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ARS Home » Midwest Area » Morris, Minnesota » Soil Management Research » Research » Publications at this Location » Publication #348009
Title: Structural equation models based on multivariate diversity assessment of diploid and tetraploid hulled wheat species

Author
item Jaradat, Abdullah
item Dykes, Linda
item Xu, Steven

Submitted to: Meeting Abstract
Publication Type: Abstract Only
Publication Acceptance Date: 1/17/2018
Publication Date: N/A
Citation: N/A

Interpretive Summary:

Technical Abstract: Hulled wheats are largely untapped genetic resources with >10,000 years of genetic memory and diversity that can be used for wheat quality improvement, development of healthy products, and adaptation to climate change. Multivariate diversity was assessed in the diploid Triticum monococcum L. var monococcum (AA genome) and tetraploid Triticum turgidum L. var. dicoccum (BBAA genome) populations representing landraces and improved cultivars from the primary and secondary centers of diversity of each species. A relational database (273 traits/repeated measurements classified into architectural, agronomic, eco-physiological, phytochemical, ionome, and rheological and textural interrelated modules, was constructed using phenotypic and high throughput data of kernels, flour, dough, bread, seedlings, and mature plants. Most (65%) of twenty principal components derived from these traits discriminated between, and explained larger variance in the BBAA (67.0%) than in the AA (43.7%) genome. Increased ploidy did not cause significant changes in 27% of all traits; while most architectural, agronomic and reproductive traits, except plant height and tillering, increased in magnitude with increased ploidy level. Both ploidy levels had comparable biochemical and ionome profiles; however, polyploidy resulted in smaller total phenolics, Beta-glucan, yellow pigment index, carotenoids, and micronutrient (Fe and Zn) content; while it resulted in better tolerance to salinity at the seedling stage, larger kernel weight, grain and protein yield, gluten index, ash content; and slightly denser and larger loaf volume. We used structural equation modeling to hypothesize a "quality" latent variable and identified causal relationships, tradeoffs, intra- and interrelationships due to direct and indirect, positive or negative correlations between traits within and among the six modules at each ploidy level. A minimum set of species-specific traits was identified and their effects on the "quality" of each species was quantified using a prediction profiling procedure. Adjustments in these traits will be used to optimize "quality" and to design an "ideotype: at each ploidy level. The procedure can be adapted to design hulled wheat species in silico with enhanced resource use efficiency and yield potential; higher tolerance to abiotic and biotic stresses; and improved nutritional profiles.