Phenotypes and Genotypes: The Search for Influential Genes

✍ Scribed by Frommlet, Florian;Bogdan, Ma Gorzata;Ramsey, David

• Publisher: Springer
• Year: 2016
• Tongue: English
• Leaves: 232
• Category: Library

✦ Synopsis

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. 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; illustrates the properties of methods for mapping quantitative trait loci using computer simulations and the analysis of real data; discusses open challenges; includes an extensive statistical appendix as a reference for those who are not totally familiar with the fundamentals of statistics.

✦ Table of Contents

Preface......Page 6
Contents......Page 8
Acronyms......Page 12
1 Introduction......Page 14
References......Page 21
2.1.1 Phenotypes and Genotypes......Page 22
2.1.3 Genetic Distance......Page 25
2.1.4 The Haldane Mapping Function......Page 26
2.1.5 Interference and Other Mapping Functions......Page 27
2.1.6 Markers and Genetic Maps......Page 29
2.2.1 Crossing Experiments......Page 30
2.2.2 The Basics of QTL Mapping......Page 34
2.2.3 Association Studies......Page 36
2.2.4 Other Types of Study......Page 40
References......Page 42
3.1 Overview......Page 44
3.2 Multiple Testing......Page 45
3.2.1 Classical Procedures Controlling FWER......Page 46
3.2.2 Permutation Tests and Resampling Procedures......Page 50
3.2.3 Controlling the False Discovery Rate......Page 54
3.2.4 Multiple Testing Under Sparsity. Minimizing the Bayesian Risk in Multiple Testing Procedures......Page 55
3.3 Model Selection......Page 64
3.3.1 The Likelihood Function......Page 65
3.3.2 Information Theoretical Approach......Page 68
3.3.3 Bayesian Model Selection and the Bayesian Information Criterion......Page 70
3.3.4 Modifications of BIC for High-Dimensional Data Under Sparsity......Page 72
3.3.5 Further Approaches to Model Selection......Page 74
References......Page 82
4.1.1 Single Marker Tests......Page 86
4.1.2 Power of a Test Based on a Single Marker as a Function of the Distance Between the Marker and a QTL......Page 87
4.1.3 Genome Wide Search with Tests Based on Single Markers......Page 89
4.2.1 Interval Mapping based on the mixture model......Page 92
4.2.2 Regression Interval Mapping......Page 94
4.2.3 Nonparametric Version of Interval Mapping......Page 95
4.2.5 Overestimation of Genetic Effects......Page 96
4.3.1 QTL mapping with mBIC......Page 97
4.3.2 Robust Version of mBIC......Page 100
4.3.3 Version of mBIC Based on Rank Regression......Page 102
4.3.4 Extensions to Generalized Linear Models......Page 103
4.3.5 mBIC for Dense Markers and Interval Mapping......Page 105
4.4 Logic Regression......Page 110
4.6 Closing Remarks......Page 114
References......Page 115
5.1 Overview......Page 118
5.2.1 Genotype Calling......Page 120
5.2.2 Imputation......Page 123
5.3.1 Case-Control Studies......Page 124
5.3.2 Quantitative Traits......Page 128
5.3.3 Covariates and Population Stratification......Page 131
5.3.4 Multiple Testing Correction......Page 134
5.3.5 Rare SNPs......Page 135
5.4.1 Motivation......Page 137
5.4.2 HYPERLASSO......Page 142
5.4.3 GWASelect......Page 145
5.4.4 MOSGWA......Page 146
5.4.5 Comparison of Methods......Page 148
5.4.6 Mixed Models......Page 151
5.5 Admixture Mapping......Page 153
5.6.1 Analyzing Gene--Gene Interaction via ANOVA......Page 157
5.6.2 Multifactor Dimensionality Reduction......Page 160
5.6.3 Logic Regression in GWAS......Page 162
5.7 Other Recent Advances and the Outlook for GWAS......Page 164
References......Page 169
6.1 Normal Distribution......Page 175
6.2.1 Chi-Square Distribution......Page 177
6.2.2 Student's t-Distribution......Page 178
6.2.3 F-distribution......Page 179
6.3.2 Inverse Gamma Distribution......Page 180
6.4.1 Asymmetric Double Exponential (ADE) Distribution......Page 181
6.5.2 Poisson Distribution......Page 182
6.5.4 Generalized Poisson Distribution......Page 183
Reference......Page 184
7.1.1 Statistical Bias......Page 185
7.1.3 Efficiency of Estimators......Page 186
7.1.4 Method of Moments......Page 187
7.1.5 Maximum Likelihood Estimation......Page 188
7.2.2 Pearson Correlation Coefficient......Page 189
Reference......Page 190
8.1 Basic Ideas of Statistical Testing: The Z-test......Page 191
8.2.1 One Sample t-Test......Page 194
8.2.2 Two Sample t-Test......Page 195
8.2.4 Robustness of t-Tests......Page 196
8.3.1 One-Way Analysis of Variance......Page 197
8.3.2 Two-Way ANOVA. Interactions......Page 198
8.3.3 Two-Way ANOVA with No Interactions......Page 201
8.3.5 Multiple Regression......Page 202
8.4 General Linear Models......Page 204
8.4.1 Cockerham's Model......Page 205
8.5 Generalized Linear Models......Page 206
8.6 Linear Mixed Models......Page 209
8.7 Nonparametric Tests......Page 212
8.7.2 Rank Regression......Page 214
8.8.1 Chi-Square Goodness-of-Fit Test......Page 215
8.8.2 Chi-Square Test of Independence......Page 216
8.8.3 Fisher's Exact Test......Page 217
References......Page 218
9.1 Bayes Rule......Page 219
9.3 Markov Chain Monte Carlo......Page 220
9.3.1 Gibbs Sampler......Page 221
9.3.3 Hierarchical Models......Page 222
9.3.4 Parametric Empirical Bayes......Page 223
References......Page 224
10.1 Principal Component Analysis......Page 226
10.2 The EM Algorithm......Page 227
References......Page 228
Index......Page 229