Mining genetic epidemiology data with Bayesian networks application to APOE gene variation and plasma lipid levels

Abstract

There is a critical need for data-mining methods that can identify SNPs that predict among individual variation in a phenotype of interest and reverse-engineer the biological network of relationships between SNPs, phenotypes, and other factors. This problem is both challenging and important in light of the large number of SNPs in many genes of interest and across the human genome. A potentially fruitful form of exploratory data analysis is the Bayesian or Belief network. A Bayesian or Belief network provides an analytic approach for identifying robust predictors of among-individual variation in a disease endpoints or risk factor levels. We have applied Belief networks to SNP variation in the human APOE gene and plasma apolipoprotein E levels from two samples: 702 African-Americans from Jackson, MS, and 854 non-Hispanic whites from Rochester, MN. Twenty variable sites in the APOE gene were genotyped in both samples. In Jackson, MS, SNPs 4036 and 4075 were identified to influence plasma apoE levels. In Rochester, MN, SNPs 3937 and 4075 were identified to influence plasma apoE levels. All three SNPs had been previously implicated in affecting measures of lipid and lipoprotein metabolism. Like all data-mining methods, Belief networks are meant to complement traditional hypothesis-driven methods of data analysis. These results document the utility of a Belief network approach for mining large scale genotype-phenotype association data.

FIG. 1

Learned Belief network relating APOE SNPs to plasma apoE levels in Jackson, MS. Node legends: numbers refer to corresponding SNPs (see Fig. 1 in Nickerson et al. [2000] for an APOE SNP map). APO_E, APO_A, APO_B, TRIG, CHOL, and HDL stand for levels of apolipoproteins E, AI and B, triglycerides, cholesterol and high-density lipoprotein cholesterol, respectively. Line thickness corresponds to the relative edge strength (see Table 1.)

FIG. 2

Belief network learned from the Rochester, MN dataset. All designations are as in Fig. 1. Line thickness corresponds to the relative edge strength (see Table 3.)