This book describes the theory and practice of methods for quantitative trait loci (QTL) detection and analysis, as well as marker assisted selection, in animal genetics and breeding. It is divided into 16 chapters: historical overview; principles of parameter estimation; random and fixed effects (the mixed model); experimental designs to detect QTL (generation of linkage disequilibrium); QTL parameter estimation for crosses between inbred lines; advanced statistical methods for QTL detection and parameter estimation; analysis of QTL as random effects; statistical power to detect QTL and parameter confidence interval; optimization of experimental designs; fine mapping of QTL; complete genome QTL scans (the problem of multiple comparisons); multiple trait QTL analysis; principles of selection index and traditional breeding programmes; marker-assisted selection (theory); marker-assisted selection (results of simulation studies); and marker-assisted introgression. The book is aimed at graduate students and research workers in quantitative genetics and animal breeding. There is an author index and a detailed subject index.

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) 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.

As a large number of single nucleotide polymorphisms (SNPs) and microsatellite markers are available, high resolution mapping employing multiple markers or multiple allele markers is an important step to identify quantitative trait locus (QTL) of complex human disease. For many complex diseases, quantitative phenotype values contain more information than dichotomous traits do. Much research has been done on conducting high resolution mapping using information of linkage and linkage disequilibrium. The most commonly employed approaches for mapping QTL are pedigree-based linkage analysis and population-based association analysis. As one of the methods dealing with multiple alleles markers, mixed models are developed to work out family-based association study with the information of transmitted allele and nontransmitted allele from one parent to offspring. For multiple markers, variance component models are proposed to perform association study and linkage analysis simultaneously. Linkage analysis provides suggestive linkage based on a broad chromosome region and is robust to population admixtures. One the other hand, allelic association due to linkage disequilibrium (LD) usually operates over very short genetic distance, but is affected by population stratification. Combining both approaches plays a synergistic role in overcoming their limitations and in increasing the efficiency and effectiveness of gene mapping.

Tenderness is one of the major meat quality factors that affects the intent of consumers to re-purchase beef. Both genetic and non-genetic factors affect the quantitative trait of tenderness. Among the genetic factors, polymorphisms in key genes, such as the myostatin (MSTN) and calpain 1(CAPN1), play important roles on tenderness. However, these genes do not explain all the genetic variation associated with tenderness. The aim of this study was to discover additional genes associated with tenderness to help integrate genetic information into beef cattle breeding programmes and meat quality assurance programmes, such as Meat Standards Australia, and produce high quality tender meat for consumers. Discovery of such genes should also aid in the understanding of mechanisms underlying tenderness. Backcross QTL mapping progeny based on crosses between two extreme Bos Taurus breeds (Limousin and Jersey) were used in the study. There were four new traits created for the QTL mapping and association studies. Two of the traits (wbld_adjusted and wbst_adjusted) were based on Warner-Bratzler (WB) shear force measurements from the M. longissimus dorsi (LD) and M. semitendinosus (ST) muscles and were derived from a multi-variate mixed model in which the environmental effects, myostatin F94L genotype effect, ageing day effect and the interaction effects were accounted for. The adjusted shear force traits offered a more accurate prediction for average tenderness. The other new trait was the amount of ageing per 25 days (called "ageing rate" herein) for the two muscles, calculated as the difference between natural log shear force values after 1 and 26 days ageing. Quantitative trait loci (QTL) mapping for these traits indicated there were 2 QTL (92 cM on BTA 5 and 52 cM on BTA 29) for adjusted shear force of the LD muscle, 3 QTL (96 cM on BTA 5, 36 cM on BTA 18 and 52 cM on BTA 29) for adjusted shear force of the ST muscle, 2 QTL (40 cM on BTA 4 and 0 cM on BTA 13) for ageing rate of the LD muscle and 2 QTL (48 cM on BTA 1 and 44 cM on BTA 19) for ageing rate of the ST muscle. Twelve candidate genes were selected for further study based on their physiological functions and the QTL mapping results from herein and elsewhere. Twenty DNA variants in these candidate genes were chosen for the association studies. The analyses were conducted with and without three known tenderness related gene variants (MSTN F94L, CAPN1-SNP316 and CAPN1-SNP530). Variants in the candidate genes were discovered to be significantly associated with traits related to tenderness, most of which were muscle specific effects. Of note, the effects of CAPN1-SNP316 were muscle specific. The heterozygous genotype (GC) of CAPN1-SNP316 had the opposite effect on LD and ST muscles in that the G allele was dominant for the LD but recessive for the ST. Another variant of large effect, MYO1G-SNP2 (myosin 1G), showed an effect on ageing rate of the LD muscle but not the ST muscle. Importantly, however, the interactions between gene variants frequently explained more of the genetic variation than the individual variants. For example, the interaction between the candidate gene variant SNIP1-SNP3 (Smad nuclear interacting protein 1) and the CAPN1-SNP316 explained more of the variation in the adjusted shear force of the ST muscle than CAPN1-SNP316 alone (9.5% vs. 5.2%). The studies also suggest that tenderness is not always affected by the genes that change the muscle weight or collagen content (eg. insulin-like growth factor 1). In fact, the results indicate that the effect of the myostatin gene on tenderness is not caused by the increased muscle mass or collagen changes associated with the myostatin F94L variant. Instead, most of the effect of myostatin on tenderness may be explained by a change in the muscle fibre types which affects calpain activity.