Patricio R. Munoz

TL;DR: Four different original methods of genomic selection that differ with respect to assumptions regarding distribution of marker effects are presented, including ridge regression–best linear unbiased prediction (RR–BLUP), Bayes A, (iii) Bayes Cπ, and (iv) Bayesian LASSO, which suggest that alternative approaches to genomic selection prediction models may perform differently for traits with distinct genetic properties.

TL;DR: The results demonstrate the feasibility and remarkable gain that can be achieved by incorporating genomic selection in breeding programs, as long as models are used at the relevant selection age and within the breeding zone in which they were estimated.

TL;DR: This work evaluates height data from a multifamily population of the tree species Pinus taeda with a systematic series of models accounting for additive, dominance, and first-order epistatic interactions, showing that the results suggest that the additive and nonadditive components of genetic variance are similar in magnitude.

TL;DR: An R package for autopolyploids to construct the relationship matrix is developed and the level and effect of double reduction in blueberry is estimated.

TL;DR: It is concluded that genome-wide prediction is feasible in potato and that it will improve selection for breeding value given the substantial amount of nonadditive genetic variance in elite germplasm.