Genomic prediction is a statistical method to predict phenotypes of polygenic traits using
high-throughput genomic data. Most diseases and behaviors in humans and animals are …

Genomic prediction exploits single nucleotide polymorphisms (SNPs) across the whole
genome for predicting genetic merit of selection candidates. In most models for genomic …

Growth of artificial intelligence and machine learning (ML) methodology has been explosive
in recent years. In this class of procedures, computers get knowledge from sets of …

Prediction of phenotypes from genotypes is an important objective to fulfill the promises of
genomics, precision medicine and agriculture. Although it's now possible to account for the …

Background Genomic prediction of breeding values from dense single nucleotide
polymorphisms (SNP) genotypes is used for livestock and crop breeding, and can also be …

Human diseases and agricultural traits can be predicted by modeling a genetic random
polygenic effect in linear mixed models. To estimate variance components and predict …

Despite important advances from Genome Wide Association Studies (GWAS), for most
complex human traits and diseases, a sizable proportion of genetic variance remains …

Because of the increased availability of genome-wide sets of molecular markers along with
reduced cost of genotyping large samples of individuals, genomic estimated breeding …

Whole-genome prediction (WGP) models that use single-nucleotide polymorphism marker
information to predict genetic merit of animals and plants typically assume homogeneous …

The increasing amount of available biological information on the markers can be used to
inform the models applied for genomic selection to improve predictions. The objective of this …