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Machine Learning In Genome Wide Association Studies
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Book Synopsis Machine Learning in Genome-Wide Association Studies by : Ting Hu
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.
Book Synopsis Deep Learning for Genome-wide Association Studies by : Deepak Sharma
Download or read book Deep Learning for Genome-wide Association Studies written by Deepak Sharma and published by . This book was released on 2022 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: "Genome-Wide Association Studies (GWAS) are a popular tool in statistical genomics that are used to identify genetic variants associated with various dis- eases. However, their success has been limited, in part because they typically do not incorporate interactions between variants to model target traits. Since Deep neural networks have been successful across domains abundant with com- plex signals, like speech, language, and vision, they are also popular candidates for modelling interactions between genetic variants. However, their black-box nature is a hindrance to their application for GWAS. In this thesis, we present a pipeline to train and interpret feedforward neu- ral networks to conduct a genome-wide association study (GWAS). We show that trained deep neural networks can be interpreted using feature-importance techniques to accurately distinguish and rank simulated causal genetic variants. We improve its accuracy by extending the pipeline to the multi-task setting, wherein we simultaneously model two related, simulated traits. We demon- strate the accuracy, reliability, and scalability of our approach by identifying most known Diabetes genetic risk factors found using a conventional GWAS on the UK Biobank"--
Book Synopsis Deep Learning for Genome-wide Association Studies and the Impact of SNP Locations by : Songyuan Ji
Download or read book Deep Learning for Genome-wide Association Studies and the Impact of SNP Locations written by Songyuan Ji and published by . This book was released on 2019 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: The study of Single Nucleotide Polymorphisms (SNPs) associated with human diseases is important for identifying pathogenic genetic variants and illuminating the genetic architecture of complex diseases. A Genome-wide association study (GWAS) examines genetic variation in different individuals and detects disease related SNPs. The traditional machine learning methods always use SNPs data as a sequence to analyze and process and thus may overlook the complex interacting relationships among multiple genetic factors. In this thesis, we propose a new hybrid deep learning approach to identify susceptibility SNPs associated with colorectal cancer. A set of SNPs variants were first selected by a hybrid feature selection algorithm, and then organized as 3D images using a selection of space-filling curve models. A multi-layer deep Convolutional Neural Network was constructed and trained using those images. We found that images generated using the space-filling curve model that preserve the original SNP locations in the genome yield the best classification performance. We also report a set of high risk SNPs associate with colorectal cancer as the result of the deep neural network model.
Book Synopsis Integration and Development of Machine Learning Methodologies to Improve the Power of Genome-wide Association Studies by : Jing Li
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.
Book Synopsis Machine Learning Methods for Genome-Wide Association Data by : Ingrid Braenne
Download or read book Machine Learning Methods for Genome-Wide Association Data written by Ingrid Braenne and published by . This book was released on 2011 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Artificial Intelligence written by and published by BoD – Books on Demand. This book was released on 2019-07-31 with total page 142 pages. Available in PDF, EPUB and Kindle. Book excerpt: Artificial intelligence (AI) is taking on an increasingly important role in our society today. In the early days, machines fulfilled only manual activities. Nowadays, these machines extend their capabilities to cognitive tasks as well. And now AI is poised to make a huge contribution to medical and biological applications. From medical equipment to diagnosing and predicting disease to image and video processing, among others, AI has proven to be an area with great potential. The ability of AI to make informed decisions, learn and perceive the environment, and predict certain behavior, among its many other skills, makes this application of paramount importance in today's world. This book discusses and examines AI applications in medicine and biology as well as challenges and opportunities in this fascinating area.
Book Synopsis Soybean Breeding by : Felipe Lopes da Silva
Download or read book Soybean Breeding written by Felipe Lopes da Silva and published by Springer. This book was released on 2017-06-10 with total page 439 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book was written by soybean experts to cluster in a single publication the most relevant and modern topics in soybean breeding. It is geared mainly to students and soybean breeders around the world. It is unique since it presents the challenges and opportunities faced by soybean breeders outside the temperate world.
Book Synopsis Machine Learning Methods for Genome-Wide Association Data by : Ingrid Braenne
Download or read book Machine Learning Methods for Genome-Wide Association Data written by Ingrid Braenne and published by . This book was released on 2011 with total page 148 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Evolutionary Computation, Machine Learning and Data Mining in Bioinformatics by : Elena Marchiori
Download or read book Evolutionary Computation, Machine Learning and Data Mining in Bioinformatics written by Elena Marchiori and published by Springer Science & Business Media. This book was released on 2007-04-02 with total page 311 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the refereed proceedings of the 5th European Conference on Evolutionary Computation, Machine Learning and Data Mining in Bioinformatics, EvoBIO 2007, held in Valencia, Spain, April 2007. Coverage brings together experts in computer science with experts in bioinformatics and the biological sciences. It presents contributions on fundamental and theoretical issues along with papers dealing with different applications areas.
Book Synopsis Developing Machine Learning and Statistical Methods for the Analysis of Genetics and Genomics by : Jiajin Li
Download or read book Developing Machine Learning and Statistical Methods for the Analysis of Genetics and Genomics written by Jiajin Li and published by . This book was released on 2021 with total page 154 pages. Available in PDF, EPUB and Kindle. Book excerpt: With the development of next-generation sequencing technologies, we can detect numerous genetic variants associated with many diseases or complex traits over the past decades. Genome-wide association studies (GWAS) have been one of the most effective methods to identify those variants. It discovers disease-associated variants by comparing the genetic information between controls and cases. This approach is simple and effective and has been used by many studies. Before performing GWAS, we need to detect the genetic variants of the sample population. A subset of these variants, however, may have poor sequencing quality due to limitations in NGS or variant callers. In genetic studies that analyze a large number of sequenced individuals, it is critical to detect and remove those variants with poor quality as they may cause spurious findings. Here, I will present ForestQC, an efficient statistical tool for performing quality control on variants identified from NGS data by combining a traditional filtering approach and a machine learning approach, which outperforms widely used methods by considerably improving the quality of variants to be included in the analysis. Once this association is identified, the next step is to understand the genetic mechanism of rare variants on how the variants influence diseases, especially whether or how they regulate gene expression as they may affect diseases through gene regulation. However, it is challenging to identify the regulatory effects of rare variants because it often requires large sample sizes and the existing statistical approaches are not optimized for it. To improve statistical power, I will introduce a new approach, LRT-q, based on a likelihood ratio test that combines effects of multiple rare variants in a nonlinear manner and has higher power than previous approaches. I apply LRT-q to the GTEx dataset and find many novel biological insights. Recent studies have shown that omics data can be used for automatic disease diagnosis with machine learning algorithms. I will introduce an accurate and automated machine learning pipeline for the diagnosis of atopic dermatitis (AD) based on transcriptome and microbiota data. I will demonstrate that this classifier can accurately differentiate subjects with AD and healthy individuals. It also identifies a set of genes and microorganisms that are predictive for AD. I will show that they are directly or indirectly associated with AD.
Book Synopsis Big Data in Omics and Imaging by : Momiao Xiong
Download or read book Big Data in Omics and Imaging written by Momiao Xiong and published by CRC Press. This book was released on 2018-06-14 with total page 736 pages. Available in PDF, EPUB and Kindle. Book excerpt: Big Data in Omics and Imaging: Integrated Analysis and Causal Inference addresses the recent development of integrated genomic, epigenomic and imaging data analysis and causal inference in big data era. Despite significant progress in dissecting the genetic architecture of complex diseases by genome-wide association studies (GWAS), genome-wide expression studies (GWES), and epigenome-wide association studies (EWAS), the overall contribution of the new identified genetic variants is small and a large fraction of genetic variants is still hidden. Understanding the etiology and causal chain of mechanism underlying complex diseases remains elusive. It is time to bring big data, machine learning and causal revolution to developing a new generation of genetic analysis for shifting the current paradigm of genetic analysis from shallow association analysis to deep causal inference and from genetic analysis alone to integrated omics and imaging data analysis for unraveling the mechanism of complex diseases. FEATURES Provides a natural extension and companion volume to Big Data in Omic and Imaging: Association Analysis, but can be read independently. Introduce causal inference theory to genomic, epigenomic and imaging data analysis Develop novel statistics for genome-wide causation studies and epigenome-wide causation studies. Bridge the gap between the traditional association analysis and modern causation analysis Use combinatorial optimization methods and various causal models as a general framework for inferring multilevel omic and image causal networks Present statistical methods and computational algorithms for searching causal paths from genetic variant to disease Develop causal machine learning methods integrating causal inference and machine learning Develop statistics for testing significant difference in directed edge, path, and graphs, and for assessing causal relationships between two networks The book is designed for graduate students and researchers in genomics, epigenomics, medical image, bioinformatics, and data science. Topics covered are: mathematical formulation of causal inference, information geometry for causal inference, topology group and Haar measure, additive noise models, distance correlation, multivariate causal inference and causal networks, dynamic causal networks, multivariate and functional structural equation models, mixed structural equation models, causal inference with confounders, integer programming, deep learning and differential equations for wearable computing, genetic analysis of function-valued traits, RNA-seq data analysis, causal networks for genetic methylation analysis, gene expression and methylation deconvolution, cell –specific causal networks, deep learning for image segmentation and image analysis, imaging and genomic data analysis, integrated multilevel causal genomic, epigenomic and imaging data analysis.
Book Synopsis Integration of Machine Learning, Network Science and Pathway Analysis in Genetic Epidemiology by : Qinxin Pan
Download or read book Integration of Machine Learning, Network Science and Pathway Analysis in Genetic Epidemiology written by Qinxin Pan and published by . This book was released on 2014 with total page 432 pages. Available in PDF, EPUB and Kindle. Book excerpt: Although genome-wide association studies (GWAS) and other high-throughput initiatives have led to an information explosion in human genetics and genetic epidemiology, the mapping from genotype to phenotype remains challenging as most of the identified loci have only moderate effect size. As a ubiquitous phenomenon, epistasis is believed to account for a portion of the presumed missing heritability. The term epistasis refers to the non-additive effect among multiple genetic variants. To detect epistasis, machine learning methods have been developed and among them Random Forest (RF) is a popular one. Meanwhile, networks have emerge as a popular tool for characterizing the space of pairwise interactions systematically, which makes it a well-suited framework for modeling interactions. Different with machine learning methods that identify risk-associated genes, pathway analysis highlights risk-associated pathways, which possess higher explanatory power. However, most extant pathway analysis methods ignore epistasis and treat each pathway independently. Here we integrate machine learning, network science, and pathway analysis to detect epistasis and address epistasis in pathway analysis. This work includes guiding random forest using interaction network for epistasis detection, examining the significance of epistasis in pathway analysis, developing pathway analysis approaches that take epistasis into account, and identifying risk-associated pathway interactions. Applications to population-based genetic studies of bladder cancer and Alzheimer's disease demonstrate the validity and potential.
Book Synopsis Essentials of Error-Control Coding Techniques by : Hideki Imai
Download or read book Essentials of Error-Control Coding Techniques written by Hideki Imai and published by Academic Press. This book was released on 2014-06-28 with total page 348 pages. Available in PDF, EPUB and Kindle. Book excerpt: Essentials of Error-Control Coding Techniques presents error-control coding techniques with an emphasis on the most recent applications. It is written for engineers who use or build error-control coding equipment. Many examples of practical applications are provided, enabling the reader to obtain valuable expertise for the development of a wide range of error-control coding systems. Necessary background knowledge of coding theory (the theory of error-correcting codes) is also included so that the reader is able to assimilate the concepts and the techniques. The book is divided into two parts. The first provides the reader with the fundamental knowledge of the coding theory that is necessary to understand the material in the latter part. Topics covered include the principles of error detection and correction, block codes, and convolutional codes. The second part is devoted to the practical applications of error-control coding in various fields. It explains how to design cost-effective error-control coding systems. Many examples of actual error-control coding systems are described and evaluated. This book is particularly suited for the engineer striving to master the practical applications of error-control coding. It is also suitable for use as a graduate text for an advanced course in coding theory.
Book Synopsis Likelihood, Bayesian, and MCMC Methods in Quantitative Genetics by : Daniel Sorensen
Download or read book Likelihood, Bayesian, and MCMC Methods in Quantitative Genetics written by Daniel Sorensen and published by Springer Science & Business Media. This book was released on 2007-03-22 with total page 745 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book, suitable for numerate biologists and for applied statisticians, provides the foundations of likelihood, Bayesian and MCMC methods in the context of genetic analysis of quantitative traits. Although a number of excellent texts in these areas have become available in recent years, the basic ideas and tools are typically described in a technically demanding style and contain much more detail than necessary. Here, an effort has been made to relate biological to statistical parameters throughout, and the book includes extensive examples that illustrate the developing argument.
Book Synopsis Investigation of Methods for Machine Learning Associations Between Genetic Varations and Phenotype by : Amanda M. Hartung
Download or read book Investigation of Methods for Machine Learning Associations Between Genetic Varations and Phenotype written by Amanda M. Hartung and published by . This book was released on 2016 with total page 140 pages. Available in PDF, EPUB and Kindle. Book excerpt: "The relationship between genetics and phenotype is a complex one that remains poorly understood. Many factors contribute to the relationship between genetic variations and differences in phenotype. An improved understanding of the genetic underpinnings of various phenotypes can help us make important advances in testing for, preventing, treating, and curing a number of diseases and disorders. The recent popularization of direct-to-consumer sequencing services, coupled with consumers releasing their genetic information for public use, has led to an unprecedented level of access to genetic information. Crowd-sourcing the problem of developing robust genome-wide association techniques for ever larger amounts of data is a promising trend. This thesis explores likely methods to data mine one such public genetic data repository, openSNP, for correlated genotypes and phenotypes. Particular care is given to data clean-up and the steps required to preprocess public data for machine learning. The preprocessing methods are detailed in such a way that they may be applied to other genetic data repositories that already exist, for example the Personal Genome Project, as well as genetic data repositories that may become available in the future. Following data clean-up, a number of machine learning techniques are investigated, applied, and assessed for their utility in such a big-data problem. No single machine learning approach was found to be sufficient; the combination of imbalanced phenotype response classes and an underdetermined system led to a difficult machine learning challenge. Additional techniques must be explored or developed in order to make such genome-wide association studies possible and meaningful."--Abstract.
Book Synopsis Genome-Wide Association Studies by : Tatsuhiko Tsunoda
Download or read book Genome-Wide Association Studies written by Tatsuhiko Tsunoda and published by Springer Nature. This book was released on 2019-10-31 with total page 209 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book examines the utility of genome-wide association studies (GWAS) in the era of next-generation sequencing and big data, identifies limitations and potential means of overcoming them, and looks to the future of GWAS and what may lay beyond. GWAS are among the most powerful tools for elucidating the genetic aspects of human and disease diversity. In Genome-Wide Association Studies, experts in the field explore in depth the impacts of GWAS on genomic research into a variety of common diseases, including cardiovascular, autoimmune, diabetic, cancer, and infectious diseases. The book will equip readers with a sound understanding both of the types of disease and phenotypes that are suited for GWAS and of the ways in which a road map resulting from GWAS can lead to the realization of personalized/precision medicine: functional analysis, drug seeds, pathway analysis, disease mechanism, risk prediction, and diagnosis.
Book Synopsis Genome-Wide Association Studies and Genomic Prediction by : Cedric Gondro
Download or read book Genome-Wide Association Studies and Genomic Prediction written by Cedric Gondro and published by Humana Press. This book was released on 2013-06-12 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: With the detailed genomic information that is now becoming available, we have a plethora of data that allows researchers to address questions in a variety of areas. Genome-wide association studies (GWAS) have become a vital approach to identify candidate regions associated with complex diseases in human medicine, production traits in agriculture, and variation in wild populations. Genomic prediction goes a step further, attempting to predict phenotypic variation in these traits from genomic information. Genome-Wide Association Studies and Genomic Prediction pulls together expert contributions to address this important area of study. The volume begins with a section covering the phenotypes of interest as well as design issues for GWAS, then moves on to discuss efficient computational methods to store and handle large datasets, quality control measures, phasing, haplotype inference, and imputation. Later chapters deal with statistical approaches to data analysis where the experimental objective is either to confirm the biology by identifying genomic regions associated to a trait or to use the data to make genomic predictions about a future phenotypic outcome (e.g. predict onset of disease). As part of the Methods in Molecular Biology series, chapters provide helpful, real-world implementation advice.
