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Network Quantitative Trait Loci Mapping of Circadian Clock Outputs Identifies Metabolic Pathway-to-Clock Linkages in Arabidopsis
Author(s) -
Rachel E. Kerwin,
José M. JiménezGómez,
Daniel Fulop,
Stacey L. Harmer,
Julin Maloof,
Daniel J. Kliebenstein
Publication year - 2011
Publication title -
the plant cell
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 5.324
H-Index - 341
eISSN - 1532-298X
pISSN - 1040-4651
DOI - 10.1105/tpc.110.082065
Subject(s) - biology , quantitative trait locus , circadian clock , circadian rhythm , arabidopsis , genetics , arabidopsis thaliana , function (biology) , computational biology , population , trait , quantitative genetics , gene , genetic variation , mutant , neuroscience , computer science , programming language , demography , sociology
Modern systems biology permits the study of complex networks, such as circadian clocks, and the use of complex methodologies, such as quantitative genetics. However, it is difficult to combine these approaches due to factorial expansion in experiments when networks are examined using complex methods. We developed a genomic quantitative genetic approach to overcome this problem, allowing us to examine the function(s) of the plant circadian clock in different populations derived from natural accessions. Using existing microarray data, we defined 24 circadian time phase groups (i.e., groups of genes with peak phases of expression at particular times of day). These groups were used to examine natural variation in circadian clock function using existing single time point microarray experiments from a recombinant inbred line population. We identified naturally variable loci that altered circadian clock outputs and linked these circadian quantitative trait loci to preexisting metabolomics quantitative trait loci, thereby identifying possible links between clock function and metabolism. Using single-gene isogenic lines, we found that circadian clock output was altered by natural variation in Arabidopsis thaliana secondary metabolism. Specifically, genetic manipulation of a secondary metabolic enzyme led to altered free-running rhythms. This represents a unique and valuable approach to the study of complex networks using quantitative genetics.

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