A multiple coefficient of determination-based method for parsing SNPs that correlate with mRNA expression

Area of Science:

• Genetics
• Bioinformatics
• Computational Biology

Background:

• Single nucleotide polymorphisms (SNPs) are crucial in understanding gene regulation.
• Identifying functional variants within complex genomic regions remains challenging.
• Existing methods may not fully capture the combinatorial effects of multiple regulatory variants.

Purpose of the Study:

• To develop a novel method for parsing single nucleotide polymorphisms (SNPs) into functionally relevant groups.
• To identify sets of index SNPs (iSNPs) that effectively represent underlying regulatory variants.
• To analyze the genetic basis of mRNA expression and gene regulation.

Main Methods:

• Utilized a matrix equation framework to calculate multiple coefficient of determination (R²_M) values.
• Employed exhaustive testing of 1-4 candidate index SNPs (iSNPs) as proxies for regulatory variants.
• Calculated pairwise r² linkage disequilibrium (LD) coefficients to parse SNPs into semi-independent families.
• Analyzed mRNA expression and genotype data from public databases (GEO, dbGAP).

Main Results:

• Successfully parsed SNPs into semi-independent families based on their regulatory potential.
• Demonstrated the method's utility in analyzing experimental mRNA expression and genotype data.
• Identified iSNP models that best explain the correlation between genotypes and mRNA expression.

Conclusions:

• The novel R²_M-based method provides a robust approach for analyzing complex genetic architectures of gene expression.
• This method facilitates the identification of functional variants influencing transcription.
• The approach is widely applicable for dissecting the genetic basis of mRNA expression and visualizing variant contributions to gene regulation.