FnR: R package for computing inbreeding and numerator relationship coefficients.

Background: Inbreeding and relationship coefficients are essential for conservation and breeding programs. Whether dealing with a small conserved population or a large commercial population, monitoring the inbreeding rate and designing mating plans that minimize the inbreeding rate and maximize the effective population size is important. Free, open-source, and efficient software may greatly contribute to conservation and breeding programs and help students and researchers.

Short Communication: Reduced GBLUP equations to core animals in the algorithm for proven and young (APY).

The number of animal genotypes is rapidly increasing, and a major challenge for animal models is inverting the genomic relationship matrix (). Matrix has a limited dimensionality, and the algorithm for proven and young (APY) makes inverting a large possible via the inverse of a block diagonal of with a size equivalent to the dimensionality of . APY divides genotyped animals into core and non-core groups, and breeding values of non-core animals are conditioned on the breeding values of core animals.

Single-step genomic evaluation for growth traits in a Mexican Braunvieh cattle population.

Objective: The objective was to compare (pedigree-based) best linear unbiased prediction (BLUP), genomic BLUP (GBLUP), and single-step GBLUP (ssGBLUP) methods for genomic evaluation of growth traits in a Mexican Braunvieh cattle population.

Methods: Birth (BW), weaning (WW), and yearling weight (YW) data of a Mexican Braunvieh cattle population were analyzed with BLUP, GBLUP, and ssGBLUP methods. These methods are differentiated by the additive genetic relationship matrix included in the model and the animals under evaluation.

A Note on the Conditioning of the Matrix Used in Single-Step GBLUP.

The single-step genomic BLUP (ssGBLUP) is used worldwide for the simultaneous genetic evaluation of genotyped and non-genotyped animals. It is easily extendible to all BLUP models by replacing the pedigree-based additive genetic relationship matrix () with an augmented pedigree-genomic relationship matrix (). Theoretically, does not introduce any artificially inflated variance.

Understanding factors influencing the estimated genetic variance and the distribution of breeding values.

This study investigated the main factors influencing the genetic variance and the variance of breeding values (EBV). The first is the variance of genetic values in the base population, and the latter is the variance of genetic values in the population under evaluation. These variances are important as improper variances can lead to systematic bias.