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Conceptually, this leads to the hypothesis that combining gene expression measurements over group of genes that fall within common pathways will be more effective means of marker identification.
When genomic and environmental covariate data are available, G×E can be modeled explicitly by means of marker × environment interactions (M×E).
In connection with this, many researchers have suggested a more effective and robust means of marker identification which combines gene expression measurements over functional or otherwise naturally defined sets of genes.
When using complete pooling, GEBVs of all individuals in the test set were predicted from the posterior means of marker effects u k estimated from the joint data set with model (4).
The statistical equating used in Part 1 and Part 2 is what Kolen [ 26] describes as a, "non-equivalent groups design", groups at different diets being allowed to differ in overall ability, with differences being estimated by means of marker, anchor or common items.
Two limitations of this approach are that it does not account for epistasis effects (interaction between markers) and adjustments are needed to control the false positive rate due to the multiple testing problem The associated markers can be used to accelerate the genetic progress by means of Marker Assisted Selection (MAS), if the total variance explained by significant markers is high [ 23].
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In cases where a QTL has been fine-mapped or the causal gene(s) have been identified, the problem of linkage drag can be overcome by means of marker-assisted selection of recombinants between the target gene or QTL and nearby unfavorable genes (Fukuoka et al. 2009>).
Genomic selection (GS) as a means of marker-assisted improvement was introduced by Meuwissen et al. [ 1] and has been implemented for dairy cattle [ 2], among other species.
By means of marker-assisted backcrossing, we have produced an introgression library using the extremely early-flowering maize (Zea mays L). variety Gaspé Flint and the elite line B73 as donor and recipient genotypes, respectively, and utilized this collection to investigate the genetic basis of flowering time and related traits of adaptive and agronomic importance in maize.
It can be shown that the posterior mean of marker effects is the best linear unbiased predictor (BLUP) of marker effects, so Bayesian ridge regression is often referred to as RR-BLUP (de los Campos et al. 2012).
Posterior mean residual and additive genetic variances and posterior mean of marker-based heritability were reported for each trait in each population.
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