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Moeller_GridQTL_BOSC2009

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  • 1. Grid-based expression QTL Analysis Ann-Kristin Grimm, Steffen Möller University of Lübeck Institute for Neuro- and Bioinformatics BOSC09 Stockholm
  • 2. Statistical genetics for complex diseases ● Isolation of homozygous strains ● susceptible or ● resistant to disease ● Identification of chromosomal markers that differ between strains ● Generating offspring with mixed genotypes, score the disease phenotypes of every individual ● Determine statistical association of markers with score BOSC09 Stockholm
  • 3. Back-Cross: F1 x P BOSC09 Stockholm
  • 4. Analysis ● Markers sufficiently dense to spot all X-overs ● Interval-mapping: neighbouring markers ● Both homozygous: no X-over ● Both heterozygous: no X-over ● One hetero-, one homozygous: X-over in between ● Continuous refinements ● Steady (directed) increase of markers ● Additional phenotypes being investigated ● Additional mice being bread – Stronger statistics – More cross-overs BOSC09 Stockholm
  • 5. Peak: inferring ML of disease location ● A stretch between two markers may be found to be influencing the strong score Effect ● The position of the controlling locus may be estimated from weak ● the fraction of individuals with X-over ● that show the same effect A/A A/B A/A A/A A/B A/B ● The exact locus remains undefined Marker-Combination ● … but what if we have more molecular scores ...? BOSC09 Stockholm
  • 6. Expression QTL ● Use gene expression levels as scores ● Disease phenotypes + sex + mitochondrial inheritance are covariates ● Every locus is described by ● genes that it controls ● pathways/GO terms/TFBS/miRNA these share ● Super-linear (epistatic) effects with other loci ● Conversely genes → genetic loci ● Direct effects from locus → gene ● Singular effects are of interest, even should the QTG never be determined BOSC09 Stockholm
  • 7. Huge amount of data ● Compute time so long you don't want to do this twice. 30000 genes x 200 markers (^2 for interactions) x 150 individuals x 20 phenotypes (^2) ● But for better insights biologists do – with updated data. BOSC09 Stockholm
  • 8. Increased communication through distributed computation ● Dynamic website to ● Ship raw data to compute nodes ● Humanely present (interim) results of computations ● Grid jobs retrieve series of work units ● Continuous input to biological researchers BOSC09 Stockholm
  • 9. Please join in: http://eqtl.berlios.de Acknowledgments Programming: Ann-Kristin Grimm, Jan Kolbaum, Hajo Krabbenhöfft, Patrick Wernhoff Data: Maja Jagodic, Mèlanie Thessèn-Hedreul, Dirk Koczan, Saleh Ibrahim Computations: Olli Tourhunen and the NDGF BOSC09 Stockholm