Yang, M.C.
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 entrée into the rapidly expanding world of genetic data analysis.|
* Emphasizes how and why statistical concepts and methods are needed and used in genetic data analysis
* Builds a background in modern genetics, from biological and molecular experiment to gene hunting and genetic engineering
* Includes exercises in each chapter, with answers and hints provided at the end of the book
* Highlights within an appendix several public domain computer programs for genetic data analysis
* Discusses applications to areas that include forensics, evolutionary history, agriculture, and intelligence
Though the basic statistical theory behind modern genetics is not that difficult, most statistical genetics papers are not easy to read for beginners, and fitting formulae to a particular area of application quickly becomes very tedious. Introduction to Statistical Methods in Modern Genetics makes a clear distinction between the necessary and unnecessary complexities. The author keeps the derivations of methods simple without losing the mathematical details. He also provides the necessary background in modern genetics for newcomers to the field, including discussion ranging from biological and molecular experiments to gene hunting and genetic engineering.
Inhaltsverzeichnis
Though the basic statistical theory behind modern genetics is not that difficult, most statistical genetics papers are not easy to read for beginners, and fitting formulae to a particular area of application quickly becomes very tedious. Introduction to Statistical Methods in Modern Genetics makes a clear distinction between the necessary and unnecessary complexities. The author keeps the derivations of methods simple without losing the mathematical details. He also provides the necessary background in modern genetics for newcomers to the field, including discussion ranging from biological and molecular experiments to gene hunting and genetic engineering.
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