An automated root phenotype platform enables nondestructive high-throughput root system architecture dissection in...

Area of Science:

• Plant Science
• Genetics
• Agricultural Engineering

Background:

• Root system architecture (RSA) is crucial for plant growth and yield, but its underground nature complicates analysis.
• Efficient and non-destructive methods are needed for large-scale RSA characterization.

Purpose of the Study:

• To develop an automated, high-throughput phenotyping platform for wheat RSA.
• To identify genetic loci and candidate genes controlling root traits.
• To explore the relationship between RSA and yield for breeding applications.

Main Methods:

• Development of an automated, non-destructive, high-throughput root phenotyping platform (Root-HTP) and data processing pipeline.
• In situ phenotyping to extract 47 RSA traits across all wheat developmental stages.
• Genome-wide association study (GWAS) using root and yield data from 155 wheat accessions.

Main Results:

• The Root-HTP system successfully characterized wheat RSA, extracting 47 traits, including novel ones.
• GWAS identified 2,650 SNPs and 233 QTLs associated with RSA, including the candidate gene TaMYB93.
• Twenty root-related QTLs were linked to yield traits, and a predictive model for wheat yield based on RSA was developed.

Conclusions:

• The developed Root-HTP platform enables efficient, large-scale wheat RSA analysis.
• Genetic insights into wheat RSA were gained, identifying key genes and QTLs.
• RSA ideotype-based breeding and yield prediction are supported by these findings.