Identification of QTL-by-environment interaction by controlling polygenic background effect.

Area of Science:

• Genetics
• Quantitative Genetics
• Bioinformatics

Background:

• Detecting quantitative trait loci (QTL) by environment (Q × E) interactions is challenging due to difficulties in controlling genomic background.
• Existing methods like meta-analysis and inclusive composite interval mapping have limitations in power and accuracy.

Purpose of the Study:

• To develop and validate a linear mixed model for robust detection of Q × E interactions.
• To improve the analysis of complex traits across multiple environments.

Main Methods:

• Proposed a linear mixed model incorporating two kinship matrices to control for main and interaction polygenic effects.
• Simulated data to compare the proposed model with existing methods.
• Applied the model to analyze agronomic traits in rice and lodging in barley across multiple environments.

Main Results:

• The proposed model demonstrated higher statistical power than meta-analysis and inclusive composite interval mapping in simulations.
• Identified a significant Q × E interaction on chromosome 7 in rice affecting grain number, yield, and 1000-grain weight, near genes PROG1 and Ghd7.
• Detected six regions with Q × E interactions for barley lodging that overlapped with previously identified single nucleotide polymorphisms (SNPs).

Conclusions:

• The developed linear mixed model is a powerful and robust tool for detecting Q × E interactions.
• The model provides valuable insights into the genetic architecture of complex traits in multi-environment studies.
• This approach advances genetic analysis in plant breeding and agricultural research.