topics and Applications, with a special focus on Integrative –omics and Systems Biology. The theoretical, methodological underpinnings of BCB, including phylogeny are covered, as are more current areas of focus, such as translational bioinformatics, cheminformatics, and environmental informatics. Finally, Applications provide guidance for commonly asked questions. This major reference work spans basic and cutting-edge methodologies authored by leaders in the field, providing an invaluable resource for students, scientists, professionals in research institutes, and a broad swath of researchers in biotechnology and the biomedical and pharmaceutical industries. Brings together information from computer science, information technology, mathematics, statistics and biotechnology Written and reviewed by leading experts in the field, providing a unique and authoritative resource Focuses on the main theoretical and methodological concepts before expanding on specific topics and applications Includes interactive images, multimedia tools and crosslinking to further resources and databases

Download or read book Integrative Analysis of Genome Wide Association Studies and Single Cell Sequencing Studies written by Sheng Yang and published by Frontiers Media SA. This book was released on 2021-09-09 with total page 113 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Biocomputing 2013 Proceedings Of The Pacific Symposium written by Russ B Altman and published by World Scientific. This book was released on 2012-11-16 with total page 472 pages. Available in PDF, EPUB and Kindle. Book excerpt: The Pacific Symposium on Biocomputing (PSB) 2013 is an international, multidisciplinary conference for the presentation and discussion of current research in the theory and application of computational methods in problems of biological significance. Presentations are rigorously peer reviewed and are published in an archival proceedings volume. PSB 2013 will be held on January 3 - 7, 2013 in Kohala Coast, Hawaii. Tutorials and workshops will be offered prior to the start of the conference.PSB 2013 will bring together top researchers from the US, the Asian Pacific nations, and around the world to exchange research results and address open issues in all aspects of computational biology. It is a forum for the presentation of work in databases, algorithms, interfaces, visualization, modeling, and other computational methods, as applied to biological problems, with emphasis on applications in data-rich areas of molecular biology.The PSB has been designed to be responsive to the need for critical mass in sub-disciplines within biocomputing. For that reason, it is the only meeting whose sessions are defined dynamically each year in response to specific proposals. PSB sessions are organized by leaders of research in biocomputing's “hot topics.” In this way, the meeting provides an early forum for serious examination of emerging methods and approaches in this rapidly changing field.