ashg2019

Transcript

1. 11
   Regional Heterogeneity in Gene
   Expression, Regulation, and Coherence in
   the Frontal Cortex and Hippocampus
   across Development and Schizophrenia
   Leonardo Collado-Torres
   Staff Scientist II
   @fellgernon
   Data Science I with Andrew Jaffe
   

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2. URGENT UNMET NEED
   Neuropsychiatric disorders cost the U.S. Economy > $80 billion/year.
   Neuropsychiatric conditions are the leading
   cause of disability in young people worldwide.
   70%
   70% of youth in the
   juvenile justice system
   are living with at least
   one mental health
   condition.
   Traumatic brain injury is
   the leading cause of
   long-term disability in
   children and adults
   younger than 35 years.
   1 in 4 experience
   mental illness in a
   given year.
   More veterans die
   of suicide than in
   combat at rate of
   20 suicides per
   day.
   2
   

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3. BrainSeq: A Human Brain Genomics Consortium
   DLPFC
   495 samples
   BrainSeq Phase I
   polyA+
   Jaffe et al., Nature Neuroscience,
   2018
   DLPFC
   453 samples
   HIPPO
   447 samples
   BrainSeq Phase II
   RiboZero
   Neuron, 2015
   Collado-Torres et al, Neuron, 2019
   551 individuals (286
   with schizophrenia)
   

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4. −6 −4 −2 0 2 4
   −6 −4 −2 0 2 4 6
   t−statistic HIPPO
   t−statistic DLPFC
   r = 0.276
   4
   SCZD expression regional heterogeneity
   48 DE genes in
   hippocampus, 243
   in DLPFC (FDR
   <5%)
   Amy Peterson
   Adjusting for:
   age, sex, chrM
   mapping, rRNA
   mapping, gene
   assignment rate,
   RIN, SNP PCs 1-5,
   quality surrogate
   variables (qSVA)
   

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5. Gene Individual 1 Individual 2
   A 10 15
   B 5 22
   C 7 9
   D 100 50
   DLPFC
   Gene Individual 1 Individual 2
   A 10 17
   B 6 16
   C 9 30
   D 120 80
   HIPPO
   correlation( (10, 5, …), (10, 6, …) ) =~ 0.9
   correlation( (15, 22, …), (17, 16, …) ) =~ 0.7
   Individual 1
   Individual 2
   Control vs SCZD
   

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6. p-values:
   Gene: 0.0164
   Exon: 0.0499
   Jxn: 1.72 * 10-5
   Tx: 0.00992
   Control SCZD
   0.54 0.55 0.56 0.57
   cleaned expr (keeping Dx) − jxnRp10m
   p−value: 1.72e−05
   Correlation
   Decreased regional coherence in SCZD
   Control SCZD
   Correlation
   

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7. 7
   HIPPO eQTLs 11,237,357 eQTL associations (FDR <1%)
   across genes, exons and junctions
   corresponding to 17,719 genes
   Emily E. Burke
   2061
   3183
   2163
   274
   5945
   2915
   1178
   gene exon
   jxn
   eQTLs grouped by gene id
   Includes 60 risk SNPs from PGC2
   8
   +,332
   E
   0 1 2
   −1 0 1 2 3 4
   chr2:5822204358229ï-[Q
   rs74563533:58250433:G:A
   r2:58 250 433
   Residualized Expression
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   0 1 2
   −1 0 1 2 3 4
   chr2:5822204358229ï-[Q
   rs75575209:58138192:A:T
   r2:58 138 192
   Residualized Expression
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   p=5.75e−34
   p=3.82e−81
   F
   

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8. 8
   205,618 region-dependent eQTLs (FDR <1%)
   corresponding to 1,484 genes
   Emily E. Burke
   Bill Ulrich
   eQTL browser at
   http://eqtl.brainseq.org/
   

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9. Analysis with PCG2 risk SNPs and proxies identifies
   eQTLs (FDR <1%) in risk loci
   HIPPO DLPFC
   # unique SNPs (unique
   index SNPs)
   5510 (103) 6780 (116)
   # unique features 1731 2525
   # Unique genes 123 171
   # Unique transcripts 244 332
   # Unique exons 857 1363
   # Unique junctions 507 659
   Emily E. Burke
   21 8
   95
   DLPFC HIPPO
   In BSP2: 163/179 (91.1%)
   FDR <1%: 124/163 (76.6%)
   

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10. Gusev et al, Nature Genetics, 2016
    Transcriptome Wide Association Study (TWAS)
    

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11. −5
    0
    5
    −5 0 5
    DLPFC
    HIPPO
    in_both
    FALSE
    TRUE
    FDR.5perc
    None
    DLPFC
    HIPPO
    Both
    TWAS Z by brain region
    Removed sex, SNP
    PCs 1-5, expression
    PCs
    Kept diagnosis
    jaffelab::cleaningY()
    TWAS Z scores are correlated among DLPFC &
    HIPPO
    GWAS: CLOZUK
    

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12. rho=−1.26e−03
    rho=−8.50e−03
    rho=7.27e−03
    rho=4.54e−02
    DLPFC HIPPO
    Other Risk Locus
    −5 0 5 −5 0 5
    −6
    −3
    0
    3
    6
    −6
    −3
    0
    3
    6
    TWAS Z score
    SCZD vs control t−statistic
    BEST GWAS
    p < 5e−08
    FALSE
    TRUE
    TWAS vs SCZD differential expression
    Risk Loci by BEST GWAS
    TWAS and SCZD statistics are not related
    

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13. Risk Loci Category HIPPO DLPFC Intersection Union
    With eQTLs <1% 103 116 95 124
    With TWAS weights 86 (83.5%) 93 (80.2%) 78 (82.1%) 101 (81.5%)
    TWAS FDR <5% 79 (91.9%) 88 (93.5%) 73 (93.6%) 94 (93.1%)
    TWAS Bonferroni <5% 61 (70.9%) 68 (73.1%) 54 (69.2%) 75 (74.3%)
    TWAS results are complementary to eQTLs in risk loci
    ~80% of risk loci with eQTLs have TWAS weights,
    ~90% and ~70% of these are FDR or Bonferroni significant
    

    View Slide

14. doi.org/10.1016/j.neuron.2019.05.013
    • Dorsolateral prefrontal cortex and hippocampus
    gene expression across development
    • Novel region-specific schizophrenia genetic risk
    features
    • Decreased regional functional coherence in
    schizophrenia
    • Public brain gene expression and eQTL resource
    at http://eqtl.brainseq.org/phase2
    BrainSeq Phase II summary
    

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15. Dentate Gyrus LCM-seq finds cell type specific eQTLs
    Jaffe, Hoeppner et al., bioRxiv, 2019
    

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16. snRNA-seq for profiling cell types in brain & spatially
    [08] In
    [09] In
    [11] In
    [12] Ex+In
    [10] Ex
    [07] Ex
    [05] Ex
    [13] NPC
    [00] Oligo
    [02] Oligo
    [06] OPC
    [03] Astro
    [04] Micro
    [01] Ambig
    SNAP25
    GAD1
    CAMK2A
    SLC17A7
    SOX11
    FOXP2
    MBP
    PDGFRA
    GFAP
    CD74
    −2
    −1
    0
    1
    2
    [00] Oligo
    [04] Micro
    [02] Oligo
    [06] OPC
    [11] In
    [03] Astro
    [05] Ex
    [08] In
    [10] Ex
    [12] Ex+In
    [13] NPC
    [09] In
    [01] Ambig
    [07] Ex
    −25
    0
    25
    −50 −25 0 25 50
    tSNE_1
    tSNE_2
    Nguyen & Maynard, In prep
    

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17. Authors
    • Leonardo Collado-Torres
    o leo.collado@libd.org
    o @fellgernon
    • Emily E. Burke
    • Amy Peterson
    • Joo Heon Shin
    • Richard E. Straub
    • Anandita Rajpurohit
    • Stephen A. Semick
    • William S. Ulrich
    • Amanda J. Price
    • Cristian Valencia
    • Ran Tao
    • Amy Deep-Soboslay
    • Thomas M. Hyde
    • Joel E. Kleinman
    • Daniel R. Weinberger+
    • Andrew E. Jaffe+
    o andrew.jaffe@libd.org
    o @andrewejaffe
    17
    • BrainSeq Consortium
    • LIBD @lieberinstitute
    Funding
    github.com/LieberInstitute/brainseq_phase2
    doi.org/10.1016/j.neuron.2019.05.013
    

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