Composite Interval Mapping

To overcome some of the shortcomings of SIM, composite interval mapping (CIM) was developed. The method was described independently by Zeng (1994) and Jansen and Stam (1994). The basis of this method is an interval test that attempts to separate and isolate individual QTL effects by combining interval mapping with multiple regression. It controls for genetic variation in other regions of the genome, thus reducing background “noise” that can effect QTL detection. To control background variation, the analysis software incorporates into the model 'cofactors', a set of markers that are significantly associated with the trait and may be located anyware in the genome. They are typically identified by forward or backward stepwise regression, with user input to determine the number of cofactors and other characteristics of the analysis.

Improved QTL detection with CIM compared to SIM is illustrated in Figs. 11 and 12. These show the same SIM curves as in Figs. 9 and 10, with the CIM curves overlaid. Note that the CIM curve in Fig. 11 narrows and shifts the position of the putative QTL region.

Fig. 11. LOD curves of simple interval mapping (SIM) and composite interval mapping (CIM) of chromosome 1 of maize for silk maysin concentration.

The CIM curve in Fig. 12 clearly shows two separate QTL peaks, compared to the broad QTL region indicated by the SIM curve. The CIM analysis also resulted in a shift of the most likely QTL positions.

Fig. 12. LOD curves of simple interval mapping (SIM) and composite interval mapping (CIM) of chromosome 6 of maize for silk maysin concentration.

Information obtained from Composite Interval Mapping: The following information is obtained from the CIM method of QTL detection. Many of these are similar to the results described previously for single-factor ANOVA.

  1. Estimate of QTL position, typically tested every 2 cM, but this can be adjusted by the user. Because of the use of cofactors to reduce background noise, QTL positions are estimated more accurately than with SIM.
  2. Measure of statistical significance: LOD score or likelihood ratio
  3. Percent variance explained (%R2)
  4. Source of desirable alleles (Parent A or Parent B)
  5. Estimates of additive and dominance effects

Limitations of Composite Interval Mapping