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| Author | : Tracy Helen Pinel |
| Publisher | : |
| Total Pages | : 183 |
| Release | : 2002 |
| Genre | : |
| ISBN | : |
| Author | : William K. Scott |
| Publisher | : John Wiley & Sons |
| Total Pages | : 340 |
| Release | : 2021-11-11 |
| Genre | : Science |
| ISBN | : 1119104076 |
Genetic Analysis of Complex Diseases An up-to-date and complete treatment of the strategies, designs and analysis methods for studying complex genetic disease in human beings In the newly revised Third Edition of Genetic Analysis of Complex Diseases, a team of distinguished geneticists delivers a comprehensive introduction to the most relevant strategies, designs and methods of analysis for the study of complex genetic disease in humans. The book focuses on concepts and designs, thereby offering readers a broad understanding of common problems and solutions in the field based on successful applications in the design and execution of genetic studies. This edited volume contains contributions from some of the leading voices in the area and presents new chapters on high-throughput genomic sequencing, copy-number variant analysis and epigenetic studies. Providing clear and easily referenced overviews of the considerations involved in genetic analysis of complex human genetic disease, including sampling, design, data collection, linkage and association studies and social, legal and ethical issues. Genetic Analysis of Complex Diseases also provides: A thorough introduction to study design for the identification of genes in complex traits Comprehensive explorations of basic concepts in genetics, disease phenotype definition and the determination of the genetic components of disease Practical discussions of modern bioinformatics tools for analysis of genetic data Reflecting on responsible conduct of research in genetic studies, as well as linkage analysis and data management New expanded chapter on complex genetic interactions This latest edition of Genetic Analysis of Complex Diseases is a must-read resource for molecular biologists, human geneticists, genetic epidemiologists and pharmaceutical researchers. It is also invaluable for graduate students taking courses in statistical genetics or genetic epidemiology.
| Author | : Kenneth Lange |
| Publisher | : Springer Science & Business Media |
| Total Pages | : 277 |
| Release | : 2013-04-17 |
| Genre | : Mathematics |
| ISBN | : 1475727399 |
Geneticists now stand on the threshold of sequencing the genome in its entirety. The unprecedented insights into human disease and evolution offered by mapping and sequencing are transforming medicine and agriculture. This revolution depends vitally on the contributions made by applied mathematicians, statisticians, and computer scientists. Kenneth Lange has written a book to enable graduate students in the mathematical sciences to understand and model the epidemiological and experimental data encountered in genetics research. Mathematical, statistical, and computational principles relevant to this task are developed hand-in-hand with applications to gene mapping, risk prediction, and the testing of epidemiological hypotheses. The book covers many topics previously only accessible in journal articles, such as pedigree analysis algorithms, Markov chain, Monte Carlo methods, reconstruction of evolutionary trees, radiation hybrid mapping, and models of recombination. The whole is backed by numerous exercise sets.
| Author | : Nan M. Laird |
| Publisher | : Springer Science & Business Media |
| Total Pages | : 226 |
| Release | : 2010-12-13 |
| Genre | : Medical |
| ISBN | : 1441973389 |
This book covers the statistical models and methods that are used to understand human genetics, following the historical and recent developments of human genetics. Starting with Mendel’s first experiments to genome-wide association studies, the book describes how genetic information can be incorporated into statistical models to discover disease genes. All commonly used approaches in statistical genetics (e.g. aggregation analysis, segregation, linkage analysis, etc), are used, but the focus of the book is modern approaches to association analysis. Numerous examples illustrate key points throughout the text, both of Mendelian and complex genetic disorders. The intended audience is statisticians, biostatisticians, epidemiologists and quantitatively- oriented geneticists and health scientists wanting to learn about statistical methods for genetic analysis, whether to better analyze genetic data, or to pursue research in methodology. A background in intermediate level statistical methods is required. The authors include few mathematical derivations, and the exercises provide problems for students with a broad range of skill levels. No background in genetics is assumed.
| Author | : Duncan C. Thomas |
| Publisher | : Oxford University Press |
| Total Pages | : 458 |
| Release | : 2004-01-29 |
| Genre | : Medical |
| ISBN | : 0199748055 |
This well-organized and clearly written text has a unique focus on methods of identifying the joint effects of genes and environment on disease patterns. It follows the natural sequence of research, taking readers through the study designs and statistical analysis techniques for determining whether a trait runs in families, testing hypotheses about whether a familial tendency is due to genetic or environmental factors or both, estimating the parameters of a genetic model, localizing and ultimately isolating the responsible genes, and finally characterizing their effects in the population. Examples from the literature on the genetic epidemiology of breast and colorectal cancer, among other diseases, illustrate this process. Although the book is oriented primarily towards graduate students in epidemiology, biostatistics and human genetics, it will also serve as a comprehensive reference work for researchers. Introductory chapters on molecular biology, Mendelian genetics, epidemiology, statistics, and population genetics will help make the book accessible to those coming from one of these fields without a background in the others. It strikes a good balance between epidemiologic study designs and statistical methods of data analysis.
| Author | : Jonathan L. Haines |
| Publisher | : John Wiley & Sons |
| Total Pages | : 507 |
| Release | : 2007-02-26 |
| Genre | : Science |
| ISBN | : 0471781134 |
Second Edition features the latest tools for uncovering thegenetic basis of human disease The Second Edition of this landmark publication bringstogether a team of leading experts in the field to thoroughlyupdate the publication. Readers will discover the tremendousadvances made in human genetics in the seven years that haveelapsed since the First Edition. Once again, the editorshave assembled a comprehensive introduction to the strategies,designs, and methods of analysis for the discovery of genes incommon and genetically complex traits. The growing social, legal,and ethical issues surrounding the field are thoroughly examined aswell. Rather than focusing on technical details or particularmethodologies, the editors take a broader approach that emphasizesconcepts and experimental design. Readers familiar with theFirst Edition will find new and cutting-edge materialincorporated into the text: Updated presentations of bioinformatics, multiple comparisons,sample size requirements, parametric linkage analysis, case-controland family-based approaches, and genomic screening New methods for analysis of gene-gene and gene-environmentinteractions A completely rewritten and updated chapter on determininggenetic components of disease New chapters covering molecular genomic approaches such asmicroarray and SAGE analyses using single nucleotide polymorphism(SNP) and cDNA expression data, as well as quantitative trait loci(QTL) mapping The editors, two of the world's leading genetic epidemiologists,have ensured that each chapter adheres to a consistent and highstandard. Each one includes all-new discussion questions andpractical examples. Chapter summaries highlight key points, and alist of references for each chapter opens the door to furtherinvestigation of specific topics. Molecular biologists, human geneticists, geneticepidemiologists, and clinical and pharmaceutical researchers willfind the Second Edition a helpful guide to understanding thegenetic basis of human disease, with its new tools for detectingrisk factors and discovering treatment strategies.
| Author | : Eleftheria Zeggini |
| Publisher | : Academic Press |
| Total Pages | : 353 |
| Release | : 2010-11-17 |
| Genre | : Medical |
| ISBN | : 0123751438 |
According to the National Institute of Health, a genome-wide association study is defined as any study of genetic variation across the entire human genome that is designed to identify genetic associations with observable traits (such as blood pressure or weight), or the presence or absence of a disease or condition. Whole genome information, when combined with clinical and other phenotype data, offers the potential for increased understanding of basic biological processes affecting human health, improvement in the prediction of disease and patient care, and ultimately the realization of the promise of personalized medicine. In addition, rapid advances in understanding the patterns of human genetic variation and maturing high-throughput, cost-effective methods for genotyping are providing powerful research tools for identifying genetic variants that contribute to health and disease. This burgeoning science merges the principles of statistics and genetics studies to make sense of the vast amounts of information available with the mapping of genomes. In order to make the most of the information available, statistical tools must be tailored and translated for the analytical issues which are original to large-scale association studies. Analysis of Complex Disease Association Studies will provide researchers with advanced biological knowledge who are entering the field of genome-wide association studies with the groundwork to apply statistical analysis tools appropriately and effectively. With the use of consistent examples throughout the work, chapters will provide readers with best practice for getting started (design), analyzing, and interpreting data according to their research interests. Frequently used tests will be highlighted and a critical analysis of the advantages and disadvantage complimented by case studies for each will provide readers with the information they need to make the right choice for their research. Additional tools including links to analysis tools, tutorials, and references will be available electronically to ensure the latest information is available. Easy access to key information including advantages and disadvantage of tests for particular applications, identification of databases, languages and their capabilities, data management risks, frequently used tests Extensive list of references including links to tutorial websites Case studies and Tips and Tricks
| Author | : Andrea S. Foulkes |
| Publisher | : Springer Science & Business Media |
| Total Pages | : 264 |
| Release | : 2009-04-28 |
| Genre | : Science |
| ISBN | : 038789554X |
Statistical genetics has become a core course in many graduate programs in public health and medicine. This book presents fundamental concepts and principles in this emerging field at a level that is accessible to students and researchers with a first course in biostatistics. Extensive examples are provided using publicly available data and the open source, statistical computing environment, R.
| Author | : Jinghang Lin |
| Publisher | : |
| Total Pages | : 99 |
| Release | : 2021 |
| Genre | : Electronic dissertations |
| ISBN | : |
The genetic etiologies of common diseases are highly complex and heterogeneous. Classic statistical methods, such as linear regression, have successfully identified numerous genetic variants associated with complex diseases. Nonetheless, for most complex diseases, the identified variants only account for a small proportion of heritability. Challenges remain to discover additional variants contributing to complex diseases. In this dissertation, we developed an expectile neural network (ENN) method and applied the method to genetic data analysis. ENN provides a comprehensive view of relationships between genetic variants and disease phenotypes and can be used to discover genetic variants predisposing to sub-populations (e.g., high-risk groups). We integrate the idea of neural networks into ENN, making it capable of capturing non-linear and non-additive genetic effects (e.g., gene-gene interactions). Through simulations, we showed that the proposed method outperformed an existing expectile regression when there exist complex relationships between genetic variants and disease phenotypes. We also applied the proposed method to the genetic data from the Study of Addiction: Genetics and Environment(SAGE), investigating the relationships of candidate genes with smoking quantity. Neural networks have been widely used in applications. However, few studies have been focused on the statistical properties of neural networks. We further investigate the Asymptotic properties of ENN (e.g., consistency). Simulations have been conducted to test the validity of the theory.
| Author | : Ingileif Bryndís Hallgrímsdóttir |
| Publisher | : |
| Total Pages | : 368 |
| Release | : 2005 |
| Genre | : |
| ISBN | : |
