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Download Between the Lines of Genetic Code book written by Leonid Padyukov, available in PDF, EPUB, and Kindle, or read full book online anywhere and anytime. Compatible with any devices.
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Between the Lines of Genetic Code lays out methodologies and tools for the measurement and evaluation of gene-gene and gene-environment studies and gives perspective on the future of this discipline. The book begins by defining terms for interaction studies, describing methodologies, and critically assessing the viability of current study designs and the possibilities for integrating designs. It then provides recent applications data with case studies in rheumatoid arthritis, multiple sclerosis, myositis and other complex human diseases. Last, it examines current studies and directions for future applications in patient care. Recent multivariate studies show that gene-gene and gene-environment interactions can explain significant variances in inheritance that have previously been undetectable in univariate analysis. These links among genes and between genes and their environments during the development of diseases may serve as important hints for understanding pathogenic mechanisms and for developing new tools for prognosis, diagnosis, and treatment of various diseases.
Book Description :
Between the Lines of Genetic Code lays out methodologies and tools for the measurement and evaluation of gene-gene and gene-environment studies and gives perspective on the future of this discipline. The book begins by defining terms for interaction studies, describing methodologies, and critically assessing the viability of current study designs and the possibilities for integrating designs. It then provides recent applications data with case studies in rheumatoid arthritis, multiple sclerosis, myositis and other complex human diseases. Last, it examines current studies and directions for future applications in patient care. Recent multivariate studies show that gene-gene and gene-environment interactions can explain significant variances in inheritance that have previously been undetectable in univariate analysis. These links among genes and between genes and their environments during the development of diseases may serve as important hints for understanding pathogenic mechanisms and for developing new tools for prognosis, diagnosis, and treatment of various diseases. Systematically integrates methods of defining and detecting gene interactions to provide an overview of the field Critically analyzes current methods and tools to aid researchers in integrating gene interaction studies Includes examples of current biomedical applications and presents current research expected to shape clinical research in the near future
Book Description :
Following the success of genome-wide association studies, it is now important to unravel interactions between genes as well as between genes and environment. Here we discuss how to interpret higher order statistical interactions in terms of underlying biological processes purportedly generating such dependencies. In this chapter, we specifically review how biological networks, such as those generated from systems biological approaches, could possibly facilitate the interpretation of observed interactions in the development of complex phenotypes, e.g., complex diseases. We conclude that the statistical challenge is essentially a high-dimensional feature selection problem and there is therefore a need to construct intelligent priors using biological networks as a tool for the discovery and interpretation of interactions.
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Functional analysis of genetic variants in association with human complex diseases remains a challenge for modern research. Discovery of gene–gene interactions may represent an additional tool for identifying specific polymorphisms involved in disease development. It not only identifies new candidate genes for mechanistic studies, but also suggests biological relations in the function of these genes with certain variants in developing disease. We describe a few examples of the studies where a link between statistical evidence of gene–gene interaction was found with the expression or function of genes of interest. Finally, we present future perspectives for integration genetic epidemiology data with biologically relevant mechanisms of disease.
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Gene–gene interactions are a critical component of the genetic architecture of complex, human traits. Unfortunately, the detection and characterization of interactions is a challenge. This is due to a number of factors including the combinatorial explosion of possible interactions in large-scale genomic data, limited power in small sample sizes, and potential difficulty interpreting or validating the resulting interaction models. A number of data mining methods have been developed to deal with one or more of these challenges. In this chapter, we discuss the importance of looking for gene–gene interactions as well as some of the challenges facing this type of analysis. Additionally, we review the alternative analytic approaches being considered to improve our ability to detect and model gene–gene interactions. Finally, we conclude with the future directions of interaction analysis.
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The idiopathic inflammatory myopathies (IIMs) represent a complex spectrum of overlapping connective tissue diseases which have skeletal muscle involvement as one of their major components. While recent immunogenetic research has shown that myositis-specific and myositis-associated autoantibodies (MSAs/MAAs) are clearly associated with and thus define many clinical IIM subsets, future research is needed to clarify whether these antibodies (Abs) are just a secondary epiphenomenon, i.e., representing surrogate markers for underlying human leukocyte antigen (HLA) genes and or genotypes, or whether they play some prime role in the immune processes characterizing the IIM. There is increasing evidence suggesting that the IIM disease subtype an individual will get following any environmental triggering event is likely predetermined by their genotype at HLA, and that epistatic genetic interactions in this region critically determine overall disease phenotype, including disease severity and treatment outcomes. It seems clear in inclusion body myositis (IBM), but not yet in other IIM, that HLA gene dosage determines disease severity, again highlighting the importance of epistatic genetic interactions in determining disease characteristics, including outcome. As for rheumatoid arthritis (RA), it appears that HLA genes and the environment interact to modify IIM disease susceptibility. That a specific IIM subtype (necrotizing myopathy characterized by infiltrating macrophages without the usual T and B cells) can be induced in some genetically susceptible individuals by the specific environmental trigger, i.e., the use of certain lipid-lowering statin drugs, suggests that this may represent a potential human model to further study IIM disease-induction mechanisms.
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Genome-wide interaction studies is an extremely challenging problem in statistics, in which conventional methods are often inadequate in terms of both power and computational efficiency. An exhaustive search for genome-wide gene–gene interactions becomes feasible with modern cluster computing run on graphics processing units. However, the large number of tests accompanying the search raises a serious multiple testing problem. A way to overcome these limits is to apply a filtering step prior to the combinatorial method and to analyze only interesting single nucleotide polymorphisms selected based on a priori (defined by statistical evidence, genetic impact, or biological plausibility). The advantage of the filter approach is speed, and the disadvantage is that attributes with poor quality scores are disregarded. Genome-wide gene–environment interaction is less problematic computational demand compared with pairwise genome-wide gene–gene interaction. Accounting for gene–gene and gene–environment interactions is important for future strategies of diagnosis, prognosis, and management of human diseases and will bring new data regarding pathogenetic mechanisms for human complex diseases.
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Rheumatoid arthritis (RA) is a chronic autoimmune disease with poorly understood etiology. Over the years, many genetic risk factors and some environmental risk factors were discovered to be important for RA. Genetic variants from the HLA-DRB1 locus, the shared epitope alleles, in different populations show a relatively strong and consistent effect on risk of RA. Non-HLA genetic risk factors have only a moderate effect. Gene–gene and gene–environment interactions were suspected to be important with regard to the risk of RA. Indeed, some combinations were demonstrated to be extremely influential. An important feature of RA is the development of autoantibodies, e.g., RF and ACPA, that serve as biomarkers and are used in diagnosing the disease. Interestingly, most of the genetic predisposition for RA observed so far concerns risk of developing the autoantibody positive subset of the disease, that is, the form where antibodies are present. In this chapter, we will review the current knowledge on genetics in RA and introduce multiple examples of interaction.
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Multiple sclerosis (MS) is a multifactorial disease where both environmental and genetic risk factors play a role. More than 50 genetic polymorphisms have been identified in association with MS mostly by mapping close to genes involved in immunoregulation. Several environmental factors have been associated with risk for developing MS, including smoking, obesity, EBV infections, and lack of sun exposure. Interactions between major genetic risk factors and environmental exposures have been observed making it possible to begin to form groups of risk factors that are jointly involved in MS pathogenesis.
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Many diseases are believed to occur as a result of interactions between genes and environment. In epidemiology, there are several study designs, such as cohort and case-control studies, that can be used to analyze biological interaction. In this chapter, we discuss crucial issues in the design stage of epidemiologic studies, how different measures of disease occurrence and measures of associations are calculated, and how systematic and random errors might hamper the accuracy of the results. By properly addressing these issues, one can design accurate epidemiologic studies and thus also decent studies of interaction. Later in this chapter, we give an example of how alcohol consumption and the genetic risk factor HLA-DRB1 shared epitope alleles interact in the development of rheumatoid arthritis, by using data from a Swedish population-based case-control study. The example demonstrates how interaction analyses in practice might be performed within epidemiologic study designs.
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Download Molecular Biology of the Cell book written by Bruce Alberts, available in PDF, EPUB, and Kindle, or read full book online anywhere and anytime. Compatible with any devices.
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This chapter deals with estimation of interaction through different statistical models. Interaction is classified into two different classes, antagonistic and synergistic effects. An antagonistic effect is said to be present if the combination of two factors decrease the measure of interest as compared to the expected sum of the single effect of each factor. Synergistic effect is present if the combination of two factors increases the measure of interest more than the expected sum based on each factor. Furthermore, this chapter also shows how estimation of multiplicative and additive interaction is carried out focusing on gene–environmental interaction.
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This book contains a series of research notes and graphical illustrations selected from the author’s amazing research work. It shows how: · The Sixty-Four hexagrams were derived by using combinatory trigrams selected from the Earlier Heaven and Later Heaven trigram cyclic sequences. · The legendary authors of the I Ching, known as Fu Hsi and King Wen, used Key Coded Matrices which enabled them to change the hexagrams of the ancient Ma-wang-tui into the Standard Modern edition. · The formulation of the Trigram Order of Completion was derived by using Knight’s Chess /binary codes and the manipulation of hexagram identification numbers. · The cyclic sequences and trigram line to line transitions were utilized to formulate the King Wen’s hexagram arrangement. · The natural numerical notation for each individual trigram was mathematically derived. · The author’s inter-face code was derived and used to determine the mathematical methodology of the Genetic Code. · Trigram lines are manipulated to form the Inner and Outer Nuclear trigrams/ hexagrams. · The Genetic Code was determined from Fu Hsi’s diagram of the derivation of the Sixty-Four hexagrams. It also includes the constructional details of an analytical model calculator which can be created from the actual design details shown within this book. This document provides the conclusive evidence that the originator(s) of the I Ching used a mathematical system which encompassed a formalistic natural philosophy that sought to embrace the entire world in a system of number symbolism. It shows the links to the ancient Indian Vedic mathematical system which reveals the relationship between the I Ching and the Binary / Genetic Codes.
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We introduce new quantitative characteristics of the population using an analogy to the system of multi-spin molecules: the disease fields, which may depend on interactions, and the susceptibility to disease as derivative of genetic vector’s (GV’s) frequency of cases with respect to these fields. The genetic vector’s approach (GVA) is applied to statistical analysis of the interaction of two SNP haplotype of HTR2A and shared epitope (SE) alleles in relation to development of rheumatoid arthritis (RA). The analysis is performed for two independent cohorts, EIRA and NARAC, and based on the evaluation of double- and triple genotype–genotype versus SE alleles correlations. The Gibbs-like parametrization of GV frequencies makes analysis transparent and easy interpretable. We find that the main contribution into association to RA comes from GVs containing double SE. GVA may resolve an opposite role in risk/protection from different pairs of genetic variations and reveal an association to RA whereas the univariate analysis does not show significant association.
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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.
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From New York Times bestselling author Sam Kean comes incredible stories of science, history, language, and music, as told by our own DNA. In The Disappearing Spoon, bestselling author Sam Kean unlocked the mysteries of the periodic table. In THE VIOLINIST'S THUMB, he explores the wonders of the magical building block of life: DNA. There are genes to explain crazy cat ladies, why other people have no fingerprints, and why some people survive nuclear bombs. Genes illuminate everything from JFK's bronze skin (it wasn't a tan) to Einstein's genius. They prove that Neanderthals and humans bred thousands of years more recently than any of us would feel comfortable thinking. They can even allow some people, because of the exceptional flexibility of their thumbs and fingers, to become truly singular violinists. Kean's vibrant storytelling once again makes science entertaining, explaining human history and whimsy while showing how DNA will influence our species' future.
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The genetic code was deciphered experimentally around 1966 and for a number of years scientists considered it to be "universal" for all forms of life. In 1981 researchers shocked the scientific community with the discovery that the code differed in mitochondria and certain other organisms, evidence that the genetic code was still evolving. This book discusses the distribution and origin of the non-universal codes and examines the possible mechanisms of code changes, making it essential reading for all those interested in evolutionary genetics.
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Everyone has heard of the story of DNA as the story of Watson and Crick and Rosalind Franklin, but knowing the structure of DNA was only a part of a greater struggle to understand life’s secrets. Life’s Greatest Secret is the story of the discovery and cracking of the genetic code, the thing that ultimately enables a spiraling molecule to give rise to the life that exists all around us. This great scientific breakthrough has had farreaching consequences for how we understand ourselves and our place in the natural world, and for how we might take control of our (and life’s) future. Life’s Greatest Secret mixes remarkable insights, theoretical dead-ends, and ingenious experiments with the swift pace of a thriller. From New York to Paris, Cambridge, Massachusetts, to Cambridge, England, and London to Moscow, the greatest discovery of twentieth-century biology was truly a global feat. Biologist and historian of science Matthew Cobb gives the full and rich account of the cooperation and competition between the eccentric characters—mathematicians, physicists, information theorists, and biologists—who contributed to this revolutionary new science. And, while every new discovery was a leap forward for science, Cobb shows how every new answer inevitably led to new questions that were at least as difficult to answer: just ask anyone who had hoped that the successful completion of the Human Genome Project was going to truly yield the book of life, or that a better understanding of epigenetics or “junk DNA” was going to be the final piece of the puzzle. But the setbacks and unexpected discoveries are what make the science exciting, and it is Matthew Cobb’s telling that makes them worth reading. This is a riveting story of humans exploring what it is that makes us human and how the world works, and it is essential reading for anyone who’d like to explore those questions for themselves.
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What would it mean to discover an ancient language—a literal message—hidden within the DNA of life itself? What we once believed of our past is about to change. . . . A coded message has been found within the molecules of life, deep within the DNA in each cell of our bodies. Through a remarkable discovery linking Biblical alphabets to our genetic code, the "language of life" may now be read as the ancient letters of a timeless message. Regardless of race, religion, heritage, or lifestyle, the message is the same in each cell of every woman, child, and man, past and present. Sharing all-new, fascinating research, Gregg Braden discusses the life-changing discovery that led him from a successful career in the aerospace and defense industries to an extensive 12-year study of the most sacred and honored traditions of humankind.