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RNA-quality-degradation

 RNA-quality-degradation

Applying Statistical Correction for Brain Tissue RNA Degradation to Gene Expression Differences in Schizophrenia

References:
BrainSeq: A Human Brain Genomics Consortium. Brainseq: neurogenomics to drive novel target discovery for neuropsychiatric disorders. Neuron 2015;88(6):1078-1083. doi:10.1016/j.neuron.2015.10.047.
Jaffe AE, Tao R, Norris AL, et al. qSVA framework for RNA quality correction in differential expression analysis. Proc Natl Acad Sci USA 2017;114(27):7130-7135. doi:10.1073/pnas.1617384114.
Leek JT, Storey JD. Capturing heterogeneity in gene expression studies by surrogate variable analysis. PLoS Genet. 2007;3(9):1724-1735. doi:10.1371/journal.pgen.0030161.

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Amy Peterson

May 04, 2018
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  1. 11 Applying Statistical Correction for Brain Tissue RNA Degradation to

    Gene Expression Differences in Schizophrenia Amy Peterson Capstone Advisors: Andrew Jaffe, PhD Leonardo Collado-Torres, PhD
  2. 2018 Elon Musk put a Tesla in space THE SCIENTIFIC

    FRONTIER 65+ YEARS: WAITING FOR A BREAKTHROUGH Molecular targets of all current psychotherapeutic drugs are the same as their 1950’s prototypes. 1957 Sputnik I 1952 Discovery of Antipsychotic Chlorpromazine (DRD2 blockade) 2018 Antipsychotics for treatment of schizophrenia all work via DRD2 blockade ? 2
  3. Animal Models Neuronal Cell Models Drug Discovery New Treatments 2300+

    Human postmortem brains 1000+ Cell lines from individuals Genomics + Transcriptomics + Proteomics 3 Mechanisms of Illness Clinical Genetics BrainSeq: A Human Brain Genomics Consortium THE SCIENTIFIC FRONTIER
  4. Animal Models Neuronal Cell Models Drug Discovery New Treatments 2300+

    Human postmortem brains 1000+ Cell lines from individuals Genomics + Transcriptomics + Proteomics 4 Mechanisms of Illness Clinical Genetics BrainSeq: A Human Brain Genomics Consortium DLPFC 495 samples BrainSeq Phase I polyA Jaffe et al., bioRxiv, 2017 THE SCIENTIFIC FRONTIER
  5. Animal Models Neuronal Cell Models Drug Discovery New Treatments 2300+

    Human postmortem brains 1000+ Cell lines from individuals Genomics + Transcriptomics + Proteomics 5 Mechanisms of Illness Clinical Genetics BrainSeq: A Human Brain Genomics Consortium DLPFC 495 samples BrainSeq Phase I polyA Jaffe et al., bioRxiv, 2017 DLPFC 453 samples HIPPO 447 samples BrainSeq Phase II RiboZero THE SCIENTIFIC FRONTIER
  6. BACKGROUND 6 RESEARCH QUESTION Do patients with schizophrenia exhibit gene

    expression differences across various brain regions compared to non-psychiatric controls?
  7. BACKGROUND 7 RESEARCH QUESTION Do patients with schizophrenia exhibit gene

    expression differences across various brain regions compared to non-psychiatric controls? • Impact of RNA quality? • Functionality of genes identified as differentially expressed?
  8. RNA-seq reads Genome (DNA) RNA transcripts (many possible variants) Measuring

    gene expression: RNA-seq Adapted from @jtleek 8
  9. SAMPLE SIZE 9 • 712 total RNA-seq samples • 379

    DLPFC, 333 HIPPO • 447 Individuals • 177 Schizophrenia cases • 270 Non-psychiatric controls Dataset summary DLPFC HIPPO CASE 153 133 CONTROL 226 200 DLPFC 453 samples HIPPO 447 samples
  10. qSVA WORKFLOW 10 quality surrogate variable analysis (qSVA) Degradation confounds

    postmortem human brain gene expression PCA
  11. qSVA WORKFLOW 11

  12. qSVA WORKFLOW 12

  13. qSVA WORKFLOW 13 PCA

  14. 14 REGION-SPECIFIC qSVs qSV1 associated with RIN and case-control status

    SCZD SCZD
  15. DEqual HIPPO 15 Model 1 (6429 genes) Model 1. Naïve

    model E = 0 + 1 DEqual plots demonstrate effectiveness of statistical correction HIPPO 333 samples r = 0.412
  16. DEqual HIPPO 16 Model 1 (6429 genes) Model 2 (63

    genes) Model 1. Naïve model E = 0 + 1 Model 2. Added RNA-quality and demographic covariates E = 0 + 1 + 2 + 3 + 4 + 5 te + 6 + 7 + ∑ γ ^ _`a DEqual plots demonstrate effectiveness of statistical correction HIPPO 333 samples r = 0.412 r = 0.0712
  17. DEqual HIPPO 17 Model 1 (6429 genes) Model 2 (63

    genes) Model 3 (48 genes) Model 1. Naïve model E = 0 + 1 Model 2. Added RNA-quality and demographic covariates E = 0 + 1 + 2 + 3 + 4 + 5 te + 6 + 7 + ∑ γ ^ _`a Model 3. Added qSVs E = 0 + 1 + 2 + 3 + 4 + 5 te + 6 + 7 + ∑ γ ^ _`a + ∑ Ζ h _`a DEqual plots demonstrate effectiveness of statistical correction HIPPO 333 samples r = 0.412 r = 0.0712 r = -0.00173
  18. DIFFERENTIAL EXPR 18 A B Comparing differentially expressed genes •

    A. T-statistics for top 400 differentially expressed genes for HIPPO compared to DLPFC (blue line: regression, red line: loess) • B. BrainSeq Phase 2 and BrainSeq Phase 1 (BSP1) DLPFC −6 −4 −2 0 2 4 −4 −2 0 2 4 t−statistic HIPPO t−statistic DLPFC r = 0.644 −6 −4 −2 0 2 4 6 −6 −4 −2 0 2 4 6 t−statistic DLPFC t−statistic BSP1 r = 0.809
  19. SUMMARY 19 • Statistical correction successfully removed confounding effect of

    RNA quality (DEqual plots) • Findings comparable with previous use of qSVA in BrainSeq Phase I (DLPFC) Final Model E = 0 + 1 + 2 + 3 + 4 + 5 te + 6 + 7 + ∑ ^ _`a + ∑ Ζ h _`a
  20. REFERENCES 20 References BrainSeq: A Human Brain Genomics Consortium. Brainseq:

    neurogenomics to drive novel target discovery for neuropsychiatric disorders. Neuron 2015;88(6):1078-1083. doi:10.1016/j.neuron.2015.10.047. Jaffe AE, Tao R, Norris AL, et al. qSVA framework for RNA quality correction in differential expression analysis. Proc Natl Acad Sci USA 2017;114(27):7130-7135. doi:10.1073/pnas.1617384114. Leek JT, Storey JD. Capturing heterogeneity in gene expression studies by surrogate variable analysis. PLoS Genet. 2007;3(9):1724-1735. doi:10.1371/journal.pgen.0030161. Code: https://github.com/LieberInstitute/qsva_brain JHPCE Cluster. https://jhpce.jhu.edu >838,168 linear regressions All analyses completed in R 3.4 Resources and Reproducibility
  21. THANK YOU! Questions? Amy Peterson MPH Candidate 2018 apeterson@jhu.edu amy-peterson.github.io

    21 Acknowledgements • Andrew Jaffe and lab members • BrainSeq Consortium • Leonardo Collado-Torres