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) is a topic of major agricultural significance for efficient livestock production. This advanced-level textbook covers all the 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, as well as new advances that have revolutionized the field since the first edition.

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.

Many traits of economic importance are determined by quantitative trait loci (QTL). QTL can be detected by their linkage to marker loci. Many different experimental designs have been proposed for detecting QTL. The relative merits of these designs can be evaluated by their power to detect a QTL segregating in a population. The test statistic most commonly used for detection of QTL is the log likelihood ratio. It has been suggested that the expected log likelihood ratio may be a good indicator of the power of a particular experimental design. In this study, two methods were used to calculate this expected value and their usefulness evaluated by simulation. The first approach approximated the expected value of the log likelihood assuming y is distributed as a mixture of multivariate normal distributions. The second approach calculated the exact log likelihood by approximating the distribution of y with a single multivariate normal distribution that has the same mean and variance-covariance structure as the mixture distribution. The utility of these approximations were studied by computing the power and the mean of the log likelihood ratio for several designs by computer simulation and relating these results to the expected value of the log likelihood for both approaches. The expected value of the log likelihood calculated using the first approach had a poor relationship with power, but the expected value of the log likelihood ratio calculated using the second approach had a strong correlation with the mean of the log likelihood ratio. Because the mean of the log likelihood ratio did not have a strong correlation with the power, this mean may not be a good indicator of the power of a design. Therefore, any approximation of the mean of the log likelihood ratio also may not be a good indicator of the power.

Over the last two decades advances in genotyping technology, and the development of quantitative genetic analytical techniques have made it possible to dissect complex traits and link quantitative variation in traits to allelic variation on chromosomes or quantitative trait loci (QTLs). In Quantitative Trait Loci (QTLs):Methods and Protocols, expert researchers in the field detail methods and techniques that focus on specific components of the entire process of quantitative train loci experiments. These include methods and techniques for the mapping populations, identifying quantitative trait loci, extending the power of quantitative trait locus analysis, and case studies. Written in the highly successful Methods in Molecular BiologyTM series format, the chapters include the kind of detailed description and implementation advice that is crucial for getting optimal results in the laboratory. Thorough and intuitive, Quantitative Trait Loci (QTLs):Methods and Protocols aids scientists in the further study of the links between phenotypic and genotypic variation in fields from medicine to agriculture, from molecular biology to evolution to ecology.