During the past decade, geneticists have constructed detailed maps of the human genome and cloned scores of Mendelian disease genes. They now stand on the threshold of sequencing the genome in its entirety and the unprecedented insights into human disease and evolution offered by mapping and sequencing will transform medicine and agriculture. This revolution depends vitally on the contributions of applied mathematicians, statisticians and computer scientists. This book is designed to equip graduate students in the mathematical sciences with the knowledge needed 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 gene mapping, risk prediction, and the testing of epidemiological hypotheses.
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Book Description
Editorial Reviews
Review
From the reviews of the second edition:
"This book is an excellent resource for statisticians and applied mathematicians seeking an overview of models and formalisms in use in a wide collection of generic analysis processes." Journal of the American Statistical Association, June 2005
"Genetics is a subject that has always attracted the attention of statisticians back to the time that Mendel did his discoveries. … The current book realizes this and tries to provide a gentle introduction to a wide range of statistical topics in genetics. … I highly recommend this book for Master’s students wishing to see many interesting applications of statistics in genomics. On their way through this book, they will be rewarded with knowledge of a wide range of statistical topics." (Dr A. Di Bucchianico, Kwantitatieve Methoden, Vol. 72B38, 2006)
"More than 100 pages have been added in this Second Edition … . The strength of this book is that it provides the foundations and rigorous details of mathematical and statistical methodologies that are applied in genetics. … A nice feature of this book is that there are about a dozen problems for solution at the end of each chapter, which will be useful for the serious reader." (Partha P. Majumder, Sankhya, Vol. 65 (3), 2003)
"This monograph is the second edition of Lange’s 1997 work, increased by more than 100 pages. … Several chapters of the first edition have been supplemented by new material. … The book is intended for readers with basic knowledge in theoretical statistics, calculus and linear algebra. … Summarizing, this monograph can be recommended to all who want to connect their knowledge of mathematical statistics and genetics and learn about the statistical solutions … for genetical research problems." (C. Becker, Metrika, September, 2003)
"The second edition of this book by Ken Lange continues to present a very broad coverage of mathematical and statistical methods in the analysis of genetic data. The text has something to offer both mathematicians already experienced in genetics, as well as those with little experience in the area … . The book allows readers to appreciate the enormous contribution that Lange has made to the area and will hopefully inspire them (in Lange’s words) 'to create new models and methods for analyzing genetic data’." (Lyle C. Gurrin and John L. Hopper, Statistics in Medicine, Vol. 23, 2004)
"This is an excellent book written in the same crisp and clear style as the first edition. It has enough in detail of the underpinning genetics to allow the mathematicians and statisticians to gain an understanding sufficient to see the challenge and richness of the area. … It is both a useful reference source and a suitable introduction … . This is a book I am happy to recommend highly, and I look forward to the third edition in a few years time." (C Cannings, Heredity, Issue 92, 2004)
"The revolution in molecular genetics during the past decade has led to an avalanche of new data whose analysis requires increasingly sophisticated mathematical, statistical and computational methods. This book provides an up-to-date introduction to several of the most important mathematical and statistical methods developed for this purpose. … this is a major text that will be most useful for researchers and students of mathematics or statistics interested or engaged in the analysis of genetic data." (R. Bürger, Monatshefte für Mathematik, Vol. 139 (3), 2003)
"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 … . 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 … ." (Zentralblatt für Didaktik, June, 2002)
"The first edition of this book appeared in 1997 … and filled an important gap by covering in a unified setting topics dispersed in the research literature. This second edition is considerably larger, updating the first edition and adding some new topics. There are two new chapters … . Several new sections have also been added to existing chapters … ." (Carlos A. Braumann, Zentralblatt MATH, Vol. 991 (16), 2002)
--This text refers to an alternate Hardcover edition.
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Most Helpful Customer Reviews
28 of 29 people found the following review helpful:
5.0 out of 5 stars
great book where few texts of a statistical nature exist,
By
This review is from: Mathematical and Statistical Methods for Genetic Analysis (Hardcover)
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.
33 of 42 people found the following review helpful:
4.0 out of 5 stars
OK, but not for me,
By wiredweird "wiredweird" (Earth, or somewhere nearby) - See all my reviews (HALL OF FAME REVIEWER) (TOP 500 REVIEWER)
This review is from: Mathematical and Statistical Methods for Genetic Analysis (Hardcover)
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.
7 of 8 people found the following review helpful:
4.0 out of 5 stars
Mathematical Details of Genetics,
By A Customer
This review is from: Mathematical and Statistical Methods for Genetic Analysis (Hardcover)
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.
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First Sentence:
In this chapter we briefly review some elementary results from population genetics discussed in more detail in the references [2, 3, 4, 6, 7, 10, 13]. Read the first page Key Phrases - Statistically Improbable Phrases (SIPs): (learn more)
obligate breaks, generalized kinship coefficients, condensed identity states, stationary renewal model, unascertained families, chiasma process, condensed identity coefficients, best rooted tree, allele vectors, genotype elimination, chromatid bundle, same unrooted tree, descent graphs, polyploid model, contemporary taxa, gamete probabilities, sampled genes, ordered genotypes, kinship matrix, founder gene, parsimony score, nucleotide substitution model, breakage probability, dominance genetic variances, ascertained family Key Phrases - Capitalized Phrases (CAPs): (learn more)
Hum Genet, New York, Monte Carlo, Continuing Problem, Amer Stat Assoc, Oxford University Press, Genet Epidemiology, Hum Hered, Johns Hopkins University Press, Academic Press, Ann Stat, Genetic Analysis Workshop, Hunt Genet, Mol Biol Evol, San Francisco, Stat Sci, Stochastic Processes, Cambridge University Press, Computational Biology, Genetic Data Analysis, Mol Evol, Pattern Recognition Letters, Proc Natl Acad Sci, Roy Stat Soc, The Genetic Structure of Populations New!
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Front Cover | Table of Contents | First Pages | Index | Back Cover | Surprise Me!
In this chapter we briefly review some elementary results from population genetics discussed in more detail in the references [2, 3, 4, 6, 7, 10, 13]. Read the first page Key Phrases - Statistically Improbable Phrases (SIPs): (learn more)
obligate breaks, generalized kinship coefficients, condensed identity states, stationary renewal model, unascertained families, chiasma process, condensed identity coefficients, best rooted tree, allele vectors, genotype elimination, chromatid bundle, same unrooted tree, descent graphs, polyploid model, contemporary taxa, gamete probabilities, sampled genes, ordered genotypes, kinship matrix, founder gene, parsimony score, nucleotide substitution model, breakage probability, dominance genetic variances, ascertained family Key Phrases - Capitalized Phrases (CAPs): (learn more)
Hum Genet, New York, Monte Carlo, Continuing Problem, Amer Stat Assoc, Oxford University Press, Genet Epidemiology, Hum Hered, Johns Hopkins University Press, Academic Press, Ann Stat, Genetic Analysis Workshop, Hunt Genet, Mol Biol Evol, San Francisco, Stat Sci, Stochastic Processes, Cambridge University Press, Computational Biology, Genetic Data Analysis, Mol Evol, Pattern Recognition Letters, Proc Natl Acad Sci, Roy Stat Soc, The Genetic Structure of Populations New!
Books on Related Topics | Concordance | Text Stats Browse Sample Pages:
Front Cover | Table of Contents | First Pages | Index | Back Cover | Surprise Me!
Citations (learn more)
This book cites 69
books:
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29
books
cite this book:
See all 29 books citing this book
- Linear Statistical Inference and Its Applications by C. Radhakrishna Rao on page 38, page 58, and page 168
- The Genetic Structure of Populations (Biomathematics, Volume 5) by Albert Jacquard on page 20, page 95, and page 113
- Genetic Data Analysis by Bruce S. Weir on page 38, page 79, and page 229
- Large Sample Theory by Thomas S. Ferguson on page 38, and page 57
- Analytic Function Theory, Volume I (AMS Chelsea Publishing) by Einar Hille on page 57, and page 316
See all 69 books this book cites
- Introduction to Statistical Methods in Modern Genetics (Modern Analysis Series) by M.C. Yang in Front Matter, and Back Matter
- The Analysis of Gene Expression Data by Giovanni Parmigiani in Front Matter, and Back Matter
- Optimization (Springer Texts in Statistics) by Kenneth Lange in Back Matter (1), and Back Matter (2)
- Likelihood, Bayesian and MCMC Methods in Quantitative Genetics by Daniel Sorensen in Back Matter (1), and Back Matter (2)
- Statistical Methods in Molecular Evolution (Statistics for Biology and Health) by Rasmus Nielsen in Front Matter
See all 29 books citing this book
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