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Heritability and quantitative trait loci of composition and structural characteristics in sorghum grain

dataset
posted on 2018-12-24, 15:13 authored by Nikhil Y. Patil, N. Ace Pugh, Robert R. Klein, Hector S. Martinez, Raul S. Martinez, Raul Rodriguez-Herrera, William L. Rooney, Patricia E. Klein

Breeding efforts in cereal crops directed toward developing or improving end-use products of grain require assessment of existing phenotypic variance and an understanding of the genetic control of grain quality traits. To this end, a grain sorghum [Sorghum bicolor (L.) Moench] mapping population consisting of 113 F2:7 recombinant inbred lines (RILs) derived from a cross between Sureño and RTx430 was evaluated in multiple environments for grain composition (fat, fiber, protein, starch) using near-infrared reflectance spectroscopy (NIRS), and size estimates of grain parts (embryo, vitreous endosperm, floury endosperm, kernel area) using an image-based phenotyping software system. Estimates of broad-sense heritability of grain compositional traits ranged from 0.11 to 0.90, whereas those of grain size ranged from 0.16 to 0.72. Composite interval mapping (CIM) was applied to a single nucleotide polymorphism (SNP)-based linkage map to identify marker-trait associations, and through these efforts, a total of 37 quantitative trait loci (QTL) for grain quality were identified across environments. Each QTL explained between 7 and 23% of the phenotypic variation for a given grain trait. Three of the five QTL that colocalized were for traits with significant negative correlation, which included grain protein content that was negatively correlated with grain starch content. In addition, several traits that were positively correlated (e.g. fat and fiber content) also revealed colocalized QTL. Finally, we compared the present study with previous studies identifying grain composition trait loci in an effort to identify genomic regions controlling grain traits across a diversity of environments and sorghum genotypes.

Funding

Financial support from USDA Hatch funds, Texas A&M AgriLife Research, and the USDA-ARS made this work possible .

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