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Browsing by Subject "arabidopsis"

Now showing 1 - 2 of 2
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    Effects of Gibberellins on Nectar Production in Arabidopsis thaliana
    (2015-12) Wiesen, Lisa
    Gibberellins (GA) are well known for their roles in regulating stem elongation and seed germination, but less understood is the role of GA in regulating floral maturation. We recently identified GA 2-OXIDASE 6 (GA2OX6, At1g02400) as being highly expressed in the actively secreting nectaries of Arabidopsis thaliana, but at low levels in other tissues. GA2OX6 was previously demonstrated to inactivate bioactive GA. Multiple independent ga2ox6 mutants displayed decreased nectar production, which suggests that elevated levels of active GA negatively regulate nectar production. Similarly, spindly (spy) mutants, which also have an increased GA signaling response, displayed decreased nectar production, further supporting the hypothesis that GA negatively regulates nectar production. Wild-type flowers also displayed an intense auxin response in actively secreting nectaries, whereas ga2ox6 and spy mutants had strongly reduced DR5-dependent signal in nectaries. This suggests significant crosstalk occurs between GA and auxin signaling pathways in the regulation of nectar production.
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    Integrating Co-Expression Networks with GWAS to Detect Causal Genes For Agronomically Important Traits
    (2015-11) Schaefer, Robert
    The recent availability of high-throughput technologies in agricultural species provides an opportunity to advance our understanding of complex, agronomically important traits. Genome wide association studies (GWAS) have identified thousands of loci linked to these traits; however in most cases the causal genes remain unknown. Analysis of a single data type is typically unsatisfactory in explaining complex traits that exhibit variation across multiple levels of biological regulation. To address these issues, we developed a computational framework called Camoco (Co-analysis of molecular components) that systematically integrates loci identified by GWAS with gene co-expression networks to identify a focused set of candidate loci with functional coherence. This framework analyzes the overlap between candidate loci generated from GWAS and the co-expression interactions that occur between them and addresses several biological considerations important for integrating diverse data types. On average, using this integrated approach, candidate gene lists identified by GWAS were reduced by two orders of magnitude. By incorporating co-expression network information, we can rapidly evaluate hundreds of GWAS experiments, producing focused sets of candidates with both strong associations with the phenotype of interest as well as evidence for functional coherence in the co-expression network. Identifying these candidates in a systematic and integrated manner is an important step toward resolving genes responsible for agriculturally important traits.