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.
Cavan Reilly is associate professor of biostatistics at the University of Minnesota.
Basic Molecular Biology for Statistical Genetics and Genomics Mendelian genetics Cell biology Genes and chromosomes DNA RNA Proteins Some basic laboratory techniques Bibliographic notes and further reading Basics of Likelihood-Based Statistics Conditional probability and Bayes theorem Likelihood-based inference Maximum likelihood estimates Likelihood ratio tests Empirical Bayes analysis Markov chain Monte Carlo sampling Bibliographic notes and further reading Markers and Physical Mapping Introduction Types of markers Physical mapping of genomes Radiation hybrid mapping Basic Linkage Analysis Production of gametes and data for genetic mapping Some ideas from population genetics The idea of linkage analysis Quality of genetic markers Two point parametric linkage analysis Multipoint parametric linkage analysis Computation of pedigree likelihoods Extensions of the Basic Model for Parametric Linkage Introduction Penetrance Phenocopies Heterogeneity in the recombination fraction Relating genetic maps to physical maps Multilocus models Nonparametric Linkage and Association Analysis Introduction Sib-pair method Identity by descent Affected sib-pair (ASP) methods QTL mapping in human populations A case study: dealing with heterogeneity in QTL mapping Linkage disequilibrium Association analysis Sequence Alignment Sequence alignment Dot plots Finding the most likely alignment Dynamic programming Using dynamic programming to find the alignment Global versus local alignments Significance of Alignments and Alignment in Practice Statistical significance of sequence similarity Distributions of maxima of sets of iid random variables Rapid methods of sequence alignment Internet resources for computational biology Hidden 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 parameters Feature Recognition in Biopolymers Gene transcription Detection of transcription factor binding sites Computational gene recognition Multiple Alignment and Sequence Feature Discovery Introduction Dynamic programming Progressive alignment methods Hidden Markov models Block motif methods Enumeration based methods A case study: detection of conserved elements in mRNA Statistical Genomics Functional genomics The technology Spotted cDNA arrays Oligonucleotide arrays Normalization Detecting 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 detection Cluster Analysis in Genomics Introduction Some approaches to cluster analysis Determining the number of clusters Biclustering Classification in Genomics Introduction Cross-validation Methods for classification Aggregating classifiers Evaluating performance of a classifier References Index Exercises appear at the end of each chapter.