Alfalfa is an important legume in the forage industry. Important traits
related to biomass yield and good nutritional quality have been successfully
improved through phenotypic selection, but genetic gain has not been
achieved due to low trait heritability, genetic complexity, and high
environmental influence. Thus, scientists reports on the utility of genomic
prediction (GP) using genotyping-by-sequencing data to determine the
predictive ability of 25 alfalfa traits related to biomass and nutrition.

The scientists used three regression methods in the GP, including BayesA,
BayesB, and BayesC. Traits with moderate to high prediction accuracies are
deemed useful in future breeding programs. These traits include mineral
element Ca, NDF digestibility, mineral element Mg, plant height in fall,
flowering date, plant regrowth, leaf to stem ratio, plant branch, and
biomass yield.

Other traits with low prediction accuracies are said to be improved using a
bigger reference population, higher SNP marker density, and more powerful
statistical tools.

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