Multiple interval mapping for quantitative trait loci

A new statistical method for mapping quantitative trait loci (QTL), called multiple interval mapping (MIM), is presented. It uses multiple marker inte rvals simultaneously to fit multiple putative QTL directly in the model for mapping QTL. The MIM model is based on Cockerham's model for interpreting genetic parameters and the method of maximum likelihood for estimating gene tic parameters. With the MIM approach, the precision and power of QTL mappi ng could be improved. Also, epistasis between QTL, genotypic values of indi viduals, and heritabilities of quantitative traits can be readily estimated and analyzed. Using the MIM model, a stepwise selection procedure with lik elihood ratio test statistic as a criterion is proposed to identify QTL. Th is MIM method was applied to a mapping data set of radiata pine on three tr aits: brown cone number, tree diameter, and branch quality scores. Based on the MIM result, seven, six, and five QTL were detected for the three trait s, respectively. The detected QTL individually contributed from similar to 1 to 27% of the total genetic variation. Significant epistasis between four pairs of QTL in two traits was detected, and the four pairs of QTL contrib uted similar to 10.38 and 14.14% of the total genetic variation. The asympt otic variances of QTL positions and effects were also provided to construct the confidence intervals. The estimated heritabilities were 0.5606, 0.5226 , and 0.3630 for the three traits, respectively. With the estimated QTL eff ects and positions, the best strategy of marker-assisted selection for trai t improvement for a specific purpose and requirement can be explored. The M IM FORTRAN program is available on the worldwide web (http://www,stat.sinic a.edu.tw/similar to chkao/).