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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


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


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


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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