This well-organized and clearly written text has a unique focus on methods of identifying the joint effects of genes and environment on disease patterns. It follows the natural sequence of research, taking readers through the study designs and statistical analysis techniques for determining whether a trait runs in families, testing hypotheses about whether a familial tendency is due to genetic or environmental factors or both, estimating the parameters of a genetic model, localizing and ultimately isolating the responsible genes, and finally characterizing their effects in the population. Examples from the literature on the genetic epidemiology of breast and colorectal cancer, among other diseases, illustrate this process. Although the book is oriented primarily towards graduate students in epidemiology, biostatistics and human genetics, it will also serve as a comprehensive reference work for researchers. Introductory chapters on molecular biology, Mendelian genetics, epidemiology, statistics, and population genetics will help make the book accessible to those coming from one of these fields without a background in the others. It strikes a good balance between epidemiologic study designs and statistical methods of data analysis.
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"This book is well organized and logically developed, such that concepts are covered in broad strokes, revisited in greater detail, and connected together. ...this is a very thorough treatise of the spectrum of methods that collectively define genetic epidemiology." --Annals of Epidemiology
"...Statistical Methods in Genetic Epidemiology is a most welcome volume that all readers...should consider having on their bookshelves. It seems likely that this will serve as a classic text for many years to come." --American Journal of Human Genetics
"This is a clear succinct tour of the analytical side of genetic epidemiology. There is a suitable balance of biology and statistical details and the text is well pitched for its target audience of graduate epidemiologists, biostatisticians, and human geneticists."--International Journal of Epidemiology
About the Author
Duncan C. Thomas is at University of Southern California School of Medicine.
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2 of 2 people found the following review helpful:
5.0 out of 5 stars
Great Text,
This review is from: Statistical Methods in Genetic Epidemiology (Hardcover)
This is not a book about statistics,it's a book about statistical methods employed in genetic epidemiology. In fact, I would consider this the most accessible text on the subject. The entire point of this book is to address problems facing epidemiology in relation to genetics and the environment, and suggest a means to statistically measure these variables. If you're looking for a stats book, grab Sokal & Rohlf. If you're trying to address problems in genetic epi, this book is a must. Suggested equations can be found in a Multivariate Analysis Text or Sokal & Rohlf.
1 of 2 people found the following review helpful:
3.0 out of 5 stars
Lacks statistical examples,
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This review is from: Statistical Methods in Genetic Epidemiology (Hardcover)
I bought this book for a survey course I am sitting in so as to learn genetics. This book does comprehensively review the subject and its literature, but it lacks so much as one completed example problem. Statistics is, after all, about getting the numbers right. I think this is the first time I've ever seen a book with "statistics" in its title that does not show the reader how to get the numbers right.
Don Champagne, Ph.D. (statistics & economics)
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Inside This Book
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Key Phrases - Statistically Improbable Phrases (SIPs):
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genetic epidemiology, linkage disequilibrium mapping, available marker information, marginal allele frequencies, mod score approach, genetic relative risk, joint genotypes, using unrelated individuals, single ascertainment, possible parental genotypes, segregation indicators, major gene model, ascertainment correction, familial relative risk, segregation parameters, penetrance function, penetrance estimates, penetrance models, haplotype assignments, joint segregation, haplotype block structure, recombination graphs, candidate gene associations, peeling algorithm, causal gene Key Phrases - Capitalized Phrases (CAPs): (learn more)
Statistical Methods, Testing Candidate Gene Associations, Ashkenazi Jews, Tying It All Together, Breast Cancer Linkage Consortium, Principles of Population Genetics, Basic Concepts of Molecular Genetics, United States, Monte Carlo, Principles of Mendelian Inheritance, Los Angeles, Human Genome Project, Presentation of Data, Gene Characterization Browse Sample Pages:
Front Cover | Table of Contents | First Pages | Index | Back Cover | Surprise Me!
genetic epidemiology, linkage disequilibrium mapping, available marker information, marginal allele frequencies, mod score approach, genetic relative risk, joint genotypes, using unrelated individuals, single ascertainment, possible parental genotypes, segregation indicators, major gene model, ascertainment correction, familial relative risk, segregation parameters, penetrance function, penetrance estimates, penetrance models, haplotype assignments, joint segregation, haplotype block structure, recombination graphs, candidate gene associations, peeling algorithm, causal gene Key Phrases - Capitalized Phrases (CAPs): (learn more)
Statistical Methods, Testing Candidate Gene Associations, Ashkenazi Jews, Tying It All Together, Breast Cancer Linkage Consortium, Principles of Population Genetics, Basic Concepts of Molecular Genetics, United States, Monte Carlo, Principles of Mendelian Inheritance, Los Angeles, Human Genome Project, Presentation of Data, Gene Characterization Browse Sample Pages:
Front Cover | Table of Contents | First Pages | Index | Back Cover | Surprise Me!
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