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Penalized Variable Selection For Gene Environment Interactions
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Book Synopsis Penalized Variable Selection for Gene-environment Interactions by : Yinhao Du
Download or read book Penalized Variable Selection for Gene-environment Interactions written by Yinhao Du and published by . This book was released on 2021 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Gene-environment (GxE) interaction is critical for understanding the genetic basis of complex disease beyond genetic and environment main effects. In addition to existing tools for interaction studies, penalized variable selection emerges as a promising alternative for dissecting GxE interactions. Despite the success, variable selection is limited in the following aspects. First, multidimensional measurements have not been taken into fully account in interaction studies. Published variable selection methods cannot accommodate structured sparsity in the framework of integrating multiomics data for disease outcomes. Second, in the big data context, no variable selection method has been developed so far to conduct tailored interaction analysis. Third, the solution to case control association GxE studies with high dimensional genomics variants in the big data context has not been made available so far. In this dissertation, we tackle these challenges rising from GxE interaction studies in the modern era through the following projects. In the first project, we have developed a novel variable selection method to integrate multi-omics measurements in GxE interaction studies. Extensive studies have already revealed that analyzing omics data across multi-platforms is not only sensible biologically but also resulting in improved identification and prediction performance. Our integrative model can efficiently pinpoint important regulators of gene expressions through sparse dimensionality reduction and link the disease outcomes to multiple effects in the integrative GxE studies via accommodating a sparse bi-level structure. Simulation studies show the integrative model leads to better identification of GxE interactions and regulators than that of the alternative methods. In two GxE lung cancer studies with high dimensional multi-omics data, the integrative model leads to improved prediction and findings with important biological implications. In the second project, we propose to conduct interaction studies in the big data context by adopting the divide-and-conquer strategy. In particular, the sparse group variable selection for important GxE effects has been developed within the framework of alternating direction method of multiplier (ADMM). To accommodate the large-scale data in terms of either samples or features, we have developed two novel parallel ADMM based variable selection methods across samples and features, respectively. The corresponding parallel algorithms can be efficiently implemented in distributed computing platforms. Simulation studies demonstrate that the parallel ADMM based penalization methods significantly improve the computational speed for analyzing large scale data from GxE interaction studies with satisfactory identification and prediction performance. In the third project, we extend the proposed parallel ADMM based variable selection for GxE interactions in the case-control association study of type 2 diabetes. Within the parallel computation framework, we have developed a penalized logistic regression model accommodating the bi-level selection tailored for the case control GxE interaction study. The advantage of the proposed parallel penalization method has been fully illustrated in the distributed learning scenario. Simulation studies show the proposed method dramatically reduces the computational time while maintaining a competitive performance compared to the non-parallel counterparts. In the case study of type 2 diabetes with environmental factors and high dimensional SNP measurements, the proposed parallel penalization method leads to the identification of biologically important interaction effects.
Book Synopsis High-dimensional Variable Selection in Longitudinal and Nonlinear Gene-environment Interaction Studies by : Fei Zhou
Download or read book High-dimensional Variable Selection in Longitudinal and Nonlinear Gene-environment Interaction Studies written by Fei Zhou and published by . This book was released on 2021 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Variable selection from both the frequentist and Bayesian frameworks has gained increasing popularity in the analysis of high-dimensional genomic data. Despite the success of existing studies, challenges still remain as tailored methods for sparse interaction structures are not available when the response variables are repeatedly measured and/or have heavy-tailed distributions. These challenges have motivated the development of novel variable selection methods proposed in the following projects. Meanwhile, powerful software packages from these projects are publically available to facilitate fast and reliable computation, as well as reproducible research. In the first project, we have developed a novel penalized variable selection method to identify important lipid-environment interactions in a longitudinal lipidomics study, where the environment factors refer to a group of dummy variables corresponding to a four-level treatment factor. An efficient Newton-Raphson based algorithm was proposed within the generalized estimating equation (GEE) framework. Simulation studies have demonstrated the superior performance of our method over alternatives, in terms of both identification accuracy and prediction performance. Analysis of the high-dimensional lipid datasets collected using mice from the skin cancer prevention study identified meaningful markers that provide fresh insight into the underlying mechanism of cancer preventive effects. In the second project, we have proposed a sparse group penalization method for the bi-level GxE interaction study under the repeatedly measured phenotype to accommodate more general environment factors. Within the quadratic inference function (QIF) framework, the proposed method can achieve simultaneous identification of main and interaction effects on both the group and individual level. We conducted simulation studies to establish the advantage of the proposed regularization methods. In the case study, the environment factors include age, gender and treatment, which are either continuous or categorical. Our method leads to improved prediction and identification of main and interaction effects with important implications. In the third project, a sparse Bayesian quantile varying coefficient model has been developed for non-linear GxE studies. The proposed model can accommodate heavy-tailed errors and outliers from the disease phenotypes while pinpointing important non-linear interactions through Bayesian variable selection based on spike-and-slab priors. Fast computation has been facilitated by the efficient Gibbs sampler. Simulation studies and real data analysis with age as the univariate environment factor have been performed to show the superiority of the proposed method over multiple competing alternatives. The open source R packages with C++ implementations of all the methods under comparison have been provided along this dissertation. The R packages interep and springer, for the first two projects respectively, are available on CRAN. The R package for the last project on Bayesian regularized quantile varying coefficient model will be released soon to the public.
Book Synopsis Variable Selection for Data Aggregated from Different Sources with Group of Variable Structure by : Camilo Broc
Download or read book Variable Selection for Data Aggregated from Different Sources with Group of Variable Structure written by Camilo Broc and published by . This book was released on 2019 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: During the last decades, the amount of available genetic data on populations has growndrastically. From one side, a refinement of chemical technologies have made possible theextraction of the human genome of individuals at an accessible cost. From the other side,consortia of institutions and laboratories around the world have permitted the collectionof data on a variety of individuals and population. This amount of data raised hope onour ability to understand the deepest mechanisms involved in the functioning of our cells.Notably, genetic epidemiology is a field that studies the relation between the geneticfeatures and the onset of a disease. Specific statistical methods have been necessary forthose analyses, especially due to the dimensions of available data: in genetics, informationis contained in a high number of variables compared to the number of observations.In this dissertation, two contributions are presented. The first project called PIGE (Pathway-Interaction Gene Environment) deals with gene-environment interaction assessments.The second one aims at developing variable selection methods for data which has groupstructures in both the variables and the observations.The document is divided into six chapters. The first chapter sets the background of this work,where both biological and mathematical notations and concepts are presented and gives ahistory of the motivation behind genetics and genetic epidemiology. The second chapterpresent an overview of the statistical methods currently in use for genetic epidemiology.The third chapter deals with the identification of gene-environment interactions. It includesa presentation of existing approaches for this problem and a contribution of the thesis. Thefourth chapter brings off the problem of meta-analysis. A definition of the problem and anoverview of the existing approaches are presented. Then, a new approach is introduced.The fifth chapter explains the pleiotropy studies and how the method presented in theprevious chapter is suited for this kind of analysis. The last chapter compiles conclusionsand research lines for the future.
Book Synopsis Sparse Simultaneous Penalized Variable Selection in Hierarchical Structure Genetic Data Analysis by : Shuang Huang
Download or read book Sparse Simultaneous Penalized Variable Selection in Hierarchical Structure Genetic Data Analysis written by Shuang Huang and published by . This book was released on 2017 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: The sparse simultaneous penalized variable selection method for data with hierarchical structure is proposed to identify the quantitative trait loci and expression traits that are related to certain clinical trait in genetic data analysis. This method is developed for data sets in which the dependency is linear, and among a large number of gene loci and expression traits candidates, relatively few are important to the interested clinical trait. The method focuses on identifying the candidates in genome set and expression traits that are significantly related to clinical observation via the hierarchical dependence structure. A penalized linear model is used to reduce the number of parameters, using a novel computational algorithm that can handle the unknowns simultaneously. A data-adaptive tuning procedure based on cross validation acts as a parameter selector. Simulation studies are conducted to check the performance of the proposed method, and to compare with some well developed methods, including several penalized methods and Step AIC method. The real data application is done on a data set from an obesity study. The data set contains 541 mice, and for each individual, over 1,000 expression traits and around 1,000 gene loci are recorded. We compare the finding of our method with previous studies on the same species of mice and the similarity and difference of the outcomes are discussed.
Book Synopsis Variable Selection in Varying Multi-Index Coefficient Models with Applications to Gene-Environmental Interactions by : Shunjie Guan
Download or read book Variable Selection in Varying Multi-Index Coefficient Models with Applications to Gene-Environmental Interactions written by Shunjie Guan and published by . This book was released on 2017 with total page 119 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Insights in Statistical Genetics and Methodology: 2022 by : Simon Charles Heath
Download or read book Insights in Statistical Genetics and Methodology: 2022 written by Simon Charles Heath and published by Frontiers Media SA. This book was released on 2023-10-24 with total page 172 pages. Available in PDF, EPUB and Kindle. Book excerpt: This Research Topic is part of the Insights in Frontiers in Genetics series.
Download or read book Epistasis written by Ka-Chun Wong and published by . This book was released on 2021 with total page 402 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Statistical Methods for the Analysis of Genomic Data by : Hui Jiang
Download or read book Statistical Methods for the Analysis of Genomic Data written by Hui Jiang and published by MDPI. This book was released on 2020-12-29 with total page 136 pages. Available in PDF, EPUB and Kindle. Book excerpt: In recent years, technological breakthroughs have greatly enhanced our ability to understand the complex world of molecular biology. Rapid developments in genomic profiling techniques, such as high-throughput sequencing, have brought new opportunities and challenges to the fields of computational biology and bioinformatics. Furthermore, by combining genomic profiling techniques with other experimental techniques, many powerful approaches (e.g., RNA-Seq, Chips-Seq, single-cell assays, and Hi-C) have been developed in order to help explore complex biological systems. As a result of the increasing availability of genomic datasets, in terms of both volume and variety, the analysis of such data has become a critical challenge as well as a topic of great interest. Therefore, statistical methods that address the problems associated with these newly developed techniques are in high demand. This book includes a number of studies that highlight the state-of-the-art statistical methods for the analysis of genomic data and explore future directions for improvement.
Book Synopsis Statistical Approaches in Omics Data Association Studies by : Qi Yan
Download or read book Statistical Approaches in Omics Data Association Studies written by Qi Yan and published by Frontiers Media SA. This book was released on 2022-06-07 with total page 169 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Gene-Environment Interaction Analysis by : Sumiko Anno
Download or read book Gene-Environment Interaction Analysis written by Sumiko Anno and published by CRC Press. This book was released on 2016-03-30 with total page 208 pages. Available in PDF, EPUB and Kindle. Book excerpt: Gene-environment (GE) interaction analysis is a statistical method for clarifying GE interactions applicable to a phenotype or a disease that is the result of interactions between genes and the environment. This book is the first to deal with the theme of GE interaction analysis. It compiles and details cutting-edge research in bioinformatics
Book Synopsis Big and Complex Data Analysis by : S. Ejaz Ahmed
Download or read book Big and Complex Data Analysis written by S. Ejaz Ahmed and published by Springer. This book was released on 2017-03-21 with total page 390 pages. Available in PDF, EPUB and Kindle. Book excerpt: This volume conveys some of the surprises, puzzles and success stories in high-dimensional and complex data analysis and related fields. Its peer-reviewed contributions showcase recent advances in variable selection, estimation and prediction strategies for a host of useful models, as well as essential new developments in the field. The continued and rapid advancement of modern technology now allows scientists to collect data of increasingly unprecedented size and complexity. Examples include epigenomic data, genomic data, proteomic data, high-resolution image data, high-frequency financial data, functional and longitudinal data, and network data. Simultaneous variable selection and estimation is one of the key statistical problems involved in analyzing such big and complex data. The purpose of this book is to stimulate research and foster interaction between researchers in the area of high-dimensional data analysis. More concretely, its goals are to: 1) highlight and expand the breadth of existing methods in big data and high-dimensional data analysis and their potential for the advancement of both the mathematical and statistical sciences; 2) identify important directions for future research in the theory of regularization methods, in algorithmic development, and in methodologies for different application areas; and 3) facilitate collaboration between theoretical and subject-specific researchers.
Book Synopsis Generalized Linear Mixed Models by : Walter W. Stroup
Download or read book Generalized Linear Mixed Models written by Walter W. Stroup and published by CRC Press. This book was released on 2016-04-19 with total page 547 pages. Available in PDF, EPUB and Kindle. Book excerpt: With numerous examples using SAS PROC GLIMMIX, this text presents an introduction to linear modeling using the generalized linear mixed model as an overarching conceptual framework. For readers new to linear models, the book helps them see the big picture. It shows how linear models fit with the rest of the core statistics curriculum and points out the major issues that statistical modelers must consider.
Book Synopsis Genetic Data Analysis for Plant and Animal Breeding by : Fikret Isik
Download or read book Genetic Data Analysis for Plant and Animal Breeding written by Fikret Isik and published by Springer. This book was released on 2017-09-09 with total page 409 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book fills the gap between textbooks of quantitative genetic theory, and software manuals that provide details on analytical methods but little context or perspective on which methods may be most appropriate for a particular application. Accordingly this book is composed of two sections. The first section (Chapters 1 to 8) covers topics of classical phenotypic data analysis for prediction of breeding values in animal and plant breeding programs. In the second section (Chapters 9 to 13) we provide the concept and overall review of available tools for using DNA markers for predictions of genetic merits in breeding populations. With advances in DNA sequencing technologies, genomic data, especially single nucleotide polymorphism (SNP) markers, have become available for animal and plant breeding programs in recent years. Analysis of DNA markers for prediction of genetic merit is a relatively new and active research area. The algorithms and software to implement these algorithms are changing rapidly. This section represents state-of-the-art knowledge on the tools and technologies available for genetic analysis of plants and animals. However, readers should be aware that the methods or statistical packages covered here may not be available or they might be out of date in a few years. Ultimately the book is intended for professional breeders interested in utilizing these tools and approaches in their breeding programs. Lastly, we anticipate the usage of this volume for advanced level graduate courses in agricultural and breeding courses.
Book Synopsis Statistical Approaches to Gene X Environment Interactions for Complex Phenotypes by : Michael Windle
Download or read book Statistical Approaches to Gene X Environment Interactions for Complex Phenotypes written by Michael Windle and published by MIT Press. This book was released on 2016-07-08 with total page 306 pages. Available in PDF, EPUB and Kindle. Book excerpt: Findings from the Human Genome Project and from Genome-Wide Association (GWA) studies indicate that many diseases and traits manifest a more complex genomic pattern than previously assumed. These findings, and advances in high-throughput sequencing, suggest that there are many sources of influence-genetic, epigenetic, and environmental. This volume investigates the role of the interactions of genes and environment (G x E) in diseases and traits (referred to by the contributors as complex phenotypes) including depression, diabetes, obesity, and substance use.
Book Synopsis Ecological Genomics by : Christian R. Landry
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.
Book Synopsis Statistical Applications from Clinical Trials and Personalized Medicine to Finance and Business Analytics by : Jianchang Lin
Download or read book Statistical Applications from Clinical Trials and Personalized Medicine to Finance and Business Analytics written by Jianchang Lin and published by Springer. This book was released on 2016-11-13 with total page 351 pages. Available in PDF, EPUB and Kindle. Book excerpt: The papers in this volume represent a broad, applied swath of advanced contributions to the 2015 ICSA/Graybill Applied Statistics Symposium of the International Chinese Statistical Association, held at Colorado State University in Fort Collins. The contributions cover topics that range from statistical applications in business and finance to applications in clinical trials and biomarker analysis. Each papers was peer-reviewed by at least two referees and also by an editor. The conference was attended by over 400 participants from academia, industry, and government agencies around the world, including from North America, Asia, and Europe.
Book Synopsis Data Analytics in Bioinformatics by : Rabinarayan Satpathy
Download or read book Data Analytics in Bioinformatics written by Rabinarayan Satpathy and published by John Wiley & Sons. This book was released on 2021-01-20 with total page 433 pages. Available in PDF, EPUB and Kindle. Book excerpt: Machine learning techniques are increasingly being used to address problems in computational biology and bioinformatics. Novel machine learning computational techniques to analyze high throughput data in the form of sequences, gene and protein expressions, pathways, and images are becoming vital for understanding diseases and future drug discovery. Machine learning techniques such as Markov models, support vector machines, neural networks, and graphical models have been successful in analyzing life science data because of their capabilities in handling randomness and uncertainty of data noise and in generalization. Machine Learning in Bioinformatics compiles recent approaches in machine learning methods and their applications in addressing contemporary problems in bioinformatics approximating classification and prediction of disease, feature selection, dimensionality reduction, gene selection and classification of microarray data and many more.
