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. 2023 Jan 25;24(3):2351.
doi: 10.3390/ijms24032351.

White Lupin Drought Tolerance: Genetic Variation, Trait Genetic Architecture, and Genome-Enabled Prediction

Affiliations

White Lupin Drought Tolerance: Genetic Variation, Trait Genetic Architecture, and Genome-Enabled Prediction

Luciano Pecetti et al. Int J Mol Sci. .

Abstract

White lupin is a high-protein crop requiring drought tolerance improvement. This study focused on a genetically-broad population of 138 lines to investigate the phenotypic variation and genotype × environment interaction (GEI) for grain yield and other traits across drought-prone and moisture-favourable managed environments, the trait genetic architecture and relevant genomic regions by a GWAS using 9828 mapped SNP markers, and the predictive ability of genomic selection (GS) models. Water treatments across two late cropping months implied max. available soil water content of 60-80% for favourable conditions and from wilting point to 15% for severe drought. Line yield responses across environments featured a genetic correlation of 0.84. Relatively better line yield under drought was associated with an increased harvest index. Two significant QTLs emerged for yield in each condition that differed across conditions. Line yield under stress displayed an inverse linear relationship with the onset of flowering, confirmed genomically by a common major QTL. An adjusted grain yield computed as deviation from phenology-predicted yield acted as an indicator of intrinsic drought tolerance. On the whole, the yield in both conditions and the adjusted yield were polygenic, heritable, and exploitable by GS with a high predictive ability (0.62-0.78). Our results can support selection for climatically different drought-prone regions.

Keywords: GWAS; drought stress; genomic selection; genotype × environment interaction; grain yield; phenology; plant adaptation.

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Conflict of interest statement

The authors declare no conflict of interest.

Figures

Figure 1
Figure 1
Linear regression of grain yield under drought stress as a function of onset of flowering for 138 white lupin inbred lines.
Figure 2
Figure 2
Assessment of population structure by a discriminant principal components analysis (DPCA). The top panel shows the Bayesian Information Content (BIC) as measured after clustering the samples by the k-means algorithm for increasing levels of K. The bottom panel shows the first two DPCA components for the selected K level. The symbol shapes represent the sweet-seed parent line (Lucky: square; MB-38: circle; Arsenio: triangle; L27PS3: cross); the symbol colours represent the bitter-seed parent accession (Gr56: red; La646: green; La246: blue; LAP123: magenta).
Figure 3
Figure 3
Manhattan plots showing the association scores between 9828 SNPs and grain yield under moisture-favourable and drought stress conditions, onset of flowering (Flowering), and the adjusted grain yield (as deviation from yield under stress expected from linear regression as a function of onset of flowering), for 134 white lupin inbred lines. The dashed and continuous lines represent Bonferroni’s threshold at p < 0.05 and p < 0.01, respectively.

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