title: Meta-analysis of metabolome QTLs in Arabidopsis: trying to estimate the network size controlling genetic variation of the metabolome.
abstract: A central goal of systems biology is to develop models that are both predictive and accurately describe the biological system. One complexity to this endeavor is that it is possible to develop models that appear predictive even if they use far fewer components than the biological system itself uses for the same process. This problem also occurs in quantitative genetics where it is often possible to describe the variation in a system using fewer genes than are actually variable due to the complications of linkage between causal polymorphisms and population structure. Thus, there is a crucial need to begin an empirical investigation into the true number of components that are used by biological systems to determine a phenotypic outcome. In this study, we use a meta-analysis of directly comparable metabolomics quantitative studies using quantitative trait locus mapping and genome wide association mapping to show that it is currently not possible to estimate how many genetic loci are truly polymorphic within Arabidopsis thaliana. Our analysis shows that it would require the analysis of at least a 1000 line bi-parental population to begin to estimate how many polymorphic loci control metabolic variation within Arabidopsis. Understanding the base number of loci that are actually involved in determining variation in metabolic systems is fundamental to developing systems models that are truly reflective of how metabolism is modulated within a living organism.
authors.affiliation: Department of Plant Sciences, University of California Davis, Davis, CA USA.
authors.forename: Bindu
authors.initials: B
authors.lastname: Joseph
authors.affiliation: Department of Plant Sciences, University of California Davis, Davis, CA USA.
authors.forename: Susanna
authors.initials: S
authors.lastname: Atwell
authors.affiliation: Department of Plant Sciences, University of California Davis, Davis, CA USA.
authors.forename: Jason A
authors.initials: JA
authors.lastname: Corwin
authors.affiliation: Department of Plant Sciences, University of California Davis, Davis, CA USA.
authors.forename: Baohua
authors.initials: B
authors.lastname: Li
authors.affiliation: Department of Plant Sciences, University of California Davis, Davis, CA USA ; DynaMo Center of Excellence, University of Copenhagen Frederiksberg, Denmark.
authors.forename: Daniel J
authors.initials: DJ
authors.lastname: Kliebenstein
keywords: QTL; RIL population; glucosinolate; metabolite; quantitative genetics