Single feature polymorphism (SFP)-based selective sweep identification and association mapping of growth-related metabolic traits in <it>Arabidopsis thaliana</it>

• Main Authors: Stitt Mark, Korff Maria V, Sulpice Ronan, Günther Torsten, Witucka-Wall Hanna, Childs Liam H, Walther Dirk, Schmid Karl J, Altmann Thomas
• Format: Article
• Language: English
• Published: BMC 2010-03-01
• Series: BMC Genomics
• Online Access: http://www.biomedcentral.com/1471-2164/11/188

<p>Abstract</p> <p>Background</p> <p>Natural accessions of <it>Arabidopsis thaliana </it>are characterized by a high level of phenotypic variation that can be used to investigate the extent and mode of selection on the primary metabolic traits. A collection of 54 <it>A. thaliana </it>natural accession-derived lines were subjected to deep genotyping through Single Feature Polymorphism (SFP) detection via genomic DNA hybridization to Arabidopsis Tiling 1.0 Arrays for the detection of selective sweeps, and identification of associations between sweep regions and growth-related metabolic traits.</p> <p>Results</p> <p>A total of 1,072,557 high-quality SFPs were detected and indications for 3,943 deletions and 1,007 duplications were obtained. A significantly lower than expected SFP frequency was observed in protein-, rRNA-, and tRNA-coding regions and in non-repetitive intergenic regions, while pseudogenes, transposons, and non-coding RNA genes are enriched with SFPs. Gene families involved in plant defence or in signalling were identified as highly polymorphic, while several other families including transcription factors are depleted of SFPs. 198 significant associations between metabolic genes and 9 metabolic and growth-related phenotypic traits were detected with annotation hinting at the nature of the relationship. Five significant selective sweep regions were also detected of which one associated significantly with a metabolic trait.</p> <p>Conclusions</p> <p>We generated a high density polymorphism map for 54 <it>A. thaliana </it>accessions that highlights the variability of resistance genes across geographic ranges and used it to identify selective sweeps and associations between metabolic genes and metabolic phenotypes. Several associations show a clear biological relationship, while many remain requiring further investigation.</p>