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Development of a QTL-environment-based predictive model for node addition rate in common bean.

Li Zhang¹, Salvador A Gezan², C Eduardo Vallejos³

¹Agricultural and Biological Engineering Department, University of Florida, Gainesville, FL, 32611, USA.

TAG. Theoretical and Applied Genetics. Theoretische Und Angewandte Genetik

|March 27, 2017

Summary

Understanding genotype by environment interactions is key for common bean breeding. This study identified four quantitative trait loci (QTLs) for node addition rate (NAR) and developed a predictive model incorporating environmental factors like temperature.

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Area of Science:

Plant Genetics
Agronomy
Quantitative Genetics

Background:

Genotype by environment interactions (GEI) are critical for selecting plant genotypes that perform well in specific environments.
Understanding the genetic basis of traits like node addition rate (NAR) is essential for common bean improvement.

Purpose of the Study:

To characterize node addition rate (NAR) in a common bean recombinant inbred line (RIL) population across diverse environments.
To identify quantitative trait loci (QTLs) influencing NAR and investigate their interactions with environmental factors.
To develop a predictive model for NAR based on genetic and environmental factors.

Main Methods:

Multi-environment QTL analysis was performed on 171 RILs of common bean grown at five locations.
QTL mixed site-effect models were used, integrating environmental covariates such as temperature, day length, and solar radiation.
Cross-validation and leave-one-site-out methods were employed to assess QTL stability and model performance.

Main Results:

Four QTLs (Nar1, Nar2, Nar3, Nar4) for NAR were identified, with Nar2 showing significant QTL by environment interactions (QEI) specifically with temperature.
Temperature was identified as the primary environmental factor influencing NAR, while day length and solar radiation had minor effects.
A predictive model incorporating genetic and environmental factors explained 73% of the phenotypic variation in NAR.

Conclusions:

Genetic makeup, environmental factors, and their interactions significantly influence NAR in common beans.
The identified QTLs are stable across different genotype sets, providing a reliable basis for marker-assisted selection.
The developed predictive model offers a valuable tool for selecting common bean genotypes suited for targeted environments, optimizing crop performance.