Analyzing metabolomics data for association with genotypes using two-component Gaussian mixture distributions.

ABSTRACT: Standard approaches to evaluate the impact of single nucleotide polymorphisms (SNP) on quantitative phenotypes use linear models. However, these normal-based approaches may not optimally model phenotypes which are better represented by Gaussian mixture distributions (e.g., some metabolomics data). We develop a likelihood ratio test on the mixing proportions of two-component Gaussian mixture distributions and consider more restrictive models to increase power in light of a priori biological knowledge. Data were simulated to validate the improved power of the likelihood ratio test and the restricted likelihood ratio test over a linear model and a log transformed linear model. Then, using real data from the Framingham Heart Study, we analyzed 20,315 SNPs on chromosome 11, demonstrating that t

SUBMITTER: Westra J 

PROVIDER: S-EPMC5757879 | biostudies-literature | 2018

REPOSITORIES: biostudies-literature