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 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.

Continued genetic improvement for milk production has been associated with decreased fertility in US Holstein cattle. A previous study (Ashwell et al., 2004) identified a putative quantitative trait locus (QTL) affecting daughter pregnancy rate at 54 cM on chromosome 18 in one Holstein grandsire family. The goal of this research is to determine the validity of the putative QTL using additional markers and an extended pedigree. Thirteen microsatellite markers located throughout the chromosome were genotyped in 973 animals that were descendants of the original grandsire in which the QTL was identified. Sons of the grandsire as well as six grandson and six great-grandson families of the original grandsire were selected for this study (range of 16 to 169 sons per family). In analysis of the sons using QTL Express, the same putative QTL affecting daughter pregnancy rate was detected and placed at 45 cM. In a joint analysis of thirteen of the largest families, each containing 10 or more sons, a significant QTL for daughter pregnancy rate was detected at 27 cM. QTL affecting daughter pregnancy rate was detected in two additional sub-families (Family II-5 and Family III-2), indicating a putative QTL affecting daughter pregnancy rate is most likely segregating within this pedigree. Across-family analysis also detected putative QTL on chromosome 18 affecting productive life at 35 cM, somatic cell score at 33 cM and percent difficult births at 72 cM. Analysis of individual families identified eight significant putative QTL and six suggestive putative QTL at the chromosome-wise level affecting somatic cell score, productive life, calving ease, percent difficult births, milk yield, fat yield, protein yield and fat percent. A complex pedigree analysis is underway to make full use of statistical power to refine the QTL region affecting fertility. Further verification of the QTL effects identified in this study will allow identification of positional candidate genes to be applied i.

Quantitative trait loci (QTL) mapping in outbred populations faces some challenges unique to the divergent genetic background and complex pedigree relationships. Motivated by a dairy cattle half-sib data set from a grand daughter design, we present in this dissertation a series of endeavors to address various challenges along the analysis flow of QTL mapping. A first step is to infer the haplotypes in sires based on the observed genotypes in sires and his offspring. Our method was shown to outperform peer methods with greater robustness and accuracy yet with fast speed performance. Then in light of adapting the multiple interval mapping method to within-family QTL analysis, we extended the modeling framework by allowing for heteroscedastic residual variances and upgraded the Windows QTL Cartographer accordingly. The advantageous post-analysis result parsing from Windows QTL Cartographer and more importantly, the improved analysis outputs due to more powerful maximum likelihood-based mixture modeling than the least squares regression manifest our efforts in delivering better methodology via practically user friendly software. We further developed a mixed model approach for the purpose of QTL mapping across multiple families that was aimed at modeling QTL effects as both the fixed effect across families and the random effect within families. Our mixed model was shown to encompass similar or higher statistical testing performance on QTL variation than the widely used variance component modeling approach, yet still allowing permutations for obtaining chromosome-wide or genome-wide significance threshold. What's more, the flexibility of our mixed model in constructing alternative hypotheses testing on either fixed or random QTL effects or both was shown to offer interesting insight into the varying sources of signal that would not be unveiled by least squares regression or variance component methods. In concluding our comprehensive approach to QTL linkage mapping in dair.