However, the difference between models is expected to be much smaller under more complex genetic architectures and, perhaps more importantly, when the marker panel does not contain markers in tight LD with QTL, e.g., Wimmer et al. (2013).
Due to this interactive connection between, or hybrid use of, EMICs and GCMs/ESMs, complementary relationships between these models are expected in the future.
It is then crucial to describe precisely the relation between these two kind of models, especially close to discontinuities, where the difference between the models are expected to be the deepest.
By accounting for changes in gene regulations between comparable conditions, graphical statistical models are expected to improve prediction precision.
Since computational models, which is the basis for most of the ToxoDB gene models, are based on the prediction of coding sequences (CDS) while transcript-based models rely on the whole RNA, variations in UTRs between computational and RNA-seq predicted gene models are expected to dominate.
As a consequence, with increasing distance between breeds or lines, the differential shrinkage models are expected to have a higher predictive ability than their GBLUP type counterparts, which is supported by a few empirical studies that reported slightly higher accuracies for differential shrinkage models applied to multi-breed training datasets [ 13, 14].
Just because three new iPhone models are expected in 2017, doesn't mean there will be parity between them.
Models are expected to be decorated in June.
