Download or read book Computational Methods for Analyzing Human Genetic Variation written by Vikas Bansal and published by ProQuest. This book was released on 2008 with total page 181 pages. Available in PDF, EPUB and Kindle. Book excerpt: In the post-genomic era, several large-scale studies that set out to characterize genetic diversity in human populations have significantly changed our understanding of the nature and extent of human genetic variation. The International HapMap Project has genotyped over 3 million Single Nucleotide Polymorphisms (SNPs) in 270 humans from four populations. Several individual genomes have recently been sequenced and thousands of genomes will be available in the near future. In this dissertation, we describe computational methods that utilize these datasets to further enhance our knowledge of the fine-scale structure of human genetic variation. These methods employ a variety of computational techniques and are applicable to organisms other than human. Meiotic recombination represents a fundamental mechanism for generating genetic diversity by shuffling of chromosomes. There is great interest in understanding the non-random distribution of recombination events across the human genome. We describe combinatorial methods for counting historical recombination events using population data. We demonstrate that regions with increased density of recombination events correspond to regions identified as recombination hotspots using experimental techniques. In recent years, large scale structural variants such as deletions, insertions, duplications and inversions of DNA segments have been revealed to be much more frequent than previously thought. High-throughput genome-scanning techniques have enabled the discovery of hundreds of such variants but are unable to detect balanced structural changes such as inversions. We describe a statistical method to detect large inversions using whole genome SNP population data. Using the HapMap data, we identify several known and putative inversion polymorphisms. In the final part of this thesis, we tackle the haplotype assembly problem. High-throughput genotyping methods probe SNPs individually and are unable to provide information about haplotypes: the combination of alleles at SNPs on a single chromosome. We describe Markov chain Monte Carlo (MCMC) and combinatorial algorithms for reconstructing the two haplotypes for an individual using whole genome sequence data. These algorithms are based on computing cuts in graphs derived from the sequenced reads. We analyze the convergence properties of the Markov chain underlying our MCMC algorithm. We apply these methods to assemble highly accurate haplotypes for a recently sequenced human.

Download or read book Computational Genome Analysis written by Richard C. Deonier and published by Springer Science & Business Media. This book was released on 2005-12-27 with total page 543 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book presents the foundations of key problems in computational molecular biology and bioinformatics. It focuses on computational and statistical principles applied to genomes, and introduces the mathematics and statistics that are crucial for understanding these applications. The book features a free download of the R software statistics package and the text provides great crossover material that is interesting and accessible to students in biology, mathematics, statistics and computer science. More than 100 illustrations and diagrams reinforce concepts and present key results from the primary literature. Exercises are given at the end of chapters.

Download or read book Handbook on Analyzing Human Genetic Data written by Shili Lin and published by Springer Science & Business Media. This book was released on 2009-10-13 with total page 340 pages. Available in PDF, EPUB and Kindle. Book excerpt: This handbook offers guidance on selections of appropriate computational methods and software packages for specific genetic problems. Coverage strikes a balance between methodological expositions and practical guidelines for software selections. Wherever possible, comparisons among competing methods and software are made to highlight the relative advantages and disadvantage of the approaches.

Download or read book Evaluating Human Genetic Diversity written by National Research Council and published by National Academies Press. This book was released on 1998-01-19 with total page 101 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book assesses the scientific value and merit of research on human genetic differencesâ€"including a collection of DNA samples that represents the whole of human genetic diversityâ€"and the ethical, organizational, and policy issues surrounding such research. Evaluating Human Genetic Diversity discusses the potential uses of such collection, such as providing insight into human evolution and origins and serving as a springboard for important medical research. It also addresses issues of confidentiality and individual privacy for participants in genetic diversity research studies.

Download or read book Computational Genetics and Genomics written by Gary Peltz and published by Springer Science & Business Media. This book was released on 2007-11-05 with total page 309 pages. Available in PDF, EPUB and Kindle. Book excerpt: Ultimately, the quality of the tools available for genetic analysis and experimental disease models will be assessed on the basis of whether they provide new information that generates novel treatments for human disease. In addition, the time frame in which genetic discoveries impact clinical practice is also an important dimension of how society assesses the results of the significant public financial investment in genetic research. Because of the investment and the increased expectation that new tre- ments will be found for common diseases, allowing decades to pass before basic discoveries are made and translated into new therapies is no longer acceptable. Computational Genetics and Genomics: Tools for Understanding Disease provides an overview and assessment of currently available and developing tools for genetic analysis. It is hoped that these new tools can be used to identify the genetic basis for susceptibility to disease. Although this very broad topic is addressed in many other books and journal articles, Computational Genetics and Genomics: Tools for Understanding Disease focuses on methods used for analyzing mouse genetic models of biomedically - portant traits. This volume aims to demonstrate that commonly used inbred mouse strains can be used to model virtually all human disea- related traits. Importantly, recently developed computational tools will enable the genetic basis for differences in disease-related traits to be rapidly identified using these inbred mouse strains. On average, a decade is required to carry out the development process required to demonstrate that a new disease treatment is beneficial.

Download or read book A Computational Analysis of Human Genetic Variation written by Asif Javed and published by . This book was released on 2008 with total page 130 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Computational Methods for Understanding Genetic Variations from Next Generation Sequencing Data written by Soyeon Ahn (Ph. D.) and published by . This book was released on 2018 with total page 246 pages. Available in PDF, EPUB and Kindle. Book excerpt: Studies of human genetic variation reveal critical information about genetic and complex diseases such as cancer, diabetes and heart disease, ultimately leading towards improvements in health and quality of life. Moreover, understanding genetic variations in viral population is of utmost importance to virologists and helps in search for vaccines. Next-generation sequencing technology is capable of acquiring massive amounts of data that can provide insight into the structure of diverse sets of genomic sequences. However, reconstructing heterogeneous sequences is computationally challenging due to the large dimension of the problem and limitations of the sequencing technology.This dissertation is focused on algorithms and analysis for two problems in which we seek to characterize genetic variations: (1) haplotype reconstruction for a single individual, so-called single individual haplotyping (SIH) or haplotype assembly problem, and (2) reconstruction of viral population, the so-called quasispecies reconstruction (QSR) problem. For the SIH problem, we have developed a method that relies on a probabilistic model of the data and employs the sequential Monte Carlo (SMC) algorithm to jointly determine type of variation (i.e., perform genotype calling) and assemble haplotypes. For the QSR problem, we have developed two algorithms. The first algorithm combines agglomerative hierarchical clustering and Bayesian inference to reconstruct quasispecies characterized by low diversity. The second algorithm utilizes tensor factorization framework with successive data removal to reconstruct quasispecies characterized by highly uneven frequencies of its components. Both algorithms outperform existing methods in both benchmarking tests and real data.

Download or read book Computational Methods for Genetics of Complex Traits written by and published by Academic Press. This book was released on 2010-11-10 with total page 211 pages. Available in PDF, EPUB and Kindle. Book excerpt: The field of genetics is rapidly evolving, and new medical breakthroughs are occurring as a result of advances in knowledge gained from genetics reasearch. This thematic volume of Advances in Genetics looks at Computational Methods for Genetics of Complex traits. - Explores the latest topics in neural circuits and behavior research in zebrafish, drosophila, C.elegans, and mouse models - Includes methods for testing with ethical, legal, and social implications - Critically analyzes future prospects

Download or read book Statistical Models for Analyzing Human Genetic Variation written by Sriram Sankararaman and published by . This book was released on 2010 with total page 328 pages. Available in PDF, EPUB and Kindle. Book excerpt: Advances in sequencing and genomic technologies are providing new opportunities to understand the genetic basis of phenotypes such as diseases. Translating the large volumes of heterogeneous, often noisy, data into biological insights presents challenging problems of statistical inference. In this thesis, we focus on three important statistical problems that arise in our efforts to understand the genetic basis of phenotypic variation in humans. At the molecular level, we focus on the problem of identifying the amino acid residues in a protein that are important for its function. Identifying functional residues is essential to understanding the effect of genetic variation on protein function as well as to understanding protein function itself. We propose computational methods that predict functional residues using evolutionary information as well as from a combination of evolutionary and structural information. We demonstrate that these methods can accurately predict catalytic residues in enzymes. Case studies on well-studied enzymes show that these methods can be useful in guiding future experiments. At the population level, discovering the link between genetic and phenotypic variation requires an understanding of the genetic structure of human populations. A common form of population structure is that found in admixed groups formed by the intermixing of several ancestral populations, such as African-Americans and Latinos. We describe a Bayesian hidden Markov model of admixture and propose efficient algorithms to infer the fine-scale structure of admixed populations. We show that the fine-scale structure of these populations can be inferred even when the ancestral populations are unknown or extinct. Further, the inference algorithm can run efficiently on genome-scale datasets. This model is well-suited to estimate other parameters of biological interest such as the allele frequencies of ancestral populations which can be used, in turn, to reconstruct extinct populations. Finally, we address the problem of sharing genomic data while preserving the privacy of individual participants. We analyze the problem of detecting an individual genotype from the summary statistics of single nucleotide polymorphisms (SNPs) released in a study. We derive upper bounds on the power of detection as a function of the study size, number of exposed SNPs and the false positive rate, thereby providing guidelines as to which set of SNPs can be safely exposed.

Download or read book Genetic Variation and Human Disease written by Kenneth M. Weiss and published by Cambridge University Press. This book was released on 1993 with total page 384 pages. Available in PDF, EPUB and Kindle. Book excerpt: Recent developments in molecular and computational methods have made it possible to identify the genetic basis of any biological trait, and have led to spectacular advances in the study of human disease. This book provides an overview of the concepts and methods needed to understand the genetic basis of biological traits, including disease, in humans. Using examples of qualitative and quantitative phenotypes, Professor Weiss shows how genetic variation may be quantified, and how relationships between genotype and phenotype may be inferred. This book will appeal to many biologists and biological anthropologists interested in the genetic basis of biological traits, as well as to epidemiologists, biomedical scientists, human geneticists and molecular biologists.

Download or read book Computational Methods to Analyze Large scale Genetic Studies of Complex Human Traits written by Huwenbo Shi and published by . This book was released on 2018 with total page 163 pages. Available in PDF, EPUB and Kindle. Book excerpt: Large-scale genome-wide association studies (GWAS) have produced a rich resource of genetic data over the past decade, urging the need to develop computational and statistical methods that analyze these data. This dissertation presents four statistical methods that model the correlation structure between genetic variants and its effect on GWAS summary association statistics to help understand the genetic basis of complex human traits and diseases. The first method employs the multivariate Bernoulli distribution to model haplotype data, allowing for higher-order interactions among genetic variants, and shows better accuracy in predicting DNase I hypersensitivity status. The second method partitions heritability into small regions on the genome using GWAS summary statistics data, while accounting for complex correlation structures among genetic variants, and uncovers the genetic architectures of complex human traits and diseases. Extending the second method into pairs of traits, the third method partitions genetic correlation into small genomic regions using GWAS summary statistics data, and provides insights into the shared genetic basis between pairs of traits. Finally, the fourth method dissects population-specific and shared causal genetic variants of complex traits in two continental populations, using GWAS summary statistics data obtained from samples of different ethnicities, and reveals differences in genetic architectures of two continental populations.

Download or read book Computational and Statistical Approaches to Genomics written by Wei Zhang and published by Springer Science & Business Media. This book was released on 2007-12-26 with total page 426 pages. Available in PDF, EPUB and Kindle. Book excerpt: The second edition of this book adds eight new contributors to reflect a modern cutting edge approach to genomics. It contains the newest research results on genomic analysis and modeling using state-of-the-art methods from engineering, statistics, and genomics. These tools and models are then applied to real biological and clinical problems. The book’s original seventeen chapters are also updated to provide new initiatives and directions.

Download or read book Computational Exome and Genome Analysis written by Peter N. Robinson and published by CRC Press. This book was released on 2017-09-13 with total page 444 pages. Available in PDF, EPUB and Kindle. Book excerpt: Exome and genome sequencing are revolutionizing medical research and diagnostics, but the computational analysis of the data has become an extremely heterogeneous and often challenging area of bioinformatics. Computational Exome and Genome Analysis provides a practical introduction to all of the major areas in the field, enabling readers to develop a comprehensive understanding of the sequencing process and the entire computational analysis pipeline.

Download or read book Algorithms and Methods for Characterizing Genetic Variability in Humans written by Christine Yanyee Lo and published by . This book was released on 2014 with total page 130 pages. Available in PDF, EPUB and Kindle. Book excerpt: Characterizing genetic variation including point mutations and structural variations, is key to understanding phenotypic variation in humans. The rapid development of sequencing technology has fueled the development of computational methods for elucidating genetic variation. In this dissertation, we develop novel computational methods to mainly target two human genetic variation problems using current and emerging sequencing technology. Capturing variation on the haplotype level is challenging with current sequencing technology as it involves linking together short sequenced fragments of the genome that overlap at least two heterozygous sites. While there has been a lot of research on correcting errors to achieve accurate haplotypes, relatively little work has been done on designing sequencing experiments to get long haplotypes. With the development of new sequencing technology and experimental haplotyping methods, we parametrize the haplotyping problem in two contexts, strobe sequencing and clone-based haplotyping, and provide theoretical and empirical assessment of the impact of different parameters on haplotype length. Variation in certain regions of the genome are harder to capture than others. Reconstruction of the donor genome from whole genome sequence data is either based on de novo assembly of the short reads or on mapping reads to a standard reference genome. While these techniques work well for inferring 'simple' genomic regions, they are confounded by regions with complex variation patterns including regions of direct immunological relevance such as the HLA and KIR regions. Characterizing these regions have previously relied on laboratory methods using traditional and quantitative PCR primers and probes which can be labor and time intensive. We address the problem of ambiguous mapping in complex regions by defining a new scoring function for read-to-genome matchings. This scoring function is applied to predicted sequence assemblies of the KIR region in order to determine the most likely KIR haplotype groups of the donor. In another approach, we developing a novel method based on barcoding (deriving signatures) known KIR templates in order to determine the copy number and allelic type of genes in the KIR region directly from whole genome sequencing data without assembly or mapping.

Download or read book Mapping and Sequencing the Human Genome written by National Research Council and published by National Academies Press. This book was released on 1988-01-01 with total page 128 pages. Available in PDF, EPUB and Kindle. Book excerpt: There is growing enthusiasm in the scientific community about the prospect of mapping and sequencing the human genome, a monumental project that will have far-reaching consequences for medicine, biology, technology, and other fields. But how will such an effort be organized and funded? How will we develop the new technologies that are needed? What new legal, social, and ethical questions will be raised? Mapping and Sequencing the Human Genome is a blueprint for this proposed project. The authors offer a highly readable explanation of the technical aspects of genetic mapping and sequencing, and they recommend specific interim and long-range research goals, organizational strategies, and funding levels. They also outline some of the legal and social questions that might arise and urge their early consideration by policymakers.

Download or read book Applied Computational Genomics written by Yin Yao Shugart and published by Springer Science & Business Media. This book was released on 2012-12-30 with total page 197 pages. Available in PDF, EPUB and Kindle. Book excerpt: "Applied Computational Genomics" focuses on an in-depth review of statistical development and application in the area of human genomics including candidate gene mapping, linkage analysis, population-based, genome-wide association, exon sequencing and whole genome sequencing analysis. The authors are extremely experienced in the area of statistical genomics and will give a detailed introduction of the evolution in the field and critical evaluations of the advantages and disadvantages of the statistical models proposed. They will also share their views on a future shift toward translational biology. The book will be of value to human geneticists, medical doctors, health educators, policy makers, and graduate students majoring in biology, biostatistics, and bioinformatics. Dr. Yin Yao Shugart is investigator in the Intramural Research Program at the National Institute of Mental Health, Bethesda, Maryland USA. ​

Download or read book New Computational Approaches for Analyzing Admixed Populations written by Danny Park and published by . This book was released on 2017 with total page 140 pages. Available in PDF, EPUB and Kindle. Book excerpt: This work revolves around developing, applying, and evaluating computational methods for the analysis of genomic datasets of recently admixed populations. Individuals from recently admixed populations, such as African Americans and Hispanic Latinos, derive their genomes from multiple genetically distinct ancestral populations. For example, African Americans have locus-specific ancestry from African and European genomes, which reflects demographic history and influences disease predisposition. Genomic studies of admixed populations therefore provide an enormous opportunity to investigate the influence of genetic variation on human phenotypic diversity. Furthermore, such studies offer a framework to test the generalizability of findings on genotype-phenotype relationships originally obtained in more homogeneous populations (i.e. Europeans), potentially yielding insights into underlying mechanisms. Here we present four novel statistical/computational approaches that leverage the unique genetic makeup of admixed populations to aid in deepening our understanding of the effect of human genetic variation on the phenome.