Targeted improvement of plant-based protein: Genome-wide association mapping of a lentil (Lens culinaris Medik.) diversity panel - Johnson, Nathan; Boatwright, Jon Lucas; Bridges, William; Thavarajah, Pushparajah; Agrawal, Shiv Kumar; Thavarajah, Dil (Wiley Open Access, 2024)
The world is increasingly looking to plant-based sources to meet its protein needs.Multiple factors are driving this progression, ranging from nutritional and ethical con-siderations to climate change and population growth. As a pulse crop, lentil is ideal tohelp meet this change in demand. However, plant-based proteins have limiting aminoacids and lower protein digestibility compared to animal-based proteins. Thisresearch identifies genetic markers that can be used to accelerate breeding of proteinquality traits in lentil to ultimately help meet the rising demand in high-quality plant-based protein and bolster global food and nutritional security.Summary• Lentil (Lens culinaris Medik.) contains 25% high-quality protein in addition to highconcentrations of prebiotic carbohydrates and micronutrients, such as folate, iron,zinc, and selenium. As animal-based protein's economic and environmental costsrise, plant-based protein sources, such as lentil, will become increasingly importantto global food systems. Consequently, evaluating and targeting protein quality traitsfor genomic-assisted breeding is a valuable objective for lentil breeding programs.• A diversity panel of 183 breeding lines was analyzed for protein quality traits, includ-ing amino acids and protein digestibility. Genotyping-by-sequencing (GBS) data wereused to assess population structure and conduct genome-wide association studies(GWAS). Genes in local linkage disequilibrium (LD) with significant single nucleotidepolymorphism (SNP) markers were identified and categorized by homology.• Protein quality traits showed a wide range of variation. Repeatability estimateswere low to moderate across traits. Twelve traits were strongly correlated witheach other (r > .7). Admixture analysis identified six ancestral subpopulations,which also demonstrated clustering in principal component analysis. Ten differenttraits had significant SNP associations; two loci were shared across multiple traits.Twenty-seven candidate genes, including glutathione S-transferase, protease fam-ily, and gibberellin 2-beta-dioxygenase genes, were identified.