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 Multimodal and Integrative Analysis of Single Cell or Bulk Sequencing Data written by Geng Chen and published by Frontiers Media SA. This book was released on 2021-04-07 with total page 116 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Design Analysis and Interpretation of Genome Wide Association Scans written by Daniel O. Stram and published by Springer Science & Business Media. This book was released on 2013-11-23 with total page 344 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book presents the statistical aspects of designing, analyzing and interpreting the results of genome-wide association scans (GWAS studies) for genetic causes of disease using unrelated subjects. Particular detail is given to the practical aspects of employing the bioinformatics and data handling methods necessary to prepare data for statistical analysis. The goal in writing this book is to give statisticians, epidemiologists, and students in these fields the tools to design a powerful genome-wide study based on current technology. The other part of this is showing readers how to conduct analysis of the created study. Design and Analysis of Genome-Wide Association Studies provides a compendium of well-established statistical methods based upon single SNP associations. It also provides an introduction to more advanced statistical methods and issues. Knowing that technology, for instance large scale SNP arrays, is quickly changing, this text has significant lessons for future use with sequencing data. Emphasis on statistical concepts that apply to the problem of finding disease associations irrespective of the technology ensures its future applications. The author includes current bioinformatics tools while outlining the tools that will be required for use with extensive databases from future large scale sequencing projects. The author includes current bioinformatics tools while outlining additional issues and needs arising from the extensive databases from future large scale sequencing projects.

Download or read book Integrative Methods for the Analysis of Genome Wide Association Studies written by Marc Andreas Schaub and published by . This book was released on 2012 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Genome Wide Association Studies (GWAS) have identified over 4,500 common variants in the human genome that are statistically associated with diseases and other phenotypical traits. Most identified associations, however, only have a small effect on disease risk, and their relevance in a clinical setting remains the subject of extensive debate. In this thesis I present three integrative analysis directions that extend on GWAS by developing new methods, by using genotyping data to ask new questions, and by integrating additional types of data to generate functional hypotheses about the biological processes underlying associations. First, I introduce a new classifier-based methodology that identifies similarities in the genetic architecture of diseases. This method can successfully identify both known and novel relationships between common diseases. Second, I show how control individuals from a GWAS can be used to detect genetic differences between the pseudoautosomal regions of chromosomes X and Y in the general population. Finally, I present an approach that integrates experimental data generated by the ENCODE consortium in order to identify functional Single Nucleotide Polymorphisms (SNPs). These functional SNPs are associated with a phenotype, either directly or through linkage disequilibrium, and overlap a functional region of the genome such as a transcribed region or a transcription factor binding site. Up to 80% of all associations previously reported in a GWAS can be mapped to a functional SNP.

Download or read book Integrative analysis of single cell and or bulk multi omics sequencing data written by Geng Chen and published by Frontiers Media SA. This book was released on 2023-03-13 with total page 189 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Genome Wide Association Studies written by Krishnarao Appasani and published by Cambridge University Press. This book was released on 2016-01-14 with total page 449 pages. Available in PDF, EPUB and Kindle. Book excerpt: Experts from academia and industry highlight the potential of genome-wide association studies from basic science to clinical and biotechnological/pharmaceutical applications.

Download or read book Statistical Methods Computing and Resources for Genome Wide Association Studies written by Riyan Cheng and published by Frontiers Media SA. This book was released on 2021-08-24 with total page 148 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Methods in Statistical Genomics written by Philip Chester Cooley and published by RTI Press. This book was released on 2016-08-29 with total page 163 pages. Available in PDF, EPUB and Kindle. Book excerpt: The objective of this book is to describe procedures for analyzing genome-wide association studies (GWAS). Some of the material is unpublished and contains commentary and unpublished research; other chapters (Chapters 4 through 7) have been published in other journals. Each previously published chapter investigates a different genomics model, but all focus on identifying the strengths and limitations of various statistical procedures that have been applied to different GWAS scenarios.

Download or read book Computational Methods for Single Cell Data Analysis written by Guo-Cheng Yuan and published by Humana Press. This book was released on 2019-02-14 with total page 271 pages. Available in PDF, EPUB and Kindle. Book excerpt: This detailed book provides state-of-art computational approaches to further explore the exciting opportunities presented by single-cell technologies. Chapters each detail a computational toolbox aimed to overcome a specific challenge in single-cell analysis, such as data normalization, rare cell-type identification, and spatial transcriptomics analysis, all with a focus on hands-on implementation of computational methods for analyzing experimental data. Written in the highly successful Methods in Molecular Biology series format, chapters include introductions to their respective topics, lists of the necessary materials and reagents, step-by-step, readily reproducible laboratory protocols, and tips on troubleshooting and avoiding known pitfalls. Authoritative and cutting-edge, Computational Methods for Single-Cell Data Analysis aims to cover a wide range of tasks and serves as a vital handbook for single-cell data analysis.

Download or read book Genetic Dissection of Complex Traits written by D.C. Rao and published by Academic Press. This book was released on 2008-04-23 with total page 788 pages. Available in PDF, EPUB and Kindle. Book excerpt: The field of genetics is rapidly evolving and new medical breakthroughs are occuring as a result of advances in knowledge of genetics. This series continually publishes important reviews of the broadest interest to geneticists and their colleagues in affiliated disciplines. Five sections on the latest advances in complex traits Methods for testing with ethical, legal, and social implications Hot topics include discussions on systems biology approach to drug discovery; using comparative genomics for detecting human disease genes; computationally intensive challenges, and more

Download or read book Genome Wide Association Studies written by Tatsuhiko Tsunoda and published by Springer. This book was released on 2020-11-14 with total page 0 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.

Download or read book Integrative Modeling for Genome wide Regulation of Gene Expression written by Zhengqing Ouyang and published by Stanford University. This book was released on 2010 with total page 135 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 Assessing Gene Environment Interactions in Genome Wide Association Studies Statistical Approaches written by Philip C. Cooley and published by RTI Press. This book was released on 2014-05-14 with total page 24 pages. Available in PDF, EPUB and Kindle. Book excerpt: In this report, we address a scenario that uses synthetic genotype case-control data that is influenced by environmental factors in a genome-wide association study (GWAS) context. The precise way the environmental influence contributes to a given phenotype is typically unknown. Therefore, our study evaluates how to approach a GWAS that may have an environmental component. Specifically, we assess different statistical models in the context of a GWAS to make association predictions when the form of the environmental influence is questionable. We used a simulation approach to generate synthetic data corresponding to a variety of possible environmental-genetic models, including a “main effects only” model as well as a “main effects with interactions” model. Our method takes into account the strength of the association between phenotype and both genotype and environmental factors, but we focus on low-risk genetic and environmental risks that necessitate using large sample sizes (N = 10,000 and 200,000) to predict associations with high levels of confidence. We also simulated different Mendelian gene models, and we analyzed how the collection of factors influences statistical power in the context of a GWAS. Using simulated data provides a “truth set” of known outcomes such that the association-affecting factors can be unambiguously determined. We also test different statistical methods to determine their performance properties. Our results suggest that the chances of predicting an association in a GWAS is reduced if an environmental effect is present and the statistical model does not adjust for that effect. This is especially true if the environmental effect and genetic marker do not have an interaction effect. The functional form of the statistical model also matters. The more accurately the form of the environmental influence is portrayed by the statistical model, the more accurate the prediction will be. Finally, even with very large samples sizes, association predictions involving recessive markers with low risk can be poor

Download or read book Efficient Design and Analysis of Genome wide Association Studies written by Emrah Kostem and published by . This book was released on 2013 with total page 88 pages. Available in PDF, EPUB and Kindle. Book excerpt: The recent advances in genomic technologies, have made it possible to collect large-scale information on genetic variation across a diverse biological landscape. This has resulted in an exponential influx of genetic information and the field of genetics has become data-rich in a relatively short amount of time. These developments have opened new avenues to elucidate the genetic basis of complex diseases, where the traditional disease study approaches had little success. In recent years, the genome-wide association study (GWAS) approach has gained widespread popularity for its ease of use and effectiveness, and is now the standard approach to study complex diseases. In GWAS, information on millions of single-nucleotide polymorphisms (SNPs) is collected from case and control individuals. SNP genotyping is cost-effective and due to their abundance in the genome, SNPs are correlated to their neighboring genetic variation, which makes them tags for genomic regions. Typically, each SNP is statistically tested for association to disease, and the genomic regions tagged by the significant SNPs are believed to be harboring the functional variants contributing to disease. In order to reduce the cost of GWAS and the redundancy in the information collected, an informative subset of the SNPs, or tag SNPs, are genotyped. Typically, the genomic regions harboring the significantly associated tag SNPs may be large and contain many additional polymorphisms. At this stage of the study it may not be clear which specific genes or polymorphisms are in fact most strongly associated to disease. We present a novel framework for designing cost-effective follow-up association studies to further characterize such regions by genotyping additional SNPs to identify all the associated polymorphisms. This identification of all associated polymorphisms provides a catalog of all possible functional variants, and the values of the actual association statistics at these polymorphisms may provide information to identify causal variants. We present the utility of our method in identifying significant associations and causal variants using simulated and real GWAS datasets. Although GWAS have been widely used to study associations of SNPs to disease phenotypes, there has been growing interest in applying the GWAS approach to high-throughput biological phenotypes, such as gene expression. In these studies, the goal is to identify genomic regions that affect gene expression levels, known as expression quantitative trait loci (eQTL). A challenge in applying GWAS to eQTL studies is that there are tens of thousands of measurements, each representing the expression level of one gene, for each sample tested, as opposed to values for one or two clinical traits. This results in a tremendous computational burden when performing the analysis, requiring computation for billions of tests and demands substantial computational resources. We present a novel two-stage approach to efficiently identify all of the significant associations without testing all the SNPs. In the first-stage, a small number of informative SNPs across the genome are tested. Based on their observed associations, our approach locates the regions that may contain significant SNPs and only tests additional SNPs from those regions. We demonstrate that this method increases the computational speed of eQTL studies by a factor of ten, and can be applied to reduce the computational burden of a wide range of association statistics. Finally, we develop a novel approach to address a problem that has been of fundamental interest to geneticists for decades. The contribution of genetics to a trait, termed as heritability, is often measured by the amount of variation in the trait that is due to genetics. Heritability, quantifies the role of genetics in a trait and provides insight about disease etiology. Traditionally, heritabilities were estimated in studies of individuals with known relatedness such as classical twin studies. Recently, estimating the heritability of a trait from unrelated individuals using GWAS data, and further, partitioning the heritability into the contributions of genomic regions has received a lot of attention. Existing methods partition the heritability by jointly estimating the contributions of all regions. However, these methods are computationally intractable and may be inaccurate when the number of regions is large. In this work, we present an alternative approach that partitions the total heritability into the contributions of an arbitrary number of regions, while performing these computations in parallel. We demonstrate that our method is more accurate and computationally efficient than existing approaches.

Download or read book Genomic Colocalization and Enrichment Analyses written by Geir Kjetil Sandve and published by Frontiers Media SA. This book was released on 2021-03-05 with total page 113 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Rigor and Reproducibility in Genetics and Genomics written by and published by Academic Press. This book was released on 2023-11-08 with total page 504 pages. Available in PDF, EPUB and Kindle. Book excerpt: Rigor and Reproducibility in Genetics and Genomics: Peer-reviewed, Published, Cited provides a full methodological and statistical overview for researchers, clinicians, students, and post-doctoral fellows conducting genetic and genomic research. Here, active geneticists, clinicians, and bioinformaticists offer practical solutions for a variety of challenges associated with several modern approaches in genetics and genomics, including genotyping, gene expression analysis, epigenetic analysis, GWAS, EWAS, genomic sequencing, and gene editing. Emphasis is placed on rigor and reproducibility throughout, with each section containing laboratory case-studies and classroom activities covering step-by-step protocols, best practices, and common pitfalls. Specific genetic and genomic technologies discussed include microarray analysis, DNA-seq, RNA-seq, Chip-Seq, methyl-seq, CRISPR gene editing, and CRISPR-based genetic analysis. Training exercises, supporting data, and in-depth discussions of rigor, reproducibility, and ethics in research together deliver a solid foundation in research standards for the next generation of genetic and genomic scientists. - Provides practical approaches and step-by-step protocols to strengthen genetic and genomic research conducted in the laboratory or classroom - Presents illustrative case studies and training exercises, discussing common pitfalls and solutions for genotyping, gene expression analysis, epigenetic analysis, GWAS, genomic sequencing, and gene editing, among other genetic and genomic approaches - Examines best practices for microarray analysis, DNA-seq, RNA-seq, gene expression validation, Chip-Seq, methyl-seq, CRISPR gene editing, and CRISPR-based genetic analysis - Written to provide trainees and educators with highly applicable tools and strategies to learn or refine a method toward identifying meaningful results with high confidence in their reproducibility

Download or read book Single Molecule and Single Cell Sequencing written by Yutaka Suzuki and published by Springer. This book was released on 2019-04-09 with total page 150 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book presents an overview of the recent technologies in single molecule and single cell sequencing. These sequencing technologies are revolutionizing the way of the genomic studies and the understanding of complex biological systems. The PacBio sequencer has enabled extremely long-read sequencing and the MinION sequencer has made the sequencing possible in developing countries. New developments and technologies are constantly emerging, which will further expand sequencing applications. In parallel, single cell sequencing technologies are rapidly becoming a popular platform. This volume presents not only an updated overview of these technologies, but also of the related developments in bioinformatics. Without powerful bioinformatics software, where rapid progress is taking place, these new technologies will not realize their full potential. All the contributors to this volume have been involved in the development of these technologies and software and have also made significant progress on their applications. This book is intended to be of interest to a wide audience ranging from genome researchers to basic molecular biologists and clinicians.