Description
Taylor & Francis Ltd Statistics In Human Genetics And Molecular Bioloy 2009 Edition by Cavan Reilly
Focusing on the roles of different segments of DNA, Statistics in Human Genetics and Molecular Biology provides a basic understanding of problems arising in the analysis of genetics and genomics. It presents statistical applications in genetic mapping, DNA/protein sequence alignment, and analyses of gene expression data from microarray experiments.The text introduces a diverse set of problems and a number of approaches that have been used to address these problems. It discusses basic molecular biology and likelihood-based statistics, along with physical mapping, markers, linkage analysis, parametric and nonparametric linkage, sequence alignment, and feature recognition. The text illustrates the use of methods that are widespread among researchers who analyze genomic data, such as hidden Markov models and the extreme value distribution. It also covers differential gene expression detection as well as classification and cluster analysis using gene expression data sets.Ideal for graduate students in statistics, biostatistics, computer science, and related fields in applied mathematics, this text presents various approaches to help students solve problems at the interface of these areas. Basic Molecular Biology for Statistical Genetics and Genomics Mendelian geneticsCell biologyGenes and chromosomesDNARNAProteinsSome basic laboratory techniques Bibliographic notes and further readingBasics of Likelihood-Based StatisticsConditional probability and Bayes theoremLikelihood-based inferenceMaximum likelihood estimates Likelihood ratio testsEmpirical Bayes analysis Markov chain Monte Carlo sampling Bibliographic notes and further readingMarkers and Physical Mapping IntroductionTypes of markersPhysical mapping of genomesRadiation hybrid mappingBasic Linkage Analysis Production of gametes and data for genetic mapping Some ideas from population geneticsThe idea of linkage analysisQuality of genetic markersTwo point parametric linkage analysisMultipoint parametric linkage analysis Computation of pedigree likelihoodsExtensions of the Basic Model for Parametric Linkage IntroductionPenetrancePhenocopiesHeterogeneity in the recombination fractionRelating genetic maps to physical maps Multilocus modelsNonparametric Linkage and Association Analysis Introduction Sib-pair method Identity by descent Affected sib-pair (ASP) methodsQTL mapping in human populationsA case study: dealing with heterogeneity in QTL mapping Linkage disequilibrium Association analysisSequence Alignment Sequence alignment Dot plots Finding the most likely alignment Dynamic programming Using dynamic programming to find the alignment Global versus local alignmentsSignificance of Alignments and Alignment in PracticeStatistical significance of sequence similarity Distributions of maxima of sets of iid random variables Rapid methods of sequence alignment Internet resources for computational biologyHidden Markov Models Statistical inference for discrete parameter finite state space Markov chains Hidden Markov models Estimation for hidden Markov models Parameter estimation Integration over the model parametersFeature Recognition in Biopolymers Gene transcription Detection of transcription factor binding sites Computational gene recognitionMultiple Alignment and Sequence Feature Discovery IntroductionDynamic programmingProgressive alignment methodsHidden Markov modelsBlock motif methodsEnumeration based methods A case study: detection of conserved elements in mRNAStatistical GenomicsFunctional genomics The technology Spotted cDNA arrays Oligonucleotide arrays NormalizationDetecting Differential Expression Introduction Multiple testing and the false discovery rate Significance analysis for microarrays Model based empirical Bayes approach A case study: normalization and differential detectionCluster Analysis in Genomics IntroductionSome approaches to cluster analysis Determining the number of clusters BiclusteringClassification in Genomics Introduction Cross-validation Methods for classificationAggregating classifiers Evaluating performance of a classifierReferencesIndexExercises appear at the end of each chapter.