Analysis of Genetic Association Studies (Statistics for Biology and Health) - Softcover

Über die Autorin bzw. den Autor

Gang Zheng is a Mathematical Statistician in the Office of Biostatistics Research, National Heart, Lung and Blood Institute, National Institutes of Health. His research interests include robust procedures, statistical genetics, inference with nuisance parameters, analysis of ordered data, and clinical trials. E-mail: zhengg@nhlbi.nih.gov

Yaning Yang is a professor in the Department of Statistics and Finance at the University of Science and Technology of China. He received his Ph.D. in Statistics from the Rutgers University. His main specialty is statistical genetics and bioinformatics. E-mail: ynyang@gmail.com

Xiaofeng Zhu is a professor in Department of Epidemiology and Biostatistics, Case Western Reserve University. His research focuses on developing statistical methods in the areas of association analysis, rare variant association analysis, population stratification, admixture mapping and searching genetic variants contributing hypertension related traits. He is currently on the editorial board of Genetic Epidemiology. E-mail: xzhu1@darwin.epbi.cwru.edu

Robert Elston, Professor of Epidemiology and Biostatistics at Case Western Reserve University, has been a leader in the field of genetic epidemiology for over forty years, having developed the software package S.A.G.E. (Statistical Analysis for Genetic Epidemiology). He has authored six books on biostatistics and genetic epidemiology prior to this one. E-mail: robert.elston@cwru.edu

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Analysis of Genetic Association Studies is both a graduate level textbook in statistical genetics and genetic epidemiology, and a reference book for the analysis of genetic association studies. Students, researchers, and professionals will find the topics introduced in Analysis of Genetic Association Studies particularly relevant. The book is applicable to the study of statistics, biostatistics, genetics and genetic epidemiology.

In addition to providing derivations, the book uses real examples and simulations to illustrate step-by-step applications. Introductory chapters on probability and genetic epidemiology terminology provide the reader with necessary background knowledge. The organization of this work allows for both casual reference and close study.