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QTL Analysis: A Robust Method for Uncovering the Genetics of Sleep and Other Complex Traits

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QTL Analysis: A Robust Method for Uncovering the Genetics of Sleep and Other Complex Traits

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    1. QTL Analysis: A Robust Method for Uncovering the Genetics of Sleep (and Other Complex Traits)

    2. Quantitative Traits Quantitative traits are continuous Human height, weight, blood pressure, 5-HT levels Sleep: amount, bout length, onset, REM/NREM

    3. Quantitative Traits and QTLs Quantitative traits caused by many genes with small effects And environment Gene x environment interactions Quantitative Trait Loci (QTLs) are regions of the genome associated with a trait: markers, genes, several linked genes, promoters/enhancers

    4. Typical QTL Analysis Approach Two strains of a model organism differ with respect to a trait Intercross or backcross the two strains, genetic recombination creates a normal distribution of the trait in F2s Measure the trait in the F2s, and genotype the extremes Use statistical techniques to associate genetic loci with the trait (Ideally) narrow the loci to individual genes

    5. QTL Analysis: Experimental Crosses

    6. QTL Analysis: Phenotypes Extremes are most informative

    7. QTL Analysis: Genotypes

    8. QTL Analysis: Association

    9. QTL Analysis: Statistical Methods ANOVA Interval Mapping EM algorithm Haley-Knott regression Each method computes LOD / LRS scores log P(H1)/ P(H0)

    10. QTL Analysis: LOD Score Significance Threshold

    11. QTL Analysis: Results

    12. QTL Analysis: Results

    13. QTL Analysis Ultimate confirmation is allelic substitution (in vivo) or functional studies (in vitro)

    14. Better QTL Analysis: RILs

    15. Better QTL Analysis: Collaborative Cross

    17. CC: Genetic Diversity

    19. Mapping Power of the Collaborative Cross

    23. Sleep Phenotype Traditional methods EEG EMG/EOG Wheel running IR beam breaking Our method Piezoelectric motion recording

    24. A novel piezoelectric system for sleep characterization Advantages Cost effective Non-invasive High throughput 90-95% accurate against human and EEG scores Disadvantages: Requires enormous computational resources to process data Does not score variables within sleep (REM vs NREM, etc.) at this time

    25. Piezoelectric pad

    26. Quad cage design

    27. Piezo system at ORNL

    28. MouseRec signal recording

    30. Computer classifier

    31. Piezo vs Pneumograph

    32. Piezo vs EEG vs EMG

    38. Future Work QTL analysis of experimental and baseline recording panels Fine mapping of QTLs Identification of genes

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