Confluence between association mapping and genomic selection to increase stem digestibility in alfalfa

Authors: MEDINA, CESAR - University Of Minnesota; Heuschele, Deborah; ZHAO, DONGYAN - Cornell University; LIN, MENG - Cornell University; BEIL, CRAIG - Cornell University; SHEEHAN, MOIRA - Cornell University; Xu, Zhanyou

Submitted to: Plant and Animal Genome Conference Proceedings
Publication Type: Abstract Only
Publication Acceptance Date: 1/10/2025
Publication Date: 1/15/2025
Citation: Medina, C.M., Heuschele, D.J., Zhao, D., Lin, M., Beil, C.T., Sheehan, M.J., Xu, Z. 2025. Confluence between association mapping and genomic selection to increase stem digestibility in alfalfa. Plant and Animal Genome Conference. San Diego, California, January 10-15, 2025.

Interpretive Summary:

Technical Abstract: Forage quality is one of the most critical traits for improvement in alfalfa, as it directly impacts livestock performance. Although alfalfa forage quality has been widely studied, few efforts have specifically targeted the improvement of the stem component, which comprises approximately 50% of the forage. This study used a stem digestibility panel composed of five populations of alfalfa generated over two cycles of divergent selection based on 16-h and 96-h in vitro neutral detergent fiber digestibility (NDFD) in stems. 500 genotypes were phenotyped for 19 traits related to forage quality across six harvests from 2021 to 2023. Multi-harvest data were modeled to obtain an overall genetic response by genotype by trait. Predicted values were then used as phenotypic response variables for association mapping and genomic selection using a DArTag genotyping panel for alfalfa. Association mapping identified 21 markers linked to 15 traits, with nine markers associated with multiple traits, including two markers linked to more than six traits. Putative candidate genes associated with these loci were also identified, including three transcription factors and two protein kinases. Genomic prediction (GP) was conducted using the genomic best linear unbiased prediction (GBLUP) method, which achieved high accuracies ranging from 0.49 (galactose) to 0.91 (Klason lignin), with a notable accuracy of 0.83 for NDFD96H. Genomic selection employed DArTag markers from 1,002 un-phenotyped plants in the stem digestibility panel to calculate genomic estimated breeding values (GEBVs). The molecular markers shared among traits identified in this study will enhance our understanding of the genetic basis of forage stem quality. Additionally, the high accuracies from GP facilitated the generation of GEBVs for all genotyped plants in this panel, allowing the selection of the top and bottom 10% of genotypes based on NDFD to develop the third selection cycle for improved alfalfa stem digestibility.