A Unifying Dynamic Framework for Mapping Metabolic Genes

• Activity Number: 468
• Type: Contributed
• Date/Time: Wednesday, August 1, 2012 : 8:30 AM to 10:20 AM
• Sponsor: Biometrics Section
• Author(s): Guodong Liu*+ and Lan Kong and Zhong Wang and Rongling Wu
• Companies: Penn State College of Medicine and Penn State College of Medicine and Penn State University and Penn State University
• Keywords: Quantitative Trait Loci ; metabolic process ; differential equations ; dynamic framework

Abstract

The formation of any complex phenotype involves a web of metabolic pathways. Traditional approaches for mapping quantitative trait loci (QTLs) are based on a direct association analysis between DNA polymorphic genotypes and end-point phenotypes. We propose a new dynamic framework for mapping metabolic QTLs (mQTLs) responsible for phenotypic formation. By treating metabolic pathways as a biological system, the new framework integrates robust differential equations into a statistical mixture model for QTL mapping. Since the mathematical parameters that define the emergent properties of the metabolic system can be estimated and tested for different mQTL genotypes, the framework allows the dynamic pattern of genetic effects to be quantified on metabolic capacity and efficacy across a time-space scale. Based on a recent study of glycolysis in Saccharomyces cerevisiae, we design simulation studies to investigate the statistical properties of the framework and validate its usefulness and utilization in practice. The framework can be generalized to mapping QTLs for any other dynamic systems and may stimulate pharmacogenetic research toward personalized drug and treatment intervention.