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Published on: August 16, 2017
Research on multi-trait genome association study method based on Shannon information entropy.
Wanping Lv1, Yiyuan Wang1, Jingyu Wang1
1College of Computer and Information Science, Fujian Agriculture and Forestry University, Fuzhou, 350002, China.
BMC Bioinformatics
|July 7, 2026
Summary
This study introduces a novel multi-trait gene region association analysis method using Shannon information entropy. The approach effectively identifies pleiotropic gene regions by integrating weak genetic signals, improving complex trait analysis.
Area of Science:
- Genetics
- Bioinformatics
- Computational Biology
Background:
- Genetic analysis of complex traits is vital for understanding disease mechanisms and inheritance.
- Traditional single-trait Genome-wide Association Studies (GWAS) often miss synergistic genetic effects across multiple traits.
Purpose of the Study:
- To develop a novel method for analyzing associations between multiple traits and gene regions.
- To enhance the identification of pleiotropic gene regions by integrating genetic information using Shannon entropy.
Main Methods:
- Proposed an Inverse Shannon Entropy-Multi-Trait Association Analysis of Gene Region (InvSE-MTAGR) model.
- Integrated gene region information as genetic entropy via Shannon information entropy.
- Developed the Inverse Partial Shannon Entropy-Multi-Trait Association Analysis of Gene Region (InvPSE-MTAGR) method using partial regression tests.
Main Results:
- The proposed multi-trait method demonstrated effective control of Type I error rates and strong statistical power in simulations.
- Validation on tomato and sorghum datasets successfully identified key gene regions containing candidate genes.
- The method effectively accumulated minor genetic effects to detect pleiotropic signals.
Conclusions:
- Multi-trait analysis offers advantages in detecting weak pleiotropic signals and understanding trait correlations.
- Provides an efficient theoretical framework for analyzing complex multi-trait genetic networks.
- Supports advancements in multi-target collaborative breeding for crops.
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