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

Genomic selection (GS) facilitates the rapid selection of superior genotypes and accelerates
the breeding cycle. In this review, we discuss the history, principles, and basis of GS and …

Genomic prediction is an effective way to accelerate the rate of agronomic trait improvement
in plants. Traditional methods typically use linear regression models with clear assumptions; …

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 …

Most traits of agronomic importance are quantitative in nature, and genetic markers have
been used for decades to dissect such traits. Recently, genomic selection has earned …

Key message Intensive public sector breeding efforts and public-private partnerships have
led to the increase in genetic gains, and deployment of elite climate-resilient maize cultivars …

Many modern genomic data analyses require implementing regressions where the number
of parameters (p, eg, the number of marker effects) exceeds sample size (n). Implementing …

Genomic selection (GS) is a promising approach exploiting molecular genetic markers to
design novel breeding programs and to develop new markers-based models for genetic …

Association analysis is used to measure relations between markers and quantitative trait loci
(QTL). Their estimation ignores genes with small effects that trigger underpinning …

Genomic Selection (GS) is a new breeding method in which genome-wide markers are used
to predict the breeding value of individuals in a breeding population. GS has been shown to …