This book details the statistical concepts used in gene mapping. It presents elementary principles of probability and statistics, which are implemented by computational tools based on the R programming language.

This book details the statistical concepts used in gene mapping, first in the experimental context of crosses of inbred lines and then in outbred populations, primarily humans. It presents elementary principles of probability and statistics, which are implemented by computational tools based on the R programming language to simulate genetic experiments and evaluate statistical analyses. Coverage reflects the modern approach of using anonymous DNA markers distributed throughout the genome to identify regions likely to contain genes of interest. Each chapter contains exercises, both theoretical and computational, some routine and others that are more challenging. The R programming language is developed in the text.

Formal statistical and computational requirements for reading the book are kept as few as seems reasonable so that the book can be understood by beginners from a variety of backgrounds Deals with ideas that have arisen in recent experimental developments Includes supplementary material: sn.pub/extras

Inhalt
Background and Preparations.- Background in Statistics.- to Experimental Genetics.- Fundamentals of Genetics: Inbreeding, Recombination, Random Mating, and Identity by Descent.- Experimental Genetics.- Testing for Linkage with a Single Marker.- Whole Genome Scans: The Significance Level.- Statistical Power and Confidence Regions.- Missing Data and Interval Mapping.- Advanced Topics.- Human Genetics.- Mapping Qualitative Traits in Humans Using Affected Sib Pairs.- Admixture Mapping.- Mapping Complex and Quantitative Traits with Data from Human Pedigrees.- Association Studies.- Inferring Haplotypes from Genotypes and Testing Association.