Professional genetic analysis software can form A matrix from pedigree data or G matrix from marker data using in the variance component estimation and BLUP of random effects. Current progeny test datasets usually have pedigree data, phenotypic data and marker data. The amount of additive variance and heritability have a great influence on the genetic gain. In a given population, an additive effect could be stably inherited from the parents to the offspring, so the ratio of additive genetic variance to phenotypic variance is defined as the narrow heritability (h2) of a target trait. In the analysis of progeny test data, breeders typically focus on the additive effects. Progeny tests are widely designed in almost all genetic improvement projects in plants and animals. Nowadays, most software uses the Restricted Maximum Likelihood (REML) method to estimate the variance components of random effects, and then estimate the fixed effects and predict the random effects. Therefore, mixed linear models are well suited for genetic data analysis. By contrast, breeders are less interested in site-specific effects or experimental replication within the site, which are generally treated as fixed effects. Breeders are often interested in predicting the future performance of a particular genotype of animal or crop in an environment, and treat the underlying genetic factors affecting the target trait as random effects. Mixed linear models are linear models with a combination of fixed and random effects to explain the degree of variation in interest traits, such as milk yield in cows or volume growth in forest trees. Mixed linear models are widely used in the analysis in the progeny test data of plants and animals. #ASREML R FREE#The AFEchidna package is developed to expand free genetic assessment software with the expectation that its efficiency could be close to the commercial software. The AFEchidna package is free, please email us ( ) to get a copy if reader is interested for it. The mixed linear models are conveniently implemented for users through the AFEchidna package to solve variance components, genetic parameters and the BLUP values of random effects, and the batch analysis of multiple traits, multiple variance structures and multiple genetic parameters can be also performed, as well as comparison between different models and genomic BLUP analysis. Therefore, this study aims to develop an R package named AFEchidna based on Echidna software. Although there is free software such as Echidna or the R package sommer, the Echidna syntax is complex and the R package functionality is limited. The current pioneer software for genetic assessment is ASReml, but it is commercial and expensive. Progeny tests play important roles in plant and animal breeding programs, and mixed linear models are usually performed to estimate variance components of random effects, estimate the fixed effects (Best Linear Unbiased Estimates, BLUEs) and predict the random effects (Best Linear Unbiased Predictions, BLUPs) via restricted maximum likehood (REML) methods in progeny test datasets.
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