Scientists Determine the Genomic Predictability of 25 Agronomic and Quality Traits in Alfalfa

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.

For more information, read the article in Frontiers in Plant Science.