Table 4.
Comparison of the mapping precision between stepwise linear mixed model (StepLMM) and other methods based on QTLMAS16 data with 50 simulated QTL
| Method | Number of false positives | Number of true QTL | Ratioa | ||||||
|---|---|---|---|---|---|---|---|---|---|
| Trait1 | Trait2 | Trait3 | Total | Trait1 | Trait2 | Trait3 | Total | ||
| RR_YDb | 9 | 15 | 5 | 29 | 8 | 6 | 8 | 22 | 0.43 |
| GRAMMARc | 0 | 0 | 0 | 0 | 2 | 3 | 5 | 10 | 1.00 |
| RHM20d | 1 | 0 | 0 | 1 | 6 | 4 | 7 | 17 | 0.94 |
| RF_YDe | 3 | 2 | 0 | 5 | 3 | 3 | 5 | 11 | 0.69 |
| LDLAf | 3 | 3 | 1 | 7 | 6 | 2 | 5 | 13 | 0.65 |
| LAg | 4 | 3 | 1 | 8 | 0 | 1 | 2 | 3 | 0.27 |
| StepLMM | 0 | 0 | 0 | 0 | 5 | 4 | 2 | 11 | 1.00 |
aCalculated as the ratio of the number of detected true QTL to the number of all detected QTL
bRidge regression on actual yield deviations [29]
cGenome-wide rapid association using mixed model and regression [30]
dRegional heritability mapping (20 SNPs) [32]
eRandom forest with yield deviations [39]
fLinkage disequilibrium and linkage analysis [31]
gLinkage analysis [33]