Book Description
The book introduces the basic concepts and methods that are useful in the statistical analysis and modeling of DNA-based marker and phenotypic data that arise in agriculture, forrestry, experimental biology, and other fields. It concentrates on the linkage analysis of markers, map construction and quantitative trait locus (QTL) mapping and assumes a background in regression analysis and maximum likelihood approaches. The strengths of this book lie in the construction of general models and algorithms for linkage analysis and QTL mapping in any kind of crossed pedigrees initiated with inbred lines of crops and plant and animal model systems or outbred lines in forest trees and wildlife species.
The book includes a detailed description of each approach and the step-by-step demonstration of live-example analyses designed to explain the utilization and usefulness of statistical methods. The book also includes exercise sets and computer codes for all the analyses used.
This book can serve as a textbook for graduates and senior undergraduates in genetics, agronomy, forest biology, plant breeding and animal sciences. It will also be useful to researchers and other professionals in the areas of statistics, biology and agriculture.
Book Description
Arnold Saxton, Ph.D., the editor of this volume, is a professor of animal science at the University of Tennessee, Knoxville. During years of research and teaching in statistics and genetics, Dr. Saxton recognized the need for a how-to introduction to SAS computer analysis for complex-trait genetics. He assembled 16 coauthors from around the world to create this unique compilation. Example-rich and experiment-driven, Genetic Analysis of Complex Traits Using SAS demonstrates how you can use SAS and SAS/Genetics to extract answers from your quantitative and molecular genetics data. The book guides you through the mix of genetic, statistical, and SAS skills that are needed, enabling you to apply what you've learned to your own experimental data. You'll find this an invaluable resource whether you are a researcher, scientist, graduate student, bioinformatician, or statistician--or any other SAS user interested in joining the highly active and exciting field of genetic analysis.
Customer Reviews:
Helpful.......2007-07-30
Regardless of how one feels about SAS as a programming language, it is readily apparent that it is very popular in areas such as financial and biological modeling. This book gives an introduction to how it is used in genetic analysis, and even though each chapter is written by a different author, the book can be useful to those (such as this reviewer) who are not experts in genetics but who may be called upon to apply their mathematical and statistical knowledge to problems in genetics (but using SAS instead of some other programming language to do so). Although the book assumes a thorough knowledge of genetics, it can still be read profitably by anyone who has a background in SAS and some knowledge of genetics. Being an interpreted language, SAS performance can be a problem with many applications, and its value in science is questionable for projects that require heavy computational power. For medium-sized projects though it can be helpful, even though its semantics can be hard to get used to for those who have programmed in more object-oriented environments.
SAS has been used widely to perform statistical studies in genetics using "classical" tools such as multivariate analysis and maximum likelihood, but there is one chapter in this book where Bayesian inference techniques are used for genetic analysis. In addition, and this makes the discussion in the chapter even more valuable, is that the estimation of the posterior distribution is done using Markov chain Monte Carlo (MCMC) techniques. The first genetics problem on which this is done regards two-point linkage analysis where Bayesian inference is used to estimate the recombination rate in a backcross between two completely homozygous lines for each of two loci. Even though this problem has an analytical solution, the authors use a simple Monte Carlo simulation to estimate the posterior mean and variance of the recombination rate to motivate how SAS can be used in this case.It should be pointed out here that the authors use SAS PROC Capability in their code and not all readers have this in their SAS implementation, but it can be replaced by PROC Univariate with no problems. This problem is generalized to the case of where there are three linked marker loci, with Bayesian inference and MCMC (via the Metropolis-Hastings algorithm) used to estimate the loci order and the recombination rates between the markers. The authors give the actual SAS code to implement this analysis, which is very readable (in spite of the ancient and annoying "goto" statements that are used within it). MCMC techniques are essential though in more general problems where analytical solutions are not possible. This is the case for a general genetic map construction that the authors discuss but do not give the explicit SAS code for (but it can be found on the Website that is associated with the book). They discuss briefly the pitfalls in doing MCMC for this case, and give a few alternatives. Bayesian inference is then applied to QTL analysis for the simple case of a single QTL model for backcrossing.
Book Description
Genetics and Analysis of Quantitative Traits brings together the diverse array of theoretical and empirical applications of quantitative genetics under one cover, in a way that is both comprehensive and accessible to anyone with a rudimentary understanding of statistics and genetics. What was originally envisioned as a single text has now become two, with the focus of this first book being on the basic biology and methods of analysis of quantitative characters. (The second book, Evolution and Selection of Quantitative Traits, will address the basic principles that govern the evolutionary dynamics of quantitative characters under the forces of mutation, random genetic drift, and natural and/or artificial selection, integrating the theory of evolution of such characters with existing empirical data from natural and domesticated plant and animal populations.) Written as a general text in quantitative genetics, the book will also be useful as a basic reference for the seasoned professional. Throughout, central theoretical concepts are developed from first principles. To aid the less statistically sophisticated reader, several chapters and appendices focus on the basic statistical tools needed to digest the book. Wherever possible, theoretical and analytical concepts are illustrated with empirical examples from diverse settings.
Three major features of Genetics and Analysis of Quantitative Traits distinguish it from earlier work. First, it reflects the explosive influx over the past few years of quantitative-genetic thinking into evolutionary biology. Second, in animal breeding, enormous strides have been made in the development of new techniques for estimating breeding values (for the purposes of identifying elite individuals in selection programs) and for estimating variance components from samples of complex pedigrees. In this text's last two chapters, the authors outline the basic principles of complex pedigree analysis, without getting bogged down in technical details. Third, Genetics and Analysis of Quantitative Traits provides a broad overview of the newly emerging array of techniques for quantitative-trait loci (QTL) analysis, currently one of the most active fields of quantitative-genetic research.
Genetics and Analysis of Quantitative Traits contains numerous fully-worked examples and illustrations of theoretical concepts, as well as over 2,000 references with indices by subject, author, and organism. In addition, the authors maintain a World Wide Web site featuring up-to-date lists of computer programs and on-line resources, and added information on various topics presented in the text.
Customer Reviews:
Comprehensive in scope.......2002-06-10
I read this book with the expectation that it would give me an idea of the extent to which organism traits or phenotypes are determined optimally. Discussions on evolution frequently regard the functioning and attributes of an organism as being optimal at the particular time in its evolutionary history. A successful theory of evolution as a theory of mathematical optimization would entail a thorough understanding of the evidence for this hypothesis of optimality of phenotypes.
Another interesting question as to what effect a certain mixing of genetic factors, each one of these factors determining a phenotype separately (and optimally), would determine an optimal phenotype. An answer to this question would be important from the standpoint of transgenic strategies.
But this book is not about optimization theory in genetics, but one that introduces the reader to an analysis, in the authors view, of how evolution happens, and not a predictive tool of what ought to evolve. And, as the authors correctly point out, the time scales needed to evolve an optimal phenotype are not usually dealt with in discussion on optimization strategies. The authors also argue that optimization theories do not consider the expected phenotypic variance or the influence of random drift or mutation. Quantitative genetics does this, they state, and they define it as a mechanistic theory of the evolutionary process.
What is also interesting about quantitative genetics is that it was responsible directly or indirectly for a large body of statistical theory, many of these results being standard material in modern classes in statistics. It is also beginning to find an intersection with the theory of molecular genetics. The authors remark that eventually both quantitative and molecular genetics will have to answer to each other, and they give a taste of this in the chapter on marked-based analysis and QTLs.
There is no question that the reading of this book will give the reader a comprehensive overview of quantitative genetics. But, it takes an very long time to get through, and there are no exercises to test the understanding. Readers will need a fair knowledge of statistics to read the book, but there are three chapters and appendices in the back of the book outlining some of the necessary statistical concepts. The level of mathematics is the most sophisticated in the last chapter, which uses techniques such as maximum likelihood, expectation maximization, and restricted maximum likelihood. Readers with a background in bioinformatics will be very familiar with these techniques. Newton-Rhapson methods and Fisher's scoring method are discusses as derivative-based methods for solving the ML/REML equations and compared with the EM methods for doing the same. The authors are very convincing in informing the reader of the difficulty in estimating genetic variance components in real populations. Also, and most importantly, there are myriads or real-world examples given to illustrate the theory.
For molecular geneticists, and for those very curious about the connection between molecular biology and quantitative genetics, chapter 14, covering the principles of marker-based analysis, would probably be the most interesting in the book. The treatment is both historical, discussing the effects of entire chromosomes, and modern, discussing topics such as using markers or the construction of nearly isogenic lines and cloning individual QTLs. In the 'classical' approach to marker-based methods the authors discuss chromosomal assays, wherein a chromosome from one line is substituted into a standard genetic background chosen to have minimum variance. Since a chromosomal segment may contain a large position of the total genome, the authors take what could be called a 'coarse-grained' approach that utilizes genetic factors rather than a 'microscopic' one emphasizing individual genes. Such a strategy requires large sample sizes if one is to detect factors that result in extremely small effects. Examples of this approach are given, and the authors discuss its weaknesses, one being that a large chromosomal section can have QTLs that have effects in opposite directions, resulting in a net effect close to zero. Thoday's method is also discussed in order to point out the limitations of using flanking-marker mapping methods. The genetics of Drosophila bristle number is also briefly treated, but many references are given.
Recoginizing that direct sequencing of DNA gives a measure of genetic variation, the authors point out though that restriction fragment length polymorphisms are suitable for most purposes, assuming that these are detectable. The advantages and disadvantages of other techniques, such as randomly amplified polymorphic DNAs, are also discussed. The arithmetic involved in genetic mapping is treated in fair detail, the authors overviewing what is involved mathematically in map distances, recombination frequencies, and in the estimation of how many randomly distributed markers are needed to gaurantee that a portion of the genome is within a given number of map units of a marker. The strategies for mapping and cloning of QTLs are the main emphasis in the rest of the chapter. Some of the more interesting discussions here include: 1. The phenomena of 'linkage drag', wherein linked undesirable geness can be dragged along with the marker; 2. Candidate loci and their use in the study of genetic disorders. The authors outline in great detail the problems with this approach, such as linkage disequilibrium; 3. Gene cloning and its use in the study of QTLs. The authors discuss two different cloning strategies, namely that of transposon tagging and positional cloning. The authors emphasize the need for inbred lines for the detection of QTLs by transposon tagging to reduce variance from segregation at other loci. Because of this need, they seem skeptical of the general use of this technique, but give a brief argument as to its possible success using homologies in sequence data between species. The authors also emphasize the complexity involved in the use of positional cloning and comparative mapping and then outline an algorithm as to how to use NILs to do positional cloning of a QTL.
Comprehensive.......1998-09-02
An amazing book that really tells you all you need to know about designing and analyzing quantitative genetic experiments.
Wonderful book........1998-07-26
An awesome book. Everything you ever wanted to know about quantitative genetics and more. A must for every evolutionary biologist.
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Genetical Analysis of Quantitative Traits
Dr M Kearsey
Manufacturer: Garland Science
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Introduction to Quantitative Genetics (4th Edition)
ASIN: 0748740821 |
Book Description
This text provides a guide to the experimental and analytical methodologies available for the study of quantitative traits, a review of the genetic control of quantitative traits, and a discussion of how this knowledge can be applied to breeding problems and evolution. Mathematics is kept simple and brief, and worked examples, both plant and animal, are used extensively. The possibilities opened up by the use of molecular genetic markers are explained and discussed in detail. The Edinburgh Iowa notation is used throughout, and the glossary of terms and list of symbols includes a guide to the equivalent Birmingham notation.
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Quantitative Trait Loci Analysis In Animals (Cabi Publishing)
Joel Ira Weller
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ASIN: 0851994024 |
Book Description
Detection of QTL has become a 'hot' topic in recent years and is of major agricultural significance. This book describes the scientific literature and principles on methods for QTL detection and analysis and marker assisted selection in animal breeding. It is an advanced textbook suitable for lecturers, graduate students and research workers in quantitative genetics and breeding.
Customer Reviews:
this is a excellent book.......2005-01-07
I met Joel last year, he is a great person with very clear thoughts and richest experiences in QTL applied research.
Book Description
This digital document is an article from Human Biology, published by Thomson Gale on October 1, 2005. The length of the article is 5225 words. The page length shown above is based on a typical 300-word page. The article is delivered in HTML format and is available in your Amazon.com Digital Locker immediately after purchase. You can view it with any web browser.
From the author: KEY WORDS: CLOTTING FACTOR VII, F7 GENE, HEMOSTASIS, THROMBOSIS, CARDIOVASCULAR DISEASE, VARIANCE COMPONENTS, QUANTITATIVE TRAIT LOCI, QUANTITATIVE TRAIT NUCLEOTIDE, GAIT STUDY.
Citation Details
Title: The F7 gene and clotting factor VII levels: dissection of a human quantitative trait locus.
Author: Jose Manuel Soria
Publication:
Human Biology (Magazine/Journal)
Date: October 1, 2005
Publisher: Thomson Gale
Volume: 77
Issue: 5
Page: 561(15)
Distributed by Thomson Gale
Book Description
This digital document is a journal article from Addictive Behaviors, published by Elsevier in 2006. The article is delivered in HTML format and is available in your Amazon.com Media Library immediately after purchase. You can view it with any web browser.
Description:
Objective: To determine whether SNPs within the dopamine transporter gene (DAT) are associated with quantitative phenotypes generated from drug frequency variables in an ADHD sample. Method: 35 SNPs were genotyped in and around DAT. We developed a quantitative phenotype at each SNP by weighting the drug frequency variables. The weights were selected to maximize the heritability at each SNP. Once a quantitative phenotype was generated at each SNP, a screening procedure was used to select and test the SNPs with the greatest power to detect an association in DAT. Results: No SNPs in DAT were associated with the quantitative phenotypes generated from the drug frequency variables after the multiple comparisons adjustment; however, some SNPs achieved nominal significance. A sliding window of analysis of 3 SNPs also resulted in only nominal associations. Conclusions: SNPs in DAT do not appear to be associated with the phenotypes generated from drug frequency variables among individuals with ADHD.
Book Description
This digital document is an article from Human Biology, published by Thomson Gale on February 1, 2005. The length of the article is 3488 words. The page length shown above is based on a typical 300-word page. The article is delivered in HTML format and is available in your Amazon.com Digital Locker immediately after purchase. You can view it with any web browser.
From the author: KEY WORDS: HEPATIC LIPASE GENE, LIPC, HDL CHOLESTEROL, LDL CHOLESTEROL, LIPOPROTEIN DIAMETER, GENOME-WIDE SCREEN, HERITABILITY.
Citation Details
Title: Joint linkage and association analysis of the hepatic lipase promoter polymorphism and lipoprotein size phenotypes.
Author: Laura Almasy
Publication:
Human Biology (Magazine/Journal)
Date: February 1, 2005
Publisher: Thomson Gale
Volume: 77
Issue: 1
Page: 17(9)
Distributed by Thomson Gale
Book Description
This digital document is an article from Human Biology, published by Wayne State University Press on June 1, 1995. The length of the article is 5711 words. The page length shown above is based on a typical 300-word page. The article is delivered in HTML format and is available in your Amazon.com Digital Locker immediately after purchase. You can view it with any web browser.
From the author: KEY WORDS: BIOLOGICAL DISTANCE, ALLOMETRY, NATIVE AMERICANS, EVOLUTIONARY THEORY
Citation Details
Title: Multivariate quantitative genetics of anthropometric traits from the Boas data.
Author: Lyle W. Konigsberg
Publication:
Human Biology (Refereed)
Date: June 1, 1995
Publisher: Wayne State University Press
Volume: v67
Issue: n3
Page: p481(18)
Distributed by Thomson Gale
Book Description
This digital document is an article from Human Biology, published by Wayne State University Press on December 1, 2001. The length of the article is 6002 words. The page length shown above is based on a typical 300-word page. The article is delivered in HTML format and is available in your Amazon.com Digital Locker immediately after purchase. You can view it with any web browser.
From the author: Abstract Activin is a multifunctional hormone playing a major role in the regulation of reproduction and growth and development. We performed a genomewide scan using multipoint linkage analysis implemented in a general pedigree-based variance component approach to identify genes with measurable effects on variation in the activin-to-estrogen ratio in baboons. A microsatellite polymorphism, D19S714, which maps to human chromosome l9pl3.2, showed marginal evidence of linkage with a lod ([log.sub.10] of the odds in favor of genetic linkage) score of 1.95 (0.014). This region contains several potential candidate genes including PKA (protein kinase, cAMP-dependent, catalytic [alpha]) and the gene pair JUN-B and JUN-D. This is the first evidence of a quantitative trait locus with a significant effect on the activin-to-estrogen ratio.
Citation Details
Title: A Quantitative Trait Locus Influencing Activin-to-Estrogen Ratio in Pedigreed Baboons Maps to a Region Homologous to Human Chromosome 19.(Statistical Data Included)
Author: Lisa J. Martin
Publication:
Human Biology (Refereed)
Date: December 1, 2001
Publisher: Wayne State University Press
Volume: 73
Issue: 6
Page: 787(14)
Article Type: Statistical Data Included
Distributed by Thomson Gale
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The Statistics of Gene Mapping (STATISTICS FOR BIOLOGY AND HEALTH)
Genetics and Analysis of Quantitative Traits
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Monte Carlo Statistical Methods (Springer Texts in Statistics)
![Family based association analysis of statistically derived quantitative traits for drug use in ADHD and the dopamine transporter gene [An article from: Addictive Behaviors]](http://ecx.images-amazon.com/images/I/215J5HV1NGL.jpg)