Simulation Research on the Methods of Multi-Gene Region Association Analysis Based on a Functional Linear Model

Area of Science:

• Genetics
• Statistical genetics
• Bioinformatics

Background:

• Complex traits are influenced by multiple interacting gene loci, necessitating advanced analytical methods.
• Genome-wide association studies (GWAS) are crucial for identifying genetic variants linked to complex traits.
• Functional linear models are underutilized in multi-gene region association analysis.

Purpose of the Study:

• To propose and evaluate a novel functional multi-gene region association analysis test method.
• To compare the performance of different multi-gene region association strategies using functional linear models.
• To enhance the understanding of genetic mechanisms underlying complex traits.

Main Methods:

• Development of a functional multi-gene region association analysis test based on functional linear models.
• Computer simulations were conducted to assess performance under various scenarios.
• Comparison of common multi-gene region, multi-gene region weighted, and multi-gene region loci weighted methods.

Main Results:

• The proposed functional multi-gene region method demonstrated higher statistical power than single-gene region analysis.
• The multi-gene region loci weighted method outperformed common and weighted multi-gene region approaches.
• The Step method and Multi-gene region loci weighted Step methods showed the best overall performance for multi-gene regions.

Conclusions:

• Functional multi-gene region association analysis provides a powerful framework for studying complex traits.
• The multi-gene region loci weighted method is a promising approach for multi-gene association studies.
• This research offers a feasible method for investigating the genetic architecture of complex traits influenced by multiple genes.