The book of abstracts is available. - PoznaÅ
The book of abstracts is available. - PoznaÅ
The book of abstracts is available. - PoznaÅ
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14 th QTL-MAS Workshop, Poznań University <strong>of</strong> Life Sciences, Poland 2010Integrating genetic markers with ~omics data using genetical genomics andmodern regression methodsAnimesh Acharjee 1,2, * , Bjorn Kloosterman 1 , Chr<strong>is</strong> Maliepaard 1,3 , Ric de Vos 4 , Chr<strong>is</strong>tianBachem 1,3 , Richard GF V<strong>is</strong>ser 1,31Wageningen UR Plant Breeding, Wageningen University and Research Center, POBox 386, 6700 AJ Wageningen, <strong>The</strong> Netherlands2 Graduate School Experimental Plant Sciences3 Center for BioSystems Genomics4 Plant Research International, P.O. Box 16, 6700 AA Wageningen, <strong>The</strong> Netherlands*Presenting author: Animesh Acharjee, email: animesh.acharjee@wur.nlUtilization <strong>of</strong> the natural genetic variation in traditional breeding programs remains a majorchallenge in crop plants. In the post genomics era, high throughput technologies give r<strong>is</strong>e todata collection in fields like transcriptomics, metabolomics and proteomics and as a result,large amounts <strong>of</strong> data have become <strong>available</strong>. We have screened a diploid potato populationfor gene-expression and obtained LC-MS data resulting in the identification <strong>of</strong> manyexpression and metabolite QTL’s across the genome. However, the integration <strong>of</strong> these datasets with phenotypic and marker data <strong>is</strong> still problematic. Here we present novel approachesto study the various ~omic datasets to allow the construction <strong>of</strong> networks integrating geneexpression, metabolites and markers. We used univariate regression and modern regressionmethods like lasso, elastic net and sparse partial least squares regression to select a subset <strong>of</strong>the metabolites and transcripts which show association with potato tuber flesh colour. <strong>The</strong>selected subset <strong>of</strong> metabolites and transcripts shows high significant (p value