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The Fundamentals of Modern Statistical Genetics Vital Source e-bog
Nan M. Laird og Christoph Lange
(2010)
Springer Nature
987,00 kr.
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The Fundamentals of Modern Statistical Genetics
Nan M. Laird og Christoph Lange
(2010)
Sprog: Engelsk
Springer New York
555,00 kr.
4 stk på lager
Hvor er varen på lager?Detaljer om varen
- Vital Source searchable e-book (Reflowable pages)
- Udgiver: Springer Nature (December 2010)
- Forfattere: Nan M. Laird og Christoph Lange
- ISBN: 9781441973382
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.
Licens varighed:
Bookshelf online: 5 år fra købsdato.
Bookshelf appen: ubegrænset dage fra købsdato.
Udgiveren oplyser at følgende begrænsninger er gældende for dette produkt:
Print: 2 sider kan printes ad gangen
Copy: højest 2 sider i alt kan kopieres (copy/paste)
Bookshelf online: 5 år fra købsdato.
Bookshelf appen: ubegrænset dage fra købsdato.
Udgiveren oplyser at følgende begrænsninger er gældende for dette produkt:
Print: 2 sider kan printes ad gangen
Copy: højest 2 sider i alt kan kopieres (copy/paste)
Detaljer om varen
- Hardback: 240 sider
- Udgiver: Springer New York (December 2010)
- Forfattere: Nan M. Laird og Christoph Lange
- ISBN: 9781441973375
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.
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.
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