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Journal club. Wouter 10 dec 2013. Why. Interest in autism Follow-up of gene-finding Interesting: two papers in same issue Cell similar findings. Overview paper. Select hcASD-genes (9) and pASD-genes (122) Use data Kang & reduce spatial and temporal number of windows

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journal club

Journal club

Wouter

10 dec2013

slide3
Why
  • Interest in autism
  • Follow-up of gene-finding
  • Interesting: two papers in same issue Cell similar findings
overview paper
Overview paper
  • Select hcASD-genes (9) and pASD-genes (122)
  • Use data Kang & reduce spatial and temporal number of windows
  • Find enrichment of pASD in coexpression networks in 4 areas
  • Test enrichment with:

1) hypergeometric test 2) hcASD permutation 3) pASD permutation

4) number of genes selected in network 5) cross validation

6) single period weighted 7) excluding TBR1

  • Focus on TBR1
  • TADA confirming pASD genes higher chance in midfetal period
  • Further improve spatial resolution to layers
  • Analyze temporal behavior of layer found
  • Find cell type
  • Immunostaining in midfetal CPi cortex
introduction
Introduction
  • No common genetic variation reproducible linked to autism
  • However, sequencing has recently led to discovery of de novo loss of function (LoF) mutation.
  • De novo LoF mutations are expected to play role in 15% of patients
  • List of associated genes is steadily growing
  • Associated loci heterogeneous with respect to biological function  challenge for translation
gene selection
Gene selection
  • Total of 1043 families (987 previously published, 56 additional exome sequenced)
  • LoF = premature stop codon, splice-site disruption, or frameshift insertion/ deletion
  • 144 LoF de novo mutations identified
chance of true asd gene
Chance of true ASD gene
  • Subset of 599 quartets: 75 LoF in 72 affected versus 34 in 32 unaffected (OR=2.21, p=5e-5)

FDR of gene ≥ 2 independent cases with LoF

  • Permutation: p=0.1975 to find 2 LoF in same gene by chance
  • 9 genes with ≥ 2 LoF genes found
  • 45.6 more often than expected (9/0.1975)
  • FDR = 0.022 (1/45.6)
  • Chance of true ASD gene is 0.978
  • Analogue chance of true ASD for 1-hit gene (0.55) and 3-hit gene (0.9998)
hcasd pasd genes
hcASD / pASD genes

hc = high confidence (m=9)

  • LoF in gene in two unrelated cases (FDR 0.02)
  • LoF in three cases (FDR 0.0002)

p = probably (m=122)

  • LoF in one case (FDR 0.45)

Use these genes to construct spatiotemporal coexpression networks

transcriptome data2
Transcriptome data
  • Expression in
    • 16 brain regions
    • 57 clinically unremarkable postmortem subjects (31M 26F)
    • 15 periods from 5.7 PCW to 82 Y

(Thus, 16*14=240 spatiotemporal units)

  • Partitioned in subsets
    • Temporal partitioning: 13 sliding windows of three consecutive time periods
    • Why?
coexpression network
Coexpression network
  • Network = hcASD gene + max 20 top correlated genes + edges
  • For each gene (M = 16,947 + 9), vector of expression values, by brain-region and brain-sample
  • Per spatiotemporal window, correlation of expression-vectors between gene-pairs
  • Per hcASD, select 20 top correlated genes with abs. cor. ≥ 0.7
  • Edges are are correlations between each gene-pair of network with abs. cor. ≥ 0.7
spatial partitioning step 1
Spatial partitioning – step 1
  • Why?
  • Select period, in which networks are most enriched for pASD genes  period 3-7 (10-38 PCW)
spatial partitioning step 2
Spatial partitioning – step 2
  • Select coherent subsets of brain regions based on period 3-7
    • Summarize gene-expression per brain region by median expression across all samples
    • Compute pairwise correlation between brain regions
    • Subsequent, hierarchical clustering (distance is 1-corr2)
  • 4 clusters of brain regions

Thus, 4*13 = 52 spatiotemporal windows, with coexpression networks constructed

hypergeometric test
Hypergeometric test
  • Probability of k successes in n draws without replacement

k = number of successes drawn (nr pASD-genes in network)

K = total number of successes (total nr pASD = 122)

n = number of draws (genes in network, ≤ 20)

N = population (16,947 genes)

Problem: larger genes more chance of de novo LoF mutations

permutation test 1
Permutation test 1
  • Tests if true hcASD genes are crucial to enrichment with pASD found
    • Select 9 pseudo hcASD genes (based on the likelihood of observing 2-hit de novo LoF mutations by chance, taking gene size and GC-content into account)
    • Build corresponding coexpression networks in concerning spatiotemporal windows & test enrichment with pASD genes
    • 100,000 iterations
permutation test 2
Permutation test 2
  • Identical, but with true hcASD, and permutation of pASD
permutation test 3
Permutation test 3
  • Permutation of hcASD, with true pASD
  • For varying number of genes in coexpression network
cross validation
Cross-validation
  • Remove 1 hcASD and 12 pASD (10%)
  • Reconstruct 52 spatiotemporal coexpression networks
  • Success = 1 of top three networks most enriched for pASD
    • top three PFC-MSC 3-5 & 4-6, MD-CBC 8-10
  • Success in 100% of 200 iterations
single period weighted analyses
Single period weighted analyses
  • Before, 3 periods equally weighted
  • Now, middle period weight 1, periods immediately before and after weighted 0.5
questions
Questions
  • How does “increasing resolution” influence subsequent results?
  • Why take expression in subjects older than say 1 year into account?
  • Why not report correlation between hcASD gene-expression?
about brainregions
About brainregions
  • V1C, ITC, IPC, A1C, STC: non-significant in permutation: dropped
  • PFC-MSC: 107 sample (period 3-5) & 140 (period 4-6)
  • MD-CBC: only 26 samples (period 8-10): dropped
  • Two PFC-MSC networks referred to as midfetal networks

PFC-MSC = Pre-Frontal-Cortex & Primary-Motor-Somatosensory-Cortex

slide27
TADA
  • = transmission and the novo association- test
  • Why? To test if pASD in midfetal network are more likely true ASD genes than estimated with FDR (55%)
  • TADA combines family and case-control data
slide28
TADA
  • Additional, case (935) control (870) data included from ARRA (Liu)

(Liu 2013. Analysis of rare, exonic variation amongst subjects with autism spectrum disorders and population controls. PLoS Genet.)

t box brain 1 tbr1
T-box, brain, 1 (TBR1)
  • TBR1=hcASD
  • Known transcription factor involved in forebrain development
  • In mice
    • Postnatal day 0 ( = human midfetal)
    • RNA-seq of cortex
    • Compare expression in TBR1-/- & TBR1+/+ (n=?)
    • 4 of differentially expressed genes (DEX) in coex- network
    • TBR1 previously known to regulate these DEX- genes
    • (not mentioned if DEX- genes are pASD- genes)
laminar specific expression data
Laminar-Specific Expression Data
  • To improve spatial resolution
  • PFC-MSC (Pre-Frontal-Cortex & Primary-Motor-Somatosensory-Cortex)
  • NB: cortex is grey matter and contains cell bodies
  • Test nine cortex-layers from 4 brains from www.brainspan.org
  • Apply original coexpression networks and estimate connectivity per layer ( = sum correlations, weighted for mean correlation in layer)
  • Permute rijk over mean(rk) = null distribution of connectivity
subsequent analyses of inner cortical plate cpi
Subsequent analyses of inner cortical plate (CPi)
  • Why? To test if localization to CPi is specific to period 3-5.

(might change over time due to neuronal migration in early brain development)

  • How?
    • Two mice brains (m&f)
    • Expression at six time points
    • Three zones of layers: select genes upregulated in 1 zone only
    • Test per zone, the zone-specific genes for enrichment in period 3-5 PFC-MSC network (hypergeometric test)
subsequent analyses of inner cortical plate cpi1
Subsequent analyses of inner cortical plate (CPi)
  • NB: CPi corresponds to deep mouse layer
  • Thus, finding of CPi as specific layer is not driven by neurons eventually migrating to superficial layer
cell type specific markers
Cell-Type-Specific Markers
  • Five cell-types specific marker genes from independent dataset
  • Enrichment for cortical glutamergic projections neurons (100,000 permutations of hcASD)
immunostaining in situ hybridization
Immunostaining / In situ hybridization
  • Staining hcASD genes: TBR1, POGZ, CHD8, DYRK1A, SCN2A (i.s.h.)
  • TBR1 restricted to CPi (inner cortical plate)
discussion willsey et al
Discussion Willsey et al
  • Results suggest marked locus heterogeneity point to a much smaller set of pathophysiological mechanisms
  • Clear evidence role synaptic proteins. Indeed, the CPi neurons of midfetal PFC-MSC are among first to form synapsis.
  • Findings suggest that ASD genes converge at additional time points and brain regions
  • Small set of hcASD genes: prioritizes specificity over sensitivity
  • Results important to subsequent further understanding of pathophysiology
parikshak et al
Parikshak et al.
  • Compares ASD to intellectual disability (ID)
  • Maps ASD and ID genes on coexpression networks
  • ASD genes enriched in superficial cortical layers & glutaminergic projections neurons
  • Distinct patterns of ASD and ID
journal club1

Journal club

Wouter

10 dec2013

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