Genome‐wide association studies (GWAS) have developed into a powerful and ubiquitous
tool for the investigation of complex traits. In large part, this was fueled by advances in …

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 …

Polygenic risk scores (PRS) have shown promise in predicting human complex traits and
diseases. Here, we present PRS-CS, a polygenic prediction method that infers posterior …

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 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; …

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 …

The success of genome-wide association studies (GWASs) has led to increasing interest in
making predictions of complex trait phenotypes, including disease, from genotype data …

Both linear mixed models (LMMs) and sparse regression models are widely used in
genetics applications, including, recently, polygenic modeling in genome-wide association …

Genomic-enabled prediction is becoming increasingly important in animal and plant
breeding and is also receiving attention in human genetics. Deriving accurate predictions of …