Sentence examples for marker effect estimates from inspiring English sources
The aim of GS is to combine whole-genome molecular markers and phenotypes in a training population to predict genetic values of future individuals in a test population for selection and no significant test is required, thus avoiding biases in marker effect estimates as well as accelerating the breeding cycle (Desta and Ortiz 2014).
In practice, these assumptions of a prior probability distribution are leveraged to limit large fluctuations in marker effect estimates, inducing shrinkage estimates of marker effects.
Because marker effect estimates based on complete cases are unbiased under the MCAR assumption, (CC) analyses served as the reference to assess consistency of imputation-based tests.
A modification of the expectation-maximization algorithm that yields heteroscedastic marker variances (i.e., RMLV) resulted in the most accurate marker effect estimates.
This value τ followed an exponential distribution Exp, where λ is a regularization parameter for the shrinkage of marker effect estimates.
In temperate environments, only one relevant phenotypic measurement can be made per year, meaning that marker effect estimates applied to progeny selections need to be robust across generations of recombination to maximize genetic gain per unit time.
It can be seen that even under the BLUP genomic model assuming equal variance for all markers, effect estimates can vary greatly between markers, and even more when new genotyped animals are added to the reference population.
Then, selection candidates that are only genotyped get their genomic estimated breeding values (GEBVs) by adding up all the marker effects estimated from the training population.
The simulated (true) QTL effects and the marker effects estimated from RRBLUP and BayesB from one random replicate of the standard scenario are shown in Figure 3.
GS_POP = genomic selection with marker effects estimated population wide and no pre-selection.
Partial pooling thus strikes a middle ground between no pooling (specific marker effects estimated from data of the specific population only) and complete pooling (common marker effects estimated from pooled training sets).
