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Related Experiment Video

Updated: Sep 18, 2025

High-throughput, Microscale Protocol for the Analysis of Processing Parameters and Nutritional Qualities in Maize Zea mays L.

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Conditional QTL Analysis and Fine Mapping for Thousand-Kernel Weight in Common Wheat.

Haoru Guo¹, Wei Liu¹, Geling Yan¹

¹Yantai Key Laboratory of Molecular Breeding for High-Yield and Stress-Resistant Crops and Efficient Cultivation, Modern Seed Industry and Green Planting & Breeding Research Center, College of Horticulture, Ludong University, Yantai 264025, China.

Plants (Basel, Switzerland)

|June 27, 2025

Summary

This study mapped quantitative trait loci (QTLs) for thousand-kernel weight (TKW) and related traits in a RIL population. Kernel length was identified as the primary contributor to increased TKW.

Keywords:

conditional QTL fine mapping thousand-kernel weight unconditional QTL wheat

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Last Updated: Sep 18, 2025

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Untargeted Liquid Chromatography-Mass Spectrometry-Based Metabolomics Analysis of Wheat Grain

Untargeted Liquid Chromatography-Mass Spectrometry-Based Metabolomics Analysis of Wheat Grain

Published on: March 13, 2020

Area of Science:

Plant genetics
Quantitative genetics
Crop improvement

Background:

Thousand-kernel weight (TKW) is a crucial yield component in crops.
Understanding the genetic basis of TKW and its relationship with kernel size traits is vital for breeding programs.
Previous studies have identified quantitative trait loci (QTLs) for TKW, but their individual effects and interactions with kernel dimensions require further elucidation.

Purpose of the Study:

To dissect the genetic architecture of thousand-kernel weight (TKW) by analyzing its relationship with kernel length (KL), kernel width (KW), and kernel diameter ratio (KDR) at the individual QTL level.
To identify both unconditional and conditional QTLs influencing TKW and related traits.
To fine-map a major-effect TKW QTL and determine the primary genetic driver of TKW variation.

Main Methods:

Utilized a recombinant inbred line (RIL) population for unconditional and conditional QTL analysis of TKW.
Employed a simplified physical map for QTL identification and analysis.
Conducted fine mapping of a major TKW QTL using near-isogenic lines (NILs).

Main Results:

Identified 37 unconditional and 34 conditional QTLs for TKW and related traits.
Detected 6 QTLs with independent effects and 18 QTLs with common influences from multiple kernel traits.
Discovered 26 pairs of epistatically interacting QTLs and fine-mapped a major TKW QTL (QTkw1B) to a specific interval on chromosome 1B.
Conditional and NIL-based analyses confirmed kernel length as the main contributor to TKW increase.

Conclusions:

The combined QTL mapping approach provides a deeper understanding of the genetic interrelationships between TKW and kernel size traits.
Kernel length plays a significant role in determining thousand-kernel weight.
The identified QTLs and fine-mapped region offer a theoretical foundation for future candidate gene discovery and marker-assisted breeding for improved TKW.