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Modeling Anxiety in Mice: Elevated Plus Maze and Genome-Environment Interactions

DESCRIPTION

This practical guide explores the effects of genetic and environmental factors on anxiety-related behaviors using the Elevated Plus Maze (EPM) model in mice. It covers hypothesis testing (H0, H1, H2, H3) and includes steps for conducting a genome scan with gender effects. Participants will learn how to analyze phenotypes in relation to sex and chromosomes, using techniques such as ANOVA and advanced intercross lines (AILs). Key methods for QTL mapping and haplotype reconstruction are also discussed, providing insights into the interplay between genetics and environment.

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Modeling Anxiety in Mice: Elevated Plus Maze and Genome-Environment Interactions

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Presentation Transcript

  1. Gene by environment effects

  2. Elevated Plus Maze (anxiety)

  3. Modelling E, G and GxE H0: phenotype ~ 1 H1: phenotype ~ covariate H2: phenotype ~ covariate + LocusX H3: phenotype ~ covariate + LocusX + covariate:LocusX

  4. PRACTICAL: Inclusion of gender effects in a genome scan To start: 1. Copy the folder faculty\valdar\FridayAnimalModelsPractical to your own directory. 2. Start R 3. File -> Change Dir… and change directory to your FridayAnimalModelsPractical directory 4. Open Firefox, then File -> Open File, and open “gxe.R” in the FridayAnimalModelsPractical directory

  5. H0: phenotype ~ sex H1: phenotype ~ sex + marker

  6. scan.markers(Phenotype ~ Sex + MARKER, • h0 = Phenotype ~ Sex, • ... etc) H0: phenotype ~ sex H1: phenotype ~ sex + marker

  7. scan.markers(Phenotype ~ Sex + MARKER, • h0 = Phenotype ~ Sex, • ... etc) H0: phenotype ~ sex H1: phenotype ~ sex + marker

  8. scan.markers(Phenotype ~ Sex + MARKER, • h0 = Phenotype ~ Sex, • ... etc) H0: phenotype ~ sex H1: phenotype ~ sex + marker

  9. head(ped.gender0) • anova(lm(Phenotype ~ Sex + m1 + Sex:m1, data=ped.gender0)) • head(ped.gender1) • anova(lm(Phenotype ~ Sex + m1 + Sex:m1, data=ped.gender1)

  10. New approaches • Advanced intercross lines • Genetically heterogeneous stocks

  11. F2 Intercross x F1 Avg. Distance Between Recombinations F2 intercross ~30 cM F2

  12. Advanced intercross lines (AILs) F0 F1 F2 F3 F4

  13. Chromosome scan for F12 QTL goodness of fit (logP) significance threshold 0 100cM position along whole chromosome (Mb) Typical chromosome

  14. PRACTICAL: AILs To start: 1. Open Firefox, then File -> Open File, and open “ail_and_ghosts.R” in the FridayAnimalModelsPractical directory

  15. Genetically Heterogeneous Mice

  16. F2 Intercross x F1 Avg. Distance Between Recombinations F2 intercross ~30 cM F2

  17. Heterogeneous Stock F2 Intercross x Pseudo-random mating for 50 generations F1 Avg. Distance Between Recombinations: HS ~2 cM F2 intercross ~30 cM F2

  18. Heterogeneous Stock F2 Intercross x Pseudo-random mating for 50 generations F1 Avg. Distance Between Recombinations: HS ~2 cM F2 intercross ~30 cM F2

  19. Genome scans with single marker association

  20. High resolution mapping

  21. Relation Between Marker and Genetic Effect QTL Marker 1 Observable effect

  22. Relation Between Marker and Genetic Effect QTL Marker 2 Marker 1 Observable effect

  23. Relation Between Marker and Genetic Effect QTL Marker 2 Marker 1 No effect observable Observable effect

  24. Multipoint method (HAPPY) calculates the probability that an allele descends from a founder using multiple markers Observed chromosome structure Hidden Chromosome Structure

  25. Haplotype reconstruction using HAPPY chromosome genotypes haplotype proportions predicted by HAPPY

  26. HAPPY model for additive effects

  27. Genome scans with single marker association

  28. Genome scans with HAPPY

  29. Results for our HS

  30. Many peaks mean red cell volume

  31. How to select peaks: a simulated example

  32. How to select peaks: a simulated example Simulate 7 x 5% QTLs (ie, 35% genetic effect) + 20% shared environment effect + 45% noise = 100% variance

  33. Simulated example: 1D scan

  34. Peaks from 1D scan phenotype ~ covariates + ?

  35. 1D scan: condition on 1 peak phenotype ~ covariates + peak 1 + ?

  36. 1D scan: condition on 2 peaks phenotype ~ covariates + peak 1 + peak 2 + ?

  37. 1D scan: condition on 3 peaks phenotype ~ covariates + peak 1 + peak 2 + peak 3 + ?

  38. 1D scan: condition on 4 peaks phenotype ~ covariates + peak 1 + peak 2 + peak 3 + peak 4 + ?

  39. 1D scan: condition on 5 peaks phenotype ~ covariates + peak 1 + peak 2 + peak 3 + peak 4 + peak 5 + ?

  40. 1D scan: condition on 6 peaks phenotype ~ covariates + peak 1 + peak 2 + peak 3 + peak 4 + peak 5 + peak 6 + ?

  41. 1D scan: condition on 7 peaks phenotype ~ covariates + peak 1 + peak 2 + peak 3 + peak 4 + peak 5 + peak 6 + peak 7 + ?

  42. 1D scan: condition on 8 peaks phenotype ~ covariates + peak 1 + peak 2 + peak 3 + peak 4 + peak 5 + peak 6 + peak 7 + peak 8 + ?

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