Inclusive composite interval mapping
In statistical genetics, inclusive composite interval mapping has been proposed as an approach to QTL (quantitative trait locus) mapping for populations derived from bi-parental crosses. QTL mapping is based on genetic linkage map and phenotypic data to attempt to locate individual genetic factors on chromosomes and to estimate their genetic effects.
Additive and dominance QTL mapping
Two genetic assumptions used in ICIM are the genotypic value of an individual is the summation of effects from all genes affecting the trait of interest; and linked QTL are separated by at least one blank marker interval. Under the two assumptions, they proved that additive effect of the QTL located in a marker interval can be completely absorbed by the regression coefficients of the two flanking markers, while the QTL dominance effect causes marker dominance effects, as well as additive by additive and dominance by dominance interactions between the two flanking markers. By including two multiplication variables between flanking markers, the additive and dominance effects of one QTL can be completely absorbed. As a consequence, an inclusive linear model of phenotype regressing on all genetic markers can be used to fit the positions and additive effects of all QTL in the genome. A two-step strategy was adopted in ICIM for additive and dominance QTL mapping. In the first step, stepwise regression was applied to identify the most significant marker variables in the linear model. In the second step, one-dimensional scanning or interval mapping was conducted for detecting QTL and estimating its additive and dominance effects, based on the phenotypic values adjusted by the regression model in the first step.Digenic epistasis mapping
Under the same assumptions in additive and dominance QTL mapping of ICIM, an additive by additive epistatic effect between two interacting QTL can be completely absorbed by the four marker interaction variables between the two pairs of flanking markers . The coefficients of four marker interactions of two pairs of flanking markers contain the genetic information of the additive by additive epistasis between the two marker intervals. As a consequence, a linear model of phenotype regressing on both markers and marker multiplications can fit the positions and effects of all QTL and their digenic interactions. Similar to the additive QTL mapping of ICIM, the two-step strategy was also adopted in additive by additive epistasis mapping. In the first step, stepwise regression was applied to identify the most significant marker and marker interactions. In the second step, two-dimensional scanning was conducted for detecting additive by additive QTL and estimating the genetic effects, based on the phenotypic values adjusted by the regression model in the first step.Applications in real mapping populations
In a barley doubled haploid population nine additive QTL affecting kernel weight were identified to be distributed on five out of the seven chromosomes, explaining 81% of the phenotypic variance. In this population additive effects have explained most of the phenotypic variance, approximating the estimated heritability in the broad sense, which indicates that most of the genetic variance was caused by additive QTL.Besides that, ICIM has been successfully used in wild and cultivated soybeans in mapping conserved salt tolerance QTL, in rice mapping tiller angle QTL, and grain length QTL, in wheat mapping flour and noodle color components and yellow pigment content, and adult-plant resistance to stripe rust QTL. Some of these detected QTL have been fine mapped.