bioRxivPlant BiologyPreprint
Genome-wide association study and genomic prediction of lucerne traits shaping living mulch performance
Zineb El GhazzaliD, Marie PégardiD, Martin GuacanemeiD, Fabien SuraultiD, Irving J. Arcia R.iD, Gaétan LouarniD, Bernadette JulieriD
Abstract
Lucerne is gaining interest as a living mulch in agroecological productions. However, its vigorous growth can lead to competition with cash crops for light and nutrients, necessitating new ideotypes. This study investigated the genetic basis of traits relevant to ideotype breeding: dormancy, spring regrowth, height, growth habit, leaflet size, stem diameter, and plant structure. Individuals from a diversity panel of 27 accessions and a synthetic population were phenotyped in a spaced plant nursery. Over 100,000 SNP markers were used for genotyping. Genome-wide association study (GWAS) and genomic prediction were conducted, considering population structure. Heritability estimates ranged from moderate to high in diversity panel (h{superscript 2} = 0.36-0.70) but were lower in synthetic population (h{superscript 2} = 0.17-0.33), reflecting reduced genetic variance. Trait correlations differed markedly between populations, indicating the possibility of recombining traits to create new ideotypes. GWAS identified a few QTL (r{superscript 2} up to 0.27) for leaflet size, height, growth habit, and plant structure, with candidate genes linked to growth, stress response, and signalling pathways. Genomic prediction was highly accurate in diversity panel, where broad genetic variation allowed reliable estimation of marker effects, with prediction accuracies exceeding 0.8 for heritable traits, including growth habit and leaflet size. In contrast, accuracies were low in synthetic population, reflecting its limited diversity and small size, whether training was based on the synthetic population itself or on the diversity panel. These results highlight the potential to recombine traits and develop lucerne ideotypes using molecular tools such as QTL detection and genomic prediction.
- organism: plant (other)
- assay: whole-genome sequencing
- research focus: GWAS / association mapping
- approach: statistical model
- Citations
- 0
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