Genomic selection, the application of genomic prediction (GP) models to select candidate
individuals, has significantly advanced in the past two decades, effectively accelerating …

Genomic data are ubiquitous across disciplines, from agriculture to biodiversity, ecology,
evolution and human health. However, these datasets often contain noise or errors and are …

Since the inception of the theory and conceptual framework of genomic selection (GS),
extensive research has been done on evaluating its efficiency for utilization in crop …

Plant genome sequences catalogue genes and the genetic elements that regulate their
expression. Such inventories further research aims as diverse as map** the molecular …

Genomic selection (GS) offers a promising opportunity for selecting more efficient animals to
use consumed energy for maintenance and growth functions, impacting profitability and …

Abstract Key message Maximizing CDmean and Avg_GRM_self were the best criteria for
training set optimization. A training set size of 50–55%(targeted) or 65–85%(untargeted) is …

We evaluated the performances of three BLUP and five Bayesian methods for genomic
prediction by using nine actual and 54 simulated datasets. The genomic prediction accuracy …

Grafting typically offers a shortcut to breed tree orchards throughout a multidimensional
space of traits. Despite an overwhelming spectrum of rootstock-mediated effects on scion …

Genomic selection predicts the genomic estimated breeding values (GEBVs) of individuals
not previously phenotyped. Several studies have investigated the accuracy of genomic …

Grafting induces precocity and maintains clonal integrity in fruit tree crops. However, the
complex rootstock× scion interaction often precludes understanding how the tree phenotype …