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For 90 SNP markers the accuracy of individual assignment increased to 98.2% (438).
However, due to the low density of these markers, the accuracy of QTL location has generally been poor.
Using markers that were in common to both methods (755 markers), the accuracy of computer scoring was determined.
It offers many advantages, including the availability of a huge number of candidate markers, the accuracy of the data, and the cost-effectiveness of the experiments.
Genome-wide breeding values were estimated by summing the effects of the markers: The accuracy of selection (acc) was calculated as the correlation between true and estimated breeding values.
With the accumulation of genomic information, such as nucleotide sequences and DNA markers, the accuracy of genomic comparisons will improve and knowledge may be transferred more efficiently from model systems to soybean.
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However, using only the QTL-linked markers the accuracies have increased for all five models (Table 1).
The haplotype model produced slightly better results in low marker density situations, but with dense markers the accuracies from the allelic and the haplotype model were very similar.
As a diagnostic marker, the accuracy was 67.6% from whole brain structural neuroanatomy (64.9% patients with depression (sensitivity) and 70.3% healthy individuals (specificity), p = 0.027).
In these cases the sensors were used only for the detection of the edges of the markers, thus, the accuracy of the laser sensors and the planarity of the robot did not affect the results.
In our phantom study, the use of 9 markers improved the accuracy of translations in all 3 axes.
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