Statistical Genomics: Linkage, Mapping, and QTL Analysis

642 Pages·1998·53.982 MB·\642

by  Ben Hui Liu (Author)

Statistical
Genomics
Linkage, Mapping,
and QTL Analysis
Ben Hui Liu
CRCPress
Boca Raton London New York Washington, D.C.
Library of Congress Cataloging-in-Publication Data
Liu, Ben-Hui.
Statistical genomics : linkage, mapping, and QTL analysis I Ben-Hui Liu.
p. em.
Includes bibliographical references and index.
ISBN 0-8493-3166-8
I. Genetics-Statistical methods. 2. Gene mapping. I. Title.
QH438.4.S73L55 1997
572.8'633'0727-dc21 97-1863
CIP
This book contains information obtained from authentic and highly regarded sources. Reprinted material is quoted with
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International Standard Book Number 0-8493-3166-8
Library of Congress Card Number 97-1863
9 0
To QIANG, MING AND ANNIE

FOREWORD
Statistical Genomics is a new book about an old subject, viewed in a new
way. The major focus of the book is genetic mapping, a technique that has
been an indispensable part of genetic analysis since Alfred Sturtevant
developed the first genetic map in Drosophila more than 85 years ago. This
book will be a valuable resource to students learning about genomic science
and to geneticists and statisticians using genomic mapping for research and
development. This book should be useful for genetic analyses across many
disciplines, including plant and animal breeding, the genetic analysis of
natural populations and human genetic analysis.
There is wide utility for the book both as a text and as a reference. For
many students, it will provide their first access to fundamental statistical
concepts essential for genomic mapping. For the more experienced scientist, it
provides useful explanations of some complex statistical concepts and provides
approaches to solving many common problems. In addition, the book provides
a considerable amount of new theory and techniques, not yet in the primary
literature, related to mating populations and estimation of genetic parameters.
Genetic mapping has become an important component of both
fundamental research and practical application in a great many studies of
plants, animals and microorganisms. The ability to correlate genetic function
and chromosome location has led to the identification of the biological roles of
a great many important genes. New technology has created the potential to
generate functional genetic maps for entire genomes of virtually any plant or
animal, limited only by the degree of genetic diversity and the ability to obtain
a modest number of progeny. This technology has resulted in an expanded and
integrated view of genomes and led to the expectation that we may be able to
understand genomes at both a molecular and a phenotypic level. The
acquisition and application of this knowledge is one part of what has come to
be called genomic science.
Our current view of genomic science has its origin in the human genome
project, an experiment of major importance for our century. Within a period of
only 50 years since Watson and Crick proposed that genetic information was
contained in a DNA sequence, it is likely that we will have a catalog of most of
the expressed human genes and a large fraction of DNA sequence for the entire
human genome. This information will lead to the determination of functions for
many genes and to the first estimates of the number of genes that are
necessary for the known functions of the human organism. The human
genome project will profoundly affect our understanding of human biology and
disease, as well as leading to new methods of diagnosis and new therapeutic
products. Similarly, the extension of the theory and technology to other
organisms will have profound effects on agriculture, forestry, bioprocessing,
material science and environmental science as well.
vi FOREWORD
Since the application of DNA based molecular markers to genomic
mapping, formidable advances have been made in human genetics and in plant
and animal breeding. Molecular marker analysis has made possible the
integration of many aspects of quantitative and molecular genetics through the
genetic dissection of quantitative traits. It is now possible to define what
fraction of quantitative variation may be assigned to major qualitative factors
and quantitative factors in an individual or in a pedigree. These developments
have made marker aided selection a practical tool with widening applications
and provided new approaches to breeding based on molecular technology. The
methods have been applied to many nontraditional genetic systems because
the technology allows development of intensive genetic analysis, even in
organisms not previously studied. Detailed genetic analysis, including genetic
mapping of forest trees from natural populations, has become possible through
these advances in technology. Genomic mapping has allowed greater
understanding of many types of complex traits, including traits with
incomplete penetrance, heterosis, genetic load, segregation distortion and
epistasis, through the correlation of molecular markers and components of a
complex phenotype.
Genomic mapping is an essential tool for the discovery of new genes and
the definition of their function. Location of a qualitative trait locus may define a
new gene. Location of a gene known only by sequence may distinguish between
members of a multigene family related directly by descent (orthologs) or
through duplication and divergence of function (paralogs). Used in this way,
mapping of quantitative effects becomes a tool for molecular genetics.
The primary focus of this book is on new statistical methods needed to use
genetic mapping in the context of this new genomic research paradigm.
Genomic science represents a new integration of many aspects of genetics
related to the determination of the function, location and evolution of all genes
in all genomes. This concept brings together molecular genetics, quantitative
genetics, cytogenetics, evolutionary biology, statistics and the technology of
computers and automated systems for structural and quantitative analysis of
nucleic acid sequences. Continued integration of these diverse disciplines
requires new statistical methods such as those found in this book.
Ronald Sederoff,
Edwin F. Conger Professor of Forestry and
Director of the Forest Biotechnology Group
North Carolina State University
PREFACE
GENOMICS
Genomics is a new science that studies whole genomes by integrating the
traditional disciplines of cytology, Mendelian genetics, quantitative genetics,
population genetics and molecular genetics with new technology from informat
ics and automated robotic systems. The purpose of genomic research is to learn
about the structure, function and evolution of all genomes, past and present. As
a branch of biological science, genomics has made great progress in the last
decade and has helped to fill the gap between the laws of chemistry, physics and
biology. Many fundamental biological questions are close to being answered as a
result of research in genomics, such as:
• What aspects of genome structure are physically and chemi
cally necessary?
• Do genes have to be located at certain sites in a genome to per
form their functions?
• What DNA sequences and structures are needed for genes to
perform their specific functions?
• How many functional genes are necessary and sufficient in a
biological system, such as an animal or a plant species?
• How many different functional genes are present in the whole
biosphere?
• How many genes are essentially the same in terms of their DNA
sequences and structures across different species?
What Does Genomics Include?
Classical genomics started in the early 20th century when the discovery of
gene linkage (genes on the same chromosome) led to the idea of making a link
age map. This classical discipline was rejuvenated following the spectacular
development of molecular marker technology beginning in 1980, especially when
the Human Genome Project and the Plant Genome Project were initiated. Classi
cal genomics, based mainly on cytogenetics and genetic mapping, is close to tra
ditional genetics. In a way, classical genomics is a bridge between genetics and
the other branches of genomics, as genomics as a whole is a bridge among differ
ent branches of genetics.
Genomic informatics was created to manage the massive amount of new
information which arose from the advances of genomics. Physical genomics
emphasizes the physical composition of genomes, such as nucleotide sequences
and DNA clones and includes subjects such as DNA sequencing, DNA library
construction, physical map assembly and DNA sequence analysis.
viii PREFACE
Where Is Genomics Heading?
Genomics has been driven by technology development since its inception.
This impetus will continue into the future. Large throughput DNA sequencing
equipment, DNA chip technology for quick and automated genotyping, high
capacity robotic stations for sample preparation and biochemical assays,
sophisticated mathematical algorithms and statistical procedures for informa
tion analyses and high power computer software packages for handling data
may dominate the development of genoinics.
Genomics is driven also by practical applications. It is widely recognized that
genomic research has a great potential to benefit the biomedical industry, agri
culture and forestry and forensic science. Genome programs (human, plant and
animal) will greatly advance the technology for DNA analysis and. generate vast
amounts of information that will ultimately lead to a deeper understanding of
biology. The technology and information from genome research can revolutionize
the health-care system by advancing the diagnosis of human diseases and for
forensic purposes by enabling more precise identification of human individuals.
For agriculture and forestry, the technology and information from genomics can
assist plant and animal breeding, disease management, genetic diversity assess
ment and variety protection. These applications will help to ensure environmen
tally friendly and sustainable agriculture and forestry.
THIS BOOK
Topics
Statistics plays an essential role in genomics because random sampling and
experimental error are greatly involved in genomic·research. This book covers
the following topics in the area of statistical genomics:
• introduction to genomics (Chapter 1)
• brief biological background for genomics, including basic genet
ics, mating design and genetic markers (Chapters 2 - 3)
• brief statistical theory for genomics, including probability distri
bution, hypothesis testing and point and confidence interval
estimation (Chapter 4)
• statistical principles and methods for screening genetic mark
ers, linkage analysis, linkage grouping, gene ordering, multi
locus models, linkage map construction, linkage map merging
and searching for genes using population .disequilibrium
(Chapters 5 - 11)
• theory and methods for identifYing genes controlling complex
traits, commonly called Quantitative Trait Loci (QTL) mapping,
including single-marker analysis, interval mapping, composite
interval mapping, QTL mapping using natural populations and
some future considerations (Chapters 12 - 17)
• computer tools for genomic map construction and QTL analysis
(Chapter 18)
• resampling techniques and computer simulations in genomics,
including jackknife and bootstrap methods, permutation analy
sis and mapping population simulations (Chapter 19)
Intended Audience
This book can be used as a handbook for biologists interested in statistical
issues related to genomics, as a textbook for upper-level undergraduate and
graduate students majoring in genetics or in genomic science and as an intra-
PREFACE ix
duction to genomics for statisticians. For biologists, information is outlined on
how statistical analyses of genomic data are implemented and how results are
interpreted and presented. Students will find information on the statistical prin
ciples and on new developments in the field of statistical genomics. Information
that ties statistics and biology together is outlined for statisticians.
The book is intended to reach many scientists in plant, animal and human
genome communities. Important topics, such as linkage analysis and QTL map
ping, are divided into different parts for experimental populations (controlled
crosses) and natural populations (uncontrolled crosses). Experimental popula
tions cover most mapping populations for plants and animals. The natural pop
ulations include some of the plant (for example, forest trees) and animal
populations and the mapping populations for the humans.
How to Read This Book
As a handbook: This book can be a handbook for genomic data analysis for
readers with either a good background in statistics (statisticians) or a good back
ground in biology (biologists). The considerable information on the biological
aspects of genomics will help the statisticians in their interpretations of the sta
tistical treatments of genomic data. For the biologists, the detailed discussion on
the statistical procedures and derivations will help them to gain a better under
standing of the principles behind computer software packages for genomic anal
ysis. I have included 222 tables and 170 figures to explain both the underlying
statistics and the biology. This certainly will help the biologists to follow the sta
tistical procedures and the statisticians to understand the kinds of biological
data with which they work.
As a textbook: This book can be used as a textbook for students majoring in
genetics or genomic science. For undergraduates with basic biology, a one
semester introduction to genomics can be covered by:
• Chapters 1, 2, 3, 6, 9, 12, 13 and 18.
For upper level undergraduates or graduates with basic genetics and statistics, a
one-semester presentation of statistical genomics can include the following
chapters:
• track one for biologists: Chapters 5 to 18 and 4; or
• track two for statisticians: Chapters 2, 3, 5 to 19.
I presume that students in track one already have basic knowledge of genomics,
such as basic genetics and DNA structure and that those in track two have a rel
atively good background in theoretical statistics.
About Exercises
A set of exercises is given at the end of each chapter. Some of the exercises
emphasizing statistical derivation usually have defined answers and are rela
tively easy for readers with a strong background in statistics. Those exercises
should be very helpful to biologists in building a firm background in statistical
genomics. Some exercises emphasize a comprehensive understanding in biology
and these exercises may not have defined answers. I included some of my
thoughts on the perspectives of genomics into some of the exercises rather than
into the text and I encourage readers to try those exercises in the context of
group discussions.