Phenotyping advances for grain size associated traits in Wheat with emphasis on maximizing QTL discovery
Year: 2024
Type: article
Abstract: Grain size is a critical trait in wheat (Triticum aestivum L.) breeding, directly impacting yield, quality, and market value. Complex and highly influenced by genetic and environmental factors, grain size-associated traits such as grain length (GL), grain width (GW), grain area size (GAS), grain perimeter length (GPL), grain length-width ratio (GLWR), grain circularity (CS), and crease depth (CD) present significant challenges for precise phenotyping. This communication highlights advancements in high-throughput phenotyping methods, with a focus on tools like Smart Grain and recent innovations in artificial intelligence (AI) and deep learning that enable multi-trait measurement. High-resolution phenotyping not only enhances the precision of trait measurement but also maximizes the discovery of quantitative trait loci (QTLs) by capturing subtle genetic variations. The integration of AI-driven tools addresses the limitations of traditional methods by improving data processing speed and accuracy, thus facilitating a more comprehensive understanding of genotype-by-environment interactions. Emphasis is placed on how enhanced phenotyping techniques can be leveraged to identify minor but significant QTLs, particularly for complex traits like CD, which play a crucial role in nutrient transport and grain morphology. By integrating multi-dimensional trait data, modern phenotyping approaches support more robust QTL mapping, paving the way for improved wheat varieties with enhanced yield stability and adaptability.
Authors: Anshu Rani, Rahul Kumar, Sachin Kumar
Institution: Chaudhary Charan Singh University
Language: English
Subfield: Plant Science
Domain: Life Sciences
SDG: Zero hunger
Open Access status: green
OpenAlex ID: W6931219035
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