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.
This book covers the statistical models and methods that are used to understand human genetics, following the historical and recent developments of human genetics.
Methodology in Medical Genetics: An Introduction to Statistical Methods
B. V. North, D. Curtis, and P. C. Sham, A note on the calculation of empirical P values from Monte Carlo procedures. Am. J. Hum. Genet. 72,498–499 (2003). 12. P. C. Sham and S. M. Purcell, Statistical power and significance testing in ...
Lunetta KL, Faraone SV, Biederman J, et al. Family-based tests of association and linkage that use unaffected sibs, covariates, and interactions. American journal of Human Genetics 2000; 66:605–614. Lynch HT, Guirgis H, Swartz M, et al.
The statistical methods required by bioinformatics present many new and difficult problems for the research community. This book provides an introduction to some of these new methods.
The book covers many topics previously only accessible in journal articles, such as pedigree analysis algorithms, Markov chain, Monte Carlo methods, reconstruction of evolutionary trees, radiation hybrid mapping, and models of recombination ...
This is the second edition of the successful textbook written by the prize-winning scientist Andreas Ziegler, former President of the German Chapter of the International Biometric Society, and Inke Konig, who has been teaching the subject ...
Probability Models and Statistical Methods in Genetics
This book presents fundamental concepts and principles in this emerging field at a level that is accessible to students and researchers with a first course in biostatistics.
Some multivariate methods have been specifically designed to decompose the variability between codon usage within the differently abundantamino acids (Grantham et al., 1981; Perrière and Thioulouse, 2002), and this enables discovery of ...