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MORE ABOUT THIS BOOK
Main description:
This book covers the statistical models and methods that are used to understand human genetics, following the historical and recent developments of human genetics. Starting with Mendel's first experiments to genome-wide association studies, the book describes how genetic information can be incorporated into statistical models to discover disease genes. All commonly used approaches in statistical genetics (e.g. aggregation analysis, segregation, linkage analysis, etc), are used, but the focus of the book is modern approaches to association analysis. Numerous examples illustrate key points throughout the text, both of Mendelian and complex genetic disorders.
The intended audience is statisticians, biostatisticians, epidemiologists and quantitatively- oriented geneticists and health scientists wanting to learn about statistical methods for genetic analysis, whether to better analyze genetic data, or to pursue research in methodology. A background in intermediate level statistical methods is required. The authors include few mathematical derivations, and the exercises provide problems for students with a broad range of skill levels. No background in genetics is assumed.
Contents:
Introduction to statistical genetics and background in molecular genetics.- Principles of inheritance: mendel's laws and genetic models.- Some basic concepts from population genetics.- Aggregation, heritability and segregation analysis: modeling genetic inheritance without genetic data.- The general concepts of gene mapping: Linkage, association, linkage disequilibrium and marker maps.- Basic concepts of linkage analysis.- The basics of genetic association analysis.- Population substructure in association studies.- Association analysis in family designs.- Advanced topics.- Genome wide assocation studies.- Looking toward the future.
PRODUCT DETAILS
Publisher: Springer (Springer-Verlag New York Inc.)
Publication date: January, 2013
Pages: 240
Weight: 373g
Availability: Available
Subcategories: Epidemiology, Genetics
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