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 Mendelas 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.In the early years of association testing, the strategy was mainly used to test specific regions, e.g., genes which were selected on the basis of function relative to the biology of the disease, or on the basis of linkage analysis. By restricting testinganbsp;...
|Title||:||The Fundamentals of Modern Statistical Genetics|
|Author||:||Nan M. Laird, Christoph Lange|
|Publisher||:||Springer Science & Business Media - 2010-12-13|