Year: 2026
Type: article
Abstract: Summary The grain quality traits of protein content, grain weight and size are socially and economically important for wheat ( Triticum aestivum ) breeding. Modern wheat germplasm has limited genetic variation for vital traits, including grain quality, underscoring the need to investigate ancestral varieties/landraces for beneficial allelic diversity. Here, we develop a reliable and sensitive single‐seed hyperspectral imaging‐based phenotyping platform to quantify grain protein, weight and size in 827 diverse wheat landraces, achieving improved predictive accuracy ( R 2 = 0.91) vs previous methods. We identified novel genetic variants (or loci) for grain protein content and its heterogeneity between individual grains in a genotype, and grain weight, and validated them through bi‐parental linkage mapping. Many loci we report are rare and absent from modern breeding germplasm. Beneficial haplotypes at these loci improved mean protein content and homogeneity without compromising grain weight; these haplotypes represent valuable genetic variation for agriculture. We identify a stable locus on chromosome 6A contributing to protein heterogeneity within single grains, suggesting homogeneity in protein content could be a selectable trait for breeding. Our findings demonstrate how integration of advanced phenotyping into breeding strategies can harness untapped genetic diversity for grain quality improvement in wheat, providing an opportunity to enhance grain nutritional value.
Source: New Phytologist
DOI: 10.1111/nph.71628
Language: English
Subfield: Plant Science
Domain: Life Sciences
SDG: Zero hunger
Open Access status: hybrid
Estimated APC paid: $5,790
Awards: Australia Partnering Award: Reimagining phenotyping of food commodities at a single grain level, Deciphering the genetic regulation of inflorescence development in wheat, Unlocking the potential of wheat grain heterogeneity using machine vision, Unlocking the potential of wheat grain heterogeneity using machine vision, Unlocking the potential of wheat grain heterogeneity using machine learning-enabled machine vision, BBSRC Institute Strategic Programme: Delivering Sustainable Wheat (DSW) Partner Grant
OpenAlex ID: W7221308379
