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14 th QTL-MAS Workshop, Poznań University of Life Sciences, Poland 2010The genetic dissection of complex traits in model organismsKarl W. Broman 1∗1Department of Biostatistics and Medical Informatics, University of Wisconsin – Madison∗ Presenting author: Karl Broman, email: kbroman@biostat.wisc.eduBoth agricultural and biomedical researchers seek to dissect the genetic architecture ofcomplex phenotypes, but their goals are somewhat different. Consequently, the appropriateanalysis strategies have important differences. I will discuss some of these differences andwill further describe recent advances in complex trait analysis in model organisms.22
14 th QTL-MAS Workshop, Poznań University of Life Sciences, Poland 2010Integrating genetic markers with ~omics data using genetical genomics andmodern regression methodsAnimesh Acharjee 1,2, * , Bjorn Kloosterman 1 , Chris Maliepaard 1,3 , Ric de Vos 4 , ChristianBachem 1,3 , Richard GF Visser 1,31Wageningen UR Plant Breeding, Wageningen University and Research Center, POBox 386, 6700 AJ Wageningen, The Netherlands2 Graduate School Experimental Plant Sciences3 Center for BioSystems Genomics4 Plant Research International, P.O. Box 16, 6700 AA Wageningen, The Netherlands*Presenting author: Animesh Acharjee, email: animesh.acharjee@wur.nlUtilization of the natural genetic variation in traditional breeding programs remains a majorchallenge in crop plants. In the post genomics era, high throughput technologies give rise todata collection in fields like transcriptomics, metabolomics and proteomics and as a result,large amounts of data have become available. We have screened a diploid potato populationfor gene-expression and obtained LC-MS data resulting in the identification of manyexpression and metabolite QTL’s across the genome. However, the integration of these datasets with phenotypic and marker data is still problematic. Here we present novel approachesto study the various ~omic datasets to allow the construction of 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 ofthe metabolites and transcripts which show association with potato tuber flesh colour. Theselected subset of metabolites and transcripts shows high significant (p value
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The book of abstracts is available. - PoznaÅ