Global eQTL Mapping Reveals the Complex Genetic Architecture of Transcript-Level Variation in Arabidopsis

Abstract

The genetic architecture of transcript-level variation is largely unknown. The genetic determinants of transcript-level variation were characterized in a recombinant inbred line (RIL) population (n ¼ 211) of Arabidopsis thaliana using whole-genome microarray analysis and expression quantitative trait loci (eQTL) mapping of transcript levels as expression traits (e-traits). Genetic control of transcription was highly complex: one-third of the quantitatively controlled transcripts/e-traits were regulated by cis-eQTL, and many trans-eQTL mapped to hotspots that regulated hundreds to thousands of e-traits. Several thousand eQTL of large phenotypic effect were detected, but almost all (93%) of the 36,871 eQTL were associated with small phenotypic effects (R 2 , 0.3). Many transcripts/e-traits were controlled by multiple eQTL with opposite allelic effects and exhibited higher heritability in the RILs than their parents, suggesting nonadditive genetic variation. To our knowledge, this is the first large-scale global eQTL study in a relatively large plant mapping population. It reveals that the genetic control of transcript level is highly variable and multifaceted and that this complexity may be a general characteristic of eukaryotes.

Genetic Dissection of Leaf Development in Brassica rapa Using a Genetical Genomics Approach

“…Given a particular eQTL region, delimited by a 5-cM interval to each side of the peak, all overlapping pQTL regions were selected (West et al, 2007). The P of two intervals of this length (10 cM) overlapping in a 1,328-cMlong genome (the length of our linkage map) is P ¼ 1 2 1 2 10 1328 2 ¼ 0:015 Sequence data from this article can be found in the Brassica Database (http://brassicadb.org/brad/).…”

Epistasis and Genetic Regulation of Variation in the Arabidopsis Metabolome

“…Eleven regions of the genome were identified as metabolite QTL clusters or hot spots that contained more metabolite QTLs than expected by chance, and five of these clusters were coincident with expression QTL hot spots identified by West et al (2007), suggesting a link with transcript variation. Metabolites were then clustered based on QTL position and allelic effect, thereby connecting the metabolite QTL clusters based on shared regulation of specific metabolites.…”

“…In recent years, the combination of QTL analysis and genomic methods, such as genome-wide gene expression analysis, has allowed greater insights into genetic mechanisms underlying phenotypic variation (e.g., Keurentjes et al, 2007;West et al, 2007). In the issue of The Plant Cell, Rowe et al (pages 1199Rowe et al (pages -1216) combine large-scale metabolomic analysis with QTL analysis of metabolic traits to investigate QTLs that influence the Arabidopsis thaliana metabolome.…”

“…Rowe et al conducted untargeted metabolomic analyses on an Arabidopsis Bayreuth-0 (Bay) 3 Shahdara (Sha) recombinant inbred line (RIL) population described by Loudet et al (2002) and previously used for targeted metabolite QTL and global expression QTL analyses (Loudet et al, 2003;Calenge et al, 2006;Kliebenstein et al, 2006;Wentzell et al, 2007;West et al, 2007). The untargeted metabolomic approach aims to measure as many metabolites as possible, rather than determining a priori what class of compounds or specific compounds will be investigated.…”

Quantitative Variation in Responses to Root Spatial Constraint within Arabidopsis thaliana

“…To begin identifying potential loci that regulate the differential rosette growth response to root constraint, we focused on the Bay and Sha accessions that are the parents of a well-studied RIL population with significant genomic and transcriptomic resources (Loudet et al, 2003a(Loudet et al, , 2003b(Loudet et al, , 2005West et al, 2006West et al, , 2007Bouteillé et al, 2012). We first tested if restricting the roots' lateral growth along the entire pot depth is too severe a constraint to measure variation between the accessions.…”