2011-2016 August 2016+ Data Science Division Leader ! ! PIs: • Jeff Leek: 2012+ • Andrew Jaffe: 2013+ Ph.D. Biostatistics Staff Scientist Data Science Team I Microarrays, little RNA-seq N = 12 (bacteria) N = 59 N = 72 potential for 1,000 BSP2: N = 900 + many more
software [...] in RNA-seq […]. We applied these methods to further our understanding of neuropsychiatric disorders using the Lieber Institute for Brain Development human brains collection (> 1000 samples).
PI: Andrew Jaffe Team: • Staff Scientist: Emily Burke • Research Associate: Madhavi Tippani • Postdocs: Amanda Price • Grad students: Matt Nguyen, Brianna Barry, Kira Perzel Mandell • Research Assistant: Nick Eagles, Stephen Semick* • Close collaborators: Carrie Wright, Nina Rajpurohit • Role details: like a postdoc (major role in some projects) with some support projects * Now at University of Maryland in med school Lab at Amy Peterson’s MPH capstone presentation
biorxiv.org/content/early/2018/10/30/445437 • DNAm on WGBS across development & cell types biorxiv.org/content/early/2018/09/29/428391 • RNA-seq DE in Schizophrenia disorder & 2 brain regions biorxiv.org/content/early/2018/09/26/426213 • RNA-seq from stem cells biorxiv.org/content/early/2018/07/31/380758 Peer reviewed: • DNAm and gene DE in Alzheimer’s disease doi.org/10.1007/s00401-019-01966-5 • RNA-seq smoking during pregnancy doi.org/10.1038/s41380-018-0223-1 • RNA-seq DE in Schizophrenia disorder on DLPFC doi.org/10.1038/s41593-018-0197-y • Histamine signaling in autism spectrum disorder doi.org/10.1038/tp.2017.87 Pre-prints:
Phase I polyA+ Jaffe et al., Nature Neuroscience, 2018 DLPFC 453 samples HIPPO 447 samples BrainSeq Phase II RiboZero THE SCIENTIFIC FRONTIER Neuron, 2015 Collado-Torres et al, bioRxiv, 2018 Manuscript status: under revision at Neuron
processing methods 2.Apply strict expression cutoffs 3.Use replication when possible 4.Adjust for RNA quality degradation confounding • Using the qSVA method 5.Avoid potential batch effects • Drop problematic samples 6.Take into account correlation at the individual level
Unaffected Controls Patients with Schizophrenia RNA Sequencing Genotyping + + + Gene Exons Expressed Regions Transcripts Junctions Age CC CA AA SZ CONT DLPFC HIPPO + region differences For public data, check recount2
on raw reads (FastQC) 2. Failed QC? Then trim reads (Trimmomatic) 3. Align reads to the genome (HISAT2) 4. Count features (featureCounts + others) 5. Calculate coverage (bam2wig) 6. Quantify transcripts (Salmon) 7. Create count tables (R) 8. Genotype samples (samtools + vcftools) Emily E. Burke Nextflow version in preparation with Winter Genomics and Nick Eagles
or fetal ages • Using only adult samples or only prenatal samples • Test for differences between DLFPC and HIPPO • Development • Linear age splines with breakpoints at developmental stages • Test for interaction between age and brain region at these splines • Case-control • By brain region • Test for differences between non-psychiatric controls and individuals with schizophrenia • For the first two models, we account for the fact that an individual can have two correlated samples: one for each brain region limma::duplicateCorrelation()
1,612 DE genes with Bonferroni < 0.01 that replicate in BrainSpan • For prenatal samples: 32 DE genes 23 6 2 0 3 0 3 gene exon jxn DE features grouped by gene id (prenatal) 328 422 778 23 839 647 388 gene exon jxn DE features grouped by gene id (adult)
processes by region G: gene, E: exon, J: exon-exon junction D: DLPFC, H: HIPPO cell−matrix adhesion extracellular matrix organization ameboidal−type cell migration microtubule bundle formation axoneme assembly cilium movement establishment of synaptic vesicle localization synaptic vesicle transport synaptic vesicle localization signal release from synapse neurotransmitter secretion presynaptic process involved in chemical synaptic transmission positive regulation of GTPase activity regulation of GTPase activity synaptic vesicle exocytosis neurotransmitter transport regulation of calcium ion−dependent exocytosis synaptic vesicle cycle regulation of synapse organization positive regulation of synaptic transmission regulation of neuron projection development regulation of small GTPase mediated signal transduction extracellular structure organization positive regulation of nervous system development positive regulation of neurogenesis positive regulation of cell development axon development axonogenesis regulation of hormone levels cardiac chamber development cardiac septum development kidney epithelium development nephron development urogenital system development cardiac chamber morphogenesis cardiac septum morphogenesis renal system development kidney development regulation of transmembrane transport actomyosin structure organization regulation of trans−synaptic signaling modulation of chemical synaptic transmission synapse organization regulation of cell morphogenesis synaptic transmission, glutamatergic regulation of synaptic transmission, glutamatergic potassium ion transmembrane transport cellular potassium ion transport potassium ion transport G.D (609) G.H (573) E.D (1101) E.H (1087) J.D (850) J.H (823) GeneRatio 0.02 0.03 0.04 0.05 0.06 0.01 0.02 0.03 0.04 p.adjust ontology: BP
development neutrophil chemotaxis regulation of B cell proliferation regulation of B cell activation positive regulation of B cell activation B cell receptor signaling pathway B cell activation lymphocyte activation regulation of leukocyte cell−cell adhesion regulation of cell−cell adhesion positive regulation of alpha−beta T cell activation regulation of leukocyte activation positive regulation of lymphocyte activation positive regulation of cell activation positive regulation of leukocyte activation leukocyte migration positive regulation of leukocyte cell−cell adhesion positive regulation of T cell activation leukocyte chemotaxis response to organophosphorus ATF6−mediated unfolded protein response positive regulation of cell adhesion cell chemotaxis positive regulation of cell−cell adhesion protein folding protein folding in endoplasmic reticulum HgC (137) HeC (306) DgC (297) DeS (250) GeneRatio 0.025 0.050 0.075 0.100 0.01 0.02 0.03 0.04 p.adjust ontology: BP DATA ANALYSIS 36 • BP enrichment in Control > SCZD at gene level • Immune processes g: gene, e: exon, j: exon-exon junction D: DLPFC, H: HIPPO, C: control, S: SCZD Case-control: by region
(84.5 %) No 17 16 116 103 PGC2 loci with eQTLs (FDR<1%): considered in TWAS? ? DLPFC HIPPO Yes 48 (48.5%) 45 (51.7%) No 51 42 99 87 Locus has a TWAS p-value among all features? ? DLPFC HIPPO Yes 26 (54.2%) 25 (55.5%) No 22 20 48 45 TWAS p-value <1x10-8?
E. Burke • Amy Peterson • Joo Heon Shin • Richard E. Straub • Anandita Rajpurohit • Stephen A. Semick • William S. Ulrich • Cristian Valencia • Ran Tao • Amy Deep-Soboslay • Thomas M. Hyde • Joel E. Kleinman • Daniel R. Weinberger+ • Andrew E. Jaffe+ o email@example.com o @andrewejaffe 53 • BrainSeq Consortium • LIBD @lieberinstitute Funding github.com/LieberInstitute/brainseq_phase2
male 1240 Male 141 Total 3640 Even when information is provided, it’s not always clear… sra_meta$Sex “1 Male, 2 Female”, “2 Male, 1 Female”, “3 Female”, “DK”, “male and female” “Male (note: ….)”, “missing”, “mixed”, “mixture”, “N/A”, “Not available”, “not applicable”, “not collected”, “not determined”, “pooled male and female”, “U”, “unknown”, “Unknown” slide adapted from Shannon Ellis
samples in recount gene, exon, exon-exon junction and expressed region RNA-Seq data SRA Sequence Read Archive N=49,848 TCGA The Cancer Genome Atlas N=11,284 GTEx Genotype Tissue Expression Project N=9,662 slide adapted from Shannon Ellis
samples in recount gene, exon, exon-exon junction and expressed region RNA-Seq data SRA Sequence Read Archive N=49,848 GTEx Genotype Tissue Expression Project N=9,662 divide samples build and optimize phenotype predictor training set test accuracy of predictor test set TCGA The Cancer Genome Atlas N=11,284 slide adapted from Shannon Ellis
samples in recount gene, exon, exon-exon junction and expressed region RNA-Seq data SRA Sequence Read Archive N=49,848 GTEx Genotype Tissue Expression Project N=9,662 divide samples build and optimize phenotype predictor training set test accuracy of predictor predict phenotypes across samples in TCGA test set TCGA The Cancer Genome Atlas N=11,284 slide adapted from Shannon Ellis
samples in recount gene, exon, exon-exon junction and expressed region RNA-Seq data SRA Sequence Read Archive N=49,848 GTEx Genotype Tissue Expression Project N=9,662 divide samples build and optimize phenotype predictor training set predict phenotypes across SRA samples test accuracy of predictor predict phenotypes across samples in TCGA test set TCGA The Cancer Genome Atlas N=11,284 slide adapted from Shannon Ellis
Black Hispanic White Tissue Site 1 Cerebral cortex Hippocampus Brainstem Cerebellum Tissue Site 2 Frontal lobe Temporal lobe Midbrain Basal ganglia Tissue Site 3 Dorsolateral prefrontal cortex Superior temporal gyrus Substantia nigra Caudate Hemisphere Left Right Brodmann Area 1-52 Disease Status Disease Neurological control Disease Brain tumor Alzheimer’s disease Parkinson’s disease Bipolar disorder Tumor Type Glioblastoma Astrocytoma Oligodendroglioma Ependymoma Clinical Stage 1 Grade I Grade II Grade III Grade IV Clinical Stage 2 Primary Secondary Recurrent Viability Postmortem Biopsy Preparation Frozen Thawed Ashkaun Razmara, in prep.
(around April 2018) https://docs.google.com/presentation/d/1FgUZZU6pW91J7zH0OqrEgxfnV1Py_ZGL3ZKHfbOZskY/edit#slide=id.g2f831fd4ae_0_306 * Dustin J. Sokolowski from Michael D. Wilson’s lab is using recount2 * Dustin joins the project and merges recount-brain with GTEx and TCGA recount_brain_v2 The SRA samples in recount-brain are complemented by 1,409 GTEx (GTEx Consortium 2015) and 707 TCGA (Brennan et al. 2013; Cancer Genome Atlas Research Network et al. 2015) samples covering 13 healthy regions of the brain and 2 tumor types, respectively. In total, there are 6,547 samples with metadata in recount-brain with 5,330 (81.4%) present in recount2
T. Leek University of Toronto Dustin J. Sokolowski Michael D. Wilson NIH Sean Davis LIBD Andrew E. Jaffe Funding NIH R01 GM105705 NIH 1R21MH109956 NIH R01 GM121459 CIHR, NSERC Ontario Ministry of Research IDIES SciServer Hosting recount2 github.com/LieberInstitute/recount-brain