Simple Interval Mapping
Recall that with the single-factor ANOVA method, the presence of a QTL is tested only at marker positions, which may be 20 cM or more apart on the chromosome map. QTL positions and effects are therefore determined imprecisely. Simple interval mapping (SIM) is an improvement because it tests for QTL presence every 2 cM between each pair of adjacent markers. Thus, the most likely position of a QTL and the size of its effects are estimated more accurately than with single-factor analysis. At each test position, the SIM method calculates a LOD score, which indicates the probability that a QTL is present at that position. LOD scores are plotted along the chromosome map, and those that exceed a threshold significance level suggest the presence of a QTL in that chromosome region. The most likely QTL position is interpreted to be the point where the peak LOD score occurs.
Note: Some programs report QTL probability as a 'likelihood ratio,' which is equal to the LOD score x 4.6052.
Examples of LOD curves based on the SIM method are shown in Figs. 8, 9, and 10. (See Byrne et al., 1998, for more details on this study). In Fig. 8, the LOD curve is straightforward, showing strong evidence for a QTL at approximately 155 cM on the chromosome map. There is a single peak and no other region of the chromosome exceeds the significance threshold (LOD thresholds are generally in the range of 2.0-3.0, but will differ depending on the genome size, number of markers, and other details of a particular study).
Fig. 9 is more complicated, with three peaks (at about 45, 75, and 105 cM) with LOD scores greater than 2. It is difficult to conclude based on this result how many QTLs are present and where they are located.
A similar result is shown in Fig. 10, with three peaks in the LOD curve, and a very broad region of the chromosome exceeding the threshold. The peak near 10 cM is fairly sharp, but the other two peaks are only vaguely distinguishable. Does this curve indicate 1, 2, or 3 QTLs? It?s hard to say.
Although SIM is an improvement over single-factor ANOVA, there are some situations in which the results are ambiguous, as demonstrated in Figs. 9 and 10. Before considering further improvements to QTL mapping, we list here some of the basic features of the SIM method.
Results Obtained from Simple Interval Mapping The following information is obtained from the SIM method of QTL detection. Many of these are similar to the results described previously for single-factor ANOVA.
- Estimate of QTL position, typically tested every 2 cM, but this can be adjusted by the user.
- Measure of statistical significance: LOD score or likelihood ratio
- Percent variance explained (%R2)
- Source of desirable alleles (Parent A or Parent B)
- Estimates of additive and dominance effects
Limitations of Simple Interval Mapping
- It requires that a linkage map be constructed first, using Mapmaker (link updated 10-25-2013) http://www.broadinstitute.org/ftp/distribution/software/mapmaker3/ or similar software.
- It requires specialized QTL analysis software Links to software.
- The indicated positions of QTLs are sometimes ambiguous, or influenced by other QTLs.
- It can be difficult to separate effects of linked QTLs.