A multiple-trait Bayesian LASSO (MBL) for genome-based analysis and prediction of
quantitative traits is presented and applied to two real data sets. The data-generating model …

The BLR (Bayesian linear regression) package of R implements several Bayesian
regression models for continuous traits. The package was originally developed for …

Motivation: Despite their success in identifying genes that affect complex disease or traits,
current genome-wide association studies (GWASs) based on a single SNP analysis are too …

Abstract The Bayesian LASSO (BL) has been pointed out to be an effective approach to
sparse model representation and successfully applied to quantitative trait loci (QTL) …

Background LASSO is a penalized regression method that facilitates model fitting in
situations where there are as many, or even more explanatory variables than observations …

Abstract Models for genome-wide prediction and association studies usually target a single
phenotypic trait. However, in animal and plant genetics it is common to record information on …

Although genome-wide association studies (GWAS) have proven powerful for
comprehending the genetic architecture of complex traits, they are challenged by a high …

We examined whether or not the predictive ability of genomic best linear unbiased
prediction (GBLUP) could be improved via a resampling method used in machine learning …

Most existing tools for constructing genetic prediction models begin with the assumption that
all genetic variants contribute equally towards the phenotype. However, this represents a …

Background The goal of genome-wide prediction (GWP) is to predict phenotypes based on
marker genotypes, often obtained through single nucleotide polymorphism (SNP) chips. The …