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. 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.

Statistical Genetics is an advanced textbook focusing on conducting genome-wide linkage and association analysis in order to identify the genes responsible for complex behaviors and diseases. Starting with an introductory section on statistics and quantitative genetics, it covers both established and new methodologies, providing the genetic and statistical theory on which they are based. Each chapter is written by leading researchers, who give the reader the benefit of their experience with worked examples, study design, and sources of error. The text can be used in conjunction with an associated website (www.genemapping.org) that provides supplementary material and links to downloadable software.

There is growing enthusiasm in the scientific community about the prospect of mapping and sequencing the human genome, a monumental project that will have far-reaching consequences for medicine, biology, technology, and other fields. But how will such an effort be organized and funded? How will we develop the new technologies that are needed? What new legal, social, and ethical questions will be raised? Mapping and Sequencing the Human Genome is a blueprint for this proposed project. The authors offer a highly readable explanation of the technical aspects of genetic mapping and sequencing, and they recommend specific interim and long-range research goals, organizational strategies, and funding levels. They also outline some of the legal and social questions that might arise and urge their early consideration by policymakers.

Overview of mapping common and genetically complex human disease traits (J. L. Haines, M. A. Pericak-Vance). Basic concepts in genetics (M. C. Speer). Defining disease phenotypes (A. S. Aylsworth). Basic concepts in linkage analysis ( M. A. Pericak-Vance). Determining the genetic component of a disease (L. A. Farrer, L. A. Cupples). Patient and family participation in genetic research studies (P. E. Cohen, C. Wolpert). Sample size and power (M. C. Speer). The collection of biological samples for DNA analysis (J. M. Vance). Methods of genotyping (J. M. Vance, K. B. Othmane). Database design for gene mapping studies (C. Haynes, C. Blach). Genomic screening (J. L. Haines). Lod score analysis (J. Xu, D. A. Meyers, M. A. Pericak-Vance). Sib pair analysis (D. E. Goldgar). Affected relative pair analysis (J. L. Haines). Linkage disequilibrium and allelic association (M. A. Pericak-Vance). Using public databases (J. L. Haines). Laboratory approaches toward gene identification (D. A. Marchuk). Examining complex genetic interactions (J. M. Schildkraut). Glossary. Appendix: useful websites for genetic analysis. Index.

Written to equip students in the mathematical siences to understand and model the epidemiological and experimental data encountered in genetics research. This second edition expands the original edition by over 100 pages and includes new material. Sprinkled throughout the chapters are many new problems.

Genomics, the mapping of the entire genetic complement of an organism, is the new frontier in biology. This handbook on the statistical issues of genomics covers current methods and the tried-and-true classical approaches.

This book introduces the basic concepts and methods that are useful in the statistical analysis and modeling of the DNA-based marker and phenotypic data that arise in agriculture, forestry, experimental biology, and other fields. It concentrates on the linkage analysis of markers, map construction and quantitative trait locus (QTL) mapping, and assumes a background in regression analysis and maximum likelihood approaches. The strength of this book lies in the construction of general models and algorithms for linkage analysis, as well as in QTL mapping in any kind of crossed pedigrees initiated with inbred lines of crops.

Focusing on the roles of different segments of DNA, Statistics in Human Genetics and Molecular Biology provides a basic understanding of problems arising in the analysis of genetics and genomics. It presents statistical applications in genetic mapping, DNA/protein sequence alignment, and analyses of gene expression data from microarray experiments.

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 and basic genetic principles; Genetic loci genetic polymorphisms; Aspects of statistical inference; Basics of linkage analysis; The informativeness of family data; Multipoint linkage analysis; Penetrance; Quantitative phenotypes; Numerical and computerized methods; Variability of the recombination fraction; Inconsistencies; Linkage analysis with mendelian disease loci; Nonparametric methods; Two-locus inheritance; Complex traits.