Statistics in Human Genetics and Molecular Biology

ISBN-13: 9781420072631 / Angielski / Twarda / 2009 / 280 str.

Cavan Reilly;Chris Chatfield;Bradley. P. Carlin

Opis

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.

Focusing on problems in contemporary genetics and molecular biology, this text describes basic statistical methods used in genetics. It covers cluster analysis, combinatorial optimization, and dynamic programming, along with the core topics of genome mapping, biological sequence analysis, and the analysis of gene expression arrays. The author also explores Bayesian approaches, such as hidden Markov models and block motif methods, as well as modern tools of Bayesian analysis, including Markov chain Monte Carlo (MCMC). The text features a number of worked examples and problem sets at the end of each chapter.

Spis treści

Basic Molecular Biology for Statistical Genetics and Genomics. Basics of Likelihood-Based Statistics. Markers and Physical Mapping. Basic Linkage Analysis. Extensions of the Basic Model for Parametric Linkage. Nonparametric Linkage and Association Analysis. Sequence Alignment. Significance of Alignments and Alignment in Practice. Hidden Markov Models. Feature Recognition in Biopolymers. Multiple Alignment and Sequence Feature Discovery. Statistical Genomics. Detecting Differential Expression. Cluster Analysis in Genomics. Classification in Genomics. References. Index.

O autorze

Cavan Reilly is associate professor of biostatistics at the University of Minnesota.