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| View Larger Image | Mathematical and Statistical Methods for Genetic Analysis
by Kenneth Lange
| | List Price: | $99.00 | | Price: | $65.00 | | You Save: | $34.00 (34%) |  | | Available: | Usually ships in 24 hours | | | | | Sales Rank: | 866515 | | Studio: | Springer | | | Binding: | Hardcover | | Number Of Pages: | 384 | | Publication Date: | June 03, 2003 | | Publisher: | Springer |
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Product Description
During the past decade, geneticists have cloned scores of Mendelian disease genes and constructed a rough draft of the entire human genome. The unprecedented insights into human disease and evolution offered by mapping, cloning, and sequencing will transform medicine and agriculture. This revolution depends vitally on the contributions of applied mathematicians, statisticians, and computer scientists. Mathematical and Statistical Methods for Genetic Analysis is written to equip students in the mathematical sciences to understand and model the epidemiological and experimental data encountered in genetics research. Mathematical, statistical, and computational principles relevant to this task are developed hand in hand with applications to population genetics, gene mapping, risk prediction, testing of epidemiological hypotheses, molecular evolution, and DNA sequence analysis. Many specialized topics are covered that are currently accessible only in journal articles. This second edition expands the original edition by over 100 pages and includes new material on DNA sequence analysis, diffusion processes, binding domain identification, Bayesian estimation of haplotype frequencies, case-control association studies, the gamete competition model, QTL mapping and factor analysis, the Lander-Green-Kruglyak algorithm of pedigree analysis, and codon and rate variation models in molecular phylogeny. Sprinkled throughout the chapters are many new problems. Kenneth Lange is Professor of Biomathematics and Human Genetics at the UCLA School of Medicine. At various times during his career, he has held appointments at the University of New Hampshire, MIT, Harvard, and the University of Michigan. While at the University of Michigan, he was the Pharmacia & Upjohn Foundation Professor of Biostatistics. His research interests include human genetics, population modeling, biomedical imaging, computational statistics, and applied stochastic processes. Springer-Verlag published his book Numerical Analysis for Statisticians in 1999. |
CUSTOMER REVIEWS (Average Customer Rating: 4.0 based on 6 reviews)
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great book where few texts of a statistical nature exist 
This second edition updates the first with the many advances in the rapidly growing field of genetics. It provides a nice treatment of the mathematical and stochastic models that are useful in genetic studies.
It is a little disappointing that it does not go into the microarray technology that has become so important for experimentation in the last few years. Other recent books that cover statistical aspects of genetic research are Weir (1996) "Genetic Data Analysis II" Sinauer Associates (publisher) and Yang (2000) "Introduction to Statistical Methods in Modern Genetics" Gordon and Breach Science Publishers.
January 23, 2008 | |
Read this book and you might learn something
This book is not for the novice dabbling in statistical genetics. This is a highly sophisticated, thought provoking book targeted to individuals with considerable mathematical ability and training. As such, this book is an invaluable tool for individuals hoping to make a real impact in the field of statistical genetics. I particularly enjoyed the chapter on Markov Chain Monte Carlo methods for pedigree data.
May 21, 2004 | |
OK, but not for me 
I'm interested in molecular genetics, this seems to be more about population genetics. There is some material, towards the back, about phylogeny. I can bash that a bit to make it match my needs, but it's still a bit of a stretch.It seems to be a pretty good presentation of population genetics, the kind of genetics taught in high schools in the 70s. I can't comment on this book's merits, but I can warn the biochem types to spend their money elsewhere.
September 23, 2003 | |
nice introduction to math. and stoch. models in genetics
This second edition updates the first with the many advances in the rapidly growing field of genetics. It provides a nice treatment of the mathematical and stochastic models that are useful in genetic studies.It is a little disappointing that it does not go into the microarray technology that has become so important for experimentation in the last few years. Other recent books that cover statistical aspects of genetic research are Weir (1996) "Genetic Data Analysis II" Sinauer Associates (publisher) and Yang (2000) "Introduction to Statistical Methods in Modern Genetics" Gordon and Breach Science Publishers.
July 08, 2002 | |
Mathematical Details of Genetics
This book has an excellent coverage on the mathematical subtlies of genetics. The complicated theories are complimented by numerous examples. The exercise at the end of each chapter has a collection of probing questions that tests the understanding of the topics covered in the respective chapter. However, there is no discussion on association studies and quantitative traits which are two of the most active areas of genetic epidemiology. Moreover, the coverage on linkage is not adequate.
March 01, 2001 | |
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