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 …

The first paradigm of plant breeding involves direct selection-based phenotypic observation,
followed by predictive breeding using statistical models for quantitative traits constructed …

Genome-wide association analysis of cohorts with thousands of phenotypes is
computationally expensive, particularly when accounting for sample relatedness or …

Linear mixed models are a powerful statistical tool for identifying genetic associations and
avoiding confounding. However, existing methods are computationally intractable in large …

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 …

Signals of local adaptation have been found in many plants and animals, highlighting the
heterogeneity in the distribution of adaptive genetic variation throughout species ranges. In …

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

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 …

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

Improper use of water resources in irrigation that contain a significant amount of salts, faulty
agronomic practices such as improper fertilization, climate change etc. are gradually …