Single-nucleotide polymorphism-gene intermixed networking reveals co-linkers connected to multiple gene expression...

Area of Science:

• Genetics
• Genomics
• Bioinformatics

Background:

• Gene expression profiles and single-nucleotide polymorphism (SNP) profiles are key data types in modern genetic analysis.
• Genetical genomics approaches can integrate these data to explore relationships among genes.

Purpose of the Study:

• To identify relationships among genes co-linked by common SNPs by integrating gene expression and SNP data.
• To investigate co-expression patterns in co-linked genes and construct gene networks.

Main Methods:

• Gene expression profiles were utilized as expression traits.
• Gene-SNP relationships were established using Haseman-Elston sib-pair regression.
• A gene-SNP intermixed network and a gene-gene network were constructed.

Main Results:

• Co-expressions among co-linked genes were significantly higher when linked to more than six SNPs.
• Genes sharing more SNPs exhibited stronger correlations.
• The gene-gene network weighted edges by the number of shared SNP co-linkers.

Conclusions:

• Integrating gene expression and SNP data is effective for identifying gene relationships and co-expression patterns.
• The number of SNP connections influences the strength of gene co-expression.
• Network construction based on SNP co-linkers provides insights into gene interactions.