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  A flowering-time gene network model for association analysis in Arabidopsis thaliana

Klotzbücher, K., Kobayashi, Y., Shervashidze, N., Borgwardt, K., & Weigel, D. (2009). A flowering-time gene network model for association analysis in Arabidopsis thaliana. Poster presented at German Conference on Bioinformatics (GCB 2009), Halle, Germany.

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 Creators:
Klotzbücher, K1, 2, Author              
Kobayashi, Y1, 2, Author              
Shervashidze, N1, 2, Author              
Borgwardt, KM1, 2, Author              
Weigel, D3, Author              
Affiliations:
1Max Planck Institute for Biological Cybernetics, Max Planck Society, ou_1497794              
2Former Research Group Machine Learning and Computational Biology, Max Planck Institute for Biological Cybernetics, Max Planck Society, ou_2528696              
3Max Planck Institute for Developmental Biology, Max Planck Society, Max-Planck-Ring 5, 72076 Tübingen, DE, ou_2421691              

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 Abstract: In our project we want to determine a set of single nucleotide polymorphisms (SNPs), which have a major effect on the flowering time of Arabidopsis thaliana. Instead of performing a genome-wide association study on all SNPs in the genome of Arabidopsis thaliana, we examine the subset of SNPs from the flowering-time gene network model. We are interested in how the results of the association study vary when using only the ascertained subset of SNPs from the flowering network model, and when additionally using the information encoded by the structure of the network model. The network model is compiled from the literature by manual analysis and contains genes which have been found to affect the flowering time of Arabidopsis thaliana [Far+08; KW07]. The genes in this model are annotated with the SNPs that are located in these genes, or in near proximity to them. In a baseline comparison between the subset of SNPs from the graph and the set of all SNPs, we omit the structural information and calculate the correlation between the individual SNPs and the flowering time phenotype by use of statistical methods. Through this we can determine the subset of SNPs with the highest correlation to the flowering time. In order to further refine this subset, we include the additional information provided by the network structure by conducting a graph-based feature pre-selection. In the further course of this project we want to validate and examine the resulting set of SNPs and their corresponding genes with experimental methods.

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 Dates: 2009-09
 Publication Status: Published online
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 Identifiers: BibTex Citekey: KlotzbucherKSBW2009
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Title: German Conference on Bioinformatics (GCB 2009)
Place of Event: Halle, Germany
Start-/End Date: 2009-09-28 - 2009-09-30

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Title: German Conference on Bioinformatics (GCB 2009)
Source Genre: Proceedings
 Creator(s):
Grosse, I, Editor
Neumann, S, Editor
Posch, S, Editor
Schreiber, F, Editor
Stadler, P, Editor
Affiliations:
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Publ. Info: -
Pages: - Volume / Issue: - Sequence Number: 39 Start / End Page: 95 - 96 Identifier: -