Until the 1990s, the
majority of work done in the field of quantitative genetics was limited to
plants and animals. During this period, the majority of gene-mapping projects
for humans involved Mendelian traits. Observations for these traits were
dichotomous; that is, the study subjects were either affected or unaffected,
sometimes with the features of reduced penetrance, age-related penetrance, and
phenocopies. Even when quantitative measures were used to define a phenotype
(e.g., blood glucose measures for non–insulin-dependent diabetes mellitus),
these measures were often treated in analyses as qualitative traits, for which
a cutoff value was used to determine which study subjects were affected and
unaffected. A problem with this scheme is that the dichotomized phenotype can
have lower heritability, as well as reduced power, when used in analyses to
detect susceptibility loci. The late 1980s and 1990s saw advances in the
availability of dense maps of microsatellite markers and computational methods.
Emphasis in the area of gene mapping shifted from Mendelian traits to “complex
traits,” which are responsible for the majority of genetic-disease morbidity.
Complex traits often have underlying quantitative phenotypes that define
disease etiology. Researchers use a variety of methods to analyze quantitative
traits to map the underlying susceptibility loci, which are often referred to
as quantitative trait loci (QTLs). A search of the literature reveals a long
list of quantitative measures that are used to study complex traits: BMI in the
study of obesity, bone mineral density to investigate osteoporosis, etc.
Quantitative measures are also used for phenotypes that usually are not thought
of as being quantitative in nature—for example, positive and negative symptom
measures for schizophrenia. Given the shift in human genetics to the study of
diseases with underlying quantitative phenotypes, researchers and students in
the field of genetic epidemiology/statistical genetics must be well versed in
the area of quantitative genetics. Lynch and Walsh’s opus Genetics and Analysis
of Quantitative Traits is a welcome addition to this area of scientific study.
Genetics and Analysis of Quantitative Traits deserves high marks. The authors,
Michael Lynch and Bruce Walsh, have covered a wealth of material in this first
volume of two. The book is written clearly and can be used as a reference book,
a self-learning resource, or a textbook. Each section has applied examples that
the reader can work through with the guidance of the authors. Answers are
provided, enabling readers to check their work. Although the authors begin the
book with some basic statistical concepts, I would not recommend this book to
students of genetics before they have learned the fundamentals of statistics
from other sources. The book is divided into four sections: “The Foundations of
Quantitative Genetics,” “Quantitative Trait Loci,” “Estimation Procedures,” and
“Appendices.” A summary of the information covered in each chapter is presented
below; however, for sake of brevity, some topics are omitted. A complete table
of contents is available at . The first section of the volume, “The Foundations
of Quantitative Genetics,” opens with “An Overview of Quantitative Genetics”
(chapter 1), which presents a historical background and states the major goals
of the field. The authors then review some basic concepts, including properties
of distributions (chapter 2) and covariance, regression, and correlation
(chapter 3). “Properties of Single Loci” (chapter 4) presents a variety of
topics, including the Hardy-Weinberg principle and the basis of dominance.
“Sources of Genetic Variation for Multilocus Traits” (chapter 5) covers the
concepts of epistasis, linkage, and linkage disequilibrium. “Components of
Environmental Variation” (chapter 6) examines causes of within-individual
variation, repeatability of measures, maternal environmental effects, and
genotype/environment interactions. “Resemblance between Relatives” (chapter 7)
presents the concepts of identity by state and by descent, coefficient of
inbreeding, assortative mating, and heritability. In brief, the next three
chapters present an overview of matrix and linear models (chapter 8), analysis
of line crosses (chapter 9), and inbreeding depression (chapter 10). The last
chapter (11) of this section, entitled “Matters of Scale,” presents
transformations to achieve normality, test for normality, and
variance-stabilizing transformations. The second section, “Quantitative Trait
Loci,” begins with a chapter (12) entitled “Polygenes and Polygenic Mutation,”
which touches on the molecular nature of QTL variation. Chapter 13, “Detecting
Major Genes,” delves into the area of segregation analysis covering mixed models,
complex segregation analysis, ascertainment bias, and estimation of
single-locus penetrance models. “Principles of Marker-Based Analysis” (chapter
14) covers a wide range of topics, including genetic maps, fine mapping of
major genes using population-level linkage disequilibrium, the
transmission/disequilibrium test, and estimation of the effects of candidate
loci. The last two chapters of this section cover mapping and characterization
of QTLs using inbred line crosses (chapter 15) and outbred populations (chapter
16). Chapter 15 examines experimental designs, QTL detection and estimation
using linear models and maximum likelihood, maximum-likelihood interval
mapping, likelihood maps, calculation of support and confidence intervals for
QTL position, sample-size requirements for detection of QTLs, and problems of
multiple testing. Chapter 16 presents measures of marker informativeness,
including mating types (fully informative, backcross, and intercross),
heterozygosity, polymorphism content, and proportion of fully informative
matings; sib-pair analysis (linear models and maximum likelihood estimation);
Haseman-Elston regression; variance-components analysis; affected sib-pair
tests; and affected pedigree-member tests. The third section, “Estimation Procedures,”
revisits some of the topics previously covered, presenting them in detail:
“Parent-Offspring Regression” (chapter 17), “Sib Analysis” (chapter 18),
“Genotype × Environment Interaction” (chapter 22), “Maternal Effects” (chapter
23), “Estimation of Breeding Values” (chapter 26), and “Variance-Component
Estimation with Complex Pedigrees” (chapter 27). This section also delves into
new areas: “Twins and Clones” (chapter 19), “Cross-Classified Designs” (chapter
20), “Correlations between Characters” (chapter 21), “Sex Linkage and Sexual
Dimorphism” (chapter 24), and “Threshold Characters” (chapter 25).