lme4GS

lme4GS extends the R package lme4 to fit linear mixed models (LMMs) for genomic selection (GS), enabling user-defined covariance structures and bandwidth selection to improve genomic prediction.

Key Features:

• User-Defined Covariance Structures: Allows specification of covariance or correlation structures between individuals or groups of individuals for LMMs used in genomic analyses.
• Bandwidth Selection: Provides functionality for bandwidth selection applicable to smoothing and fitting models with continuous genomic data.
• Genomic Prediction Models: Fits LMMs with various variance-covariance matrices to support implementation of diverse genomic selection models.
• Real-Data Examples: Includes demonstrations and examples using real datasets to illustrate model application in genomic prediction.

Scientific Applications:

• Genomic Selection and Breeding Programs: Enables prediction of genetic values to inform selection decisions in plant and animal breeding programs.
• Prediction of Genetic Traits: Supports genomic prediction of quantitative traits by modeling genetic relationships and variance components.
• Research in Statistical Genetics and GS: Facilitates methodological and applied research by geneticists and bioinformaticians working on genomic selection problems.

Methodology:

Extends lme4 to fit linear mixed models using user-defined covariance/correlation structures and various variance-covariance matrices, includes bandwidth selection for model fitting, and is demonstrated on real datasets.