Although hybrid crop varieties are among the most popular agricultural innovations, the
rationale for hybrid crop breeding is sometimes misunderstood. Hybrid breeding is slower …

Regression models are a form of supervised learning methods that are important for
machine learning, statistics, and general data science. Despite the fact that classical …

Background Transforming large amounts of genomic data into valuable knowledge for
predicting complex traits has been an important challenge for animal and plant breeders …

The large number of markers in genome-wide prediction demands the use of methods with
regularization and model comparison based on some hold-out test prediction error measure …

Sugarcane is a C4 and agro-industry-based crop with a high potential for biomass
production. It serves as raw material for the production of sugar, ethanol, and electricity …

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

Use of genomic prediction (GP) in tetraploid is becoming more common. Therefore, we think
it is the right time for a comparison of GP models for tetraploid potato. GP models were …

Variation in complex traits is the result of contributions from many loci of small effect. Based
on this principle, genomic prediction methods are used to make predictions of breeding …

Key message Complementing or replacing genetic markers with transcriptomic data and use
of reproducing kernel Hilbert space regression based on Gaussian kernels increases hybrid …

Breeding programs require exhaustive phenotyping of germplasms, which is time-
demanding and expensive. Genomic prediction helps breeders harness the diversity of any …