Two studies were conducted on gene mapping analysis. For the first study, genetic simulation experiments were conducted to address the effects of marker density, method of mapping analysis, and gaps in a marker map on the efficiency of QTL detection and the accuracy of QTL parameter estimation. The simulated genome consisted of seven chromosomes with seven or eight segregating QTL affecting the simulated quantitative trait. A set of six randomly segregating QTL outside the test region was consistently used to represent 40% of phenotypic variation. An individual QTL or a linkage block of two QTL on a target chromosome contributed 10% of phenotypic variation. The marker map was either dense (with markers every 4 cM) or sparse (with markers every 20 cM). The gap in the marker map was either 32 cM or 56 cM. Interval mapping and composite interval mapping were used to map QTL on the target chromosome. A dense map provided more power of QTL detection, better accuracy of QTL parameter estimation, and higher false-positive error rates for the target chromosome than a sparse map. Composite interval mapping provided more power of QTL detection, better accuracy of QTL parameter estimation, and lower false-positive error rates than interval mapping. Presence of a large gap in a marker map affected QTL detection and QTL parameter estimation for a QTL inside or near the gap. The use of a dense map with composite interval mapping was the most efficient combination tested in this study. For the second study, a mixed factorial regression model for interval mapping was developed for conducting QTL-by-environment interaction analysis and for providing inferences about QTL that are applicable beyond the environments used in the experiments. Genetic simulation was used to test the model for the power of detecting QTL-by-environment interaction and identifying the types of such interaction as crossover or non-crossover, and for the accuracy of estimating QTL parameters. The model prov

Mather, Diane E. (Supervisor)