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 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.
Biography
Cavan Reilly is associate professor of biostatistics at the University of Minnesota.
Thankfully, some brave souls are willing to serve as guides to rigorous application and understanding of statistical approaches to genetically informative data. Cavan Reilly is among them. … The book is self-contained and well organized, covering a substantial breadth of the core topics in genetics and genomics. … this book is a valuable reference source for both statistics-oriented and human-genetics-oriented researchers and graduate students to learn the specialized methodology for analysis of diverse genetic data. … a useful textbook for beginners trained in applied mathematics and statistics to take in a panoramic snapshot of the very evolving field of statistical genetics and genomics.
—Xiang-Yang Lou and David B. Allison, Biometrics, December 2011Very useful for those taking courses in statistics and geneticists.
—Pediatric Endocrinology Reviews, Vol. 7, No. 4, June 2010
