Statistics in Human Genetics and Molecular Biology  book cover
1st Edition

Statistics in Human Genetics and Molecular Biology

ISBN 9781420072631
Published June 19, 2009 by Chapman and Hall/CRC
280 Pages 24 B/W Illustrations

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Book Description

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.

Table of Contents

Basic Molecular Biology for Statistical Genetics and Genomics

Mendelian genetics

Cell biology

Genes and chromosomes




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


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




Heterogeneity in the recombination fraction

Relating genetic maps to physical maps

Multilocus models

Nonparametric Linkage and Association Analysis


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


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


Detecting Differential Expression


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


Some approaches to cluster analysis

Determining the number of clusters


Classification in Genomics



Methods for classification

Aggregating classifiers

Evaluating performance of a classifier



Exercises appear at the end of each chapter.

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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 2011

Very useful for those taking courses in statistics and geneticists.
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