Title: "Global transcriptome variation in Arabidopsis thaliana"
Speaker: Dina St. Clair, Department of Plant Science, UC Davis
Place: Krannert (KRAN) G016; October 31, 2006, Tuesday, 4:30pm


The genetic architecture of transcript level variation is largely unknown. We characterized the genetic determinants of transcript level variation in a Bay-0 x Shahdara recombinant inbred line (RIL) population of Arabidopsis thaliana. Transcript level variation in 211 RILs subjected to a replicated experiment was measured using whole genome Affymetrix ATH1 microarrays and used as expression quantitative traits (e-traits) to map eQTLs using a framework map of single feature polymorphism (SFP) markers. Genetic control of transcription was highly complex: one-third of the quantitatively controlled transcripts were regulated by cis-eQTLs and many trans-eQTLs mapped to 'hotspots' that regulated hundreds to thousands of transcripts. Single eQTLs were detected for 69% of the 22,746 e-traits. Many transcripts were controlled by multiple eQTLs with opposite allelic effects and transgressive segregation was evident.

To examine the genomic distribution of the determinants of transcript level variation, we surveyed the transcriptome of seven diverse Arabidopsis thaliana accessions with Affymetrix ATH1 microarrays. The accessions were subjected to a replicated factorial experiment in the presence and absence of exogenous salicylic acid. These accessions encompassed ~80% of the moderate-to-high frequency nucleotide polymorphisms in Arabidopsis. Between any pair of accessions, we detected on average 2234 genes that were significantly differentially expressed. Sequence diversity exhibited a significant positive association with diversity in gene expression.

The relationship between variation in the transcriptome with downstream phenotypic traits was explored using 20 a priori-defined gene networks. Significant variation between Bay-0 and Shahdara was detected for 8 of 20 gene networks. In the Bay-0 x Sha RIL population, we detected significant eQTLs controlling network responses for 18 of the 20 gene networks. These network eQTLs control genes sharing a common biological function. By mapping QTLs for specific traits in the same population used for global gene expression analysis, the genetic architecture of eQTLs controlling the transcripts can be compared to the resulting measurable phenotype.

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