Two genetic assumptions used in ICIM are (1) the genotypic value of an individual is the summation of effects from all genes affecting the trait of interest; and (2) linked QTL are separated by at least one blank marker interval.
By including two multiplication variables between flanking markers, the additive and dominance effects of one QTL can be completely absorbed.
In the first step, stepwise regression was applied to identify the most significant marker variables in the linear model.
When population size is greater than 200, the position estimation of ICIM for QTL explaining more than 5% of the phenotypic variance is unbiased.
When population size is greater than 200, the effect estimation of ICIM for QTL explaining more than 5% of phenotypic variance is unbiased.
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.
; (2) QTL linkage analysis more than twenty mapping populations derived from bi-parental cross, including backcross, double haploid, recombinant inbred lines, etc.
; (3) Power analysis for simulated populations under the genetic models user defined; and (4) QTL mapping for non-idealized chromosome segment substitution lines.