Exploiting Differential Gene Expression and Epistasis to Discover Candidate Genes for Drought-Associated QTLs in Arabidopsis thaliana. Issue 4 (14th April 2015)

• Record Type: Journal Article
• Authors: Lovell, John T.; Mullen, Jack L.; Lowry, David B.; Awole, Kedija; Richards, James H.; Sen, Saunak; Verslues, Paul E.; Juenger, Thomas E.; McKay, John K.
• Is Part Of: The Plant Cell. Volume 27:Issue 4(2015)
• Page Start: 969
• Page End: 983
• Publication Date: 2015-04-14
• DOI: 10.1105/tpc.15.00122
• Languages: English
• ISSNs: 1040-4651

Abstract

Abstract : Analysis of the causal connections between environmental variation and the genotype-phenotype map revealed candidate genes for constitutive and plastic responses to soil moisture reduction in the context of QTL mapping. Abstract: Soil water availability represents one of the most important selective agents for plants in nature and the single greatest abiotic determinant of agricultural productivity, yet the genetic bases of drought acclimation responses remain poorly understood. Here, we developed a systems-genetic approach to characterize quantitative trait loci (QTLs ), physiological traits and genes that affect responses to soil moisture deficit in the TSUxKAS mapping population of Arabidopsis thaliana . To determine the effects of candidate genes underlying QTLs, we analyzed gene expression as a covariate within the QTL model in an effort to mechanistically link markers, RNA expression, and the phenotype. This strategy produced ranked lists of candidate genes for several drought-associated traits, including water use efficiency, growth, abscisic acid concentration (ABA ), and proline concentration. As a proof of concept, we recovered known causal loci for several QTLs . For other traits, including ABA, we identified novel loci not previously associated with drought. Furthermore, we documented natural variation at two key steps in proline metabolism and demonstrated that the mitochondrial genome differentially affects genomic QTLs to influence proline accumulation. These findings demonstrate that linking genome, transcriptome, and phenotype data holds great promise to extend the utility of genetic mapping, even when QTL effects are modest or complex.