UID:
almafu_9961427165702883
Format:
1 online resource (232 p.)
Edition:
1st ed. 2016.
ISBN:
1-4471-5310-3
Series Statement:
Computational Biology, 18
Content:
This timely text presents a comprehensive guide to genetic association, a new and rapidly expanding field that aims to elucidate how our genetic code (genotypes) influences the traits we possess (phenotypes). The book provides a detailed review of methods of gene mapping used in association with experimental crosses, as well as genome-wide association studies. Emphasis is placed on model selection procedures for analyzing data from large-scale genome scans based on specifically designed modifications of the Bayesian information criterion. Topics and features: Presents a thorough introduction to the theoretical background to studies of genetic association (both genetic and statistical) Reviews the latest advances in the field, highlighting the advantages and shortcomings of the currently most commonly used methods Illustrates the properties of methods for mapping quantitative trait loci using computer simulations and the analysis of real data Discusses open challenges that remain to be solved in the future Includes an extensive statistical appendix as a reference for those who are not totally familiar with the fundamentals of statistics This important work is essential reading for researchers and graduate students wishing to enter the field of statistical genetics and/or interested in statistical issues related to the analysis of high-dimensional data. No prior background knowledge of genetics is required, although some basic mathematical and computer science skills are assumed. Dr. Florian Frommlet is a researcher in the Center for Medical Statistics, Informatics, and Intelligent Systems at the Medical University of Vienna, Austria. Dr. Małgorzata Bogdan is an Associate Professor in the Institute of Mathematics at Wrocław University, Poland. Dr. David Ramsey is a Senior Lecturer in the Department of Operations Research at Wrocław University of Technology, Poland.
Note:
Description based upon print version of record.
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Preface; Contents; Acronyms; 1 Introduction; References; 2 A Primer in Genetics; 2.1 Basic Biology; 2.1.1 Phenotypes and Genotypes; 2.1.2 Meiosis and Crossover; 2.1.3 Genetic Distance; 2.1.4 The Haldane Mapping Function; 2.1.5 Interference and Other Mapping Functions; 2.1.6 Markers and Genetic Maps; 2.2 Types of Study; 2.2.1 Crossing Experiments; 2.2.2 The Basics of QTL Mapping; 2.2.3 Association Studies; 2.2.4 Other Types of Study; References; 3 Statistical Methods in High Dimensions; 3.1 Overview; 3.2 Multiple Testing; 3.2.1 Classical Procedures Controlling FWER
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3.2.2 Permutation Tests and Resampling Procedures3.2.3 Controlling the False Discovery Rate; 3.2.4 Multiple Testing Under Sparsity. Minimizing the Bayesian Risk in Multiple Testing Procedures; 3.3 Model Selection; 3.3.1 The Likelihood Function; 3.3.2 Information Theoretical Approach; 3.3.3 Bayesian Model Selection and the Bayesian Information Criterion; 3.3.4 Modifications of BIC for High-Dimensional Data Under Sparsity; 3.3.5 Further Approaches to Model Selection; References; 4 Statistical Methods of QTL Mapping for Experimental Populations; 4.1 Classical Approaches
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4.1.1 Single Marker Tests4.1.2 Power of a Test Based on a Single Marker as a Function of the Distance Between the Marker and a QTL; 4.1.3 Genome Wide Search with Tests Based on Single Markers; 4.2 Interval Mapping; 4.2.1 Interval Mapping based on the mixture model; 4.2.2 Regression Interval Mapping; 4.2.3 Nonparametric Version of Interval Mapping; 4.2.4 Specific Models; 4.2.5 Overestimation of Genetic Effects; 4.3 Model Selection; 4.3.1 QTL mapping with mBIC; 4.3.2 Robust Version of mBIC; 4.3.3 Version of mBIC Based on Rank Regression; 4.3.4 Extensions to Generalized Linear Models
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4.3.5 mBIC for Dense Markers and Interval Mapping4.4 Logic Regression; 4.5 Applying mBIC in a Bayesian Approach; 4.6 Closing Remarks; References; 5 Statistical Analysis of GWAS; 5.1 Overview; 5.2 Inferring Genotypes; 5.2.1 Genotype Calling; 5.2.2 Imputation; 5.3 Single Marker Tests; 5.3.1 Case-Control Studies; 5.3.2 Quantitative Traits; 5.3.3 Covariates and Population Stratification; 5.3.4 Multiple Testing Correction; 5.3.5 Rare SNPs; 5.4 Model Selection; 5.4.1 Motivation; 5.4.2 HYPERLASSO; 5.4.3 GWASelect; 5.4.4 MOSGWA; 5.4.5 Comparison of Methods; 5.4.6 Mixed Models; 5.5 Admixture Mapping
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5.6 Gene--Gene Interaction5.6.1 Analyzing Gene--Gene Interaction via ANOVA; 5.6.2 Multifactor Dimensionality Reduction; 5.6.3 Logic Regression in GWAS; 5.7 Other Recent Advances and the Outlook for GWAS; References; 6 Appendix A: Basic Statistical Distributions; 6.1 Normal Distribution; 6.2 Important Distributions of Sample Statistics; 6.2.1 Chi-Square Distribution; 6.2.2 Student's t-Distribution; 6.2.3 F-distribution; 6.3 Gamma and Beta Distributions; 6.3.1 Exponential Distribution; 6.3.2 Inverse Gamma Distribution; 6.3.3 Beta Distribution; 6.4 Double Exponential Distribution and Extensions
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6.4.1 Asymmetric Double Exponential (ADE) Distribution
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English
Additional Edition:
ISBN 1-4471-5309-X
Language:
English
DOI:
10.1007/978-1-4471-5310-8
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