In Quantitative Trait Loci: Methods and Protocols, a panel of highly experienced statistical geneticists demonstrate in a step-by-step fashion how to successfully analyze quantitative trait data using a variety of methods and software for the detection and fine mapping of quantitative trait loci (QTL). Writing for the nonmathematician, these experts guide the investigator from the design stage of a project onwards, providing detailed explanations of how best to proceed with each specific analysis, to find and use appropriate software, and to interpret results. Worked examples, citations to key papers, and variations in method ease the way to understanding and successful studies. Among the cutting-edge techniques presented are QTDT methods, variance components methods, and the Markov Chain Monte Carlo method for joint linkage and segregation analysis.

Quantitative Trait Loci (QTL) is a topic of major agricultural significance for efficient livestock production. This book covers various statistical methods that have been used or proposed for detection and analysis of QTL and marker-and gene-assisted selection in animal genetics and breeding.

Quantitative trait loci (QTL) are those chromosome regions that contribute to variation of quantitative traits. Analysis of QTL is helpful for further study of molecular basis of the quantitative genetic variation. The discovery of highly abundant and dense polymorphic markers (e.g., single nucleotide polynorphisms, or SNPs) covering a whole genome provides an opportunity to localize QTL in a variety of populations. While classical linkage studies have a relatively limited resolution in QTL localization, the association mapping or linkage disequilibrium (LD) mapping approach can offer an alternative way for fine mapping of genes. Currently there are few LD methods available for QTL mapping in natural populations. Development of more efficient methods is still a challenging problem. In this thesis, we first review the LD approach for fine mapping of QTL in Chapter 1. Some basic issues in LD analysis and recent developments in LD methodology are discussed. Particular attention is paid to limitations and potential problems of these methods. This provides the motivation for the research in this thesis. Next, we explore Cockerham's genetic model (Cockerham, 1954) for quantitative traits in Chapter 2. A revised form of the Cockerham model is presented using some coding variables. The relationship between Cockerham model and some specific genetic models for designed experimental populations, such as backcross or F2, is then established. We study extensively the properties of QTL effects and partitions of various genetic variance components for these reduced models under both linkage equilibrium and linkage disequilibrium situations. A general multi-locus-two-allele model is also proposed that may serve as a basis for mapping QTL in natural populations. The main research of the thesis is on development of an exact multipoint likelihood approach to infer QTL in natural populations. In Chapter 3, we first generalize the formulation of the likelihood analysis for a polymorphic.

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.