Multilocus analysis of SNP and metabolic data within a given pathway

Area of Science:

• Genetics
• Bioinformatics
• Statistical Methodology

Background:

• Complex traits are influenced by multiple genes, posing challenges for geneticists.
• Identifying susceptibility genes for multifactorial diseases and quantitative traits is a key research area.

Purpose of the Study:

• To develop and apply statistical methods for identifying genetic factors influencing complex traits.
• To investigate the association of single nucleotide polymorphisms (SNPs) with metabolic pathway activity.

Main Methods:

• Analysis of categorical SNP data and quantitative metabolic trait data.
• Application of combinatorial partitioning and leave-one-out cross-validation methods.
• Utilized pattern recognition and Monte Carlo simulation for SNP set identification.

Main Results:

• Identified significant SNPs in CYP19, CYP1B1, and CYP1A1 associated with metabolic levels.
• Six SNPs collectively predicted phenotype with 65% accuracy.
• Discovered intragenic and intergenic haplotypes associated with increased metabolic pathway activity.

Conclusions:

• Developed methods to study multiple low-penetrance genetic factors for quantitative traits.
• Preliminary data indicate that genes in common pathways and chromosomal regions, forming haplotypes, contribute to higher pathway activity.