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Introduction to Statistical Methods in Modern Genetics (Modern Analysis Series) [Hardcover]

By M.C. Yang (Author)
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Item description for Introduction to Statistical Methods in Modern Genetics (Modern Analysis Series) by M.C. Yang...

Although the basic statistical theory behind modern genetics is not very difficult, most statistical genetics papers are not easy to read for beginners in the field, and formulae quickly become very tedious to fit a particular area of application. Introduction to Statistical Methods in Modern Genetics distinguishes between the necessary and unnecessary complexity in a presentation designed for graduate-level statistics students. The author keeps derivations simple, but does so without losing the mathematical details. He also provides the required background in modern genetics for those looking forward to entering this arena. Along with some of the statistical tools important in genetics applications, students will learn: * How a gene is found * How scientists have separated the genetic and environmental aspects of a person's intelligence * How genetics are used in agriculture to improve crops and domestic animals * What a DNA fingerprint is and why there are controversies about it Although the author assumes students have a foundation in basic statistics, an appendix provides the necessary background beyond the elementary, including multinomial distributions, inference on frequency tables, and discriminant analysis. With clear explanations, a multitude of figures, and exercise sets in each chapter, this text forms an outstanding entre into the rapidly expanding world of genetic data analysis.



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Item Specifications...


Studio: CRC Press
Pages   260
Est. Packaging Dimensions:   Length: 9.1" Width: 6.3" Height: 0.8"
Weight:   1.1 lbs.
Binding  Hardcover
Release Date   Feb 23, 2000
Publisher   CRC
ISBN  9056991345  
ISBN13  9789056991340  


Availability  0 units.


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Product Categories

1Books > Special Features > New & Used Textbooks > Sciences > Biological Sciences
2Books > Special Features > New & Used Textbooks > Sciences > Mathematics > Statistics
3Books > Subjects > Medicine > General
4Books > Subjects > Medicine
5Books > Subjects > Professional & Technical > Professional Science > Evolution > Genetics
6Books > Subjects > Professional & Technical > Professional Science > Mathematics > Applied > Statistics
7Books > Subjects > Science > Biological Sciences > Biology > General
8Books > Subjects > Science > Biological Sciences > Biology
9Books > Subjects > Science > Evolution > Genetics
10Books > Subjects > Science > Mathematics > Applied > General
11Books > Subjects > Science > Mathematics > Applied > Probability & Statistics
12Books > Subjects > Science > Mathematics > General



Reviews - What do customers think about Introduction to Statistical Methods in Modern Genetics (Modern Analysis Series)?

early book on statistical genetics  Mar 26, 2008
Mark Yang is a University of Florida Professor of Statistics. He has a strong engineering background and good training in biology and biochemistry. This interest led him to a careful understanding of the subject and the modern methods that are employed which rely more and more on statistics.
What is particularly nice about the book is its simplicity. The study of genetics can be very complicated. Also various useful statistical tools including Markov chain Monte Carlo, bootstrap methods, the EM algorithm and multiple comparisons can be complex as well. But Professor Yang is careful to extract only the essentials to make both the statistics and teh genetics understandable to the layperson.

On the statistical side the emphasis is on the Hardy-Weinberg equilibrium and the likeihood equations in pedigree analysis. Rather than go through complicated Markov Chain theory to demonstrate the equilibrium result Professor Yang demonstrates using simple conditional probabilities. This approach works because the convergence occurs exactly after two steps.

For the benefit of the statistician who knows nothing about genetics Chapter 1 is a primer on molecular genetics that gives the essential to understand recombinant methods, recombination fraction, the Haldane distance mapping function, linkage, RFLP and PCR techniques needed for subsequent chapters.

Most of the work in genetic inference involves calcualting the formula for the likelihood equations. This involves basic discrete probability calculations and combinatorics for the main part.

Once we learn the basics we are ready to understand gentic fingerprinting which is used to establish paternity or identify criminals.

The book has interesting examples and exercises and many good references. The only major drawback to the book is that it does not cover microarray analysis.

However, I took a short course from Dr. Yang based on this book. In the course he spent a great deal of time on microarray technology by supplementing the text with some notes on microarrays and a recent paper he published on the reliabiity on the technique. The text certainly gives the reader the proper background for microarray analysis or other genetic developments.

At the time this was the only book available but now severral good books on bioinformatics and Kenneth Lange text are available and better written than Mark Yang's book.

 
nice introduction to stat methods in genetic research  Mar 11, 2002
Mark Yang is a University of Florida Professor of Statistics. He has a strong engineering background and good training in biology and biochemistry. This interest led him to a careful understanding of the subject and the modern methods that are employed which rely more and more on statistics.

What is particularly nice about the book is its simplicity. The study of genetics can be very complicated. Also various useful statistical tools including Markov chain Monte Carlo, bootstrap methods, the EM algorithm and multiple comparisons can be complex as well. But Professor Yang is careful to extract only the essentials to make both the statistics and teh genetics understandable to the layperson.

On the statistical side the emphasis is on the Hardy-Weinberg equilibrium and the likeihood equations in pedigree analysis. Rather than go through complicated Markov Chain theory to demonstrate the equilibrium result Professor Yang demonstrates using simple conditional probabilities. This approach works because the convergence occurs exactly after two steps.

For the benefit of the statistician who knows nothing about genetics Chapter 1 is a primer on molecular genetics that gives the essential to understand recombinant methods, recombination fraction, the Haldane distance mapping function, linkage, RFLP and PCR techniques needed for subsequent chapters.

Most of the work in genetic inference involves calcualting the formula for the likelihood equations. This involves basic discrete probability calculations and combinatorics for the main part.

Once we learn the basics we are ready to understand gentic fingerprinting which is used to establish paternity or identify criminals.

The book has interesting examples and exercises and many good references. The only major drawback to the book is that it does not cover microarray analysis.

However, I recently took a short course from Dr. Yang based on this book. In the course he spent a great deal of time on microarray technology by supplementing the text with some notes on microarrays and a recent paper he published on the reliabiity on the technique. The text certainly gives the reader the proper background for microarray analysis or other genetic developments.

 

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